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Biomarkers in Pharmaceutical Research

生物标志物 药物开发 生物标志物发现 医学 临床试验 疾病 药学 药品 个性化医疗 精密医学 监管科学 生物信息学 肿瘤科 药理学 内科学 病理 蛋白质组学 生物 基因 生物化学
作者
Xuemei Zhao,Vijay Modur,Leonidas N. Carayannopoulos,Omar Laterza
出处
期刊:Clinical Chemistry [American Association for Clinical Chemistry]
卷期号:61 (11): 1343-1353 被引量:54
标识
DOI:10.1373/clinchem.2014.231712
摘要

Biomarkers are important tools in drug development and are used throughout pharmaceutical research.This review focuses on molecular biomarkers in drug development. It contains sections on how biomarkers are used to assess target engagement, pharmacodynamics, safety, and proof-of-concept. It also covers the use of biomarkers as surrogate end points and patient selection/companion diagnostics and provides insights into clinical biomarker discovery and biomarker development/validation with regulatory implications. To survey biomarkers used in drug development--acknowledging that many pharmaceutical development biomarkers are not published--we performed a focused PubMed search employing "biomarker" and the names of the largest pharmaceutical companies as keywords and filtering on clinical trials and publications in the last 10 years. This yielded almost 500 entries, the majority of which included disease-related (approximately 60%) or prognostic/predictive (approximately 20%) biomarkers. A notable portion (approximately 8%) included HER2 (human epidermal growth factor receptor 2) testing, highlighting the utility of biomarkers for patient selection. The remaining publications included target engagement, safety, and drug metabolism biomarkers. Oncology, cardiovascular disease, and osteoporosis were the areas with the most citations, followed by diabetes and Alzheimer disease.Judicious biomarker use can improve pharmaceutical development efficiency by helping to select patients most appropriate for treatment using a given mechanism, optimize dose selection, and provide earlier confidence in accelerating or discontinuing compounds in clinical development. Optimal application of biomarker technology requires understanding of candidate drug pharmacology, detailed modeling of biomarker readouts relative to pharmacokinetics, rigorous validation and qualification of biomarker assays, and creative application of these elements to drug development problems.
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