Data Governance and Integrity: Pharma, Biotech, and the Data Deluge, Part 4

Welcome back to our ongoing series!

The last few months have taken us deep into one of the biggest challenges and greatest opportunities in today’s drug development industry: the fast-growing data deluge flooding today’s life sciences organizations. 

Every year that flood gets petabytes deeper, leaving many drug developers swamped with massive amounts of unstructured, fragmented, and ultimately unusable information. As we’ve seen in this series, the vast majority of that data will remain forever stranded in file cabinets, SharePoints, and inboxes – and it’s costing the life sciences industry a fortune in development delays, regulatory setbacks, and missed opportunities

Now if you’ve been following the first three chapters in our series, I suspect I know what’s on your mind: “Yash, this all makes sense, I see why drug developers are drowning in seas of unmanaged data, and I can clearly identify the operational challenges we could address by tackling our unstructured CMC knowledge… but how does that actually work?”

Well! I’m glad you stayed tuned for Chapter 4, because this is where rubber hits road. So let’s dive straight in, and have a  look at how structured data principles can help drug developers achieve a long-sought operational holy grail: automation.

The CMC lifecycle is more complex than ever

Taking a step back, let’s start by revisiting the CMC lifecycle. It’s become dauntingly complicated as more and more cell-and-gene modalities enter the global pipeline. 

For today’s product candidates, the journey to validated processes still follows the familiar pathway of design, to qualification, to continuous validation (CPV). But in parallel, product programs must also plan for an increasingly complex array of other development and risk management activities:

As manufacturing processes go through their “formal” development steps, drug sponsors and contract manufacturers must navigate a series of related development tasks from fit-finding, to process characterization, to PPQ. All while also managing multiple tech transfers needed to scale up production and deliver a high-quality supply of clinical trial material.  

At the same time, drug developers also need to create a detailed risk management framework that demonstrates robust process understanding, process capability, and continually evolving mitigation. And it’s this last step that frequently leaves drug developers grasping for real control and understanding of their risks. 

A truly comprehensive control strategy should span every single facet of the manufacturing process – whether it’s a raw material, unit operation, or finished product. Yet all too often, sponsors struggle to keep that strategy in step with their core development activities. Risk assessments are often performed retroactively, and based on tacit knowledge instead of direct data tying precise process capabilities to specified risks.

Why? As we saw in the first 3 chapters of our series, the source of this disconnect often comes back to the way drug developers manage their CMC data. Or don’t

After all, how is a CMC contributor supposed to show control of a specific parameter when the CQAs are on the SharePoint in a PDF labeled “RX1075tpp_rev 3_updated_Nicksversion_R2” and the last official risk analysis is on the desktop of someone’s old computer? How do you establish deep understanding and comprehensive control of risks without equally holistic and integrated process knowledge?

This is just one of the areas where we see both an acute need for better data structures and the profound value of establishing them. And that begs the first big question we need to answer: which structures are the right ones for a CMC data framework?

Luckily, that one’s easy.

ICH Q8 can be the cornerstone of CMC workflow automation

When it comes to planning a comprehensive CMC data structure, the ICH guidelines are an optimal place to start. They define the essential quality and compliance parameters for the pharmaceutical development process. 

For a risk-based control strategy, we can zoom in immediately at ICH Q8. It provides a perfect working example of how structured data principles can be applied to core CMC information – by creating a digital QTPP. 

Let’s take a closer look at how. 

ICH Q8 specifies that a control strategy should: 

  • Describe and justify how in-process controls and the controls of input materials (drug substance and excipients), intermediates (in-process materials), container closure system, and drug products contribute to the final product quality.
  • Show that those controls are based on product, formulation, and process understanding.
  • Include (at a minimum) control of critical process parameters and material attributes. 

 

So far, so familiar – but when it comes that vital second bullet, often easier said than done. I’m looking at you, RX1075tpp_rev 3_updated_Nicksversion_R2.pdf

Demonstrating “product, formulation, and process understanding” requires robust, multidimensional risk assessments (ICH Q8 section 2.3) that clearly “[identify] which material attributes and process  parameters potentially have an effect on product CQAs,” defined via the QTPP. And that’s notoriously hard to do when those material attributes, process parameters, CQAs – all your product and process information, really – are scattered across documents, departments, and memory banks. 

That’s why it’s no surprise that lots of drug development organizations struggle to build the understanding required by ICH Q8, much less the control strategy grounded in that understanding. And it’s why the QTPP – the foundation of an effective, comprehensive control strategy – is the perfect place to start structuring CMC data.

