Product lifecycle management (PLM): A familiar concept with many definitions
PLM is a theoretical business concept that can be defined and applied many different ways, depending on the product, process and lifecycle involved. Implementation typically focuses on software technologies and business processes that help manage multiple dimensions of information and unite them in a holistic view of a product’s evolution.Â
In the drug development industry, PLM is typically associated with the commercial life cycle of a drug product – sales processes, market-scale manufacturing, regional variations of the drug product, and change management. Some forms of PLM may include discovery and R&D phases. But historically, few solutions provide the flexibility needed to manage a product’s pre-commercial life cycle. Many legacy PLM systems available today are focused on the management of discrete manufacturing (i.e. making parts that go into subassemblies and then final assembly) meaning these systems are not well suited to the process-intensive activities of pharma and biotech manufacturing.
Now, with QbDVision, drug developers have a cutting edge tool purpose-built to bring comprehensive PLM to the entire lifecycle – from translation to clinical, to clinical development, to translation to commercial.
Let’s take a closer look at what that can look like for drug developers, and how it can help them drive operational efficiency and lower their risks.
Value of pre-commercial PLM
Translation to clinical
- Establish clear product requirements for patient safety/efficacy: “Start with the end in mind”
- Organize CMC data to accelerate IND preparation
- Establish sound data governance and data integrity practices early on
- Minimize risks of IND clinical hold due to CMC deficiencies
Clinical development
- Execute development roadmap using QbD-based best practices
- Simplify CDMO interactions and clinical phase tech transfers
- Keep CMC optimization and characterization activities in sync with clinical studies
- Drive technical & regulatory success with a world-class CMC operation
Translation to commercial
- Accelerate commercial translation with Digital Tech Transfer & MES integration
- Demonstrate robust process understanding and control based on knowledge and QRM
- Build institutional knowledge for lifecycle management
- Reduce risk of regulatory actions due to CMC deficiencies and data integrity issues
Value of pre-commercial PLM
Translation to clinical
- Establish clear product requirements for patient safety/efficacy: “Start with the end in mind”
- Organize CMC data to accelerate IND preparation
- Establish sound data governance and data integrity practices early on
- Minimize risks of IND clinical hold due to CMC deficiencies
Clinical development
- Execute development roadmap using QbD-based best practices
- Simplify CDMO interactions and clinical phase tech transfers
- Keep CMC optimization and characterization activities in sync with clinical studies
- Drive technical & regulatory success with world-class CMC operation
Translation to commercial
- Accelerate commercial translation with Digital Tech Transfer & MES integration
- Demonstrate robust process understanding and control based on knowledge and QRM
- Build institutional knowledge for lifecycle management
- Reduce risk of regulatory actions due to CMC deficiencies and data integrity issues
Accelerating drug development with a cutting-edge digital CMC solution
QbDVision is a commercial, off-the-shelf DIgital CMC platform that redefines how drug developers approach product evolution and process development – and how this information can be captured as structured datasets.Â
It does so by shifting how users approach critical CMC workflows like process development and validation, tech transfer, and continued process verification. With QbDVision, drug developers no longer need to sink time and resources into inefficient, siloed, document-centric frameworks. Instead, they can move these key steps to data-centric digital structures that create transparency, foster collaboration, and enable agility.
Figure 1 shows the “ideal state” that development programs can move toward with QbDVision – one that’s optimally aligned with the ICH Q8-Q12 framework of process validation, process development, and control strategy. In practice, of course, these workstreams are often far less well-aligned. But by deploying a Digital CMC strategy as early as possible – even pre-IND – drug developers can put themselves on a path to holistic data governance, development best practices, and effective PLM.
For drug developers racing to gain efficiency, compress development timelines, and streamline operations, now’s the time to take a long, strong look at how Digital CMC solutions: how they can help organizations adapt to a challenging market, and how they can unlock new value from within those organizations.
The foundation of this strategy is simple: developing the product and its processes in a structured framework with a predefined CMC data model.Â
Structured data models have multiple key advantages. Instead of indexing documents, they can index the content within those documents – enabling much more efficient data/knowledge management across the product lifecycle. In today’s data deluge, structured data frameworks also provide a firm foundation for data governance and integrity strategies.
With QbDVision, implementing a Digital CMC strategy is as easy as using the platform. By design, it applies FAIR principles to captured data, implements QbD-based best practices, and generates robust metadata based on ICH taxonomies and ontologies. Together, these features of the QbDVision framework convert data into a vertically integrated knowledge base that can evolve over the lifecycle.Â
QbDVision can help drug developers achieve 5 key CMC objectives.
