Quantitative structure-activity relationship (QSAR) analysis of plant-derived compounds with larvicidal activity against Zika Aedes aegypti (Diptera: Culicidae) vector using freely available descriptors

QSAR modelling of larvicidal phytocompounds against Aedes aegypti using index of ideality of correlation

Aedes aegypti is the primary vector of several infectious viruses that cause yellow, dengue, chikungunya, and Zika fevers. Recently, plant-derived products have been tested as safe and eco-friendly larvicides against Ae. aegypti. The present study aimed to improve QSAR models for 62 larvicidal phytocompounds against Ae. aegypti via the Monte Carlo method based on the index of the ideality of correlation (IIC) criterion. The representation of structures was done with SMILES. Three splits were prepared randomly and three QSAR models were constructed using IIC target function. The molecular descriptors were selected from SMILES descriptors and the hydrogen-filled molecular graphs. The predictability of three models was evaluated on the validation sets, the r ² of which was 0.9770, 0.8660, and 0.8565 for models 1 to 3, respectively. The statistical results of three randomized splits indicated that robust, simple, predictive, and reliable models were obtained for different sets. From the modelling results, important descriptors were identified to enhance and reduce the larvicidal activity of compounds. Based on the identified important descriptors, some new structures of larvicidal compounds were proposed. The larvicidal activity of novel molecules designed further was supported by docking studies. Using the simple QSAR model, one can predict pLC₅₀ of new similarity larvicidal phytocompounds.

Discovery of new small-molecule cyclin-dependent kinase 6 inhibitors through computational approaches

Excessive cell proliferation due to cell cycle disorders is one of the hallmarks of breast cancer. Cyclin-dependent kinases (CDKs), which are involved in the transition of the cell cycle from G1 phase to S phase by combining CDKs with cyclin, are considered promising targets with broad therapeutic potential based on their critical role in cell cycle regulation. Pharmacological evidence has shown that abnormal cell cycle due to the overexpression of CDK6 is responsible for the hyperproliferation of cancer cells. Blocking CDK6 expression inhibits tumour survival and growth. Therefore, CDK6 can be regarded as a potential target for anticancer therapeutics. Thus, small molecules that can be considered CDK inhibitors have been developed into promising anticancer drugs. In this study, combined structure-based and ligand-based in silicon models were created to identify new chemical entities against CDK6 with the appropriate pharmacokinetic properties. The database used to screen drug-like compounds in this thesis was based on the best E-pharmacophore hypothesis and the best ligand-based drug hypothesis. As a result, 147 common compounds were identified by further molecular docking. Surprisingly, the in vitro evaluation results of 20 of those compounds showed that the two had good CDK6 inhibitory effects. The best compound was subjected to kinase panel screening, followed by molecular dynamic simulations. The 50-ns MD studies revealed the pivotal role of VAL101 in the binding of inhibitors to CDK6. Overall, the identification of two new chemical entities with CDK6 inhibitory activity demonstrated the feasibility and potential of the new method.

Rational design of novel coumarins: A potential trend for antioxidants in cosmetics

Coumarins are well-known for their antioxidant effect and aromatic property, thus, they are one of ingredients commonly added in cosmetics and personal care products. Quantitative structure-activity relationships (QSAR) modeling is an in silico method widely used to facilitate rational design and structural optimization of novel drugs. Herein, QSAR modeling was used to elucidate key properties governing antioxidant activity of a series of the reported coumarin-based antioxidant agents (1-28). Several types of descriptors (calculated from 4 softwares i.e., Gaussian 09, Dragon, PaDEL and Mold2 softwares) were used to generate three multiple linear regression (MLR) models with preferable predictive performance (Q 2 LOO-CV = 0.813-0.908; RMSE LOO-CV = 0.150-0.210; Q 2 Ext = 0.875-0.952; RMSE Ext = 0.104-0.166). QSAR analysis indicated that number of secondary amines (nArNHR), polarizability (G2p), electronegativity (D467, D580, SpMin2_Bhe, and MATS8e), van der Waals volume (D491 and D461), and H-bond potential (SHBint4) are important properties governing antioxidant activity. The constructed models were also applied to guide in silico rational design of an additional set of 69 structurally modified coumarins with improved antioxidant activity. Finally, a set of 9 promising newly design compounds were highlighted for further development. Structure-activity analysis also revealed key features required for potent activity which would be useful for guiding the future rational design. In overview, our findings demonstrated that QSAR modeling could possibly be a facilitating tool to enhance successful development of bioactive compounds for health and cosmetic applications.

A non-conformational QSAR study for plant-derived larvicides against Zika Aedes aegypti L. vector

A set of 263 plant-derived compounds with larvicidal activity against Aedes aegypti L. (Diptera: Culicidae) vector is collected from the literature, and is studied by means of a non-conformational quantitative structure-activity relationships (QSAR) approach. The balanced subsets method (BSM) is employed to split the complete dataset into training, validation and test sets. From 26,775 freely available molecular descriptors, the most relevant structural features of compounds affecting the bioactivity are taken. The molecular descriptors are calculated through four different freewares, such as PaDEL, Mold², EPI Suite and QuBiLs-MAS. The replacement method (RM) variable subset selection technique leads to the best linear regression models. A successful QSAR equation involves 7-conformation-independent molecular descriptors, fulfiling the evaluated internal (loo, l30%o, VIF and Y-randomization) and external (test set with N_test = 65 compounds) validation criteria. The practical application of this QSAR model reveals promising predicted values for some natural compounds with unknown experimental larvicidal activity. Therefore, the present model constitutes the first one based on a large molecular set, being a useful computational tool for identifying and guiding the synthesis of new active molecules inspired by natural products.