That resource isn’t just a PDF that everyone links to on SharePoint (or at least it shouldn’t be!). It contains all the essential building blocks of a risk-based approach to complying with the safety and efficacy requirements of product quality. The first place to start: Freeing those blocks from the document and turning them into foundational data points.

A 3-step approach to building a digital QTPP

In my recent feature on FAIR data principles – co-authored with my colleagues Paul Denny-Gouldson, Sujeegar Jeevanandam, and John F. Conway – we laid out a simple but transformative way to turn CMC resources into multidimensional datasets. The ultimate goal: liberating data from a document so it can be structured, linked, contextualized, and used for bigger purposes.

The ICH defines a QTPP as a “prospective summary of the quality characteristics of a drug product that ideally will be achieved to ensure the desired quality, taking into account safety and efficacy of the drug product.” To extract those characteristics in usable form, we can follow these three steps:

Step 1: Atomize 

Break down the QTPP into individual nodes of information categorized by product (requirements and attributes), process (operational steps and variables), or plant (assets and materials).  

 

Step 2: Assemble 

Group those data points into nodes of related information using industry-relevant taxonomies like ICH, QBD, or GMP. In the case of a QTPP, let the ICH be your guide. 

 

Step 3: Link 

Identify associations and define relationships between the nodes based on industry-relevant ontologies such as risk, process capability, and control strategy.

 

Once you’ve completed those steps, you’ll find that you no longer have a traditional QTPP: a document that provides the only indexable reference for ALL the information it contains. Instead, you have an array of easily findable, readily accessible, fully interoperable, and reusable data points – the raw material of a modern data framework for your whole CMC program.

Let’s take a quick look at how each step works for a QTPP.

Step 1: Atomizing a QTPP

Typically, a QTPP is managed as a comprehensive, controlled document. What many drug developers don’t realize, though, is that this “document” is really a collection of separate elements – primarily product quality attributes.  

So, to begin the process of digitizing the QTPP, start by atomizing the information it contains by converting the document into separate data objects. Here’s an example:

Once the elements of the QTPP are defined separately, they can be managed and tracked that way as well. Your team can then index the content of QTPP, rather than just the document.  

Once you take individual control of these points, you can also manage and update them individually as well – instead of iterating entire documents every time, no matter how small the change. Create a canonical version of a data point, and updating it once updates it everywhere. And there never needs to be a RX1075tpp_rev 3_updated_Nicksversion_R3.pdf.

Step 2: Assembling QTPP data objects

Once you’ve atomized the data points in your QTPP, you can easily sort this information into a coherent structure by categorizing the elements based on language of ICH, QbD, and GMP. 

The example below shows how typical QTPP components can be grouped as general attributes, quality attributes, and performance attributes.  

Bonus: Categorizing elements this way gives you a rich set of metadata you can apply to each one, such as risk assessments, acceptance criteria, and control methods. The labeling schema you create is the critical bridge between this organizing step and the next one – where you connect these data points in ways that help the real magic happen.

Step 3: Linking QTPP data elements

After atomizing and assembling the discrete components of the QTPP, the true power of this process begins to emerge. 

Now, you can link those records, create contextualizing relationships between data points, assess and trace causality, and ultimately provide justification for decisions made throughout the development process. Here’s what that can look like for a production-ready QTPP:

As you can see, these links can extend far beyond the core QTPP components and connect with attributes and parameters of many other processes, units, equipment, and materials. You can also further dimensionalize these linked data points by connecting associate risks, effects, controls, and more. 

And while we’re focused on ICH for the purpose of digitizing a QTPP, there are many other potential ontologies that can be applied to other essential datasets. ICH, QbD, and GMP all provide valuable concepts that can be translated into metadata.

See it in action: Transforming auditor inquiries into instant CMC insights

Okay, so now we’ve translated your QTPP into a structured set of individually linkable, manageable, and leverageable data points – and put them at the heart of a multidimensional CMC data framework. What does it look like when you put that structure to work?

Fast forward to your next PAI. Your inspector has questions about your CMC program (don’t they always?). And they want those answers to be supported by data that shows strong governance and consistent integrity. 

You know how that exchange will typically go: 

  • Gather all your CMC documents in one room (and hope you haven’t missed any)
  • Get the inspector and all your SMEs together in a different room (and hope you’ve included all the right brains and memories)
  • Shuffle documents back and forth between both rooms until all the inspector’s questions have been answered

It’s… time-consuming. There’s lots of frantic flipping through binders and last-minute file collation. There are always spreadsheets that have to be reformatted so they can fit on the biggest piece of paper you can print them on. 

