Senior Director
Education, Outreach, and Digital Experience
Open Biopharma Research and Training Institute

Richard Jaenisch

Industry experts from Sanofi, GxP Frame, and Open Biopharma outline how AI is transforming drug discovery, manufacturing, and workforce strategy through biopharma partnerships in the latest episode of Drug Digest.

Drug Digest: How Advanced Analytics Partnerships Enhance the Biopharma Value Chain

, global head of Computational and AI Strategy for R&D at Sanofi, 

, founder and principal consultant of GxP Frame, and 

, senior director of Education, Outreach, and Digital Experience at Open Biopharma Research and Training Institute, discuss artificial intelligence (AI) integration across drug discovery, 

, supply chain operations, and workforce development in this special combined episode of 

Truppo describes AI's primary value as connecting signals across previously siloed data types, including molecular structure, biological assay results, patient demographics, and clinical site performance, to improve scientific decision quality rather than generate autonomous answers. At Sanofi, for example, disease-specific target identification engines integrate genetics, proteomics, transcriptomics, and single-cell data to help researchers determine target druggability, tissue expression, and optimal therapeutic modality, he explains. 

All three experts emphasize that human oversight must be structurally embedded in AI workflows, not applied as an afterthought. 

How should biopharma companies govern AI tools to maintain data integrity and regulatory compliance?

, Drapeau emphasizes that the most valuable AI applications are not headline-grabbing but operational, such as deviation management, batch record review, predictive maintenance, and supply and demand forecasting. In cell and gene therapy (CGT), specifically, he notes that supply chain is patient-specific, making chain of identity and chain of custody the true measures of AI's value. He also notes that no single manufacturer owns the full CGT supply chain, making cross-company partnerships essential.

Drapeau adds that effective partnerships between biopharma and AI tech and/or advanced analytics companies require a partner capable of integrating across multiple domains while maintaining strict data governance. "Completion is not competency," he stresses, cautioning against operators who can execute AI-assisted tasks but lack the independent judgment to troubleshoot or critically evaluate model outputs.

All three experts emphasize that human oversight must be structurally embedded in AI workflows, not applied as an afterthought. Jaenisch states that explainability is non-negotiable, cautioning that AI outputs used in regulated environments must be traceable and defensible to inspectors.

“There are [many] pieces that an explainable AI system is able to retrieve and provide with every output, and if you do not have that, then you are probably not going to have a good time when an audit comes,” he explains.

Truppo adds that data governance, source traceability, and clearly stated model uncertainty are prerequisites for trustworthy AI integration across biopharma R&D and manufacturing operations when dealing with a strategic partner.

Matt Truppo, Global Head of Computational & AI Strategy, R&D, Sanofi

As the global head of Computational and AI Strategy at Sanofi, Dr Truppo oversees platform capabilities across all therapeutic areas and modalities to advance the company’s drug discovery ambitions. He focuses on embedding AI and predictive technologies throughout R&D to accelerate timelines and enable the development of first-in-class therapies. Prior to Sanofi, he led Protein Therapeutics API Development and Biologics Discovery at Johnson & Johnson and spent more than a decade at Merck working across both small- and large-molecule discovery and development. He holds a PhD in Chemistry from the University of Manchester and undergraduate degrees in Chemical Engineering and Engineering Biology from Princeton University. Over a 20+ year career, Truppo has contributed to bringing 13 medicines to market and advanced many more into the clinic, a quest driven by his commitment to improve global health through innovative science.

Richard Jaenisch, Senior Director of Education, Outreach, and Digital Experience, Open Biopharma Research and Training Institute

Jaenisch has spent the past 15 years in patient advocacy since he began and fought off end stage liver disease and more than 15 years in STEM education, marketing, and consulting. Among his duties at Open Biopharma, he is leading several data projects regarding generative AI integration and exploring generative AI in biomanufacturing training. At Prompting Integration and Consulting LLC, he develops training courses and works alongside his brother, a seasoned AI/ML engineer to develop bespoke generative AI integrations for business in life sciences and healthcare and deliver educational programming on generative AI.

Brian Drapeau, Founder and Principal Consultant, GxP Frame

A private consultant to some of the largest biotech companies in the industry, Drapeau brings more than 5 years of experience in workforce development and training across the biotechnology and pharmaceutical sectors. His expertise includes new hire training, instructional program development, contract development and manufacturing organization (CDMO) training requirements, and high-volume ramp-ups. Drapeau also authors 

, a newsletter covering FDA enforcement trends, CDMO training failures, and pharmaceutical manufacturing operations.

® Group editors, who interview industry experts to discuss the emerging opportunities, obstacles, and advances in the pharmaceutical and biopharmaceutical industry for the research, development, formulation, analysis, upstream and downstream processing, manufacturing, supply chain, and packaging of drug products.

Videos

In this episode of Drug Digest, experts from Sanofi, GxP Frame, and Open Biopharma explain how AI is transforming drug discovery, manufacturing, and workforce strategy and how strategic partnerships play a role.

Screenshot 2026-06-12 at 1.20.08 PM.png

CAI's Jon Thompson explains how digital twins cut cleanroom rework by up to 50% and pressure excursion investigation time by up to 70%.

Manufacturing Intelligence Episode 4: Cleanroom Digital Twins

, co-hosts Chris Cole and Richard Jaenisch explore the emerging role of digital twins in cleanroom design and qualification in pharmaceutical manufacturing with guest Jonathon Thompson, global director of the digital center of excellence at CAI.

