Mercor

Key Responsibilities

• • Designing and executing in vivo studies that link molecular mechanism to disease-relevant phenotypes
• • Selecting appropriate preclinical systems (in vitro, ex vivo, animal models) with a clear rationale for human translatability
• • Developing biomarker strategies that span target engagement through clinical response, including practical considerations around sample collection and assay performance
• • Evaluating formulation and delivery approaches for tissue access across different modalities
• • Troubleshooting inconclusive or negative preclinical results and recommending next steps
• • Building exposure-activity relationships from in vivo datasets to inform clinical predictions
• • Evaluating whether preclinical evidence supports drug activity at the intended site of action
• • Updating mechanistic hypotheses as new data emerges and designing experiments to resolve ambiguity
• • Assessing early safety observations and developing hypotheses for their biological basis
• • Evaluating immunogenicity risk and its potential downstream consequences
• • Supporting portfolio-level decisions (advance, pivot, terminate) grounded in data quality and residual uncertainty
• • Determining safe and pharmacologically relevant starting doses for human studies, including cross-species scaling and its limitations
• • Designing dose escalation schemes informed by expected pharmacodynamic timecourses and safety margins
• • Powering early-phase studies appropriately given biological variability and expected effect sizes
• • Defining patient selection and enrichment strategies using available biomarker and epidemiological data
• • Selecting endpoints - including when surrogate measures are sufficient vs. when clinical endpoints are required
• • Planning interim analyses, safety monitoring, and adaptive decision rules
• • Exposure-response analysis and model-informed dose optimization
• • Population PK and PK/PD modeling, including covariate identification and impact assessment
• • Model-based support for dose escalation decisions using accumulating trial data
• • Longitudinal efficacy modeling, including time-to-effect and trajectory-based analyses
• • Sensitivity analyses addressing missing data, protocol deviations, and intercurrent events
• • Statistical analysis planning across endpoint types (binary, continuous, time-to-event)
• • Multiplicity-adjusted hypothesis testing and sample size determination
• • Subgroup and heterogeneous treatment effect analyses with appropriate false discovery controls
• • Handling of estimand-related considerations, including missing data frameworks and dropout patterns
• • Adaptive and interim monitoring design, including futility boundaries and alpha-spending functions