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The present study explores the important chemical features of diverse petroleum hydrocarbons (PHCs) responsible for their biodegradation by developing partial least squares (PLS) regression-based quantitative structure-property relationship (QSPR) models. The biodegradability is estimated in terms of biodegradation half-life (Logt1/2). All the PLS models were extensively validated by different internationally acceptable internal (R2= 0.849–0.861; Q2 = 0.833–0.849; R2adj = 0.845–0.858) and external (Q2F1= 0.825-0.848; Q2F2 = 0.822–0.845) validation parameters. The consensus predictions were also performed by using the “intelligent consensus predictor” (ICP) tool, which improves the predictive ability of individual models based on mean absolute error (MAE)-based criteria. The models suggested that the biodegradation of PHCs is dependent on the presence of substituents on the aromatic ring, 12 atom containing ring system, thiophene moiety, electron rich chemicals, large molecular size, degree of unsaturation, degree of branching, cyclization, and hydrophobicity.

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.

Background: Alzheimer’s disease (AD), a neurological disorder, is the most common cause of senile dementia. Butyrylcholinesterase (BuChE) enzyme plays a vital role in regulating the brain acetylcholine (ACh) neurotransmitter, but in the case of Alzheimer’s disease (AD), BuChE activity gradually increases in patients with a decrease in the acetylcholine (ACh) concentration via hydrolysis. ACh plays an essential role in regulating learning and memory as the cortex originates from the basal forebrain, and thus, is involved in memory consolidation in these sites. Methods: In this work, we have developed a partial least squares (PLS)-regression based two dimensional quantitative structure-activity relationship (2D-QSAR) model using 1130 diverse chemical classes of compounds with defined activity against the BuChE enzyme. Keeping in mind the strict Organization for Economic Co-operation and Development (OECD) guidelines, we have tried to select significant descriptors from the large initial pool of descriptors using multi-layered variable selection strategy using stepwise regression followed by genetic algorithm (GA) followed by again stepwise regression technique and at the end best subset selection prior to development of final model thus reducing noise in the input. Partial least squares (PLS) regression technique was employed for the development of the final model while model validation was performed using various stringent validation criteria. Results: The results obtained from the QSAR model suggested that the quality of the model is acceptable in terms of both internal (R2= 0.664, Q2= 0.650) and external (R2 Pred= 0.657) validation parameters. The QSAR studies were analyzed, and the structural features (hydrophobic, ring aromatic and hydrogen bond acceptor/donor) responsible for enhancement of the activity were identified. The developed model further suggests that the presence of hydrophobic features like long carbon chain would increase the BuChE inhibitory activity and presence of amino group and hydrazine fragment promoting the hydrogen bond interactions would be important for increasing the inhibitory activity against BuChE enzyme. Conclusion: Furthermore, molecular docking studies have been carried out to understand the molecular interactions between the ligand and receptor, and the results are then correlated with the structural features obtained from the QSAR models. The information obtained from the QSAR models are well corroborated with the results of the docking study.

Site-specific delivery of chemotherapeutics specifically to neoplastic hepatocytes without affecting normal hepatocytes should be a focus for potential therapeutic management of hepatocellular carcinoma (HCC). The aptamer TLS 9a with phosphorothioate backbone modifications (L5) has not been explored so far for preferential delivery of therapeutics in neoplastic hepatocytes to induce apoptosis. Thus, the objective of the present investigation was to compare the therapeutic potential of L5-functionalized drug nanocarrier (PTX-NPL5) with those of the other experimental drug nanocarriers functionalized by previously reported HCC cell-targeting aptamers and non-aptamer ligands, such as galactosamine and apotransferrin. A myriad of well-defined investigations such as cell cycle analysis, TUNEL (terminal deoxynucleotidyltransferase-mediated deoxyuridine triphosphate nick end labeling) assay, and studies related to apoptosis, histopathology, and immunoblotting substantiated that PTX-NPL5 had the highest potency among the different ligand-attached experimental formulations in inducing selective apoptosis in neoplastic hepatocytes via a mitochondrial-dependent apoptotic pathway. PTX-NPL5 did not produce any notable toxic effects in healthy hepatocytes, thus unveiling a new and a safer option in targeted therapy for HCC. Molecular modeling study identified two cell-surface biomarker proteins (tumor-associated glycoprotein 72 [TAG-72] and heat shock protein 70 [HSP70]) responsible for ligand-receptor interaction of L5 and preferential internalization of PTX-NPL5 via clathrin-mediated endocytosis in neoplastic hepatocytes. The potential of PTX-NPL5 has provided enough impetus for its rapid translation from the pre-clinical to clinical domain to establish itself as a targeted therapeutic to significantly prolong survival in HCC., Graphical Abstract

To study the relationship between toxicity of organic chemicals (OCs) with their structural and physicochemical features, the authors have developed a linear discriminant analysis (LDA) model for the classification of organic chemicals based on their acute observed toxicity in algae. This is done by employing a sufficiently large dataset of 352 chemicals following the strict Organization for Economic Cooperation and Development (OECD) guidelines for the quantitative structure-activity relationship (QSAR) validation. Additionally, 3D toxicophore models were generated to explore for the presence of common features contributing to the toxicity making chemicals a major concern for the future. Both of the models were rigorously validated following stringent validation criteria such as Wilks' λ statistic, canonical index (Rc), squared Mahalanobis distance, and chi-squared. Finally, a confusion matrix was employed to check for the quality of classification/prediction obtained for the LDA and pharmacophore models both in the training and the test sets.

