New Computer Simulation Cracks Mystery of Cancer Drug Resistance
UMD-led team uses machine learning to explain why a ‘miracle drug’ might not work in all cases
Computer simulation methods developed by Pratyush Tiwary’s lab identified two optimum pathways that Gleevec, a cancer drug, could take to unbind from the protein. The “fast” pathway allows Gleevec to leave the protein three times faster than that of the “slow” pathway, ultimately leading to drug resistance. Photo courtesy of Pratyush Tiwary.
Imatinib, better known as Gleevec, was hailed as a “miracle” cancer drug when it entered the market in the early 2000s. Though it has been highly successful at treating early-stage chronic myeloid leukemia (CML)—a rare cancer that forms in bone marrow cells—many late-stage patients experience drug resistance caused by mutations of vital proteins in the body.
A new study led by Pratyush Tiwary, an assistant professor in the University of Maryland’s Department of Chemistry and Biochemistry and Institute for Physical Science and Technology, used computational chemistry to figure out what causes resistance to Gleevec at the molecular level.
The study was published online in the journal Angewandte Chemie on April 29, 2022. The journal’s editors labeled it a 'Hot Paper'—a distinction granted to papers of utmost interest and importance. The metrics developed by Tiwary’s lab could have broad applications for the pharmaceutical industry, potentially yielding drugs that target a variety of diseases with higher rates of efficacy.
