problem solving techniques pharmaceutical industry

Problem Solving: What’s the Best Approach?

June 20, 2016

Post-it note saying 'Houston, we have a problem'

Those of you in the pharmaceutical, biotech and medical device industries who encounter process and product problems on a regular basis, you likely grimace when one lands in your lap. There is a degree of dread associated with a problem solving requirement as it represents a diversion from other pressing matters and will require effort you hadn't necessarily budgeted for yourself. Indeed, effective problem solving takes time and attention; but that's generally a small investment compared to the impact and ramifications of a serious problem not solved.

So, the reality being what it is, what is the best approach to problem solving? That's a rhetorical question for sure as there's not a one-size-fits-all answer; however, one can form a foundation of problem solving skills and techniques that can be tweaked to fit the specific nature of the problem at hand.

The table below lists the step-by-step approaches used in 3 different disciplines: Six-Sigma, CAPA and Risk Management. Although terminology differs slightly, notice how very similar each discipline utilizes essentially the same problem solving approach: problem identification (Houston, we have a problem!), evaluation (how bad is the problem?), investigation (what caused the problem?), resolution (how do we fix the problem?), improve (what's the plan for fixing the problem?), and verification of the effectiveness of the implemented remediation plan (did we truly fix the problem?).

As mentioned, there is not a universally accepted problem solving approach, especially one that will guarantee the problem will be solved effectively every time. However, one could do worse by not using the industry accepted, best-practice approaches used by those who are six-sigma, CAPA and Risk Management experts.

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problem solving techniques pharmaceutical industry

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9 Important Skills to Score a Job in the Pharmaceutical Industry

These are some of the important skills you should

Finding a job in the pharmaceutical industry can be challenging. Find out which skills can help you score your dream pharmaceutical company job.

The pharmaceutical industry is constantly evolving and growing . That makes it one of the most desirable industries to work in. Qualified, intelligent people from various educational backgrounds tend to gravitate towards pharmaceutical company jobs.

Like many other jobs, a position at a pharmaceutical company requires you to have some exceptional skills. The options for pharmaceutical industry jobs are endless, and people with various skill sets can find a place for themselves.

When building your CV for a pharmaceutical industry job , you need to be clear about your education and work history, as well as your skills. Writing your top skills for a pharma job can be tricky. You need to focus on the core qualities and skills that can set you apart from other applicants.

The pharmaceutical industry is incredibly vast, and you can choose the area you will excel at based on the skills you have. Some of the positions you can hold at pharmaceutical company jobs include:

Production and quality control
Research and development
Clinical research associate
Clinical research assistant
Analytical chemist
Distribution
Research scientist
Pharmacologist
Product specialist
Science writer
Pharmaceutical sales
Process/product development scientist

Must-Have Skills to Find a Job in the Pharmaceutical Industry

Deciding which field of the pharmaceutical industry you want to join can be overwhelming for many. However, before you make a choice, you must familiarize yourself with all the skills that can help you become successful in the pharma sector.

1. Project Planning Skills

Pharmaceutical companies have numerous projects they have to complete at all times. These projects are what help the companies grow and are the reason for the ongoing evolution of the industry. To fit in seamlessly with the company, you need to be skilled at planning projects.

Project planning skills in the pharma industry include identifying and allocating resources for the production of substances and devices. Managing time and budget for various projects is also a part of planning. You should be able to identify all issues and problems faced by users and be clear on how you plan to solve these problems.

2. Specializations

Your knowledge and educational background are also valuable skills when it comes to scoring pharmaceutical company jobs. Before you move on to listing other qualities you have that help you stand out; you need to specialize in a particular area of expertise. Of course, you can choose the field that interests you the most.

Like every other industry, employers in the pharma sector value some talents more than others. You need to make your decision after considering everything. Being a specialist in any field is always a plus when you are job hunting. It is important that you choose an area and learn about it in exceptional detail to become successful in the field.

3. Transferrable Skills

As crucial as specialized skills are for securing a job, transferrable soft skills are just as essential. These skills are what help you maintain your position and success at work. Your communication and emotional intelligence skills can take you places. To become a valuable asset for a pharma company, you need a wide variety of transferable skills and qualities.

Professional communication is vital in any field. You need to be aware of how to put your point across and inform people about any product or service. When working a sales job in pharma, you need to be an excellent listener as well and engage with consumers in a convincing manner. You must also be emotionally intelligent to understand and recognize your coworkers’ and clients’ feelings.

4. Strategic Thinking and Creative Problem Solving

Strategic thinking and creative problem-solving are skills that can help you become successful in any field of work. These skills are even more crucial for pharmaceutical company jobs. You need strategic thinking to make difficult decisions and provide solutions to various problems. The pharma industry requires you to become a part of different projects.

You need to think innovatively to ensure the success of complex projects. Strategic thinking can also help you plan projects and ensure that everything goes well. Becoming a valuable asset for a pharma company requires you to provide quick and efficient solutions to problems. These skills can help you gain the reputation of a responsible worker, and someone others can rely on.

5. Management Skills

There are various positions at a pharmaceutical company that require you to have exceptional management skills. Whether you are managing a pharmacy or a project, you need to prove to your employers that you are reliable and can get the work done. Pharmaceutical industry jobs require you to evaluate risks and make tough managerial decisions.

When working at a pharmaceutical company, you must realize the importance of time and resource management. There are countless jobs in the industry that require you to work with teams, which is why you need to have brilliant management skills and the ability to adapt to various situations. You should also have fantastic organizational skills and track the progress your team and company are making.

6. Awareness Of Industry Trends

Having adequate business skills that help you analyze and recognize industry trends is essential for pharmaceutical company jobs. The pharma industry is known for evolving and changing. To keep up and progress in this sector, you need to be aware of key business trends and influences on the various sectors of the industry.

Employers in the pharma industry value candidates who have the ability to predict industry trends, manage budgets, and make projections for future finances. It is crucial that you are capable of recognizing global business opportunities and trends and making informed decisions regarding the future of the company and its products.

7. Understanding of Regulations and Legal Matters

When obtaining your formal education, you learn how to follow the rules and regulations and work in a disciplined environment. Working in a pharma company is not any different. You must learn to comply with the regulations set by agencies and regulatory bodies like the FDA.

It is crucial that you follow the laws and guidelines of the pharma industry. These rules are not just related to scientific research and development. You must recognize the regulatory laws that affect the organization and company. Whether it is related to compliance or intellectual property laws, you must keep your work in check with all the regulations.

8. Conflict Resolution and Teamwork

Whether you are working in a pharma company or a research lab, you should be aware that working alone with data is not possible. Although you can get by working alone at an educational institution, it is significantly more difficult to do that at a pharma company.

To become a part of the company and team, you need the ability to work well with others. The environment inside a pharma company is incredibly collaborative, and you need teamwork. To become a part of a team, you need excellent communication skills, interpersonal skills, diplomatic skills, and more. Working with a team can be challenging when others have different opinions, but your conflict resolution skills can help you manage such situations.

9. Relationship Building

It is difficult to become successful in an industry without being efficient at communicating with others. To progress at a pharmaceutical company, you need to be exceptional at dealing with differences in opinion, communicating with colleagues, and sharing responsibilities.

You need to get along with your coworkers and be accommodating. If you are not good at building relationships with people that are alongside you, there is little to no chance for you to be successful at your work.

Key takeaway

Finding pharmaceutical company jobs is not an easy task. Recruiters often require an excellent CV with your educational and work background.

And while your resume and cover letter are important, they are not all you need. You need to have various skills that can help you work in the ever-evolving environment of a pharmaceutical company. Different skills can help you excel in various positions and roles, and it’s crucial that you hone these skills and use them to establish a prosperous place in the industry.

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Decision-Making and Problem-Solving Approaches in Pharmacy Education

Affiliations.

1 Bon Secours Memorial Regional Medical Center, Mechanicsville, Virginia.
2 Virginia Commonwealth University School of Pharmacy, Richmond, Virginia.
PMID: 27170823
PMCID: PMC4857647
DOI: 10.5688/ajpe80352

Domain 3 of the Center for the Advancement of Pharmacy Education (CAPE) 2013 Educational Outcomes recommends that pharmacy school curricula prepare students to be better problem solvers, but are silent on the type of problems they should be prepared to solve. We identified five basic approaches to problem solving in the curriculum at a pharmacy school: clinical, ethical, managerial, economic, and legal. These approaches were compared to determine a generic process that could be applied to all pharmacy decisions. Although there were similarities in the approaches, generic problem solving processes may not work for all problems. Successful problem solving requires identification of the problems faced and application of the right approach to the situation. We also advocate that the CAPE Outcomes make explicit the importance of different approaches to problem solving. Future pharmacists will need multiple approaches to problem solving to adapt to the complexity of health care.

Keywords: CAPE outcomes; decision-making; pharmacy education; problem-solving.

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Applying lean six sigma methodology to a pharmaceutical manufacturing facility: a case study.

1. Introduction

2. materials and methods, 2.1. loss identification, 2.2. loss stratification, 3. project selection.

Within the packaging area line being reviewed, tablet feed issues are the highest cause of downtime within the short stop category.
The impact is 335 h of downtime over a 4-month period at an average 20 h per week with an upward trend observed in downtime for tablet feed of which one fifth (72 h) is being contributed by packaging line C80/2.

3.1. Team Creation

3.2. problem solving, 3.3. problem-solving approach, 3.4. coating department, 3.5. refined problem statement, 3.6. analysis of robustness of the tablets.

It will take 2 years to implement a change as a 2-year stability reference will need to be established for dissolution for regulatory authorities.
Increased hardness affects dissolution time. As can be seen below in Figure 18 , an increase of 1 KP to the product SKU 10C821 (currently at 10 KP) will mean the tablet product will fail on dissolution testing. An increase to 12 KP ensured over 50% sampled failed batch testing for dissolution of the tablet.

3.7. Benefits Realization and Results

3.8. future value stream map.

Product backlog into the packaging area reduced by 84%
The cycle per batch improved by 8.3%.
The line changeover time reduced by 25%
The line availability improved by 11%.

3.9. Roll out and Share

4. conclusions.

The project demonstrated the benefits of implementing change through effective and structured problem solving by eliminating downtime, improving product flow, reducing backlog, eliminating product wastage, increasing productivity and ultimately enhancing customer experience by reducing the backlog for the product to leave the factory.
This project successfully utilized the Lean Six Sigma methodologies to determine root causes and implement corrective actions. This resulted in eliminating the problems under investigation without negatively impacting manufacturing cost, production time or product quality.

Author Contributions

Institutional review board statement, informed consent statement, data availability statement, acknowledgments, conflicts of interest.

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Click here to enlarge figure

Process Step	Work Performed	Average Time Taken (s)
1	Operator removes broken half tablet from feed chute line using a spatula tool	20
2	Operator gets tablets from feed bowl to use to fill empty tablet pockets	10
3	Operator refills empty tablet pockets manually	20
4	Operator resets and restarts production line	10

Action	Why?
Conduct diasorting Trial on riddle plate	Potential to remove 79% of broken tablets found on the packaging line
Complete maintenance check to get specific plattens in place and allocate storage areas	Incorrect sized plattens will not remove defects (half tablets) effectively. There is no area to store plattens to ease changeover
Agree storage for plattens to enable changeover when running different size tablets	Current system not working. Plattens being cross shared between lines. Sets being mixed up
Create standard settings to the packaging line transportation system	No standard settings in place. Standard optimized settings will reduce variation output from setups to improve quality of production outputs

Minutes Downtime	20,888
Projected Blisters Lost per year	7,912,200
Projected Blisters rejected on restart	180,000
Contingency of 20%	6,473,760
Recovery cost per Blister	(£0.06)

Line	Minutes Lost	Total Blisters Lost
C80/2	4189	418,900
C80/6	3662	439,440
C95/5	3605	612,850
C65/1	3347	267,760
C95/4	3210	545,700
C95/3	2075	352,750

Measure (Waste)	Current VSM (Before)	Future VSM (After)	% Improvement
Backlog into Packaging (in days)	11.6	1.8	84
Cycle Time per batch in Packaging (in hours)	24	22	8.3
Line changeover time in Packaging (in minutes)	120	90	25
Packaging Line availability (in seconds)	81,000	72,000	11
Overall Factory Lead Time (in days)	60.1	18.85	69
Overall Factory Value Added Activity (in days)	2.8	2.4	14

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Share and Cite

Byrne, B.; McDermott, O.; Noonan, J. Applying Lean Six Sigma Methodology to a Pharmaceutical Manufacturing Facility: A Case Study. Processes 2021 , 9 , 550. https://doi.org/10.3390/pr9030550

Byrne B, McDermott O, Noonan J. Applying Lean Six Sigma Methodology to a Pharmaceutical Manufacturing Facility: A Case Study. Processes . 2021; 9(3):550. https://doi.org/10.3390/pr9030550

Byrne, Brian, Olivia McDermott, and John Noonan. 2021. "Applying Lean Six Sigma Methodology to a Pharmaceutical Manufacturing Facility: A Case Study" Processes 9, no. 3: 550. https://doi.org/10.3390/pr9030550

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Reinventing Lean Six Sigma for the Pharmaceutical Industry

Instead of rigidly applying statistical tools, experts suggest that pharma embrace statistical thinking, but focus on reducing variability and adding value for patients.

Six Sigma, both a statistical term and a method developed by Bill Smith, an engineer at Motorola in the 1980s, has allowed companies in a number of industries to improve their business processes, product quality, and overall financial performance. The concept was championed by Jack Welsh when he was CEO of GE, and a number of pharmaceutical companies, including Merck and Johnson & Johnson (J&J) adopted the practice enthusiastically in the past. The principles of Lean Manufacturing, including its focus on the customer and employees and its emphasis on minimizing waste, enriched Six Sigma’s overall framework of “Design, Measure, Analyze, Improve, and Control” (DMAIC). The resulting “Lean Six Sigma” (LSS) programs allowed companies in a number of industries to achieve significant improvements in efficiency and product quality. However, LSS programs need senior management support if they are to work, says consultant and statistician Ron Snee, coauthor of Leading Six Sigma - A Step-by-Step Guide Based on Experiences with GE and Other Six Sigma Companies . In cases where this has happened, improvements have been dramatic, he says. Snee recalls one company outside of pharma whose Six Sigma programs were driven first by three managers, saving a few million dollars a year, snowballing into $30 million in savings over five years, after the CEO assigned a vice-president-level champion to the project. A decade ago, LSS programs were trumpeted by many pharma companies. Today, efforts are more subdued, and not only in the pharmaceutical industry. “It has been a challenge in other industries as well, to maintain excitement and initiatives around Lean Six Sigma,” says consultant Tara Scherder, a chemical engineer and statistician. Now a partner with Synolostats, Scherder trained employees throughout the enterprise as part of Merck Sigma, Merck’s formal enterprise-level Lean Six Sigma program. Today, LSS programs that once spanned the enterprise live on in a muted fashion, limited mostly to smaller initiatives in manufacturing.

Misunderstandings

Why has LSS failed to become standard practice at more pharmaceutical companies? For one thing, Snee says, people misunderstand the concept. “Some think it’s only about quality improvement, while others just see it as training. It’s actually about improving the overall performance of an organization.” In addition, he notes, some may be fixated on the definition of Six Sigma, in terms of number of defects. In the process, Snee says, managers may fail to integrate programs and efforts with the bigger picture, and overall management goals. “Programs must be relevant to the needs of the business,” notes Scherder. “You must be agile and focus on business context and solutions, not statistical tools,” she says, especially because these tools are a relatively small piece of a larger business solution, and they are often not required at all.

Too little problem solving?

That overemphasis on statistics was a weakness in many corporate training programs in the past, says Scherder, at the expense of other beneficial, relevant principles and activities that should be part of LSS. For example, she says, it takes time, and mentoring, for individuals to absorb statistical concepts, and to apply them properly to specific business issues, considering the context of the data involved. “Typically, non-trivial statistical concepts (e.g., design of experiments) come fast and furious at the student without time for practice, or adequate interpretation,” she says. This can lead to a statistical toolbox mentality, in lieu of statistical thinking. Often, she notes, problems won’t even require a statistical solution. “Learning how to assess business and data context and the need and choice of statistical method requires time and contextual practice. This won’t be found in JMP or Minitab or any other statistical software out there,” she says, noting that the “toolbox mentality” naturally leads to non-value-added analysis instead of the simplest solution.

Pharma bashing

Scherder is also concerned about pharma being compared to other manufacturing industries and criticized for failing to achieve Six Sigma levels. For one thing, notes Snee, Six Sigma has not been achieved for all processes, even within electronics or automotive industries, and it may not always be desirable to achieve it, given business goals. Consider the myriad processes involved in producing a pharmaceutical tablet, Snee says. Which steps would make sense to bring to Six Sigma levels? “Implementing the six sigma improvement process does not mean that a company has attained, or planned to attain six sigma quality in its processes.”

Room for improvement

“Admittedly, the industry has room for improvement in the adoption of LSS principles,” says Scherder, “but simple comparisons to other industries are unfair.” Consider, for instance, the relative ease of incremental change. The regulatory burden associated with process changes in pharmaceutical manufacturing is extremely high compared to that in other industries, which impedes motivation for continual improvement, she says. Industry and regulators recognize the benefit associated with reducing this burden over the lifecycle of a product. Signs of progress include the 2016 draft FDA guidance for comparability protocols, and the drafting of a guidance (ICH Q12) for lifecycle management of post approval changes by the International Council for Harmonization (ICH).

Customer-based specifications

In addition, in pharma, manufacturers do not always have customer-based specifications, such as tolerance for a car component, or the dimensions of a semiconductor wafer, says Scherder. Instead, specs are often derived from process performance. This is particularly true for some biopharma attributes, where the mechanism of action to the patient cannot be simply described, she says. In these cases, the sigma quality level is essentially bounded to three, she explains, because the specifications are based on the expected distribution (the mean +/- 3 standard deviations, or similar statistical interval). Historically, Scherder notes, when variability has improved over time, regulators have required update of the specifications to reflect the tighter performance even though the tighter specifications were not required for patient safety or efficacy. This practice caps the sigma quality level at three or less, she says, because the specifications bracket only the new performance. This type of specification adjustment is not prevalent in other industries. The use of specification ranges that simply bracket the expected process variability in lieu of true customer derived specifications must be considered in any valuation or comparison of pharma industry sigma quality performance. Clinically relevant specifications (customer derived in terms of LSS), are receiving more attention within the industry. The topic is discussed in a paper by Yu and Kopcha (1) (see Sidebar ), and the International Society of Pharmaceutical Engineers has established a working group to focus on this issue, she says.

Lack of leadership support

Although Merck’s CEO, and a few other CEOs in pharma, supported Six Sigma and Lean Sigma, “pharma has not yet had a Jack Welch figure,” notes Snee, while companies may abandon an approach that focuses on individual projects.

But problems with some past programs may also be to blame, says Scherder. At some companies, she says, there may have been too much emphasis on training large numbers of Black Belts who went forth with an arsenal of complex statistical tools to solve problems. Over time, at some companies, the title Black Belt may have become synonymous with ‘someone who complicates things.’ “Today, business priorities have become highly focused,” says Scherder. “You have to prove yourself to be relevant and effective in moving product along the lifecycle,” she says, or improvement programs will be considered a cost that can be eliminated.

Returning to basics

Scherder sees a need for pharma’s LSS programs to focus on the fundamental concept of understanding and controlling variability and driving that throughout the organization. She also sees a need to incorporate the best aspects of Lean thinking in training. “The concentration on many statistical tools taught too quickly comes at the price of holistic problem solving. Instead, we need to develop problem solvers who will drive statistical thinking throughout an organization, to understand variability, reduce waste, and improve processes,” she says. “Everyone needs to have a line of sight from product/process development to commercial supply and back. Application of statistical thinking across an organization would enable the connections needed to optimally develop and continually improve processes,” she says. What would happen, she asks, if process and analytical development professionals were made aware of long term variability that could affect process capability and continual improvement? Resource prioritization should be given to activities that will provide value or protection to the patient.“If a process exhibits high process capability, there’s no patient benefit to chasing variability that has negligible safety or efficacy implications. In such cases, patient needs are better served by resources spent on new product development, and less capable processes,” she notes. Finally, she says, there needs to be a focus on activities that will add value to the patient. Some companies have developed LSS approaches incorporating the best elements of both DMAIC and Lean. Amgen, for example, took an approach that focused on process monitoring, incorporating elements of Lean, and setting targets for process capability to improve individual process performance and reduce cycle time as well as overall waste (2).

1. L. Yu and M. Kopcha, International Journal of Pharmaceutics , 528 (2017), pp. 354-359 (June 2017). 2. M. Van Trieste, “The Journey from Good to Great: Process Monitoring Leads to Improving Product Quality,” paper presented at the Second Annual FDA/PQRI Conference, October 5, 2015.

Article Details

Pharmaceutical Technology Vol. 41, No. 10 Pages: 76-80

When referring to this article, please cite it as A. Shanley, “Reinventing Lean Six Sigma for the Pharmaceutical Industry," Pharmaceutical Technology 41 (10) 2017.

XRD in Pharmaceutical Analysis: A Versatile Tool for Problem-Solving

Introduction.

There is a wealth of published material on specific pharmaceutical applications of XRD [1-6]. The non-destructive nature and relative ease of sample preparation make XRD ideal here. In particular, the XRD pattern represents a crystalline drug “fingerprint” needed for patent descriptions, and to identify different drug batches. Other XRD pharmaceutical applications are excipient compatibility, optimization of process parameters, detection of form impurities, crystal morphology of active, and monitoring batch or dosage uniformity.

The purpose of this article is less specific, and directed more toward the non-expert XRD user; i.e., how XRD can be practically utilized to solve real-life problems in preformulation and formulation. In these situations, XRD is normally part of a multi technique approach. Therefore, we show XRD in context with some of these techniques. We also discuss disadvantages of XRD, and describe techniques potentially useful as alternative methods.

Mention is necessary regarding XRD sample preparation and data acquisition conditions. These aspects are not trivial and can lead to serious errors if improperly performed. Particle size, particle orientation, and data collection parameters influence data quality. The sample must be representative of the bulk material with particles uniformly distributed. The need for sample pre-treatment (grinding, etc.) should be assessed; if employed, samples phases must not be altered. Many methods are available for XRD sample preparation, each having advantages and disadvantages. These aspects are discussed by Buhrke et al [7].

Experimental

Samples were run on quartz zero background holders with a commercially available XRD system. A liquid nitrogen cooled solid-state Germanium detector was used with Cu k-alpha radiation at 45kv/40ma.

Case 1: Drug Substance Form Identification

Knowledge of drug substance form(s) is essential, especially identification of the most stable form. Polymorphism and pseudo-polymorphism (hydrated or solvated crystal forms) often are discovered during screening. Because physical changes in drug substance can potentially affect solubility, ease of manufacturing, bioavailability, and product stability, forms discovered in screening may require further solid-state characterization. Techniques like HPLC, solution NMR, and mass spectrometry are important for verifying chemical purity and composition, but not crystal form.

However, XRD in conjunction with other techniques can be quite informative. Consider Compound A, isolated as either a dihydrate or monohydrate. The amount of water in Compound A can be quantified by TGA, but TGA weight-loss curves for the hydrates do not give any information regarding physical structure. DSC thermograms of the two forms are similar, although the monohydrate has a higher melting point. While FTIR can be sensitive to O-H vibrational modes of hydrates, here the differences between the two forms were slight. But XRD—a non-destructive technique, unlike DSC—definitively distinguished the two forms. Note that as an alternative technique, solid-state NMR could also provide this information.

The relationship between the two hydrate forms was further examined using variable temperature XRD (VTXRD). Of interest was whether the dihydrate converted to the monohydrate upon heating. The VTXRD data, Figure 1, suggest no interconversion. The monohydrate appears more stable, retaining its crystal structure at 300oC, while the dihydrate crystal structure has disappeared. These observations agree with DSC and hot-stage microscopy data.

Characterization of the Compound A hydrates is relatively straightforward; application of XRD and TGA allow definitive identification of the monohydrate and dihydrate. Complications occurred when an intravenous formulation of Compound A was needed. The drug substance had a very limited solubility at neutral pH; to achieve the target drug concentration, the solution pH range was adjusted in buffers (pH 4 to pH 5). Several co-solvent combinations (e.g., with propylene glycol) were also evaluated. Initially all solution formulations were clear, but haziness developed, then precipitation. The precipitate was bright yellow, suggesting a transformation of the pale yellow dihydrate starting material. Precipitation appeared related to the amount of co-solvent, although it was also observed in buffers alone. To identify the precipitate, a characterization protocol was developed.