In their 2022 article on the future of pharmaceutical technical development, McKinsey lays out a compelling case for why CMC transformation is essential. They point out multiple driving factors, including the faster pace of development, the increasing complexity of manufacturing processes, and the dramatic growth in the volume of manufacturing data.Â
So where should that transformation focus first? McKinsey outlines five imperatives, each of which can be achieved by using QbDVision to pursue science- and risk-based development and comprehensive pre-commercial PLM:Â
- Making technical development (CMC) more patient-centric
- Adopting data-driven technical development
- Unlocking the full scientific potential of CMC
- Cross-functional collaboration across your organization
- Embedding sustainability into pharma development Â
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While the path to each goal may be different, they all start with the same first step: digitizing CMC workflows and information. To sustain the collaboration that makes these objectives achievable, drug developers need a structured data framework that supports centralized data and knowledge management.
Figure 2 shows how QbDVision’s technological foundation supports a data framework that delivers multiple levels of functional and strategic value. Ultimately, the platform helps streamline and accelerate a range of essential steps in the pre-commercial lifecycle.
An M&A upswing will have many stakeholders reviving sweet dreams of a handshake and a payout. But for many drug developers, it should also lead to some cold night-sweats: after all, what will those future deal partners find when they look closer at the development data behind that potential new asset?
The time to implement those principles is now, not when a deal partner is adding up how much time it will take them to find, organize, or recreate key development data. If M&A is in the plan or on the horizon, data structure and taxonomy should be critical priorities – so when the time comes, that vital information can pass from organization to organization with maximum efficiency and minimal confusion.
How to make that happen? With QbDVision, drug developers can establish effective knowledge management with every keystroke. ICH guidelines and QbD principles are built into the framework of our structured platform, so adding necessary taxonomies and ontologies is as simple as entering the data.Â
QbDVision is also purpose-built to enable a FAIR approach for CMC data and data quality. So when a potential deal partner needs to see that critical process information – and understand how easily they can leverage it – that data will already be in a Findable, Accessible, Interoperable, and Reusable form. No scrambling to find or generate the reports. Just click and show.
Without that kind of structure, it can take 15 people 10 days to a month to complete a painstaking process like FMEA – time that’s either coming out of a dwindling cash runway or the value of an asset or business. With QbDVision, it can be completed in as little as 10 seconds.
QbDVision redefines what’s possible with drug development transformation.
The last several years have finally seen many drug developers move to adopt digital technologies. But many of these digitization efforts continue to come up short.
All too often, drug developers make the mistake of focusing on managing their primary data via ELNs, LIMS, historians, and more. This approach hasn’t solved the primary challenge: contextualizing data to create information and institutional knowledge.Â
Other organizations have struggled with the profusion of data management point solutions that have recently flooded the industry. Many of these tools are narrowly focused on specific activities and associated data – such as risk management, digital recipe management, or pre-commercial MES – and often further reinforce information silos.Â
But now, with QbDVision, drug developers can finally tap a solution that connects multidimensional data across multiple functions and systems. In doing so, the platform creates structured data sets that are robustly labeled and comprehensively linked to other relevant information. The result: a deep knowledge base that enables many valuable capabilities (Fig. 3), and that functions far more efficiently than legacy Microsoft solutions and SharePoint databases.
As a growing number of organizations have realized, drug developers’ information resources are more than vital assets for their day-to-day operations: they can also be a valuable product in and of themselves.
QbDVision user Glycomine has built a highly productive business model around this strategy. While they have their own promising therapeutic candidate in their pipeline, they actively support their own development program by licensing packaged CMC assets to other organizations. With our platform’s robust, FAIR-based data framework, the “packaging” process is as simple as generating those assets in QbDVision. For customers, accessing and onboarding purchased resources is as easy as logging in.Â
This strategy can be particularly productive for smaller organizations (<200 FTEs) and CMC consultancies, both of which can often benefit from supplementary revenue streams and new partnership opportunities. For organizations that purchase those development resources, doing so can be a valuable “plug-and-play” way to reduce R&D cycle times.Â
To efficiently create knowledge and data products, though, the producer needs the right information infrastructure – a system that channels product components into customer-ready structures that can be easily accessed, navigated, and leveraged. That’s precisely what QbDVision provides in one off-the-shelf solution.Â
Adoption challenges: What’s holding back PLM for pharma and biotech products?