The authors have developed an artificial neural network model using odor threshold (OT) property data for diverse odorant components present in black tea (76 components) and coffee (46 components). The models were validated in terms of both internal and external validation criteria signifying acceptable results. The authors found the significant features controlling the OT property using Mean Absolute Error (MAE)-based criteria in a backward elimination of descriptors, one in each turn. The present results well-corroborated the previously published PLS-regression based chemometric model results.

We have developed a robust quantitative structure-activity relationship (QSAR) model employing a dataset of 98 heterocycle compounds to identify structural features responsible for BACE1 (beta-secretase 1) enzyme inhibition. We have used only 2D descriptors for model development purpose thus avoiding the conformational complications arising due to 3D geometry considerations. Following the strict Organization for Economic Co-operation and Development (OECD) guidelines, we have developed models using stepwise regression analysis followed by the best subset selection, while the final model was developed by partial least squares regression technique. The model was validated using various internationally accepted stringent validation parameters. From the insights obtained from the developed model, we have concluded that heteroatoms (nitrogen, oxygen, etc.) present within to an aromatic nucleus and the structural features such as hydrophobic, ring aromatic and hydrogen bond acceptor/donor are responsible for the enhancement of the BACE1 enzyme inhibitory activity. Moreover, we have performed the pharmacophore modelling to unveil the structural requirements for the inhibitory activity against the BACE1 enzyme. Furthermore, molecular docking studies were carried out to understand the molecular interactions involved in binding, and the results are then correlated with the requisite structural features obtained from the QSAR and pharmacophore models.

The authors have developed an artificial neural network model using odor threshold (OT) property data for diverse odorant components present in black tea (76 components) and coffee (46 components). The models were validated in terms of both internal and external validation criteria signifying acceptable results. The authors found the significant features controlling the OT property using Mean Absolute Error (MAE)-based criteria in a backward elimination of descriptors, one in each turn. The present results well-corroborated the previously published PLS-regression based chemometric model results.

In order to understand the physicochemical properties as well as the mechanisms behind adsorption of hazardous synthetic organic chemicals (SOCs) onto single walled carbon nanotubes (SWCNTs), we have developed partial least squares (PLS)-regression based QSPR models using a diverse set of 40 hazardous SOCs having defined adsorption coefficient (logK). The models were extensively validated using different validation parameters in order to assure the robustness and predictivity of the models. We have also checked the consensus predictivity of all the individual models using “Intelligent consensus predictor” tool for possible enhancement of the quality of predictions for test set compounds. The consensus predictivity of the test set compounds were found to be better than the individual models based on not only the MAE based criteria (MAE(95%) = Good) but also some other validation parameters (Q2F1 = 0.938, Q2F2 = 0.937). The contributing descriptors obtained from the QSPR models suggested that the hazardous SOCs may get adsorbed onto the SWCNTs through hydrophobic interaction as well as hydrogen bonding interactions and electrostatic interaction to the functionally modified SWCNTs. Thus, the developed models may provide knowledge to scientists to increase the efficient application of SWCNTs as a special adsorbent, which may be useful for the management of environmental pollution.

Nowadays, the risk assessment of engineered nanoparticles (NPs) on human health and animals is of great importance. We have used here simple periodic table based descriptors for mixture compounds t...

Nanotechnology has introduced a new generation of adsorbents like carbon nanotubes (CNTs), which have drawn a widespread attention due to their outstanding ability for the removal of various inorganic and organic pollutants. The goal of this study was to develop regression-based quantitative structure–property relationship (QSPR) models for organic pollutants and organic solvents using only easily computable 2D descriptors to explore the key structural features essential for adsorption to multi-walled CNTs and improve the dispersibility index of single-walled CNTs. The statistical results of the developed models showed good quality and predictivity based on both internal and external validation metrics (dataset 1: R2 range of 0.893–0.920, Q2(LOO) range of 0.863–0.895, Q2F1 range of 0.887–0.919; dataset 2: R2 range of 0.793–0.845, Q2(LOO) range of 0.743–0.798, Q2F1 range of 0.783–0.890; dataset 3: R2 = 0.830, Q2(LOO) = 0.775, Q2F1 = 0.945). We have also tried to explore whether the quality of the predictions of test set compounds can be enhanced through an “intelligent” selection of multiple models using the “Intelligent consensus predictor” tool. The consensus results suggested that the consensus predictivity of the test set compounds gave better results than those from the individual MLR models based on different criteria (dataset 1: Q2F1 = 0.935, Q2F2 = 0.935, MAE(95%) = good; dataset 2: Q2F1 = 0.887, Q2F2 = 0.879, MAE(95%) = good). The contributed descriptors obtained from different models suggested that the organic pollutants may adsorb to the CNTs through hydrogen bonding interactions, π–π interactions, hydrophobic interactions and electrostatic interaction. Based on the observations obtained from the developed models, we have inferred that the adsorption of the organic pollutants onto the CNTs can be enhanced by the following factors: a higher number of aromatic rings, high unsaturation or electron richness of molecules, the presence of polar groups substituted in the aromatic ring, the presence of oxygen and nitrogen atoms, the size of the molecules, and the hydrophobic surface of the molecules. On the other hand, the presence of C–O groups, aliphatic primary alcohols and the presence of chlorine atoms may retard the adsorption of organic pollutants. The results also suggest that the organic solvents bearing the >N- fragment, a higher degree of branching (compactness), polar solvents with low donor number and lower ionization potential may be better solvents for enhancing the dispersibility of single-walled CNTs.

