Behind the Headlines Episode 27

Ask the Expert

Small Molecules, APIs, and Excipients—Trends, Challenges, and Opportunities

Drug Digest September 2025

Artificial intelligence, machine learning, and novel instruments are helping drug developers.

Enhancing Bioanalysis and Drug Development with Advanced Tools and Technologies

J.P. Morgan Report Lists Biologics, Obesity, China

Top Trends Reshaping Pharma R&D

Adam C. Fisher, Phd, is Director of Science Staff, Office of Pharmaceutical Quality, Center for Drug Evaluation and Research, FDA.

Cyborg hand holding a Medical icon and connection 3d rendering | Image Credit: ©Production Perig - stock.adobe.com

Hollywood has long presented artificial intelligence (AI) as a futuristic concept far from reality, but technology has now eliminated the gap between science fiction and science fact. AI is here and here to stay; 50% of global healthcare companies plan to implement AI strategies by 2025 (1). For pharmaceutical manufacturers, AI has the potential to revolutionize process design and control, and thus bring benefits to patients and challenges to regulators.

Owing to its fictionalized past, the term AI can mean wildly different things to different people. FDA’s Center for Drug Evaluation and Research (CDER) describes AI rather inclusively as “a branch of computer science, statistics, and engineering that uses algorithms or models that exhibit behaviors such as learning, making decisions, and making predictions” (2). The National Academies of Sciences, Engineering, and Medicine issued a report on innovations in pharmaceutical manufacturing that highlighted AI’s potential role in the measurement, modeling, and control used for pharmaceutical manufacturing (3).

AI offers potential benefits to pharmaceutical manufacturers in the form of optimized process design and control, and smart monitoring and maintenance, to drive continuous improvement. AI in concert with other innovative technologies might advance pharmaceutical quality, build more resilient supply chains, and improve the availability of medicine for patients (4). In anticipation of these benefits, FDA is proactively preparing for the arrival of AI technologies in pharmaceutical manufacturing.

AI is an enabler of Industry 4.0, the fourth industrial revolution characterized by integrated, autonomous, and self-organizing production systems (5). The Industry 4.0 paradigm brings the hope of a well-controlled, hyper-connected, digitized ecosystem and supply chain. The COVID-19 public health emergency seems to have accelerated the development of Industry 4.0 technologies, such as AI, that might respond to rapidly changing supply and demand and reduce dependence on human intervention.

So how might manufacturers deploy AI in process design and control?

 AI models employing machine learning might use process development data to more quickly design and identify optimal process parameters or scale-up strategies, reducing development time and waste.

Advanced process control might allow for dynamic control of a manufacturing process which, in combination with real-time sensor data, might be used to develop process controls that can precisely predict the trajectory of a process. Pharmaceutical manufacturers are expecting to adopt advanced process control approaches that combine AI techniques with chemistry and physics knowledge to improve manufacturing efficiency and output.

 AI methods might be used to better monitor equipment and detect changes from optimal performance. AI-detected deviations might trigger maintenance activities in a manner that minimizes process downtime.

AI methods integrated with process performance metrics might offer better trend monitoring, even across products or locations, and consequently allow for proactive corrective and preventive actions to address manufacturing discrepancies before they impact the supply chain, or worse, cause drug shortage.

The potential use of AI to monitor quality doesn’t even end at the process. AI might also be used to monitor a product’s quality after manufacturing, for example, for the integrity of its packaging or presence of particulates. A vision-based quality control system might use AI to analyze images of packaging, labels, or glass vials to detect deviations (6). Beyond the product itself, AI might be used to identify cluster problems from consumer complaints and deviation reports that might contain large volumes of unstructured text. Though the potential applications of AI in pharmaceutical manufacturing will continue expanding, whether a manufacturer employs AI or not, high-quality drugs should be consistently available to patients.

CDER established a Framework for Regulatory Advanced Manufacturing Evaluation (FRAME) initiative to prepare a regulatory framework to support the adoption of advanced manufacturing technologies that could benefit patients, such as AI. FDA recognizes potential benefits, and risks, of AI throughout the drug product lifecycle. As some manufacturers look to employ AI in their manufacturing processes, the regulatory framework will need to enable the timely adoption of these technologies while also keeping patients safe. CDER recognizes the need to better understand how this new manufacturing paradigm could impact pharmaceutical operations and regulation. The regulatory strategies of the past might not work as effectively in an AI-enabled Industry 4.0. However, regulators do not develop and implement manufacturing technologies. There is a need for the stakeholders developing these technologies to provide FDA a better understanding of where and how AI innovations might be used in drug manufacturing. Industry and regulators will not be able to address the issues related to AI by working separately; true innovation requires science-based collaboration.

