AI-driven discovery of synergistic drug combinations against pancreatic cancer

Treatment regimens, especially in cancer, often include more than one medicine in order to achieve durable outcomes. Identifying the optimal combination of treatments has historically been done through clinical trial and error. And for many conditions, such as pancreatic cancer, an optimal treatment protocol has remained elusive, and the best available treatment combinations provide only modest benefit. Recent developments have led to the application of both experimental screening approaches and in silico modeling methods to identify synergistic drug combinations and expand the therapeutic options for multiple diseases. Here we conduct a study to compare different predictive approaches for identifying new treatment combinations for pancreatic cancer using cell line growth as an initial proxy for clinical utility. NCATS performed screening involving 496 pairwise combinations of 32 antineoplastic drugs, tested against the PANC-1 human pancreatic carcinoma cell line in duplicates using a 10 × 10 matrix format. This dataset served as the basis for generating and training advanced AI/ML models focused on pancreatic cancer. Next, three independent groups (NCATS, UNC and MIT), though in a collaborative manner, utilized three different workflows with AL/ML approaches to discover new perspective drug combinations against pancreatic cancer among over 1.5 million drug combinations. As a result of this collaboration, 88 proposed combinations were tested in a cell-based assay; 53 of them were synergistic (hit rate ~60%). While all machine learning approaches demonstrate advances in the direction of predicting synergistic drug combinations, graph convolutional networks resulted in the best performance with a hit rate ~83%, and Random Forest delivered the highest precision of 65%. Interestingly, all utilized AL/ML methods among the three groups proposed different drug combinations with a small overlap of only two combos from 90. This study demonstrates the potential of a collaborative modeling approach for prioritizing drug combinations in large-scale screening campaigns, particularly when focusing on maximizing the efficacy of drugs known to exhibit synergy.