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

The present chapter reports a partial least squares (PLS)-regression-based chemometric model for the carbazole class of dyes used in dye-sensitized solar cells (DSSCs) to predict the absorption maxima (λmax) values. Quantitative prediction of the λmax values can be an important criterion for molecular design of new dye molecules. We have used only 2D descriptors for the model development purpose as the quantum chemical and electrochemical analyses are time consuming. The developed model was validated extensively using internationally acceptable statistical and validation parameters. A seven-descriptor PLS model with one latent variable (LV) was developed. The statistical results suggested that the model was statistically significant. The majority of the descriptors involved in the model are easily interpretable 2D atom pair descriptors. The model suggests that presence of nitrogen and sulfur atoms at the topological distance of 8, presence of nitrogen and oxygen atoms at the topological distance of 4, higher frequency of oxygen and sulfur atoms at the topological distance of 5, higher frequency of two nitrogen atoms at the topological distance of 5, presence of two oxygen atoms at the topological distance of 4, presence of carbon atoms connected with three aromatic bonds, and presence of two sulfur atoms at the topological distance of 4 in the dye molecules shifted the λmax value toward the longer wavelength. From the information obtained from the model, it has also been suggested that highly conjugated π-systems shift the λmax values toward the longer wavelength. Finally, it can be concluded that the identified features from the PLS model for the carbazole derivatives may be employed for the design and development of new carbazole dyes and to predict λmax values before they are synthesized.

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. 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.

After the 1918 Spanish Flu pandemic caused by the H1N1 virus, the recent coronavirus disease 2019 (COVID-19) brought us to the time of serious global health catastrophe. Although no proven therapies are identified yet which can offer a definitive treatment of the COVID-19, a series of antiviral, antibacterial, antiparasitic, immunosuppressant drugs have shown clinical benefits based on repurposing theory. However, these studies are made on small number of patients, and, in majority of the cases, have been carried out as nonrandomized trials. As society is running against the time to combat the COVID-19, we present here a comprehensive review dealing with up-to-date information of therapeutics or drug regimens being utilized by physicians to treat COVID-19 patients along with in-depth discussion of mechanism of action of these drugs and their targets. Ongoing vaccine trials, monoclonal antibodies therapy and convalescent plasma treatment are also discussed. Keeping in mind that computational approaches can offer a significant insight to repurposing based drug discovery, an exhaustive discussion of computational modeling studies is performed which can assist target-specific drug discovery. Graphic abstract

Recent tuberculosis (TB) drug discovery programme involve continuous pursuit for new chemical entity (NCE) which can be not only effective against both susceptible and resistant strains of Mycobacterium tuberculosis (Mtb) but also safe and faster acting with the target, thereby shortening the prolonged TB treatments. We have identified a potential nitrofuranyl methyl piperazine derivative, IIIM-MCD-211 as new antitubercular agent with minimum inhibitory concentration (MIC) value of 0.0072 μM against H37Rv strain. Objective of the present study is to investigate physicochemical, pharmacokinetic, efficacy and toxicity profile using in-silico, in-vitro and in-vivo model in comprehensive manner to assess the likelihood of developing IIIM-MCD-211 as a clinical candidate. Results of computational prediction reveal that compound does not violate Lipinski's, Veber's and Jorgensen's rule linked with drug like properties and oral bioavailability. Experimentally, IIIM-MCD-211 exhibits excellent lipophilicity that is optimal for oral administration. IIIM-MCD-211 displays evidence of P-glycoprotein (P-gp) induction but no inhibition ability in rhodamine cell exclusion assay. IIIM-MCD-211 shows high permeability and plasma protein binding based on parallel artificial membrane permeability assay (PAMPA) and rapid equilibrium dialysis (RED) assay model, respectively. IIIM-MCD-211 has adequate metabolic stability in rat liver microsomes (RLM) and favourable pharmacokinetics with admirable correlation during dose escalation study in Swiss mice. IIIM-MCD-211 has capability to appear into highly perfusable tissues. IIIM-MCD-211 is able to actively prevent progression of TB infection in chronic infection mice model. IIIM-MCD-211 shows no substantial cytotoxicity in HepG2 cell line. In acute toxicity study, significant increase of total white blood cell (WBC) count in treatment group as compared to control group is observed. Overall, amenable preclinical data make IIIM-MCD-211 ideal candidate for further development of oral anti-TB agent.

