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

Richard Jaenisch

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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

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, 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.

In this clip from episode 1 of Manufacturing Intelligence, Richard Jaenisch of Open Biopharma discusses uses of AI for deviations and investigations in pharma.

Manufacturing Intelligence Episode 1 - Part 2: Automating Deviations with AI

According to Rick Jaenisch, senior director of Education, Outreach, and Digital Experience at OpenBiopharma, every large life sciences company is currently adopting either proprietary AI tools or enterprise versions of models like ChatGPT, Gemini, and Claude. He notes that Claude has been particularly popular because it features a model specialized for life sciences that has been utilized by the FDA. 

Jaenisch identifies the most significant use case for generative AI in the industry as the management of deviations, specifically by turning traditional paperwork into AI-fillable forms. He explains to Christopher Cole, associate editorial director at 

, that this application can save a writer anywhere from one to four hours per report, which is especially valuable since manual typing is often slowed down by multitasking and distractions. 

In more complex investigations, Jaenisch describes how AI assists teams in gathering disparate data and summarizing video transcripts to support root cause analysis. However, he warns against the rise of "work slop," which he defines as low-effort AI output that can easily be identified through pattern recognition. He points out that this "slop" creates a calculable economic loss because reviewers must spend more time on the back end fixing errors than the original writer saved. To combat this, Jaenisch argues that companies must implement training that goes beyond basic "read and understand" instructions.

Finally, regarding manufacturing, Jaenisch observes that data silos and regulations like the EU’s Annex 22—which restricts probabilistic models in GxP environments—remain significant hurdles. While AI is already established in early drug discovery, Jaenisch is looking toward a future in which "AI-native" pharma companies fully integrate these technologies into the production lifecycle.

Richard Jaenisch

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