Getting started: What you need in a PLM system for Digital CMC

Product lifecycle management (PLM) is poised to revolutionize the development cycle.

“The life cycle” is a ubiquitous concept for much of the life sciences industry. There are many well-established approaches to managing one. Lifecycle management gets special attention in ICH Q12. It has a diverse ecosystem of solutions and technologies to enable it.

For many development teams, though, PLM is often seen as more of a theoretical business concept than a solution to the data deluge swamping today’s CMC programs. Even as that flood gets deeper and deeper, traditional PLM systems have gained little traction with drug developers drowning in their unmanaged data lakes.

So why not? Why have drug development programs been slow to adopt PLM? And what could help them unlock its benefits?

Very glad you asked. Let’s take a look.

Discrete vs Process-Based Manufacturing

The main reason for low PLM adoption in drug development: Most industries and disciplines that deploy traditional PLM solutions have fundamentally different manufacturing needs. 

Take any of the 50 industries that have left pharma in the digital dust over the last decade – Aerospace, Automotive, you name it – and you’ll find no shortage of PLM deployments. Many top 50 life science organizations also use platforms like Oracle and SAP to manage commercial-stage manufacturing processes.

But whether these organizations produce passenger jets, smartphones, or doses of a GLP-1, they all have one thing in common: a focus on discrete manufacturing with distinct outputs, locked parameters, and a heavy emphasis on supply chain management. 

Sound like many CMC programs you know?

The main reason for low PLM adoption in drug development: Most industries and disciplines that deploy traditional PLM solutions have fundamentally different manufacturing needs.

The PLM platforms these organizations use are simply too rigid and protocol-dependent for scenarios where parameters are still being defined, characterization is just ramping up, and the team is still figuring it out. Jam those workflows into a traditional PLM system, and you’ll know exactly how it feels to stuff yourself into XS Lululemon leggings right after Christmas dinner.

No, while development and commercial manufacturing may be two stages of the same life cycle, they have vastly different lifecycle management needs. Therein lies the rub – and not just those lycra legs scraping each other – when it comes to extending the PLM concept to pre-commercial workflows. 

Closing the PLM gap: Where should drug developers begin?

At this point, we might fairly ask: Where does a PLM solution need to kick in for it to be considered a true “life cycle” solution?

Today, many of the “PLM” solutions used in the life sciences are actually blind to large, fundamental phases of a drug’s evolution. Many – if not most – commercial manufacturing processes run off a predefined recipe with minimal access to the development history and scientific rationale underlying the final recipe selections. It’s this all-too-common gap that must be closed to unlock truly comprehensive PLM for drug products. 

And there’s just one place to start: drug development data.

Today’s development teams – and their document-centric processes – are drowning in petabytes of information they have no hope of absorbing, much less leveraging. CMC programs are now treading water at a crossroads: modernize how they manage their ever-accumulating data, or continue perpetuating alarmingly low levels of efficiency and effectiveness.

The top 50 are quickly realizing that this isn’t a choice: it’s an imperative. Three-sixty knowledge management isn’t a when-you-get-to-it operational refinement; it’s essential to predictably efficient development processes. And for that, you need an effective approach to PLM.

If that’s on the roadmap for your organization, I’m excited to share some thoughts on what you can expect and how to set yourself up for success. Let’s take a look at how to prepare for pre-commercial PLM, and how to evaluate today’s rapidly evolving options.

Before you adopt a PLM system, start with the data that will power it.

By now, the benefits of pre-commercial PLM need little introduction: holistically managing the development cycle and its data can help accelerate key workflows, maximize program productivity, and save organizations millions in overhead costs.

But all these transformational opportunities have a catch: They all rely on a consistent, program-wide flow of structured, contextualized data. Without that flow… 

Well, to put it in colloquial terms: “Sh*te in, sh*te out.”

All that to say: to achieve success with a pre-commercial PLM system, you first need to start by optimizing the data that will power that system. Most importantly, setting up a consistent contextual framework that’s aligned with the scientific, quality, and regulatory standards that define the drug development pathway.

