PK/PD Modeling

vor 2 Wochen

Berlin, Berlin, Deutschland Mercor Vollzeit 80.000 € - 120.000 € pro Jahr

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.

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