OR WAIT 15 SECS
JosÃ© C. Menezes is CEO and founder of 4Tune Engineering, and director of a pharmaceutical engineering program focused on QbD and PAT at the Technical University of Lisbon. He has co-authored numerous papers and three books on these subjects. 4Tune Engineering Ltd has been working on QRM and materials science and technology with biopharma companies for more than a decade.
Applied throughout a product’s lifecycle and across a company’s portfolio, quality risk management and knowledge management will enable more agile manufacturing and better quality standards in the future.
The biopharmaceutical industry started its drive to modernization by bringing better science into manufacturing. Its goal was more predictable quality, from early development in R&D, through process development and into commercial manufacturing. It soon became clear, however, that the industry still faced fundamental challenges in leveraging its knowledge and filling in gaps in its understanding of core aspects of the industry’s products and processes. Manufacturers often turned to risk management as a way to harvest prior and tacit knowledge to support decisions related to poorly understood systems.
In 2008, with the publication of the International Council for Harmonization’s (ICH) Q10, knowledge management (KM) entered the industry lexicon. This KM focus added a new dimension to previous guidance documents, which addressed two challenges that biopharmaceutical manufacturers face: uncertainty in decision-making and poor retention and use of prior knowledge over a product’s lifecycle.
Global regulators have suggested that manufacturers could use quality risk management (QRM) and knowledge-based approaches to address and justify improvements in their current legacy control strategies (e.g., FDA and EMA pioneering guidelines addressing ongoing process validation and new GMP requirements, now captured in ICH Q11 and Q12), and to support post-approval changes. To use QRM correctly throughout each product’s lifecycle, however, manufacturers will need to improve the way they manage knowledge.
QRM is to KM what process analytical technology (PAT) is to quality by design (QbD). Currently, biopharmaceutical manufacturers may not have sophisticated tools and platforms in place to manage knowledge, but risk management tools can still allow them to capture and retain the information required to support knowledge-driven lifecycle activities. This article shows how these tools are already helping biopharmaceutical manufacturers change their approach to quality, compliance, and culture, and how regulators are integrating risk and knowledge requirements in new approaches to evaluating biosimilars.
The move to knowledge and risk-based management is coming at an opportune time, given the trends that are already in evidence in biopharma, such as the dawn of Industry 4.0 approaches to quality and control, and the seamless linking of design, industrialization, and commercialization. There has also been a realization that QbD by itself is no longer enough, but that quality must be optimized end-to-end throughout a product’s lifecycle. The industry is also seeing some companies become true learning-organizations and developing in-depth KM of product and technology portfolios. This approach is already building a foundation of agile, sustainable innovation and client responsiveness.
ICH guidelines (1–5) related to quality as a manufacturing-science (i.e., from ICH Q8, published in 2005, through ICH Q12, published in 2017) address risk and knowledge management in ways that biopharmaceutical companies are only now starting to appreciate (see Figure 1).
Figure 1: International Council for Harmonization, guidelines relevant to quality risk and knowledge management [all figures courtesy of the author].
ICH guidelines dealing with quality as a manufacturing science address both QRM and KM. [Normalized instances; total 975 on Risk (R); Risk-Management (RM); Knowledge (K); Knowledge-Management (KM)] Both intra- and inter-guideline comparisons of the different terms can be made due to the scaling used (document relative size in words and overall keyword counts across different documents, were taken into account).ICH Q12 provides an operational view of how risk and knowledge management can work together to ensure quality, operational, and cultural excellence at the corporate level. These benefits will come from making risk- and science-based decisions consistently for: individual products and processes over their lifecycles (6); product portfolios (7); and technology platforms (8).
The different elements required for the implementation of QRM, first outlined in ICH Q9–Risk Identification, Analysis, Evaluationand Control, require tools and practices that are at the core of good knowledge management. The initial stages, dealing with risk identification and analysis, can effectively capture prior knowledge; then mapping processes and defining an ontology of objects (e.g., unit operations) with specific attributes (e.g., inputs and outputs); and finally establishing causality between process parameters (inputs) and quality attributes (outputs), from which the criticality analysis exercise will be developed and a control strategy established.
