Developing Machine Learning Powered Solutions for Cell and Gene Therapy Candidate Validation

There are over 1,000 ongoing clinical trials evaluating the safety and efficacy of cell and gene therapies in a broad array of therapeutic areas. While only a handful of these new therapies have been FDA-approved, biopharmaceutical investors are banking heavily on cell and gene therapy companies and their potential to unleash powerful cures for rare genetic diseases and many cancers. 

However, the infrastructure to support the entire journey from pre-clinical research to approval and commercialization is in its infancy. Most cell and gene therapies being evaluated are in phase I/II trials. They have yet to navigate the scale-up process, and with the current manufacturing capabilities, some may be destined for disappointment. Even therapeutics that have successfully navigated approval face challenges due to excessive pricing and lack of clarity around reimbursement. 

New techniques and technologies are needed to make research, development, manufacturing, and commercialization more efficient. Artificial intelligence (AI) algorithms offer approaches to positively impact these processes, improving the quantity and quality of manufactured products and production efficiency. Here, we explore the use of deep learning (DL) to make cell and gene therapies more manufacturable, advancing them into a new era of innovation. 

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