In March 2023, CDER released a discussion paper to solicit public and stakeholder input on AI in drug manufacturing to better identify areas of policy consideration for AI technologies (2). This discussion paper proposed areas of consideration based on CDER’s evaluation of the existing regulatory framework: standards for developing and validating AI models, clarification on regulatory oversight for AI in pharmaceutical manufacturing, maintenance of cloud applications and continuously learning AI systems that adapt to real-time data, and data management practices commensurate with the volume of data generated.

FDA’s public workshop on the use of AI in drug Manufacturing on September 26–27, 2023, in conjunction with the Product Quality Research Institute, was designed to provide another opportunity for stakeholders to discuss key topics with regulators, such as:

Artificial Intelligence in Process Development, Process Monitoring, and Commercial Batch Trend Monitoring

Artificial Intelligence and the Use of Big Data and Data Management within the Pharmaceutical Quality System

Lifecycle Approaches to Management of Artificial Intelligence.

Discussions on topics like these will help further inform the evaluation, development, and implementation of a regulatory framework that considers the benefits and risks of AI.

Like industry, regulators can employ AI to improve processes. In fact, FDA currently uses AI for translating documents, screening adverse event reports, and forecasting the volume of incoming regulatory submissions. In the future, both manufacturers and regulators might benefit from the extensive data and analysis provided by the expanded use of AI. International regulators have projected using AI to detect false or misleading drug information, scan scientific literature, identify safety signals, and respond to public inquiries (7).

While there are potential benefits of AI, there are also risks. Access to high-quality data is a fundamental requirement for effective AI training or learning. AI can be particularly sensitive to the characteristics of the data used for training, testing, and validation. The process analytical technologies providing data to AI systems must be accurate and representative. For learning purposes, data must represent not only process successes but also process failures. It will be critical to ensure that data used for AI training or learning are fit for use based on quality, reliability, and representativeness.

Humans, and not machines, are ultimately responsible for assuring that high-quality drugs are available to patients. Humans must be able to interpret the information generated by AI enough to ensure, for example, adherence to current good manufacturing practice requirements. Even with the best intentions, there are risks for unintended consequences. Might some even use AI for unethical outcomes?

Still, continued technology advancement is undeniable. There’s already talk of an Industry 5.0 paradigm which focuses on man and machine working together, instead of separately, and considers the manufacturer’s impact on the workforce and environment (8). In looking ahead, it feels only appropriate to ask the uber popular AI chatbot, ChatGPT, how things will turn out, “Will AI improve pharmaceutical manufacturing?” “Yes, AI has the potential to significantly improve pharmaceutical manufacturing.”

The combined efforts of regulators, industry, and scientists can now help to realize this potential while safeguarding against risks to patients.

USM. Top 5 AI Use Cases in Pharma & Bio Medicine. 

FDA. Artificial Intelligence in Drug Manufacturing. 

National Academies of Sciences, Engineering, and Medicine. Innovations in Pharmaceutical Manufacturing on the Horizon: Technical Challenges,
Regulatory Issues, and Recommendations. The National Academies Press online, DOI:10.17226/26009 (2021).

Arden, S. et al. Industry 4.0 for Pharmaceutical Manufacturing: Preparing for the Smart Factories of the future.

FDA. Artificial Intelligence/Machine Learning Assisted Image Analysis for Characterizing Biotherapeutics. 

International Coalition of Medicines Regulatory Authorities. Horizon Scanning Assessment Report–Artificial Intelligence (2021).

https://research-and-innovation.ec.europa.eu/research-area/industrial-research-and-innovation/industry-50_en

Adam Fisher, PhD is Director of Science Staff and Immediate Office within the Office of Pharmaceutical Quality at the Center for Drug Evaluation and Research at FDA.


Vol. 47, No. 9
September 2023
Pages: 32–34

When referring to this article, please cite it as Fisher, A. The Future is the Present: Artificial Intelligence in Pharmaceutical Manufacturing. 

Articles

FDA is anticipating how AI may advance manufacturing and improve supply chain security.

The Future is the Present: Artificial Intelligence in Pharmaceutical Manufacturing

Martin J. Lipa, PhD, martin.lipa@prst.ie, is a senior research fellow at Pharmaceutical Regulatory Science Team.

Anne Greene, PhD, is director PRST, Technological University Dublin.

The current International Council for Harmonisation (ICH) Q9(R1) 

 (QRM) guideline is a targeted revision of the initial guidance issued in 2005, intended to address subjectivity, formality, risk-based decision-making, and other specific areas for improvement. While not a formally defined area for improvement, the revised guidance invokes many new references to “knowledge” and “knowledge management (KM)” in supporting improvement in QRM practice and, by extension, to the goals of an effective pharmaceutical quality system. 