Precipitate material was isolated from bulk solutions by centrifugation or vacuum filtration, and air-dried; drying is necessary to remove co-solvent. Solution methods (HPLC, LC-MS, NMR) confirmed the precipitate was chemically identical to Compound A. The precipitate DSC, Figure 2, indicated it was a hydrate. The dehydration endotherm (Event 1) showed a Tmax similar to that of the known forms. The precipitate melting point (Event 2) was similar to that of the dihydrate. The precipitate exhibited an additional thermal event, a broad endotherm with Tmax at 70oC, suggestive of increased water.

Likewise, the precipitate FTIR spectrum exhibited modest differences in the O-H stretching region (3300-3500cm-1) compared to the other hydrate forms. So, both DSC and FTIR suggest the precipitate was a hydrate, but these results do not tell us anything about water stoichiometry or crystal structure. The former is confirmed by TGA, indicating the precipitate is a Compound A tetrahydrate. With respect to tetrahydrate crystal structure and its relationship to the other hydrate forms, XRD can address this question, Figure 3. The tetrahydrate clearly has a crystal structure distinct from the other hydrate forms.

It was later found that both monohydrate and dihydrate convert to tetrahydrate under high humidity conditions. The tetrahydrate has a lower solubility in the solution formulation, causing precipitation. What initiates tetrahydrate formation is not totally understood. Increasing the amount of co-solvent favors tetrahydrate appearance, but this would not account for observed precipitate in buffer solutions alone. Thus, solution pH may also play a role.

A potential challenge here was the relatively small amount of precipitate isolated from solution (frequently less than 5 mg), highlighting the need for sample conservation and avoidance of nondestructive characterization methods if possible. XRD data acquisition conditions were adjusted for sample size, Table 1.

Again, solid-state NMR could have been utilized in this work, although the smaller sample amounts (<5mg) might have been problematic.

Case 2: Stability of an Amorphous Drug Substance

Poor water solubility of drugs poses a difficult challenge for formulation. Many approaches have been taken to overcome this problem: Particle size reduction is often the first option pursued, but introduces the possibility of contamination from the grinding equipment. Another approach is to prepare the drug substance as an amorphous form. This can lead to a significant enhancement of solubility. However, it should be emphasized that amorphous formulations are inherently unstable and may crystallize, especially at high humidity storage. If amorphous material precipitates from a delivery system, drug dissolution and bioavailability may be compromised. Thus, it becomes critical to identify the onset of crystallization, and if possible set a detection limit for crystalline content.

The present example involves Compound B. Crystalline B, with or without PVP, was dissolved in methanol and isolated by rotary evaporation; spray-drying was carried out for scale-up. Table 2 shows that amorphous B has significantly enhanced solubility.

Amorphous B can be characterized by various techniques for information about crystallinity. In optical microscopy, crystalline and amorphous B appear dramatically different. Crystalline B exhibits obvious birefringence and well-defined particle morphology; amorphous B shows no birefringence and has much smaller, less distinct particles. While microscopy is an excellent tool for confirming amorphous material existence, it cannot make quantitative estimates of crystalline content in the amorphous matrix.

Another technique is modulated DSC (MDSC), which allows determination of the glass transition (Tg). For dry amorphous B, Tg is quite high, ca. 210oC, suggesting this material will remain stable if moisture is minimized. It is well known that increased moisture content lowers Tg, making the amorphous material increasingly susceptible to crystallization. This has significant implications for product storage; i.e., by minimizing moisture content, the amorphous form should remain stable. Tg is thus useful as a predictive tool, but can’t directly quantify crystalline content.

Regarding the onset of crystallization, XRD is useful to detect crystallinity in an amorphous matrix. This is because the crystalline and amorphous patterns are strikingly different: Crystalline forms exhibit relatively sharp, well-defined peaks, while amorphous forms display a diff use “halo.”

Preparing mixtures of crystalline and amorphous reference drug forms offers a way to quantify the amount of crystalline drug, Figure 4. The intensity or area of selected crystalline peaks diminishes as the amount of crystalline material in the amorphous matrix decreases; this change can be applied for quantification.

Typically XRD assesses crystallinity from peak height or area. Using the peak area at 10.4 2-theta gave the best linearity (Figure 5). The data also indicate the limit for detection of crystalline material is below 2.5%.

Note that PVP is amorphous, so its presence does not interfere with XRD crystallinity detection. However, it is not uncommon for crystalline excipient peaks to overlap with active peaks in drug product, necessitating selective subtraction procedures [8].

While application of XRD for crystallinity quantification can work well, there are caveats. One is preferred orientation, which will affect peak intensities. This can sometimes be overcome by grinding, or by using a capillary. With grinding, care must be taken that the sample does not change physically. Another problem relates to the quality of the reference standards and homogeneity of the mixtures used; incomplete mixing can lead to large errors when a small amount of one component is not uniformly distributed.

In addition, one must consider underlying assumptions of the quantification model. Here the two-state model was used, i.e., completely ordered (100% crystalline) and completely disordered (100% amorphous). However, this model may not truly describe the crystalline lattice disordering process [9]. Furthermore, there is concern that the “amorphous halo” concept is too simplistic for meaningful analysis. Treatment of more complicated amorphous systems is described in the literature [3,10].

While XRD and DSC are the most widely used techniques for determination of crystalline (or amorphous) content, many others are available. Shah et al. compares the merits of several methods [11].

Case 3 Identification of Active in Solid Dosage Form

Besides the “amorphous” strategy of Case 2, there are other ways to enhance solubility. One is salt formation; salts tend to be significantly more soluble, with a higher dissolution rate than the original acid or base drug substance. This strategy was applied to Compound C, a poorly soluble free acid. Here free acid starting material is treated with excess sodium hydroxide and a carrier (e.g., mannitol) acting as a conversion aid. This yields a crystalline dispersion, facilitating transformation of free acid to sodium salt [12]. The resulting in situ sodium salt requires characterization at different intervals of the formulation process, including the finished product (tablet).

Before salt form assessment can be done in such samples, one must consider excipients also present. In the mannitol formulation, the non-active components include: mannitol, microcrystalline cellulose, cornstarch, and a disintegrant. It is necessary to obtain the XRD pattern of each excipient to determine any areas of overlap with the drug substance. Furthermore, other conversion aids were evaluated for formulation; these were also analyzed by XRD. From the patterns of the excipients and various conversion aids (D-sorbitol, various non-ionic surfactants, propylene glycol, and PEG 400), the 2-12 2-theta region appeared best for sodium salt form determination. No excipients or conversion aids exhibited peaks there; all observed peaks were unique to the drug substance.

One concern relates to the identity of the sodium salt within the dosage form. Interestingly, an automated salt screen was unable to find any sodium salts of Compound C. Manual screening was more successful. Seven sodium salts were synthesized; all were crystalline, with differing XRD patterns. However, only salt forms 6 and 7—those containing the carrier material with sodium hydroxide—had XRD patterns matching those of the situ salts in the dosage form, Figure 6.

Initially it appeared that salt forms 6 (dihydrate) and 7 (trihydrate) were unrelated, but this turned out not to be the case; it became clear that forms 6 and 7 could interconvert rapidly, within one hour. Depending on the humidity level, one or both forms could be present, which is reflected in the XRD pattern.

Another concern is maintaining the desired product solubility. It is essential that the free acid be totally converted to the corresponding salt form(s) to avoid precipitation. XRD and FTIR are both useful for monitoring this. From Figure 6, it’s evident the main peaks of the free acid are generally distinct from those of the salt forms, suggesting a qualitative and possibly quantitative XRD method is feasible.

However, due to other considerations—availability of instrumentation at different manufacturing sites—an FTIR method was chosen. As with XRD, the FTIR method used known mixtures of acid and salt to establish a detection limit for the free acid, in this case by monitoring disappearance of the carboxylic acid carbonyl at 1734cm-1, where the sodium salt has minimal absorbance. A direct correlation between free acid content and carbonyl peak absorbance loss was established, with a limit of quantification (LOQ) of 5% and a limit of detection (LOD) of 2% acid.

Note with either XRD or FTIR, there was never any evidence of acid in the solid formulations, suggesting conversion to salt was complete.

Representative granulations were stored under different conditions and analyzed by XRD. Results are summarized in Table 3.

Despite the limited angular range, Forms 6 and 7 exhibit distinctly different patterns. Fortuitously the peaks at 11.3 2-theta (form 6) and 4.5 2-theta (form 7) are well separated and easily identified. Compound C granulations generally contain mixtures of Form 6 (dihydrate) and Form 7 (trihydrate). But the mannitol formulation, which contains only form 6 with desiccant, shows Form 7 formation without desiccant, as indicated in Table 3.

XRD works well in distinguishing forms 6 and 7, even in a tablet. FTIR cannot distinguish these two forms; on the other hand, parallel studies with Raman spectroscopy gave good results on the same samples, suggesting this technique could be an excellent back-up method for XRD.

The three examples given are intended to illustrate the diversity of XRD applications in pharmaceutical analysis. In Case 1, XRD was used to identify drug substance forms, including an unknown material. In Case 2, XRD was applied to quantify crystalline content in an amorphous formulation. In Case 3, XRD was used to identify two related drug forms in a solid formulation.

In summary, as a stand-alone technique XRD is very useful for problem solving in pharmaceutical analysis, although sample preparation and data interpretation require care. Also, by combining it with other techniques—FTIR/Raman, DSC/TGA, etc.—XRD can provide greater clarity and completeness in the understanding of events.

Acknowledgements

We thank Valerie Jobeck and Nathaniel Schuster for excellent technical contributions. We also thank Dr. Anthony Severdia for his encouragement and keen scientifi c insights.

1. Davidovich, M., Mueller, R., Raghavan, K., Ranadive, S., Vitez I., Sarsfield, B., The role of powder X-ray diffraction as a powerful tool in characterization of various hydrates of drug substance, American Pharmaceutical Review, 2001, 4, 53-60.

2. Sampath, I.S., Phadnis, N.V., Suryanarayanan, R., Quantitative analysis of complex pharmaceutical mixtures by the Rietveld method, Powder Diffraction, 2001, 16, 20-24.

3. Bergese, P., Colombo, I., Gervasoni, D., Depero, L.E. Assessment of the X—ray diffraction-absorption method for quantitative analysis of largely amorphous pharmaceutical composites, J. Applied Crystallography, 2003, 36, 74-79.

4. Harris, K.D.M., Modern applications of Powder X-ray diffraction in pharmaceutical sciences, American Pharmaceutical Review, 2004, 7, 86-91.

5. Davidovich, M., Gougoutas J.Z., Scaringe, R.P., Vitez, I., Yin, S., Detection of polymorphism by powder X-ray diffraction: Interference by preferred orientation American Pharmaceutical Review, 2004, 7, 10-17

6. Varasteh, M., Deng, Z., Hwang, H., Kim, Y.J., Wong, G.B., Quantitative determination of polymorphic impurity by X-ray powder diffractometry in an OROS formulation, Int. J. Pharm., 2009, 366, 74-81.

7. A Practical Guide for the Preparation of Specimens for X-ray Fluorescence and X-ray Diffraction Analysis, Buhrke, V.E., Jenkins, R. Smith, D.K. (eds.), Wiley VCH, 1998

8. Phadnis, N.V., Cavatur, R.K., Suryanarayanan, R., Identification of drugs in pharmaceutical dosage forms by X-ray powder diffractometry, J. Pharm Biomed. Analysis, 1997, 15, 929-943.

9. Rani, M., Govindarajan, R., Surana, R., Suryanarayanan, R., Structure in trehalose dihydrate— evaluation of the concept of partial crystallinity, Pharm. Res., 2006, 23, 2356-2367

10. Bates, S., Zograf,i G., Engers, D., Morris, K., Crowley, K., Newman, A., Analysis of amorphous and nanocrystalline solids from their X-ray diffraction patterns, Pharm. Res., 2006, 23, 2333-2349.

11. Shah, B., Kakumanu, V.K., Bansal, A.K., Bansal, J., Analytical techniques for quantification of amorphous/crystalline phases in pharmaceutical solids, J. Pharm. Sci., 2006, 95, 1641-1665.

12. United States patent 583771

Author Biographies

Cynthia Randall received her degrees in chemistry from the University of Wisconsin-Madison and the University of Michigan. Since joining the Analytical Sciences Department at sanofi-aventis, she has been primarily involved in solid-state characterization of new drug entities. Cynthia is the co-author of 17 scientific publications, including 4 book chapters. Her research interests include protein denaturation/stabilization, drug-liposome interactions, and pharmaceutical applications of spectroscopic and calorimetric techniques.

William Rocco was a Principal Research Investigator in the Pharmaceutical Sciences Department of sanofi-aventis for over 20 years. His primary interests are in preformulation and development of polymorphic forms. He has degrees in chemistry from SUNY-Oneonta and Chemical Engineering from SUNY Buffalo. He is currently a research chemist at Merck Research and Development.

Pierre Ricou received his doctoral degree from Ecole des Mines de Nantes (France), and then joined his now former employer sanofi-aventis as a postdoctoral scientist in the Analytical Sciences Department. Among his interests are the applications of XRD and XRF to characterize polymer formulations.

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Emerging from disruption: The future of pharma operations strategy

In the past, many pharmaceutical companies (pharmacos) deprioritized operations strategy in the face of competing business pressures. This is now changing. Factors such as the COVID-19 pandemic, inflation, geopolitics, new therapeutic modalities, and new ways of working make it vital for pharmacos to carefully reconsider their long-term choices in sourcing, manufacturing, and supply chain.

Now is exactly the right time for this renewed emphasis on operations strategy, as pharmacos emerge from two years of intense firefighting. Succeeding in pharma under these new and challenging conditions will require succeeding in operations.

The focus for operational leaders may need to shift from the prevailing emphasis on continuous improvement—including cost savings, quality assurance, and constant readiness to deliver—to longer-term external challenges. These include high inflation and an increase in complexity and risk, as well as the compounding effects these forces have on each other.

Pharma operations leaders now have an opportunity to deliver even greater value to their organizations by achieving this shift in focus, but they must act quickly to keep abreast of the challenges confronting the industry. The effort will require enormous mobilization and thoughtful prioritization. This task will fall to leadership; only the CEO and head of operations are in the right positions to make it happen.

This article explores the challenges facing pharma leaders and the steps they can take to develop a more strategic, long-term, and integrated approach to operations strategy. It presents questions leaders can ask as they design the solutions needed to make sure operations can protect enterprise continuity while still delivering to patients.

A perfect storm of external challenges

The pharma industry is facing a multitude of challenging trends (Exhibit 1). Global demand is growing rapidly, and the unprecedented need for COVID-19 vaccines and therapeutics has put additional pressure on the industry. The industry’s ability to find innovative solutions to deliver COVID-19 vaccines while still meeting overall demand is a remarkable achievement, but rising global demand is still a significant challenge for the industry in the long term.

The product landscape also is changing swiftly. New modalities, such as cell and gene therapy and mRNA vaccine technology, have increased from 11 to 21 percent of the drug development pipeline—the fastest growth ever seen in the sector. This change is likely to bring more fragmentation of technology, new supply chains, and unique product life cycles.

In addition to these industry-specific trends, pharma has also been affected by broader global trends, such as supply chain pressures. While the pharma industry is considered somewhat protected by its high inventory levels and long-standing dual sourcing, over a given ten-year period, the likelihood of supply chain disruptions still represents a potential loss of 25 percent of EBITA . Inflation has risen in recent months to levels not seen for decades, leading to increasing costs for labor, raw materials, and transportation. This is over and above the persistent price pressures pharma is already facing, particularly in generics. Since pharma customers are not expected to fully absorb these cost increases, profit margins are under pressure.

Meanwhile, increased state interventions and protectionist trade policies are creating new pressures on manufacturing networks and could drive increased regionalization. This would be a capital-intensive exercise: to regionalize just 10 percent of current vaccine trade in one particular geographical region, governments would need to invest an estimated $100 million.

Would you like to learn more about our Operations Practice ?

The pharma industry is also facing talent shortages linked to wider labor market trends, including the 20 percent increase in demand for STEM-related roles across the life sciences industry in the United States. The current pool of pharma digital talent is at least 14 percent lower than demand, and many companies are finding it challenging to recruit technical talent. Compounding this challenge is the rise of remote working, which has increased employee expectations for flexibility. In response, nearly all pharmacos are experimenting with hybrid working models.

A few major trends point to an industry tailwind; one of them is the advancement of digital and analytics tools. Digital tools, robots, and sensors are becoming cheaper and easier to access, and they can be used to capture all manner of raw data. In addition, edge computing and cloud analytics are providing real-time optimization and transparency. Pharmacos are working to leverage the power of data to become more agile and resilient. However, to date, no pharmaco has emerged as a true global leader in this field.

The pharma industry is facing a multitude of industry-specific and global trends. But a few major trends point to an industry tailwind; one of them is the advancement of digital and analytics tools.

Each of these global trends represents significant challenges in and of itself, and the trends may be compounded and strengthened through their interactions. This compounding effect can add to the complexity of evaluating an effective strategic response.

Major implications for pharma

These global trends have six major implications for pharmacos: rising operational complexity, increasing risk, shifting capability requirements, higher capital expenditure requirements, variable-cost increases, and opportunities for savings (Exhibit 2).

Operations leaders may need to become comfortable navigating a more complex ecosystem as they respond to increased operational complexity. Risks may increase due to rising environmental, social, and governance (ESG) expectations and skills gaps, while new modalities and digital acceleration will also likely lead to a shift in capability requirements. This could necessitate reskilling and upskilling of staff, as well as a renewed focus on recruiting from outside of the pharma industry.

From a cost perspective, the pharma industry may see significantly increased capital expenditure requirements related to the construction of new sites and new digital infrastructure. Increases are also likely in variable costs in areas such as raw materials, transportation, and employee attrition, reskilling, and salaries.

Future of pharma operations

Pharma companies are experiencing a wave of innovations – from new treatment modalities, to smart machines, advanced analytics, and digital connectivity.

Although these implications are challenging, they may represent possible opportunities for savings in several areas. For example, ESG commitments on waste reduction could reduce costs, as could successful digital implementation. However, the challenge lies in monetizing these cost savings, given that the industry has long created value largely through revenue expansion rather than through cost savings.

Rising to the challenge: Actions to deliver value

To respond to these challenges, pharmaco leaders may now need to emphasize the importance of their operations strategy. They should consider taking a longer-term view and scaling activity across four key themes: network strategy and resilience, digital, operating model, and talent.

Expand focus on longer-term, transformative solutions

Operations leaders can address these challenges through several short-term and long-term responses. For example, problems associated with a more unpredictable supply chain could be addressed with a short-term approach of increasing inventory or a long-term initiative to establish an end-to-end supply chain digital nerve center.

Short-term levers can be an important part of the total response but are insufficient to fully mitigate the challenges facing the industry. To respond effectively, companies may need to accelerate new ways of working and embrace long-term thinking. This will require concrete action with a focus on making sure that strategies are put in place to weather the long-term headwinds the industry is facing.

Accelerate and scale responses across four strategic domains

To identify the actions that pharmacos could take, it may help to group these in terms of four strategic domains: network and resilience, digital strategy, operating model and ecosystem, and talent strategy (Exhibit 3). While these themes are likely to be familiar to any business leader, they now require a substantial shift in mindset. Acting on them also calls for a large investment of resources.

Plan for and manage future resilience and reliability needs . Recent supply chain disruptions have pushed supply chain resilience up corporate agendas. Companies have been forced into reactive modes that employ short-term levers like building inventory. However, companies could better position themselves by solving multiple variables and building resilience into their operations strategy through longer-term actions like network design and dual sourcing.
Scale end-to-end adoption of digital and automation . Digital has proven itself highly valuable to pharma operations. However, many companies struggle to move from targeted, single use cases to a fully scaled suite of solutions. And while the adoption of full-scale digital solutions can require heavy investment—around $50 million to $100 million per year for two to three years—the rewards can include significant cost savings, improved quality, and increased resilience, as well as greater employee effectiveness. Companies that truly scale and implement digital can better protect themselves from the pressures of the forces increasing costs for the industry. More and more companies are moving toward network-wide and end-to-end digitization; to date, the World Economic Forum has recognized 103 as “lighthouses,” based on their advanced application of digital technologies . Johnson & Johnson, for example, has successfully launched multiple Industry 4.0 lighthouses, including some focused on end-to-end patient connectivity and order fulfillment.
Expand adoption of end-to-end partner ecosystems . Companies could also consider changing their operating model from a traditional hub configuration around originators to an end-to-end ecosystem of true strategic partners. More than 50 percent of companies already expect to intensify their collaboration models with other industry players through, for example, service agreements, joint ventures, or ecosystems. Some are already in motion; examples include Pfizer and BioNTech, which have already established a strategic partnership in mRNA technology discovery, and AstraZeneca and Huma, which are collaborating to scale innovation for digital health. These partnerships are indicative of increasing collaborations throughout the industry across functions.

Automation, centralization, and new job requirements may affect nearly 90 percent of today’s workforce, and to deal with this challenge, companies could adopt effective long-term strategies. Retaining talent is challenging in the present environment, with the share of workers planning to leave their jobs in the next three to six months standing at 40 percent since 2021 . 1 Aaron De Smet, Bonnie Dowling, Bryan Hancock, and Bill Schaninger, “ The Great Attrition is making hiring harder. Are you searching the right talent pool? ” July 13, 2022. Strategies for talent retention should therefore be broad and focus on more than just salary.

A viable long-term solution to talent shortages may need to involve more than increasing wages to attract people. To solve structural talent gaps, companies could ensure long-term reskilling and upskilling of the existing workforce. For example, Roche runs an operations rotational program to attract top talent with bachelor’s and master’s degrees, and early in the COVID-19 pandemic, Novartis launched a “choice with responsibility” policy to improve overall employee experience.

Successfully developing a robust operations strategy is complex and requires dedicated resources with the ability to focus on the medium to long term. This means the C-suite will need to prioritize efforts and provide adequate resourcing. Only the CEO and head of operations can set the appropriate direction for their organization, steer their company’s effort, gather the right skills and teams, and manage complex interdependencies and resource-intensive interventions.

Are companies doing enough?

As COOs look to emerge from the disruption of the past two years, reflecting on several questions could help them evaluate their organizations’ level of preparedness to respond to the trends affecting the industry. The process could provide foundational answers to inform a renewed operations strategy.

Have you projected the impact of today’s current trends on your business?
Do you have a focused, skilled, and scaled operations strategy team that identifies, prioritizes, and deploys initiatives across different horizons?
Are your resilience measures proactive and dynamic, and are they being built on talent and digital capabilities to achieve greater agility and reliability?
Have you experienced greater access to innovation and flexibility as a result of expanding your services and strategic partnerships?
Has your digital strategy created benefits across your network and transformed your operation from digitally enabled to digitally driven?
Have you achieved ESG improvements, and do you have a broad, long-term road map for ESG commitments (beyond net zero)?
Has your operating model been agile enough to adapt to rapidly changing operations requirements, such as new modalities and potential disruptions?
Have you successfully transformed your operations workforce and comprehensively improved the employee experience?
Do you have an established governance process that incorporates past lessons into future strategy?

Although the pharma industry has performed a remarkable feat in delivering COVID-19 vaccines while also meeting growing demand, current trends create a challenging environment for pharmaceutical companies. Companies face greater costs, complexity, and risk.

Now is the time to rethink operational strategy to respond to these trends and remain competitive. Such change may have associated challenges and will require bold and innovative leadership. But if companies successfully implement new strategies, they could position themselves to take advantage of the industry’s remarkable growth.

Hillary Dukart is an associate partner in McKinsey’s Denver office, Laurie Lanoue is a partner in the Montreal office, Mariel Rezende is a consultant in the Miami office, and Paul Rutten is a partner in the Amsterdam office.

The authors wish to thank Joe Hughes and Jean-Baptiste Pelletier for their contributions to this article.

