Genetics in Drug Development, 24-28 November 2025Info Location Contact More Info Event Information
DescriptionThe majority of biologic and small molecule drugs perturb protein targets to exert their effects. With the recent explosion in the availability of large-scale genetic association data, it is increasingly feasible to identify genetic variants that proxy the effect of perturbing a protein drug target. Leveraging such genetic data thus offers an efficient and cost-effective approach for identifying drug targets and studying their effects. This short course “Genetics in drug development” will provide theoretical and practical advice on using genetic data to: University of Cambridge Students and Staff, whose department will be covering the participation cost, please raise a Purchase Order (or contact a member of your department who can assist with this) and send a PDF of the Purchase Order to [email protected] . You will then be sent the passcode which you will need to use along with the Purchase Order number to complete registration below, selecting the 'UoC Student/Staff Registration' option.
Event Location
More InformationThe course will be held online over one week and will consist of a series of recorded video lectures, live interactive sessions with practical examples, and participation in an online community that allows for interaction with peers and tutors throughout the course. Course tutors: Dr Dipender Gill, Advanced Clinical Research Fellow, Imperial College London Dr Ville Karhunen, Senior Research Associate, MRC Biostatistics Unit, University of Cambridge Dr David Ryan, NIHR Academic Clinical Fellow and ST2 in Clinical Pharmacology and Therapeutics, University College London Dr Stephen Burgess, MRC Biostatistics Unit Group Leader and Senior Scientist in the Cardiovascular Epidemiology Unit, University of Cambridge Intended audience: This course is designed for all those interested in leveraging genetic data for the drug development and investigation of drug effects, most notably:
Prerequisites: Previous experience of using the R statistical software is desirable, although not essential. All code for the practical examples used during the course will be provided for R.
Full refunds will be given for cancellation 28 or more working days before the course start date. Otherwise the full course fee will be charged. However, registrations may be transferable to another course or individual. | ||||||||||||||||||||||||