Tea and coffee are the most attractive non-alcoholic beverages used worldwide due to the odorant properties of diverse components present in these beverages. The aim of this work is to investigate the key structural features which regulate the odorant properties of constituents present in black tea and coffee using regression-based chemometric models. We have also investigated the key structural properties which create the odor difference between tea and coffee. We have employed different variable selection strategies to extract the most relevant variables prior to development of final partial least squares (PLS) models. The models were extensively validated using different validation metrics, and the results justify the reliability and usefulness of the developed predictive PLS models. The best PLS model captured the necessary structural information on relative hydrophobic surface area, heteroatoms with higher number of multiple bonds, hydrogen atoms connected to C3(sp3)/C2(sp2)/C3(sp2)/C3(sp) fragments, electron-richness, C–O atom pairs at the topological distance 10 and surface weighted charged partial negative surface areas for explaining the odorant properties of the constituents present in black tea. On the other hand, C–S atom pairs at the topological distance 1, C–C atom pairs at the topological distance 5, donor atoms like N and O for hydrogen bonds, hydrogen atoms connected to C3(sp3)/C2(sp2)/C3(sp2)/C3(sp) fragments and R–CX–X fragments (where, R represents any group linked through carbon and X represents any heteroatom (O, N, S, P, Se, and halogens)) are the key structural components captured by the PLS model developed from the constituents present in coffee. The developed models can thus be successfully utilized for in silico prediction of odorant properties of diverse classes of compounds and exploration of the structural information which creates the odor difference between black tea and coffee.

The current quantitative structure-activity relationship (QSAR) study seeks to explore the underlying causes of fluctuations in growth rate and biomass of microalgae mainly due to textile dyes. The derived QSAR models cover two endpoints: ErC50 (growth rate) and EbC50 (biomass) of Raphidocelis subcapitata. In order to extract the structural features involved, multiple PLS (partial least squares) models have been developed with easy to interpret and uncomplicated 2D descriptors having proper physico-chemical meaning. These descriptors were calculated from Dragon, SiRMS, and PaDEL-descriptor software. Then, the models were developed initially using stepwise regression followed by partial least squares (PLS) regression, and the model development procedure for both the endpoints (ErC50 and EbC50) followed the stringent Organization for Economic Cooperation and Development (OECD) rules. Later on, the model validation was carried out with statistically significant and internationally accepted metrics (both internally and externally) in both the cases. Next, we have used the "Intelligent Consensus Predictor" tool (available from http://teqip.jdvu.ac.in/QSAR_Tools/DTCLab/) to test the prediction quality with an "intelligent" approach to select multiple models. The estimated prediction quality for the appropriate test sets reveals that the consensus models (CM) surpass the quality shown by individual models (IM) for both the endpoints (ErC50 and EbC50). Finally, the developed models were able to identify the major contributing features (hydrophobic units, unsaturation, saturation, electronegativity, branched atoms and charged fragments) related to aquatic toxicity of textile dyes.

Ionic liquids (ILs) have significant uses in academic research and industrial process development. The tunable physicochemical properties like low vapor pressure, exceptional solvating properties, low inflammability etc. make them eco-friendly and green solvents. For industrial or laboratory purposes, it is very difficult to operate with high viscous ILs. Therefore, in this work, our attention is focused on modeling of viscosity of ILs using quantitative structure-property relationship (QSPR), in order to provide essential information to chemists enabling design of lower viscous ILs for addressing operational problems. In the present study, we have developed a QSPR model using a pool of 291 ILs using easily computable two-dimensional descriptors. Different strategies were applied to select appropriate variables for development of the final partial least squares (PLS) regression model. The model was validated extensively using different validation metrics to check the robustness and predictivity of the model for regulatory acceptance and enhancing confidence in QSPR predictions. The results showed that the model was statistically robust. Based on the insights obtained from the PLS model as mapped by the loading plot, scores plot, regression coefficient plot etc., we have concluded that intrinsic properties of cations, number of path count of order three at the anionic part, temperature, cation containing branched alkyl chain with large numbers of heteroatoms, shape of anions (spherical or rod), presence or absence of CH2RX fragment, presence or absence of quaternary nitrogen at the cationic part, aliphatic keto group at the anionic part, a C S bond at topological distance of 4 at the cationic part and presence or absence of a pyrrolidine ring at the cationic part of ILs play crucial roles to regulate the viscosity of ILs.

