PKPD Modeling Pharmacometrics Lead

This person complements the clients Translational / Clinical Pharmacology Decision-Maker team by grounding dose selection and exposureresponse analysis in quantitative structure and parameter plausibility .

Who were looking for

• Deep hands-on experience in PK PD exposureresponse modeling and ideally population PK or QSP .

• Expert at model fitting sensitivity analysis and identifying non-plausible parameter spaces.

• Can evaluate the validity of doseexposure 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

• 812 years of quantitative pharmacology experience in pharma CROs or modeling consultancies .

• Strong portfolio in population PK/PD exposureresponse 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 exposureresponse 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.