Laboratory of Medical Biochemistry and Clinical Analysis, Faculty of Pharmaceutical Sciences, Ghent University, Ghent, Belgium.
Drug Metab Dispos. 2011 Nov;39(11):2066-75. doi: 10.1124/dmd.111.039982. Epub 2011 Aug 10.
The aim of this study was to evaluate three different metabolite prediction software packages (Meteor, MetaSite, and StarDrop) with respect to their ability to predict loci of metabolism and suggest relative proportions of metabolites. A chemically diverse test set of 22 compounds, for which in vivo human mass balance studies and metabolic schemes were available, was used as basis for the evaluation. Each software package was provided with structures of the parent compounds, and predicted metabolites were compared with experimentally determined human metabolites. The evaluation consisted of two parts. First, different settings within each software package were investigated and the software was evaluated using those settings determined to give the best prediction. Second, the three different packages were combined using the optimized settings to see whether a synergistic effect concerning the overall metabolism prediction could be established. The performance of the software was scored for both sensitivity and precision, taking into account the capabilities/limitations of the particular software. Varying results were obtained for the individual packages. Meteor showed a general tendency toward overprediction, and this led to a relatively low precision (∼35%) but high sensitivity (∼70%). MetaSite and StarDrop both exhibited a sensitivity and precision of ∼50%. By combining predictions obtained with the different packages, we found that increased precision can be obtained. We conclude that the state-of-the-art individual metabolite prediction software has many advantageous features but needs refinement to obtain acceptable prediction profiles. Synergistic use of different software packages could prove useful.
本研究旨在评估三种不同的代谢物预测软件包（Meteor、MetaSite 和 StarDrop）在预测代谢部位和提示代谢物相对比例方面的能力。使用具有体内人体质量平衡研究和代谢方案的 22 种化合物的化学多样性测试集作为评估的基础。为每个软件包提供了母体化合物的结构，并将预测的代谢物与实验确定的人体代谢物进行了比较。评估分为两部分。首先，研究了每个软件包中的不同设置，并使用被确定为给出最佳预测的设置对软件进行了评估。其次，使用优化设置将三个不同的软件包进行了组合，以观察是否可以建立关于整体代谢预测的协同效应。考虑到特定软件的功能/限制，对软件的性能进行了灵敏度和精度评分。个别软件包的结果各不相同。Meteor 表现出普遍的过度预测趋势，这导致相对较低的精度（约 35%）但较高的灵敏度（约 70%）。MetaSite 和 StarDrop 均表现出约 50%的灵敏度和精度。通过组合不同软件包的预测结果，我们发现可以提高精度。我们得出的结论是，最先进的个体代谢物预测软件具有许多有利的功能，但需要改进才能获得可接受的预测结果。协同使用不同的软件包可能会很有用。
