Abstract 13263: Utilization of Proteomic Surrogates for Early Detection of Unexpected Drug Benefits

Abstract

Introduction: Detection of benefits and adverse effects of therapies in early clinical trial phases could improve the safety, efficiency, and cost of clinical trials. For example, while SGLT2i and GLP-1 RA drugs are recognized success stories, earlier identification of their benefits beyond improved diabetic control may have had the potential to save loss of patients’ lives and years of sales. Hypothesis: Using the EXSCEL and SUGAR-DM-HF trials as examples to study GLP1-RA and SGLT2i responses, we hypothesized that previously validated proteomic surrogates of cardiovascular (CV) events, and kidney health could have enabled detection of these unexpected drug benefits earlier in development in a smaller number of participants over a shorter follow-up period. Methods: CV risk and kidney prognosis SomaSignal tests (each derived from ~5000 plasma proteins measurements using SomaScan® assay) were applied to paired plasma samples at baseline and 9-months (SUGAR-DM-HF) or 1-year (EXSCEL) in intervention (EXSCEL n=1840; SUGAR-DM-HF n=45) and control (EXSCEL n=1833; SUGAR-DM-HF n=52) participants. Power calculations were performed to determine the minimum number of samples needed to detect a significant change within the treatment period with alpha = 0.05, 80% power using a t-test comparing two-sample means. Results: We demonstrated that the cardiovascular benefits of exenatide were detectable with a proteomic surrogate within 1-year (p=0.002), with power analysis indicating a significant 1-year change is observable with group sizes of n=1368 compared with >7000 participants for up to 6.8 years follow-up. Additionally, kidney protection (p=0.037) and CV protection (p=0.06) impacts of empagliflozin within 36 weeks were detectable using proteomic surrogates in small sample sizes (n ~ 50) compared to published outcomes studies requiring thousands of participants followed for >2 years. Conclusions: SomaSignal tests were able to predict cardiometabolic benefits of GLP-1 RA and SGLT2i drugs within a significantly shortened interval and fewer participants than in the outcome trials. Proteomics may provide a powerful tool for improving the efficacy, and cost of drug development by predicting effects of novel therapeutics in smaller, shorter studies.