1149P An automated platform for rapid drug screening in patient-derived micro-organospheres

Abstract

Across published in vivo studies related to 20 FDA-approved cancer therapies, drugmodel pairs which achieved a high DELVE score showed greater than or equal to 90% tumor growth inhibition in vivo 78% of the time. Drug-model pairs with low DELVE scores showed less than or equal to 60% tumor growth inhibition in vivo 77% of the time. Across 71 FDA-approved drugs, using chemical structure alone, the platform was able to predict at least one approved solid tumor indication 84% of the time. Random chance indication-therapy pairing was correct only 21% of the time. Across 10 failed therapies, the platform was able to predict failure with 82% accuracy.Conclusions: DELVE is able to predict drug efficacy on cancer models and to correctly select indications for existing therapies, supporting its utility in predicting new indications for cancer therapies. Because the platform can operate on any single transcriptome, it is possible to use this tool to match patients with therapies from which they may benefit without reliance on a previously classified target.Legal entity responsible for the study: Shepherd Therapeutics.