The identification of optimal drug combinations is a challenging task because of the large number of possible combinations as well as the high cost of preclinical and clinical trials of testing each selection. This MSK invention addresses these problems by using data-driven computational models to systematically predict cellular responses to perturbations from drugs targeting certain signaling pathways in tumors.
These models are developed through an integrative experimental-computational approach. The experiments include a systematic series of perturbations of cancer cell lines by targeted drugs, singly or in combination. The response to perturbation is quantified in terms of relative changes in the measured levels of proteins, phospho-proteins, and cellular phenotypes such as viability. Computational network models are derived de novo (without prior knowledge of signaling pathways) and are based on simple non-linear differential equations.
The predictive models provided by this invention can be utilized to identify and evaluate combinations of targeted drugs in cancer therapy that might improve treatment efficacy, reduce off-target effects, and prevent or overcome evolutionary drug resistance.
- Data-driven, context-specific, and ready-to-use
- Published validations in multiple cancer cell lines with different drug combinations
This computational model facilitates potential discoveries of new molecular interactions and predicts efficacious novel drug perturbations, and can be used by the pharmaceutical and biotechnology industry as a powerful tool for the rational design of drug combinations to treat cancers and other potential diseases.
Chris Sander, PhD, formerly Chair and Laboratory Head, Computational Biology Program, Sloan Kettering Institute, Memorial Sloan Kettering