Analysis of system structure-function relationships
Analysis of system structure-function relationships
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DOI：
10.1002/cmdc.200700153
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发表时间：
2007-12-01
期刊：
影响因子：
3.4
通讯作者：
Volkmann, Robert A.
中科院分区：
文献类型：
--
作者：
Fliri, Anton F.;Loging, William T.;Volkmann, Robert A.
临床前药理学研究进行了实验药物目前集中在评估药物的作用归因于药物的推定机制。然而，药物在临床试验中的高失败率突出表明，从这些研究中收集的信息不足以预测在患者中实际观察到的药物效果。改善药物效应预测和提高新药临床试验的成功率是制药行业目前面临的一些关键挑战。解决这些挑战需要开发新的方法，用于在细胞和生物体水平上捕获和比较药物的“全系统”结构-效应信息。目前的研究描述了一种策略，通过使用六种不同的描述符集，在细胞和生物体水平上研究1064种药物的分子结构和广泛的效应信息之间的关系，朝着这个方向发展。为了比较不同药物之间的广泛药效信息，为1064种药物中的每一种创建信息谱，并通过层次聚类确定信息谱之间的相似性。通过这些比较确定的结构-效应关系表明，通过临床前配体结合实验，使用模型蛋白质组获得的信息光谱相似性提供了有用的估计，这些1064药物在orgonisms广泛的药物效应概况。使用数据集中选定药物的配体结合谱作为生物标志物来说明这一前提，用于预测未包括在初期1064药物分析中的药物的全系统效应观察。
Preclinical pharmacology studies conducted with experimental medicines currently focus on assessments of drug effects attributed to a drug's putative mechanism of action. The high failure rate of medicines in clinical trials, however, underscores that the information gathered from these studies is insufficient for forecasting drug effect profiles actually observed in patients. Improving drug effect predictions and increasing success rates of new medicines in clinical trials are some of the key challenges currently faced by the pharmaceutical industry. Addressing these challenges requires development of new methods for capturing and comparing "system-wide" structure-effect information for medicines at the cellular and organism levels. The current investigation describes a strategy for moving in this direction by using six different descriptor sets for examining the relationship between molecular structure and broad effect information of 1064 medicines at the cellular and the organism level. To compare broad drug effect information between different medicines, information spectra for each of the 1064 medicines were created, and the similarity between information spectra was determined through hierarchical clustering. The structure-effect relationships ascertained through these comparisons indicate that information spectra similarity obtained through preclinical ligand binding experiments using a model proteome provide useful estimates for the broad drug effect profiles of these 1064 medicines in orgonisms. This premise is illustrated using the ligand binding profiles of selected medicines in the dataset as biomarkers for forecasting system-wide effect observations of medicines that were not included in the incipient 1064-medicine analysis.