A Multi-layered Variable Selection Strategy for QSAR Modeling of Butyrylcholinesterase Inhibitors

Chronic aquatic toxicity assessment of diverse chemicals on Daphnia magna using QSAR and chemical read-across

We have modeled here chronic Daphnia toxicity taking pNOEC (negative logarithm of no observed effect concentration in mM) and pEC50 (negative logarithm of half-maximal effective concentration in mM) as endpoints using QSAR and chemical read-across approaches. The QSAR models were developed by strictly obeying the OECD guidelines and were found to be reliable, predictive, accurate, and robust. From the selected features in the developed models, we have found that an increase in lipophilicity and saturation, the presence of electrophilic or electronegative or heavy atoms, the presence of sulphur, amine, and their related functionality, an increase in mean atomic polarizability, and higher number of (thio-) carbamates (aromatic) groups are responsible for chronic toxicity. Therefore, this information might be useful for the development of environmentally friendly and safer chemicals and data-gap filling as well as reducing the use of identified toxic chemicals which have chronic toxic effects on aquatic ecosystems. Approved classes of drugs from DrugBank databases and diverse groups of chemicals from the Chemical and Product Categories (CPDat) database were also assessed through the developed models.

In silico study to recognize novel angiotensin-converting-enzyme-I inhibitors by 2D-QSAR and constraint-based molecular simulations

Cardiovascular diseases (CVD) such as heart failure, stroke, and hypertension affect 64.3 million people worldwide and are responsible for 30% of all deaths. Primary inhibition of the angiotensin-converting enzyme (ACE) is significant in the management of CVD. In the present study, the genetic algorithm-multiple linear regressions (GA-MLR) method is used to generate highly predictive and statistically significant (R2 = 0.70-0.75, Q2LOO=0.67-0.73, Q2LMO=0.66-0.72, CCCex=0.70-0.78) quantitative structure-activity relationships (QSAR) models conferring to OECD requirements using a dataset of 255 structurally diverse and experimentally validated ACE inhibitors. The models contain simply illustratable Padel, Estate, and PyDescriptors that correlate structural scaffold requisite for ACE inhibition. Also, constraint-based molecular docking reveals an interaction profile between ligands and enzymes which is then correlated with the essential structural features associated with the QSAR models. The QSAR-based virtual screening was utilized to find novel lead molecules from a designed database of 102 thiadiazole derivatives. The Applicability domain (AD), Molecular Docking, Molecular dynamics, and ADMET analysis suggest two compound D24 and D40 are inflexibly linked to the protein binding site and follows drug-likeness properties.Communicated by Ramaswamy H. Sarma.

MOZART, a QSAR Multi-Target Web-Based Tool to Predict Multiple Drug-Enzyme Interactions

Developing models able to predict interactions between drugs and enzymes is a primary goal in computational biology since these models may be used for predicting both new active drugs and the interactions between known drugs on untested targets. With the compilation of a large dataset of drug-enzyme pairs (62,524), we recognized a unique opportunity to attempt to build a novel multi-target machine learning (MTML) quantitative structure-activity relationship (QSAR) model for probing interactions among different drugs and enzyme targets. To this end, this paper presents an MTML-QSAR model based on using the features of topological drugs together with the artificial neural network (ANN) multi-layer perceptron (MLP). Validation of the final best model found was carried out by internal cross-validation statistics and other relevant diagnostic statistical parameters. The overall accuracy of the derived model was found to be higher than 96%. Finally, to maximize the diffusion of this model, a public and accessible tool has been developed to allow users to perform their own predictions. The developed web-based tool is public accessible and can be downloaded as free open-source software.

Assessment of the CYP1A2 Inhibition-Mediated Drug Interaction Potential for Pinocembrin Using In Silico, In Vitro, and In Vivo Approaches

Pinocembrin, a bioflavonoid, is extensively used in complementary/alternative medicine. It turns out as a promising candidate against neurodegenerative diseases because of its multifaceted pharmacological action toward neuroprotection. However, literature evidence is still lacking for its inhibitory action on CYP1A2, which is responsible for xenobiotic metabolism leading to the generation of toxic metabolites and bioactivation of procarcinogens. In the present study, our aim was to evaluate the CYP1A2 inhibitory potential of pinocembrin via in silico, in vitro, and in vivo investigations. From the results of in vitro studies, pinocembrin is found to be a potent and competitive inhibitor of CYP1A2. In vitro-in vivo extrapolation results indicate the potential of pinocembrin to interact with CYP1A2 substrate drugs clinically. Molecular docking-based in silico studies demonstrate the strong interaction of pinocembrin with human CYP1A2. In in vivo investigations using a rat model, pinocembrin displayed a marked alteration in the plasma exposure of CYP1A2 substrate drugs, namely, caffeine and tacrine. In conclusion, pinocembrin has a potent CYP1A2 inhibitory action to cause drug interactions, and further confirmatory study is warranted at the clinical level.