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Against the odds: How life sciences companies excel in large transformations

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Operations can launch the next blockbuster in pharma

Current trends and challenges in the pharmaceutical industry – we are here to make a difference

Journal of Business Chemistry October 2022

October 2022

Daniel Götz, Timo Flessner

Introduction

The pharmaceutical industry is currently undergoing a major shift, driven primarily by groundbreaking new therapeutic approaches and a radical technological revolution. Issues such as the growing population of our planet, high unmet medical needs in various disease areas, and the rise of certain emerging markets to global players represent further challenges the industry must overcome.

On top of this, there is the lasting impact of the pandemic and the energy crisis caused by the war in Ukraine, which have led to supply chain disruptions and the need to adapt business and operating models to the “new normal”. Finally, sustainability has stepped up from being a “feel-good topic” to being a business-critical success factor across the entire value chain. We need to act responsibly now to achieve challenging climate goals and safeguard our planet’s resources for generations to come.

Against this backdrop, the pharmaceutical industry has to refocus and prepare for the future by establishing highly efficient and innovative R&D, agile marketing, and especially resilient and sustainable product supply organizations.

This commentary aims to reflect on a number of questions associated with industry trends and challenges. How do current trends impact our core business? How can we leverage our key strengths to unleash the full potential of our assets? How are we to deal with increasing regulatory requirements? Why should we make sustainability our top priority? How are we to rise to the challenges resulting from the pandemic and continue with renewed strength to make a difference for patients – now and in the future?

Megatrends and current challenges shaping the future of our industry

The effects of the pandemic are currently still very present in our daily routine, in both our working environment and our private lives. On top of this has come the terrible war in Ukraine, which has led to an unforeseen energy crisis, a rise in inflation, and higher raw material prices. Even without these global crises, the pharmaceutical industry was already undergoing a major transformation, driven primarily by the combination of radical inventions in the biology and biomedicine fields and digital innovations. This is still ongoing. Overall, we are facing a situation that makes one thing very obvious – we have to critically question ourprocesses and long-established modes of (inter)action in the pharmaceutical industry to reorient and reinvent ourselves and be prepared for current challenges and those that lie ahead.

Amongst the most pressing global trends that will heavily affect our industry are the following:

Our planet’s growing and aging population – two billion people worldwide will be over the age of 60 by 2050, a doubling of this age group as compared to 2015.
The increase in “prosperity” diseases – for example, the number of people with diabetes is predicted to rise to 578 million by 2030, and to 700 million by 2045. (Datta, 2019)
High unmet medical need for various indications – more than ten million patients globally suffer from Parkinson’s disease, which is one of the fastest-growing diseases in terms of prevalence, disability, and mortality.

In line with our primary aim and purpose – improving patients’ lives – we in the pharmaceutical industry need to switch gears and find ways to address these challenges. And we have already taken up the gauntlet. The combination of breakthrough discoveries in biological science (CRISPR- CAS was awarded the Nobel Prize in 2020) and the dynamic development of digital and computer science (from computing and automation to artificial intelligence) is propelling an unprecedented wave of innovation, referred to as the “biorevolution” (Chue et al., 2020). As a consequence, novel biological, biochemical, and biomedical applications are already improving our response to global challenges, with the best example being the recent pandemic. The speed at which scientists sequenced the coronavirus genome within weeks rather than months would not have been possible just a few years ago. In addition, new biochemical tools such as mRNA technology have reached maturity, enabling real-life applications such as the rapid introduction of mRNA-based vaccines to fight the pandemic. The development of smart devices, remote health monitoring, and precision medicines tailor-made to meet the need of individual patients are further examples of recent technological advances.

The rapid evolution of new technologies obviously opens up a range of unforeseen opportunities, especially for customers and patients. For example, we could unlock applications for biomedical tools that actually cure rather than just treat some of the most serious and deadly diseases such as cancer. As always, however, those opportunities are accompanied by challenges that need to be overcome first.

Tansforming Active Pharmaceutical Ingredient (API) portfolios from conventional small molecules to new chemical modalities and biomolecules requires an expansion of expertise and the establishment of new production technologies.
End-to-end supply chains have been hit hard by the pandemic and must be turned into a future-proof setup with improved resilience.
Sustainability has evolved into a huge, business-critical task that is increasingly relevant to keeping our license to operate.
Artificial Intelligence opens up a vast array of options, but we still have some miles to go as an industry if we are to leverage the huge potential of digital solutions.

Further current challenges include reduced development timelines. One major focus of the pharmaceutical industry is to make new treatment options available to patients in need – as safely and yet as quickly as possible. In addition, new regulatory requirements and changes in reimbursement regimes are being driven forward. Speeding up development cycles raises the need to ramp up external innovation and partnerships, requiring new ways of thinking, for example by focusing on creative collaboration models with the clear goal of making full use of the knowledge capacity of other agile companies and start-ups.

Finally, the lasting impact of the pandemic has brought to the fore the need to transition to what is being called the “new normal”, with remote working models (e.g. working from home, virtual audits, etc.) putting employees and new ways of working even more prominently at the center of attention. This is of crucial importance – not least because people and employees are the most valuable resource for companies to build on. Beyond the highly motivated workforces in the companies and organizations, we must put all patients in need at the heart of everything we do.

Selected challenges – dangers to our business or opportunities for growth?

Portfolio transformation – from conventional small molecules to new (chemical) modalities.

As we have already pointed out, large parts of our industry are currently undergoing a portfolio transformation based on the increasing importance of new modalities. Over the past decade, the conventional drug discovery toolbox has been expanding continuously from traditional, small molecule APIs to new chemical modalities such as peptides, oligonucleotides, PROTACs, RNA-targeting therapeutics, antibody drug conjugates, and gene-editing approaches (Blanco et al., 2020).

While the transformational breakthroughs in bioscience offer invaluable opportunities for patients in need of new therapeutic approaches, the situation also poses challenges to the pharmaceutical industry. Firstly, we must extend our technological capabilities – including employees’ knowledge, but specifically also engineering and manufacturing expertise – across the whole value chain from development to routine production on both a medium and a large scale.

Accepting that many key players in the pharmaceutical industry aim to build their future portfolio on new modalities to a significant extent, the question arises: “What about conventional small molecules and traditional organic chemistry – will they disappear?” Our clear answer is no! Instead, we are convinced they will continue to play a key role going forward. There will be many unsolved problems where chemical know-how, creativity and a deep knowledge of conventional process chemistry are of vital importance when it comes to uncovering the best methods of synthesis and scaling up, thus making next generation APIs accessible for patients (Nising and von Nussmaum, 2022). Chemical API manufacturing that applies state-of-the-art technology platforms in all necessary volumes will significantly contribute to driving economical and sustainable supply and growth.

However, even the small molecule business is currently undergoing a significant transformation. Most importantly, there has been a clear trend towards APIs that are structurally more complex. Among the main reasons for this progression over recent years are the lower druggability of several disease-specific targets and a narrow patent space due to increasing competition. Having said that, complexity in the small molecules API field is increasing in many directions. Increasing molecular complexity (i.e., higher molecular weights, moving toward more complex structural features such as macrocycles, greater number of stereogenic elements, etc.) often results in a higher step count in the initial synthetic approach. In addition, a broad range of production scales across the API portfolio – from small- scale APIs for treating rare diseases to chronic treatment of cardio-vascular indications typically characterized by high peak demands – often places additional pressure on the manufacturing and supply chain setup. Quite regularly, the volume variances in larger API portfolios range from just a few hundred grams to three-digit ton demands at peak sales. Special technologies in the areas of highly potent APIs, biocatalysis, electrochemistry, photochemistry, or unit operations such as ultrafiltration, lyophilization, and chromatography are often required to produce next- generation APIs and drug products at scale.

The rising complexity and the trend toward more complex clinical trials, sometimes involving a higher number of patients in earlier clinical phases and higher costs per patient, result in a significantly increased cost of goods pressure. Contrary to previous experiences, this is increasingly driven by API manufacturing. The need to support even shorter development timelines so as to bring life-saving medications to patients as quickly as possible often means the production process is less well developed at launch. This increases the need for post-launch changes or the development of “second-generation processes”. It goes without saying thatthe very high quality standards for pharmaceutical products need to be safeguarded at all times.

In summary, there is an increase in the challenges for and expectations of pharmaceutical manufacturing organizations to supply innovative drugs in a fully reliable, cost-efficient, competitive, and sustainable way. At the same time, these reasons are offering opportunities to adapt traditional ways of working and make a difference in people’s lives.

As an industry, we should always aim to establish first-in- class development and technology platforms with state- of-the-art, digitally transformed product supply networks. We must strive to be at the forefront of innovation and technological development. To safeguard the reliability of supply and commercial competitiveness, we firmly believe that strong in-house launch capabilities with a fully integrated interface between chemical and pharmaceutical development and product supply functions are key success factors for ensuring best-in-class supply of key brands to meet patients’ needs and impact their lives for the better.

Resilient supply chains – impact during the pandemic and how we safeguard our future

The shock to supply and demand that arose out of the epidemic situation in China in January 2020 and the resulting global pandemic exposed vulnerabilities in the production strategies and supply chains used across many different industries. Concrete effects of the pandemic included:

Lockdown and quarantine regulations affecting resource availability in production plants and distribution centers
Temporary trade restriction
Transport capacity shortages, especially air- and sea- freight container
Changed demands and stockpiling
Shortages of many products, highlighting weaknesses in supply chains

The economic turmoil caused by the pandemic has exposed many vulnerabilities in supply chains. There has therefore recently been widespread speculation in policy circles and the popular press as to whether globalization is still a valuable guiding principle for the future. Will or should the experience of the pandemic lead to supply chains becoming more local again?

It is our firm view that it should not! Going back to a domestic setup will not solve the issues described above but would instead cause knock-on effects such as increased costs and capacity shortages, the rise of a global recession and decreased flexibility. We strongly believe that the key to success is a global supply network with strong regional footprints, relying on free trade and cooperation in industrial operations. If we are to overcome future crises with global impact, what we need are global and resilient supply chain solutions, globally diversified production and distribution networks, and global capacity reserves that offer flexible room to maneuver, among other things.

As the pharmaceutical industry, we should stand up for a transparent, rules-based system of trade, managed globally by international institutions. We must strive for resilience rather than isolation. At the same time, we should foster balanced and diversified global networks, including back- up systems within well-defined, segmented supply chains. Localization reduces the scope for fallback options and capacity reserves. In addition, we must further exploit the opportunities that Industry 4.0 offers for increasing supply chain transparency and improving risk management.

Digitalization is a key lever for optimizing supply chain processes and creating end-to-end transparency. Although “end-to-end” is regularly used as a buzzword, all too often we fail to look beyond the bounds of our own companies. The pandemic taught us that we must have a deep understanding (and ideally a real-time picture) of the entire value chain from 1st/2nd tier suppliers all the way downstream to distributors and pharmacies and ultimately the patient. We need to be able to receive early alerts when supplier problems arise and proactively manage demand signals. This includes merging suppliers and CMO partners on integrated platforms, increasing data transparency, cloud-based, real-time tracking of transports, and intelligent control tower solutions.

Digitalization and Industry 4.0 – a challenge that offers immeasurable chances for improvement

The ongoing automation of traditional manufacturing and industrial practices using modern smart technology based on cyber-physical systems (CPS) and dynamic data processing is commonly referred to as “Industry 4.0” (Arden et al., 2021). This is having a major impact on manufacturing in the chemical and pharmaceutical industry, too. While it seems obvious that digitalization offers a variety of options for improvement in production, the challenge we often face is keeping pace with the overwhelming speed at which digital solutions sometimes evolve in adjacent industries. To be honest, the traditional pharmaceutical industry (especially the big players) is not noted for its ability to adapt quickly to new technologies, and isn’t typically known for being particularly agile and dynamic in this regard. Even so, we are facing the reality of having to digitize our processes and change data and business processes to achieve digital transformation. This is a must – even in a strongly regulated environment.

In the pharmaceutical industry, we have access to data from research studies, production campaigns and marketing and sales, for instance. Digital technologies such as machine learning and artificial intelligence (AI) have the ability to connect and leverage this data so as to uncover new insights and improve decision making. With the help of data and digital technologies, we are able to develop new solutions that better fit our patients’ needs. This enables us to manufacture more efficiently in digital factories and reduce our own environmental footprint.

A clear vision for implementing digital solutions in API production is typically driven by advanced analytics technologies, alongside transforming the ongoing automation into smart manufacturing. It might seem natural to assume that, for the majority of mature products, the respective manufacturing processes have been exhaustively optimized over the years. It was therefore all the more surprising and highly motivating to learn that there is still potential to unleash by applying data science tools, even for long-established products. One thing is essential for the digital transformation – it has to be closely linked to the business by using digital insights to unlock additional value with relevant impact. It does not make sense to simply introduce technology just because it’s all the rage. Doing so can bring with it a major risk of failure (Hotz, 2022). Taking this as a basic guiding principle for selecting meaningful use cases, it is advisable to follow a gradual process, starting with a plant readiness assessment (rating digital maturity), followed by a process improvement analysis (problem identification), clustering, and finally, prioritizing potential digitalization projects through the creation of implementation plans. In our experience, the key success factors of every advanced analytics project are best summarized as follows:

10% of the success is due to the algorithms identified
20% is based on successful application of new tools and improving the IT and computing landscape
70% can be attributed to sensitivity in the change management process, driven by the excitement and involvement of the interdisciplinary teams

Most importantly, “digital” must become an integral part of our thinking and mindset. In this context, it is worth pointing out that there is much to learn from next-generation employees – let’s empower them to speak up, to share their views and knowledge and thus propel our digital transformation efforts in our industry to reach unprecedented speeds.

Sustainability – a challenge we must take seriously to safeguard our planet’s resources

The scientific evidence could not be clearer. Global climate change caused by human activity is happening now and poses a growing threat to society. The pace of change and the evidence of harm have increased markedly over recent years. The time to significantly reduce greenhouse gas emissions is therefore now. Sustainability has quickly grown from a “feel-good” topic to a business-critical success factor. Society at large is increasingly aware of sustainability issues and has developed a strong desire for responsible action and change. As the pharmaceutical industry, we have a responsibility to deliver on the expectations of customers, policy makers, and investors. Most importantly, however, we should strive to push ourselves even beyond benchmarks and regulations imposed from the outside, since we are aware that it is us who can make a difference for the generations to come. API manufacturing organizations in particular have a huge lever when it comes to improving our industry’s ecological balance (Flessner, 2022).

In order to improve our ecological footprint, we need to consider the following relevant aspects – science-based climate targets, water and waste reduction targets applying the principles of circularity, and the responsible use of substances, focusing on green chemistry. A strong basis for fulfilling challenging climate targets is typically a sound and forward-looking sustainability and green energy strategy. We need to aim for compliance with the carbon reduction ambitions defined by the European Green Deal as a business imperative. We should foster an even more intense dialog and partner up across divisions, companies, and even industries to reach the most challenging goals. Especially in the area of circularity, we strongly believe there are many opportunities beyond those currently being addressed. Once we enter into open discussions, we can come up with joint ideas that make the most of our creative potential and technological expertise.

Furthermore, sustainability must be a factor in everything we do, including hiring next-generation scientists and leaders with an eco-centered mindset to proactively design production and packaging processes, focusing our downstream distribution on reducing, recycling and/or reusing waste streams and optimizing our value streams across the entire value chain – from suppliers to customers and patients. Examples from API manufacturing include the recycling of starting materials, reagents or catalysts from production waste, with subsequent repurposing of the recovered material, and the use of biocatalytic platforms to replace the conventional chemical routes of synthesis with less resource-intense alternatives. Using raw materials from biogenic sources can further reduce our overall footprint.

We should also keep in mind that sustainability goes beyond waste treatment, circularity, and handling emissions. The final – but no less important – dimension is social responsibility. For example, it is well worth acknowledging the valuable impact of funding regional initiatives, engaging in the field of social innovation and hands-on support for science education. Both in the communities around our sites and in society as a whole, these efforts can bring about improvements and really make a difference.

What actually makes the difference – people are our greatest asset

As the saying goes, “If you want to go fast, go alone. If you want to go far, go together!” A high level of commitment among employees is essential when it comes to achieving progress in our industry, mastering the challenges of today so as to be prepared for the future, and coming up with innovative solutions that address the needs of both our own generation and those to come – from sustainability to unmet medical need in a growing and aging population. To achieve this, we need to go from “command and control” to “agility and adaptability”, with a key element of personal development and leadership being empowerment. In addition, we need to really live up to our commitment to inclusion and diversity. As employers, we must therefore take on the challenge of creating a working environment that values all people, supports them in their (self-)development and fosters creativity. On the other hand, it is up to every single employee to embrace a mindset based on a constant willingness to learn and adopt a culture of interaction.

Summary and conclusion – we are here to make a difference!

There are several key challenges that we need to take seriously to successfully shape the future of the pharmaceutical industry – the increasing cost pressure in production, the need to implement supply chain concepts with greater resilience, full commitment to driving sustainability, and the question of how to unleash the full potential of digital solutions in an industry that has historically not been very agile.

We need to act now to maintain the strong momentum of our industry and safeguard pharmaceutical companies as innovative and competitive global players with strong footprints in their home countries. If we approach this proactively, it is a big opportunity – but it might become a threat if we do not foster collaboration and joint action now. We must also not forget that it’s all about the people, because every change needs strong commitment among staff, coworkers, and collaborators. Ultimately, this means we need to face and tackle the challenges for our industry together – across leading pharma companies, across the interface between industry and academia, and across colleagues with diverse backgrounds and opinions.

Our challenge to you today, therefore, is this – let’s tackle it together. Let’s team up and strengthen collaboration to shape the future of our industry together. Let’s go hand in hand, using collaborative network approaches as a basis wherever feasible.

Acknowledgement

We would like to thank Kai Victor, Olivia Krampe, Janina Jansong, and Georg von Dziembowski for their valuable input and advice.

Arden, N. S., Fisher, A. C., Tyner, K., Yu, L. X., Lee, S. L., Kopcha, M. (2021): Industry 4.0 for pharmaceutical manufacturing: Preparing for the smart factories of the future, Int. Journal of Pharmaceutics, 602, 120554, published online and available at https://www.sciencedirect.com/sdfe/reader/pii/ S0378517321003598/pdf, accessed July 27th 2022.

Blanco, M. J., Gardinier, K. M. (2020): New Chemical Modalities and Strategic Thinking in Early Drug Discovery, ACS Med. Chem. Lett., 11 (3), pp. 228-231.

Chui, M., Evers, M., Manyika, J., Zheng, A., Nisbet, T. (2020): The Bio Revolution, available at https://www.mckinsey.de/~/media/mckinsey/locations/europe%20and%20middle%20east/deutschland/news-/presse/2020/2020-05-14%20 mgi%20bio%20revolution/mgi-bio-revolution-report- may-2020_neu.pdf, accessed July 27th 2022.

Datta, J. (2019): China, India and US home to the largest number of adults with diabetes: Intenrational Diabetes Foundation, available at https://www.thehindubusinessline. com/news/variety/china-india-and-us-home-to-the-largest- number-of-adults-with-diabetes-international-diabetes- federation/article62235303.ece, accessed July 27th 2022.

Hotz, N. (2022): Why Big Data Science & Data Analytics Projects Fail, referring to studies of Gartner and VentureBeat, available at https://www.datascience-pm. com/project-failures/#:~:text=Indeed%2C%20the%20 data%20science%20failure,outcomes%E2%80%9D%20 (Gartner%2C%202019), accessed July 29th 2022.

Flessner, T (2022): Nachhaltigkeit in der Wirkstoffproduktion: Herausforderungen und Chancen, BIOspektrum, submitted.

Nising, C. F., von Nussbaum, F. (2022): Industrial Organic Synthesis in Life Sciences – Today and Tomorrow, Eur. J. Org. Chem., published online and available at https://chemistry- europe.onlinelibrary.wiley.com/doi/epdf/10.1002/ ejoc.202200252, accessed at July 27th 2022.

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Pharmaceutics
PMC10385763

Artificial Intelligence in Pharmaceutical Technology and Drug Delivery Design

Lalitkumar k. vora.

1 School of Pharmacy, Queen’s University Belfast, 97 Lisburn Road, Belfast BT9 7BL, UK; [email protected]

Amol D. Gholap

2 Department of Pharmaceutics, St. John Institute of Pharmacy and Research, Palghar 401404, Maharashtra, India; ni.ude.rpijs@gloma

Keshava Jetha

3 Department of Pharmaceutics and Pharmaceutical Technology, L. M. College of Pharmacy, Ahmedabad 380009, Gujarat, India; moc.liamg@ahtejvahsek

4 Ph.D. Section, Gujarat Technological University, Ahmedabad 382424, Gujarat, India

Raghu Raj Singh Thakur

Hetvi k. solanki.

5 Pharmacy Section, L. M. College of Pharmacy, Ahmedabad 380009, Gujarat, India

Vivek P. Chavda

Associated data.

No new data were created or analyzed in this study. Data sharing is not applicable to this article.

Artificial intelligence (AI) has emerged as a powerful tool that harnesses anthropomorphic knowledge and provides expedited solutions to complex challenges. Remarkable advancements in AI technology and machine learning present a transformative opportunity in the drug discovery, formulation, and testing of pharmaceutical dosage forms. By utilizing AI algorithms that analyze extensive biological data, including genomics and proteomics, researchers can identify disease-associated targets and predict their interactions with potential drug candidates. This enables a more efficient and targeted approach to drug discovery, thereby increasing the likelihood of successful drug approvals. Furthermore, AI can contribute to reducing development costs by optimizing research and development processes. Machine learning algorithms assist in experimental design and can predict the pharmacokinetics and toxicity of drug candidates. This capability enables the prioritization and optimization of lead compounds, reducing the need for extensive and costly animal testing. Personalized medicine approaches can be facilitated through AI algorithms that analyze real-world patient data, leading to more effective treatment outcomes and improved patient adherence. This comprehensive review explores the wide-ranging applications of AI in drug discovery, drug delivery dosage form designs, process optimization, testing, and pharmacokinetics/pharmacodynamics (PK/PD) studies. This review provides an overview of various AI-based approaches utilized in pharmaceutical technology, highlighting their benefits and drawbacks. Nevertheless, the continued investment in and exploration of AI in the pharmaceutical industry offer exciting prospects for enhancing drug development processes and patient care.

1. Introduction

Numerous industries are striving to enhance their progress to meet the demands and expectations of their customers, utilizing various methodologies. The pharmaceutical industry is a critical field that plays a vital role in saving lives. It operates based on continuous innovation and the adoption of new technologies to address global healthcare challenges and respond to medical emergencies, such as the recent pandemic [ 1 ]. In the pharmaceutical industry, innovation is typically predicated on extensive research and development across various domains, including but not limited to manufacturing technology, packaging considerations, and customer-oriented marketing strategies [ 2 ]. Novel pharmaceutical innovations are range from small drug molecules to biologics, with a preference for better stability with high potency to fulfil unmet needs to treat diseases. The assessment of the significant levels of toxicity associated with new drugs is an area of considerable concern, necessitating extensive research and exploration in the foreseeable future. One of the primary aims is to provide drug molecules that offer optimal benefits and suitability for utilization in the healthcare industry. Despite this, the pharmacy industry faces numerous obstacles that necessitate further advancement using technology-driven methods to address worldwide medical and healthcare demands [ 3 , 4 , 5 ].

The need for a proficient workforce in the healthcare industry is persistent, necessitating the continuous provision of training to healthcare personnel to augment their involvement in routine duties. Identifying skill gaps in the workplace is a crucial undertaking within the pharmaceutical industry. It is imperative to effectively address the identified gaps through appropriate remedial measures while acknowledging that providing adequate training can also pose a significant challenge. As per a report presented by certain authorities, it has been observed that approximately 41% of supply chain disruptions occurred in June 2022. The report further highlights that supply chain disruption has emerged as the second-most-formidable challenge to overcome. Several pharmaceutical industries are anticipating further advancements in their supply chain, as well as innovative models to address these challenges, with the potential to enhance business resilience [ 6 ]. The global outbreak of coronavirus disease 2019 (COVID-19) has caused significant disruptions to various operations worldwide, including ongoing clinical trials [ 7 ].