This workflow, from risk identification to risk control, will result in a science-based foundation for the risk-management exercise, in which residual uncertainties are mapped, measured, and controlled. A failure mode and effects analysis (FMEA), which is crucial to develop a robust risk control strategy, will be difficult and extremely time-consuming to complete if the initial knowledge-based steps have been skipped. Crucial questions include the following:
Figure 2 summarizes the different risk management tools that are available at each QRM stage, showing which ones are most useful for each stage. FMEA, for instance, would not be used initially to identify risks, because it is incapable of doing that. Generally, for a QRM project to succeed, different tools will be required. In addition, mapping and brainstorming tools will help ensure team alignment, empathy across multiple functions, and an effective knowledge-sharing and harvest right from the beginning.
Figure 2: Risk management tools and their relevance at each quality risk management (QRM) stage. Risk management tools and their relevance at each quality risk management (QRM) stage.
Multiple tools exist to perform each of the tasks involved in risk-management as described in International Council for Harmonization (ICH) Q9. Failure mode and effect analysis (FMEA) is inadequate for the risk identification stage and for the initial causality assessment stage, both required to derive and justified robust control strategies.
After an end-to-end risk-assessment has been completed (i.e., risks have been identified, understood, quantified, and ranked, and a control strategy has been proposed), a lifecycle management plan should be put in place to manage unacceptable risks. This plan should also evaluate the control strategy’s performance, detect opportunities for improvement, and manage additional risks that stem from new events (e.g., post-approval changes).
This plan can then be used to aggregate all actions, findings, and experience (i.e., manage product and process knowledge over the product’s lifecycle). Such foundation will enable science-based justification and risk-based decisions to be taken when deviations occur; corrective actions and preventive actions (CAPAs) are implemented; improvement opportunities are evaluated; or any other change-management activities are considered.
ICH Q12 introduces a number of different concepts that will require a level of sophistication in knowledge-management that most biopharmaceutical companies have not yet reached (see Figure 3). In the words of the organizational quality guru, W. Edwards Deming, “Knowledge has a temporal dimension” (9). This dimension is related to the experience a company gains over a product’s lifecycle and that it uses to determine its current actions and future decisions, with the goal of improving process control, as well as ameliorating its technology platforms and portfolio products.
Figure 3: Quality system fundamentals for quality risk management (QRM). PACMP is post approval change management program; PQS is pharmaceutical quality system. ICH Q12 concepts  require a robust pharmaceutical quality system with a risk-management foundation used for science-based decisions.
However, biopharma is known for being a data-driven industry that repeats experiments rather than reusing information or knowledge. For decades, biopharmaceutical manufacturers seem to neglect the fact that their products take a generation to develop and have a commercial life of at least another generation.
As subject matter experts retire or leave companies, most product launches or commercial projects today are shared by multiple teams. Most members of these teams have only been working on the project for a short time, without access to the full history and knowledge of the product and process, acquired over many years.
ICH Q12 provides opportunities for companies to formalize their knowledge assets-first, in terms of establishing quality commitments, which can be compared to the concept of “established conditions.” It then requires that manufacturers take an end-to-end lifecycle view of all operations over each product’s lifecycle, identifying risks and categorizing types of changes.
Q12 then proposes that post-approval change management protocols (PACMP) be developed for each type of change, and, finally, that they be used to encourage rigorous science- and risk-based decisions. Neither QbD, as outlined in ICH Q8, nor QRM in ICH Q9, or KM in ICH Q10, have yet achieved its bottom-line goal: enabling regulators to grant more flexibility to companies that have adopted these practices within the framework of a modern and robust pharmaceutical quality system (PQS) than they would grant to companies that do not use these approaches.
There is a renewed expectation on the industry’s part that with ICH Q12 in place, regulators’ oversight can be based in quality culture and quality excellence metrics. Using this approach, greater agility is obtained in all lifecycle management changes, inspections, and review processes.
One modern vision of risk management is that it will allow different risk-management tools to be integrated into standardized workflows or templates and these templates would inherit causality and attributes from objects that represent the whole process being analyzed end-to-end and over its lifecycle (7).
QRM used within a lifecycle-management framework is an effective way to ensure consistency at all levels of modern pharma operations. Taking this approach will help take biopharmaceutical manufacturing closer to the paradigm of Industry 4.0, rooted in a culture of quality and operational-excellence. An elite group of manufacturers, which aim to be “learning organizations,” are already moving toward this goal.
When we consider a biologic, the number of potentially critical quality attributes (pCQA) entailed can be enormous, due, for example, to all the possible combinations of post-translation modifications, which is in the range of 16,9202 or 285 million. Factor in other pCQAs related to structural and activity attributes, and this number becomes unmanageable.
