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

Richard Jaenisch

, 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.

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

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.

Videos

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.

In this clip from episode 1 of Manufacturing Intelligence, Richard Jaenisch of Open Biopharma discusses step 1 in adopting AI in pharma manufacturing.

Manufacturing Intelligence Episode 1 - Part 1: The First Step in AI Use in Pharma Manufacturing

The integration of AI in pharmaceutical manufacturing has become a critical focus, as evidenced by its prominence at events like 

. Despite this high interest, many industry professionals find themselves in a position in which they understand that they 

 be using AI but are unsure of the practical starting steps or how to implement it effectively. 

According to Rick Jaenisch, the first essential move is a critical evaluation of one's workflow to determine if AI integration offers a genuine benefit to specific tasks. This involves analyzing every step of a process to see where the technology can be realistically put into play.

Currently, many professionals limit their AI usage to basic "Copilot" functions, such as email summarization. Jaenisch tells Christopher Cole that these are low-value trades; once such tools become standard, the baseline for efficiency simply shifts for everyone, providing no unique advantage. Moreover, AI is often "mid" at searching and lacks the ability to summarize verbatim consistently. Because users generally cannot control technical parameters like temperature or 

-value, AI may produce complex, unwanted outputs—such as a 40-page essay when a user only needs a simple list of valves.

Jaenisch also cautions against "work slop," which refers to presenting AI-generated materials, such as presentations or reports, without human oversight or effort. He maintains a general rule: if you would not want to receive low-effort content, you should not produce it. 

To bridge the gap between hype and reality, Jaenisch recommends specialized education tailored for life sciences. Learning what not to prompt is as vital as the prompting itself. Ultimately, AI must be treated as a focused tool used with specific intent to meet business goals.

Richard Jaenisch and Christopher Cole discuss AI in pharma manufacturing, covering learning steps, output evaluation, hallucinations, and FDA/EMA guiding principles.

Manufacturing Intelligence: AI Integration in Pharmaceutical Lifecycle Management

In this inaugural episode of Manufacturing Intelligence—a biweekly, conversational series taking deep dives into all things digital technology in pharma manufacturing and development, Christopher Cole, associate editorial director at 

, sits down with Richard Jaenisch, senior director of Education, Outreach, and Digital Experience at Open Biopharma, to explore the practical integration of AI within pharmaceutical manufacturing and development. 

This discussion is specifically designed for industry professionals seeking to move beyond the hype and toward technical proficiency and begins with a foundational inquiry as Cole asks about the essential first step for professionals learning to use AI in their specific contexts. Jaenisch shares insights into the educational frameworks required to build internal expertise. 

A core focus of the conversation is the necessity of human oversight. Cole poses critical questions regarding how to train professionals to critically evaluate AI outputs and how to determine if a system is operating within its competency or merely “hallucinating confidently.” Jaenisch further explores the pedagogy of limitation—teaching staff to recognize exactly when a model is incapable of answering a question. 

Finally, the dialogue shifts to the regulatory environment, specifically an analysis of the EMA and FDA guiding principles for good AI practice throughout the drug lifecycle. Jaenisch evaluates the applicability of these principles and highlights the specific areas where current guidance may be missing key details. Click the video above to join a comprehensive roadmap for pharmaceutical professionals navigating the intersection of cutting-edge technology and rigorous regulatory standards.

Richard Jaenisch

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