Developing and Deploying Digital & AI Solutions in Pharma: The Reality vs. the Hype

Advances in extreme ultraviolet lithography have unlocked the computational power needed to produce chips and GPUs capable of training and deploying GenAI models that are reshaping the way we work and interact with technology. These breakthroughs have been both frightening and inspiring for the manufacturing industry. The ability of these models to process massive amounts of data and deliver insights in ways humans can easily interpret is truly remarkable.

Yet, because AI has not been part of foundational education curricula, widespread misunderstandings persist—even among technically skilled professionals outside the AI field. Market pressures to include “AI” in products and business strategies are also creating significant pressure on executive teams to invest in AI applications without a clear understanding of potential use cases, implementation requirements, or expected return on investment.

In this webinar, I will explore key considerations for integrating various forms of AI—not just GenAI—across the pharmaceutical value chain, with a particular focus on manufacturing and quality. My goal is to provide executives and managers with actionable insights to define realistic strategic objectives, achieve measurable ROI, and avoid common pitfalls in AI adoption.

Presented by Saly Romero-Torres, Ph.D. Owner, Hyperplane LLC

Saly Romero-Torres, Ph.D. is an experienced senior leader with over 20 years of expertise in advanced manufacturing and strategy. She has a diverse track record spanning digital transformation, operations, global network strategy, strategic planning, financial management, quality systems, regulatory compliance, data analytics, artificial intelligence, workforce development, and operational excellence.

She is a proud former Board Member of the Chemical Sciences and Technologies Board at the U.S. National Academies, recognized for shaping science and technology policy at the national level. Saly has authored and co-authored more than 20 publications in advanced manufacturing.

She is the founder and owner of Hyperplane LLC, a consulting firm dedicated to helping customers develop and operationalize their digital and AI strategies.

Crawl, Walk, Run – On the Journey of Delivering AI and Maximizing Value as a Biopharmaceutical CDMO

The topic of Artificial Intelligence in Biopharmaceutical CMC has been well-explored from the innovator’s perspective. Many innovators across large and small pharma/biopharma have described strategies and use cases where AI has the potential to reshape the business, significantly accelerating the delivery of medicines while reducing cost. Perhaps less explored is the potential AI has in reshaping the way CDMOs operate creating additional acceleration and supply agility. This talk will describe how one CDMO views the potential of AI and where the technology could transform the delivery of medicines to patients.

Presented by Dan Hill, Director, Process Analytics at Fujufilm Biotechnologies

Dan Hill is a senior digital leader and Process Analytical Technology (PAT) expert with extensive experience developing and executing global digital transformation strategies, within and across CMC and R&D organizations. He is passionate about enabling seamless operations and high productivity through digital application and employee up-skilling and is committed to capturing business value by integrating processes, people, systems, and products with the right data and digital solutions at the right time.

Dan is currently a Director of Process Analytics at FUJIFILM Biotechnologies in Holly Springs, NC. He has served in prior roles at Thermo Fisher Scientific, Biogen, Boehringer-Ingelheim, Cargill North America, and Eli Lilly and Company. Dan earned a Bachelor of Science from Ball State University and an MBA from NC State University. In his spare time, Dan enjoys spending time with his family and running.

From Silos to Synergy: Leveraging Data Flow and Intelligent Automation Across the Pharma Value Chain

In today’s pharma landscape, the flow of data between R&D and manufacturing is transforming how organizations achieve continuous improvement and innovation. Rather than linear workflows, it is the bidirectional journey of data, enabling knowledge transfer, process optimization, and real-time decision-making, that drives progress. This webinar explores how intelligent orchestration, advanced analytics, and automation empower scientific managers and heads of manufacturing to break down silos and foster a truly connected ecosystem. Attendees will discover practical examples, from closed-loop experimentation and high-throughput screening to real-time process control and distributed manufacturing, illustrating how integrated platforms and vendor-agnostic solutions support end-to-end digitalization. We will highlight the importance of robust data strategies, standardization, and partnerships in building AI-ready environments where scientists are augmented, not replaced, by technology. Join us to learn how leveraging data across the value chain accelerates innovation, enhances quality, and enables continuous learning from discovery through to production and back.

Presented by Paul Gillham, Innovations Director, SciY

Paul is the Director of Innovation at SciY where he leads a team of scientists, mathematicians and engineers. He has over twenty years of experience in the implementation of digitalization for life sciences, notably in the field of PAT (Process Analytical Technologies), with a focus on data management, analysis and communication. He read Physics at the University of Bath and has worked in different industries before joining Optimal, to work on the synTQ suite of software. In April 2022 Optimal was purchased and placed into the SciY group of companies, which has a broad portfolio of software applications within its data management platform. His main aim at SciY is to advance data use, analysis and to action control of our customer’s processes, based on understanding and good science. 

