Risk Management Plan Configuration: Process Risk Assessments (Part 6)

Editor’s Note: This is part 6 of a 7 part series on Process Risk Assessments.

Last week, we discussed the importance of considering your people in the design and structure of your risk management process. We showed how applying their skills to a structured and data-driven approach can accelerate and maximize risk understanding. Furthermore, we discussed that while designing that structure, it is important to advocate for a strong foundation that produces easily understandable assessments to guide future work.

This week, we’re going to put the pieces together and review the structure of risk configuration in QbDVision that bring these concepts to life.

Let’s review!

Risk Management Plan Configuration

QbDVision provides significant flexibility in defining your risk management plan (RMP) because there is no consistent prescription for these plans. As a result, pharma organizations have myriad different designs for their plans in terms of the number of levels, terminology, definitions, etc. The QbDVision RMP object focuses on an FMEA/FMECA approach allowing you to define the following discrete and calculated layers.

  • Impact/Severity of Harm (Discrete)
  • Uncertainty/Likelihood of Harm (Discrete)
  • Criticality (Calculated)
  • Capability Risk/Occurrence (Discrete)
  • Process Risk (Calculated)
  • Detectability Risk (Discrete)
  • Risk Priority Number (Calculated)

For each Discrete layer, the user can define the number of levels and for each level they can define:

  • Score and Score Label (e.g. 5 – Medium)
  • Color
  • Description

For each Calculated Layer, the user can define the number of levels and for each level they can define:

  • Range of calculated values (e.g. 1% – 10%)
  • Score Label for the Range (e.g. Low)
  • Color
  • Risk Label (e.g. Not Critical, Possibly Critical, Critical)
  • Development Activity
  • Control Strategy

The Criticality layer has a unique column labeled Critical with checkboxes. Any variable having a Criticality value in the range where the box is checked will be marked as Critical. This is how quality attributes and process parameters get designated as CQAs and CPPs in the QbDVision system.

Always Critical

The Impact layer also has a special override function to handle the assignment of Criticality when the impact can be catastrophic. As an example, let’s say that you are evaluating a final quality attribute and you decide that the Impact of this variable being out of specification is catastrophic to patient safety. Given the seriousness of the impact, the prudent decision is to make this variable Critical regardless of the Uncertainty or Likelihood of it happening. To enable this functionality, the Impact layer has an Always Critical checkbox for each layer. If a layer has this box checked, then any variable with an Impact value corresponding to this level will automatically be categorized as Critical.

Normalized Values

The RMP structure in QbDVision has four discrete layers and three calculated layers. The Criticality layer is calculated by taking the product of Impact and Uncertainty. The Process Risk layer is calculated by taking the product of Criticality and Capability Risk. And, the RPN layer is calculated by taking the product of Process Risk and Detectability Risk. Let’s take an example where the max value for each Discrete layer is 5. For the calculated layers, you can calculate the raw values as shown in the table below.

Assigned ValueCalculated Raw Value
Impact = 5
Uncertainty = 5
Criticality = 25
Capability Risk = 2Process Risk = 50
Detectability Risk = 1RPN = 50

It is interesting that even though the Capability Risk and Detectability Risk are relatively low, the overall risk-based on raw value seems to be going up which doesn’t make sense. Even though the Criticality is high, a well-controlled process that has low detectability risk should lower the overall risk. The focus on raw values is misleading because it does not take into account the scale of each layer. Let’s take the scale into account and use the scale of each layer to normalize the raw value between 0 and 100%.

Assigned ValueCalculated Raw ValueMax Value of Calculated LayerNormalized Value
Impact = 5
Uncertainty = 5
Criticality = 2525100%
Capability Risk = 2Process Risk = 5012540%
Detectability Risk = 1RPN = 506258%

Using the Normalized Value, which takes the scale of the calculated layer into account, makes more sense and correctly shows the overall risk dropping because of a well-controlled process and low detectability risk. For this reason, the RMP configuration in QbDVision works with normalized ranges for the Criticality, Process Risk, and RPN layers.

Pretty powerful, right?

Now that you’ve seen some of the capabilities to implement the structures discussed, we will wrap up our series and bring it all together in one final post next week. Stay tuned!

This post is part 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

Life Sciences Luminary and Influencer

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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