Drug repositioning is an increasingly attractive strategy to rapidly discover new treatments using existing medicines. A major motivation for pursuing repositioned medicines is their shorter timelines to the patient by utilizing safety data and accelerated approval pathways to the clinic. Drug repositioning successes can be categorized into three major methodologies; disease-centric (related diseases treated by the same medication), target-centric (a known target is linked to multiple pathologies), and drug-centric (unrecognized off-target interactions lead to therapeutic use in other diseases). Thus far, most repurposing examples fall into the first two methods, with the latter being a challenge due to insufficient approaches to fully assess a drug’s protein binding profile. Cyclica’s MatchMaker technology is a deep learning approach that can rapidly assess the proteome binding profile of a small molecule.<br/>MatchMaker can be pivotal to unlocking the drug-centric repositioning strategy by identifying off-target interactions that can be meaningful for other diseases. Cyclica has developed PolypharmDB, a collection of computed proteome binding profiles for molecules that have been evaluated to some degree in the clinics. We will demonstrate how PolypharmDB was used to identify new opportunities for existing medications.

Drug repositioning with PolypharmDB: A polypharmacological approach using cyclica’s matchmaker technology

ACS Spring Meeting 2021

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