Pandemics, natural catastrophes, pricing changes, cyberattacks, logistical delays, and product issues increase supply chain disruptions. Transportation challenges caused by the epidemic have devastated the supply chain network and global industries. Decision-induced delays for price updates from suppliers owing to misunderstanding over whether to utilize the new price or the existing price for commodities or materials create price fluctuation delays. New obstacles arise from countries’ cross-border trade cooperation strategies, increasing criminal activity and instability in the availability of crucial resources for operation and production. The manufacturing of footprint modifications is needed to suit patient needs and compliance.

Within the pharmaceutical industry, a significant quantity of COVID-19 vaccines ended up being unusable during the pandemic because of complications related to the maintenance of the cold chain. The primary cause of supply chain disruption resulting from the delayed response can be attributed to insufficient innovation and imprecise forecasting in industrial and commercial operations. Supply chain disruptions within the pharmaceutical industry have significant ramifications on customer satisfaction, corporate reputation, and potential profits [ 8 , 9 ].

The implementation of AI is poised to bring about a significant transformation in the way the pharmaceutical industry handles supply chain operations ( Figure 1 ). It also consolidates numerous AI research endeavors from recent decades to create effective solutions for diverse supply chain issues. Additionally, the study suggests potential research areas that could enhance decision-making tools for supply chain management in the future [ 10 , 11 ].

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Depicts a possible artificial intelligence (AI) solution to the pharmaceutical industry’s challenges: acquiring a proficient workforce is a prerequisite in all sectors to leverage their expertise, proficiency, and aptitude in product innovation. The second pertains to supply chain disruption and clinical trial experimentation challenges. The incidence of cyberattacks is on the rise, with data breaches and security emerging as significant concerns for the industry.

The primary impact of the pandemic is receding, but it still has some influence on clinical trials. Many pharmaceutical companies are looking to adopt newer technologies, including platforms such as AI and virtual platforms in this field. These new technologies may be helpful in the restart or recreation of these clinical trials, with minimal interaction for face-to-face types [ 12 , 13 , 14 , 15 , 16 , 17 , 18 ], as presented in Figure 1 . At present, highly skilled workers and high maintenance costs pose a larger challenge. The fourth main challenge in seeking a technology-based solution is data breaches and cybersecurity threats. The number of cyberattacks on available patient data has also increased in the 21st century, and many pharmaceutical companies are more concerned about confidential medical records and patient data, which are especially vulnerable to cybersecurity attacks. Some of the major challenges associated with traditional clinical trials are data fragmentation and disconnected system involvement, which generally result from scattered data generated during the trials and hence require extensive manual data transcription efforts for documents along with those of the systems. There is a lack of innovation in the trial models, which thus requires the rework and repetition of the ongoing work. In the healthcare sector, patient recruitment, enrollment, monitoring, retention, and medical adherence are the key points that require special attention due to clinical trials. The enrollment of the patient is affected due to the traveling process at the trial sites, which is time-consuming for the participants, and frequent visits to sites contribute to patient re-enrollment in the same context. The application of AI to the study design helps with optimization as well as accretion for the work related to the creation of the patient-centric type of design. AI uses techniques for the collection of the huge amounts of data generated from those clinical trials, thus reducing the amount of data manpower required for the same. Such technologies implement body sensors along with wearable devices to record the patient’s vital signs and valuable information in a remote mode, which helps meet the patient’s requirement for face-to-face interaction on a routine basis. AI algorithms using wearable technology provide real-time insights during the study process [ 19 ].

A new technology platform and solution are required for the implementation of effective cybersecurity inside the office and for remote workers. Special attention must also be paid to data security and breach techniques. Technology is also required to address political fraud, and many cases have been reported, especially during the pandemic in the last few years around the world. Therefore, there is a need to take appropriate steps for the prevention of healthcare fraud, along with constant encouragement for internal discussions about fraudulent behaviors, which may help in the inhibition of the same.

2. Current Pharmaceutical Challenges and the Role of AI

In the pharmaceutical industry, research on small molecules for better products and customer satisfaction is ongoing due to their multiple advantages. The chemical synthesis process is simple, while the synthetic derivative preparation is economical. Thus, many stable and potent small-molecule-loaded formulations are present in the pharmacy sector. Except for the treatment of rare diseases, many innovative small molecules face competition from generic molecules, and complex data are required for them to be launched, along with clinical trials. These processes increase the economic pressure on companies to engage in more innovation. However, the biomolecular drug industry is still growing at a rapid pace to compensate for the crisis induced by the small molecular size and poor dissemination of research and innovations. Small-molecule actions are based on their conformation and reactivity [ 20 , 21 , 22 , 23 , 24 , 25 , 26 ]. Biomolecules, which are large units, mostly contain amino acids from the protein source along with nucleotides or ribonucleotides for the nucleic acid. Their stability and function are also influenced by the supramolecular sequence and the spatial conformation [ 27 ]. Some biomolecules are very successful products, such as insulin and adalimumab. The pharmacokinetic aspects of these molecules are complex, as infusion is the preferred and most usable route of administration for these biomolecules. Pharmacokinetic modulation and molecular stabilization are important aspects of nucleic acid-based research. The pharmacokinetic exposure and enhancement of these molecular forms are crucial goals. New technological advancement may be helpful to address these challenges and solve related issues [ 28 , 29 , 30 , 31 , 32 , 33 ]. Although there is huge scope for AI in drug delivery innovation and drug discovery, it still presents some major limitations that ultimately require human interference or intellectuals to interpret the complex results. The major contributions of AI predictions are based on the datasets, but the interpretation of the results, owing to the gray zone, require human interference to reach the appropriate conclusion. AI can experience issues with algorithm bias regarding the processing of information for predictions and the assessment of hypotheses. Moreover, it is not uncommon for docking simulations to result in the discovery of inactive molecules [ 34 ]. Therefore, a critical analysis of these parameters still requires human involvement for effective decision-making and cross-verifications, to rule out system bias issues. Nevertheless, there is huge potential in AI for possible application, and thus, extensive work may be able to reduce the limitations associated with AI and make it effective and reliable [ 35 ].

Regarding AI, the methodology employed involves the utilization of machine learning or its subsets, such as deep learning and natural language processing. The learning process can be either supervised or unsupervised, and the type of algorithm employed is also a crucial factor. Supervised learning is a machine learning methodology that involves the use of known inputs (features) and outputs (labels or targets), as opposed to unsupervised learning, which deals with unknown outputs. The supervised approach involves the prediction of output, such as labels or targets, based on multiple inputs or features. On the other hand, unsupervised classification aims to create groups that are homogeneous in terms of features [ 36 ].

In pharmaceutical product development, various AI models have been explored to enhance different aspects of the process. A list of commonly explored AI models in this domain is described in Table 1 and Figure 2 .

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Different supervised and unsupervised AI learning models/tools for pharmaceutical applications.

2.1. Supervised AI Learning

Supervised learning refers to a type of machine learning in which an algorithm is trained on a labeled dataset, where the desired output is already known. The algorithm learns to map input data to the correct output by analyzing the patterns and relationships within the labeled data. This approach is commonly used in various applications, such as image recognition, natural language processing, and predictive modeling. Task-driven strategies involve setting specific goals for achieving desired outcomes from a given set of inputs. This approach utilizes labeled data to train algorithms for tasks such as data classification or outcome forecasting. The predominant supervised learning tasks are classification, which involves predicting a label, and regression, which involves predicting a quantity. A variety of techniques are available for solving supervised learning tasks, depending on the nature of the data in a given problem domain. These techniques include Naïve Bayes, K-nearest neighbors, support vector machines, ensemble learning, random forest, linear regression, support vector regression, and others [ 37 ]. It has several applications in the pharmaceutical industry, as described below:

Drug Discovery and Design: Supervised learning algorithms can be used to predict the activity or properties of new drug candidates. By training on a dataset of known compounds and their associated activities, the model can learn patterns and relationships between molecular features and desired outcomes. This enables the prediction of the activity, potency, or toxicity of novel compounds, aiding in drug discovery and design [ 38 ].
Predictive Maintenance and Quality Control: In pharmaceutical manufacturing, supervised learning can be utilized for predictive maintenance and quality control. By training on data from manufacturing processes, equipment sensor data, or quality testing results, the model can learn to predict equipment failure, product quality deviations, or process abnormalities, allowing for proactive maintenance and quality assurance [ 39 ].
Drug Target Identification: Supervised learning techniques can help identify potential drug targets by analyzing biological data. By training on data that include information about genetic, proteomic, or transcriptomic features and their relationship to drug response or disease progression, the model can learn patterns and identify potential targets for further investigation [ 40 ].
Disease Diagnosis and Prognosis: Supervised learning models can be used to diagnose diseases or predict patient outcomes based on medical data. By training on labeled datasets containing patient characteristics, clinical data, and disease outcomes, the model can learn to classify patients into different disease categories or predict disease progression or treatment response [ 41 ].
Adverse Event Detection: Supervised learning algorithms can be applied to pharmacovigilance data to identify and classify adverse events associated with drugs. By training on labeled adverse event reports, the model can learn to recognize patterns and identify potential safety signals, helping in the detection and characterization of adverse events [ 42 ].
Predictive Modeling for Clinical Trials: Supervised learning can be used to predict outcomes in clinical trials. By training on historical clinical trial data, including patient characteristics, treatment interventions, and trial outcomes, the model can learn to predict patient response, treatment efficacy, or safety outcomes. This information can guide trial design and optimize patient selection [ 43 ].

These are just a few examples of how supervised learning can be applied in the pharmaceutical industry. Supervised learning techniques, combined with appropriate feature selection, data preprocessing, and model evaluation, can provide valuable insights and support decision-making in various stages of pharmaceutical research, development, and manufacturing.

2.2. Unsupervised AI Learning

Unsupervised learning refers to a type of machine learning where the algorithm is not provided with labeled data. Instead, it is tasked with identifying patterns and relationships within the data on its own. This approach is often used in exploratory data analysis and can be useful for discovering hidden structures or clusters within a dataset. The approach being described is commonly known as a “data-driven methodology,” which aims to extract patterns, structures, or insights from unannotated data. There are several prevalent unsupervised tasks, including clustering, dimensionality reduction, visualization, finding association rules, and anomaly detection. Various unsupervised learning tasks can be addressed using popular techniques such as clustering algorithms (e.g., hierarchical clustering, K-means, K-medoids, single linkage, complete linkage, BOTS), association learning algorithms, and feature selection and extraction techniques (e.g., Pearson correlation, principal component analysis) based on the data’s characteristics [ 44 , 45 ]. Unsupervised learning techniques in AI can be valuable for pharmaceutical applications, particularly for exploratory analysis, pattern recognition, and data visualization, as described below:

Clustering: Clustering algorithms group data points based on their similarities, allowing the identification of natural groupings or clusters within the data. In pharmaceutical applications, clustering can be applied to various datasets, such as gene expression profiles, chemical structures, or patient data, to uncover subgroups with similar characteristics. This can aid in target identification, patient stratification, and identifying distinct classes of compounds or diseases [ 46 ].
Dimensionality Reduction: Dimensionality reduction techniques, such as principal component analysis (PCA) and t-distributed stochastic neighbor embedding (t-SNE), are used to reduce the complexity of high-dimensional datasets while preserving meaningful information. These methods can help visualize and explore complex datasets, identify key variables or features, and support decision-making processes. Dimensionality reduction can be applied to various types of pharmaceutical data, including gene expression data, drug activity profiles, or imaging data [ 47 ].
Anomaly Detection: Anomaly detection algorithms identify rare or unusual data points that deviate significantly from the expected patterns. In the pharmaceutical industry, anomaly detection can be useful for detecting adverse events, identifying potential safety concerns, and uncovering data quality issues. Unsupervised anomaly detection techniques, such as the local outlier factor (LOF) or isolation forest, can help highlight abnormal patterns or data points that warrant further investigation [ 48 ].
Association Rule Mining: Association rule mining techniques, such as the Apriori algorithm, aim to discover interesting relationships or associations between items in a dataset. In the pharmaceutical context, association rule mining can be applied to drug–drug interactions, adverse event data, or co-occurrence patterns between medical conditions and medications. These techniques can provide insights into potential drug interactions, identify medication patterns, or support pharmacovigilance activities [ 49 ].
Topic Modeling: Topic modeling algorithms, such as latent Dirichlet allocation (LDA), extract latent topics or themes from large text datasets. In the pharmaceutical industry, topic modeling can be used to analyze the scientific literature, clinical trial reports, or social media data to identify key research themes, emerging trends, or patient sentiments. This can aid in literature mining, competitive intelligence, or understanding patient perspectives [ 50 , 51 ].

Unsupervised learning techniques offer valuable insights and exploratory analysis in pharmaceutical applications. However, it is important to note that the interpretation of results from unsupervised learning methods often requires domain expertise and further validation to extract actionable knowledge and ensure the reliability of the findings.

Top 10 list of commonly used AI models in the pharmaceutical industry.

AI/Machine Learning Models	Description/Usage	References
Generative Adversarial Networks (GANs)	GANs are widely used in drug product development to generate novel chemical structures and optimize their properties. GANs consist of a generator network that creates new molecules and a discriminator network that evaluates their quality, leading to the generation of structurally diverse and functionally optimized drug candidates.	[ ]
Recurrent Neural Networks (RNNs)	RNNs are commonly employed for sequence-based tasks in drug development, such as predicting protein structures, analyzing genomic data, and designing peptide sequences. They capture sequential dependencies and can generate new sequences based on learned patterns.	[ ]
Convolutional Neural Networks (CNNs)	CNNs are effective in image-based tasks, including analyzing molecular structures and identifying potential drug targets. They can extract relevant features from molecular images and aid in drug design and target identification	[ ]
Long Short-Term Memory Networks (LSTMs)	LSTMs are a type of RNN that excel in modeling and predicting temporal dependencies. They have been used in pharmacokinetics and pharmacodynamics studies to predict drug concentration-time profiles and evaluate drug efficacy.	[ ]
Transformer Models	Transformer models, such as the popular BERT (Bidirectional Encoder Representations from Transformers), have been employed in natural language processing tasks in the pharmaceutical domain. They can extract useful information from the scientific literature, patent databases, and clinical trial data, enabling researchers to make informed decisions in drug development.	[ ]
Reinforcement Learning (RL)	RL techniques have been applied to optimize drug dosing strategies and develop personalized treatment plans. RL algorithms learn from interactions with the environment to make sequential decisions, aiding in dose optimization, and improving patient outcomes.	[ ]
Bayesian Models	Bayesian models, such as Bayesian networks and Gaussian processes, are employed for uncertainty quantification and decision-making in drug development. They enable researchers to make probabilistic predictions, assess risks, and optimize experimental designs.	[ , ]
Deep Q-Networks (DQNs)	DQNs, a combination of deep learning and reinforcement learning, have been used to optimize drug discovery processes by predicting the activity of compounds and suggesting high-potential candidates for further experimentation.	[ , ]
Autoencoders	Autoencoders are unsupervised learning models used for dimensionality reduction and feature extraction in drug development. They can capture essential characteristics of molecules and assist in compound screening and virtual screening.	[ , ]
Graph Neural Networks (GNNs)	GNNs are designed to process graph-structured data, making them suitable for drug discovery tasks that involve molecular structures. They can model molecular graphs, predict properties, and aid in virtual screening and de novo drug design.	[ , ]

3. AI for Drug Discovery

AI has revolutionized drug research and discovery in numerous ways. Some of the key contributions of AI in this domain include the following:

3.1. Target Identification

AI systems can analyze diverse data types, such as genetic, proteomic, and clinical data, to identify potential therapeutic targets. By uncovering disease-associated targets and molecular pathways, AI assists in the design of medications that can modulate biological processes.

3.2. Virtual Screening

AI enables the efficient screening of vast chemical libraries to identify drug candidates that have a high likelihood of binding to a specific target. By simulating chemical interactions and predicting binding affinities, AI helps researchers prioritize and select compounds for experimental testing, saving time and resources.

3.3. Structure-Activity Relationship (SAR) Modeling

AI models can establish links between the chemical structure of compounds and their biological activity. This allows researchers to optimize drug candidates by designing molecules with desirable features, such as high potency, selectivity, and favorable pharmacokinetic profiles.

3.4. De Novo Drug Design

Using reinforcement learning and generative models, AI algorithms can propose novel drug-like chemical structures. By learning from chemical libraries and experimental data, AI expands the chemical space and aids in the development of innovative drug candidates.

3.5. Optimization of Drug Candidates

AI algorithms can analyze and optimize drug candidates by considering various factors, including efficacy, safety, and pharmacokinetics. This helps researchers fine-tune therapeutic molecules to enhance their effectiveness while minimizing potential side effects.

3.6. Drug Repurposing

AI techniques can analyze large-scale biomedical data to identify existing drugs that may have therapeutic potential for different diseases. By repurposing approved drugs for new indications, AI accelerates the drug discovery process and reduces costs.

3.7. Toxicity Prediction

AI systems can predict drug toxicity by analyzing the chemical structure and characteristics of compounds. Machine learning algorithms trained on toxicology databases can anticipate harmful effects or identify hazardous structural properties. This helps researchers prioritize safer chemicals and mitigate potential adverse responses in clinical trials.

Overall, AI-driven approaches in drug research and development offer the potential to streamline and expedite the identification, optimization, and design of novel therapeutic candidates, ultimately leading to more efficient and effective medications [ 66 ].

For example, in silico target fishing technology (TF) is used in pharmaceuticals for biological target prediction based on chemical structure. This information is provided depending on the information available in the chemical database in the biological annotated form. Along with this, several other methods, such as data mining and docking of the chemical structure, were used for the exploration of the mechanism of action along with target class information required for effective planning. The target fishing technique was used in drug discovery with the help of machine learning along with cheminformatics tools. These two are used to obtain detailed knowledge related to the proper analysis of complex structures and the design of novel drug ingredients for the effective treatment of complex diseases. The routine drug discovery methods run by different industries are quite costly, as they involve several complicated events that must be addressed properly to conclude, such as the selection and identification of the target proteins and the mechanism of action of the small molecules in depth. To speed up this process, the TF was applied, which assisted in reducing the total experimental cost during the drug development processes. The reference molecules are used for the prediction of the ligand-target with the help of the 3D descriptors. This technique was used for the identification of the high binding ability of diethylstilbestrol, while the TF technique is widely implemented for the study of the phytopharmacology of the drug along with monthly similarity assessments. It is a computational and a proteomics-based method, which is based on the ranking of the data points depending on the similarity of data fusion along with drug targets. It is also used for the prediction of potential toxicities for the ligand-based approach used in drug discovery. Some of the critical points required in the drug development and drug discovery phases, such as novel target identification, selection, prediction of the phytopharmacological profiles, and prediction of the adverse effects associated with novel therapeutic indications, are explored with the TF. For these events, the bioactive compound similarity is applied for target identification to that of the unrecognized compounds. Some of the drugs that have been successfully characterized by using this method are loperamide and emetine, along with methadone, while the targets identified for the same are muscarinic, adrenergic, and neurokinin receptors [ 2 , 67 , 68 , 69 , 70 , 71 , 72 , 73 , 74 , 75 ].

The field of drug discovery has seen significant advancements with the use of AI models and tools. Some of the popular AI model tools used for drug discovery are described in Table 2 . These are just a few examples of the AI model tools available for drug discovery. The field is rapidly evolving, and new tools and models are continuously being developed to accelerate the discovery of new drugs.

Popular AI model tools used for drug discovery.

AI Model Tools	Summary
DeepChem	An open-source library that provides a wide range of tools and models for drug discovery, including deep learning models for molecular property prediction, virtual screening, and generative chemistry.
RDKit	A widely used open-source cheminformatics library that offers various functionalities for molecule handling, substructure searching, and descriptor calculation. It can be integrated with machine learning frameworks for drug discovery applications.
ChemBERTa	A language model specifically designed for drug discovery tasks. It is based on the Transformer architecture and is pretrained on a large corpus of the chemical and biomedical literature, allowing it to generate molecular structures, predict properties, and assist in lead optimization.
GraphConv	A deep learning model architecture that operates on molecular graphs. It has been successful in predicting molecular properties, such as bioactivity and toxicity, by leveraging the structural information encoded in the graph representation of molecules.
AutoDock Vina	A popular docking software that uses machine learning techniques to predict the binding affinity between small molecules and protein targets. It can assist in virtual screening and lead optimization for drug discovery.
SMILES Transformer	A deep learning model that takes Simplified Molecular Input Line Entry System (SMILES) strings as input and generates molecular structures. It can be used for de novo drug design and lead optimization.
Schrödinger Suite	A comprehensive software package for drug discovery that incorporates various AI-driven tools. It includes modules for molecular modeling, virtual screening, ligand-based and structure-based drug design, and predictive modeling.
IBM RXN for Chemistry	An AI model designed to predict chemical reactions. It utilizes deep learning algorithms and large reaction datasets to generate potential reaction outcomes, aiding in the discovery of new synthetic routes and compound synthesis.
scape-DB	scape-DB (Extraction of Chemical and Physical Properties from the Literature-DrugBank) is a database that combines natural language processing and machine learning to extract chemical and biological data from the scientific literature. It provides valuable information for drug discovery research.
GENTRL (Generative Tensorial Reinforcement Learning)	A deep learning model that combines reinforcement learning with generative chemistry to design novel molecules with desired properties. It has been used for de novo drug design and optimization.

4. AI Tool Application in Dosage Form Designs

The human body system is divided into several compartments to understand the impact of drug delivery. The compartments are further simplified based on biological membranes. Physicochemical barriers are vital for biological compartments and can be implemented based on the mode of drug delivery inside the body. One of the most significant criteria for efficient drug delivery system monitoring is the rate of permeation based on the route of administration. The orally administered drug, after entering the gastric environment, must permeate through the intestinal or gastric epithelium. This step is vital for the further distribution of the drug into the bloodstream. The distribution step conveys the drug to the target site, which can be tissue or any of the specific cellular components [ 76 , 77 , 78 , 79 , 80 ]. Intracellular molecules can also act as targets for drug entry into the body. Most of the permeation of drugs is facilitated through biological barriers, either passively or actively. Passive diffusion is based on the drug’s molecular features. The in silico models are used to predict drug distribution through computation analysis, but these results are somewhat different from the actual drug distribution study. The drug’s interaction with biological components and the availability of the drug in biological environments have a great impact on the drug’s fate in the body. This process is governed by the molecular features of the drug. For many biologically active entities and small molecules, passive permeation is inefficient and requires a specific drug delivery system. The active permeation process is driven by membrane transport and depends on complex biological interactions. This complex process must be explored by using many specific parameters through computation and systematic modeling approaches. This newer computational model is used to study the pharmacokinetic parameters of the drug delivery system. One of the major loopholes present in the research and development of the pharmacy industry is the predictability of preclinical models. The predictability assumption is based on the selected parameters, and the same applies to complex in silico models as well. All these cases are linked to drug interactions with membranes and can be better analyzed by the modeled environment, as presented in Figure 3 . This modeled environment can be studied and analyzed more effectively through AI [ 81 , 82 , 83 ]. AI provides sophisticated technology for the analysis of such multilayer data. The thoroughness of the analysis will contribute to a better understanding of the research units. The systematically applied model along with parameter evaluation is based on many factors, such as simulation, scoring, and refinement, in each step of the research to determine the best outcomes. AI could provide an automated system that can be implemented for all these functions for better guessing and predicted refinement of the data for consistent improvement. For better AI training in the biological environment, a proper understanding of the drug–biological interaction is essential, which is indicated by the system biology type of the databases. Pharmacokinetic studies can be performed using many novel AI technologies, such as artificial neural networks. Along with this, many databases are provided by AI, such as chemical, genomic, and phenotypical databases, for a better understanding of the drug interaction and the effective study of the molecules’ complex unit roles within the same. Some of the methods are also applied to study the impact of the drug delivery system on the pharmacokinetics of the drug, for an effective understanding of the disposition and toxicity. Many new approaches to drug delivery systems involve the design of quality attributes along with critical attributes and studying their impacts on experimental trials before actual experiments.

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AI contribution to drug development and research. AI can be used to enhance nanosystem design, expand the present drug testing modeling system, and increase the accuracy of parameter and factor selection in drug design, drug discovery, and drug repurposing methods. It also helps to better understand the mechanism of membrane interaction with the modeled human environment by studying drug permeation, simulation, human cell targets, etc.