Smarter biomanufacturing

Delivering interconnected, intelligent, and predictable bioprocessing

The rapid proliferation of immature digital tools and heterogeneous artificial intelligence (AI) platforms in the biopharmaceutical sector poses the risk of introducing operational complexity, redundant functionalities, suboptimal solution performance, and substantial challenges in system integration and lifecycle management. To mitigate these risks, Cytiva is developing a new ecosystem of smarter biomanufacturing assets that represents a next-generation, fully integrated digital manufacturing framework unifying both physical and digital bioprocessing components. This architecture is being designed to enable seamless interoperability, real-time data acquisition, and AI-driven process intelligence to enhance control, consistency, and predictability in bioproduction. Inspired by advanced digitized industries, including automotive, electronics, and semiconductor manufacturing, this paradigm emphasizes comprehensive system connectivity, continuous monitoring, and autonomous optimization. By embedding intelligent capabilities directly within bioprocess hardware, the platform delivers actionable insights across the entire bioprocess workflow and supports scalable, user-friendly implementation suitable for diverse manufacturing environments. Collectively, smarter biomanufacturing establishes a robust foundation for interconnected, intelligent, and highly predictable bioprocess operations, thereby accelerating the digital transformation of biopharmaceutical manufacturing and enabling organizations to realize value more rapidly.

Presented by Ashley Howard, Sr. Director, Product Management A&D at Cytiva

Ashley Howard is Senior Product Director, Automation & Digital at Cytiva, advancing smarter, more connected biomanufacturing through practical digital and analytics-led solutions. Earlier in her career, she worked in the semiconductor industry and spent time in biologics manufacturing—experience that shaped her focus on scalable, disciplined technology deployment in regulated environments. She is passionate about cross‑industry collaboration and real-world adoption, helping translate complex manufacturing needs into approaches teams can implement and sustain. In this Biopharma Asia forum webinar, Ashley will discuss how AI can complement Process Analytical Technology (PAT) to strengthen process understanding and improve decision-making.

Streamlining Bioprocess Development: A Novel Perspective on the Role of Hybrid Models and Risk-Based Optimal Experimental Designs

In the rapidly evolving field of pharmaceutical bioprocess development, the application of hybrid models represents a transformative opportunity to enhance process understanding, while highly improving R&D effectiveness through a significant reduction of experiments and resources. These capabilities are further enhanced by (i) transfer learning, which enables the effective reuse of historical process data—across different products, scales, and equipment, to inform the development of models for new products; and (ii) through Pareto-based Bayesian optimization, which effectively leverages model prediction confidence to enable optimal, risk-informed decisions—such as designing new experiments.

These technologies represent the core of the “DataHow methodology”, which closely resembles the well-known PDCA cycle used in lean development and manufacturing. In this context, the novelty is represented by the introduction of ML tools embedded into our hybrid models. This is reshaping the scope of models from a tool to understand and closely reproduce mature production processes, to a tool that is continuously learning the features of new processes while providing continuous support for decision making, from the early screening phases to the tech transfer to manufacturing. The overall outcome of embracing such methodology is not only more effectiveness, but also an enhanced flexibility in all phases of the product life cycle, with more robust decisions and shorter time-to-decision.

In our talk, many study cases from different modalities, from mAbs to cell & gene therapies, and from different unit operations will be discussed.

Presented by Dr. Alessandro Butté, CEO DataHow

Alessandro Butté is a chemical engineer, entrepreneur, and CEO of DataHow, with over 15 years of experience in the pharmaceutical industry and in developing machine learning solutions for manufacturing processes.

He earned his Ph.D. in Chemical Engineering from ETH Zurich in 2000, followed by a postdoctoral fellowship at the Georgia Institute of Technology. He later returned to ETH Zurich as a senior researcher in the group of Prof. Morbidelli.

In 2008, Alessandro joined Lonza (Switzerland), where he led the downstream technologies group. He returned to ETH Zurich in 2013 as a senior lecturer and researcher to lay the groundwork for a new venture, DataHow.  Alessandro is the author of several patents and has published over 100 peer-reviewed articles.

Followed by a live Question and Answer session

Sponsored by SciY, Cytiva and DataHow

About SciY

SciY is a brand of Bruker, born from collaborations and majority-acquisitions of renowned vendor-agnostic software partners such as Mestrelab Research, Arxspan, Optimal Industrial Technologies, Optimal Industrial Automation, LOGS and ZONTAL. As a vendor-agnostic platform, SciY offers a broad range of software solutions across the life sciences research, development, and manufacturing automation and QC functions – easy to integrate, flexible, user-centric, and uncompromisingly performant. By streamlining data analysis and management, SciY empowers scientists and researchers, accelerates scientific discoveries, and enables precise decision-making. For more information, visit www.sciy.com    

About Cytiva

At Cytiva, our mission is to advance and accelerate the development of therapeutics. With nearly 16,000 associates in more than 40 countries, we’re driven to use our expertise and talent to achieve better flexibility, capacity, and efficiency for our customers. Our broad and deep portfolio of tools and technologies, global scale, and best-in-class service provides critical support from discovery to delivery, for customers spanning researchers, emerging biotech, large-scale biopharma, and contract manufacturers. Learn more at cytiva.com.

About DataHow

DataHow is a pioneer and leader in ML/AI-driven process modeling, combining first-principles process knowledge with data-driven machine learning to extract maximum insight from experimental and manufacturing data.

Founded at ETH Zurich in 2017, DataHow is a Swiss technology company dedicated to democratizing the extraction of meaningful value from process data through hybrid modeling technologies embedded in its flagship intelligence platform, DataHowLab. The platform supports a wide range of bioprocess modalities, including mammalian, microbial, mRNA, as well as cell and gene therapies.  

DataHow works closely with biopharmaceutical companies, academic institutions, and technology partners to translate advanced modeling methods and reliable digital twin technologies into practical, deployable solutions that advance the Pharma 4.0 agenda.