DMPK Lead, London, Lausanne

Essential:

PhD in either Pharmaceutical Science, Pharmacy, Chemistry, or another relevant subject.

3+ years’ professional experience as a DMPK Lead, solving DMPK-related problems in drug discovery with a track record of working collaboratively to deliver high quality candidates.

Practical experience developing DMPK and quantitative pharmacology packages to support early clinical development, including authoring and reviewing regulatory documents, ideally in oncology or immunology.

Experience of partnering closely with Preclinical and Clinical teams.

In depth knowledge of biotransformation.

At Isomorphic Labs, DMPK Leads drive the DMPK strategy of drug discovery projects to deliver high quality clinical candidates whilst using and contributing to the optimisation of state-of-the-art computational and AI ADME models.

As part of the Drug Design and Medical Research team and through close collaboration with the Technology teams, you will be supporting the business alongside world leading AI/ML Drug Design Platform teams. You will collaborate to deliver scientific expertise and strategic direction to drive both improvements in the Platform and apply this to real drug design projects.

If you are a skilled and ambitious scientist looking to take the next step in your career, joining Isomorphic Labs will lift you out of your comfort zone and provide you with an amazing opportunity for personal growth and development.

Design and implement stage-appropriate DMPK strategies with AI-first approach, integrating quantitative pharmacology and overall project strategies.

Design screening cascade and mechanistic experiments to support project progression in a data-driven manner.

The DMPK and Quantitative Pharmacology team, part of the Preclinical Development group, accelerates the delivery of high quality drug candidates by developing innovative and creative AI-first solutions. By applying our domain expertise and through collaboration with the entirety of our world-class drug discovery disciplines and ML researchers, we ensure that drug-like properties are at the heart of our drug design engine. We shape the development of state-of-the-art ADME and toxicology predictive models. We rethink drug discovery from Hit ID to first-in-human (FIH) trials by optimising the context of use of these models.