Synergistic terpene combinations as larvicides against the dengue vector Aedes aegypti Linn

Most of the essential oils (EO) known for mosquitocidal activity contain terpenes as part of their chemical constituents. In this study, four monoterpenes namely γ-terpinene (T), R-(+)-limonene (L), carvacrol (C), and trans-anethole (A) were screened against late third instar Aedes aegypti Linn. larvae singly and in combination to find out the synergistic interaction. The monoterpenes γ-terpinene, R-(+)-limonene, trans-anethole, and carvacrol showed larvicidal activity with LC₅₀ values of 9.76, 11.88, 50.19, 48.57 ppm and LC₉₀ values of 16.99, 17.78, 65.21, 75.02 ppm, respectively for Ae. aegypti when tested individually. Prepared binary combinations at LC₂₀ values and studied the synergistic interactions based on cotoxicity factor (CTF) and found that all the binary combinations showed synergistic interactions (CTF > 20). Further, the binary (1:1), ternary (1:1:1), and quaternary (1:1:1:1) terpene combinations on equal weight basis were prepared, evaluated against Ae. aegypti larvae and synergistic interactions were studied by Wadley's method. Among the eight combinations two combinations viz. LC (LC₅₀ 10.42 ppm) and LA (LC₅₀ 11.86 ppm) were showing synergistic interactions (R > 1.5) and others were exhibiting additive effect (1.5 ≥ R > 0.5) and none of the combinations showed antagonism (R < 0.5). Among the two methods used the Wadley's method found suitable when combinations were made on an equal weight basis. The two binary combinations found in this study are promising synergistic mixtures which has scope in designing mosquito larvicidal EOs by enriching the EOs with terpenes or by producing EO combinations with these terpenes as chemical constituents in the correct proportions.

Substituent effects on the ultraviolet absorption properties of stilbene compounds-Models for molecular cores of absorbents

The effects of substituent X and Y on ultraviolet (UV) absorption properties of stilbene compounds XPhCHCHPhY (XSBY) were studied both experimentally and computationally from the viewpoint of UV maximum absorption wavelength (λ_max) and the corresponding energy (υ_max). In the studies, the contribution of substituents on υ_max shift was explored. The results show that with increase of electron withdrawing or electron donating ability of X or Y, there is an enhanced electron delocalization of XSBY that leads to bathochromic shift. Computational analyses based on density functional theory were conducted to elucidate the phenomena. It is disclosed that the υ_max values are significantly affected by the excited state, though the electronic effect of ground state cannot be ignored. Finally, on the basis of the respective influences of X and Y, a quantitative model, which was proved reliable by the leave-one-out method, was developed to scale the effects of terminal substituents on υ_max. According to the model, the effects of substituents X or Y exhibit almost the same action on υ_max owing to the symmetric skeleton of the XSBY compounds. The findings provide deep insight into the effects of terminal substituents on UV absorption properties of stilbene compounds, and the derived model enables practical expression of the relationship between substituents and UV absorption.

Exploration of good and bad structural fingerprints for inhibition of indoleamine-2,3-dioxygenase enzyme in cancer immunotherapy using Monte Carlo optimization and Bayesian classification QSAR modeling

Indoleamine-2,3-dioxygenase 1 (IDO1) is an extrahepatic, heme-containing and tryptophan-catalyzing enzyme responsible for causing blockade of T-cell proliferation and differentiation by depleting tryptophan level in cancerous cells. Therefore, inhibition of IDO1 may be a useful strategy for immunotherapy against cancer. In this study, 448 structurally diverse IDO1 inhibitors with a wide range of activity has been taken into consideration for classification QSAR analysis through Monte Carlo Optimization by using different splits as well as different combinations of SMILES-based, graph-based and hybrid descriptors. The best model from Monte Carlo optimization was interpreted to find out the good and bad structural fingerprints for IDO1 and further justified by using Bayesian classification QSAR modeling. Among the three splits in Monte Carlo optimization, the statistics of the best model was obtained from Split 3: sensitivity = 0.87, specificity = 0.91, accuracy = 0.89 and MCC = 0.78. In Bayesian classification modeling, the ROC scores for training and test set were found to be 0.91 and 0.86, respectively. The combined modeling analysis revealed that the presence of aryl hydrazyl sulphonyl moiety, furazan ring, halogen substitution, nitro group and hetero atoms in aromatic system can be very useful in designing IDO1 inhibitors. All the good and bad structural fingerprints for IDO1 were identified and are justified by correlating these fragments to the inhibition of IDO1 enzyme. These structural fingerprints will guide the researchers in this field to design better inhibitors against IDO1 enzyme for cancer immunotherapy.Communicated by Ramaswamy H. Sarma.