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Updated: Sep 16, 2025

Biosensor-based High Throughput Biopanning and Bioinformatics Analysis Strategy for the Global Validation of Drug-protein Interactions
Published on: December 1, 2020
Utilization of Proteomic Measures for Early Detection of Drug Benefits and Adverse Effects
Jessica Chadwick1, Colin Berry2, Meredith A Carpenter1
1Standard BioTools, South San Francisco, CA, USA.
Abstract:
Recognition of benefits and adverse effects of therapies in earlier clinical trial phases could improve the safety, efficiency, and cost of clinical trials. Using four clinical trials representing a diverse set of diseases and drug classes (EXSCEL: exenatide/GLP-1 RA, SUGAR-DM-HF: empagliflozin/SGLT2i, PRADA: epirubicin/anthracycline, and AMPLE: abatacept/immunomodulator and adalimumab/TNF inhibitor), we hypothesized that previously validated proteomic measures for cardiometabolic outcomes could enable the detection of beneficial and adverse drug effects in fewer participants over a shorter follow-up period. Changes in SomaSignalTM proteomic tests over time in response to treatment were assessed in the EXSCEL (baseline vs 1 year; once-weekly exenatide (EQW) (n) = 1812 vs control (n) = 1787), SUGAR-DM-HF (baseline vs 12 weeks and 36 weeks; empagliflozin (n) = 45 vs control (n) = 52), AMPLE (baseline vs 85 days and 1 year; abatacept (n) = 210, adalimumab (n) = 222), and PRADA (baseline vs 7-10 days and 3 months, n = 120) trial. Improvement of cardiovascular risk and cardiometabolic traits with EQW was detectable within 1 year (P = .002) in sample sizes significantly smaller than the original study. Cardio- and kidney-protective (P = .06, P = .037) effects of empagliflozin were detectable within 36 weeks in a small sample size (n ∼ 50). Abatacept and adalimumab treatment demonstrated significant improvements in cardiovascular risk (P ≤ .001, P ≤ .001) and cardiorespiratory fitness (P ≤ .001, P ≤ .001) within 85 days. In contrast, anthracycline treatment led to significant increases in heart failure mortality risk (P ≤ 0.001) and cardiovascular risk (P = .004) after the first cycle of chemotherapy treatment. These findings provide preliminary evidence that proteomics may provide a powerful tool for optimizing drug pipelines by predicting the effects of novel therapeutics in smaller, shorter trials.
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