Ready for AI to Transform CMC? Here’s How It Could Happen

AI solutions are already reshaping drug development—with one big exception.

At this point in the hype cycle, we hardly need to say it: AI is poised to deliver potentially transformative benefits for the pharmaceutical industry. In fact, many drug development disciplines have already moved past discussing the many possibilities of AI and are busy putting AI-powered solutions into practice.

For CMC programs though, the story is… well, a little different. Like its cousins in discovery and clinical development, CMC should be at the gates of a transformational new age of AI-enabled efficiency and predictability – were it not for persistent data challenges that keep that golden apple out of reach. The industry is ramping up its efforts to address that issue, inspired by the value waiting to be tapped by successful AI solutions. But we’ve got work to do.

In the meantime, though, I’ll borrow a phrase: “you musn’t be afraid to dream a little bigger, darling.” Let’s take a peek at what an AI-powered future could look like for CMC programs – once they make the right investment in their data.

AI in drug development: Where it’s already making an impact

As you’ve probably noticed, the application of AI technology is already well underway in many aspects of drug development. 

In drug discovery, AI platforms are being used to both identify and engineer molecules with promising clinical potential. In the clinical development enterprise, a host of AI-powered solutions are already transforming trial design, site selection, recruitment, endpoint analysis, and more.

Of course, there’s still a long way to go when it comes to realizing the full potential of AI in any of these areas. AI solutions for clinical trials have likely only scratched the surface of what’s truly possible. And while AI has shown it can accelerate discovery of promising molecules, the first generation of those drug candidates has delivered middling results at best. 

But still, it’s a game-changing start for these areas of the drug development process. Proofs of concept are stacking up quickly, and drug developers are rapidly figuring out how to expand and capitalize on them. 

And then there’s CMC. Sigh.

CMC should be at the gates of a transformational new age of AI-enabled efficiency and predictability, were it not for persistent data challenges that keep that golden apple out of reach. The industry is ramping up its efforts to address that issue, inspired by the value waiting to be tapped by successful AI solutions. But we’ve got work to do.

CMC is falling behind, and the culprit is clear.

While AI may already be making a big impact on discovery, clinical development, and even commercial initiatives, CMC is still waiting for its moment in the sun. And if you follow our blog, it’s no surprise why: It’s all about the data

Not the lack of it, of course. CMC programs continue to generate immense amounts of data. But most of it puddles in SharePoints and inboxes full of PDFs, spreadsheets, PPTs, and Word docs – a galaxy of unstructured formats completely unsuited to powering AI. 

ML, NLP, LLM, or any other model or modality, AI needs a vast amount of high-quality, structured data to deliver its much-hyped benefits. That’s exactly what most CMC programs don’t have enough of, leaving visions of AI enablement on a far more distant horizon than other drug development disciplines.

I’m excited to say that that’s beginning to change. We’re beginning to see many top drug developers commit to structuring their data, connecting their CMC data ecosystems, and purposefully laying the groundwork for AI solutions. And as that effort gathers momentum, we’re starting to see what AI-powered CMC could truly look like. 

Here are a few ways it could come to life for CMC programs that join the shift to data-centric drug development.

Use case 1: Synthetic process design

Drug manufacturing processes are complex, multidimensional workflows with thousands of inputs, variables, parameters, and risk factors. Like all such complex entities, developing them takes a healthy amount of experimentation, exploration, and “what-if” scenarios. 

Necessary as that may be, however, it takes time and resources that drug developers have less and less of every year. 

Luckily, as clinical trial innovators have already shown, today’s data models and algorithms are more than up to the task of simulating incredibly complex processes and scenarios. Data-centric CMC programs have a scintillating opportunity to take a page from that playbook, and use AI to design and run simulations of their own sophisticated processes or systems. 

We’re beginning to see many top drug developers commit to structuring their data, connecting their CMC data ecosystems, and purposefully laying the groundwork for AI solutions. And as that effort gathers momentum, we’re starting to see what AI-powered CMC could truly look like.