And then there’s the data supporting your answers to the inspector’s questions. Breadcrumbing conclusions to analyses and raw data can be a painful process with no guarantee of success – and also a high risk of a 483 if the inspector throws a data integrity flag on that play. 

But with all that data integrated into a structured framework, answering tough questions can be as simple as a click – one that connects to a seamless chain of data custody all the way back to the raw data. 

Here are just few examples of the kinds of questions you can instantly answer with your CMC data in FAIR format: 

  • “Can I see a process flow map of your manufacturing process?” Absolutely. That’s a cinch when your entire process is digitally connected from start to finish, down to the most nuanced parameter. 
  • “Can I see a list of your CQAs and their control methods?” Easy. Your CQAs will be stored and managed in one central location, and directly linked to specific controls for each attribute.
  • “Which process parameters affect this particular CQA? Are they critical?” Simple. Just pull up that specific CQA and click the associated CPP(s).
  • “Can you show me the FMEA for unit operation X in your process?” No problem. The record for Unit Op X connects directly to every FMEA report it influences.
  • “What’s the control strategy for sterility for your drug product fill-finish?” Click. There are all the steps, parameters, and risks associated with sterility. 
  • “What’s your process capability for Bioburden as an in-process control for sterility?” Click again. There are all process parameters related to bioburden.

Atomize, assemble, and link the data in your QTPP – and that’s just one key CMC document – and demonstrating compliance can truly be that easy and fast. All it takes is consistent application of structured data principles. Implement from the ground up, and answering those common questions can be as simple as click, show, “approved.” 

Of course, this is just what you can do by digitizing a single cornerstone CMC resource. What happens when you scale that up to an entire CMC program? 

Just ask one of our QbDVision power users!

Check out how top drug developers are putting structured data principles to work

If you caught any of the great content from last year’s Digital CMC Summit, you’ve seen how seriously many life science organizations are now taking their data governance and integrity initiatives. For many, it’s already starting to pay off in powerful ways. 

And it’s not just streamlined audits and inspections. With a comprehensive Digital CMC framework in place, top drug developers are discovering that they can unlock a range of exciting new capabilities – from automated reporting, to instant generation of key regulatory documents, to accelerated tech transfers. 

Here are just a few examples: 

Xeris Biopharma has turned to digital CMC tools to streamline risk assessments, systematize regulatory submissions, and keep CMC off the critical path for tech transfers. VP CMC Peter Knauer calls it an “astounding change to the way we do things.”

Sanofi and Bayer have both used cutting-edge digital CMC solutions to connect their entire CMC ecosystem, a step that’s helping them safeguard data integrity, save development time, and maximize quality.

A major regional generics manufacturer turned to QbDVision to help them modernize their CMC program, and ultimately helped triple the speed at which they can execute tech transfers.

All of these organizations are already discovering the impact digitization can have on their CMC workflows, and capturing multidimensional value from their investment. Together, they offer some powerful examples of structured data principles at work – and a preview of what other drug developers can achieve with similar initiatives.

One last stop! Don’t miss Chapter 5 of our series

Here in Chapter 4, we’ve just laid out an approach to applying modern data principles in CMC workflows – and shown how many ways that effort can deliver value and accelerate organizational performance. For chapter 5, we’ll look closer at how this same effort can help drug developers prepare for another looming industry shift: digitizing regulatory submissions. 

In case you missed Sue Plant’s excellent presentation at the 2023 Digital CMC Summit, now’s the time to stream it. She takes a prescient look at how digital regulatory submissions are poised to shake up the industry, one that beautifully sets the stage for the last chapter in our series. 

Until then!

 

Miss a chapter? Catch up on the whole series: 

Chapter 1: Pharma, Biotech, and the data deluge >

Chapter 2: Modernizing the structure and management of drug development data >

Chapter 3: Taking a structured digital approach to CMC data >

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Mark Fish

Managing Director, Scientific Informatics, Accenture

Mark Fish is Managing Director and Global Lead for Accenture’s Scientific Informatics Services Business. Mark has over 25 years of experience in leadership roles in Accenture, Brooks Life Sciences and Thermo Fisher Scientific delivering innovative solutions to the pharmaceutical sector and is passionate about drug discovery and development, translation research and manufacturing transformation. Mark has extensive experience in agile software development, data strategy, process engineering and robotic automation for research, analytical development and quality control in Life Sciences.

