Event Brief: Digital CMC Basecamp – Boston, MA, hosted by QbD Vision
This exclusive event brought together some of the leading voices in Biopharma. The intimate setting fostered energetic discussions challenging the status quo of the drug development lifecycle management process.
Digital CMC Basecamp addressed several topics, including the need for integrating Research, Development, and Manufacturing, data frameworks as foundations for AI in CMC workstreams, end-to-end Product Lifecycle Management (PLM), and knowledge management. It concluded with panels showcasing real-world examples of how Digital CMC frameworks are being applied by some of the world’s largest and most innovative Biopharmaceutical companies.
The event featured insights from industry thought leaders, including:
It Takes an Ecosystem to Solve Complex Problems
To kick off the event, Luke Guerrero, Chief Operating Officer at QbDVision, set the expectations for the Basecamp with three important takeaways for the event:
“There are very few people working to solve these complex problems in the world, but fortunately, most of them are here,” said Luke Guerrero.
After all, it takes an ecosystem to solve complex problems and a movement to get people together.
Lots of Data, But Where’s the Knowledge?
Yash Sabharwal, President and CEO at QbDVision, provided the opening presentation, which focused on the need to mind the data early in the product lifecycle. He explored the latest thinking and offered a roadmap for the appropriate and effective use of AI leveraging structured data frameworks.
-Yash Sabharwal, President & CEO QbdVision
He advocated using agile software development methodologies and comprehensive strategies, integrating data across research, development, and manufacturing to reduce cycle-time, streamline tech transfer into manufacturing and accelerate time to market
In the wake of the AI revolution, Biopharma companies grapple with excitement and anxiety about deploying this powerful tool. Structuring data frameworks has become a critical concern for businesses worldwide. A primary challenge is distilling and transforming vast amounts of data into actionable knowledge.
Our industry suffers from what Axendia calls DRIP (we are Data Rich and Intelligence Poor).
While there is no shortage of raw, unprocessed data, little knowledge and insights are derived from this data. Unfortunately, too often, this knowledge resides in the minds of subject matter experts (SMEs). The journey from data to knowledge is crucial, yet organizations usually focus disproportionately on data collection rather than process understanding, leading to inefficient outcomes.
According to Axendia research, “Life science companies are obsessed with collecting dumb data. We assemble it, retain it, and hoard it to support regulatory and legal requirements. But what happens to these vast amounts of collected data? Unfortunately, most of the data is “lobotomized” as soon as it hits the paper it was printed on or the electronic document it was saved to — vast amounts of product and process intelligence that could be used to improve patient outcomes go unused.”
“We usually put effort into the data (instead of the process design for example) and then you do not have the right information. We do not make the effort or investment on the data today and we pay for it later. Unstructured data increases costs and risk.”, said Yash. We go from document to document in the data document pyramid!
Transforming data into domain-specific frameworks leads to the development of digital knowledge models. These models can then train predictive AI systems, creating digital twins that enhance and further refine AI capabilities.
As FDA Commissioner Calif said, “By themselves they (data) are meaningless; only when we add critical context about what is being measured and how, do they become information. That information can then be analyzed and combined to yield evidence, which in turn, can be used to guide decision making.”
AI should be trained with knowledge, not just data. This approach ensures more reliable and contextually accurate AI models.
Make it FAIR!
A crucial step towards effective AI implementation is democratizing data and knowledge. This involves standardizing data and making it accessible in a knowledge base. Importantly, AI should be trained with knowledge, not just raw data. This distinction ensures that AI models are built on robust, contextual insights rather than fragmented information.
To facilitate this effort, Biopharma organizations and the solutions providers in this ecosystem must adhere to the FAIR Principle that is creating a structured data framework that is Findable, Accessible, Interoperable, and Reusable.
To be useful, these FAIR principles must span the entire lifecycle.
For AI to be effective, data needs rich contextualization. This involves creating a knowledge layer that integrates structured data frameworks with relevant technologies. By moving from technology to the knowledge layer, organizations can build comprehensive models.
Building a Digital Bridge Between Research, Development and Manufacturing
Mike Stapleton, QbDVision, led a thought-provoking panel challenging the current drug development pre-commercial lifecycle process featuring industry insights from Sanofi’s Greg Troiano, Flagship Pioneering’s Matthew Schulze and Vertex Pharmaceuticals’ Rachelle Howard.
Panelists shared their thoughts on the value of bringing together Development and Manufacturing into a single organization, with Research on its own.
The panel agreed that, ideally, manufacturing is part of development from the inception of a new drug. Design and development should occur throughout the entire lifecycle. Using Quality by Design principles, this becomes a 2-way bridge that enables learnings to be fed into the development process to support continuous improvement across the product lifecycle and organization.
Matthew Schulze, Head of Digital Pioneering Medicines & Regulatory Systems, Flagship Pioneering addressed the need to change culture to accelerate the pace of digital drug development. Mathew was emphatic, “The winners will and the losers won’t… the organizations that will win will have a collective culture and view of how to go fast, because what is needed in research is different than what is needed in CMC until it’s not. And then everyone needs the same thing, to aggregate all the information at that pivotal milestone moment,” he added.
Rachelle Howard, Director of Manufacturing Systems Automation and Digital Strategy at Vertex Pharmaceuticals discussed how her organization is undergoing reorganization and focussing on foundational technology platforms to accelerate knowledge transfer from Development to Manufacturing. Rochelle shared that Vertex underwent “a recent reorganization to bring the <Development and Manufacturing> teams under the same organization. This was working even before that was the model. But there is that just proximity, there are meetings, there is an earlier stage in trying to look forward, look back into what worked in our last products, what is working now in manufacturing, having some of that knowledge transfer back to the development teams, trying to leverage these platform approaches as well and so that we can capitalize on what has worked previously and not start from scratch.”