This article was peer reviewed by a member of 

Submitted: April 24, 2023
Accepted: June 14, 2023

Recognizing that QRM and risk-based decision-making (RBDM) both depend on knowledge to be effective, it could be suggested that knowledge is the currency of managing risk. This paper presents research and perspectives on the opportunity to better connect risk and knowledge through improved integration of QRM and KM, including presenting several potential benefits of such integration. A framework is proposed, the 

), which could be used to improve the connectivity of risk and knowledge along with preliminary and practical recommendations to operationalize the 

. In addition, the paper invokes concepts of systems thinking in connecting QRM and KM as well as the opportunity to consider the role of knowledge and KM more holistically across the pharmaceutical business through application of a process classification framework.

There has been a bolus of activity associated with ICH Q9(R1) (1) finalized in January 2023, as the long-awaited update to the 2005 guidance, ICH Q9 

 (2). This activity included scrutiny of the concept paper (3), comments on the draft revision, and revision of the guideline itself. In addition, there has been a focus on the development of supporting training material by the ICH Expert Working Group (EWG) and to interpreting what it all means to industry and to organizations on an individual level as the deployment and adoption process begins.

Concurrent to this activity surrounding ICH Q9(R1), the Pharmaceutical Regulatory Science Team (PRST) at Technological University Dublin have continued their ongoing research into related topics, including QRM, KM, and RBDM. In particular, the PRST are exploring the relationships and interconnectivity between these practices, applying through “systems thinking” mindset and aspects of complexity theory where appropriate, which provide a more holistic approach to analysis and how the parts of a system interact over time (4).

One of the key areas of focus for the PRST is regarding the practice of KM, including:

The role of KM as a pharmaceutical quality system (PQS) enabler as defined by ICH Q10, 

The relationship between QRM and KM as dual enablers of the PQS

The role of KM in informing risk-based decision making.

The purpose of this paper is to compile and synthesize this KM-related research describing how it supports the intent of the ICH Q9(R1) revision and, ultimately, the objectives of the PQS itself as outlined by ICH Q10 

 (5), which are to achieve product realization, establish and maintain a state of control, and facilitate continual improvement.

To start with, however, it is important to demystify the relationship between the terms “knowledge” and KM. Authoritative definitions are presented in the next section of this paper, and the authors have discussed these concepts in more detail in a 2022 paper (6). Consider the following pragmatic descriptions by the authors.

In a pharmaceutical context, “knowledge” is an asset that includes what is collectively known (e.g., scientific understanding and insight) about products, processes, and platforms (e.g., technology transfer documents, regulatory filings, process experience, etc.) inclusive of both documented (explicit) knowledge and know-how, experience, and other tacit knowledge. To further break it down:

“Explicit knowledge” is “knowledge” that can be expressed in words, numbers, and symbols and can be stored (e.g., in books, computers, etc.). Explicit knowledge can be articulated and easily communicated between individuals and organizations (7).

“Tacit knowledge” refers to “knowledge” that resides in the minds of individuals and is surfaced in response to a situation or action (8). Tacit knowledge is often referred to as “know-how.”

Whereas, KM is a set of practices and associated mindsets and behaviors that determine how knowledge is managed (e.g., captured and made available in the future) in an intentional, repeatable, and systematic manner.Common KM practices include content management processes, lessons learned, knowledge transfer activities, among others. KM as a discipline is most successful when certain enabling practices are in place, such as standard processes, dedicated roles (e.g., community stewards, lessons learned facilitators), communications, and training (9) as well as foundational practices of effective data management (e.g., data strategy, standards, and stewardship).

Essentially, if “knowledge” is considered a noun, then “KM” can be considered a verb. This is perhaps a seemingly obvious but important distinction—as in the authors’ experience—these terms of “knowledge” and “KM” are often used loosely, almost interchangeably. It is crucial that the reader is mindful of the difference and—as the authors will make the case—knowledge is the currency of managing risk, and the smart investment is to ensure that the best possible knowledge is available when and where it is needed—via KM—to inform QRM and risk-based decision making, and to support the PQS objectives.

The foundational role of knowledge and KM for risk management, risk-based decision-making, and pharmaceutical quality

When one looks holistically across the ICH quality guidelines (10), the expectations for managing knowledge are implied through the repeated references to the availability and application of knowledge in support of risk management, science and risk-based decision-making, product and process understanding, and, ultimately, ensuring availability of a safe and efficacious product to the patient. While these references are not each overtly stated as “knowledge” or “knowledge management,” they do repeatedly invoke the availability and application of “knowledge” under the guise of many synonyms, including prior knowledge, platform knowledge, scientific knowledge, science, evidence, product knowledge, process knowledge, experience, product development history, expertise, know-how, product understanding, process understanding, and lessons learned (6).

Knowledge and KM across ICH and other regulatory guidance.

