Continuous Product Quality Assurance (CPQA) Working Team

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Goal

To increase the assurance of our product quality by becoming a predictive organization through the power of AI to provide more robust information to make more informed decisions than we can today.

 

Team Leadership

Full list of working team members are listed at the bottom of page.

Co-Leaders

  • Charlene Banard.  Senior Vice President – Global Quality, Shire
  • David Lonza.  Head of EU Operations, Lachman

Team Administrator

  • Mark Zhong.  Associate, PwC

Mentor from AI Core Team

  • Tony Barnes.  Bucks County Advisors

AI Expert from AI Core Team

  • Matthew Schmucki.  Senior Validation Engineer.  AstraZeneca

 

Problem Statement

Despite the controls our industry has in place to assure product quality, we still have failures and recalls. The assurance of our product quality is limited by the ability of our organization to access, assess and connect all relevant data (known and unknown) real-time to inform our decisions.

 

Scope

The Team will determine the scope.  One suggestion is to choose one specific system that interconnects with other systems (both GMP and non-GMP)

 

Problem Solving Process

  1. Map out the interconnected systems of the chosen system.
  2. Gather relevant variables/information (internal/external).
  3. Interview process of expert auditors and FDA investigators.
     

Obstacles

  1. Need to harmonize the wording used across the systems, which requires cross-functional involvement and alignment.
  2. How do you run some of these advanced models in a reasonable amount of time – not months.
  3. How do we identify and ask the unknown/unknown questions, or have AI identify them?
  4. Data governance:  who will own the AI process, and do they have the capacity to own/implement the solution?
  5. Who will be the champion through to the end of implementation and ongoing use?
  6. Data genealogy- recommend how to guide data robustness.
  7. Testing approaches to suggest.  Pilot/proof of concept.  Crawl, then walk, then run.
  8. Identifying the appropriate variables that would lead to possible failures.  The data out is only as good as the data in.
  9. Deployment:  Guidance on how the information is used and by whom.  Are people trained to use the AI tools chosen?
  10. How to gather external data.
  11. Resources needed to data mine
  12. Money and public trust
  13. Scalability

 

Expected Deliverables

  1. System Map demonstrating interconnectivity and potential causal relationships between GMP systems, non-GMP systems and the complaint system.
  2. Following from the system map, a summary of data elements that represent risks and/or potential failure modes for the events captured in the complaint system 
  3. Definitions of AI relevant terms to harmonize understanding.
  4. Harmonized root cause definitions and examples.

 

CPQA Working Team Members

  • Thilini Ariyachandra, Professor, Xavier University
  • Isaac Brown, Vice President, Landmark Ventures
  • Patrick Caines Sr., Dir, Quality & Compliance, Baxter Healthcare
  • Vizma Carver, CEO, Clear Road Map
  • Haley Clark, Project Lead - Quality Assurance, AMRESCO
  • Leonardo Estevez, Quality Systems & Compliance, AstraZeneca
  • Lacey Harbour, Ken Block Consulting
  • Syed Hoda, Sight Machine
  • Jennifer Monaldi, Production, Shire Pharmaceuticals
  • Agnes Ortega, DVP, Compliance & Operation Services, Abbott Labs
  • Bhavesh Patel, Manager, QA Analysis|Quality Analytics, Janssen Pharmacutical Company of Johnson & Johnson
  • Sebastian Pazderski, Global Data Analyst, Medela
  • David Preikszas, Quality Assurance Director, Clorox Corporation
  • Chad Puterbaugh, Global Process Owner, Midmark Corp
  • Nick Ranly, Account Executive, Siemens
  • Susanne Rommel, Director Quality, Gilead
  • Victoria Sowemimo, Asc. Director Quality, Purdue Pharma L.P.
  • Cenk Undey, Executive Director, Process Development, Amgen
  • Heather Vezner, Director, Quality Systems, Astellas
  • Oliver Yu, CTO, FDAzilla