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 ...