Phanus-Umporn Chuleeporn, Shoombuatong Watshara, Prachayasittikul Veda, Anuwongcharoen Nuttapat, Nantasenamat Chanin
Center of Data Mining and Biomedical Informatics, Faculty of Medical Technology, Mahidol University Bangkok 10700 Thailand
RSC Adv. 2018 Feb 7;8(11):5920-5935. doi: 10.1039/c7ra12079f. eCollection 2018 Feb 2.
Sickle cell disease (SCD), an autosomal recessive genetic disorder, has been recognized by the World Health Organization (WHO) as a major public health problem as it affects 300 000 individuals worldwide. Complications arising from SCD include anemia, microvascular occlusion, severe pain, stokes, renal dysfunction and infections. A lucrative therapeutic strategy is to employ anti-sickling agents that can disrupt the formation of the HbS polymer. This study therefore employed cheminformatic approaches, encompassing classification structure-activity relationship (CSAR) modeling, to deduce the privileged substructures giving rise to the anti-sickling activity of an investigated set of 115 compounds, followed by substructure analysis. Briefly, the compiled compounds were described by fingerprint descriptors and used in the construction of CSAR models several machine learning algorithms. The modelability of the data set, as exemplified by the MODI index, was determined to be in the range of 0.70-0.84. The predictive performance was deduced by the accuracy, sensitivity, specificity and Matthews correlation coefficient, which was found to be statistically robust, whereby the former three parameters afforded values in excess of 0.7 while the latter statistical parameter provided a value greater than 0.5. An analysis of the top 20 important substructure descriptors for anti-sickling activity revealed that 10 important features were significant in the differentiation of actives from inactives, as illustrated by aromaticity/conjugation ( SubFPC287, SubFPC171 and SubFPC5), carbonyl groups ( SubFPC137, SubFPC139, SubFPC49 and SubFPC135) and miscellaneous groups ( SubFPC303, SubFPC302 and SubFPC275). Furthermore, an analysis of the structure-activity relationship revealed that the length of alkyl chains, choice of functional moiety and position of substitution on the benzene ring may affect the anti-sickling activity of these compounds. Thus, this knowledge is anticipated to be useful for guiding the design of robust compounds against the gelling activity of HbS, as preliminarily demonstrated in the data-driven compound design presented herein.
镰状细胞病（SCD）是一种常染色体隐性遗传疾病，已被世界卫生组织（WHO）认定为一个主要的公共卫生问题，因为它影响着全球30万人。SCD引发的并发症包括贫血、微血管阻塞、剧痛、中风、肾功能障碍和感染。一种有前景的治疗策略是使用能够破坏HbS聚合物形成的抗镰状化药物。因此，本研究采用了化学信息学方法，包括分类结构-活性关系（CSAR）建模，以推断出115种被研究化合物产生抗镰状化活性的优势子结构，随后进行子结构分析。简而言之，所汇编的化合物通过指纹描述符进行描述，并用于构建基于几种机器学习算法的CSAR模型。以MODI指数为例，数据集的可建模性被确定在0.70 - 0.84范围内。通过准确性、敏感性、特异性和马修斯相关系数推导出预测性能，发现其在统计学上具有稳健性，其中前三个参数的值超过0.7，而后者的统计参数值大于0.5。对抗镰状化活性的前20个重要子结构描述符的分析表明，10个重要特征在区分活性化合物和非活性化合物方面具有显著性，如芳香性/共轭性（SubFPC287、SubFPC171和SubFPC5）、羰基（SubFPC137、SubFPC139、SubFPC49和SubFPC135）以及其他基团（SubFPC3