The benefits of AI are that it collects information from multiple sources and provides indications for the selected drug delivery system to work as per the anticipated results. The evaluation of the molecular information, patient data, and pharmacokinetic data are considered part of the complex data for analysis for the possible selection of the best active pharmaceutical against patient diseases or requirements. The passive type of AI is implemented for the identification of molecular entity features against those of known molecules for comparison. Effective treatment depends on the accuracy of the selection of drug delivery systems, which are provided by AI.

AI is also useful for the drug discovery process along with the drug repurposing method. This addresses the application of the existing therapeutics to that of the new disease. The requirement of the patients and disease condition are major factors contributing to formulation, pharmacokinetics, and drug development. One of the major challenges associated with the application of AI in full scope to develop delivery systems is the availability of databases with detailed information. This is required for the evaluation of the models, along with parameters, in an unbiased way. AI provides help for future applications by using current knowledge. A large quantity of the data can be handled or digested by using AI tools for a better approach to the rational design of the product, as presented in Figure 4 . A more vigorous codification inside the knowledge database can be performed with excellent self-supervised experimental results and related to proper parameter recording [ 84 , 85 , 86 , 87 , 88 , 89 , 90 ].

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Application of AI tools in the pharma sector. AI tools are helpful for the analysis of multilayered data. Automated AI systems are used to perform effective searches, simulations, and refinements of data and processes involved in research and product development. The system biology database, chemical database, genomic database, phenotypic database, and AI bots are used for better exploration of drug models, drug release, and activity predictions along with recommendations for effective drug delivery systems.

5. AI for Drug Delivery

The integration of AI and big data in the field of pharmaceutics has led to the development of computational pharmaceutics, which aims to enhance drug delivery processes by utilizing multiscale modeling approaches. Computational pharmaceutics employs AI algorithms and machine learning techniques to analyze large datasets and predict drug behavior ( Table 3 ). By simulating drug formulation and delivery processes, researchers can evaluate various scenarios and optimize drug delivery systems without the need for extensive trial-and-error experiments. This accelerates the drug development timeline, reduces costs, and increases productivity. Computational pharmaceutics involves modeling drug delivery systems at different scales, ranging from molecular interactions to macroscopic behavior. AI algorithms can analyze complex relationships between drug properties, formulation components, and physiological factors to predict drug behavior at each scale. This allows for a more comprehensive understanding of drug delivery mechanisms and aids in designing efficient drug delivery systems. It helps in the prediction of the physicochemical properties of the drug, the in vitro drug release profile, and the stability of the drug. The same technology is also implemented for the better assessment of in vivo pharmacokinetic parameters and drug distribution along with in vivo-in vitro correlation studies. By utilizing the right set of AI tools, researchers can identify potential risks and challenges associated with drug delivery systems early in the development process. This allows for proactive modifications and adjustments to mitigate risks and optimize drug performance. The use of AI and computational modeling reduces the reliance on time-consuming and expensive trial-and-error experiments, minimizing the chances of unforeseen outcomes [ 91 , 92 ].

5.1. AI for Oral Solid Dosage Form Development

AI involves the use of advanced tools and software to achieve human-like capabilities. Such innovation has helped in many sectors, such as the pharmaceutical industry, especially in the product development phase over the past few years. The implementation of these technological innovations can save time, money, and resources required for manufacturing and proper distribution to end customers through the supply chain. It also provides a better platform to understand the impact of process parameters on the formulation and manufacturing of products.

Run Han et al. explored the utilization of machine learning methods for the prediction of solid dispersion stability for six months. Hanlu Gao et al. investigated the application of machine leaching for solid dispersion dissolution studies. They used a random forest algorithm to generate a classification model that further helps to distinguish between the spring and parachute types of dissolution profiles. It also contributed to maintaining supersaturation with eighty-five percent accuracy and eighty-six percent sensitivity. The time-dependent drug release was predicted based on the regression model created by the random forest algorithm [ 93 ].

In the pharmaceutical market, solid dosage forms are predominant, and tablets are one of the dominant dosage forms in this domain. The preparation of the tablet includes many factors based on the type of tablet. AI can help in the search for optimized formulation and the study of the desired attributes involved in the same. AI is also expected to process obligations with the help of automated algorithms and technologies. The implementation of AI also poses a challenge to the regulatory authorities to redefine the policies regarding current good manufacturing practice (cGMP). Different technologies in AI, such as artificial neural networks (ANNs), fuzzy logics, and neural networks, along with genetic algorithms, are implemented for the development of solid dosage forms and a better understanding between the inputs and outputs for processing and operations. ANN is used for better prediction abilities for solid dosage forms, while genetic algorithms are used to predict the results obtained from the utilization of input parameters [ 94 ].

List of commonly explored AI models in pharmaceutical product development.

AI/Machine Learning Models	Description/Usage	References
Genetic Algorithms	Genetic algorithms are optimization techniques inspired by the principles of natural selection and genetics. They can be applied to optimize formulation compositions, drug release profiles, and process parameters to achieve desired dosage form characteristics.	[ ]
Artificial Neural Networks (ANNs)	ANNs have been employed to model and optimize drug release kinetics from different dosage forms. They can assist in determining optimal formulations and predict the release behavior of active pharmaceutical ingredients (APIs) under various conditions.	[ ]
Support Vector Machines (SVMs)	SVMs have been used in dosage form optimization to predict and model relationships between formulation variables, such as excipient composition, processing parameters, and drug release profiles. They aid in optimizing formulation design space.	[ ]
Particle Swarm Optimization (PSO)	PSO is a population-based optimization algorithm that can be used for dosage form optimization. It has been applied to optimize particle size distribution, dissolution profiles, and other formulation parameters.	[ ]
Artificial Intelligence-based Expert Systems	Expert systems utilize AI techniques, including rule-based systems and fuzzy logic, to simulate the decision-making process of human experts. They can be applied to dosage form optimization by considering multiple formulation and process variables.	[ ]
Monte Carlo Simulation	Monte Carlo simulation methods have been used to optimize drug product performance by considering uncertainties and variability in formulation and process parameters. They aid in robust formulation and process design.	[ ]
Computational Fluid Dynamics (CFD)	CFD simulations enable the optimization of fluid flow and mixing within dosage form manufacturing processes, such as granulation, coating, and drying. They help in designing efficient and uniform processes.	[ , ]
Response Surface Methodology (RSM)	RSM is a statistical technique that helps optimize dosage form formulations by modeling and analyzing the relationship between multiple variables and their effect on formulation responses. It aids in understanding and optimizing formulation parameters.	[ , , ]
Artificial Neural Network–Genetic Algorithm (ANN-GA) Hybrid Models	Hybrid models combining ANN and GA techniques have been used for dosage form optimization. They can efficiently search the formulation space to identify optimal solutions and predict formulation characteristics.
Multivariate Analysis Techniques	Multivariate analysis methods, such as principal component analysis (PCA) and partial least squares (PLS), have been employed in dosage form optimization. They aid in identifying critical formulation variables, reducing dimensionality, and optimizing formulation performance.	[ , , ]

Tablets are a highly used solid dosage, occupying a substantial portion of the market within the drug delivery segment. The process of creating this product involves the utilization of active pharmaceutical ingredients along with excipients, which are subsequently compressed or molded to achieve the intended form and dimensions. Numerous excipients are incorporated into tablets to manage the desired product outcome, including tablet disintegration, dissolution, and drug release. These factors are predetermined by the formulator to meet the specific needs of the target patient population. Certain excipients are essential in facilitating the manufacturing process, including glidants and lubricants. AI can also be utilized in the context of systemic drug delivery to predict drug release. Additionally, it is employed to investigate the effects of crucial processing parameters that are integral to tablet manufacturing, with the potential to ensure consistent quality control measures. Certain AI applications have been utilized to identify defects in tablets [ 109 , 110 ].

5.1.1. Prediction of Dug Release through Formulations

The prediction of drug release certainly has the potential for stable quality control. Drug release studies are performed through in vivo and in vitro methods, which are treated as fundamental technologies regularly evaluated or tested during product development. The release of the drug from oral solid dosage forms is based on the contribution of critical material attributes along with the processing parameters. Some of the common factors affecting drug release include compaction parameters such as the pressure used for tablet hardness setting, geometric aspects of the tablets, and drug loading characteristics. Many analysis techniques, including spectrophotometric analysis methods, have been implemented, or drug release studies are usually required for extensive analysis.

The drug release results must be set as per the formulator’s requirements and require repetitive testing and preparation of the batches to obtain an optimized batch, which makes this task tedious and time-consuming [ 111 ]. AI is implemented in the drug formulation and will assist in the prediction of drug release; hence, there is a limited number of runs required to optimize the batch, which further induces a reduction in the work and cost during pilot batch scale and production processes. AI can help predict the drug release profiles and dissolution profiles and explore the disintegration time for the effective selection of the best batch for further scale processing. Some researchers have implemented AI algorithms for the prediction of dissolution profiles into the hydrophilic matrix type of sustained-release tablets with the help of artificial neural networks (ANNs). The support machine vector (SVM), as well as regression analysis, are also implemented during the analysis of the data and prediction of the dissolution profile. The data for the modeling study of drug release were obtained with the help of process analytical technology (PAT) along with critical material attributes. The particle size distribution was found to be the most crucial variable during model prediction. Finally, the ANN was implemented for the identification of the most accurate models as part of the evaluation metrics, as presented in Figure 5 [ 112 , 113 ].

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AI for Oral Dosage Forms. Conventional tablet analysis is performed by screening many factors, such as drug release, drug loading, and study of the tablet geometry and hardness, by using in-process quality control tests along with ultraviolet spectrophotometry. These methods are often time-consuming and cost-ineffective to the industry. To address these issues, the combination of such traditional techniques along with AI was performed by using ANN, SVM, PAT, and regression trees. The data analysis and drug release predictions indicated that particle size distribution was a crucial factor for the same. Defective tablet surface crack analysis is performed by XRCT in combination with AI, containing three modules for distinguishing features for effective application in the healthcare sector.

5.1.2. Application of AI for 3D-Printed Dosage Forms

The application of AI in the field of 3D-printed dosage forms has revolutionized pharmaceutical manufacturing by enabling personalized medicine and enhancing drug delivery systems. AI algorithms can optimize the design and formulation of 3D-printed dosage forms based on patient-specific factors, such as age, weight, and medical history, leading to tailored drug therapies. By leveraging machine learning and computational modeling, AI can analyze large datasets and simulate the behavior of 3D-printed dosage forms, allowing for the rapid prototyping and optimization of drug release profiles, dosage strengths, and geometries. AI also aids in predicting and overcoming potential manufacturing challenges, optimizing printing parameters, and ensuring quality control. Furthermore, AI-driven feedback systems can continuously improve the 3D-printing process by learning from real-time data, enhancing accuracy, reproducibility, and scalability. Overall, the application of AI in 3D-printed dosage forms holds tremendous potential in advancing personalized medicine and improving patient outcomes [ 114 , 115 ].

The 3D-printed tablets are prepared by using the fused-filament type of fabrication, jetting of the binder, utilization of laser sintering, and pressure microsyringe. Some of the crucial processing parameters impacting the 3D-printed tablets are the temperature of the nozzle and platform along with the speed of the printing. Obeid et al. demonstrated the impact of the processing parameters on a 3D-printed tablet containing diazepam and its subsequent drug release study with the help of an ANN model. They explored the infill pattern, infill density, and other input variables for effective drug dissolution into 3D-printed tablets. The interactions between the different variables were evaluated with the help of self-organizing maps. Further modeling studies were performed by keeping the infill density along the surface area and volume ratio as the crucial factors contributing to the same. The higher dissolution resulted after extensive testing and ANN modeling along with validation [ 116 , 117 ].

5.1.3. Application of AI for the Detection of Tablet Defects

The application of AI in the detection of tablet defects has revolutionized quality control processes in pharmaceutical manufacturing. AI algorithms and computer vision techniques are employed to analyze images of tablets, enabling the automated and efficient detection of defects such as cracks, chips, discoloration, or variations in shape and size. By training AI models on large datasets of labeled images, the system learns to accurately classify and identify different types of defects, achieving high levels of precision and recall. Conventional methods, such as X-ray computed tomography, have been used to analyze the internal structure of tablets, but they are still time-consuming and affect the demand for the rapid production of tablets. Deep learning is implemented along with X-ray tomography to detect tablet defects. Ma et al. explored the application of neural networks for tablet defect detection with the help of image analysis completed through X-ray tomography. These researchers have manufactured several batches of tablets by using excipients such as microcrystalline cellulose along with mannitol. The prepared batches were analyzed with the help of the so-called image augmentation strategy. Three different models were used during the same research, including UNetA, which is applicable for the identification of distinguished characteristics of tablets from those of bottles. Module 2 was used for the identification of individual tablets with the help of augmented analysis. The internal cracks in the internal structure of the tablet were analyzed with the help of UNetB. Such UNet networks have been used to check tablet defects with better accuracy and thus provide ease of identification of defects with significant reductions in time, financial costs, and workload [ 118 , 119 ]. This AI-powered detection not only improves the speed and accuracy of defect identification but also reduces the dependence on manual inspection, minimizing human errors and subjective judgment. The real-time monitoring capabilities of AI systems ensure the prompt detection of defects, facilitating timely intervention and preventing the release of faulty tablets into the market. Ultimately, the integration of AI into tablet defect detection enhances product quality, increases productivity, and ensures the safety and efficacy of pharmaceutical products.

5.1.4. AI for the Prediction of Physicochemical Stability

AI has emerged as a powerful tool for predicting the physicochemical stability of oral dosage forms in pharmaceutical research. By leveraging machine learning algorithms and computational models, AI can analyze and interpret large datasets, including drug properties, formulation parameters, and environmental conditions, to predict the stability of oral formulations. AI models can assess factors such as drug degradation, interaction with excipients, and environmental effects on formulation stability. These predictive capabilities enable researchers to optimize formulation designs, identify potential stability issues early in the development process, and make informed decisions to enhance the shelf life and efficacy of oral dosage forms. The integration of AI into stability prediction contributes to more efficient and cost-effective drug development processes, ultimately leading to the delivery of safe and effective medications to patients. Some researchers have studied the utilization of machine learning for the determination of solid dispersion with the help of several algorithms. Han et al. explored the application of machine learning for the prediction of solid dispersion by implementing ANN along with K-nearest neighbor (KNN) algorithms as well as a light gradient boosting machine (LightGBM). The SVM was also applied in the same way. KNN is a nonparametric type of supervised learning classifier. It was used to classify or complete the predictions for the grouping along with the individual data point [ 120 ]. The free- along with the open-source distributed gradient boosting framework implemented with machine learning was the LightGBM. It is usually utilized for ranking assessments and classification along with machine learning tasks. In this study, approximately fifty drug molecules with six hundred forty-six data points for physical stability were collected from the public database and implemented for the training model. The generation of the database was performed with the help of molecular representations and molecular descriptors, such as molecular weight, along with the hydrogen bond acceptor count. The melting point and heavy atom count also acted as molecular descriptors. For three months, an accelerated stability study was conducted for the further evaluation of the model performance as a part of the physical stability prediction. They found an overall 82% accuracy for the same experiments [ 121 , 122 ].

5.1.5. Contribution of AI to Dissolution Rate Predictions

The dissolution rate of a drug, which refers to the rate at which it dissolves in a biological fluid, is a crucial parameter that determines its bioavailability and therapeutic effectiveness. AI has made significant contributions to the prediction of dissolution rates, aiding in the optimization of drug formulations and dosage forms. Through the analysis of vast amounts of experimental data, AI models can identify key physicochemical properties and molecular features that influence the dissolution process. These models leverage machine learning algorithms to learn complex patterns and relationships between drug properties and dissolution rates, enabling accurate predictions. By providing insights into the dissolution behavior of different drug formulations, AI facilitates the design of more effective drug delivery systems and helps in the selection of optimal formulation strategies for enhanced drug solubility and absorption. This advancement in dissolution rate prediction powered by AI empowers pharmaceutical scientists with valuable tools to accelerate drug development, optimize formulation strategies, and ultimately improve patient outcomes [ 97 ].

Many researchers have studied the dissolution profiles of routine drugs, and they have documented the rapid dissolution of some drugs and supersaturation of related drugs. Amorphous drug recrystallization and precipitation are also crucial factors associated with this process. Some studies have shown that solid dispersions do not precipitate due to the addition of excipients. Dong et al. explored a method for predicting dissolution along with the dissolution rate by using AI for at least 50 active pharmaceutical ingredients along with 25 polymers. Some of the AI algorithms they have used include SVM, LightGBM, and extreme grading boosting (XGBoost) [ 123 ]. XGBoost is a scalable machine learning-related library consisting of a distributed gradient-boosted decision tree, which is helpful in the prediction of problems associated with unstructured data, including images and texts. The artificial neural network was used to outperform all other types of algorithms or frameworks. In the same study, the same team used molecular computational software for the descriptors for the active pharmaceutical ingredients as well as the polymers. The input variables selected for the same study were temperature, drug loading, and volume, while dissolution was recognized as that of the binary output including precipitation or supersaturation. The dissolution rate was considered the research output for the same and resulted in the greater accuracy of the prediction of the results for the dissolution profiles of the selected active pharmaceutical ingredients, along with the polymers [ 124 , 125 ].

5.2. AI for Nanomedicine

By harnessing AI’s capabilities in data analysis, pattern recognition, and optimization, nanomedicine researchers can accelerate the development of novel nanoscale interventions, improve diagnostics, enhance drug delivery, and advance personalized medicine. AI in nanomedicine holds great potential for revolutionizing healthcare by enabling precise and targeted therapeutic approaches at the nanoscale [ 126 ]. Nanoparticles are used for targeted drug delivery, imaging, and sensing. AI algorithms can aid in designing and optimizing nanoparticles by predicting their physicochemical properties, stability, and efficacy. This helps researchers develop nanoparticles with desired characteristics for specific applications. Nanomedicines are used effectively as drug delivery carriers for drugs or combinations of drugs based on the concept of drug synergy, especially for the treatment of cancer patients. They contain major impactful inputs, such as drug selection, dose selection, and stimuli-responsive material selection. The deep learning type of algorithm was used for melanoma and has shown great accuracy in caring for patients and assisting in diagnostic procedures [ 127 , 128 ].

AI algorithms can model the behavior and interactions of nanoscale materials within biological systems. This enables the prediction of nanoparticle behavior, drug release kinetics, and potential toxicity, facilitating the development of safe and effective nanomedicine formulations. AI can be used in nanosensors and biosensors for the real-time monitoring of biomarkers, drug levels, or disease progression. These sensors can provide continuous feedback to healthcare providers, enabling timely interventions and personalized treatment adjustments [ 129 ].

The AI-based database is useful for scaling up nanocarriers by using an automated system. AI is also used in nanocarrier drug delivery systems, particularly in the optimization of nanocarriers and drug compatibility testing by using computational approaches. Such approaches are used for the evaluation of drug loading, formulation stability, and drug retention. Thus, AI intervention contributes to the enhancement of the therapeutic nanocarriers required for specific cell types for the treatment of tumors. Yuan He et al. studied the application of machine learning methods to the prediction of nanocrystals prepared by high-pressure homogenization along with the wet ball milling method. The demands for a repetition of the experiments can also be decreased by using computational techniques through Monte Carlo simulations and molecular dynamics, along with theoretical techniques. The simulation techniques are helpful for quantitative measurements in critical experiments. AI is also implemented for the creation of the database repository required for nanocarriers, which further helps in the determination of 3D structures along with physical and chemical property investigations in collaboration with structural nanobiology. Such repositories are essential to investigate the relationship between nanocarrier structure and toxicological, physical, and biological data [ 130 , 131 , 132 , 133 , 134 , 135 , 136 , 137 ]. In another study performed by Lutz Nuhn for the application of AI for better analysis, it was found that AI helped to reveal the heterogeneous vascular permeability for prepared nanoparticle-based drug delivery systems using an analysis of single blood vessels. Such findings may help in the design of a protein nanoparticle drug delivery system to obtain an active type of transendothelial permeability into tumors [ 138 ]. Zhoumeng Lin et al. used AI for better assessment with a PBPK modeling approach to study cancer medicine effectively. The same is also helpful to obtain a better understanding of the causes of low nanoparticle tumor delivery efficacy [ 139 ].

5.3. AI Application for Parenteral, Transdermal and Mucosal Route Products

Injectables, biologics, and other complicated formulations can be developed and manufactured using AI. Predicting complicated drug formulation physicochemical parameters using AI systems may help formulation development. AI models optimize pH, solubility, stability, and viscosity by analyzing formulation components, excipients, and manufacturing processes. This helps create stable parenteral formulations. AI can optimize parenteral product production for quality, efficiency, and variability. AI algorithms may discover process factors that affect product qualities and offer appropriate modifications by analyzing real-time process data. Thus, product consistency, batch failures, and manufacturing productivity increase. AI algorithms may find trends and product quality variations in huge datasets from analytical tests, including particle size analysis, spectroscopy, and chromatography. This helps identify and fix quality concerns early, assuring high-quality goods. AI models may anticipate contamination, stability, and regulatory deviations using historical data and process factors. AI-based monitoring systems may analyze important process parameters in real time during parenteral product manufacture. AI algorithms can identify abnormalities and forecast deviations and take quick action by combining data from sensors, instruments, and process controls. This maintains product quality and minimizes noncompliance. AI optimizes maintenance procedures for complicated parenteral product manufacturing equipment. AI models analyze sensor data, equipment performance history, and maintenance records to forecast equipment failure or deterioration and schedule proactive maintenance. This saves unnecessary downtime, boosts output, and cuts maintenance. AI can help ensure parenteral and complex biological product regulatory compliance. AI algorithms may analyze compliance, detect possible noncompliance concerns, and provide process improvement ideas by analyzing process data and product properties. This aids GMP compliance and regulatory compliance [ 140 ].

For example, AI was used in the inspection of the particles to check whether the particles were swimming, sinking, or sticking into the inner side of the container. For proper inspection of the individual particles, the optical setup, strategy, algorithm, and inspection were recommended. The particle tracking algorithm along with image subtraction was used for the analysis of the floating particles. The liquid inside the container is allowed to move so that the behavior of the moving particles can be recorded with the help of high-resolution images, while the particle movement direction can also be traced with the help of AI. The deep learning algorithm is also used for the proper isolation of the particles. One of the greater issues associated with parenteral batch flaws is bubble formation, which is normally not harmful to patients, but there is a great need to distinguish between particles and bubbles. The AI-based image processing type of algorithm was used for these types of visual inspection and the issues associated with them. One of the other camera-based applications of AI was surface crack detection by using surface qualifies 7500, which is used to analyze hundreds of millions of data points per second with the help of graphical processing subunits [ 127 , 128 , 140 , 141 , 142 ]. Manufacturers may optimize product performance, decrease manufacturing hazards, and provide safe and effective parenteral and technologically advanced pharmaceutical products using AI data analysis, pattern recognition, and predictive modeling.

Bannigan et al. highlight the availability and potential of cutting-edge machine learning (ML) technologies in the field of pharmaceutical and materials science. They demonstrate that ML can accelerate the development of innovative drug delivery technologies by accurately predicting in vitro drug release from long-acting injectables (LAIs). The study emphasizes the interpretability of ML models, which can provide insights into the decision-making process. Although neural network models did not perform well due to the small dataset, tree-based models such as LGBM showed promise in reducing the time and cost associated with LAI formulation development. The study presents a proof-of-concept for ML in drug formulation and hopes to inspire more advanced and tailored ML approaches in the future [ 143 , 144 ].

The conventional trial-and-error approach in formulating ocular, transdermal, pulmonary and other mucosal drug delivery systems lacks in-depth understanding, making it inefficient for complex formulations. However, recent advancements in computational pharmaceutics, specifically machine learning and multiscale simulations, have opened up new possibilities. Recent progress in using molecular simulations, mathematical modeling, and PK/PD modeling for these drug delivery routes has led to more efficient product development. In silico modeling and simulations offer unique advantages by providing detailed insights and facilitating rational formulation design. The integration of in silico methodologies, overcoming data challenges, and interdisciplinary collaborations can lead to more efficient and objective-oriented drug formulation design in the era of Pharma 4.0 [ 145 , 146 , 147 , 148 ].