One compelling version of this application is already beginning to take shape: creating “digital twins” of a manufacturing process based on historical data of similar products. With enough of that (high-quality, structured) data, a CMC program could potentially design, simulate, and pressure-test processes 100% in silico, saving time, reducing risk, and accelerating PD timelines.

In fact, this approach might make a host of questions more efficient to answer: what kinds of materials are best to use, which attributes of the product will be critical to its quality outcomes, how changing any single variable or parameter might affect the other components, and more. And crucially, it could be done virtually, using a fraction of the time and resources that drug developers currently expend experimenting, analyzing, and answering similar questions. 

Of course, the caveat remains: The success of the digital model—and the accuracy and reliability of the simulations it generates—all depends on the quality of the data that it’s trained on. But for drug developers who can harness the right amount of high-quality CMC data, the payoff could be significant.

Use case 2: Streamlined studies & experiments

Digitally modeling entire processes feel a bit distant for your program? You might be able to start much smaller: some AI tools could potentially be used to streamline the generation of key data on stability, solubility, toxicity, and much more. 

In today’s CMC programs, the studies and experiments used to produce this data typically lay the groundwork for much of the manufacturing processes that follow. But they’re undeniably time-consuming and expensive in their current state. That’s where AI solutions can offer a potential shortcut. 

As software engineers and data scientists have already discovered, generative AI tools can be highly effective at extrapolating new code, datasets, and analyses from past data – all you need is the right prompt. Well-trained LLMs may hold similar potential for CMC programs: feed them enough historical data on similar products and prompt them based on your study design, and it’s feasible that a robust model could produce either largely accurate testing data. Or at the very least, data directional enough to refine the study design.

Whether it provides usable results or “just” valuable guidance, this capability could cut down on the overall time and cost currently required to conduct this iterative testing today.

Use case 3: Generative regulatory submissions

The regulatory submission process is another critical but time-consuming step in the CMC process – and one that’s primed for AI optimization. 

Many of the industry’s hallmark regulatory documents – including modules of the NDA, IND, and eCTD – are heavily templated, making them ideal for automation. If you’ve tried the smart content authoring tool in QbDVision, you’ve already gotten a taste of how much more efficient automated reporting and document generation can be. Generative AI tools have the potential to vastly amplify the power of solutions like ours.

Trained on enough structured submissions – the kind regulators are actively moving toward – a specialized LLM could conceivably be used to generate net-new submissions with impressive accuracy and consistency. With that kind of AI tool, document-generating tasks that typically take weeks could potentially be reduced to mere minutes – freeing up time that’s usually spent chasing data, consolidating documents, and MacGyvering modules.

The regulatory submission process is another critical but time-consuming step in the CMC process – and one that’s primed for AI optimization. Many of the industry’s hallmark regulatory documents – including modules of the NDA, IND, and eCTD – are heavily templated, making them ideal for automation.

Use case 4: Accelerated risk analyses

Along with regulatory submissions, analyzing and reporting on process risks is often one of the more laborious, time-consuming tasks in today’s CMC programs. 

All too frequently, that’s because risk-related data is scattered across departments and even facilities – even for established products and platforms. So analysts often have to start new analyses from scratch (or nearly so) each time. 

But what if you could train an ML algorithm on all the existing, structured risk analyses from that product or platform’s previous CMC programs? If you had that kind of training data, you could potentially create a tool with deep awareness of past risks and how they evolved, and the ability to predictively identify how those risks would evolve with modified product parameters. 

QbDVision functionality is already moving in this direction, starting with a clone-forward feature that enables users to build new risk analyses on a foundation of historical structured data. But as with regulatory documentation, an LLM trained on enough structured risk analyses could potentially have an even bigger impact, enabling CMC programs to generate brand new analyses based on specified attributes, parameters, and materials.