Thanks to capabilities like those enabled by QbDVision, structured, FAIR-based data frameworks can be a huge operational asset for CMC programs. But despite the efficiency and transparency they provide, the transition to modern data curation methods can often meet with some resistance.Â
The primary challenge is no surprise: change management. The CMC function has a long history of document-centric workflows powered by purpose-built, single-use datasets that are shelved after one analysis. Shifting to a FAIR standard of data stewardship can turn those isolated resources into valuable data assets for the whole organization. But multiple barriers often stand in the way of that important transition.Â
Here are four of the most important cultural, behavioral, and organizational obstacles to overcome:
Legacy technology
CMC needs to be better than Microsoft, PDF, and email.
The R&D workforce is still married to document- and spreadsheet-centric data management. To drop these tools, CMC contributors need better ones – solutions that can automate repetitive, time-consuming tasks and implement FAIR principles with every keystroke.
Missing incentives
Data producers need a good reason to change the way they work.
A company-wide knowledge base may seem counterintuitive to scientists used to owning “their” own purpose-built data. To encourage adoption of FAIR principles, organizations need to clearly articulate how these valuable contributors will benefit from sharing, connecting, and repurposing data.
Metadata ontologies
Consistent, standardized labeling is vital to efficient data management.
FAIR principles depend on a clear, domain-relevant labeling strategy that can be used to search, link, contextualize, and organize data. Luckily, ICH guidelines, cGMP, and QbD concepts and principles all provide valuable taxonomies and ontologies that CMC programs can leverage.
ALCOA mindset
Many CMC programs default to document-centric compliance.
Even with updates like ALCOA+ and ALCOA++, this document-centric framework is still utterly outmatched by today’s vast and ever-increasing volumes of digital data. It’s time to accept that ALCOA++ is not a data integrity system: it’s an outcome of a strong data culture.
Legacy technology
CMC needs to be better than Microsoft, PDF, and email.
The R&D workforce is still married to document- and spreadsheet-centric data management. To drop these tools, CMC contributors need better ones – solutions that can automate repetitive, time-consuming tasks and implement FAIR principles with every keystroke.
Missing incentives
Data producers need a good reason to change the way they work.
A company-wide knowledge base may seem counterintuitive to scientists used to owning “their” own purpose-built data. To encourage adoption of FAIR principles, organizations need to clearly articulate how these valuable contributors will benefit from sharing, connecting, and repurposing data.
Metadata ontologies
Consistent, standardized labeling is vital to efficient data management.
FAIR principles depend on a clear, domain-relevant labeling strategy that can be used to search, link, contextualize, and organize data. Luckily, ICH guidelines, cGMP, and QbD concepts and principles all provide valuable taxonomies and ontologies that CMC programs can leverage.
ALCOA mindset
Many CMC programs default to document-centric compliance.
Even with updates like ALCOA+ and ALCOA++, this document-centric framework is still utterly outmatched by today’s vast and ever-increasing volumes of digital data. It’s time to accept that ALCOA++ is not a data integrity system: it’s an outcome of a strong data culture.
Not surprisingly, as cash gets tight and timelines get tighter, many drug developers are racing to consolidate their tech stacks – which are often bloated with siloed tools for managing risk, process knowledge, product knowledge, tech transfers, and more.
As top drug developers like Sanofi and Bayer have already discovered, these capabilities can make QbDVision a lynchpin for information systems that span the product lifecycle – as well as a valuable solution to tech stack sprawl. Using the platform’s open API and fast-growing range of integrations, these organizations have leveraged QbDVision to unify an array of disparate data sources and fully connect their CMC ecosystems from discovery to commercial manufacturing.
Doing so has enabled them to work more efficiently, manage risk more comprehensively, and harness their data at a much greater scale – and do so with a right-sized tech stack. That can be a transformative shift for drug developers navigating today’s choppy market waters, and it’s one that QbDVision can readily support.
QbDVision-powered PLM supports multiple personas at pharma & biotech organizations.
A persona is simply a way to summarize trends and communicate patterns about a specific person or role. They provide a useful snapshot of a target user or stakeholder, their needs, goals, and pain points. Ultimately, clear personas help product developers create solutions for a specific somebody – not a generic anybody.
