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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Predicting drug susceptibility and the emergence of drug resistance

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While there are now over 30 FDA-approved selective kinase inhibitors available for the treatment of cancer, the median progression-free survival is still <1 year for a majority of these drugs. Drug resistance is responsible for >90% of deaths in patients with metastatic cancer. In many of these cases, mutations in the target of therapy drive resistance by abolishing or reducing inhibitor affinity while maintaining or increasing kinase activity.

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Nanoparticles for targeted drug delivery

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The Heller lab at MSKCC has discovered that poorly soluble kinase inhibitors mixed with specific indocyanine dye excipients will spontaneously form nanoparticles with very high (90% by mass) drug loadings, and that these dyes specifically target certain tumors while maintaining high blood stability. These nanoparticles offer the potential for avoiding both off- and on-pathway toxicities while delivering high quantities of targeted kinase inhibitors directly to 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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