Identification of potential antivirals against 3CLpro enzyme for the treatment of SARS-CoV-2: A multi-step virtual screening study

The severe acute respiratory syndrome coronavirus-2 (SARS-CoV-2) outbreak is posing a serious public health threat worldwide in the form of COVD-19. Herein, we have performed two-dimensional quantitative structure-activity relationship (2D-QSAR) and three-dimensional pharmacophore modelling analysis employing inhibitors of 3-chymotrypsin-like protease (3CLpro), the leading protease that is crucial for the replication of SARS-CoV-2. The investigation aims to identify the important structural features responsible for the enzyme inhibition and the search for novel 3CLpro enzyme inhibitors as effective therapeutics for treating SARS-CoV-2. Furthermore, we carried out molecular docking studies using the most and least active compounds in the dataset, aiming to validate the contributions of various features as appeared in the QSAR models. Later, the stringently validated 2D-QSAR model was used to estimate the 3CLpro inhibitory activity of compounds from five chemical databases. Compounds with the significant predicted activity were then subjected to pharmacophore-based virtual screening to screen the top-rated compounds, which were then further subjected to molecular docking analysis, absorption, distribution, metabolism, excretion - toxicity (ADMET) profiling, and molecular dynamics (MD) simulation. The multi-step virtual screening analyses suggested that compounds CASAntiV-865453-58-3, CASAntiV-865453-40-3, and CASAntiV-2043031-84-9 could be used as effective therapeutic agents for the treatment of SARS-CoV-2.

Effect of Myricetin on CYP2C8 Inhibition to Assess the Likelihood of Drug Interaction Using In Silico, In Vitro, and In Vivo Approaches

Myricetin, a bioflavonoid, is widely used as functional food/complementary medicine and has promising multifaceted pharmacological actions against therapeutically validated anticancer targets. On the other hand, CYP2C8 is not only crucial for alteration in the pharmacokinetics of drugs to cause drug interaction but also unequivocally important for the metabolism of endogenous substances like the formation of epoxyeicosatrienoic acids (EETs), which are considered as signaling molecules against hallmarks of cancer. However, there is hardly any information known to date about the effect of myricetin on CYP2C8 inhibition and, subsequently, the CYP2C8-mediated drug interaction potential of myricetin at the preclinical/clinical level. We aimed here to explore the CYP2C8 inhibitory potential of myricetin using in silico, in vitro, and in vivo investigations. In the in vitro study, myricetin showed a substantial effect on CYP2C8 inhibition in human liver microsomes using CYP2C8-catalyzed amodiaquine-N-deethylation as an index reaction. Considering the Lineweaver-Burk plot, the Dixon plot, and the higher α-value, myricetin is found to be a mixed type of CYP2C8 inhibitor. Moreover, in vitro-in vivo extrapolation data suggest that myricetin is likely to cause drug interaction at the hepatic level. The molecular docking study depicted a strong interaction between myricetin and the active site of the human CYP2C8 enzyme. Moreover, myricetin caused considerable elevation in the oral exposure of amodiaquine as a CYP2C8 substrate via a slowdown of amodiaquine clearance in the rat model. Overall, the potent action of myricetin on CYP2C8 inhibition indicates that there is a need for further exploration to avoid drug interaction-mediated precipitation of obvious adverse effects as well as to optimize anticancer therapy.

Ecotoxicological QSTR and QSTTR Modeling for the Prediction of Acute Oral Toxicity of Pesticides against Multiple Avian Species

The ever-increasing use of pesticides in response to the rising agricultural demand has threatened the existence of nontarget organisms like avian species, disrupting the global ecological integrity. Therefore, it is critical to protect and restore different endangered bird species from the perspective of ecosystem safety. In the present work, we have developed regression-based two-dimensional quantitative structure toxicity relationship (2D QSTR) and quantitative structure toxicity-toxicity relationship (QSTTR) models to estimate the toxicity of pesticides on five different avian species following the Organization for Economic Co-operation and Development (OECD) guidelines. Rigorous validation has been performed using different statistical internal and external validation parameters to ensure the robustness and interpretability of the developed models. From the developed models, it can be stated that the presence of electronegative and lipophilic features greatly enhance pesticide toxicity, whereas the hydrophilic characters are shown to have a detrimental impact on the toxicity of pesticides. Moreover, the developed QSTTR models have been employed to the in silico toxicity prediction of 124, 154, and 250 pesticides against bobwhite quail, ring-necked pheasant, and mallard duck species, respectively, extracted from the Office of Pesticides Program (OPP) Pesticide Ecotoxicity Database. The information obtained from the modeled descriptors might be used for pesticide risk assessment in the future, with the added benefit of providing an early caution of their possible negative impact on birds for regulatory purposes.

