Dr Mike Dalrymple

Staff Enterprise (company formation support)

Background

Early career in basic research at Yale and Edinburgh. Moved into industry, firstly with Inveresk Research and then PPL Therapeutics ("Dolly the Sheep"). Joined the Medical Research Council (MRC) as Director of the Collaborative Centre Scotland, a wet lab partnering MRC science with industry. MRC CCS transitioned into MRC Technology (latterly LifeArc) in 2000 creating a 'technology transfer' company with both office based and wet translational capabilities. In 2001 we created 'proof of concept' funding for MRC Units and Institutes and, in 2007, we established one of the first 'Academic Drug Discovery' labs in the UK with both antibody and small molecule therapeutic capabilities (now ~80 scientists).

I headed up Business Development between 2007 and 2016, executing a number of licensing deals including that for the drug that became Keytruda. During that period, built a 'due diligence' team to assess opportunities from a technical and commercial perspective. By 2020 this team had grown to 9 PhD level analysts and assisted the UK government to assess novel drug repurposing opportunities during the first phase of the Covid pandemic.

Created and subsequently directed an ISO13485 accredited molecular diagnostics development lab within LifeArc, designed to collaborate with academia and industry. This grew from 2 scientists to 20+ scientists by 2021 and is well advanced in delivering clinical evaluations of new diagnostic tests. Around 2018 helped create LifeArc's first seed investment fund, building the team to five people and making investments in 9 early stage companies in two years. Joined Edinburgh innovations in March 2022 as advisor in Staff Enterprise.

Qualifications

BSc(Hons) Molecular Biology, University of Edinburgh

PhD (Virology), University of Glasgow

MBA, University of Edinburgh

Research summary

Commercialisation of basic research in the following areas:

Early stage drug discovery and novel drug targets

Diagnostic technologies and biomarkers

AI/ML as it relates to therapeutics discovery and diagnostics development