Director Clinical Pharmacology MIDD Lead

Job Description

The Clinical Pharmacology and Quantitative Medicine (CPQM) group at GSK is a newly established organization dedicated to becoming a Centre of Excellence in Model‑Informed Drug Development (MIDD). CPQM uniquely integrates expertise in clinical pharmacology, digital medicine, translational imaging, and mechanistic and systems modelling.

Under the strategic leadership of the MIDD Head, the MIDD team drives strategy and innovation in clinical pharmacology and quantitative medicine. This function is essential for advancing GSK's MIDD capabilities, efficiency, and impact, with a strong focus on respiratory, immunology, inflammation, hepatology, neurodegeneration, and emerging areas within the Respiratory, Immunology & Inflammation Research Unit (RIIRU). The MIDD team is responsible for leading the development and execution of comprehensive therapeutic or sub‑therapeutic area MIDD evidence strategies across RIIRU.

The MIDD Lead is a scientific and strategic leader who applies modelling and simulation to inform drug development decisions. The MIDD Lead is responsible for leading the development and implementation of MIDD evidence strategies, serving as an advanced technical and subject‑matter expert for a therapeutic or sub‑therapeutic area within RIIRU. This role oversees integration and leads on the delivery of advanced modelling and simulation approaches, building‑on or extending model‑informed clinical pharmacology strategies.

Emphasis is placed on incorporating disease‑level data and novel digital and imaging endpoints to inform asset and mechanism‑level decision‑making. This role integrates advanced pharmacometrics approaches, alongside systems pharmacology, biostatistics, and data science to optimise dosing, clinical trial design, regulatory strategy, and go/no‑go decisions. The role supports optimisation and promotion of the effective use of established M&S methodologies (including MBMA and disease progression modelling) and the integration of emerging technologies (including AI/ML and SciML).

A key objective is to ensure CPQM delivers robust and transparent MIDD evidence to de‑risk and accelerate programmes supporting both internal and regulatory decision‑making. The Lead will advocate for and advise on optimising regulatory MIDD strategies to accelerate the delivery of transformative therapies to patients. Collaboration is central to this role, requiring close partnership with AI/ML lead & experts within the MIDD team, Clinical Pharmacology Modelling & Simulation (CPMS), Quantitative Systems Pharmacology (QSP), digital, imaging, and biomarker teams within CPQM, as well as multiple functions (e.g., Biostatistics, Clinical/Medical, Translation Sciences, Epidemiology) across RIIRU and GSK.

• Design & Execute MIDD Strategies: Drive optimal use of MIDD approaches, associated data plans and model evaluation strategies, to inform disease‑, programme‑ and business‑level decisions. Develop and implement robust evidence strategies within RIIRU, advancing MIDD evidence frameworks and documentation standards to de‑risk and accelerate programmes, optimising internal and regulatory strategies to deliver transformative therapies.
• Technical Expertise: Act as a subject‑matter expert for modelling and simulation (M&S) for a (sub)‑therapeutic area, serving as a contributor and technical coach. Lead development of the more technically challenging M&S plans, analyses and reports. Contribute to regulatory interactions involving quantitative evidence, ensuring clear communication of modelling assumptions and rigor in alignment with impact on decision‑making.
• Optimize & Innovate: Promote effective use of current M&S methodologies (e.g., MBMA, disease progression modelling) and integrate with emerging technologies (AI/ML, SciML). Develop good practices, training and wider adoption strategies.
• Shape Future Methods: Collaborate with global CPMS, academic partners and external experts to develop scalable methodologies aligned with RIIRU needs.
• Cross‑Functional Partnerships: Build strong networks across AI/ML, CPMS, QSP, biomarkers, digital, imaging, biostatistics, clinical and…