 While ICH Q10 does not define the term “knowledge,” the 2018 International Organization for Standardization (ISO) standard on KM, 

ISO 30401:2018, Knowledge Management Systems—Requirements

 provides a helpful definition of knowledge as:

“Human or organizational asset enabling effective decisions and action in context” (11).

The ISO definition is further clarified as follows:

“Knowledge can be individual, collective, or organizational.

“There are diverse views on the scope covered within knowledge, based on context and purpose. The definition above is general as to the various perspectives. Examples of knowledge include insights and know-how.

“Knowledge is acquired through learning or experience” (11).

Regarding the term “knowledge management,” ICH Q10 introduced the concept of KM and provided a definition in 2008 as:

“Knowledge management is a systematic approach to acquiring, analysing, storing and disseminating information related to products, manufacturing processes and components”

It further stated that product and process knowledge should be managed throughout the product lifecycle and includes examples of sources of knowledge:

“Sources of knowledge include, but are not limited to, prior knowledge (public domain or internally documented); pharmaceutical development studies; technology transfer activities; process validation studies over the product lifecycle; manufacturing experience; innovation; continual improvement; and change management activities”

It is in ICH Q10 that QRM and KM were linked as the dual enablers for an effective PQS by “providing the means for science- and risk-based decisions related to product quality” (5). It is worth noting that while ICH Q10 provided a definition of KM, it does this without any references to KM in the literature, or by discussing KM guidance in other industries.

In addition to ICH Q10, there are many other direct and indirect references to knowledge and KM across ICH quality guidelines, including ICH Q8(R2), ICH Q8/Q9/Q10 Questions & Answers, ICH Q11, and ICH Q12, as well as in other regulatory guidance issued by the World Health Organization (WHO), EudraLex in the European Union, and Pharmaceutical Inspection Co-operation Scheme (PIC/S), which have previously been reviewed in detail by the authors in 2020 (12).Furthermore, since the 2020 review, references to the role of knowledge and KM are also included in ICH Q13, 

Continuous Processing of Drug Substances and Drug Products

 (14), and further in FDA’s pharmaceutical quality initiative, known as knowledge-aided assessment and structured applications (KASA) (15).

Considering regulatory guidance taken in aggregate, while it is ICH Q10 that defined KM in 2008, it is heartening to see the concepts of knowledge and KM emerging and repeatedly reinforced in the interceding 15 years. Indeed, a closer look at the recent ICH Q9(R1) extends this theme and provides an opportunity for risk and knowledge to be intentionally connected.

A closer look: the emergence of knowledge management in ICH Q9(R1).

 The concept paper for ICH Q9(R1) (3) identified four primary areas for improvement in the practice of QRM, for both pharma companies and regulatory authorities as follows:

High levels of subjectivity in risk assessments and in QRM outputs

Lack of understanding as to what constitutes formality in QRM

Lack of clarity and understanding on risk-based decision-making.

Furthermore, two additional points were raised in the concept paper to be addressed, namely:

Emphasis on “hazard identification” (rather than “risk identification”).

Collectively these six areas of focus are referred to as the “revision topics.”

While knowledge-related or KM-related areas for improvement were not identified in the concept paper directly (i.e., as one of the six revision topics), a closer look at the revised ICH Q9(R1) guideline reveals that the concepts of both “knowledge” and “knowledge management” have a significant presence. This is demonstrated by an evaluation carried out by the authors, presented in 

 as a summary of the frequency of the use of each team in the original ICH Q9 guideline, and in the revised ICH Q9(R1) guideline. 

 Frequency of “knowledge” and “knowledge management” in ICH Q9(R1).

While the term “knowledge management” is not as prevalent as “knowledge” there is a significant change in the recognition of the centrality and importance of knowledge to support QRM. This is welcomed by the authors, given the nature of ICH Q9(R1) as a “targeted revision” with “limited and specific adjustments” (1).

In fact, a review of the six revision topics could indeed suggest a dependency of each of these six revision topics on knowledge and/or knowledge management, as summarized in 

 Mapping “knowledge” and “knowledge management” (KM) references to ICH Q9(R1) revision topics. QRM is quality risk management. PQS is pharmaceutical quality system.

Knowledge as an invaluable commodity for risk management and decision making.

 A further literature review by the authors in 2020 (12,16) explored the relationship between knowledge and risk—and knowledge management and risk management—in pharmaceutical regulatory guidance and in other industries and organizations, including the National Aeronautics and Space Administration (NASA). A key assertion resulting from this review is that knowledge is both an input to and an output from QRM, thus highlighting the opportunity for QRM and KM to be thoughtfully and intentionally connected.

