Tu135 - Genetic, Transcriptomic and Proteomic Evidences Identify Targets with Higher Clinical Probability of Success

Abstract Text: The low success rate of drugs in clinical trials across most therapeutic areas presents a significant challenge for pharmaceutical development. Previous studies and the Open Targets Platform have shown that human genetic evidence is a strong predictor of clinical trial success. However, the impact of multi-omics evidence on clinical trial outcomes remains less quantified. To address this, we compiled a comprehensive dataset of multi-omics target-disease pair associations, including 18,723 genetic associations (GWAS), 332,460 transcriptomic differential gene expressions (bulkRNA) across various tissues and cell types, and 96,283 plasma proteomic measurements from OLINK panels, covering 123 immunoscience diseases. We then calculated the probability of success (POS) of targets and target-indication pairs with or without multi-omic evidence. Our study reveal that targets supported by multi-omics evidence are significantly enriched in of approved targets and the have a higher probability of successful transition from phase I to approval (POS): confirming the importance of genetics (OR = 5.1 for approved targets and OR = 2.64 for POS), but also differential expression (OR = 5.4 for FDA-approved targets and OR = 2.68 for POS), and plasma protein (OR = 3.65 for POS). These results suggest that not only genetic but also transcriptomic and proteomic can improve clinical trial POS. Furthermore, we demonstrate that combining multi-omics evidence can result in better enrichment for approved targets (OR = 15.1). In conclusion, our data-driven, end-to-end target and drug indication positioning approach can accelerate the delivery of critical therapies to patients by accelerating clinical development.