Only a fraction of these pCQAs will be clinically relevant and critical for that matter. Currently, it is not possible to measure all those pCQAs to decide afterwards about their criticality. Instead, a tiered approach is used involving a combination that balances science- and evidence-based evaluations with a risk or residual uncertainty evaluation.
Taking the biopharmaceutical manufacturing industry one step in that direction are FDA guidance documents on similarity assessments and a tiered risk-based approach that ranks the clinical relevancy of residual uncertainties (related to non-similarities) (8). Going a step further toward more complete assessments is FDA’s proposed approach of “Totality of Evidence”(10), including evaluation of the complete bioanalytical package together with the clinical package, thus establishing a stronger foundation and linkage between quality, safety and efficacy (Figure 4).
Figure 4: Concepts behind the Totality of Evidence approach to biosimilars review.
The foundational steps for FDA’s Totality of Evidence approach also apply to comparability assessments, and can be compared with ICH’s Q5E protocols.
For managing post-approval change for reference products, the same rationale would hold. In this case, the approach could be even easier to justify because differences between before and after a change should be smaller than those found between reference and biosimilars (10).
Tools are available (11) to assess both the data-driven part of the analytical package within the framework, shown in Figure 4, as well as the comprehensive platform for lifecycle management of its risk-based aspects (Figures 5 and 6).
Figure 5. An example of different aspects of QRM applied over lifecycle with a dedicated platform .
A Product, Equipment, Supply Chain and Facilities or Sites overview of risks, with different tools, templates and workflows, allowing for more agile and higher excellence levels of quality as manufacturing science & culture.
In short, the biopharmaceutical industry is entering a new and exciting era, in which tools and practices will no longer come from the small-molecule world but will be specific to large molecules and also intentionally address the inherent complexity of biologics. That is reassuring at a time the industry is launching new modalities that depart from the classical antibody model.
QRM and a life-cycle approach (11) to knowledge management will be instrumental to developing a new science that can quantitate the explicit parts of both evidence- and risk-based components and accept non-critical uncertainties. As a result, the industry will be better prepared to “deliver high-quality, safe and effective medicines to patients, in an agile and efficient manner, without extensive regulatory oversight” (12).
1. International Council for Harmonization (ICH), Q8 Quality Guidance (ICH 2006), “Pharmaceutical Development,” ich.org
2. ICH, Q9 Quality Guidance (ICH 2006), “Quality Risk Management,” ich.org
3. ICH, Q10 Quality Guidance (ICH 2009), “Pharmaceutical Quality Systems,” ich.org
4. ICH , Q11 Quality Guidance (ICH 2012), “Development and Manufacture of Drug Substances,” ich.org
5. ICH, Q12 Quality Guidance (ICH 2017), “Technical and Regulatory Considerations for Pharmaceutical Product Lifecycle Management: Core Guideline,” ich.org
6. US Patent Number 2018 / 0075379 A1, “Method for Risk-Management over Lifecycle of Complex Products and Processes,” published March 15th, 2018.
7. Calnan N, Lipa M, Kane P, Menezes JC, A Lifecycle Approach to Knowledge Excellence in the Biopharmaceutical Industry (CRC Press, 2017).
8. FDA, “Specific Considerations in Demonstrating Biosimilarity of a Therapeutic Protein Product to a Reference Product".
9. W. Deming, The New Economics ,2nd Ed., p. 106, W.E. Deming’s Institute (1994).
10. FDA, “Clinical Pharmacologicaly Data to Support a Demonstration of Biosimilarity to a Reference Product,” (2016).
11. Risk Management Platform iRISK (www.iRisk.com).
12. FDA, Final Report on Pharmaceutical CGMPs for the 21st Century – A Risk-Based Approach, 2004.
José C. Menezes is CEO and founder of 4Tune Engineering, and director of a pharmaceutical engineering program focused on QbD and PAT at the Technical University of Lisbon. He has co-authored numerous papers and three books on these subjects. 4Tune Engineering Ltd has been working on QRM and materials science and technology with biopharma companies for more than a decade.
Vol. 31, No. 10
Pages: 34, 36–37, 40–41
When referring to this article, please cite it as J. C. Menezes, “FDA Clarifies Worldwide Inspection Policies," BioPharm International 31 (10) 2018.