And of course, one of the built-in advantages of AI technology is that it’s designed to become more precise and reliable over time. The more you use it, and the more high-quality data inputs you feed it, the better the output. For CMC programs, that could translate to significant time savings and increasingly accurate risk analyses over time.

Use case 5: Predictive risk control

As all these potential use cases demonstrate, AI technology and automation capabilities come with two huge perks that CMC programs could profit from: time and cost savings. 

But those aren’t the only long-term benefits that CMC workflows stand to gain from investing in the data infrastructure needed to develop AI solutions. AI also has the potential to help CMC programs better understand, adapt to, or proactively control risks throughout the drug manufacturing process. 

For example, with enough high-quality, structured risk data, it’s feasible that AI solutions could predictively extrapolate future risks and proactively recommend process adaptations, parameter changes, causality changes, and more. Imagine being able to map risks and model control strategies throughout an entire process, all using AI tools trained on historical analyses from analogous products. Today’s ML algorithms are just waiting for the data. 

Yes, that kind of predictive ability and risk control would also likely yield time savings of its own in the long run. But it will also aid decision-making, alert scientists to risks that may not be on their radar, and overall contribute to a more robust risk management strategy and manufacturing processes that are more dynamic, adaptable, and proactive than ever before.

A closer look at LLMs: It takes a lot to make these silver bullets fly.

While there are many good reasons to get excited about these use cases, we’ll happily be the ones to inject a cold dose of reality into the picture. Here it is: AI, ironically, takes a ton of work.

Perhaps the best example is LLM-powered generative AI: By far the shiniest AI object lighting up the industry right now. 

Ask Claude to run a risk analysis for you, and you’ll quickly see why adapting LLMs for specialized purposes requires intensive additional training with substantial amounts of data – tens of thousands of examples, according to one recent publication. That’s because most off-the-shelf, generalist foundation models are trained on vast amounts of general information. Reliable, business-specific use cases typically require additional training on large quantities of robustly labeled, domain-specific data.

Creating the data to even get started is no mean feat. As this recent experiment from Cleanlab demonstrated, fine-tuning an application-specific LLM can require multiple rounds of both manual and automated data curation to achieve “acceptable” rates of accuracy (~75%…). Now imagine multiplying that effort by tens of thousands of CMC datasets and… there’s a chair right over there if you need to sit down for a moment. 

Ponder that scenario for a moment, though, and you’ll quickly see why some AI thought leaders are beginning to ask if generative AI may be creating more work than it saves. They require so much high-quality data, and the labor required to curate it can be so immense, that – paradoxically – there may ultimately be domains where it’s more efficient to not use AI. 

We’re a long way from making that determination for CMC, of course. But as our industry begins to explore the potential of AI for process development, it’s worth reminding ourselves of what that investment will entail. Does AI have the potential to unlock a transformative new level of performance for CMC programs? Absolutely. But will it be as simple as Sam Altman says? 

We’ll let you take a look at your SharePoint.

GET IN TOUCH

Let’s get your CMC program on the AI onramp.

Reach out to our team to learn how QbDVision can help you lay the groundwork for automated and generative solutions.

Laurent Lefebvre - Headshot

Laurent Lefebvre

RA CMC Director, Novartis

Laurent is the Director of RA GDD CMC at Novartis. With over 10 years of experience working as a worldwide Regulatory CMC Project Lead on blockbuster brands, he is an expert in the entire CMC product lifecycle in global regulatory environments. Laurent has been a core team member of the Novartis Regulatory Strategy and Intelligence for IDMP since 2014 and a member of the EFPIA ICH M4Q support team. He is involved in regular collaborations cross-industry (IDMP roundtables, Pistoia Alliance), digital initiatives (RIM structured authoring, master data & PLM), reviewer of the ISO IDMP guidelines and a Novartis contributor to regulatory intelligence discussions.
Laurent Lefebvre - Headshot