5.4. AI Tools for Biologics Product Development

AI helps create newer proteins, peptides, nucleic acid biologics and immunotherapeutics [ 144 , 145 , 146 , 147 , 148 , 149 , 150 , 151 , 152 ]. AI algorithms could help to build proteins and peptides with desired features [ 153 , 154 , 155 , 156 , 157 ]. AI models may produce therapeutic sequences with better stability, binding affinity, or immunogenicity by analyzing massive volumes of protein structure and function data. This allows for customized biologics with improved effectiveness and safety [ 158 , 159 ].

AI systems can find therapeutic targets using genetic, proteomic, and clinical data. AI helps researchers build protein and peptide biologics that alter biological pathways or target illness-causing proteins by finding disease targets. AI models can predict protein folding from amino acid sequences. Understanding protein function and creating optimized biologics requires protein folding. Deep learning and molecular dynamics simulations can anticipate protein folding patterns, helping design stable and functioning biologics ( Figure 6 ) [ 160 ].

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AI can contribute to protein development and customized biologics by using data analysis, predictive modeling, and pattern recognition tools for better improvisation in the protein development process and customized proteins. Knowledge of altered biological pathways and finding disease targets are required for the same. The prediction of protein folding from amino acid sequences and the use of deep learning and molecular dynamic simulation for better understanding can be performed by AI. The prediction of protein/peptide binding affinity and toxicity studies can be performed effectively by AI with the help of SAR and toxicological datasets.

AI algorithms predict protein/peptide-target molecule binding affinity. AI models may reliably estimate binding strength by training on huge protein–protein or protein–peptide datasets. This improves treatment effectiveness by choosing or creating biologics with a high affinity and specificity for targets. AI could help to optimize protein and peptide biologics formulations. Stability, aggregation tendency, and formulation factors affect biologic quality and effectiveness. AI algorithms can optimize formulation conditions and biologic stability and shelf life by analyzing protein physicochemical parameters, formulation components, and manufacturing processes [ 161 ].

AI algorithms predict protein and peptide biologic toxicity. AI systems can anticipate biologic adverse effects and immunogenicity by analyzing structure–activity relationships while being trained on toxicological datasets. This allows researchers to find and alter harmful sequences or structures. AI is being utilized to optimize clinical trials for protein and peptide biologics. AI algorithms are capable of predicting patient responses and refining trial procedures using patient data, illness features, and treatment results. This streamlines patient enrollment, study design, and personalized treatment [ 162 , 163 , 164 ]. AI has the potential to significantly enhance research, diagnostics, and therapeutics in the fields of exosomes, CAR T-cell therapy, and CRISPR/Cas9 [ 164 , 165 , 166 ].

By utilizing AI’s capabilities in data analysis, pattern recognition, and predictive modeling, the development of protein/peptide and gene therapy biologics can be accelerated, and the design and optimization of therapeutic molecules can be more efficient and targeted. AI holds immense potential to revolutionize the field by enabling the creation of novel biologics with enhanced properties and improving the success rate of biologic development [ 167 ].

5.5. AI in Medical Devices

The medical device is a sort of apparatus, implement, instrument, implant, or machine appliance as well as a reagent for specific medical purposes and can be used alone or in combination with the help of software or other related systems in vitro to address medical issues of patients. AI has made significant advancements in the field of medical devices, revolutionizing healthcare in various ways. Due to the pandemic, personalized medicine along with remote health monitoring has become essential and quite popular in many countries, which has boosted AI and machine learning applications in the healthcare sector. Some examples of how AI is being utilized in medical devices are described below:

Diagnostic Assistance: AI algorithms can analyze medical imaging data such as X-rays, CT scans, and MRIs to assist healthcare professionals in detecting and diagnosing diseases. For example, AI-powered algorithms can help identify cancerous lesions in medical images or detect abnormalities in electrocardiograms (ECGs) [ 168 ].
Remote Monitoring: AI-enabled medical devices can remotely monitor patients’ health conditions, allowing for the continuous tracking of vital signs and other relevant parameters. This is particularly useful for patients with chronic conditions who can receive personalized care from the comfort of their homes. AI algorithms can analyze the collected data and provide alerts or insights to healthcare providers [ 169 ].
Wearable Devices: AI is integrated into wearable devices such as smartwatches, fitness trackers, and biosensors. These devices can monitor various health parameters, such as heart rate, sleep patterns, physical activity, and even blood glucose levels. AI algorithms help interpret the data and provide users with actionable insights for improving their health and well-being [ 170 ].
Prosthetics and Rehabilitation: AI is used in advanced prosthetic devices to provide more natural movement and functionality. Machine learning algorithms can learn from user movements and adapt the prosthetic to better match the user’s intentions. AI can also assist in rehabilitation by analyzing motion and providing feedback to patients to improve their movements and track progress [ 171 ].
Surgical Assistance: AI has found applications in surgical devices, aiding surgeons during procedures. For instance, robotic surgical systems use AI algorithms to assist surgeons in performing precise and minimally invasive procedures. AI can also analyze preoperative and intraoperative data to provide real-time guidance and improve surgical outcomes [ 172 ].
Medication Management: AI-powered devices can help patients manage their medications effectively. Smart pill dispensers can remind patients to take their medications on time, dispense the correct dosage, and track adherence. AI algorithms can also analyze patient data, such as medical history and medication usage, to provide personalized recommendations for medication management [ 173 ].

These examples demonstrate how AI is integrated into medical devices to enhance diagnostics, monitoring, treatment, and patient care. AI’s ability to analyze large amounts of data, identify patterns, and provide personalized insights contributes to more accurate diagnoses, improved treatment outcomes, and better overall healthcare delivery. It also contributes to the development of new products for patient benefits and to effectively reaching out to new customer segments to captivate large businesses and create more business potential in the healthcare sector. Currently, medical technology-based companies are using AI in major sectors, such as diagnosis, prevention, and care, along with personalized medicine work for patients.

For example, Medtronic, a global medical technology company, has indeed developed innovative applications of AI to help patients with diabetes manage their condition effectively. One notable example is the Medtronic Guardian Connect system, which combines AI and continuous glucose monitoring (CGM) technology to provide real-time insights and support to individuals living with diabetes. In 2016, Medtronic collaborated with IBM Watson to develop the Medtronic Sugar IQ app, which serves as a mobile personal assistant for individuals managing diabetes. This app incorporates AI technology to provide valuable features for effective diabetes disease management. One of the major features of the Sugar IQ app is “insights.” The app analyzes the user’s glucose patterns over time, identifies trends, and provides personalized messages and notifications to the patient. These insights help individuals understand how specific actions, habits, and external factors impact their glucose levels. By gaining this understanding, users can make informed decisions and take proactive steps to manage their diabetes more effectively. The second important feature of the Sugar IQ app is “glycemic assistance.” The app utilizes AI algorithms to provide real-time guidance and recommendations to users based on their current glucose readings. If the glucose levels are trending high or low, the app can suggest actions to help the user maintain a more stable glucose range. This feature acts as a virtual assistant, providing personalized support and reminders to help users make appropriate choices regarding their diabetes management. Last, the Sugar IQ app incorporates a “food logging” functionality. Users can log their meals and track carbohydrate intake through the app. The app can then analyze the impact of different foods on glucose levels and provide insights into how specific meals or food choices affect blood sugar. This information enables individuals to make more informed dietary decisions, leading to better glycemic control. By combining AI technology with glucose monitoring and personalized messaging, the Medtronic Sugar IQ app offers valuable tools for individuals with diabetes. It helps users gain insights into their glucose patterns, provides real-time assistance in managing blood sugar levels, and assists in making informed decisions about diet and lifestyle choices. These features contribute to improving disease management and supporting patients in achieving better control of their diabetes [ 174 , 175 , 176 , 177 , 178 ].

6. AI for Pharmacokinetics and Pharmacodynamics

Drug development is a complex process that involves several stages, including drug discovery, preclinical studies, clinical trials, and regulatory approval. Pharmacokinetics and pharmacodynamics are crucial aspects of drug development, as they determine the optimal dosage, administration route, and safety of a drug in the body [ 85 ]. Traditional experimental methods for pharmacokinetics and pharmacodynamics studies can be time-consuming and expensive and may not always provide accurate predictions of drug efficacy and safety [ 179 , 180 ].

Traditionally, pharmacokinetics and pharmacodynamics studies have been conducted using experimental methods such as animal studies and human clinical trials. These methods have critical challenges, such as ethical concerns, sample size, and interindividual variability. Furthermore, these studies may not always provide accurate predictions of drug pharmacokinetics and pharmacodynamics in humans. To overcome these limitations, computational models and AI methods have been developed to predict drug pharmacokinetics and pharmacodynamics in a faster, more cost-effective, and more accurate manner [ 181 , 182 ].

AI has shown tremendous potential in the fields of pharmacokinetics, pharmacodynamics, and drug discovery [ 183 ]. With the advent of powerful computing and machine learning algorithms, AI has emerged as a valuable tool for predicting and optimizing drug pharmacokinetics and pharmacodynamics. Although the challenges of large data and reliable datasets are hard to ignore, AI can open new doors in PKPD studies and their impact on therapies [ 183 , 184 , 185 , 186 , 187 ].

6.1. AI-Based Methods to Predict Pharmacokinetic Parameters

The utilization of machine learning (ML) and deep learning (DL) algorithms is prevalent in the prediction of pharmacokinetic parameters. Various ML algorithms, including the Bayesian model, random forest, support vector machine, artificial neural network, and decision tree, have been employed to forecast drug absorption, distribution, metabolism, and excretion (ADME) characteristics. DL algorithms, including convolutional neural networks (CNNs), long short-term memory (LSTM), and recurrent neural networks (RNNs), are commonly employed in the prediction of various pharmacokinetic parameters, such as drug absorption, bioavailability, clearance, volume of distribution, and half-life. Quantitative structure–activity relationship (QSAR) is a computational approach that utilizes the chemical structure of a molecule to predict its biological activity. This method has found application in pharmacokinetics, where it can be employed to anticipate drug ADME properties, including solubility, permeability, and metabolism ( Figure 7 ) [ 121 , 188 , 189 , 190 , 191 ].

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Role of AI in PKPD studies. Pharmacokinetic studies include absorption (A), distribution (D), metabolism (M), and excretion (E) studies. A pharmacodynamic study includes the drug’s effect on the target. Understanding the effect of drug molecules and their distribution requires a large number of calculations. A smaller miscalculation or missed dataset may lead to a huge error that may be critical. AI helps to accelerate complicated calculations without missing datasets and provides more accurate, faster, and cost-effective results. It converts complicated data into easily understandable and representable graphs, which might help to identify the root cause of the problem. It can also help to minimize animal studies by calculating the impact of different conditions such as enzymes, diseased conditions, dosing differences, patient data, etc., in different animals and reduce the number of animals required for clinical trials.

6.2. AI-Based Computational Method for PBPK

PBPK models are widely used to simulate drug distribution and clearance in the body. These models are complex, and the development of such models requires extensive data and computational resources. AI-based methods can simplify the development of PBPK models by using machine learning algorithms to identify the most relevant features of the model ( Table 4 ). AI-based computational methods can also optimize the parameters of the PBPK model, which can reduce the need for animal studies and human clinical trials [ 192 , 193 , 194 ].

The efficacy and safety of drug molecules are largely based on their pharmacokinetic parameters. Drug safety is based on the total time the active drug is present in the body, while the dose of the drug depends on its elimination from the body. Therefore, in vivo exposure is a very important tool for drug safety and efficacy assessment. The drug discovery and development process involves assessment and evaluation prior to clinical trials. Absorption, distribution, metabolism, and elimination (ADME) are the major factors in compound attrition for the development of drug molecules. Drug discovery studies involve in vivo pharmacokinetic studies in animals, while in vitro systems are used for humans along with animal studies. The first, in human dosing, is used for the optimization of the drug’s exposure to humans. In vitro and in vivo extrapolations are used for liver microsomes and hepatocytes. Hepatic clearance is performed with the help of in vivo studies in humans and animals, while in vitro assays are used for liver microsome studies. The human pharmacokinetic parameters are estimated by using allometric scaling methods along with in vivo preclinical data. The volume of distribution, drug clearance, and bioavailability are also estimated by the same method. The simulation of the time course along with ADME properties is simulated by the mathematical framework along with PBPK modeling. The latter are used to understand the in vivo behavior for extrapolation to humans, and normally these are applied to the later stages of drug discovery. The complexity of in vivo data is higher than that of in vitro pharmacokinetic parameters, and AI and ML are implemented for the analysis and assessment of the same [ 195 ].

6.3. Prediction of Drug Release and Absorption Parameters

AI-based models have been successfully employed to predict drug release and absorption parameters. AI algorithms can analyze data from various drug delivery systems and predict the release kinetics of drugs. By considering factors such as the drug’s physicochemical properties, formulation characteristics, and release mechanism of the delivery system, AI models can estimate the rate and extent of drug release over time. AI-based models can also predict the release kinetics of drugs from different drug delivery systems, such as oral tablets, transdermal patches, and inhalers [ 196 ].

AI-based models can predict drug absorption parameters, such as bioavailability and absorption rate, by considering factors such as drug solubility, permeability, and formulation characteristics. These models can analyze the physicochemical properties of the drug, such as lipophilicity and molecular weight, and correlate them with absorption data to estimate how efficiently the drug is absorbed into the bloodstream. Overall, AI-based models provide a powerful tool for predicting drug release and absorption parameters. By analyzing various factors and leveraging machine learning algorithms, these models can optimize drug formulations, guide drug development decisions, and contribute to the design of more effective drug delivery systems [ 189 , 190 , 191 , 192 , 193 , 194 , 197 ].

6.4. Prediction of Metabolism and Excretion Parameters

AI-based models have proven valuable in predicting drug metabolism and excretion parameters, providing insights into drug pharmacokinetics. AI algorithms can analyze the molecular structure and physicochemical properties of drugs to predict their metabolic pathways. By training on large datasets of known drug metabolism information, AI models can identify structural features associated with specific metabolic transformations. These models enable the prediction of potential metabolites and provide insights into the major enzymes involved in drug metabolism [ 198 ].

AI-based models can calculate enzyme kinetics, such as reaction rates and enzyme–substrate interactions, to estimate the metabolic fate of drugs. By considering factors such as enzyme expression levels, genetic variations, and drug–drug interactions, AI models can assess the potential impact of metabolism on drug clearance and efficacy. This information is valuable in optimizing drug dosing regimens and predicting potential drug interactions [ 199 ].

AI algorithms can analyze drug physicochemical properties, such as molecular weight, lipophilicity, and ionization, to predict drug clearance rates. By training on datasets that include information on drug clearance pathways, AI models can estimate the rate at which drugs are eliminated from the body. This information is crucial for determining appropriate dosing regimens and ensuring drug efficacy and safety [ 200 ].

AI models can predict drug interactions with transporters involved in absorption, distribution, metabolism, and excretion processes. By considering drug physicochemical properties and transporter characteristics, AI models can assess the potential for drug–drug interactions or altered pharmacokinetics due to transporter-mediated effects. This knowledge aids in understanding drug disposition and optimizing drug formulations [ 201 , 202 , 203 , 204 ].

By utilizing AI algorithms and analyzing vast amounts of data on drug metabolism and excretion, these models contribute to predicting drug fate in the body. They assist in optimizing drug dosing, identifying potential drug interactions, and aiding in the design of safer and more effective medications. Additionally, AI models enable researchers and pharmaceutical companies to prioritize drug candidates based on their predicted metabolic and excretion profiles, facilitating more efficient drug development processes.

Algorithms used for the development of AI models for various PKPD studies along with their advantages and limitations.

Algorithm/Software	Aim/Target	PK/PD/Both	Reference
Bayesian/WinBUGS	To handle data below the limit of quantification	Both	[ ]
Bayesian/PKBUGS (v 1.1)/WinBUGS (v 1.3)	Pharmacokinetic analysis of sirolimus concentration data for therapeutic drug monitoring	PK	[ ]
Support Vector Machine/Least Square-SVM	Drug concentration analysis of sample drug based on individual patient profile	PK	[ ]
Support Vector Machine/Drug Administration Decision Support System (DADSS) and Random Sample Consensus RANSAC	Prediction of drug concentration, ideal dose, and dose intervals for a new patient	PK	[ ]
Support Vector Machine/Profile Dependent SVM	Therapeutic drug monitoring of kidney transplant recipient	PK	[ ]
Support Vector System + Random Forrest Model	Pharmacodynamic drug interaction based on Side-Effect Similarity (SES), Chemical Similarity (CS), and Target Protein Connectedness (TPC)	PD	[ ]
Linear Regressions (LASSO)/Gradient Boosting Machines/XGBoost/Random Forest	Prediction of the plasma concentration–time series and area under the concentration-versus-time curve from 0 to 24 h after repeated dosing of Rifampicin	PK	[ ]
XGBoost	Estimation of drug area under the curve (AUC) of tacrolimus or mycophenolate mofetil (MMF)	PK	[ , ]
Simulated Annealing k-Nearest-Neighbor (SA-kNN)/Partial Least-Square (PLS)/Multiple Linear Regression (MLR)/Sybyl version 6.7	Prediction of pharmacokinetic parameters of antimicrobial agents in humans based on their molecular structure	Both	[ ]
Drug Target Interaction Convolutional Neural Network (DTICNN)	Identification of the drug-target interactions and predict potential drug molecules	PD	[ ]
Deep Long Short-Term Memory (DeepLSTM)	Computational methods to validate the interaction between drugs and target	PD	[ ]

7. Limitations of AI Tools

Despite their benefits, AI-based models have some limitations, such as the need for large datasets, potential biases, and lack of interpretability. Therefore, AI-based models should be used in combination with traditional experimental methods to ensure the safety and efficacy of drugs. Some of the limitations are highlighted below:

7.1. Lack of Transparency

AI models use complex algorithms and are often referred to as “black boxes” because it is difficult to understand how the model arrives at its predictions. This lack of transparency can make it challenging to gain regulatory approval for AI-based drug development tools, as it can be challenging to demonstrate that the model is making accurate and reliable predictions. Furthermore, the lack of transparency can also lead to a lack of trust in the model’s predictions, particularly if the model makes predictions that conflict with the expectations of clinicians or researchers [ 216 , 217 ].

7.2. Limited Availability of Data

AI models require a significant amount of data for accurate predictions. However, in some cases, there may be limited data available for a particular drug or population, leading to less accurate predictions or biased results. For instance, rare diseases may have limited data available, which can be a significant challenge for developing AI models. Additionally, the data used to train AI models may not be representative of the population of interest, which can lead to biased results. Moreover, some types of data, such as longitudinal data or real-world evidence, may not be readily available, which can limit the utility of AI models. These limitations highlight the need for the careful consideration of the quality and representativeness of the data used to develop AI models.

7.3. Biases in Data

The efficacy and precision of AI models are contingent upon the quality of the data utilized for their training. In instances where the data exhibit bias or incompleteness, the resulting predictions may also be biased. The homogeneity of patient populations in clinical trials is a significant problem within the realm of pharmacology. If a specific demographic or disease state is inadequately represented in the training dataset, the model’s ability to make precise predictions regarding the drug’s efficacy in that particular population may be compromised. Moreover, in the case of incomplete or inaccurate data, the model may generate erroneous assumptions, which can result in imprecise predictions. The utilization of an AI model to direct clinical decision-making can pose a significant challenge. Therefore, it is essential to guarantee that the training data used to create AI models are representative of the population for whom the model will be utilized and that the data are trustworthy, comprehensive, and impartial [ 218 , 219 ].

7.4. Inability to Incorporate New Data

Once an AI model is trained, it is often challenging to incorporate new data or update the model. This can be a significant limitation in the context of drug development processes, where new information and data are constantly emerging. For example, as new drugs are introduced or as clinical trials produce additional data, an AI model may need to be updated to reflect this new information. However, updating an AI model can be challenging, and it may require significant time and resources to retrain the model with the new data. Furthermore, as drug development processes continue to evolve, AI models must be able to keep up with these changes. Failure to do so could result in inaccurate predictions and flawed decision-making. Thus, it is crucial to carefully consider the limitations of AI models and to develop strategies for updating them as new information becomes available. This can include designing models that can be easily updated or integrating the model into a larger framework that can be continuously refined over time.

7.5. Limited Ability to Account for Variability

AI models are generally trained on large datasets, which can be biased toward the average responses observed in the data. As a result, the models may not be able to accurately predict drug responses for individuals who deviate significantly from the average response. This is particularly concerning for drugs that have a wide range of responses in different patients (such as in cancer), where the variability can be significant [ 220 ].

7.6. Interpretation of Results

AI models can be complex and can generate outputs that are difficult to interpret, even for experts in the field. The models may not be able to provide a clear explanation of how they arrived at their predictions, which can make it challenging for clinicians and researchers to understand and interpret the results. In some cases, the results may be difficult to translate into actionable insights that can be used in clinical practice or drug development. Additionally, the use of AI models may require a level of technical expertise that is not readily available to all clinicians and researchers, which can further limit their usefulness. As a result, there is a need for an improved interpretability and explainability of AI models, to ensure that their predictions can be understood and used effectively [ 221 , 222 ].

7.7. Ethical Considerations

As with any use of AI, there are ethical considerations that must be taken into account when using these technologies in drug development. One major concern is patient privacy, as sensitive health data are often used to train AI models. Data safety and security represent crucial parameters that demand significant attention and cannot be overlooked. It is important to ensure that patient data are collected and used in a way that protects their privacy and respects their rights. Data ownership is another ethical concern when using AI in drug development. In some cases, data may be collected from patients without their explicit consent, and it may not be clear who owns the data or who has the right to use it. This can lead to conflicts between patients, researchers, and pharmaceutical companies [ 223 , 224 ]. Regulatory agencies are tasked with the development of stringent protocols, guidelines, and standardized evaluation processes to effectively integrate AI into drug development. These measures should encompass multiple dimensions, including the ethical considerations of animal welfare and patient safety. Animal testing, which plays a pivotal role in drug development, necessitates a commitment to reducing, refining, and replacing animal models whenever feasible, aligning with ethical principles. Prioritizing patient safety, AI models must undergo thorough validation and testing to ensure their reliability and accuracy. An important step in addressing the regulatory and ethical implications of AI in drug development is the release of the discussion paper by the U.S. Food and Drug Administration (FDA) entitled, “Using Artificial Intelligence & Machine Learning in the Development of Drug and Biological Products.” This document provides an overview of the role of AI in drug discovery, nonclinical research, and clinical research. Additionally, it outlines recommended practices for the application of AI and machine learning. This FDA initiative marks an important milestone in regulating the use of AI in healthcare and paves the way for new opportunities in the sector. It signifies the recognition of the potential benefits and challenges associated with AI in drug development and sets the stage for future regulatory advancements in this domain [ 225 ].

7.8. Complex Biological Systems

AI’s ability to accurately mimic the complexity of biological systems as a whole is limited. Biological systems are intricate and dynamic, encompassing a multitude of interconnected pathways, feedback loops, and intricate molecular interactions. This complexity poses challenges for AI models, which often simplify and abstract the underlying biological processes. AI models heavily rely on training data to learn patterns and make predictions, but the available data may not fully capture the intricacies and nuances of biological systems [ 226 ]. Factors such as genetic variations, environmental conditions, and interindividual variability contribute to significant complexity and variability that may not be adequately captured by AI models [ 45 , 227 ]. Moreover, the emergent properties of biological systems, where the collective behavior of individual components gives rise to system-level behaviors, are difficult to predict solely based on the properties of individual components. A limited understanding of certain biological processes and mechanisms further hampers the accurate incorporation of this knowledge into AI models [ 228 ].

7.9. Lack of Clinical Expertise

While AI can identify correlations, it is essential to recognize that individual patient therapies can vary despite these correlations. AI algorithms typically operate on a statistical framework, which may limit their comprehension of the intricate factors and the profound effects certain parameters can have. The complex nature, where treatment decisions are influenced by various individualized factors, poses a challenge for AI models primarily focused on statistical associations [ 229 ]. Therefore, the ability of AI to fully capture the critical aspects and implications of specific parameters may be limited.

