Developing a strategy to identify & validate pharmacodynamic kinase inhibitor biomarkers

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 DESCRIPTION (provided by applicant): Small molecule kinase inhibitors comprise one of the most promising classes of oncology-directed therapies. Phenomenal clinical successes have already been achieved in a few cases, and intense efforts to broaden these successes are currently underway. More than 100 kinase inhibitors are currently in clinical trials, and hundreds of others are in the development pipeline. Unfortunately, it is highly likely that the vast majorit of these will fail, given that the current success rate for targeted oncology drugs is ~5%. A primary contributing factor to this unacceptably high failure rate is the paucity of pharmacodynamic biomarkers to assess drug efficacy at each stage of drug development. The dearth of kinase inhibitor biomarkers is the direct result of our more general lack of knowledge regarding the physiological targets of most protein kinases. In this application, we propose to develop and validate an innovative new approach for the identification of pharmacodynamic kinase inhibitor biomarkers. The current state-of-the-art in the industry is to employ anti-phosphosite antibodies to semi-quantitatively measure phosphorylation decreases in direct, or even indirect kinase substrates when the target kinase is inhibited. The choice of an appropriate biomarker is most often driven by anti-phosphosite antibody availability, rather than by pharmacodynamic characteristics of the responsive substrate. Here, we intend to vet a new paradigm that incorporates absolute quantification of phosphorylated and non-phosphorylated forms of inhibitor-responsive kinase substrates. We intend to profile protein phosphorylation stoichiometry in a kinase-specific manner, a goal that has not been achieved to date. This will be accomplished by incorporating stable isotope labeling into a reverse in-gel kinase assay, and analyzing tryptic peptides from ERK2 inhibitor-treated tumors using high resolution mass spectrometry. Quantification will be achieved using AQUA peptides as internal standards. By quantifying substrate responses at various intervals after drug administration, a panel of inhibitor-responsive substrates that accurately report on drug efficacy will be generated and validated. We anticipate that panels of biomarkers will outperform single biomarkers to determine optimal dose and scheduling of promising new kinase inhibitor therapies. The successful completion of this work will fulfill a pressing need to develop new technologies to reduce the failure rate of kinase inhibitors.