The Case for AI-driven Cancer Clinical Trials - The Efficacy Arm in Silico

Biochimica et Biophysica Acta (BBA) - Reviews on Cancer

Pharmaceutical agents in oncology currently have high attrition rates from early to late phase clinical trials. Recent advances in computational methods, notably causal artificial intelligence, and availability of rich clinicogenomic databases have made it possible to simulate the efficacy of cancer drug protocols in diverse patient populations, which could inform and improve clinical trial design. Here, we review the current and potential use of in silico trials and causal AI to increase the efficacy and safety of traditional clinical trials. We conclude that in silico trials using causal AI approaches can simulate control and efficacy arms, inform patient recruitment and regimen titrations, and better enable subgroup analyses critical for precision medicine.

Fred Gruber
Fred Gruber
Senior Principal Scientist

My research interests include causal inference, Bayesian networks, causal discovery, machine learning.