Arguably, the authors suggest that emergence of the importance of knowledge and KM in ICH Q9(R1) is a logical evolution of the recognition of the foundational role each play in informing risk and improving risk-based decision-making, as is evident through a holistic review of the document as shown in 

At the end of the day, the pharmaceutical industry is a knowledge industry (17) and, on a daily basis, formally and informally assesses risk and makes countless decisions—each of which should seek to ensure quality products and to protect the patient. Knowledge is an indispensable commodity necessary to make this happen effectively. With greater knowledge comes greater understanding. Greater understanding leads to reduced uncertainty. Reduced uncertainty in turn informs risk management activities and ultimately risk-based decision-making. Knowledge, indeed, is the currency for managing risk—and managing such knowledge is a smart investment for benefitting patients.

Exploring the practical connection between knowledge and risk

The authors, having recognized the disconnect between QRM and KM in practice, turned to systems-thinking principles to explore the connections between risk and knowledge, and between QRM and KM, starting with an assessment of practice at the time.

Current state connectivity of quality risk management and knowledge management. 

In 2021, the authors collected data through a survey (18), which reported that, while 96% of survey respondents believe QRM and KM are highly interdependent as concepts supporting the PQS (i.e., how connected should they be), no one believes they are fully integrated in practice today (0%), with 4% responding they are intentionally but not optimally connected, and 85% reporting they are only partially connected. These data are summarized in 

ALL FIGURES ARE COURTESY OF THE AUTHORS. 

 Exploring the integration of quality risk management (QRM) and knowledge management (KM).

Furthermore, when asked about the potential benefits of improved QRM–KM integration, a variety of important benefits associated with better connectivity between risk and knowledge were identified, including:

Better risk-based decisions where decisions are informed by risk and knowledge

More data/knowledge-driven risk assessments

Increased ability to leverage off of prior knowledge

Improved control strategies that better reflect risk and knowledge

Improved PQS effectiveness, better supporting decision making, validation, change management, outsourcing, etc.

Improved protection and value for patients through reduced risk of defects, drug shortages, etc.

Notably, these anticipated benefits reflect the role of knowledge as a commodity—or currency—in delivering improved risk assessments, control strategies, decision making, and quality outcomes.

 coupled with anticipated benefits identified by survey responses clearly suggest that QRM and KM were not well-connected, nor as connected as they should be, and that important benefits are possible if QRM and KM are better integrated in the future. The authors are hopeful that this connection between risk and knowledge will be more prevalent with the adoption of ICH Q9(R1) which clearly recognizes the role knowledge plays in quality risk management and risk-based decision-making.

A framework to connect risk and knowledge.

 As one potential means to address this need to connect risk and knowledge, a framework was developed by the authors in partnership with Kevin O’Donnell, PhD, market compliance manager, Health Products Regulatory Authority. This framework, the 

 (19), was proposed based on the previously referenced literature review of pharmaceutical regulatory guidance, input from industry and regulatory agency experts, practices in other industries (e.g., aerospace), and fundamental principles of knowledge management (e.g., enabling knowledge to “flow”). 

, highlighting the relationship between risk and knowledge, and underlying key concepts reinforced throughout regulatory guidance.

 as applied to ICH Q10, with proposed guiding principles to connect QRM and KM as a means to operationalize the integration of risk and knowledge.

 over the product lifecycle, illustrating that knowledge increases over time, providing the opportunity to apply this to increase understanding, decrease uncertainty, and, ultimately, reduce risk. 

. QRM is quality risk management. KM is knowledge management.

 and supporting concepts and principles, the following sections will explore its application and contribution to ICH Q9(R1).

 can be applied in different ways to help address the disconnect between risk and knowledge. In the simplest manner, the 

 can readily be applied as a conceptual model for organizations to reflect on whether risk and knowledge are intentionally connected via a connectivity between an organization’s QRM and KM practices. The six guiding principles (

) provide “target conditions” throughout the cycle, which can be qualitatively assessed for alignment of an organization’s practices.

 can also be applied as a detailed process model to operationalize the 

 via deeper assessment of QRM and KM practices and their interdependency, resulting in the identification of gaps and recommended deployment actions to better connect QRM and KM, and thus risk and knowledge. Efforts by the authors are ongoing to develop detailed 

 operationalization guidance. Several preliminary recommendations include the following:

Create a “knowledge map” for QRM (node 1 on 

 A knowledge map is a KM practice used to understand the knowledge necessary for a given activity, in this case, a quality risk assessment (or risk review). An illustrative case study of a knowledge map for the installation of an autoclave has been documented (20), for which the industry participants provided overwhelmingly positive feedback on the knowledge-mapping experience, with 97% of the attendees agreeing that the knowledge mapping exercise helps them think more expansively about knowledge needed for QRM (inclusive of know-how, expertise, and other tacit knowledge). The insights from knowledge maps can be used as requirements to inform KM practices (or improve current practices).