Tumor angiogenesis and invasion are deregulated biological processes that drive multistage transformation of tumors from a benign to a life-threatening malignant state activating multiple signaling pathways including MD-2/TLR4/NF-κB. Development of potential inhibitors of this signaling is emerging area for discovery of novel cancer therapeutics. In the current investigation, we identified Iturin A (A lipopeptide molecule from Bacillus megaterium) as a potent inhibitor of angiogenesis and cancer invasion by various in vitro and in vivo methods. Iturin A was found to suppress VEGF, a powerful inducer of angiogenesis and key player in tumor invasion, as confirmed by ELISA, western blot and real time PCR. Iturin A inhibited endothelial tube arrangement, blood capillary formation, endothelial sprouting and vascular growth inside the matrigel. In addition, Iturin A inhibited MMP-2/9 expression in MDA-MB-231 and HUVEC cells. Cancer invasion, migration and colony forming ability were significantly hampered by Iturin A. Expressions of MD-2/TLR4 and its downstream MyD88, IKK-α and NF-κB were also reduced in treated MDA-MB-231 and HUVEC cells. Western blot and immunofluorescence study showed that nuclear accumulation of NF-κB was hampered by Iturin A. MD-2 siRNA or plasmid further confirmed the efficacy of Iturin A by suppressing MD-2/TLR4 signaling pathway. The in silico docking study showed that the Iturin A interacted well with the MD-2 in MD-2/TLR4 receptor complex. Conclusively, inhibition of MD-2/TLR4 complex with Iturin A offered strategic advancement in cancer therapy.

Environmental transformation products of pesticides (ETPPs) have a great deal of ecological impact owing to their ability to cause toxicity to the aquatic organisms, which can then be translated to the humans. The limited experimental data on biochemical and toxic effects of ETPPs, the high test costs together with regulatory limitations and the international push to reduce animal testing encourage greater dependence on predictive in silico techniques like quantitative structure-activity relationship (QSAR) models. The aim of the present work was to explore the key structural features, which regulate the toxicity towards fishes, for 85 ETPPs using a partial least squares (PLS) regression based chemometric model developed according to Organisation for Economic Co-operation and Development (OECD) guidelines. The model was extensively validated using both internal and external validation metrics, and the results so obtained justify the reliability and usefulness of the developed model (Q2 = 0.648, R2pred or Q2F1 = 0.734 and Q2F2 = 0.733). From the developed model, we can conclude that lipophilicity, polarity, presence of branching and the functional form of O-atom in the transformed structures of pesticides are the important features that are to be considered during ecotoxicity assessment of ETPPs. The information obtained from the descriptors of the developed model could be utilized in the future for assessing ETPPs with the benefit of providing an early warning of their potentially detrimental effect on fishes for regulatory purposes.

The discharge of huge amount of chemicals from industries into the environment has led to toxicity towards different living species. Therefore, risk assessment of these chemicals is essential. In order to comply with the ethical issues, in this present work, we have developed quantitative structure-activity relationship (QSAR) models for cytotoxicity against GFS (goldfish scale) tissue (Crassius auratus) and enzymatic activity against PLHC-1 cell line (topminnow hepatoma cell line) (Poeciliopsis lucida). The final models were developed by means of PLS (Partial Least Squares) regression method applying only ETA (extended topochemical atom) descriptors. The results obtained from various validation parameters (obtained from the both datasets) suggested that the developed models are statistically robust and predictive. From the insights obtained from the models developed from the Neutral Red dye (NR) dataset, it can be concluded that presence of bulky atoms, unsaturation, branching and hetero atoms (most importantly N, Cl) enhance the cytotoxicity towards the Goldfish scale tissue. On the other hand, in case of the Ethoxyresorufin-O-deethylase (EROD) dataset, presence of higher electronegative atoms (O, Cl), polycyclic aromatic hydrocarbons (PAHs) with more number of rings and absence of polar groups and hydrogen bond acceptors enhance enzymatic activity of the PLHC-1 cell line.

The present study reports chemometric modeling of power conversion efficiency (PCE) of dye sensitized solar cells (DSSCs) using the biggest available data set till date which comprises around 1200 dyes covering 7 chemical classes. To extract the best structural features required for higher PCE, we have developed multiple partial least squares (PLS) quantitative structure-property relationship (QSPR) models for the Triphenylamine, Phenothiazine, Indoline, Porphyrin, Coumarin, Carbazole and Diphenylamine chemical classes using descriptors derived from the best subset selection method followed by selection of best five models in each dataset based on the Mean Absolute Error (MAE) values. The models were validated both internally and externally followed by the consensus predictions employing “Intelligent Consensus Predictor” tool to examine whether the quality of predictions can be improved with the “intelligent” selection of multiple PLS models. The quality of predictions for the respective external sets showed that the consensus models (CM) are better than the individual models (IM) in most of the cases. From the insights of the developed models, we concluded that attributes like a packed structure toward higher conductivity of electrons, auxiliary donor fragment of aromatic tertiary amines, number of thiophenes inducing the bathochromic shift and augmenting the absorption, presence of additional electron donors, enhancement of electron-donating abilities, number of non-aromatic conjugated C(sp2) which helps as conjugation extension units to broaden the absorption and highly conjugated π-systems exert positive contributions to the PCE. On the contrary, features negatively contributing to PCE are the followings: fragments which lower the tendency of localized π–π* transition, fragments related to larger volume and surface area of dyes along with hydrophobicity resulting in poor adhesion, fragment RC = N causing dye hydrolysis, steric hindrance for π electronic mobility, fragments enhancing polarity, etc. The identified features from the best QSPR model of the coumarin dataset was employed in designing of ten more efficient coumarin dyes (predicted %PCE ranging from 8.93 to 10.62) than the existing ones.

