PK/PD Modeling / Pharmacometrics Lead
18 days ago
Simi Valley
This person complements the client’s “Translational / Clinical Pharmacology Decision-Maker” team by grounding dose selection and exposure–response analysis in quantitative structure and parameter plausibility. ### Who we’re looking for - Deep hands-on experience in PK, PD, exposure–response modeling, and ideally population PK or QSP. - Expert at model fitting, sensitivity analysis, and identifying non-plausible parameter spaces. - Can evaluate the validity of dose–exposure predictions and detect high-risk extrapolations. - Comfortable designing model evaluation rubrics that distinguish between acceptable vs. non-credible outputs. - Able to articulate how quantitative checks should complement narrative decision logic. Nice-to-have: - Experience supporting translational or clinical pharmacology leads in dose justification. - Familiarity with integrating nonclinical PK/PD data (2-species GLP → human FIH extrapolation). ### Experience level - ~8–12 years of quantitative pharmacology experience in pharma, CROs, or modeling consultancies. - Strong portfolio in population PK/PD, exposure–response, and parameter estimation using NONMEM, Monolix, or equivalent tools. - Demonstrated ability to interpret model results for decision-making, not just fit data. - Can create fit-for-purpose models and critique model structures or assumptions under uncertainty. ### Expectations - Design and refine micro-evaluations for PK/PD performance (curve fits, parameter checks, error taxonomies). - Encode quantitative sanity checks into model rubrics for automated evaluation. - Define failure conditions (e.g., unsafe extrapolation, poor coverage curves, invalid assumptions). Inputs we give: - PK/PD datasets, tox summaries, and performance prompts (e.g., “fit exposure–response curves, interpret safety margins”). - Example model outputs from automated systems. Expected outputs: - Quantitative Rubrics: clear thresholds for acceptable parameter fits, coverage curve quality, and model integrity checks. - Golden Fit Examples: representative “ideal” PK/PD model outputs and visualizations for calibration. - Error Taxonomy: structured list of typical modeling or fitting errors, with root-cause annotations. - Meta-Layer Commentary: short note per rubric capturing how expert modelers recognize implausible or unsafe fits beyond numeric error values. ### Engagement Model & Compensation - Contract / part-time, remote, outcome-based deliverables.