Greg Troiano, Head of cGMP Strategic Supply & Operations, mRNA Center of Excellence, Sanofi emphasized the importance of early investment in process knowledge to ensure successful scaling and manufacturing. According to Greg, “…a lot of our tech transfers as a platform are leveraging existing know-how, existing processes, existing analytical methods. And then if you’ve done a good job with your knowledge management, then you can do it potentially very quickly. We run a manufacturing suite where we are actually manufacturing a different product every week in mRNA leveraging the platform. The tech transfer phase goes about three months back and they are all running in parallel because there are knowledge differences or recipe differences. But you can achieve quite quickly if you are leveraging a platform.”
The panel also discussed the need for a harmonized, integrated, and closed-loop approach to a product lifecycle management (PLM) platform to accelerate Time To Market, Biopharma PLM.
Standardizing the CMC Development Practices Across a Portfolio of Products
Vijay Raju, Vice President of CMC Management at Flagship Pioneering, shared several key trends and ideas on the future of digital innovation in the biopharma industry. He discussed the idea of establishing a unified development platform that could be shared across multiple Flagship Pioneering companies to enhance efficiency in medicine development.
Additionally, he discussed the need to create a digital innovation supply chain to maximize the value of therapeutic assets. This approach aligns with several industry trends, such as the rise of new modalities, the increasing role of Contract Development and Manufacturing Organizations (CDMOs), and the disruption brought about by generative AI. These trends underscore the need for biopharma companies to adapt and integrate digital solutions to remain competitive and meet regulatory expectations while improving quality and transparency.
“Digitalization presents a transformative opportunity for the biopharma sector, similar to how it has enabled artists to become influencers through social media. By leveraging data lakes, ontologies, FAIR data principles, knowledge graphs, and advanced analytics, scientists can gain valuable insights and enhance their impact on drug development. Technologies such as visualization tools, robotics, generative AI, and process automation can streamline research and development processes,” commented Vijay.
“For example, computational approaches and digital twins have already shown significant improvements in drug design efficiency and selectivity. Moreover, tools like study trackers and robotic labs can drastically reduce the time required for formulation development, enabling faster and more robust product creation,” he added.
To drive the successful implementation of digitalization, the industry must foster a digital mindset that prioritizes user-centric approaches and reimagines traditional workflows. Emphasizing digital literacy, psychological safety, and preparing the workforce for future challenges are essential to this transition. Additionally, forming strategic partnerships that focus on shared value, complementarity, and joint ownership can drive the collective advancement of digital initiatives.
Effective change management, with a bottom-up approach that empowers frontline employees to embrace digital tools, will be critical in achieving a sustainable and impactful transformation in Biopharma.
What does this all mean? “Strong foundation is key to successful and sustainable digitalization,” said Vijay, “and as thought leaders, we must integrate into our industry to prepare our organization for the future. Digitalization will allow us to redefine our way of working. What would take months now may take weeks in the future.”
Real-World Use Cases for CMC Digitalization
The event ended with a dynamic panel discussion, during which industry experts from AbbVie, Eli Lilly and Company, and Zaether shared real-world applications for their deployment of Digital CMC Solutions. The extremely interactive session encouraged attendees to also share how modernizing CMC operations is a must.
Diana Bowley, AbbVie.
“Our north star is that data is an asset, with this approach knowledge is the primary benefit, but efficiency is a side effect” declared Diana Bowley, Associate Director, Data & Digital Strategy at AbbVie.
Victor Goetz, Eli Lilly and Company, thoughtfully stated, “Don’t build a house on a quicksand foundation”. You need to enable your digital future by ensuring you pre-condition your data, to ensure well-organized data. Overall, you do need a sustainable foundation to enable CMC Digitalization.
In Brief
We must embrace Change Management and the Opportunity!
The most significant barrier to implementing these frameworks is change management. Organizations must adopt an agile mindset, focusing on people, defining outcomes, integrating technology, and adjusting business processes to align with new technological capabilities.
Turning data into knowledge requires the use of FAIR principles spanning the entire lifecycle. We must also build a digital bridge between Research, Development, and Manufacturing as well as standardizing CMC development practices across the product portfolios. As an industry, we must foster a digital mindset that prioritizes user-centric approaches. We must treat data as an asset and the foundation of knowledge, not an end in and of itself.
In Yash’s words, we must “embrace the opportunity”. The path to knowledge lies in structuring data frameworks that transform raw data into actionable insights. By embracing this opportunity, businesses can leverage technology to its full potential, ensuring security and relevance tailored to their specific needs.
Digital CMC Basecamp Boston emphasized the critical role of digital transformation in modernizing the pharmaceutical and biotech industries. The event underscored that embracing digital solutions is essential for companies aiming to stay competitive and drive forward scientific and medical advancements. Developing a strong sense of community between technology providers and industry will be crucial in fostering collaboration, innovation, and the seamless integration of these digital tools. This synergy is not only be pivotal for the future growth and efficiency of the Biopharma sector, but also for accelerating the development of groundbreaking therapies that improve patient outcomes globally.
We will continue to provide updates on QbdVision as they become available.
To discuss how this initiative impacts your organization, Click on this link to schedule an Analyst Inquiry on this topic.
The opinions and analysis expressed in this post reflect the judgment of Axendia at the time of publication and are subject to change without notice. Information contained in this post is current as of publication date. Information cited is not warranted by Axendia but has been obtained through a valid research methodology. This post is not intended to endorse any company or product and should not be attributed as such.