Home AI in Cell and Gene Therapy Manufacturing: Enhancing Cost-Efficiency and Scalability Through Data, Equipment, and Robotics
2025

AI in Cell and Gene Therapy Manufacturing: Enhancing Cost-Efficiency and Scalability Through Data, Equipment, and Robotics

by cyb2025
K. Harris
Autologous Cell Therapy Manufacturing
Cell Therapy Cost Reduction
ell Therapy Automation
Machine Learning for Cell and Gene Therapy
Robotics and Automation in CAR-T

KENNETH HARRIS
Chief Strategy and AI Officer, OmniaBio Inc., Hamilton, Canada

ABSTRACT

The clinical adoption of cell and gene therapies remains primarily focused on rare diseases and patient populations for whom conventional treatments have failed. Despite significant advancements in demonstrating clinically meaningful responses, these therapies are hindered by high manufacturing costs, complex cold chain logistics, and intricate post-treatment care. This review identifies where implementation of specific use-cases for artificial intelligence and robotic automation can address the highest impact challenges for reducing costs and streamlining processes.

Introduction

Autologous Chimeric Antigen Receptor (CAR) T-cell therapy represents the most significant class of approved cell and gene therapies to date. However, regulatory approvals thus far have been in specific hematologic malignancies. The promising and demonstrated clinical efficacy of CAR-T therapies in blood cancers is also driving substantial investment toward expanding their applications to solid tumors and autoimmune diseases – meaning scaling of dose throughput will be mandatory to meet the target patient populations. So, since CAR-T therapies account for over 50% of the preclinical and clinical development pipeline within cell and gene therapies, and the growing number of potential patients with the new indications, the necessity for optimizing manufacturing processes and improving patient accessibility has reached a necessity.

 

One of the major challenges facing autologous CAR-T therapies is their prohibitive cost, exceeding $1 million USD per treatment (1), which limits access to patients with both socioeconomic means and proximity to advanced medical centers. Integrating machine learning (ML) into patient selection and manufacturing processes offers a promising avenue for cost reduction and enhanced therapeutic efficacy. By leveraging ML algorithms to identify patients most likely to achieve a positive clinical response and implementing scalable, high-throughput manufacturing strategies such as robotics, the industry can significantly enhance the financial feasibility of CAR-T therapies. Specifically, by focusing treatment to patients whose T cells can generate a high-quality drug product and producing CAR-T cells within roboticized cleanroom environments, cost reductions of approximately 50% should achievable alongside improved therapeutic outcomes.

ABOUT THE AUTHOR

Ken Harris has dedicated over three decades to revolutionizing healthcare. With a profound expertise spanning 35 years in the field, Ken has been at the forefront of innovation and leadership in technology, medical devices, and therapeutics.
Ken currently serves as the Chief Strategy Officer and Head of AI at OmniaBio, an Ontario-based, technology-focused cell and gene therapy contract development and manufacturing organization. In this role, he leads the company’s corporate strategy and AI team, ensuring sustainable growth and cutting-edge implementation, safety, and scalability in healthcare robotics.
Previously, Ken spearheaded the Academic Medicine and Public Health vertical within Amazon’s $90 Billion annual revenue group – Amazon Web Services. He charted new territories by developing the business from its inception leading a dynamic team comprised of Principal Trusted Healthcare Advisors, Physician Advisors, Health Informaticists, and Healthcare IT architects.
Prior to his tenure at Amazon, Ken’s illustrious career showcased his adeptness in executive leadership. He notably founded and successfully took public a pioneering cell and gene therapy precision medicine company, marking a milestone in the advancement of cell therapy in cardiovascular medicine. Additionally, he played C-level and Board roles with a Stanford University cell and gene therapy start-up, and led Pall Corporations global biomedical business for 13 years (now a Danaher company).
Ken’s commitment to staying at the forefront of healthcare innovation is evidenced by his completion of a certification in Healthcare AI/ML from Harvard Medical School. Ken serves on several US Federal Advisory Groups, including most recently the National Institutes of Health’s Advanced Research Program Agency for Healthcare, and the newly empaneled Coalition for Health AI a 501(C) organization to drive safe and valid deployment of AI in healthcare.

Latest issue

September / October 2025

View Issue

Archive

Lorem ipsum dolor sit amet, consectetur adipiscing elit, sed do eiusmod tempor incididunt!

Chemistry Today - Archive
Go to archive

Content from magazine

Lorem ipsum dolor sit amet, consectetur adipiscing elit, sed do eiusmod tempor incididunt ut labore et dolore magna aliqua. Qui blanditiis praesentium voluptatum deleniti atque corrupti quos dolores.
View all content

The most read

@2019 - All Right Reserved. Designed and Developed by PenciDesign

Login