Department of Immunogenetics, Institute of Tropical Medicine (NEKKEN), Nagasaki University, Nagasaki, Japan.
Leading Program, Graduate School of Biomedical Sciences, Nagasaki University, Nagasaki, Japan.
Antimicrob Agents Chemother. 2018 Apr 26;62(5). doi: 10.1128/AAC.02424-17. Print 2018 May.
The rapid spread of strains of malaria parasites that are resistant to several drugs has threatened global malaria control. Hence, the aim of this study was to predict the antimalarial activity of chemical compounds that possess anti-hemozoin-formation activity as a new means of antimalarial drug discovery. After the initial anti-hemozoin-formation high-throughput screening (HTS) of 9,600 compounds, a total of 224 hit compounds were identified as hemozoin inhibitors. These 224 compounds were tested for erythrocytic antimalarial activity at 10 μM by using chloroquine-mefloquine-sensitive strain 3D7A. Two independent experiments were conducted. The physicochemical properties of the active compounds were extracted from the ChemSpider and SciFinder databases. We analyzed the extracted data by using Bayesian model averaging (BMA). Our findings revealed that lower numbers of S atoms; lower distribution coefficient (log D) values at pH 3, 4, and 5; and higher predicted distribution coefficient (ACD log D) values at pH 7.4 had significant associations with antimalarial activity among compounds that possess anti-hemozoin-formation activity. The BMA model revealed an accuracy of 91.23%. We report new prediction models containing physicochemical properties that shed light on effective chemical groups for synthetic antimalarial compounds and help with screening for novel antimalarial drugs.
抗药性疟原虫株的快速传播已威胁到全球疟疾控制。因此，本研究旨在预测具有抗疟原虫血红素形成活性的化合物的抗疟活性，作为发现抗疟新药的新方法。在最初对 9600 种化合物进行抗疟原虫血红素形成高通量筛选（HTS）后，共鉴定出 224 种血红素抑制剂。这些 224 种化合物在 10 μM 浓度下通过氯喹-甲氟喹敏感株 3D7A 进行红细胞抗疟活性测试。进行了两项独立的实验。从 ChemSpider 和 SciFinder 数据库中提取活性化合物的物理化学性质。我们通过贝叶斯模型平均（BMA）分析提取的数据。我们的研究结果表明，具有抗疟原虫血红素形成活性的化合物中，S 原子数量较少、pH 值为 3、4 和 5 时的分配系数（log D）值较低、pH 值为 7.4 时的预测分配系数（ACD log D）值较高，与抗疟活性有显著相关性。BMA 模型的准确率为 91.23%。我们报告了新的预测模型，其中包含有助于了解合成抗疟化合物的有效化学基团并有助于筛选新型抗疟药物的物理化学性质。
