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定量系统药理学建模在药物研发中的前景。

The promises of quantitative systems pharmacology modelling for drug development.

作者信息

Knight-Schrijver V R, Chelliah V, Cucurull-Sanchez L, Le Novère N

机构信息

Babraham Institute, Babraham Research Campus, Cambridge CB22 3AT, UK.

European Molecular Biology Laboratory, European Bioinformatics Institute (EMBL-EBI), Wellcome Trust Genome Campus, Hinxton, Cambridge CB10 1SD, UK.

出版信息

Comput Struct Biotechnol J. 2016 Sep 23;14:363-370. doi: 10.1016/j.csbj.2016.09.002. eCollection 2016.

DOI:10.1016/j.csbj.2016.09.002
PMID:27761201
原文链接:https://pmc.ncbi.nlm.nih.gov/articles/PMC5064996/
Abstract

Recent growth in annual new therapeutic entity (NTE) approvals by the U.S. Food and Drug Administration (FDA) suggests a positive trend in current research and development (R&D) output. Prior to this, the cost of each NTE was considered to be rising exponentially, with compound failure occurring mainly in clinical phases. Quantitative systems pharmacology (QSP) modelling, as an additional tool in the drug discovery arsenal, aims to further reduce NTE costs and improve drug development success. Through mathematical modelling, QSP can simulate drug activity as perturbations in biological systems and thus understand the fundamental interactions which drive disease pathology, compound pharmacology and patient response. Here we review QSP, pharmacometrics and systems biology models with respect to the diseases covered as well as their clinical relevance and applications. Overall, the majority of modelling focus was aligned with the priority of drug-discovery and clinical trials. However, a few clinically important disease categories, such as and , were poorly covered by computational models. This suggests a possible disconnect between clinical and modelling agendas. As a standard element of the drug discovery pipeline the uptake of QSP might help to increase the efficiency of drug development across all therapeutic indications.

摘要

美国食品药品监督管理局(FDA)每年批准的新治疗实体(NTE)数量近期有所增长,这表明当前的研发产出呈积极趋势。在此之前,每个NTE的成本被认为呈指数级增长,复合失败主要发生在临床阶段。定量系统药理学(QSP)建模作为药物发现工具库中的一种额外工具,旨在进一步降低NTE成本并提高药物开发成功率。通过数学建模,QSP可以将药物活性模拟为生物系统中的扰动,从而理解驱动疾病病理、化合物药理学和患者反应的基本相互作用。在此,我们就所涵盖的疾病以及它们的临床相关性和应用,对QSP、药物计量学和系统生物学模型进行综述。总体而言,大多数建模重点与药物发现和临床试验的优先级一致。然而,一些临床重要疾病类别,如[此处原文缺失具体疾病名称],计算模型对其覆盖不足。这表明临床和建模议程之间可能存在脱节。作为药物发现流程的一个标准要素,QSP的应用可能有助于提高所有治疗适应症的药物开发效率。

https://cdn.ncbi.nlm.nih.gov/pmc/blobs/62d9/5064996/408456cc3e28/gr4.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/62d9/5064996/c800b71ac12e/gr1.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/62d9/5064996/8eff752fe13a/gr2.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/62d9/5064996/c6805ef07f7a/gr3.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/62d9/5064996/408456cc3e28/gr4.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/62d9/5064996/c800b71ac12e/gr1.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/62d9/5064996/8eff752fe13a/gr2.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/62d9/5064996/c6805ef07f7a/gr3.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/62d9/5064996/408456cc3e28/gr4.jpg

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