Recognize and manage the knowledge outputs from QRM (node 3 on 

 In addition to the routine outputs of a quality risk assessment (e.g., results of risk ranking) is sufficient tacit knowledge captured to improve future risk assessment and review activities, such as decisions and supporting rationale, alternatives considered, what was known at the time? Other knowledge outputs might include important experiential knowledge surfaced by subject-matter experts that should be codified for broader dissemination, gaps in control strategies, knowledge to inform supply chain, and other types of risks, gaps in knowledge where specific knowledge must be acquired, etc. Organizations can take steps to define expectations for where and how this knowledge is managed, ideally for use in the future (e.g., risk review) or across similar risk assessments.

Inventory existing KM practices (nodes 4, 5, and 6 on 

 Regardless of whether an organization has a formal KM program, understand what KM or “KM-like” practices exist in the categories of content management, search, expertise location, communities of practice, after action review/lessons learned, tacit knowledge transfer, and critical knowledge retention. These KM practices should be catalogued and made visible broadly across the organization. A more comprehensive (and complex) opportunity would be to perform a KM capability assessment (21) to understand an organization’s relative KM maturity and potential actions to improve.

Conduct KM awareness training for QRM practitioners (nodes 1, 2, and 3 on 

 Build KM competency for QRM practitioners via basic training that includes introductory KM concepts (e.g., definitions, principles), mindsets and behaviors, and awareness of existing KM practices at an organization. In the opinion of the authors, the more awareness QRM practitioners have about KM and the opportunity for KM to support QRM, the greater the opportunity to recognize challenges and opportunities, and create a “pull” from QRM to improve KM—and from KM to improve QRM.

Develop and deploy simple KM practices (nodes 4, 5, and 6 on 

 These practices should be based on known challenges and/or insights from QRM knowledge mapping (i.e., the first recommendation on this list). Such practices could be simple steps to provide guidance on where content should be stored (and not stored) and create lists of subject matter experts and decisions or could be more involved, such as creating communities of practice or deploying search tools.

The role of knowledge in enhancing realization of ICH Q9(R1).

 Considering the six revision topics of ICH Q9(R1) and how each invokes a dependency on knowledge availability and application (

), ICH Q9(R1) further reinforces this relationship between knowledge and risk. It is perhaps obvious to say each of the six topics—subjectivity, product availability risks, formality, risk-based decision-making, risk review, and hazard identification—would each benefit by having the “maximum and collective knowledge of the organization available on demand,” as is the intent of the 

The authors anticipate further evolution of KM practices (and, therefore, improved knowledge access and availability) can benefit several of these targeted revision topics.For example:

 Consider the use of a “hazard library” (or “risk bank”) to identify common hazards for a given risk scenario. Such lists could reduce subjectivity by minimizing variability across different teams of experts in identifying common hazards. Furthermore, other KM processes such as lessons learned could funnel new hazards into such standardized lists.

 In addition to such a “hazard library,” processes such as directed recognition (22), aim to reduce “unknown-unknowns.” Many of the processes, such as those described in the referenced article, focused on reducing unwelcome surprises (e.g., checklists and interviews) and are, in fact, KM-like processes to surface, connect, and transfer tacit knowledge.

 Research into risk-based decision-making in the pharmaceutical industry has identified 21 characteristics of risk-based decision-making in high-reliability organizations (e.g., aerospace, nuclear) (23). Many of these characteristics—eight in total—have a dependency on KM practices, including concepts of data and knowledge sources, data and knowledge availability, and taxonomy.

 In addition to the recommendations above involving knowledge capture from QRA to inform future risk review, consider the use of “risk registers” as a KM tool to monitor and track actions identified during risk assessments.

Knowledge as the currency of managing risk

Knowledge is a fundamental enabler to pharmaceutical quality. QRM should strive to apply the collective knowledge of the organization to recognize and understand risk and, in turn, to inform risk-based decisions that can have direct impact to patient safety and product availability. Said differently, knowledge is the currency of managing risk. Investing in the practice of knowledge management can provide an intentional, standard, and repeatable “how” for knowledge to be more effectively managed.

Enhancing knowledge via KM practices:a smart investment.

 Indeed, the opportunity is that knowledge is central to managing risk, making decisions, and ensuring product quality. Ensuring that knowledge is effectively managed (e.g., on-demand access and availability) via KM practices is a smart investment in the interest of patients. Further connecting QRM and KM with a framework such as the 

 can help the “system” perform as intended while further improving the outcomes of each QRM and KM practices.

Ask oneself: “What would I expect as a patient?”Would you not expect the best possible knowledge to be available to support risk assessments, risk control, risk review, and risk communication if you were waiting on a pharmaceutical company to deliver you a new lifesaving therapy? Would you not expect that pharmaceutical organizations “know what they know” and “can find and apply what they know” to deliver the best possible outcomes to you as a customer?

