MATCH: Machine learning algorithms for toxicity and cardiac health

Senior Scientist | Molecular Medicine

Current drug discovery and animal models of cardiac function do not accurately predict cardiac activity or toxicity, resulting in the failure of 30% of drug candidates in development, market removal of 16% of approved drugs, and as a result, the loss of billions of drug development dollars. To identify cardiotoxic compounds earlier in the drug discovery continuum, Dr. Jason Maynes, the Director of Research for Anesthesia and Pain Medicine and a Staff Anesthesiologist at SickKids, has led a team in the development of a machine-learning based platform called MATCH. MATCH incorporates multiple gold-standard tests of cardiac function into a unified, predictive report, and has proven its utility in existing preclinical drug discovery programs to efficiently identify the cardiotoxicity profile of investigational drugs.

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