As the use of the pesticides has increased extensively in the farming fields to have a better agricultural production, the negative impacts of such use have also increased exponentially. Hence, the toxic effects of pesticides along with the targeted organisms affect the non-targeted terrestrial organisms such as earthworm. Therefore, in the present work, we have developed a classification-based quantitative structure-activity relationship (QSAR) model using linear discriminant analysis (LDA) to capture the specific information of pesticides / diverse chemicals in order to determine the structural information responsible for toxicity manifestation towards the non-targeted organism, i.e., earthworm (Eisenia foetida). After variable selection, the model was developed using 2D descriptors only and was subjected to rigorous statistical validation. The best discriminant model obtained with 8 descriptors showed appreciable Wilks' λ value of 0.490, F (Fischer's statistics) value of 14.03, χ2 value of 79.098, canonical regression coefficient (R) value of 0.714 and ρ value of 14.63. The sensitivity, specificity, accuracy, precision and F-measure values of the training set are 90.00, 80.52, 83.76, 70.59 and 79.12 respectively whereas for the test set, these are 58.82, 79.31, 71.74, 62.50 and 60.61 respectively. The insights obtained from the LDA model suggested that lipophilicity, electronrichness, and lower degree of branching of the organic compounds are responsible for earthworm toxicity through various mechanisms. On the other hand, polar and bulky diverse chemicals do not have such toxic effects on earthworm. Hence, this model can be an effective tool to tailor molecular structures of the existing pesticides to develop novel compounds or pesticides which would be less toxic to the non-targeted organisms, specifically earthworm.

In the current research, we have developed robust two-dimensional quantitative structure-activity relationship (2D-QSAR) and pharmacophore models using a dataset of 314 heterocyclic β-amyloid aggregation inhibitors. The main purpose of this study is to determine the essential structural features which are responsible for the inhibition of β-amyloid aggregation. Prior to the development of the 2D-QSAR model, we applied a multilayered variable selection method to reduce the size of the pool of descriptors, and the final models were built by the partial least squares (PLS) regression technique. The models obtained were thoroughly analysed by applying both internal and external validation parameters. The validation metrics obtained from the analysis suggested that the developed models were significant and sufficient to predict the inhibitory activity of unknown compounds. The structural features obtained from the pharmacophore model, such as the presence of aromatic rings and hydrogen bond acceptor/donor or hydrophobic sites, are well corroborated with those of the 2D-QSAR models. Additionally, we also performed a molecular docking study to understand the molecular interactions involved in binding, and the results were then correlated with the requisite structural features obtained from the 2D-QSAR and 3D-pharmacophore models.

Earthworm provides sustainability towards the agroecosystem which can be degraded day by day by the extensive use of pesticides (e.g., fungicides, insecticides and herbicides). The present study attempts to develop a predictive quantitative structure-activity relationship (QSAR) model for toxicity of pesticides to earthworm in order to give a suitable guidance for designing new analogues with lower toxicity by exploring the important chemical features which are required to develop safer alternatives. The QSAR model was developed by using the negative logarithm of lethal concentration (pLC50) values of pesticides towards earthworm. We have used various 2D descriptors along with extended topochemical atom (ETA) indices as independent variables for the development of the model. The developed partial least squares (PLS) model was subjected to statistical validation tests proving that the model is statistically reliable and robust (R2 = 0.765, Q2 = 0.614, Q2F1 = 0.734, Q2F2 = 0.713). The contributing descriptors in the model suggested that the pesticides may affect the earthworm nucleic acid through various physicochemical interactions including hydrophobicity, hydrogen bonding, electron donor acceptor complex formation, π-π stacking interaction and charge transfer complex formation.

To address the nanomaterial exposure threat, it is imperative to understand how nanomaterials are recognized, internalized, and distributed within diverse cell systems. Targeting of nanomaterials to a specific cell type is generally attained through the modification of the nanoparticle (NP) surface leading to required cellular uptake. The enhanced cellular uptake to normal cells can direct to the higher interaction of NPs with subcellular organelles resulting the provocation of various signaling pathways. The successes of NPs rely on the prospect for the synthesis of functionalized NPs with necessary properties and their enhanced potential for cellular uptake for specific targeting. In the present study, we have modeled the cellular uptake of 109 surface modifiers of metal oxide nanoparticles (MNPs) for three different cell lines: HUVEC (Human endothelial cells), U937 (human macrophage cells), and PaCa2 (cancer cell lines). Along with the quantitative structure-activity relationship (QSAR) models, for the very first time we have developed and performed quantitative inter cell line uptake specificity (QICLUS) modeling to identify the physicochemical properties, as well as majorly structural fragments responsible for cellular uptake differences between two specific cell lines. The present work provides a comprehensive understanding of the cellular uptake of MNPs and the underlying structural parameters controlling the nano-cellular interactions. This phenomenon has also been analyzed from the QSAR and QICLUS models that concluded the functional groups of surface modifiers like amine, anhydride, halogen atoms, nitro group, acids have the dominating roles for the uptake of MNPs into the cell lines. Thus, the developed models may be used for designing of novel surface modifiers of MNPs of desired characteristics for proper cell-NPs interactions, as well as in the context of virtual screening aspect. Moreover, the MNP-cell interactions can give some idea about the toxicity for target-specific drug delivery treatment as higher cellular uptake is required for specific cells to treat the disease and lower uptake to the neighboring cells for lower toxicity.

