Kinase inhibitor selectivity and design

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Selective kinase inhibitors---such as the blockbuster drug imatinib---have shown tremendous promise in the treatment of cancers involving kinase dysregulation. Currently, over 27 small molecule targeted kinase inhibitors have received FDA approval, representing a substantial fraction of the $37B U.S.~market for oncology drugs. Despite this, major challenges remain in their widespread application in cancer treatment. To meet these challenges, our laboratory develops quantitative physical models of kinase inhibitor efficacy to accelerate the rational design of kinase inhibitors with desired selectivity profiles, an understanding of mutational mechanisms of resistance, and prediction of drug sensitivity and resistance in individual patient tumors.

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Bayesian inference and error modeling for experimental data

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All experimental assay data contains error arising from uncertainties in initial compositions, dispensed masses or volumes, measurement noise, model fitting error, and intrinsic biological variability. Accounting for this error to produce a reliable estimate of the uncertainty of experimentally-derived quantities is critical, as this is the basis for testing hypotheses or building predictive models, but it is often difficult to even identify the dominant sources of assay error, let alone propagate them. Our lab uses two primary tools to both build predictive models of assay error and incorporate all sources of error and uncertainty in data analysis: the bootstrap principle and Bayesian inference.

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