7.10. Inactive Molecules

AI uses a computational approach to predict the binding interactions between a small molecule and a target protein by employing algorithms and scoring functions. However, such simulations can lead to the identification of inactive molecules. One major challenge is accurately representing the conformational flexibility of both the small molecule and the target protein, as docking algorithms sample a limited range of conformations, potentially resulting in false-positive or false-negative binding affinities [ 34 ]. Moreover, if the protein structure used in docking or AI is incomplete or inaccurate, it can lead to erroneous predictions. Difficulties in accounting for solvation effects, receptor flexibility, and other influential factors further contribute to the limitations of docking. Hence, it is crucial to conduct experimental validation to confirm the activity of identified compounds, assessing their potency and selectivity. Continuous efforts to refine docking algorithms, scoring functions, and incorporate factors such as protein flexibility and solvent effects aim to enhance the reliability of docking-based screening. Integrating additional computational methods, such as molecular dynamics simulations, can provide a more comprehensive representation of molecular interactions [ 230 ].

Despite the limitations of AI tools, they hold significant potential and cannot be overlooked in the field of pharmaceutical development. It is crucial to promptly identify and address these limitations to facilitate smoother and faster advancements in the industry. Recent years have witnessed rapid progress in resolving these challenges, driven by improvements in data availability, deep learning algorithms, explainability, integration with other modeling approaches, and increased computational power [ 231 ].

However, one persistent problem that remains unresolved is the issue of misreported data, which introduces bias and distorts the accuracy of AI models. To mitigate this, it is imperative to adopt the principles of FAIR data (Findable, Accessible, Interoperable, Reusable), which align with the fundamental principles of ALCOA (Attributable, Legible, Contemporaneous, Original, and Accurate) [ 232 ]. By adhering to these principles, data quality can be improved, enhancing the reliability of AI-driven analyses.

While challenges associated with AI in PKPD studies are substantial and will require time to overcome, the ever-evolving nature of the field instils hope for continuous improvement. However, it is crucial to exercise caution and not overly rely on AI without considering potential limitations and verifying results through rigorous scientific validation. Although AI has shown great potential in improving and enhancing PKPD studies, it is not ready to completely replace humans in this field. AI is a powerful tool that can assist researchers and clinicians in analyzing large amounts of data, identifying patterns, and making predictions. While AI can automate certain tasks and assist in data analysis, the collaborative effort between AI and human experts is crucial for successful PKPD studies. The integration of AI in pharmaceutical research should be approached with a balanced perspective, acknowledging both its potential and the need for careful evaluation and validation.

8. Current Trend: Fairy Tale to the Holy Grail

The current trends demonstrate the wide-ranging impact of AI in pharmaceutics, spanning drug discovery, precision medicine, formulation optimization, clinical trials, safety monitoring, and supply chain management. Here are some prominent trends:

Drug Discovery and Development: AI is revolutionizing the drug discovery process by enabling virtual screening, molecular modeling, and predictive analytics. AI algorithms can analyze vast amounts of chemical and biological data to identify potential drug candidates, optimize lead compounds, and predict their properties. This expedites the identification and development of novel therapeutics.
Precision Medicine: AI is being utilized to advance precision medicine approaches. By analyzing patient data, including genomics, proteomics, and clinical records, AI algorithms can identify patient subgroups, predict treatment responses, and assist in personalized treatment decision-making. AI also contributes to the development of biomarkers for disease diagnosis and prognosis.
Drug Repurposing: AI is being applied to identify new uses for existing drugs, a process known as drug repurposing. By analyzing large datasets and biological knowledge, AI algorithms can identify potential drug–disease associations and repurpose approved drugs for new therapeutic indications. This approach offers a faster and more cost-effective route to drug development.
Drug Formulation and Delivery: AI plays a role in optimizing drug formulations and delivery systems. AI models can predict drug release kinetics and absorption profiles and optimize formulations for enhanced efficacy and targeted delivery. AI is also used to design drug delivery devices and systems that improve patient adherence and convenience.
Clinical Trial Optimization: AI is being leveraged to optimize clinical trials, improving efficiency and reducing costs. AI algorithms can aid in patient recruitment, identify suitable trial populations, and optimize trial protocols. AI also assists in the real-time monitoring and analysis of trial data, allowing for adaptive trial designs and faster decision-making.
Regulatory Compliance and Safety: AI is increasingly used to support regulatory compliance and ensure drug safety. AI algorithms can analyze real-world data, adverse event reports, and the literature to identify potential safety issues and monitor post-marketing drug safety. AI also helps in pharmacovigilance, signal detection, and adverse event prediction.
Supply Chain Optimization: AI is applied to optimize pharmaceutical supply chains, ensuring efficient manufacturing, inventory management, and distribution. AI algorithms can predict demand, optimize production schedules, and enhance quality control processes, contributing to more streamlined and cost-effective operations.

Pharmaceutical companies are increasingly recognizing the potential of AI in PKPD studies. AI offers valuable tools and approaches that can enhance drug discovery and development processes. These companies are leveraging AI to analyze large datasets, predict drug–target interactions, optimize drug candidates, and simulate drug responses in biological systems. Some examples include GNS Healthcare [ 233 ], AstraZeneca [ 234 ], Atomwise [ 235 ], Recursion Pharmaceuticals, and Insilico Medicines [ 236 ]. AI has helped to improvise strategies for rapid and more accurate dosage form development. Pfizer has utilized AI algorithms to predict drug–drug interactions (DDIs) by analyzing vast datasets of drug structures, clinical outcomes, and adverse effects [ 237 ]. This approach has enabled Pfizer to identify potential DDIs more efficiently and prioritize drug combinations for further investigation, minimizing the risk of adverse reactions. Novartis has leveraged AI in drug formulation and delivery optimization, employing algorithms to analyze physicochemical properties, solubility, and permeability data to design optimal drug formulations and delivery systems. This has streamlined the drug development process and improved bioavailability and therapeutic efficacy. Additionally, Roche has made significant strides in personalized medicine by integrating patient-specific data into AI models [ 238 ]. By incorporating genetic profiles, medical histories, and biomarker measurements, Roche can predict individual drug responses and tailor treatment regimens, leading to more effective and personalized therapies. These examples highlight the innovative use of AI by pharmaceutical companies and showcase how it has revolutionized PKPD studies, paving the way for enhanced drug development strategies and improved patient outcomes. Some of the major applications of AI in pharmaceutical companies are tabulated in Table 5 .

Lit of companies using AI and ML technologies in pharmaceutical research [ 239 ].

Sr. No.	Domain	Technology and Outcome	Industry and Collaborations
1	Drug design	Novel therapeutic antibodies	Exscientia
2	Molecular drug discovery	AtomNet–deep learning-driven computational platform for structure-based drug design	AtomWise
3	Gene mutation related disease	Machine learning based recursion operating system for biological and chemical datasets	Recursion
4	Drug design	Ligand- and structure-based de novo drug design, especially in multiparametric optimization	Iktos
5	Drug discovery	Generative modeling AI technology	Iktos and Galapagos
6	Drug development	Potential preclinical candidates	Iktos and Ono Pharma
7	Drug design	Rapid drug design by software “Makya”	Iktos and Sygnature Discovery
8	Drug discovery and Drug development	Pharma.AI, PandaMics, ALS.AI	Insilico Medicine
9	Drug target and Drug development	ChatPandaGPT	Insilico Medicine
10	Drug development	Protein motion in drug development lie RLY-4008 (Novel allosteric, pan mutant and isoform selective inhibitor of PI3Kα	Relay therapeutics
11	Drug discovery	AI and machine learning for selection of drug target	BenevolentAI
12	Drug target	Drug target selection for chronic kidney disease and idiopathic pulmonary fibrosis	BenevolentAI and AstraZeneca, GlaxoSmithKline, Pfizer
13	Clinical trials	AI in clinical trials	Pfizer and Vysioneer
14	Disease treatment	AI and supercomputing for oral COVID-19 treatment Paxloid	Pfizer
15	Drug discovery	NASH drugs and sequencing behemoth Illumina	AstraZeneca and Viking therapeutics
16	Drug development	Trials360.ai platform in clinical trials for site feasibility, site engagement and patient recruitment	Janssen
17	Drug research	Automate medical literature review by using natural language processing	Sanofi
18	Drug development	AI in drug development	BioMed X and Sanofi
19	Drug research and drug development	AI empowerment and AI exploration platforms	Novartis and Microsoft
20	Drug discovery	AI drug discovery platform	Bayer

9. Futuristic Overview

AI might revolutionize the pharmaceutical industry in the future to accelerate drug discovery and drug development. Virtual screening techniques will rapidly analyze enormous chemical libraries and find therapeutic candidates with required features, accelerating lead compound identification. AI-enabled precise medicine could categorize patients, predict therapy responses, and customize medicines by analyzing genomes, proteomes, and clinical records. Scientists may create innovative compounds with target-binding characteristics using deep learning and generative models, improving medication effectiveness and lowering adverse effects. Additionally, AI will allow patient-specific dose formulations. AI algorithms will optimize medicine compositions and delivery methods to improve treatment results by considering patient-specific parameters, including age, weight, genetics, and illness status. AI algorithms will revolutionize safety assessment by predicting drug candidate side effects and toxicity.

AI-powered monitoring systems will allow remote patient care and medication adherence. Wearable gadgets and sensors will continuously gather data for AI algorithms to propose personalized therapy and better compliance. AI improves clinical trial design, patient selection, and recruitment. AI algorithms will use electronic health records, biomarkers, and genetic profiles to find appropriate patients, lower trial costs, and speed up approval.

The real-time monitoring and control of important parameters by AI models will optimize continuous manufacturing operations. AI algorithms will make pharmaceutical manufacture uniform and efficient via data analysis and feedback. AI will analyze large amounts of data to inform regulatory decisions. It will assist regulatory bodies in speeding up medication approval and improving safety.

The use of artificial intelligence in various segments of healthcare is growing daily, from the triage and screening of clinical risk prediction to diagnosis [ 141 , 240 ]. Clinical applications of AI have the potential to increase diagnosis accuracy and healthcare efficiency. The massive amount of time and money spent on medication research and development necessitates the use of more inventive methodologies and tactics [ 241 ]. Artificial intelligence is providing large opportunities in the medical field, such as multivariate data analysis of abundant amounts; resolving the complicated issues involved in the creation of viable medication delivery systems; making decisions with more accuracy, disease categorization, and modeling; establishing the correlation between formulations and processing factors; dosage ratio optimization; rapid drug development; anticipating drug bioactivities and interactions; cellular response; the effectiveness of the drugs used in combination; the outcomes of treatment; and many more. As demonstrated in all sections, AI and machine learning have considerable potential in revolutionizing medication delivery to improve infectious disease treatment effectiveness. Unfortunately, there are currently limited practical uses of AI in medication delivery, particularly in the therapeutic setting. Various AI methods used in drug delivery for the treatment of infectious diseases, such as Boost, 𝑘-nearest neighbors, decision trees and random forest, Naïve Bayes, ANN, Feedback System Control (FSC), SVM, Set Covering Machine (SCM), and logistic regression, have not been widely evaluated or used in clinical settings, demonstrating the existence of significant hurdles in the clinical translation of AI for medication administration in the treatment of infectious diseases [ 96 , 144 ]. Machine learning and artificial intelligence combined with PBPK modeling are important tools for drug development and risk assessment of environmental chemicals. A recently developed model of PBPK was used to describe how chemicals enter the body, the bioavailability of drugs, the movement of drugs between tissues, and how drugs are metabolized and eliminated from the body by a mathematical description. For the determination of the toxicity of the various classes of nanomaterials, PBPK-based toxicity models are most suitable. Because the chemical ADME routes are not well described or mathematically formulized, developing mechanistically valid PBPK models for novel compounds with limited prior knowledge is difficult and complex. With the recent development of Neural-ODE (Neural-ordinary differential equation) algorithms, it is now feasible to build PBPK simulations for a novel medication based on its properties, which can learn the governing ODE equations algorithmically and directly from PK data without the need for well-characterized previous knowledge. Overall, advances in AI approaches, particularly for the deep neural network model, may help to solve some of today’s challenges, thereby improving the performance of PK and PBPK modeling and simulations aimed at drug discovery and development, as well as a human health risk assessment of environmental chemicals [ 242 ]. The ultimate goal of the development of AI in PKPD depends on the understanding of the fundamentals associated with different scientific principles. This is only possible by developing standard regulations with strict measures that prevent the abuse of AI but at the same time accelerate its growth. Such a tedious task requires the collaboration of multiple pharmaceutical companies and regulatory bodies along with various healthcare professionals, including doctors, nurses, pharmacists, data scientists, etc.

While this futuristic overview presents exciting possibilities, it is important to recognize that challenges related to data quality, regulatory frameworks, and ethical guidelines will need to be addressed for the full realization of AI’s potential in pharmaceutical product development. However, with continued advancements and collaborations between industry, academia, and regulatory bodies, AI-driven innovations have the potential to revolutionize the pharmaceutical industry and improve patient outcomes in the years to come.

10. Conclusions

AI is transforming drug delivery technologies, enabling targeted, personalized, and adaptive therapies. By leveraging AI’s capabilities in data analysis, pattern recognition, and optimization, pharmaceutical researchers and healthcare professionals can enhance drug efficacy, minimize side effects, and improve patient outcomes. AI-based methods have revolutionized the field of pharmacokinetics and pharmacodynamics. They offer several advantages over traditional experimental methods. AI-based models can predict pharmacokinetic parameters, simulate drug distribution and clearance in the body, and optimize drug dosage and administration routes. AI-based computational methods for PBPK models can simplify the development of such models and optimize their parameters, reducing the need for animal studies and human clinical trials. Computational pharmaceutics, facilitated by AI and big data, revolutionizes the drug delivery process by providing a more efficient, cost-effective, and data-driven approach. It enables the optimization of drug formulations, personalized therapies, regulatory compliance, and risk reduction, ultimately leading to improved drug manufacturing processes and enhanced patient outcomes. Overall, the integration of AI technologies holds great promise for accelerating drug development, improving patient outcomes, and revolutionizing the pharmaceutical industry, promoting its evolution from era 4.0 to era 5.0.

Funding Statement

This research received no external funding.

Author Contributions

Conceptualization L.K.V.; validation, L.K.V. and V.P.C.; formal analysis, L.K.V. and V.P.C.; resources, R.R.S.T. and L.K.V.; writing—original draft preparation, A.D.G., K.J., V.P.C. and H.K.S.; review and editing, L.K.V. and V.P.C.; supervision, L.K.V. and R.R.S.T. All authors have read and agreed to the published version of the manuscript.

Institutional Review Board Statement

Not Applicable.

Informed Consent Statement

Data availability statement, conflicts of interest.

The authors declare no conflict of interest in this study.

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GMP Problem Solving and Root Cause Analysis course

Deal with gmp problems in an effective, efficient and compliant way.

This highly interactive one-day GMP Problem Solving course will allow you to learn about a wide range of problem-solving tools to help address your deviations, incidents and quality issues in a compliant way. From documenting the problem, through effective Root Cause Analysis, to confirmation of the effectiveness of corrective actions, delegates will leave the course with the knowledge and skills necessary not only to identify, document and solve problems, but to avoid these problems occurring in the first place .

Inadequately dealing with problems, not identifying root-cause and not implementing effective corrective actions are one of the major findings at regulatory inspections . This course will give confidence that problems are addressed in a systematic, consistent, compliant and auditable manner.

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Many pharmaceutical manufacturing sites have gotten into difficulty in the past few years for being unable to deal with problems in a systematic, consistent, compliant and auditable way. With increasing GMP requirements to deal with problems adequately it is inevitable that how an organisation deals with problems, incidents and deviations will be inspected more closely at regulatory inspections. In order to avoid difficulties both now and in the future make sure you are up to date with current tools and techniques needed to deal with problems in an effective, efficient and compliant way.

Our highly interactive Pharma Problem Solving course introduces delegates to a wide range of problem solving tools that can be used to help solve problems that occur in a pharmaceutical environment. During the course a large number of methods that can be used to deal with problems are introduced, explained and examples provided of when they may be used. The course focuses on ensuring that the correct cause of a problem is identified, that the right measures are employed to deal with the problem and that monitoring occurs to ensure that the problem has been solved for good. In addition the Pharma Problem Solving course looks at common reasons for problems and highlights how many systems have inadequacies within them that actually cause problems themselves.

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Types of problems and deviations
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Is/Is not, 5 “W”‘s and a “H”
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Human error – or not?
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Over the past 20 years he has worked as a leading pharmaceutical trainer and Quality Management System specialist. He works closely with pharmaceutical companies and their suppliers on evaluating, developing and continually improving their quality systems so that they add real-value to organisations. He has a real passion for training in the pharmaceutical industry and has presented GMP and Quality Management related training courses all over the world. He is an IRCA registered GMP Lead Auditor and regularly performs audits of pharmaceutical companies and their suppliers.

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Find out about our course ranges, gmp training, qms training, gdp/rp training, gmp compliance, qp training, gdp compliance, annex 1 2022 section 10: quality control (qc).

This final article looks at the new requirements in section 10 on Quality Control. It is also a useful point at which to reflect on the 2022 version of annex and what might it mean.

Annex 1 2022 Section 9: Environmental and Process Monitoring

This article is covering section 9 which describes the annex 1 guidance for Environmental and Process Monitoring. Section 9 is now a centralized location for monitoring activities.

Annex 1 2022 Section 8: Production and Specific Technologies – part 3

In this final part of the article on section 8 we will look at Sterilization processes. The selection of a sterilization process should be based on scientific principles.

Annex 1 2022 Section 8: Production and Specific Technologies – part 2

This is the second part of the article covering section 8 of Annex 1 2022 and here we are looking at the requirements around Form fill seal and Blow fill seal.

Annex 1 2022 Section 8: Production and Specific Technologies – part 1

This next article in our series is about section 8, which is the largest section of Annex 1 and covers production and specific technologies

Annex 1 2022 Section 7: Personnel

In this article, we move onto the personnel section, section 7.

Annex 1 2022 Section 6: Utilities

In this article we expand upon the equipment section but looking at Utilities as described in section 6.

Annex 1 2022 Section 5: Equipment

In this article we look at the general requirements for the equipment used in the manufacturing of sterile products.

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By Wayne Stottler , Kepner-Tregoe

Problem Solving & Decision Making Over time, developing and refining problem solving skills provides the ability to solve increasingly complex problems Learn More

For over 60 years, Kepner-Tregoe has been helping companies across industries and geographies to develop and mature their problem-solving capabilities through KT’s industry leading approach to training and the implementation of best practice processes. Considering that problem solving is a part of almost every person’s daily life (both at home and in the workplace), it is surprising how often we are asked to explain what problem solving is and why it is important.

Problem solving is at the core of human evolution. It is the methods we use to understand what is happening in our environment, identify things we want to change and then figure out the things that need to be done to create the desired outcome. Problem solving is the source of all new inventions, social and cultural evolution, and the basis for market based economies. It is the basis for continuous improvement, communication and learning.

If this problem-solving thing is so important to daily life, what is it?

Problem-solving is the process of observing what is going on in your environment; identifying things that could be changed or improved; diagnosing why the current state is the way it is and the factors and forces that influence it; developing approaches and alternatives to influence change; making decisions about which alternative to select; taking action to implement the changes; and observing impact of those actions in the environment.

Each step in the problem-solving process employs skills and methods that contribute to the overall effectiveness of influencing change and determine the level of problem complexity that can be addressed. Humans learn how to solve simple problems from a very early age (learning to eat, make coordinated movements and communicate) – and as a person goes through life problem-solving skills are refined, matured and become more sophisticated (enabling them to solve more difficult problems).

Problem-solving is important both to individuals and organizations because it enables us to exert control over our environment.

Fixing things that are broken

Some things wear out and break over time, others are flawed from day-1. Personal and business environments are full of things, activities, interactions and processes that are broken or not operating in the way they are desired to work. Problem-solving gives us a mechanism for identifying these things, figuring out why they are broken and determining a course of action to fix them.

Addressing risk

Humans have learned to identify trends and developed an awareness of cause-and-effect relationships in their environment. These skills not only enable us to fix things when they break but also anticipate what may happen in the future (based on past-experience and current events). Problem-solving can be applied to the anticipated future events and used to enable action in the present to influence the likelihood of the event occurring and/or alter the impact if the event does occur.

Improving performance

Individuals and organizations do not exist in isolation in the environment. There is a complex and ever-changing web of relationships that exist and as a result, the actions of one person will often have either a direct impact on others or an indirect impact by changing the environment dynamics. These interdependencies enable humans to work together to solve more complex problems but they also create a force that requires everyone to continuously improve performance to adapt to improvements by others. Problem-solving helps us understand relationships and implement the changes and improvements needed to compete and survive in a continually changing environment.

Seizing opportunity

Problem solving isn’t just about responding to (and fixing) the environment that exists today. It is also about innovating, creating new things and changing the environment to be more desirable. Problem-solving enables us to identify and exploit opportunities in the environment and exert (some level of) control over the future.

Problem solving skills and the problem-solving process are a critical part of daily life both as individuals and organizations. Developing and refining these skills through training, practice and learning can provide the ability to solve problems more effectively and over time address problems with a greater degree of complexity and difficulty. View KT’s Problem Solving workshop known to be the gold standard for over 60 years.

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_Drug Excipients Database
_FDA Inactive Ingredient
_QbD in FRD
_QbD in ARD
Pharma Quiz
_Basic Pharmacy
_Industrial Pharmacy
_Clinical Pharmacy
_Pharmacology
_Daily Pharma Quiz
_Dissolution vs Disintegration
_Validation vs Calibration
_Bulk Density vs Tapped Density
_LOD vs Water Content
_GMP vs cGMP
_GMP vs GLP
_Manufacturing
_Quality Control
_Compliance

1. Brainstorming

A group problem-solving technique that encourages the generation of a large number of ideas in a short amount of time. It is used to generate creative solutions to complex problems.

2. Root Cause Analysis

A problem-solving technique that helps identify the underlying causes of a problem. It is used to identify the root cause of a problem and develop effective solutions.

3. Fishbone Diagram

A visual tool used to identify the possible causes of a problem. It is used to organize and analyze information related to a problem.

4. SWOT Analysis

A strategic planning tool used to identify an organization's strengths, weaknesses, opportunities, and threats. It is used to develop strategies that capitalize on an organization's strengths and opportunities while minimizing its weaknesses and threats.

5. Pareto Chart

A graphical tool used to identify the most significant factors contributing to a problem. It is used to prioritize actions based on their impact on the problem.

6. 5 Whys

A technique used to identify the root cause of a problem by asking "why" questions repeatedly until the underlying cause is identified. It is used to uncover hidden causes of problems and develop effective solutions.

7. Mind Mapping

A visual tool used to organize information related to a problem or project. It is used to generate ideas, organize thoughts, and facilitate communication among team members.

8. Decision Matrix

A tool used for decision-making that helps evaluate options based on multiple criteria or factors. It is used to make informed decisions by considering all relevant factors and weighing their importance.

9. Flowcharting

A visual tool that represents processes or systems using symbols and arrows to show how they work or how they should work in an ideal situation. It is useful for identifying bottlenecks, inefficiencies, or areas for improvement in processes or systems.

10. TRIZ (Theory of Inventive Problem Solving)

A systematic approach for solving problems by analyzing patterns of problems and solutions across different industries and fields. It is used to generate innovative solutions by applying principles and concepts from other domains.

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30 Pharmaceutical Analyst Interview Questions and Answers

Common Pharmaceutical Analyst interview questions, how to answer them, and example answers from a certified career coach.

In the critical world of pharmaceuticals, a Pharmaceutical Analyst plays a pivotal role in ensuring that products meet quality standards and regulatory requirements. As such, securing a position as a Pharmaceutical Analyst requires not only technical knowledge but also strong problem-solving skills and meticulous attention to detail.

If you’re preparing for an interview for this role, it’s vital to be ready to answer questions about your experience, methodologies, and how you handle specific situations that may arise during analysis. In this article, we’ve curated some common Pharmaceutical Analyst interview questions along with insightful tips on how to craft compelling responses that will help you demonstrate your proficiency and readiness for the job.

1. Can you describe your experience with analytical techniques used in pharmaceutical analysis?

Insight into your hands-on experience with key analytical techniques is what hiring managers are after with this question. As a pharmaceutical analyst, you’ll be required to use a wide range of methodologies – from chromatography to spectroscopy – to ensure the safety and efficacy of drugs. Interviewers want to ensure you have a strong grasp of these techniques and can apply them effectively in real-world situations.

Example: “Throughout my career, I have gained substantial experience in various analytical techniques used in pharmaceutical analysis. These include High-Performance Liquid Chromatography (HPLC), Gas Chromatography (GC), and Mass Spectrometry (MS).

