Does The FDA's Plausible Mechanism Pathway Revolutionize The Way All Risks Are Analyzed?

By Mark F. Witcher, Ph.D., biopharma operations subject matter expert

One of the pharmaceutical industry’s best examples of risk-based decision-making is the FDA’s decision to approve a new therapy. While the FDA has not approved new therapies for some time without an explanation of how it works, their new guidance on plausible mechanisms¹ represents the first regulatory guidance driven example of where the primary information source of a risk analysis is shifted from data-based to mechanism-based evidence.

The development and licensing of highly specialized targeted therapies have a number of unique challenges, one of which is obtaining sufficient clinical data to provide a statistically significant indication of safety and efficacy. This intrinsic limitation is overcome by shifting the risk analysis from seeking data to defining and evaluating a plausible causal risk mechanism that a risk analysis team of experts can use to provide a reasonable rationale supported belief that the patient is more likely to benefit from the therapy than if the therapy is not used, given the existence or absence of alternative treatments.

Risks are currently defined as events, such as ISO 31000’s “effect of uncertainty on an objective,”² or the FDA’s ICH Q9 — “the combination of the probability of occurrence of harm and the severity of that harm.”³ While neither definition is incorrect, they have led to the practice of viewing and analyzing risks almost exclusively based on event data obtained by testing the risk, such as is done with clinical trials, or by extrapolating “data” from similar past risks to establish regulatory guidelines for controlling future risks.

However, if risks are viewed and defined as connected relationships between events, an alternative, potentially more powerful, risk definition can be developed. Relational risk analysis (ReRA) is a risk modeling strategy that defines a risk as a cause – (system/process/mechanism) – effect relationship that provides a more robust definition of a risk as the “effect of uncertainty on a process, system, or mechanism that produces a consequence or objective.”⁴ The Q9 definition might be restated as a risk is “the combination of the severity and probability of occurrence of a consequence or objective produced by a system, process, or mechanism.”

ReRA provides an alternative approach for analyzing risks by including the intuitively obvious causal mechanism by which a process, system, actions, activities, or mechanisms produce an outcome after being initiated or triggered by a cause event. By defining the process, system, or mechanism as a causal mechanism (CM), ReRA can be used to formulate a risk analysis method such as proposed by causal mechanism and effect analysis (CMEA).⁵

Obviously, in the early days of drug approvals, the therapeutic mechanism of action (MOA) of how a human population will likely respond to a particular therapy would be virtually unknown, so approving a new therapy would rely almost completely on the results of carefully conducted clinical trials. However, as biological and medical technologies have advanced, analyzing causal risk mechanisms, at least intuitively, has steadily increased. The implementation of the plausible mechanism approval pathway is a recognition that MOAs for both safety and efficacy have advanced to the point where medical expertise and experience can play a major role in analyzing and managing the risks associated with the decision to approve both the initiation of clinical trials and the final approval of the therapy.

If analyzing causal mechanisms is good enough for approving medical therapies, why not apply the concept to all risks?

A Possible Revolution In Analyzing And Managing Risks

While the events and causal mechanisms vary widely, a good case can be made that a risk is a risk. A risk is either suffering a harmful consequence or failing to achieve a sought-after beneficial objective. The ReRA/CMEA modeling strategy provides an efficient, concise approach for analyzing, understanding, communicating, and ultimately managing any risk. From approving a new therapy for patients to climbing a ladder, risks can be modeled based on connected cause and effect relationships.

While using CMEA requires estimating the subjective probability of the future performance of a CM, in the absence of significant amounts of data from identical or highly similar causal CMs, a CMEA-like risk analysis approach may ultimately prove to be the best way to analyze most risks, especially those where finding and generating relevant data is challenging, if not impossible.

CM-based risk analysis is used all the time for making decisions, such as whether to climb a rickety old ladder, walk home alone on a dark night, or jump over a protective barrier. The FDA has used CM-based risk analysis for inspections and reviews for a long time. Form 483s may cite events, but they mostly cover deficiencies in how things are done (aka causal mechanisms). In the establishment license application era, team biologics inspectors were more interested in how things were constructed and how they would be operated — a CM-based risk analysis — than they were on the very limited data from the conformance lots.

A Bright Future For Risk Analysis

As medical technologies advance exponentially, combined with using AI analysis methods, evaluating the MOA for therapies should significantly reduce many medical risks associated with testing, approving, and using both existing and future therapies. If the same approach is used for analyzing the industry’s procedures, supply chains, manufacturing operations, contamination risks, minimizing human errors, etc., the pharmaceutical and medical device industries can significantly improve the way they analyze and manage risks.3

But in the even wider world of risk analysis, as the focus shifts from an exclusive emphasis on data to a more comprehensive approach of analyzing the causal mechanisms that generated past data and will generate future data, risk analysis should improve significantly, providing many industries with better methods for analyzing, understanding, and managing their risks.

References:

1. FDA Draft Guidance for Industry – Considerations for the use of the Plausible Mechanism Framework to Develop Individualized Therapies that Target Specific Genetic Conditions with Known Biological Cause, February 2026.
2. ISO 31000:2018 – Risk Management Guidelines – Principles and Guidelines, International Organization of Standardization, 2018.
3. ICH Q9 (R1) – Quality Risk Management, FDA, May 2023.
4. Witcher, M., Relational Risk Analysis For the Bio/Pharma Industry, Bioprocess Online, January 29, 2024. https://www.bioprocessonline.com/doc/relational-risk-analysis-for-the-bio-pharma-industry-0001
5. Witcher, M., Causal Mechanism And Effect Analysis (CMEA): FMEA’s Simpler, Effective Alternative, Bioprocess Online, May 1, 2026. https://www.bioprocessonline.com/doc/causal-mechanism-and-effect-analysis-cmea-fmea-s-simpler-effective-alternative-0001

About The Author:

Mark F. Witcher, Ph.D., has over 35 years of experience in biopharmaceuticals. He currently consults with a few select companies. Previously, he worked for several engineering companies on feasibility and conceptual design studies for advanced biopharmaceutical manufacturing facilities. Witcher was an independent consultant in the biopharmaceutical industry for 15 years on operational issues related to: product and process development, strategic business development, clinical and commercial manufacturing, tech transfer, and facility design. He also taught courses on process validation for ISPE. He was previously the SVP of manufacturing operations for Covance Biotechnology Services, where he was responsible for the design, construction, start-up, and operation of their $50-million contract manufacturing facility. Prior to joining Covance, Witcher was VP of manufacturing at Amgen. You can reach him at witchermf@aol.com or on LinkedIn (linkedin.com/in/mark-witcher).