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药物研究中翻译风险的评估:生物标志物分类和基于机制的 PKPD 概念的作用。

Assessment of translational risk in drug research: Role of biomarker classification and mechanism-based PKPD concepts.

机构信息

Quantitative Pharmacology & Pharmacometrics, Pharmacokinetics, Pharmacodynamics and Drug Metabolism, Merck & Co. Inc., Kenilworth, NJ, USA.

Preclinical ADME, Pharmacokinetics, Pharmacodynamics and Drug Metabolism, Merck & Co. Inc., Kenilworth, NJ, USA.

出版信息

Eur J Pharm Sci. 2017 Nov 15;109S:S72-S77. doi: 10.1016/j.ejps.2017.08.006. Epub 2017 Aug 13.

DOI:10.1016/j.ejps.2017.08.006
PMID:28811131
Abstract

In 2005, Danhof and coauthors proposed a new biomarker classification in the context of the application of mechanism-based PKPD modeling. They defined the term 'biomarker' as a measure that characterizes a drug-induced response, which is on the causal path between drug administration and clinical outcome. The biomarker classification identified seven categories that provide different insights into the kinetics of drug action, such as target site distribution, target engagement, or into the impact of the drug on physiology or disease. The original biomarker classification has been further modified into a translational biomarker scheme that is used as a communication tool for drug hunting teams to guide designing translational and early clinical development plans as part of an integrated model-informed drug discovery and development strategy. It promotes a dedicated discussion on the topic of the translational relevance of biomarkers and enables efficient identification of translational gaps and opportunities. Based on the elucidated PKPD characteristics exhibited by a novel drug and the kinetics of the investigated biomarker, prospective predictions can be made for the drug response under new conditions; translating from the preclinical arena to the clinical setting, from the healthy volunteer to the patient, or from an adult to an elderly or a child. These drug response predictions provide support to decisions on appropriate next steps in the development of the drug, while keeping clear line of sight on the potential to address unmet medical need. Moreover, this framework enables a transparent translational risk assessment for drug hunting projects, and as such can underpin decisions at program and portfolio level.

摘要

2005 年,Danhof 及其同事在应用基于机制的 PKPD 建模的背景下提出了一种新的生物标志物分类。他们将“生物标志物”定义为一种用于描述药物诱导反应的度量,这种反应处于药物给药和临床结果之间的因果路径上。该生物标志物分类确定了七个类别,为药物作用的动力学提供了不同的见解,例如靶位分布、靶标结合或药物对生理学或疾病的影响。原始的生物标志物分类已进一步修改为转化生物标志物方案,作为药物研发团队的沟通工具,用于指导转化和早期临床开发计划的设计,作为综合模型指导的药物发现和开发策略的一部分。它促进了对生物标志物转化相关性主题的专门讨论,并能够有效地识别转化差距和机会。基于新药物表现出的阐明的 PKPD 特征和所研究的生物标志物的动力学,可以对新条件下的药物反应进行前瞻性预测;从临床前领域转化到临床环境,从健康志愿者到患者,或从成人到老年人或儿童。这些药物反应预测为药物开发的下一步提供了决策支持,同时清楚地了解解决未满足的医疗需求的潜力。此外,该框架还为药物研发项目提供了透明的转化风险评估,从而为项目和投资组合层面的决策提供支持。

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