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Remote PK/PD Module expert: Translational / Clinical Pharmacology Decision-Maker - AI Trainer ($175-$250 per hour)
Remote PK/PD Module expert: Translational / Clinical Pharmacology Decision-Maker - AI Trainer ($175-$250 per hour)Mercor • Garland, Texas, US
Remote PK / PD Module expert : Translational / Clinical Pharmacology Decision-Maker - AI Trainer ($175-$250 per hour)

Remote PK / PD Module expert : Translational / Clinical Pharmacology Decision-Maker - AI Trainer ($175-$250 per hour)

Mercor • Garland, Texas, US
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The client is seeking an external expert who brings a

  • decision-maker’s mindset
  • — someone who has personally driven translational pharmacology choices under governance and uncertainty.

###

  • Who we’re looking for
  • Has owned FIH starting dose, SAD / MAD design, and Phase 2 dose selection for multiple assets. - Participated in governance or review boards, defending dose / exposure rationale in front of senior leadership or regulators. - Demonstrates ability to balance
  • nonclinical → clinical translation
  • : tox, PK, PD, potency, and MoA. - Can articulate the
  • real decision calculus
  • behind dose selection (safety, pharmacology, speed-to-proof). - Capable of turning narrative judgment into
  • explicit decision rules and rubrics
  • for AI training and evaluation. ###
  • Experience level
  • ~10+ years in major biopharma or biotech (e.g., Lilly, Roche, AbbVie, Amgen, Novartis, GSK, Regeneron, Vertex, Incyte, Gilead) or in specialized translational pharmacology consulting. - Has
  • personally led
  • FIH dose and / or SAD / MAD escalation for ≥2–3 assets. - Proven governance experience : IND sign-off, dose justification memos, exposure–response presentations. - CV or track record includes statements like _“led clinical pharmacology strategy and FIH dose selection”_ or _“accountable for exposure–response in Phase 1 / 2.”_ - Avoid pure modeling profiles; the ideal expert has
  • decision accountability
  • , not just analytical skill. ###
  • Expectations
  • Write “golden” FIH dose rationales and escalation strategies for representative programs. - Encode the
  • decision heuristics and trade-offs
  • used by senior translational leaders into structured guidance and rubrics. - Surface
  • unwritten decision rules
  • — when and why experienced teams override model-based recommendations.
  • Inputs given :
  • Representative drug programs or data packets (GLP tox summaries, PK / PD tables, potency & MoA context). - Target prompts for translational decision-making (e.g., _“Recommend FIH dose and escalation strategy given these data”_).
  • Expected outputs :
  • Golden Decision Memos :
  • exemplar dose justification memos that reflect the level of reasoning and format expected from top-tier translational pharmacologists. -
  • Decision Rubrics :
  • scoring guides capturing acceptable vs. unsafe dose rationales, missed edge cases, or failure modes. -
  • Meta-Layer Commentary :
  • short narrative explaining unspoken heuristics — how senior teams weigh risk tolerance, potency data, or tox uncertainty.
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    Module Translational • Garland, Texas, US

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