To effectively power a pre-commercial PLM system, that structure should clearly and globally define both the product you’re making and the manufacturing processes that support it – from CQAs to unit operations, steps, process parameters, materials, material attributes, and equipment. All with integrated QRM and their own rich set of contextualizing metadata for each attribute, parameter, and object.

To achieve success with a pre-commercial PLM system, you first need to start by optimizing the data that will power that system. Most importantly, setting up a consistent contextual framework that’s aligned with the scientific, quality, and regulatory standards that define the drug development pathway.

This structure and context is key: ultimately, when your PLM system is in place, this framework will be the source for data that flows between teams and tools throughout your organization. You wouldn’t introduce a new material to a bioreactor without knowing its pH, osmolality, and oxygenating properties. Nor do you want low-quality, unstructured, de-contextualized data flowing from your knowledge management system to your LIMS, MES, or QMS. 

But don’t sweat the budget you’ll need for a data science team. Today, CMC programs can implement this kind of holistically structured data framework with specialized, off-the-shelf solutions like QbDVision. It’s an essential step before moving ahead with any pre-commercial PLM strategy, or any tools you’ll use to implement it. 

But while that framework will be the informational backbone of your PLM system, it won’t be the only groundwork you need to lay to support that system’s success. Here’s a quick look at what to plan for before your rollout.

Pre-implementation planning: What should be on your PLM roadmap

So you’ve implemented a global, contextual, and QRM-centric framework for your CMC data. Big, essential box checked! Now your PLM engine has the high-quality fuel it needs to become your engine of efficient, effective development. 

Next big questions: How does your organization need that engine to run, who will be responsible for running it, and what should your workforce be prepared for when the engine revs up?

There are a few important steps to take to ensure you know the answers: 


1. Assess your company’s specific needs

No two development cycles are exactly the same. Workflows for a small molecule will look wildly different from those for a personalizable gene therapy. Same for organizations with comprehensive in-house resources and those with a robust network of outsourced development partners. 

Before you launch your pre-commercial PLM system, make sure you have a detailed idea of what workflows you’ll need it to support, where your data flows will need to reach, and how each group of stakeholders will expect to access your development knowledge. Knowing all these parameters will help ensure your system is set up to meet your exact needs.


2. Build a cross-functional implementation team

Today’s drug development cycle is an intensively multidisciplinary endeavor. Your CMC workflows likely have contributors from at least process development, analytics, MSAT, Quality, Regulatory, Materials, and IT. And that’s the short list. 

When you assemble the team responsible for your PLM initiative, be selective but comprehensive: you need a group that’s compact enough to move efficiently but holistic enough to represent all the stakeholders who’ll be impacted by rollout. 


3. Make a change management plan

It’s no secret: Adopting PLM-driven knowledge management may be a big and uncomfortable shift for a lot of your stakeholders. Especially those who may be heavily invested in legacy systems and document-centric processes. 

Before you launch a PLM system to your team, it’s critically important to prepare, educate, and incentivize them. Like all transformation initiatives, this one will live and die by your colleagues’ willingness to try it, use it, and stick with it – and that all comes down to how well you communicate, manage, and guide them through the change they’ll experience.


4. Carefully vet potential PLM partners

PLM is a specialized arena with a fast-growing and rapidly evolving tech stack. You may find yourself evaluating a range of different tools and their supporting teams, from enterprise-scale platforms with pre-commercial widgets to more narrowly focused solutions with some extensible PLM capabilities. 

When you assess these options, ask questions, ask a few more, then ask a few others: How do they handle development data? How well do they integrate with other solutions? What’s their cybersecurity posture? How do they ensure the security of your IP? 

Get clear, comprehensive answers before engaging. It can save you a lot of headaches downstream.


5. Build a comprehensive, collaborative implementation plan

Once you’ve found the right PLM partner, they won’t simply ship you a pre-commercial PLM platform. They’ll work closely with you to build a step-by-step plan for customization, pilot testing, and company-wide rollout.

One key part of this plan will be a strategy for data migration and system integration. 

