Chemometric modeling of larvicidal activity of plant derived compounds against zika virus vector Aedes aegypti: application of ETA indices

Abstract

Dengue, zika and chikungunya have severe public health concerns in several countries. Human modification of the natural environment continues to create habitats in which mosquitoes, vectors of a wide variety of human and animal pathogens, thrive, which can bring about an enormous negative impact on public health if not controlled properly. Quantitative structure-activity relationship (QSAR) modeling has been applied in this work with the aim of exploring features contributing to promising larvicidal properties against the vector Aedes aegypti (Diptera: Culicidae). A dataset of 61 plant derived compounds reported in previous literature was used in this present study. A genetic algorithm (GA) was used for QSAR model development employing the "Double Cross Validation" (DCV) tool available at http://teqip.jdvu.ac.in/QSAR_Tools/. The DCV tool removes any bias in descriptor selection from a fixed composition of a training set and often provides an optimum solution in terms of predictivity. Simple topological descriptors, the "Extended Topochemical Atom" (ETA) indices developed by the present authors' group, were used for model development. These descriptors do not require pretreatment of molecular structures by conformational analysis or energy minimization before model development, thus saving computational time and resources. They also avoid ambiguities with respect to the existence of compounds in various conformational states leading to the loss of predictive capability in QSAR models. A number of models were generated from GA, and further, the descriptors appearing in the best model obtained from GA were subjected to partial least squares (PLS) regression to obtain the final robust model. The developed model was validated extensively using different validation metrics to check the reliability and predictivity of the model for enhancing confidence in QSAR predictions. Based on the insights obtained from the PLS model, we can conclude that the presence of hydrogen bond acceptor atoms, the presence of multiple bonds as well as sufficient lipophilicity and a limited polar surface area play crucial roles in regulating the activity of the compounds.

Fig. 1. Regression coefficient plot of the final PLS model.

Fig. 2. Variable importance plot of the final PLS model.

Fig. 3. Contribution of ETA_dEpsilon_D to pLC₅₀ of the compounds.

Fig. 4. Effect of ETA_EtaP_F on pLC₅₀ of the compounds.

Fig. 5. Contribution of ETA_dAlpha_B to pLC₅₀ of the compounds.

Fig. 6. Contribution of ETA_BetaP_s on pLC₅₀ of the compounds.

Fig. 7. Effect of ETA_dEpsilon_C on pLC₅₀ of the compounds.

Fig. 8. Score plot of the final PLS model.

Fig. 9. Loading plot of the final PLS model.