Accelerating Drug Translation

翻译(生物学) 药品 计算机科学 医学 药理学 生物 遗传学 基因 信使核糖核酸
作者
Lohendran Baskaran,Utkarsh Dutta,Xiaomeng Wang,Michael P. Gray,Mark Y. Chan,Kelley R. Branch,Matthew J. Budoff,Ron Blankstein,Pamela S. Douglas,Gemma A. Figtree
出处
期刊:Jacc-cardiovascular Imaging [Elsevier BV]
卷期号:18 (8): 912-927 被引量:3
标识
DOI:10.1016/j.jcmg.2025.03.017
摘要

There is an unmet need for new pathways to evaluate therapeutic targets and drugs for coronary artery disease (CAD). The use of major adverse cardiovascular events as a primary outcome is advantageous for rigor, but challenges related to large sample size requirements, long study durations, and associated costs and risks remain major disincentives for key stakeholders. Until now, a robust, well-validated surrogate biomarker for cardiovascular events that reflects atherosclerosis itself has been lacking. This is particularly important as new therapeutic candidates targeting inflammation and factors downstream of low-density lipoprotein and other well-recognized factors continue to emerge. Coronary computed tomography angiography (CTA) has emerged as an ideal modality to image, characterize, and quantify specific plaque components, with potential for a more formal role in the CAD drug development pipeline. This review examines both morphological (high-risk plaque) and quantitative (specific plaque components) measures, considering their pathophysiological correlation with other imaging measures of atherosclerosis, evidence for the association of each component with major adverse cardiovascular events, and their responsiveness to effective drug treatments. Given the rapid advancement in coronary CTA acquisition and analysis technology, there is an urgent need for coordination and collaboration among key stakeholders, including imaging hardware and software companies, pharmaceutical companies, as well as clinicians, patients, regulators, and payers. Progress in this area, and the opportunities that may follow for the broader cardiovascular community if standardized coronary CTA endpoints are validated as surrogates for clinical drug trials, could accelerate innovation and enhance efficiency in CAD drug development.
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