Unless you’re a brand-new drug development organization with a green field of product and process data – we love you too!! – you’ll have a lot of existing and historical knowledge resources that will need to be transposed to your new pre-commercial PLM system and its data framework. A smart PLM partner will help guide you through every step of that process.

Today’s pre-commercial PLM options come in many forms. Here’s what to know about them.

So lots to do to prepare for the starting line! But what kind of platform do you want to have with you there??

Excellent question – and one you can answer a few different ways.

Like any evolving software ecosystem, there are a lot of ways in which the pre-commercial PLM tech stack is “still figuring out who it wants to be.” Solutions in that stack can come in many different configurations, so you’ll want to take a close look at each one’s advantages and limitations.

First off, though, it’s important to understand the full range of capabilities you’ll ultimately need from a PLM system – so you can determine the best way to source and implement them all. A comprehensive PLM system should enable you to manage: 


Via a workspace customized for your process design and development workflows


Especially an audit-ready chain of versions, changes, and justifications

Quality & compliance

With a focus on opportunities for continuous improvement and regulatory readiness

Program planning & projects

Including stages, gates, and milestones on your critical path to market


An essential resource that will continually evolve throughout your CMC program


Via a comprehensive library of risk analyses, FMEA, compliance documents, and more


Centralizing key assets like your TPP and qTPP, along with all their atomized components

Program planning & projects

Including stages, gates, and milestones on your critical path to market

That’s a diverse and sophisticated array of functions – and one, admittedly, that pre-commercial solutions are still evolving toward. Today, most pre-commercial PLM tools come in one of three forms, each with different advantages: 


Enterprise platforms with pre-commercial modules

In recent years, numerous large, commercial- and manufacturing-focused platforms have extended their capabilities by adding components or special functions focused specifically on pre-commercial workflows. 

If your organization already uses one of these platforms, it can be easy and convenient to extend its capabilities by adding pre-commercial modules. But in many cases, those modules are retrofitted from other platform functions, rather than purposefully designed for earlier-stage workflows.


Point solutions with some pre-commercial capabilities

Some narrowly-focused solutions for other use cases may offer capabilities that overlap with those that make up a PLM system. For example, eQMS tools offer robust document management capabilities and ERP systems provide extensive program planning functionality – though neither is designed for PLM.

If needed, these overlapping functions can be used to meet some PLM needs. But tools optimized for specific non-PLM functions will rarely be a replacement for a true PLM system. 


Digital CMC solutions designed for ready integration

This type of solution is purpose-built to provide the backbone infrastructure of pre-commercial PLM – effective, comprehensive knowledge management – while readily integrating with other tools that leverage that data for specific purposes (including LIMS, MES, ERP, and more). 

Thanks to this versatile integrability, this class of solution is often a smart starting point for new pre-commercial PLM systems. It provides a strong, data-centric foundation that can be extended by a range of other tools and functions. 

The “con”: Today, there’s only one solution that provides this unique capability. But as numerous top drug developers can attest, that one solution can be the lynchpin for an entire connected, continuous CMC ecosystem – one that spans the entire development cycle and beyond.

But however you decide to consolidate all the functions of a PLM system, there’s substantial value waiting to be captured through that investment. So what’s the next step toward unlocking it?

Let’s start shaping your path to pre-commercial PLM.

By “substantial value,” we mean real 7-figure impact: a conservative model shows that investment in a Digital CMC solution can deliver personnel costs savings of $4.3M and manufacturing costs savings of $2.2M over a typical 7-year development cycle. A tidy 114% ROI! 

To capture that return, though, we need to finally turn the page on yesterday’s CMC processes and fully refocus on today’s data-centric development principles. That’s the first essential step on a path that leads to greater efficiency, higher productivity, and accelerated results throughout the development cycle. All it takes is the right data framework – one that’s built for the iterative, evolutionary, and, yes, scientific realities of the development process.

Let’s definitely chat when you’re ready to take a look at one!


Ready to see QbDVision in action?

Connect with our team to schedule a QbDVision demo and see how our platform can transform your knowledge management strategies.

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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