Leveraging Data for the Identification of Viable Drug Repurposing Opportunities for Multiple Sclerosis
Featuring Charles Bury, Cheminformatics Data Scientist at Medicines Discovery Catapult
Abstract: 'Leveraging Data for the Identification of Viable Drug Repurposing Opportunities for Multiple Sclerosis'.
Medicines Discovery Catapult (MDC) has worked closely with the MS Society, the leading UK charity for people affected by multiple sclerosis, to support their goal of accelerating the translation of effective medicines into the clinic that slow, stop or reverse the advancement of disability. To enable new treatments to reach patients faster, the MS Society has commissioned an expert consortium to investigate drugs used in the treatment of other diseases as potential clinical candidates for repurposing in progressive MS. To support their expert-led approach, MDC has developed a complementary data-driven approach for the systematic identification of potential drug candidates for MS. By incorporating existing MS clinical studies data, and known molecular targets associated with MS, all approved and late-stage clinical trial drugs have been assessed based on their potential application to MS. This has ultimately led to the proposal of 300 candidate drugs for further review by the MS Society. To then facilitate the prioritisation of drugs within this new list of 300 proposed drugs, MDC retrieved extensive biological data associated with each candidate drug. This included safety and toxicology data, patent information, cost, side effects, contraindications, the ability to cross the bloodbrain barrier, potential drug interactions with standard MS treatments and the available administration routes for each drug. MDC's workflow has now been integrated into MS Society's drug selection process for future clinical trials, providing further confidence that the drug candidates selected to enter the MS Society's clinical trials platform have the best chance of success and the potential to provide patients with better treatments faster.
About the speaker:
Dr Charles Bury has been working as a cheminformatics data scientist at Medicines Discovery Catapult (MDC) for the past two years. His work has focused on the application of machine learning methods to aid drug design, and in particular to facilitate UK SM E access to recent advancements in QSAR modelling and generative Al for de nova drug design. Prior to MDC, Charles completed a DPhil in systems biology from the University of Oxford, where his work involved the development of computational tools for the tracking and correction of detrimental radiation damage in macromolecular structures solved by X-ray crystallography.