Cañizares-Carmenate Yudith, Hernandez-Morfa Mirelys, Torrens Francisco, Castellano Gloria, Castillo-Garit Juan A
Unit of Computer-Aided Molecular "Biosilico" Discovery and Bioinformatic Research (CAMD-BIR) Unit, Facultad de Química-Farmacia, Universidad Central "Marta Abreu".
Institut Universitari de Ciència Molecular, Universitat de València, Edifici d'Instituts de Paterna, València, Spain.
J Vector Borne Dis. 2017 Apr-Jun;54(2):164-171.
BACKGROUND & OBJECTIVES: Aedes aegypti is an important vector for transmission of dengue, yellow fever, chikun- gunya, arthritis, and Zika fever. According to the World Health Organization, it is estimated that Ae. aegypti causes 50 million infections and 25,000 deaths per year. Use of larvicidal agents is one of the recommendations of health organizations to control mosquito populations and limit their distribution. The aim of present study was to deduce a mathematical model to predict the larvicidal action of chemical compounds, based on their structure.
A series of different compounds with experimental evidence of larvicidal activity were selected to develop a predictive model, using multiple linear regression and a genetic algorithm for the selection of variables, implemented in the QSARINS software. The model was assessed and validated using the OECDs principles.
The best model showed good value for the determination coefficient (R2 = 0.752), and others parameters were appropriate for fitting (s = 0.278 and RMSEtr = 0.261). The validation results confirmed that the model hasgood robustness (Q2LOO = 0.682) and stability (R2-Q2LOO = 0.070) with low correlation between the descriptors (KXX = 0.241), an excellent predictive power (R2 ext = 0.834) and was product of a non-random correlation R2 Y-scr = 0.100).
INTERPRETATION & CONCLUSION: The present model shows better parameters than the models reported earlier in the literature, using the same dataset, indicating that the proposed computational tools are more efficient in identifying novel larvicidal compounds against Ae. aegypti.
埃及伊蚊是登革热、黄热病、基孔肯雅热、关节炎和寨卡病毒热传播的重要媒介。据世界卫生组织估计，埃及伊蚊每年导致5000万例感染和25000人死亡。使用杀幼虫剂是卫生组织控制蚊虫数量并限制其分布的建议之一。本研究的目的是基于化合物结构推导一个数学模型，以预测其杀幼虫作用。
选择一系列具有杀幼虫活性实验证据的不同化合物，使用多元线性回归和变量选择的遗传算法来开发预测模型，该算法在QSARINS软件中实现。根据经合组织（OECD）原则对模型进行评估和验证。
最佳模型的决定系数具有良好值（R2 = 0.752），其他参数适合拟合（s = 0.278和RMSEtr = 0.261）。验证结果证实该模型具有良好的稳健性（Q2LOO = 0.682）和稳定性（R2 - Q2LOO = 0.070），描述符之间的相关性较低（KXX = 0.241），具有出色的预测能力（R2 ext = 0.834），并且是非随机相关性R2 Y - scr = 0.100的产物。
使用相同数据集，本模型显示出比文献中先前报道的模型更好的参数，表明所提出的计算工具在识别针对埃及伊蚊的新型杀幼虫化合物方面更有效。
