Kévin Contrepois - Ph.D.

Lead a global team (USA and Europe) supporting oncology and immunology drug discovery and development efforts across drug modalities (SM, PROTAC, ADC, mAb, ASO) and development stages (preclinical and clinical). • Design and execute translational strategies in oncology including biomarker discovery in pre-clinical and clinical samples, biomarker validation, clinical implementation and forward/reverse-translation. • Lead strategic planning and implementation of innovative discovery strategies leveraging omics technologies, AI-driven analytics, and advanced 3D culture models to accelerate drug discovery, discover biomarkers (PD, safety, precision medicine) and unravel complex biological mechanisms (toxicity/resistance/response). • Translate high-dimensional large-scale datasets into actionable insights to support decision-making and inform clinical development strategies (patient selection). • Collaborate cross-functionally with discovery and development teams, translational medicine, biomarker operations, computational biologists and clinicians in a matrix environment. • Present translational research strategies, biomarker plans, and clinical implementation frameworks at governance and leadership forums. • Supervise and mentor scientific teams, fostering innovation, career development, and collaborative leadership to drive project execution and scientific excellence. • Lead cross-program initiative to build cloud-based infrastructure and harmonize large-scale molecular, safety and efficacy datasets facilitating integrative analysis and predictive modeling. • Oversee external collaborations with CROs and academic partners for biomarker assay validation and clinical sample testing and exploration of novel technologies and approaches. • Serve as a scientific expert partnering with business development to support external innovation sourcing, scientific due diligence, and strategic evaluation of portfolio expansion opportunities.

Oversee all research activities of the Metabolic Health Center which goal is to monitor child health through metabolic profiling of newborns, mothers and children using cutting-edge MS technologies and develop new diagnostic tests for direct patient testing and returnable results. • Led team of wet-laboratory research staff and data scientists focused on generating and interpreting high quality metabolomics and lipidomics data. • Collaborated closely with data management engineers to develop an automated and standardized data processing pipeline and improve biological interpretation of high content omics datasets. • Key stakeholder in implementing clinical workflows for patient enrollment (e-consent), sample collection in a clinical setting, transportation and processing by clinical labs, sample deidentification, barcoding and biobanking. • Established SOPs for sample preparation, MS data acquisition (quality controls) as well as data processing, analysis and visualization (cloud computing) to generate reports and create a database. http://med.stanford.edu/metabolichealthcenter.html

Lead metabolomics and lipidomics research and technological innovation. • Supervised staff research associates, provide coaching and mentorship, assign tasks and oversee work to ensure timely and high-quality project completion. Mentored 6 postdoctoral fellows. • Contributed (data generation and computational analysis) to multi-disciplinary projects and large consortia aiming at discovering predictive molecular signatures of disease onset and unraveling driving mechanisms of diseases including the Integrative Human Microbiome Project, NASA Twins Study, Undiagnosed Disease Network, etc. • Performed advanced statistical analysis of omics datasets as well as multi-omics integration across a variety of disease areas including type 2 diabetes (precision medicine), oncology, hypertrophic cardiomyopathy, immunology and respiratory. • Oversaw the acquisition and analysis of 5,000+ samples from biofluids (plasma and urine), cells and tissues.

Principal Investigator: Pr Michael SNYDER Developed a streamlined workflow for deep metabolomics profiling at scale including automated sample preparation, optimized LC-MS data acquisition (HILIC and RPLC), data processing (i.e. noise removal, normalization, batch effect correction, imputation), quality controls and confident metabolite annotation (in-house and public repository).

PhD supervisor: Dr Carl MANN « Chromatin modifications associated with cellular senescence » • Development of an UHPLC-MS methodology to characterize and quantify by label-free histone variants and PTMs (top-down, middle-down, bottom-up). • Characterization of H2A.J, a poorly studied H2A variant found only in mammals, and its role in regulating the expression of inflammatory genes that contribute to the senescent-associated secretory phenotype. • Discovery that SIRT2-mediated global deacetylation of H4-K16Ac is involved in heterochromatin assembly upon senescence.

« Biochemical and structural characterization of the guanine nucleotide exchange factor BRAG2 » Cloning, expression and purification of the GTPase Arf6 and the sec7 domain of BRAG2. Biochemical characterization by fluorimetry of the nucleotidic exchange reaction, circular dichroism and crystallogenesis. Characterization of the nucleotidic exchange reaction between Arf6 and BRAG2 and functional effect of interaction inhibitors and production of crystals of the sec7 domain of BRAG2.

« Identification and differential quantification of targets of the tyrosine kinase SYK using SILAC by nanoHPLC-MS »

Research of an optimal home-made enzymatic cocktail in order to commercialize an hydrolyzed raw product (whey protein) conform to agro-alimentary industry.