Beyond the framework: opportunities for biopharma as a knowledge industry.

 It is easy for organizations and the people employed by them to become consumed with day-to-day business and lose sight of the broader system to which their functional work contributes.Organizational structures, differing priorities, divergent governance processes, and countless other factors can contribute to “silos” in the business where people become narrowly focused. Systems thinking is a means to counter this trend, where one can “zoom out” to understand how the system works as a whole and how the major elements interrelate. It was such an opportunity the authors recognized in proposing the 

—that QRM and KM generally were focused on excellence in their respective domains—but little attention was paid to how they worked together to enable the PQS.

When one expands that aperture even further, it is possible to see a broad-knowledge “ecosystem,” where knowledge is connected and enables a great many processes in pharmaceutical organizations, including technology transfer, change management, deviation management and investigations, process validation, and others. In fact, if one were to zoom out to see the entire business unit or enterprise mapped at a process level, one could envision the relationships between these processes and the knowledge generated and consumed by each, and how that knowledge must flow between these various processes.

Multiple such frameworks exist, including the Process Classification Framework (PCF) tailored for the pharmaceutical industry, developed by the American Productivity & Quality Center (APQC) and IBM (24), which provides a detailed breakdown of processes and activities to run a business. At the highest level, this consists of process categories such as operating processes (e.g., develop vision and strategy, deliver products) and support processes (e.g., develop and manage human capital, manage information technology). One can then drill down into deeper levels of the PCF to reveal process groups, processes, and activities. The current version of the APQC PCF for the pharmaceutical industry in fact contains over 1200 rows across the four-tier structure to fully define the activities associated with operating a pharmaceutical business. With such an understanding of the business, one can envision developing and deploying holistic KM practices to enable the business at large (notably, such a foundational capability is called out by the APQC PCF as a support process).

Yet a further opportunity is to recognize and exploit the strengths and synergies of related practices in supporting the overall mission of the business, including the practices of knowledge management, operational excellence, organizational change management, learning, and development, digital, and human performance, at a minimum. Each of these aims to drive improvements of some sort in how business is conducted and can be co-deployed for enhanced impact and benefit.

KM practice is still highly variable within pharmaceutical organizations, many of which treat KM as discretionary even though it is called out as a key enabler to an effective PQS in ICH Q10 (5). Yet, expectations for managing knowledge appear throughout regulatory guidance (and also in industry guidance which was not discussed in this paper) and can be directly linked to effectiveness in risk management and risk-based decision-making. ICH Q9(R1) provides a renewed opportunity for the industry to recognize the foundational role knowledge and KM must play in support of risk management and risk-based decision making. Devoting energy and resources toward managing knowledge can clearly deliver worthwhile results.

Beyond the ICH Q9(R1) driver to recognize KM practice, as a society, we face a time of rapid and unprecedented change. Considering product complexity (e.g., advanced therapy medicinal products) cost pressure, continued realignment of supply chains, societal expectation for accelerated development, advancement of new technologies, continued pressure on innovation, and a constantly evolving regulatory and economic landscape, never before has it been more important to integrate risk and knowledge to benefit patients and, indeed, our businesses and other stakeholders.

The views expressed in this article are those of the authors and are not necessarily those of any organizations with which the authors are affiliated.

, Final concept paper (2020).
4. TechTarget, What is Systems Thinking? 

www.techtarget.com/searchcio/definition/systems-thinking

, Step 4 version (2008).
6. Greene, A.; Lipa, M. J. Steps Towards Digital Transformation in the Pharma Manufacturing Landscape—Knowledge-Enabled Technology Transfer. 

 (2) 1.
7. Cambridge Dictionary, Explicit Knowledge. 

; APQC, 2022.
9. Kane, P. E.; Lipa, M. J. The House of Knowledge Excellence—A Framework for Success. In 

A Lifecycle Approach to Knowledge Excellence in the Biopharmaceutical Industry

, 1st ed.; Calnan, N.; Lipa, M. J.; Kane, P.; Menezes, J. C., Eds.; Taylor & Francis, 2018; pp. 181–224. DOI: 10.1201/9781315368337
10. ICH, 

. www.ich.org/page/quality-guidelines (accessed April 23, 2023).
11. ISO, 

Knowledge Management Systems—Requirements

; ISO 30401:2018; ISO: Geneva, Switzerland, 2018. https://www.iso.org/standard/68683.html(accessed April 23, 2023).
12. Lipa, M. J.; O’Donnell, K.; Greene, A. Managing Knowledge and Risk—A Literature Review on the Interdependency of QRM and KM as ICH Q10 Enablers. 

