Guiding design and performance of nonviral nucleic acid delivery vehicles via machine learning

Machine Learning (ML) techniques have enabled the advancement of many technologies throughout the pharmaceutical industry, especially for drug discovery. One of the most rapidly growing technologies within the pharmaceutical space is gene therapy, with twenty six FDA-approved genetic medicines and over three thousand treatments currently undergoing clinical trials. A key challenge in the successful employment of gene therapy is effective nucleic acid delivery, and nonviral delivery vectors provide a cost-effective and highly customizable solution to this challenge. However, the vast design space also poses a large challenge for traditional development, which relies heavily on iterative trial-and-error and costly in vivo and in vitro experiments. This review identifies key ML techniques and discusses how these approaches have been utilized to improve the design of nonviral nucleic acid delivery vehicles. ML has the potential to radically transform the design space for nucleic acid therapies, like it has already done in drug discovery and drug formulations. This potential is being realized in research and has already led to the advent of several commercial enterprises seeking to build full end-to-end platforms for rapidly decreasing development time for new gene therapies.