To view the full fascinating, practical, engaging conversation, click here!

Digital twins of cleanrooms are offering measurable reductions in commissioning time, rework, and pressure excursion investigation, but only when paired with rigorous real-time data verification, explains Thompson. He adds that the technology's core value lies not in replacing IQ/OQ/PQ, but in catching design flaws before construction locks them in.

The traditional cleanroom qualification workflow exposes problems late, Thompson notes, often after equipment is already installed and early-phase processes are running. Digital twins, fed with 3D building information modeling (BIM) models, HVAC specifications, personnel flow data, and physics-based fluid dynamics simulations, allow manufacturers to stress-test facility designs upstream. Thompson points to tangible returns: 10–30% reductions in commissioning time and resource hours, 20–50% reductions in rework, and 30–70% faster root cause identification during pressure excursions.

Regulatory acceptance, Thompson argues, depends on how the twin is used. "If you're using this digital twin as your single source of truth without verifying that data in real time with what's actually happening out in the field," he warns, "they're going to start to question it." Continuous process verification—cross-referencing the model against live environmental monitoring data, particle counts, and pressure differentials—is what earns FDA and EMA confidence in the approach.

For CDMO and modular facilities, where equipment configurations and product mixes change frequently, the stakes around model maintenance are especially high. Thompson observes that "we've got to change the mindset" to treat the BIM model as a living asset subject to change management, not a static deliverable from the capital project phase. As generative AI and light detection and ranging-based reality capture mature, automated model updating may eventually reduce that burden, making the digital twin a true operational constant rather than a commissioning tool.

CAI's Jon Thompson details how digital twins cut cleanroom commissioning time by up to 30% and accelerate GMP deviation investigations.

Manufacturing Intelligence: How Digital Twins Are Changing Cleanroom Validation

, co-hosts Chris Cole and Richard Jaenisch explore the emerging role of digital twins in pharmaceutical container systems with guests Mihaela Simianu, president of Concordia MCS and scientific advisor to SmartSkin Technologies, and Austin Caudle, vice president at SmartSkin Technologies.

The discussion centers on how “smart” pharmaceutical container replicas—equipped with sensors and data-capture capabilities—can help manufacturers better understand how vials, syringes, cartridges, and other container closure systems interact with manufacturing equipment throughout the product lifecycle. Simianu explains that while today’s technology is not yet a fully virtualized digital twin, these physical smart replicas provide critical real-time data about seal forces, equipment interactions, and operational stresses that can impact product quality and patient safety.

The guests emphasize that container intelligence can play a significant role across development, clinical manufacturing, commercial scale-up, and tech transfer between manufacturing sites and CDMOs. By using sensor-enabled replicas to monitor equipment-container interactions, companies can reduce glass breakage, minimize downtime, optimize line setup, and better maintain container closure integrity throughout production and distribution.

A key theme throughout the conversation is risk reduction. Simianu and Caudle argue that integrating these tools early in development creates continuity as products move between facilities and scales, helping manufacturers preserve process understanding and reduce variability tied to changing equipment environments.

The episode also highlights broader industry trends toward data-driven manufacturing, predictive analytics, and digital transformation, positioning smart container technologies as foundational building blocks for the future of pharmaceutical digital twins and advanced manufacturing intelligence.

Mihaela Simianu and Austin Caudle join the show to discuss how digital container twins can improve pharma manufacturing, validation, and risk reduction.

Manufacturing Intelligence: Digital Twins Meet Pharma Containers

Manufacturing Intelligence Episode 3 - Digital Twins of Containers

In this episode of Manufacturing Intelligence, we break down digital twin use in pharma, from risk reduction and scale-up modeling to AI integration and virtual inspections.

Manufacturing Intelligence: Unpacking Digital Twin Use in Pharma Manufacturing

In this episode of Manufacturing Intelligence, Rick Jaenisch, Senior Director of Education, Outreach, and Digital Experience at Open BioPharma and Chris Cole, Associate Editorial Director at 

, offer a wide-ranging explainer on digital twins in pharmaceutical manufacturing, covering definitions, current applications, practical requirements, and future possibilities.

Jaenisch grounds the discussion in the FDA's own definition, describing a digital twin as information constructs that mirror a physical asset and are dynamically updated with real-world data, emphasizing the crucial distinction between a static virtual model and a true digital twin. "If it isn't updated with either real time or somewhat periodic updates... it's not really a digital twin,” he says. “It's a virtual model."

The conversation highlights two primary use cases: risk reduction and scale-up modeling. By running hundreds or thousands of simulations virtually, manufacturers can identify potential errors and optimize processes before committing to a physical run, effectively letting "the computer fail" cheaply and quickly. Facilities management, AR-assisted operator training, and inventory tracking via RFID are identified as active applications today.

Scaling digital twins to full manufacturing lines requires robust data unification across building management, laboratory information management systems, and inventory systems, a significant integration challenge. Jaenisch identifies AI as essential, noting that "Digital twin without AI is pretty much just a virtual model." Generative AI's ability to find the needle in a haystack makes it indispensable for anomaly detection at scale.

Looking ahead, Jaenisch sees virtual inspection—in which regulators conduct non-disruptive audits through a real-time digital twin—as a compelling frontier. He also flags a persistent human challenge: first-generation employees often resist the increased sensor and camera surveillance that digital twins require, even when the monitoring is automated rather than human-led.

Richard Jaenisch

Articles