 (2), 3. DOI: 10.21427/2jdq-jq09
13. ICH, 

Q13 Continuous Manufacturing of Drug Substances and Drug Products

, Draft version (2022).
15. Yu, L. X.; Raw, A.; Wu, L.; et al. FDA’s New Pharmaceutical Quality Initiative: Knowledge-Aided Assessment & Structured Applications. 

, 100010. DOI: 10.1016/j.ijpx.2019.100010.
16. Lipa, M. J. Unlocking Knowledge to Benefit the Patient: How Connecting KM and QRM Can Strengthen Science and Risk-Based Decision Making. Ph.D. Thesis, Technological University Dublin, Dublin, Ireland, 2021.
17. Wikipedia, Knowledge Industries. 

en.wikipedia.org/wiki/Knowledge_industries 

(accessed June 04, 2022).
18. Lipa, M. J.; O’Donnell, K.; Greene, A. A Survey Report on the Current State of Quality Risk Management (QRM) and Knowledge Management (KM) Integration. 

 (3), 5. DOI: 10.21427/rpc7-sp95
19. Lipa, M. J.; O’Donnell, K.; Greene, A. Knowledge as the Currency of Managing Risk: A Novel Framework to Unite Quality Risk Management and Knowledge Management. 

 (2), 4. DOI: 10.21427/4mzp-vn67
20. Lipa, M. J.; Mulholland, V.; Greene, A. Integrating Knowledge Management and Quality Risk Management. 

 (4), 14–23.
21. APQC, Knowledge Management Capability Assessment Tool. 

 (accessed April 23, 2023).
22. Browning, T. R.; Ramasesh, R. V. Reducing Unwelcome Surprises in Project Management. 

 (3), 53–62.
23. Mulholland, V.; Greene, A.; Lipa, M. J. Steps Beyond Risk Assessment in QRM: RBDM, the Next Horizon. 

The Pharmaceutical Process Classification Framework

; APQC: Houston, Texas, 2008. https://www.apqc.org/system/files/PCF_Pharm_Ver_5.0.1.pdf.

Martin J. Lipa*, PhD, martin.lipa@prst.ie, is senior research fellow, Pharmaceutical Regulatory Science Team. Anne Greene, PhD, is professor, Technological University Dublin, and director, Pharmaceutical Regulatory Science Team.

*To whom all correspondences should be addressed.

Recent research and perspectives shed light on an opportunity to better connect risk and knowledge through improved integration of systems.

Knowledge as the Currency of Managing Risk: A Smart Investment for Patients

Cheryl Barton is director of PharmaVision, 

TILT Biotherapeutics (TILT) is a Finland-based private biotechnology spin-out from the University of Finland focusing on the development of cancer immunotherapies utilizing oncolytic viruses encoded with a variety of cytokines to treat various cancers. The company was founded in 2013 by Akseli Hemminki, a cancer clinician, and aims to use an oncolytic virus to enhance T-cell therapy, including tumour-infiltrating lymphocytes (TILs), chimeric antigen receptor T cells (CAR-Ts), and checkpoint inhibiting antibody therapy of solid tumours (1).

The therapeutic benefit of various oncolytic viruses has been widely published in the literature, and researchers have improved the efficacy of first-generation products using genetic engineering (2). TILT coordinated a European Union (EU)-funded project, UNLEASHAD, which has developed an adenovirus-based oncolytic product, TILT-123 (igrelimogene litadenorepvec), that specifically targets and lyses cancer cells. It also releases TNF-α and interleukin-2 (IL-2) cytokines into the microenvironment, providing an alert and attracting immune cells to the tumour. This strategy ensures anti-tumour immunity and may be suitable for tumours that are refractory to other types of immunotherapies (3).

TILT-123 is currently in Phase I clinical trials as monotherapy and in combination with TIL therapy in Europe in metastatic melanoma. Preclinical results have been published, and the virus backbone has been demonstrated to be safe and well-tolerated in humans. Trials for other solid tumour indications are planned in combination with TILT-123 and a checkpoint modulator or a CAR-T product (2), and the company has other patent-protected products in the pipeline, including oncolytic viruses coding for bispecific antibodies TILT-321 and TILT-322 (3).

In May 2023, the company announced that the first patient had been dosed in a Phase I solid tumour trial, which will assess TILT-123 in combination with avelumab in squamous cell carcinoma of the head and neck (SCCHN) and melanoma in patients with advanced solid tumours refractory to, or progressing after, anti-programmed cell death ligand 1 (anti-PD-L1) (4). The study is part of TILT’s agreement with Merck KGaA, Darmstadt, Germany, and Pfizer, and the European trial is underway at the Docrates Cancer Center (Helsinki, Finland). The company expects to open its US site in the third quarter of 2023 (5). In November 2022, TILT opened an office in Boston to support its US clinical trial programme.

Tumour-infiltrating lymphocyte (TIL) therapies offer a new route to target cancer.

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