Predicting Drug Response and Synergy Using a Deep Learning Model of Human Cancer Cells

By B. Kuenzi, Jisoo Park, Samson H. Fong, Kyle S. Sanchez, John J. Lee, Jason F. Kreisberg, Jianzhu Ma, Trey Ideker. Cancer Cell, 2020

421 citations recorded by the source corpus.

SUMMARY Most drugs entering clinical trials fail, often related to an incomplete understanding of the mechanisms governing drug response. Machine learning techniques hold immense promise for better drug response predictions, but most have not reached clinical practice due to their lack of interpretability and their focus on monotherapies. We address these challenges by developing DrugCell, an interpretable deep learning model of human cancer cells trained on the responses of 1,235 tumor cell lines to 684 drugs. Tumor genotypes induce states in cellular subsystems that are integrated with drug structure to predict response to therapy and, simultaneously, learn biological mechanisms underlying the drug response. DrugCell predictions are accurate in cell lines and also stratify clinical outcomes. Analysis of DrugCell mechanisms leads directly to the design of synergistic drug combinations, which we validate systematically by combinatorial CRISPR, drug-drug screening in vitro, and patient-derived xenografts. DrugCell provides a blueprint for constructing interpretable models for predictive medicine. Graphical Abstract

View Paper: https://www.semanticscholar.org/paper/8d19c68cada5e1d50d801a06b3acae0ab32c451a

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Citations in the corpus

Artificial Intelligence (AI) Applications in Drug Discovery and Drug Delivery: Revolutionizing Personalized Medicine (2024, methodology): “Additionally, this approach was used to predict synergistic drug combinations and these predictions were able to be validated through patient-derived xenograft models [63].”
Empowering Biomedical Discovery with AI Agents (2024, unknown): “These models have advanced modeling of proteins [22–26], genes [27], phenotypes [28], clinical outcomes [29–31], and chemical compounds [32, 33] through mining of biomedical data.”
Artificial intelligence to deep learning: machine intelligence approach for drug discovery (2021, methodology): “Similarly, Kuenzi et al. 2020 predicted drug response and synergy using a DL model of human cancer cells.”
Artificial Intelligence in Cancer Research and Precision Medicine (2021, methodology): “To address this, Kuenzi and colleagues developed DrugCell, an interpretable deep learning model that uses a “visible neural network” (VNN) to ensure that the underlying neural network hierarchy resembled known biological processes (119).”