Focus on FMEA – Process Risk Assessments (Part 3)

Editor’s Note: This is part 3 of a 7 part series on Process Risk Assessments. If you’re just getting started with the series, check out part 1.

Last week we discussed how to use the concepts of ISO 14971 to create a generalized framework for risk management that works across pharma and is compatible with (and leverages the best parts from) medical devices.

This week, we’re going to take that one step further. We will explore how these concepts line up with FMEA/FMECA methodologies.

Let’s review!

Focus on FMEA/FMECA

The QbDVision platform is intended to align with the recommendations outlined in the various ICH guidelines, specifically ICH Q8 to ICH Q12. ICH Q9 specifically discusses the concepts of quality risk management but does not recommend a specific risk methodology for use in the risk assessment of manufacturing processes. However, we find that recent writings by the FDA do recommend the use of FMEA/FMECA for the risk assessment of pharmaceutical manufacturing processes related to their knowledge-aided assessment & structured applications (KASA) initiative. For this reason, the risk assessment features and functionalities available in QbDVision are based on the FMEA/FMECA methodologies. ICH Q9 defines FMEA and FMECA as follows.

FMEA – Failure Mode Effects and Analysis

FMEA provides for an evaluation of potential failure modes for processes and their likely effect on outcomes and/or product performance. Once failure modes are established, risk reduction can be used to eliminate, contain, reduce or control the potential failures. FMEA relies on product and process understanding and methodically breaks down the analysis of complex processes into manageable steps. It is a powerful tool for summarizing the important modes of failure, factors causing these failures and the likely effects of these failures.

FMECA – Failure Mode Effects and Criticality Analysis

FMEA might be extended to incorporate an investigation of the degree of severity of the consequences, their respective probabilities of occurrence, and their detectability, thereby becoming a Failure Mode Effect and Criticality Analysis (FMECA; see IEC 60812). In order for such an analysis to be performed, the product or process specifications should be established. FMECA can identify places where additional preventive actions might be appropriate to minimize risks.

In practice, and in QbDVision, risk assessment is a progressive exercise, as diagrammed last week where the quantitative estimation of each layer should be based on data and/or sound scientific rationale. Risk assessments begin with the evaluation of hazardous situations that have the potential to cause harm. The harm could be to patients or to a downstream operation or output in the manufacturing process. Figure 1 provides a summary of how these layers are assembled into a full risk profile.

Figure 1. Progressive risk assessment model.

  • Criticality is defined as the product of the Severity of Harm and the Likelihood of Harm. In some scenarios and especially with Critical Quality Attributes, there is an alternate definition used where the Criticality is the product of the Impact and the Uncertainty around the Impact. In this case, Uncertainty refers to how well the Impact is understood and whether the defined Impact is scientifically justified by direct data. We will explore Uncertainty in more detail later. In ISO 14971 terms, Criticality equals the product of the Severity of Harm (S) * the Probability of the Hazardous Situation leading to Harm (P2).
  • Process Risk is defined as the product of Criticality and Occurrence. In QbDVision we also refer to Occurrence as Capability Risk. In a manufacturing process, if one thinks of occurrence as the probability that a process variable will be out-of-specification, then this is directly related to the process capability which can be statistically calculated from manufacturing data. Lower process capability means higher Capability Risk or probability of being out-of-specification. In ISO 14971 terms, Occurrence/Capability Risk is the Probability of the Hazardous Situation Occurring (P1) and the Risk as defined in this standard is equivalent to the Process Risk defined in QbDVision.
  • Risk Priority Number is an overall risk metric that adds in the layer of Detectability Risk. Detectability is the concept that the hazardous situation can be detected before there is the incidence of harm. Detectability is often defined in terms of when the hazardous situation is detected relative to when the harm can occur. As an example, consider the pH of a drug formulation which can affect aggregation subsequently leading to cytotoxicity In the patient. If a pH out-of-specification can be detected at the time of manufacturing the bulk formulation, then the Detectability Risk is much lower than if it cannot be detected until testing at release. In QbDVision, RPN is calculated as the product of Process Risk and Detectability Risk which follows the general definition of Severity (S) * Occurrence (O) * Detectability (D). ISO 14971 does not include the concept of detectability in the calculation of risk or the idea of the RPN.

Figure 2 provides a graphical comparison of the QbDVision risk assessment structure with the ISO 14971 structure to demonstrate their alignment. Even though ISO 14971 does not include the concepts of detectability and RPN, they are included in figure 2 to allow comparison across all layers of risk.

