Modelling in vitro hepatotoxicity using molecular interaction fields and SIMCA
Modelling in vitro hepatotoxicity using molecular interaction fields and SIMCA
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DOI：
10.1016/j.jmgm.2004.03.009
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发表时间：
2004-07-01
影响因子：
2.9
通讯作者：
Sussman, NL
中科院分区：
文献类型：
--
作者：
Clark, RD;Wolohan, PRN;Sussman, NL
目前，人们对创建用于预测药物开发候选物的药理学性质的计算工具非常感兴趣，所述药理学性质的范围从物理化学性质如pK（a）和溶解度到更复杂的生物学性质如口服生物利用度和毒性。在许多情况下，限制因素是缺乏用于构建训练集的良好数据。在其他情况下，大量的数据是可用的，但它们使用替代终点或由与药物发现和开发中通常遇到的化合物非常不同的化合物组成。在这种情况下，大型训练集和全局模型不一定比基于较小数据集的局部模型更好。这些考虑使得仔细检查可用数据以避免对所获得的模型进行过度解释变得同样重要，因为这是为了最大限度地减少预测本身的误差。的各种并发症可能会遇到体外肝毒性建模进行了一般性的讨论，并说明特别是SIMCA分析的数据从培养的肝细胞的测定一个大的，结构多样的数据集和一个较小的，更集中的。(C)2004年爱思唯尔公司All rights reserved.
There is currently a great deal of interest in creating computational tools for predicting the pharmacological properties of drug development candidates, ranging from physicochemical properties such as pK(a) and solubility to more complex biological properties such as oral bioavailability and toxicity. The limiting factor in many cases is a shortage of good data from which to construct training sets. In other cases, large amounts of data are available, but they use surrogate end-points or are comprised of compounds very different from those usually encountered in drug discovery and development. In such cases large training sets and global models are not necessarily better than local models based on smaller data sets. Such considerations make it as important to examine the available data carefully so as to avoid over-interpretation of the models obtained as it is to minimise errors in prediction per se. The kinds of complications likely, to be encountered for in vitro hepatotoxicity modelling are discussed in general terms and illustrated in particular by SIMCA analysis of data obtained from assays of cultured hepatocytes for a large, structurally diverse data set and a smaller, much more focussed one. (C) 2004 Elsevier Inc. All rights reserved.