Harnessing the power of artificial intelligence to advance cell therapy

2023/07/06 by Sara Capponi, Kyle G. Daniels · 2 citations
Biochemistry, Genetics and Molecular Biology · Medicine · #CAR-T cell therapy research #CRISPR and Genetic Engineering #Viral Infectious Diseases and Gene Expression in Insects

paper · pdf · doi:10.1111/imr.13236

openalex publication_date 2023/07/06 · openalex created_date 2025/10/10 · openalex updated_date 2026/07/28

Abstract

Cell therapies are powerful technologies in which human cells are reprogrammed for therapeutic applications such as killing cancer cells or replacing defective cells. The technologies underlying cell therapies are increasing in effectiveness and complexity, making rational engineering of cell therapies more difficult. Creating the next generation of cell therapies will require improved experimental approaches and predictive models. Artificial intelligence (AI) and machine learning (ML) methods have revolutionized several fields in biology including genome annotation, protein structure prediction, and enzyme design. In this review, we discuss the potential of combining experimental library screens and AI to build predictive models for the development of modular cell therapy technologies. Advances in DNA synthesis and high-throughput screening techniques enable the construction and screening of libraries of modular cell therapy constructs. AI and ML models trained on this screening data can accelerate the development of cell therapies by generating predictive models, design rules, and improved designs.

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Cited by

• CAR T Cells from Code to Clinic: Framing Modeling Approaches with Current Translational Research Goals
• Opportunities for artificial intelligence and synthetic biology in designing living drug delivery systems

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