Figure 2. Mathematical comparison of the risk models of ISO 14971 and QbDVision Risk Assessment.

Finally, it is important to note that Criticality is driven by Impact (Severity) and Uncertainty (Likelihood) and is not improved by reduction in Capability Risk (Occurrence) or Detectability Risk. Better process capability which reduces the probability of an attribute or parameter being outside of the acceptable range as demonstrated by manufacturing data, will reduce Process Risk. Similarly, earlier or more sensitive detection capability will reduce Detectability Risk and drive lower overall risk. With this structure, an attribute or parameter can be Critical but have a low overall risk with robust process capability and control. This is the methodology used to define your design space where these attributes and parameters can vary as long as they are within predefined limits without impacting critical quality attributes (CQAs).

Pretty simple, right?

Now that we have reviewed FMEA/FMECA, next week we’ll explore the impostors: uncertainty and detectability risk. While not as widely used, these two categories are important to creating clarity while assessing risk. We’ll discuss their usage and importance.

This post is part 3 of 7 in a series on practical risk management for pharmaceutical process development.

Robert Dimitri, M.S., M.B.A.

Director Digital Quality Systems, Thermo Fisher Scientific

Robert Dimitri is a Director of Digital Quality Systems in Thermofisher’s Pharma Services Group. Previously he was a Digital Transformation and Innovation Lead in Takeda’s Business Excellence for the Biologics Operating Unit while leading Digital and Data Sciences groups in Manufacturing Sciences at Takeda’s Massachusetts Biologics Site.

Devendra Deshmukh

Global Head, Digital Science Business Operations, Thermo Fisher Scientific

Devendra Deshmukh currently leads Global Business Operations for Digital Science Solutions at Thermo Fisher Scientific. In this role he oversees operations broadly for the business across its product portfolio and leads the global professional services, technical support, and product education teams.

Grant Henderson

Sr. Dir. Manufacturing Science and Technology, VernalBio

Grant Henderson is the Senior Director of Manufacturing Science and Technology at Vernal Biosciences. He has years of expertise in pharmaceutical manufacturing process development/characterization, advanced design of experiments, and principles of operational excellence.

Ryan Nielsen

Life Sciences Global Sales Director, Rockwell Automation

Ryan Nielsen is the Life Sciences Global Sales Director at Rockwell Automation. He has over 17 years of industry experience and a passion for collaboration in solving complex problems and adding value to the life sciences space.

Shameek Ray

Head of Quality Manufacturing Informatics, Zifo

Shameek Ray is the Head of Quality Manufacturing Informatics and Zifo and has extensive experience in implementing laboratory informatics and automation for life sciences, forensics, consumer goods, chemicals, food and beverage, and crop science industries. With his background in services, consulting, and product management, he has helped numerous labs embark on their digital transformation journey.

Max Peterson​

Lab Data Automation Practice Manager, Zifo

Max Petersen is the Lab Data Automation Practice Manager at Zifo responsible for developing strategy for their Lab Data Automation Solution (LDAS) offerings. He has over 20 years of experience in informatics and simulation technologies in life sciences, chemicals, and materials applications.

Michael Stapleton

Board Director, QbDVision

Michael Stapleton is a life sciences leader with success spanning leadership roles in software, consumables, instruments, services, consulting, and pharmaceuticals. He is a constant innovator, optimist, influencer, and digital thought leader identifying the next strategic challenge in life sciences, executing and operationalizing on high impact strategic plans to drive growth.

Matthew Schulze

Head of Digital Pioneering Medicines & Regulatory Systems, Flagship Pioneering

Matt Schulze is currently leading Digital for Pioneering Medicines which is focused on conceiving and developing a unique portfolio of life-changing treatments for patients by leveraging the innovative scientific platforms and technologies within the ecosystem of Flagship Pioneering companies.

Daniel R. Matlis

Founder and President, Axendia

Daniel R. Matlis is the Founder and President of Axendia, an analyst firm providing trusted advice to life science executives on business, technology, and regulatory issues. He has three decades of industry experience spanning all life science and is an active contributor to FDA’s Case for Quality Initiative. Dan is also a member of the FDA’s advisory council on modeling, simulation, and in-silico clinical trials and co-chaired the Product Quality Outcomes Analytics initiative with agency officials.