A robust digital solution like QbDVision has to consider the many personas involved over the pre-commercial lifecycle, from pre-IND to commercial translation. Here’s a sample of the many technical development stakeholders whose roles and functions can benefit from our platform:
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Analytical Development Lead/Scientist
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Manufacturing Science & Technology (MSAT)
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Process and Facilities Design
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DS/DP Pharmaceutical Dev Lead/Scientist
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CMC Lead
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|---|---|---|---|---|
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Validation Scientist
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Materials Management Lead
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Tech Transfer Lead
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Information Technology (IT)
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Quality Assurance Scientist
|
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Analytical Development Lead/Scientist
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Manufacturing Science & Technology (MSAT)
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|---|---|
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Process & Facilities Design
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DS/DP Pharmaceutical Dev Lead/Scientist
|
|
CMC Lead
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Validation Scientist
|
|
Materials Management Lead
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Tech Transfer Lead
|
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Information Technology (IT)
|
Quality Assurance Scientist
|
Effectively addressing the needs of each persona requires an understanding of their responsibilities, objectives, environment, and challenges. It also takes a clear understanding of their relationship to data – creator or consumer – and which dimensions of the FAIR paradigm are most important for them.Â
BioPhorum provides several useful examples of relevant personas for Digital CMC solutions. Here’s a good representative example of a common QbDVision user:
The real impact: ROI of PLM for pharma and biotech
Looking closer at personas like these, we often discover a diverse range of interests, priorities, concerns, and needs. But when it comes to organizational leadership, the focus often narrows to one key topic: What return can drug developers expect from their investment in digital CMC solutions like QbDVision?
To show what that ROI can look like, we’ve used a very conservative model to perform a detailed financial analysis of development costs and potential savings over a pre-commercial development lifecycle. Here’s a look at our assumptions and associated findings:
Assumptions
- FTE cost of $250K
- Pre-Commercial CMC lifecycle of 7.5 years
- Total QBDVision investment of $2.0M over lifecycle per product
- 10% savings in personnel costs or 10% reduction in lifecycle
- 10% reduction in clinical mfg. costs
ROI per program
- Personnel costs savings of $4.3M
- Manufacturing costs savings of $2.2M
- Total savings of $6.6M
- 228% return on investment over lifecycle per program
ROI (not included)
- Risk mitigation of IND clinical hold
- Higher likelihood of clinical success
- Time saved on tech transfers
- Reduce costs from rework
- Higher valuations from partners and investors with robust CMC program
- First-to-market advantage
- Longer patent life after launch
Assumptions
- FTE cost of $250K
- Pre-Commercial CMC lifecycle of 7 years
- Total QBDVision investment of $3.075M over lifecycleÂ
- 10% savings in personnel costs or 10% reduction in lifecycle
- 10% reduction in clinical mfg. costs
ROI per program
- Personnel costs savings of $4.3M
- Manufacturing costs savings of $2.2M
- Total savings of $6.6M
- 114% return on investment over lifecycle per program
ROI (not included)
- Risk mitigation of IND clinical hold
- Higher likelihood of clinical success
- Time saved on tech transfers
- Reduce costs from rework
- Higher valuations from partners and investors with robust CMC program
- First-to-market advantage
- Longer patent life after launch
Real success will start with collaboration.
While Digital CMC can unlock many game-changing opportunities, no one software solution can make them feasible for a pre-commercial development program. Not while primary data still sits in ELNs, LIMS, and historians, and commercial recipes are cobbled together across ERPs, MES, and SCADAs. Or while documents are controlled using eQMS and PDF regulatory submissions are assembled and submitted as eCTDs via RIMS.Â
In each of these all-too-familiar scenarios, floods of dispersed, unstructured data lie waiting to be transformed into a robust, centralized knowledge base. The missing piece: a digital CMC solution capable of enabling that transformation.
QbDVision is the first platform to fill this crucial gap, providing a comprehensive knowledge management framework that can integrate with other platforms across the pre-commercial lifecycle. Top drug developers across the industry are already discovering how this purpose-built tool can help them integrate their entire CMC programs and streamline key development workflows.Â
But despite these growing successes, we know that the long-term success and sustainability of the Digital CMC ecosystem will require collaboration. Our most successful deployments have always been the result of QbDVision and our customer coming together to rethink data and information workflows in the context of a truly digital platform.Â
Ready to make that fundamental shift in your CMC program? We’d love to share the effort of accelerating your innovative therapy to patients. Â
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Reach out to our team at any time to learn more about implementing comprehensive lifecycle management for your pre-commercial products.
