Director, Computational DMPK – Drug Metabolism & Pharmacokinetics

🕒 5 dias atrás

🌐 Estados Unidos, Reino Unido – Remoto

🇺🇸 Estados Unidos – Remoto (EUA) 🇬🇧 Reino Unido – Remoto (RU)

⏰ Tempo Integral

🔴 Especialista

👔 Diretor

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Deep Origin

SiteLinkedInTodas as Vagas

11 - 50 funcionários

🏥 Saúde

💼 Consultoria

📦 Logística

Healthcare • Consulting • Logistics

A Deep Origin é uma empresa de ponta focada em acelerar a pesquisa e o desenvolvimento nas ciências da vida por meio de sua plataforma e de ferramentas inovadoras. Ela oferece um conjunto de ferramentas de simulação molecular e uma plataforma de P&D que aproveitam o poder da inteligência artificial e de uma gestão de dados integrada. As soluções da Deep Origin são projetadas para ajudar cientistas a enfrentar desafios complexos, biológicos e relacionados a doenças, fornecendo ferramentas para análise de dados, simulação molecular e descoberta de fármacos. A empresa busca revolucionar a biologia computacional e simular processos biológicos para apoiar a descoberta de novas terapias e a compreensão de doenças. As ofertas da Deep Origin incluem assistentes de análise com IA, workstations de alto desempenho e recursos de triagem virtual de fármacos, tornando-a uma poderosa aliada para pesquisa e desenvolvimento científicos interdisciplinares.

Descrição

• Lead a team of computational scientists and data curators across sites; set scientific direction, priorities and review standards, and develop team members. • Own the roadmap and release gates for four metabolism models for the VHAT platform, standalone products and external review. • Own metabolism-to-PBPK integration, including parameter transfer, clearance scaling to in vivo hepatic clearance, and validation against clinical PK and drug-drug interaction data. • Ensure parent and metabolite exposure flows correctly into organ models. • Design rigorous validation experiments with leakage-aware data splits, applicability-domain and confidence reporting, locked prospective predictions, and external, blinded and independent benchmarks. • Set data strategy, plan curation targets and throughput, oversee AI-assisted literature mining and quality control, support data licensing, and design wet-lab experiments with partners. • Extend model coverage to Phase II enzymes, FMO, AOX, multistep metabolic networks, enzyme polymorphism and population variability. • Manage relationships with consortium partners, contract research organizations, academic reviewers and independent validation teams. • Author technical milestone reports and present results to ARPA-H. • Contribute metabolism content to grant proposals and regulatory engagement on new approach methodologies. • Carry uncertainty from metabolism models through PBPK to final organ-level risk outputs. • Within six months, take ownership of the four models and team, release metabolite identity and enzyme-isoform predictions, pass clearance and kinetic parameters into PBPK, score prospective predictions, establish a kinetics model and define curation and coverage plans. • Within twelve months, support independent validation, feed parent and metabolite concentration profiles into PBPK and organ models, expand enzyme coverage, propagate confidence estimates, and operate the team against a defined roadmap.

🎯 Requisitos

• A PhD in computational chemistry, cheminformatics, drug metabolism, computational biology, pharmacology or a related field. • Deep, hands-on expertise in drug metabolism, including CYP and non-CYP enzymes, sites and products of metabolism, enzyme kinetics and clearance, and assessing whether predictions are chemically and biologically sound. • Strong track record in computational modeling, including machine learning for molecular property or reactivity prediction, mechanistic or kinetic modeling, ODE-based models, parameter estimation, Bayesian or frequentist inference, and fluent Python use. • Demonstrated rigor in model validation, including appropriate data splits, applicability domains, external or blinded benchmarks, and explaining model limitations. • Experience leading a scientific team and delivering against milestones and deadlines. • Clear written and spoken communication with scientists, partners and funders. • Strongly preferred: hands-on experience building or parameterizing PBPK models in Simcyp, GastroPlus, PK-Sim, SimBiology or a custom ODE framework. • Strongly preferred: experience translating in vitro or predicted intrinsic clearance into in vivo hepatic clearance and human PK, including IVIVE scaling factors. • Strongly preferred: experience using PBPK to assess metabolic drug-drug interactions, such as CYP inhibition and induction. • Strongly preferred: experience combining machine learning predictions with mechanistic PK models and carrying uncertainty into downstream predictions. • Nice to have: experience with metabolomics or LC-MS metabolite identification data. • Nice to have: experience running data curation at scale, including annotation standards and quality control. • Nice to have: experience on government-funded research programs with milestone-based reporting. • Nice to have: exposure to regulatory science, such as FDA guidance on new approach methodologies, model-informed drug development or the ISTAND program. • Nice to have: familiarity with medicinal chemistry and addressing metabolism liabilities during lead optimization. • Must be authorized to work for any employer in the U.S.; employment visa sponsorship or transfer is unavailable.

🏖️ Benefícios

• Competitive compensation package with meaningful equity. • Comprehensive health, dental, and vision coverage. • A remote-friendly culture across the U.S. and Europe, with flexible, cross-time-zone working. • Opportunity to shape the future of health, longevity, and our ability to simulate life. • Annual team gatherings and company events.

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