The 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:
1.    Identify drug targets
2.    Investigate the effects of drug target perturbation for different indications
3.    Explore potential adverse effects and repurposing potential for drug targets
4.    Identify biomarkers to explore drug efficacy in early stage clinical trials
5.    Identify population subgroups for which drug effects may vary
6.    Explore interactions between different drugs and exposures

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 burgess-group-admin@mrc-bsu.cam.ac.uk .  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. 

Course aims
This short course introduces students to Bayesian statistical methods in biomedical settings, and provides skills for designing, assessing and interpreting Bayesian analyses using the R and JAGS statistical software. The emphasis throughout will be on practical, applied modelling: code to carry out analyses will be provided.

Course outline
The course runs on the Moodle online learning platform, and involves 7 sessions:
1.    Quantifying uncertainty with probability
2.    Bayesian inference
3.    Bayesian regression models
4.    Critiquing and comparing Bayesian models
5.    Hierarchical models
6.    Modelling with missing and censored data
7.    Integrating multiple sources of data

Course delivery
The course will be delivered via the Moodle online learning platform. The course consists of 7 half-days worth of content, and will take place over 7 half-days across 3 weeks starting on Friday 7th November 2025.
It will consist of some on-demand content and some timetabled (live) sessions.

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 courses@mrc-bsu.cam.ac.uk .  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. 

Many confirmatory clinical trials turn out to be negative and do not lead to the registration of a new drug or the introduction of a new treatment. Consequently, there is growing interest amongst clinical researchers in the use of efficient methods for conducting early and late phase trials to identify the most promising compounds, and in using interim analyses to allow trials to be stopped as soon as there is sufficient evidence to reach a convincing conclusion.

Adaptive designs allow mid-trial modifications to be made to the trial so that the accumulating data guides investigators to the most appropriate form of evaluation. While offering great flexibility during the trial, they require specialised approaches to be designed and analysed. 

Dates
Monday 17th of November – Wednesday 19th of November 2025 (3 full days)

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 courses@mrc-bsu.cam.ac.uk .  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.