Malaria, the most virulent parasitic disease, has become a devastating health problem in tropical and subtropical regions, especially in Africa, due to favorable temperature and rainfall conditions for the development of the causative vector. Due to the spread of multidrug resistance to the marketed antimalarial drugs including the "magic bullet" artemisinin, discovery and development of new antimalarial drugs is one of the utmost challenges. Different government and non-government chemical regulatory authorities have recommended the application of non-animal, alternative techniques and in particular, in silico, methods in order to provide information about the basic physicochemical properties as well as the ecological and human health effects of chemicals before they reach into the market for public use. In this aspect, application of chemometric methods along with structure-based approaches may be useful for the design and discovery of new antimalarial compounds. The quantitative structureactivity relationship (QSAR) along with molecular docking and pharmacophore modeling techniques play a crucial role in the field of drug design. QSAR focuses on the chemical attributes influencing the activity and thereby allows synthesis of selective potential candidate molecules. In this communication, we have reviewed the QSAR reports along with some pharmacophore modeling and docking studies of antimalarial agents published during the year 2011 to 2014 and attempted to focus on the importance of physicochemical properties and structural features required for antimalarial activity of different chemical classes of compounds. Note that this is not an exhaustive review and all the given examples should be considered as the representative ones. The reader will gain an insight of the current status of QSAR and related in silico models developed for different classes of antimalarial compounds. This review suggests that combination of both ligand and structure-based drug designing approaches may be a promising tool for the discovery and development of new molecules with potential antimalarial activity.

We have modelled here odor threshold properties (OTP) of various aroma components present in different types of wine using quantitative structure–property relationship (QSPR) studies employing both two-dimensional and three-dimensional descriptors. The aim has been to identify the molecular properties essential for lowering the OTP. We have applied different variable selection strategies to select the most relevant descriptors prior to the development of the final partial least squares (PLS) regression model, which was validated extensively using different validation metrics in terms of acceptability and predictivity of the model for enhancing confidence in QSPR predictions. Using the developed PLS model, we have also predicted the “composite” OTP of different types of wine using the “composite” descriptor values based on individual components according to the PLS model and the results were well corroborated with the observations reported by Wang et al. [Food Chem., 2017, 226, 41–50]. The developed model may guide us to understand the dependence of the odor quality of different types of wines obtained under different manufacturing conditions on their aroma constituents.

Quantitative structure-activity relationship (QSAR) modeling has travelled a long journey in drug discovery process as well as in prediction of property and/or toxicity data of diverse chemicals in order to fill the data gaps. The goodness-of-fit and quality of a model and its prediction capability for untested compounds are assessed through diverse validation metrics. There is a constant endeavor among QSAR researchers to get better the quality of predictions for lowering the predicted residuals for external compounds. The objective of the present study has been to improve the prediction quality for external compounds with implication of “intelligent” consensus modeling approach. Three different forms of consensus models were developed for six different datasets to explore their prediction capability on query chemicals. The types are average of predictions from all qualifying individual models (CM1), weighted average predictions from all qualifying individual models (CM2), and best selection of predictions (compound-wise) from individual models (CM3). Among three consensus models, newer strategies like CM2 and CM3 are evolved as the “winners” considering prediction errors of query compounds for the studied six data sets irrespective of diverse responses, number of data points as well as dissimilar modeling algorithm. We have also developed a tool named “Intelligent Consensus Predictor” which is freely accessible via the web http://teqip.jdvu.ac.in/QSAR_Tools/ and http://dtclab.webs.com/software-tools. The details of this work have been presented in Conferentia Chemometrica http://cc2017.ttk.mta.hu/ in Hungary during September 3-6, 2017. References Dearden JC. The history and development of quantitative structure-activity relationships (QSARs). IJQSPR. 2016;1(1):1–44. Roy K, Kar S, Das RN. Understanding the basics of QSAR for applications in pharmaceutical sciences and risk assessment. Academic press. 2015. http://teqip.jdvu.ac.in/QSAR_Tools/ http://dtclab.webs.com/software-tools Roy K, Das RN, Ambure P, Aher RB. Be aware of error measures. Further studies on validation of predictive QSAR models. Chemom Intell Lab Syst. 2016;152:18-33. Roy K, Ambure P, Kar S, Ojha PK, Is it possible to improve the quality of predictions from an intelligent” use of multiple QSAR/QSPR/QSTR models? J Chemom 2017 (Submitted)

