Drug off-target effects predicted using structural analysis in the context of a metabolic network model.
Drug off-target effects predicted using structural analysis in the context of a metabolic network model.
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DOI：
10.1371/journal.pcbi.1000938
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发表时间：
2010-09-23
影响因子：
4.3
通讯作者：
Palsson BØ
中科院分区：
文献类型：
--
作者：
Chang RL;Xie L;Xie L;Bourne PE;Palsson BØ
结构生物信息学的最新进展使得能够基于其配体结合位点预测蛋白质-药物脱靶。系统生物学的同步发展允许使用大规模网络模型预测系统扰动的功能影响。这两种功能的整合为计算机模拟评价代谢药物反应表型提供了一个框架。这种联合方法被应用于研究胆固醇酯转移蛋白抑制剂torcetrapib在人类肾功能背景下的高血压副作用。生成代谢肾模型，以模拟药物治疗。据预测，因果性药物脱靶以前曾观察到影响基因缺陷患者的肾功能，并可能在临床试验中观察到的不良副作用中发挥作用。还预测了药物治疗的遗传风险因素，其对应于表征的和未知的肾代谢疾病以及除了药物治疗外预计不会表现出肾脏疾病表型的隐性遗传缺陷。这项研究代表了结构和系统生物学的新整合，也是迈向计算系统医学的第一步。本文介绍的方法对药物开发和个性化医疗具有重要意义。药物科学才刚刚开始触及药物作用的确切机制的表面，这些机制导致药物对患者的广泛反应，包括预期的和副作用。几十年的临床试验、分子研究和最近的计算分析都试图描述药物和细胞分子机制之间的相互作用。我们设计了一种综合计算方法来评估药物如何影响特定系统，在我们的研究中，我们研究了人类肾脏的代谢及其过滤血液内容物的能力。我们应用这种方法回顾性地研究了导致药物torcetrapib临床试验参与者血压升高的潜在因果药物靶点，以展示我们的方法如何直接用于药物开发过程。我们的研究结果表明，特定的代谢酶可能是直接负责的副作用。我们开发的药物筛选框架可用于将不良副作用与特定的药物靶点联系起来，发现旧药物的新用途，识别代谢疾病和药物反应的生物标志物，并提出遗传或饮食风险因素，以帮助指导个性化的患者护理。
Recent advances in structural bioinformatics have enabled the prediction of protein-drug off-targets based on their ligand binding sites. Concurrent developments in systems biology allow for prediction of the functional effects of system perturbations using large-scale network models. Integration of these two capabilities provides a framework for evaluating metabolic drug response phenotypes in silico. This combined approach was applied to investigate the hypertensive side effect of the cholesteryl ester transfer protein inhibitor torcetrapib in the context of human renal function. A metabolic kidney model was generated in which to simulate drug treatment. Causal drug off-targets were predicted that have previously been observed to impact renal function in gene-deficient patients and may play a role in the adverse side effects observed in clinical trials. Genetic risk factors for drug treatment were also predicted that correspond to both characterized and unknown renal metabolic disorders as well as cryptic genetic deficiencies that are not expected to exhibit a renal disorder phenotype except under drug treatment. This study represents a novel integration of structural and systems biology and a first step towards computational systems medicine. The methodology introduced herein has important implications for drug development and personalized medicine. Pharmaceutical science is only beginning to scratch the surface on the exact mechanisms of drug action that lead to a drug's breadth of patient responses, both intended and side effects. Decades of clinical trials, molecular studies, and more recent computational analysis have sought to characterize the interactions between a drug and the cell's molecular machinery. We have devised an integrated computational approach to assess how a drug may affect a particular system, in our study the metabolism of the human kidney, and its capacity for filtration of the contents of the blood. We applied this approach to retrospectively investigate potential causal drug targets leading to increased blood pressure in participants of clinical trials for the drug torcetrapib in an effort to display how our approach could be directly useful in the drug development process. Our results suggest specific metabolic enzymes that may be directly responsible for the side effect. The drug screening framework we have developed could be used to link adverse side effects to particular drug targets, discover new uses for old drugs, identify biomarkers for metabolic disease and drug response, and suggest genetic or dietary risk factors to help guide personalized patient care.
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影响因子：
12.3
作者：
Gentleman RC;Carey VJ;Bates DM;Bolstad B;Dettling M;Dudoit S;Ellis B;Gautier L;Ge Y;Gentry J;Hornik K;Hothorn T;Huber W;Iacus S;Irizarry R;Leisch F;Li C;Maechler M;Rossini AJ;Sawitzki G;Smith C;Smyth G;Tierney L;Yang JY;Zhang J
通讯作者：
Zhang J
影响因子：
14.8
作者：
Becker, Scott A.;Feist, Adam M.;Herrgard, Markus J.
通讯作者：
Herrgard, Markus J.
影响因子：
--
作者：
Borghese, Maria F. Albertoni;Majowicz, Monica P.;Vidal, Norberto A.
通讯作者：
Vidal, Norberto A.
DOI：
10.1001/archpedi.1991.02160060079025
发表时间：
1991-06-01
影响因子：
--
作者：
ESHEL, G;LAHAT, E;ALADJEM, M
通讯作者：
ALADJEM, M
影响因子：
4.3
作者：
Durrant, Jacob D.;Amaro, Rommie E.;McCammon, J. Andrew
通讯作者：
McCammon, J. Andrew