Evaluating the Utility of Proteomics for the Identification of Circulating Pharmacodynamic Biomarkers of IFNβ‐1a Biologics

药效学 鉴定(生物学) 蛋白质组学 计算生物学 药理学 医学 化学 药代动力学 生物 生物化学 基因 植物
作者
Paula L. Hyland,Lakshmi Manasa S. Chekka,Deepti P. Samarth,Barry A. Rosenzweig,Erica Decker,Esraa G. Mohamed,Yan Guo,Murali K. Matta,Qin Sun,William Wheeler,Carlos Sanabria,James L. Weaver,Sarah J. Schrieber,Jeffry Florian,Yow‐Ming Wang,David G. Strauss
出处
期刊:Clinical Pharmacology & Therapeutics [Wiley]
卷期号:113 (1): 98-107 被引量:11
标识
DOI:10.1002/cpt.2778
摘要

Proteomics has the potential to identify pharmacodynamic (PD) biomarkers for similarity assessment of proposed biosimilars without relying on clinical efficacy end points. In this study, with 36 healthy participants randomized to therapeutic doses of interferon‐beta 1a products (IFNβ‐1a) or pegylated‐IFNβ‐1a (pegIFNβ‐1a) approved to treat multiple sclerosis or placebo, we evaluated the utility of a proteomic assay that profiles > 7,000 plasma proteins. IFNβ‐1a and pegIFNβ‐1a resulted in 248 and 528 differentially expressed protein analytes, respectively, between treatment and placebo groups over the time course. Thirty‐one proteins were prioritized based on a maximal fold change ≥ 2 from baseline, baseline adjusted area under the effect curve (AUEC) and overlap between the 2 products. Of these, the majority had a significant AUEC compared with placebo in response to either product; 8 proteins showed > 4‐fold maximal change from baseline. We identified previously reported candidates, beta‐2microglobulin and interferon‐induced GTP‐binding protein (Mx1) with ~ 50% coefficient of variation (CV) for AUEC, and many new candidates (including I‐TAC, C1QC, and IP‐10) with CVs ranging from 26%–129%. Upstream regulator analysis of differentially expressed proteins predicted activation of IFNβ1 signaling as well as other cytokine, enzyme, and transcription signaling networks by both products. Although independent replication is required to confirm present results, our study demonstrates the utility of proteomics for the identification of individual and composite candidate PD biomarkers that may be leveraged to support clinical pharmacology studies for biosimilar approvals, especially when biologics have complex mechanisms of action or do not have previously characterized PD biomarkers.
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