Division of Medicinal Chemistry and Natural Products, School of Pharmacy, University of North Carolina at Chapel Hill, North Carolina 27599, United States.
J Med Chem. 2010 Nov 11;53(21):7573-86. doi: 10.1021/jm100600y.
Some antipsychotic drugs are known to cause valvular heart disease by activating serotonin 5-HT(2B) receptors. We have developed and validated binary classification QSAR models capable of predicting potential 5-HT(2B) actives. The classification accuracies of the models built to discriminate 5-HT(2B) actives from the inactives were as high as 80% for the external test set. These models were used to screen in silico 59,000 compounds included in the World Drug Index, and 122 compounds were predicted as actives with high confidence. Ten of them were tested in radioligand binding assays and nine were found active, suggesting a success rate of 90%. All validated actives were then tested in functional assays, and one compound was identified as a true 5-HT(2B) agonist. We suggest that the QSAR models developed in this study could be used as reliable predictors to flag drug candidates that are likely to cause valvulopathy.
一些抗精神病药物通过激活血清素 5-HT(2B)受体已知会导致心脏瓣膜疾病。我们已经开发并验证了能够预测潜在 5-HT(2B)激活剂的二进制分类 QSAR 模型。用于区分 5-HT(2B)激活剂和非激活剂的模型的分类准确率对于外部测试集高达 80%。这些模型用于计算机筛选包含在世界药物索引中的 59,000 种化合物，有 122 种化合物被预测为具有高置信度的活性化合物。其中 10 种在放射性配体结合测定中进行了测试，发现 9 种为活性化合物，提示成功率为 90%。所有经过验证的活性化合物随后都在功能测定中进行了测试，其中一种化合物被鉴定为真正的 5-HT(2B)激动剂。我们建议，本研究中开发的 QSAR 模型可作为可靠的预测因子，标记出可能导致瓣膜病的候选药物。
