Drug repositioning for cancer in the era of AI, big omics, and real-world data

Drug repositioning in cancer has been pursued for years because of slowing drug development, increasing costs, and the availability of drugs licensed for other indications with anticancer effects in the laboratory. Repositioning has encountered obstacles due to generally insufficient single-agent clinical anticancer effects of licensed drugs and a subsequent reluctance by pharmaceutical companies to invest in phase III combination studies with them. Here we review potential machine learning/artificial intelligence (ML/AI) approaches for using real-world data (RWD) that could overcome the limitations of clinical trials and retrospective analyses. We outline a two-tiered filtering approach of identifying top-ranked drugs based on their drug-target binding affinity scores while considering their challenges and matching the top-ranked drugs with their top-ranked specific scenarios from among the multitude of real-world scenarios for efficacy and safety. This approach will generate RWD scenario-specific hypotheses that can be tested in randomized clinical trials with high probabilities of success.