Chris Puzzo

Solution Architect, Digital & Data, Zaether

Chris is a Solution Architect with Zaether, focusing on delivering next-generation digital and data solutions for GxP Life Sciences customers. Chris has previously held technical operations roles within multiple gene therapy manufacturers, including Thermo Fisher Scientific’s CDMO organization where he supported various capital projects including the design, build, and startup of new GxP manufacturing capacity.

Victor Goetz, Ph.D

Executive Director, TS/MS New Modalities and Data Strategy, Eli Lilly and Company

Victor Goetz, Ph.D. is the Executive Director of Technical Services New Modalities and Data Strategy at Eli Lilly and Company. He has over 35 years of industry experience in developing and commercializing nine novel medicines to enhance the exchange of knowledge needed to speed the delivery of new medicines to patients. Dr. Goetz holds a BS in chemical engineering from Stanford University and a PhD in chemical and biochemical engineering from the University of Pennsylvania.

Rachelle Howard

Director of Manufacturing Systems Automation and Digital Strategy, Vertex Pharmaceuticals

Rachelle is the Director of Manufacturing Systems Automation and Digital Strategy for Vertex’s Small Molecule Manufacturing Center. She oversees the site Automation Engineering function and has co-led Vertex’s global Digital Manufacturing Transformation program since 2019. She leads several initiatives related to data integrity, data management, and employee education. Rachelle is a graduate of Tufts University and the University of Connecticut where she has degrees in Chemical Engineering and a PhD in Process Control.

Vijay Raju

Vice President, CMC Management, Flagship Pioneering

Vijay currently leads CMC activities to deliver on Pioneering Medicines portfolio. The portfolio is built on Flagship Pioneering’s bio-platforms covering multiple modalities (small molecules, biologics, cell & gene therapies). Vijay was previously in technical leadership roles at Novartis.

Greg Troiano

Head of cGMP Strategic Supply & Operations, mRNA Center of Excellence, Sanofi

Greg serves as Head of cGMP Strategic Supply and Operations at the mRNA Center of Excellence at Sanofi, where he is responsible for all aspects of clinical production and raw material supply chain. He joined Sanofi via acquisition of Translate Bio, where he was Chief Manufacturing Officer and responsible for Technical Operations. Over his 20+ year career in the drug delivery field, Greg had various roles leading the pharmaceutical development of complex formulations, including numerous nano- and microparticle based systems. Greg received his MSE and BS in Biomedical Engineering from The Johns Hopkins University and was elected and inducted into the American Institute for Medical and Biological Engineering (AIMBE) College of Fellows in 2020 for recognition of his accomplishments in drug delivery.

Pat Sacco

Senior Vice President Manufacturing, Quality, and Operations, SalioGen

Pat is a Biotechnology technical operations executive with 30+ years of experience leading and managing technical operations functions at numerous innovative companies in the biotech and life sciences industries. He has a passion for advancing and implementing best practices in pharmaceutical manufacturing.

Diana Bowley

Associate Director, Data & Digital Strategy, AbbVie

Diana is the Associate Director, Data & Digital Strategy in S&T-Biologics Development and Launch leading the organization’s Digital Transformation since October 2021. She joined AbbVie in 2012 in the R&D-Discovery Biologics group focused on antibody and multi-specific protein screening and engineering, leading multiple programs to the cell line development stage. In 2017 she joined Information Research and led a team of IT professionals who supported AbbVie’s Discovery Scientists in Biotherapeutics, Chemistry, Immunology and Neuroscience. She has a PhD in Molecular Biology from The Scripps Research Institute and Bachelor of Science in Chemistry from The University of Northern Iowa.

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 a Senior Director in the Flagship Digital, IT, and Informatics team, where he leads and manages the digital evolution for Pioneering Medicines. His role is pivotal in ensuring that digital strategies align with the overall goals and objectives of the Flagship Pioneering initiative.

His robust background in digital life sciences includes expertise in applications, informatics, data management, and IT/OT management. He previously spearheaded Digital Biomanufacturing Applications at Resilience, a CDMO start-up backed by Arch, where he established a team responsible for implementing global manufacturing automation systems, Quality Assurance applications, laboratory systems, and data management applications.

Matt holds a B.S. in Biology and Biotechnology from Worcester Polytechnic Institute and an M.B.A. from the Boston University Questrom School of Business, where he focused on Strategy and Innovation.

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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