I’ve utilized HPLC for the separation, identification, and quantification of each component in a mixture. It’s particularly useful when dealing with complex biological samples.

In contrast, GC is excellent for volatile organic compounds. My work often involved using this technique to analyze drug metabolism.

Finally, MS has been instrumental in identifying unknown compounds and elucidating the structure and chemical properties of molecules.

Each technique has its unique advantages and applications, and understanding when to use each one was crucial in my role as an analyst.”

2. How have you utilized HPLC in your previous roles?

High-performance liquid chromatography (HPLC) is a fundamental technique in pharmaceutical analysis. It’s used for determining the content and purity of a sample, and for separating the components of a mixture. The question is designed to assess your hands-on experience with this essential tool, as well as your understanding of its applications in a real-world laboratory setting. It can also reveal your problem-solving skills and how you approach technical challenges.

Example: “In my experience, High-Performance Liquid Chromatography (HPLC) has been an essential tool for analytical chemistry. I’ve used it primarily for separating, identifying, and quantifying each component in a mixture.

One specific application was during the quality control process of pharmaceuticals. Here, HPLC helped to determine the exact quantities of active ingredients, impurities, or degradation products in the samples.

Moreover, I have also utilized this technique in method development and validation processes. This involved adjusting parameters such as mobile phase composition, flow rate, and column type to achieve optimal separation conditions.

Through these experiences, I’ve gained a solid understanding of how to effectively use HPLC in a pharmaceutical context.”

3. What strategies have you used to ensure compliance with Good Manufacturing Practices (GMP)?

Compliance with GMP is critical in the pharmaceutical industry to ensure the quality of the products and the safety of the consumers. Hiring managers want to know your understanding of these practices and how you’ve applied them in your previous roles. They are looking for evidence of your commitment to quality control, safety protocols, and process integrity.

Example: “In ensuring GMP compliance, I have used a combination of training, audits, and continuous improvement strategies.

Training is crucial in establishing understanding and adherence to GMP standards among staff. Regular refresher courses ensure that all team members are up-to-date with the latest procedures.

Audits provide an opportunity to assess our current practices against GMP guidelines. They help identify areas for improvement and monitor progress towards achieving full compliance.

Continuous improvement involves regularly reviewing and updating processes based on audit findings and changes in regulations or industry best practices. This proactive approach ensures we maintain high standards and adapt effectively to any changes in GMP requirements.”

4. How do you approach the validation of analytical methods?

This question is designed to assess your technical knowledge and practical skills. As a pharmaceutical analyst, you will be expected to develop and validate methodologies for the analysis of drugs and their components. The interviewer wants to know if you understand the importance of method validation in ensuring the accuracy, precision, and reliability of the tests you conduct. Your ability to answer this question effectively will demonstrate your competence in this critical aspect of the role.

Example: “In validating analytical methods, I first identify the purpose of the method and its key parameters. This includes accuracy, precision, specificity, detection limit, quantitation limit, linearity, range, robustness, and system suitability.

I then design a validation protocol which outlines how each parameter will be tested. The protocol also specifies acceptance criteria for each test.

After executing the protocol, I analyze the results to confirm if the method meets all predefined criteria. If it does not, adjustments are made until it does.

Finally, I document everything in a validation report which provides evidence that the method is suitable for its intended use.”

5. Can you provide an example of a complex data analysis you have conducted and the results?

The essence of a pharmaceutical analyst’s job revolves around analyzing complex data and interpreting the results. This question is designed to assess your analytical skills, your ability to work with complex data sets, and your ability to communicate the results of your analysis effectively. It also gives insight into your problem-solving skills, understanding of the field, and your capacity to make data-driven decisions.

Example: “In a recent project, I was tasked with analyzing patient data to identify factors influencing the effectiveness of a new drug. Using statistical software, I conducted regression analysis on various variables including age, gender, dosage, and duration of treatment.

The results indicated that while age and gender had minimal impact, higher dosages and longer treatment durations significantly improved drug efficacy. This information helped guide future clinical trials and dosage recommendations.”

6. How have you dealt with a situation where a drug did not meet its quality standards?

The crux of a pharmaceutical analyst’s role is ensuring the safety and efficacy of drugs. This means you’ll encounter situations where a drug doesn’t meet the required standards. In such scenarios, how you handle the situation is critical. Therefore, interviewers want to gauge your problem-solving skills, your understanding of quality control protocols, and your commitment to upholding the highest pharmaceutical standards.

Example: “In a situation where a drug did not meet quality standards, I initiated an investigation to identify the root cause. After identifying manufacturing process deviations, I worked with the production team to rectify them.

I also recommended improvements in the Quality Management System (QMS) to prevent future occurrences. This included more rigorous checks and balances within our processes and additional staff training on GMP compliance.

Moreover, I ensured that all these steps were documented as per regulatory requirements for transparency and traceability. It was crucial to communicate effectively with all stakeholders during this process, ensuring everyone understood their role in maintaining high-quality standards.”

7. What is your experience with stability testing of pharmaceutical products?

Stability testing is a fundamental part of pharmaceutical analysis. It ensures that products are viable and safe for use over time and under varying conditions. When hiring for a pharmaceutical analyst, employers want to know that you have a deep understanding of this process. Your experience with stability testing can indicate your ability to predict and prevent potential issues, thereby maintaining the quality and safety of the product.

Example: “I have extensive experience with stability testing of pharmaceutical products. My expertise covers both physical and chemical stability tests, including accelerated and long-term stability studies.

I’ve worked on designing protocols for these tests, ensuring they comply with ICH guidelines. This includes defining test conditions, frequency of testing, and the number and size of sample batches.

In my work, I’ve used a variety of analytical techniques such as HPLC, GC, and dissolution testing to assess product stability. I’m also proficient in data analysis and interpretation, which is crucial for making informed decisions about product shelf life and storage conditions.

My experience has taught me that meticulous planning, rigorous execution, and careful analysis are key to successful stability testing.”

8. How do you handle unexpected results during pharmaceutical analysis?

Unanticipated results are an inherent part of scientific research, and in the field of pharmaceutical analysis, they can have significant implications. Interviewers want to gauge your problem-solving skills, resilience, and adaptability. With this question, they are trying to understand how you approach unexpected situations, analyze data, troubleshoot issues, and ensure accuracy in your work.

Example: “In pharmaceutical analysis, unexpected results are not uncommon. When I encounter such situations, my initial step is to recheck the procedure and data for any potential errors or anomalies. If no issues are found, I would then repeat the experiment to verify the results.

If the unexpected result persists, it may indicate a new discovery or an unconsidered variable. In this case, I’d conduct further research to understand the cause of the anomaly. This could involve consulting relevant literature, discussing with colleagues, or performing additional tests.

Throughout the process, maintaining detailed documentation is crucial. It helps in identifying patterns, tracing back steps, and ensuring that all findings, whether expected or unexpected, contribute to our understanding and improvement of the pharmaceutical product.”

9. Could you discuss a time when you had to develop a new analytical method?

This question is designed to probe your problem-solving skills and your ability to innovate. In the ever-evolving world of pharmaceuticals, new challenges arise frequently. It’s not enough to just understand and perform existing analytical methods; you need to be able to develop and adapt new ones as well. This question gives you a chance to demonstrate that you have this capability.

Example: “In a previous project, we faced challenges with an existing HPLC method for drug purity testing. It was not sensitive enough to detect impurities at required levels.

I developed a new analytical method using UHPLC coupled with mass spectrometry. This increased sensitivity and specificity significantly, allowing us to accurately quantify even trace amounts of impurities.

This process involved extensive research, rigorous validation, and cross-functional collaboration. The successful implementation improved our product quality and compliance with regulatory standards.”

10. How have you ensured the safety and efficacy of pharmaceutical products in your previous roles?

This question is intended to gauge your ability to uphold the highest standards in pharmaceutical practice. Ensuring the safety and efficacy of pharmaceutical products is paramount in this industry and it’s pivotal to understand your approach to meeting these standards. This will also provide insight into your understanding of quality assurance, regulatory compliance, and risk management.

Example: “In my experience, ensuring the safety and efficacy of pharmaceutical products involves rigorous testing and quality control. I’ve employed a range of analytical techniques such as HPLC, GC-MS, and FTIR for product analysis.

I also ensured adherence to regulations like GMP and GLP in all processes. For instance, I maintained detailed documentation to ensure traceability.

Moreover, I worked closely with cross-functional teams to address any potential issues promptly. This proactive approach helped minimize risks and maintain high standards of product quality.”

11. What is your understanding of regulatory requirements in pharmaceutical analysis?

It’s essential for pharmaceutical analysts to be well-versed in the laws and regulations that govern their work. This is because the pharmaceutical industry is heavily regulated to ensure safety, efficacy, and quality of drugs. Compliance with these regulations is not just a matter of law, but it is also critical for the company’s reputation and customer trust. Therefore, hiring managers ask this question to assess your knowledge and understanding of these regulations, and to ensure you can maintain the standards required in this field.

Example: “Regulatory requirements in pharmaceutical analysis ensure the safety, efficacy, and quality of drugs. They involve stringent guidelines for drug development, production, distribution, and post-market surveillance. Key regulations include Good Manufacturing Practice (GMP), Good Laboratory Practice (GLP), and Good Clinical Practice (GCP) which govern manufacturing processes, lab testing, and clinical trials respectively.

Understanding these requirements is crucial as non-compliance can lead to severe penalties including product recalls or withdrawal from the market. Moreover, different regions have specific regulatory bodies like FDA in the US, EMA in Europe, each with its own set of rules that must be adhered to. Therefore, staying updated on global regulatory changes is vital.”

12. Could you explain your approach to troubleshooting issues with analytical equipment?

This question is designed to gauge your problem-solving skills and your familiarity with the equipment typically used in a pharmaceutical lab. As a pharmaceutical analyst, you will likely encounter issues with analytical equipment at some point, and your ability to troubleshoot these issues effectively is key to maintaining productivity and accuracy in your work. It also reflects on your technical knowledge and adaptability in the face of unexpected challenges.

Example: “Troubleshooting analytical equipment involves a systematic approach. I start by identifying the problem, which could be an error message or abnormal results. Next, I refer to the user manual or technical guides for possible solutions.

If that doesn’t resolve the issue, I perform a detailed inspection of the machine parts and software settings. Sometimes, it’s as simple as a calibration error or a part needing replacement.

For complex issues, I consult with manufacturers or service engineers. Documenting each step is crucial for future reference and ensuring compliance in our industry. This process helps maintain accuracy and reliability of equipment, essential for quality control in pharmaceutical analysis.”

13. How have you contributed to the development or improvement of pharmaceutical products?

The pharmaceutical industry is always looking for ways to improve existing products and develop new ones to better serve patients. As an analyst, you play a critical role in these processes. Employers want to know you have the analytical skills, creativity, and industry knowledge to contribute to their ongoing efforts to create superior products. Your answer gives them insight into your problem-solving skills, attention to detail, and understanding of pharmaceutical processes and regulations.

Example: “In my experience as a Pharmaceutical Analyst, I’ve contributed to the improvement of pharmaceutical products by conducting thorough research and analysis. This includes evaluating the composition of drugs, their stability, and their potential interactions with other substances.

I have also been involved in method development for new drug testing. My work has led to more efficient processes, reducing both time and cost without compromising on accuracy or quality.

Moreover, I’ve collaborated closely with formulation scientists to optimize drug formulations. By providing valuable data and insights, we were able to enhance product efficacy and safety profiles.”

14. How familiar are you with the process of drug formulation and manufacturing?

As a pharmaceutical analyst, you’re not just testing the end product. You’re a key player in the entire process, from initial formulation to final manufacturing. Understanding the whole process is essential for identifying potential problems and ensuring that the final product is safe and effective. Your answer will show whether you have the comprehensive knowledge required to excel in this role.

Example: “I have a comprehensive understanding of drug formulation and manufacturing, encompassing the entire process from pre-formulation studies to final product testing. I’m well-versed in various techniques such as granulation, compression, coating, and packaging.

My expertise extends to ensuring quality control and compliance with regulatory standards. This includes knowledge on stability studies, bioequivalence tests, and dissolution testing.

In terms of manufacturing, I understand the importance of Good Manufacturing Practices (GMP) and how it impacts every stage of production. My skills also include troubleshooting any issues that may arise during the manufacturing process.”

15. Can you discuss a time when you had to work under tight deadlines to deliver analytical results?

Pharmaceutical analysis often goes hand-in-hand with high-stakes, time-sensitive projects. Whether it’s a critical drug trial or a potential product launch, delays can have significant implications. Thus, hiring managers want to know whether you can perform under pressure, manage your time effectively, and deliver accurate results, even when the clock is ticking.

Example: “In my experience, tight deadlines often come with the territory in pharmaceutical analysis. One instance that comes to mind is when we were conducting a new drug efficacy study. The results of our analysis were critical for the next phase of clinical trials.

We had less than two weeks to complete the project due to regulatory timelines. To meet this deadline, I streamlined our testing procedures and coordinated closely with team members to ensure efficient workflow.

Despite the pressure, we successfully delivered accurate and comprehensive analytical results on time. This experience underscored the importance of efficiency and teamwork in meeting demanding deadlines without compromising on quality.”

16. How do you ensure accuracy and precision in your work?

Accuracy and precision are the cornerstone of a pharmaceutical analyst’s job. In a field where a small miscalculation or oversight can lead to serious consequences, an employer wants to be assured that you have a systematic approach to maintaining the highest standards of accuracy and precision in your work. This question helps them understand your attention to detail and your methods for ensuring quality in your results.

Example: “Accuracy and precision in pharmaceutical analysis are crucial. I ensure this through strict adherence to established protocols and guidelines.

I double-check all data entries, calculations, and results for any possible errors. Regular calibration of equipment is also essential to maintain accuracy.

Furthermore, I believe in the power of teamwork and peer review. Having another set of eyes look over my work can catch mistakes that might have been missed.

Continuous learning and staying updated with industry standards also play a significant role in ensuring accuracy and precision in my work.”

17. What is your experience with bioanalytical methods in drug discovery and development?

Understanding bioanalytical methods is a key part of the pharmaceutical analyst role. It’s important because these methods are used to quantify drugs and their metabolites in biological systems, which is a critical step in drug discovery and development. Therefore, employers want to know if you have the necessary experience and understanding to help their company develop safe and effective drugs.

Example: “I have extensive experience with bioanalytical methods in drug discovery and development. I’ve utilized techniques such as HPLC, LC-MS, and GC-MS for the quantification of drugs and metabolites.

My expertise includes method development and validation following FDA guidelines, ensuring accuracy and reliability of results. I’m also skilled at interpreting data to support pharmacokinetic and toxicokinetic studies.

In terms of drug discovery, I’ve worked on target identification and validation using molecular biology techniques. My work has contributed to understanding disease mechanisms and identifying potential therapeutic targets.”

18. How have you used statistical methods in your previous roles?

Statistical methods are the backbone of pharmaceutical analysis. They enable analysts to accurately interpret data and make informed decisions. By asking this question, hiring managers are looking to understand your proficiency and experience with these methods. It’s also a way to gauge how you apply theoretical knowledge to real-world situations, which is critical in this field.

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19. What role have you played in preparing documents for regulatory submissions?

Regulatory submissions are a critical part of the pharmaceutical industry, ensuring the safety and efficacy of drugs. Interviewers ask this question to gauge your understanding of this process and your ability to contribute effectively. It’s important to note that your role in preparing these documents can directly impact the company’s ability to market new pharmaceuticals, so your experience, attention to detail, and understanding of regulatory requirements are key components they are looking for.

20. Could you discuss a challenging project you worked on and how you overcame the difficulties?

The crux of this question lies in assessing your problem-solving skills and resilience, which are paramount in the role of a Pharmaceutical Analyst. The field of pharmaceuticals is fraught with complex projects and unforeseen challenges. Your ability to navigate these hurdles, make critical decisions, and still deliver high-quality results is what the hiring manager is trying to gauge.

21. How do you stay updated on the latest advancements in pharmaceutical analysis?

This question is key because it digs into your passion and commitment to the field. The world of pharmaceuticals is constantly evolving with new advancements and discoveries. Therefore, it’s essential to stay updated to ensure your work remains relevant, effective, and cutting-edge. Employers are seeking candidates who are not just competent, but also engaged, enthusiastic, and proactive about their professional growth and development.

22. Could you explain how you have used spectroscopy in your work?

Whether it’s infrared, atomic absorption or ultraviolet-visible, spectroscopy is a key tool in the pharmaceutical analyst’s arsenal. The question is designed to gauge your technical expertise and how you apply it in your work. Your response showcases your understanding and application of complex scientific principles and techniques, which are critical in ensuring the safety and efficacy of pharmaceutical products.

23. What is your experience in handling and disposing of hazardous chemical waste?

Safety is paramount in the pharmaceutical industry, so it’s critical to ensure that hazardous waste is handled and disposed of correctly. By asking this question, hiring managers are trying to gauge your understanding and experience in this aspect of the job. They want to be certain that you can perform these tasks safely and in compliance with all relevant regulations, thereby reducing the risk of accidents and keeping the workplace safe for all employees.

24. How do you ensure the integrity of data in your work?

This question is posed to gauge your understanding and commitment to maintaining high-quality data. In the pharmaceutical industry, accuracy and consistency of data is paramount. It is used to make important decisions about drug efficacy, safety, and overall quality. Therefore, as a pharmaceutical analyst, the manner in which you handle and ensure data integrity is critical to the success of drug development and the safety of patients.

25. Can you discuss a time when you had to communicate complex analytical data to non-technical team members?

A key part of any analytical role involves not just crunching numbers, but making those numbers understandable to others. In pharmaceuticals, this might mean explaining to a marketing team why certain drugs are more effective than others, or helping a sales rep understand why one product is priced higher. This question helps employers assess your communication skills and your ability to translate complex concepts into accessible language.

Example: “During a project, I had to explain the results of a clinical trial to our marketing team. The data was complex with statistical jargon and medical terms. I simplified it by using everyday language and metaphors. For instance, I compared our drug efficacy to winning a race – more wins meant higher efficacy. I also used visuals like graphs and charts for better understanding. This approach made them understand the implications of the data on their work, leading to an effective marketing strategy.”

26. How have you managed quality control in a high-volume production environment?

Quality control is a critical aspect in a high-volume production environment, especially in the pharmaceutical industry where even tiny errors can have serious consequences. Hence, your potential employer wants to understand how you’ve navigated this responsibility in the past. They want to know if you can maintain precision and accuracy when dealing with a large quantity of samples, and how you ensure that every product meets the required standards despite the pressure of high-volume demands.

Example: “In a high-volume production environment like pharmaceuticals, I’ve managed quality control by implementing effective process controls and regular monitoring. This involves setting clear specifications for each product and ensuring all processes consistently meet these standards.

I also rely heavily on statistical process control (SPC) to monitor real-time data trends and catch potential issues before they become bigger problems.

Another key aspect is fostering an open communication culture where staff can report any concerns or anomalies without fear of repercussion. This aids in early detection and quick resolution of potential quality issues.

Finally, conducting regular audits and reviews helps ensure compliance with both internal and regulatory standards. These measures have proven effective in maintaining high-quality output even under high volume demands.”

27. What is your experience with in-vitro and in-vivo bioequivalence studies?

This question is designed to gauge your technical knowledge and experience in the field of pharmaceutical analysis. Bioequivalence studies are a fundamental part of drug development and regulatory compliance, and an understanding of both in-vitro and in-vivo methods is essential. So, employers need to know if you’re equipped with the necessary skill set to contribute to their objectives.

Example: “I have substantial experience with both in-vitro and in-vivo bioequivalence studies. In my work, I’ve conducted numerous in-vitro dissolution tests to assess the rate and extent of drug release from generic products compared to innovator drugs.

In terms of in-vivo studies, I’ve been involved in designing and analyzing pharmacokinetic studies, ensuring they meet regulatory standards for demonstrating bioequivalence. This includes understanding factors like absorption, distribution, metabolism, and excretion.

My expertise also extends to interpreting results, identifying potential issues, and suggesting solutions to ensure successful study outcomes. Overall, this knowledge allows me to contribute effectively to pharmaceutical analysis projects.”

28. Could you discuss your role in a cross-functional team in your previous job?

Pharmaceutical companies operate in a complex, highly regulated environment. They often require collaboration across many departments such as research, production, quality control, and regulatory affairs. Hence, potential employers in this industry want to see that you are capable of working across different functions and teams. Your ability to effectively collaborate with diverse teams can greatly impact the success of a project or even the entire company.

Example: “In my previous job, I was part of a cross-functional team that included professionals from quality control, research and development, and regulatory affairs. My role involved analyzing pharmaceutical products to ensure their safety and efficacy.

I collaborated with the R&D department by providing them with analytical data for new product development. With the quality control team, I worked on routine testing and validation processes to maintain our high-quality standards.

For the regulatory affairs team, I helped prepare necessary documentation based on my analysis results. This ensured compliance with various health authority regulations.

Overall, my role required effective communication and collaboration skills to achieve common goals while maintaining stringent industry standards.”

29. How have you used chromatography in the separation and analysis of complex mixtures?

The question is posed by interviewers to ascertain your practical expertise and hands-on experience with chromatography, a quintessential technique used in the pharmaceutical industry. Being able to discuss specific instances of its usage, demonstrates your technical competency, understanding of the technique’s principles, and your ability to apply it effectively in a real-world lab setting.

Example: “In my experience, chromatography is an essential tool for separating and analyzing complex mixtures. For instance, I’ve used high-performance liquid chromatography (HPLC) to separate active pharmaceutical ingredients from excipients.

This technique allowed me to identify each component based on their different affinities towards the stationary phase. Furthermore, gas chromatography has been useful in volatile organic compound analysis by vaporizing and injecting samples into a mobile phase.

These methods have proven crucial in ensuring drug purity and understanding molecular interactions. They also provide valuable data during formulation development and stability testing.”

30. Can you explain your approach to maintaining and calibrating analytical instruments?

This question is raised to assess your technical skills and attention to detail. As a pharmaceutical analyst, the accuracy of your data is paramount and directly linked to the health and safety of patients. Therefore, your ability to maintain and calibrate the instruments you work with is critical. It ensures consistent, reliable results and prevents costly errors or delays.

Example: “Maintaining and calibrating analytical instruments is crucial for ensuring accurate results. Regular maintenance involves cleaning, replacing worn-out parts, and routine checks to verify performance.

Calibration ensures the instrument’s readings are consistent with a standard. This process includes identifying any deviation from the standard, adjusting the instrument accordingly, and documenting these changes.

Instruments should be calibrated at regular intervals or when there is a significant change in environment that could affect measurements. A proactive approach to this task aids in preventing inaccurate data and costly downtime.”

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Problem-solving enables us to identify and exploit opportunities in the environment and exert (some level of) control over the future. Problem solving skills and the problem-solving process are a critical part of daily life both as individuals and organizations. Developing and refining these skills through training, practice and learning can ...
Top 10 Problem Solving Tools and Their Applications in Pharma
2. Root Cause Analysis. A problem-solving technique that helps identify the underlying causes of a problem. It is used to identify the root cause of a problem and develop effective solutions. 3. Fishbone Diagram. A visual tool used to identify the possible causes of a problem. It is used to organize and analyze information related to a problem.
30 Pharmaceutical Analyst Interview Questions and Answers
Interviewers want to gauge your problem-solving skills, resilience, and adaptability. With this question, they are trying to understand how you approach unexpected situations, analyze data, troubleshoot issues, and ensure accuracy in your work. Example: "In pharmaceutical analysis, unexpected results are not uncommon.
PDF XRD in Pharmaceutical Analysis: A Versatile Tool for Problem-Solving
In Case 1, XRD was used to identify drug substance forms, including an unknown material. In Case 2, XRD was applied to quantify crystalline content in an amorphous formulation. In Case 3, XRD was ...

Problem Solving: What’s the Best Approach?

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1. Project Planning Skills

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Decision-Making and Problem-Solving Approaches in Pharmacy Education

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Reinventing Lean Six Sigma for the Pharmaceutical Industry

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XRD in Pharmaceutical Analysis: A Versatile Tool for Problem-Solving

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Case 1: Drug Substance Form Identification

Case 2: Stability of an Amorphous Drug Substance

Case 3 Identification of Active in Solid Dosage Form

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