Desirability-Based Methods of Multiobjective Optimization and Ranking for Global QSAR Studies. Filtering Safe and Potent Drug Candidates from Combinatorial Libraries
Desirability-Based Methods of Multiobjective Optimization and Ranking for Global QSAR Studies. Filtering Safe and Potent Drug Candidates from Combinatorial Libraries
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DOI：
10.1021/cc800115y
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发表时间：
2008-11-01
影响因子：
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通讯作者：
Rosa Dominguez, Elena
中科院分区：
文献类型：
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作者：
Cruz-Monteagudo, Maykel;Borges, Fernanda;Rosa Dominguez, Elena
到目前为止，文献中关于多目标优化（MOOP）技术在定量构效关系（QSAR）研究中的应用报道极少。然而，这些报道中没有一个涉及与药物最终药学特性直接相关的目标优化。本文介绍了一种基于德林格合意性函数的MOOP方法，该方法可进行全局QSAR研究，同时考虑一组候选药物的效力、生物利用度和安全性。基于合意性的MOOP结果（预测变量的水平同时在决定最佳候选药物的特性之间产生最佳可能的折衷）被用于实施一种排序方法，该方法同样基于合意性函数的应用。这种方法能够根据与先前确定的最佳候选药物的相似程度对组合库中具有未知药学特性的候选药物进行排序。该方法的应用将使筛选库中最有希望的候选药物（排名最高的候选药物）成为可能，这些候选药物应具有最佳的药学特性（效力、安全性和生物利用度之间的最佳折衷）。此外，还提出了一种排序过程的验证方法以及一种排序质量的定量衡量指标——排序质量指数（W）。通过将基于合意性的MOOP和排序方法应用于一个包含95种氟喹诺酮的库，报道它们的革兰氏阴性菌抗菌活性和哺乳动物细胞毒性，证明了这些方法的有效性。最后，本文提出的基于合意性的MOOP和排序方法的联合使用似乎是合理药物发现和开发的一种有价值的工具。
Up to now, very few applications of multiobjective optimization (MOOP) techniques to quantitative structure-activity relationship (QSAR) studies have been reported in the literature. However, none of them report the optimization of objectives related directly to the final pharmaceutical profile of a drug. In this paper, a MOOP method based on Derringer's desirability function that allows conducting global QSAR studies, simultaneously considering the potency, bioavailability, and safety of a set of drug candidates, is introduced. The results of the desirability-based MOOP (the levels of the predictor variables concurrently producing the best possible compromise between the properties determining an optimal drug candidate) are used for the implementation of a ranking method that is also based on the application of desirability functions. This method allows ranking drug candidates with unknown pharmaceutical properties from combinatorial libraries according to the degree of similarity with the previously determined optimal candidate. Application of this method will make it possible to filter the most promising drug candidates of a library (the best-ranked candidates), which should have the best pharmaceutical profile (the best compromise between potency, safety and bioavailability). In addition, a validation method of the ranking process, as well as a quantitative measure of the quality of a ranking, the ranking quality index (W), is proposed. The usefulness of the desirability-based methods of MOOP and ranking is demonstrated by its application to a library of 95 fluoroquinolones, reporting their gram-negative antibacterial activity and mammalian cell cytotoxicity. Finally, the combined use of the desirability-based methods of MOOP and ranking proposed here seems to be a valuable tool for rational drug discovery and development.