James Maxwell

Life Sciences Innovation Lead, Accenture

James Maxwell is an Innovation Lead at Accentures Global Centre for R&D and Innovation. He leads strategic innovation programs with global Life Sciences organizations to solve challenges, rapidly prototype and prove value for future solutions across the end-to-end Life Sciences value chain. With a background in design, research and innovation strategy he has worked with multiple organizations to take an innovation approach for solving challenges across CMC.
Paul Denny-Gouldson - Headshot

Paul Denny-Gouldson

CSO, Zifo

Paul is the CSO at Zifo RnD Solutions, a global specialist scientific and process informatics service provider working across research, development, manufacturing and clinical domains. He obtained his Ph.D. in Computational Biology from Essex University in 1996 and started his career as a Post Doc, and subsequently Senior Scientist at Sanofi-Synthelabo Toulouse (now Sanofi) for five years, where he managed a multidisciplinary molecular and cell biology department. He has also founded a number of companies focused on combining science, technology and business, and authored more than 25 scientific papers and book chapters.
Chris McCurdy

Chris McCurdy

Chief Architect of Healthcare and Life Sciences at Amazon Web Services

Chris McCurdy serves as Chief Architect of Healthcare and Life Sciences (HCLS) for Amazon Web Services (AWS), where he leads teams responsible for architecting cutting-edge services, unlocking data assets, and opening novel analytics capabilities for customers. With over 20 years of industry experience, Chris plays a key role in envisioning and developing innovative solutions and services that accelerate customer value while improving patient outcomes.
Isabell Hagemann Headshot - Digital CMC Basecamp - QbDVision

Isabell Hagemann

Scientific Assistant, Biological Development, Downstream, Bayer AG

Isabell Hagemann is a biochemical engineer by training and has worked at Bayer AG in the biological development downstream department in 2017. In that time, she has worked on process development, process characterization, and the technology transfers of several biologics using high-throughput development systems, modeling approaches, and knowledge management tools.

Ganga Kalidindi

Global Head TRD Data Assets & Insights, Novartis

As the Global Head TRD Data Assets and Insights at Novartis, Ganga Kalidindi brings a unique combination of Information Technology and Product Development expertise to delivering in a regulatory landscape. Throughout his career, he has striven to make direct positive impact on business providing leadership that creates cross-functional high-performing teams. Focusing on complex business and technical challenges, leading through change, and creating success that takes programs and companies to a winning status.
Fran Leira Headshot - Digital CMC Basecamp - QbDVision

Fran Leira

Global Head of Process Engineering CoE, CSL Behring

Fran Leira is a biopharma Professional with over 20 years of experience in QC, MSAT/Tech Ops at companies like Genentech, GSK, Merck, and Lonza where he supported Product and Process Lifecycle Management at site-based and global roles. He is currently the Global Head of Process Engineering CoE at CSL Behring.

Florian Aupert Headshot - Digital CMC Basecamp - QbDVision

Florian Aupert

Lab Head, Biological Development, Bayer AG

Florian has a B. Sc. and M. Sc. in pharmaceutical biotechnology with a focus on bioprocess engineering. Since 2018, he’s worked at Bayer AG in Biological Development, concentrating on portfolio program management and tech transfer.

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.

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

Director of Product Management, QbDVision

Joschka Buyel is the Director of Product Management at QbDVision. He was previously responsible for the rollout and integration of QbDVision at Bayer and 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.)

Speaker Name

Speaker’s Pretty Long Title, Specialty, and Business

Lorem ipsum dolor sit amet, consectetur adipiscing elit. Aliquam dignissim velit et est ultricies, a commodo mauris interdum. Etiam sed ante mi. Aliquam vestibulum faucibus nisi vel lacinia. Nam suscipit felis sed erat varius mollis. Mauris diam diam, viverra nec dolor et, sodales volutpat nulla. Nam in euismod orci.