An Improved Artificial Bee Colony for Feature Selection in QSAR

Quantitative Structure–Activity Relationship (QSAR) aims to correlate molecular structure properties with corresponding bioactivity. Chance correlations and multicollinearity are two major problems often encountered when generating QSAR models. Feature selection can significantly improve the accuracy and interpretability of QSAR by removing redundant or irrelevant molecular descriptors. An artificial bee colony algorithm (ABC) that mimics the foraging behaviors of honey bee colony was originally proposed for continuous optimization problems. It has been applied to feature selection for classification but seldom for regression analysis and prediction. In this paper, a binary ABC algorithm is used to select features (molecular descriptors) in QSAR. Furthermore, we propose an improved ABC-based algorithm for feature selection in QSAR, namely ABC-PLS-1. Crossover and mutation operators are introduced to employed bee and onlooker bee phase to modify several dimensions of each solution, which not only saves the process of converting continuous values into discrete values, but also reduces the computational resources. In addition, a novel greedy selection strategy which selects the feature subsets with higher accuracy and fewer features helps the algorithm to converge fast. Three QSAR datasets are used for the evaluation of the proposed algorithm. Experimental results show that ABC-PLS-1 outperforms PSO-PLS, WS-PSO-PLS, and BFDE-PLS in accuracy, root mean square error, and the number of selected features. Moreover, we also study whether to implement scout bee phase when tracking regression problems and drawing such an interesting conclusion that the scout bee phase is redundant when dealing with the feature selection in low-dimensional and medium-dimensional regression problems.

Amalgamation of in-silico, in-vitro and in-vivo approach to establish glabridin as a potential CYP2E1 inhibitor

CYP2E1 is directly or indirectly involved in the metabolism of ethanol and endogenous fatty acids but it plays a major role in the bio-activation of toxic substances that produce reactive metabolites leading to hepatotoxicity. Therefore, identification of CYP2E1 inhibitor from bioflavonoids class having useful pharmacological properties has dual benefit regarding avoidance of severe food-drug/nutraceutical-drug interaction and scope to develop a phytotherapeutics through an intended pharmacokinetic interaction.In the present study, we aimed to identify CYP2E1 inhibitor from experimental bioflavonoids which are unexplored for CYP2E1 inhibition till date using in-silico, in-vitro and in-vivo approaches.Results of in-vitro CYP2E1 inhibitory studies using CYP2E1-mediated chlorzoxazone 6-hydroxylation in human liver microsomes showed that glabridin have the highest potential than fisetin, epicatechin, nobiletin, and chrysin to inhibit CYP2E1 enzyme. Mechanistic investigations indicate that glabridin is a competitive CYP2E1 inhibitor. Molecular docking study results demonstrate that glabridin strongly interacted with the active site of human CYP2E1 enzyme. Pharmacokinetics of a CYP2E1 substrate in mice model indicates a significant alteration of chlorzoxazone and 6-hydroxychlorzoxazone plasma levels in the presence of glabridin. Further studies are needed to confirm the results at clinical level.Overall, glabridin is found to be a potential CYP2E1 inhibitor.

In silico modeling for dual inhibition of acetylcholinesterase (AChE) and butyrylcholinesterase (BuChE) enzymes in Alzheimer's disease

In this research, we have implemented two-dimensional quantitative structure-activity relationship (2D-QSAR) modeling using two different datasets, namely, acetylcholinesterase (AChE) and butyrylcholinesterase (BuChE) enzyme inhibitors. A third dataset has been derived based on their selectivity and used for the development of partial least squares (PLS) based regression models. The developed models were extensively validated using various internal and external validation parameters. The features appearing in the model against AChE enzyme suggest that a small ring size, higher number of -CH2- groups, higher number of secondary aromatic amines and higher number of aromatic ketone groups may contribute to the inhibitory activity. The features obtained from the model against BuChE enzyme suggest that the sum of topological distances between two nitrogen atoms, higher number of fragments X-C(=X)-X, higher number of secondary aromatic amides, fragment R--CR-X may be more favorable for inhibition. The features obtained from selectivity based model suggest that the number of aromatic ethers, unsaturation content relative to the molecular size and molecular shape may be more specific for the inhibition of the AChE enzyme in comparison to the BuChE enzyme. Moreover, we have implemented the molecular docking studies using the most and least active molecules from the datasets in order to identify the binding pattern between ligand and target enzyme. The obtained information is then correlated with the essential structural features associated with the 2D-QSAR models.