Kir Henrici

CEO, The Henrici Group

Kir is a life science consultant working domestically and internationally for over 12 years in support of quality and compliance for pharma and biotech. Her deep belief in adopting digital technology and data analytics as the foundation for business excellence and life science innovation has made her a key member of PDA and ISPE – she currently serves on the PDA Regulatory Affairs/Quality Advisory Board

Oliver Hesse

VP & Head of Biotech Data Science & Digitalization, Bayer Pharmaceuticals

Oliver Hesse is the current VP & Head of Biotech Data Science & Digitalization for Bayer, based in Berkeley, California. He has a degree in Biotechnology from TU Berlin and started his career in a Biotech start-up in Germany before joining Bayer in 2008 to work on automation, digitalization, and the application of data science in the biopharmaceutical industry.

John Maguire

Director of Manufacturing Sciences, Sanofi mRNA Center of Excellence

With over 18 years of process engineering experience, John is an expert in the application of process engineering and operational technology in support of the production of life science therapeutics. His work includes plant capability analysis, functional specification development, and the start-up of drug substance manufacturing facilities in Ireland and the United States.

Chris Kopinski

Business Development Executive, Life Sciences and Healthcare at AWS

As a Business Development Executive at Amazon Web Services, Chris leads teams focused on tackling customer problems through digital transformation. This experience includes leading business process intelligence and data science programs within the global technology organizations and improving outcomes through data-driven development practices.

Tim Adkins

Digital Life Science Operations, ZÆTHER

Tim Adkins is a Director of Digital Life Sciences Operations at ZÆTHER, serving the life science industry by assisting companies reach their desired business outcomes through digital IT/OT solutions. He has 30 years of industry experience as an IT/OT leader in global operational improvements and support, manufacturing system design, and implementation programs.

Blake Hotz

Manufacturing Sciences Data Manager, Sanofi

At Sanofi’s mRNA Center of Excellence, Blake Hotz focuses on developing data ingestion and cleaning workflows using digital tools. He has over 5 years of experience in biotech and holds degrees in Chemical Engineering (B.S.) and Biomedical Engineering (M.S.) from Tufts University.

Anthony DeBiase

Offering Manager, Rockwell Automation

Anthony has over 14 years of experience in the life science industry focusing on process development, operational technology (OT) implementation, technology transfer, CMC and cGMP manufacturing in biologics, cell therapies, and regenerative medicine.

Andy Zheng

Data Solution Architect, ZÆTHER

Andy Zheng is a Data Solution Architect at ZÆTHER who strives to grow and develop cutting-edge solutions in industrial automation and life science. His years of experience within the software automation field focused on bringing innovative solutions to customers which improve process efficiency.

Sue Plant

Phorum Director, Regulatory CMC, Biophorum

Sue Plant is the Phorum Director of Regulatory CMC at BioPhorum, a leading network of biopharmaceutical organizations that aims to connect, collaborate, and accelerate innovation. With over 20 years of experience in life sciences, regulatory, and technology, she focuses on improving access to medicines through innovation in the regulatory ecosystem.

Yash Sabharwal​

President & CEO, QbDVision

Yash Sabharwal is an accomplished inventor, entrepreneur, and executive specializing in the funding and growth of early-stage technology companies focused on life science applications. He has started 3 companies and successfully exited his last two, bringing a wealth of strategic and tactical experience to the team.

Joschka Buyel

Senior MSAT Scientist at Viralgen, Process and Knowledge Management Scientist at Bayer AG

Joschka is responsible for the rollout and integration of QbDVision at Bayer Pharmaceuticals. He previously worked on various late-stage projects as a Quality-by-Design Expert for Product & Process Characterization, Process Validation, and Transfers. Joschka has a Ph.D. in Drug Sciences from Bonn University and a M.S. and B.S. in Molecular and Applied Biotechnology from the RWTH University.

Luke Guerrero

COO, QbDVision

A veteran technologist and company leader with a global CV, Luke currently oversees the core business operations across QbDVision and its teams. Before joining QbDVision, he developed, grew, and led key practices for international agency Brand Networks, and spent six years deploying technology and business strategies for PricewaterhouseCoopers’ CIO Advisory consulting unit.

Gloria Gadea Lopez

Head of Global Consultancy, Business Platforms | Ph.D., Biosystems Engineering

Gloria Gadea-Lopez is the Head of Global Consultancy at Business Platforms. Using her prior extensive experience in the biopharmaceutical industry, she supports companies in developing strategies and delivering digital systems for successful operations. She holds degrees in Chemical Engineering, Food Science (M.S.), and Biosystems Engineering (Ph.D.)

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