With the emergence of multi-drug resistance of the currently available antimalarial drugs including the "magic bullet" artemisinin derivatives in the market, there is an urgent need for discovery and development of new potent antimalarial molecules. The present work deals with quantitative structure-activity relationship (QSAR) modeling, pharmacophore mapping and docking studies of a series of 35 thymidine analogs as inhibitors of Plasmodium falciparum thymidylate kinase (PfTMPK), an enzyme that catalyzes phosphorylation of thymidine monophosphate (TMP) to thymidine diphosphate (TDP). The models were validated both internally and externally and significant statistical results were obtained, indicating the robustness and reliability of the developed models. The docking study was performed using the LigandFit option of receptor-ligand interactions protocol section available in Discovery Studio 2.1 where lower RMSD values (0.6931Å) between the co-crystallized ligand and re-docked ligand assured that the ligand was bound in the same binding pocket. The QSAR, pharmacophore mapping and docking studies provide an understanding of important structural requirements or essential molecular properties, or features of molecules, and important binding interactions, and provide an important guidance for the chemist to synthesis of new molecules with improved PfTMPK inhibitory activity profile. This work revealed the importance of -NH-fragment, electrophilicity of the molecules and the number of oxygen atom towards the PfTMPK inhibitory activity of the molecules. To the best of our knowledge, this work presents the first QSAR and pharmacophore report for thymidine analogs which may serve as an efficient tool for the design and synthesis of potent molecules as PfTMPK inhibitors to address the increasing threat of multi-drug resistance against P. falciparum.

Quantitative structure-activity relationship (QSAR) techniques have found wide application in the fields of drug design, property modeling, and toxicity prediction of untested chemicals. A rigorous validation of the developed models plays the key role for their successful application in prediction for new compounds. The r(m)(2) metrics introduced by Roy et al. have been extensively used by different research groups for validation of regression-based QSAR models. This concept has been further advanced here with introduction of scaling of response data prior to computation of r(m)(2). Further, a web application (accessible from http://aptsoftware.co.in/rmsquare/ and http://203.200.173.43:8080/rmsquare/) for calculation of the r(m)(2) metrics has been introduced here. The present study reports that the web application can be easily used for computation of r(m)(2) metrics provided observed and QSAR-predicted data for a set of compounds are available. Further, scaling of response data is recommended prior to r(m)(2) calculation.

Due to the emergence of multidrug resistance of malaria parasites, there is an urgent need to modify existing antimalarial drugs through structural changes as well as to search new pharmacophores to counteract the induced resistance. In the present work, different quantitative structure-activity relationship (QSAR) models have been developed using a series of 73 1,2,3,4-tetrahydroacridin-9(10H)-one (THA) analogs with well defined antimalarial activity against W2 clone to provide detailed insight into the main structural fragments that impart antimalarial activity to these molecules. To the best of our knowledge, this is first QSAR report of THA analogs as antimalarials. From the developed models, it may be inferred that for an enhancement in the antimalarial activity of the molecules: i) R2 position should be substituted with a bulky group (like phenyl, methoxy, isopropyl, tertiary butyl group) which may be further substituted with electrostatically favored positively charged group; ii) R3 position should be substituted with hydrophobically favored but sterically disfavored group; iii) R4 position should be substituted with positively charged group lacking any carbon atom and iv) R5 position should be substituted with two methyl groups. On the contrary, substitution with bulky group at R6 position may reduce the antimalarial activity. Using the developed models, we have designed and proposed some new compounds which showed good in silico predicted activity. The designed compounds may be experimentally proved to be active and useful THA derivatives as antimalarial compounds.

In the present report, we have developed a predictive QSPR model using only easily computable two-dimensional (2D) descriptors from diverse classes of sweetening agents to find out the key structural properties which regulate their sweet potency. The available data set was curated to remove salts, mixtures and compounds without having a definite structure. A k-fold double cross validation technique was employed for variable selection prior to development of the final model. The final model was developed using partial least squares (PLS) regression analysis and selected based on a mean absolute error (MAE) based criteria for the validation sets. The model was validated extensively using different internal and external validation strategies in accordance with the Organization for Economic Co-operation and Development (OECD) guidelines. This work presented development of a validated quantitative structure-property relationship (QSPR) model obtained from k-fold double cross-validation technique in order to find out the key structural information required to enhance the sweet potency of the molecules. Finally, we have designed and proposed 13 new molecules based on the insights obtained from the QSPR model. The designed compounds showed good in silico predicted sweetness potency with acceptable ADMET profile.

In silico techniques involving the development of quantitative regression models have been extensively used for prediction of activity, property and toxicity of new chemicals. The acceptability and subsequent applicability of the models for predictions is determined based on several internal and external validation statistics. Among different validation metrics, Q2 and R2pred represent the classical metrics for internal validation and external validation respectively. Additionally, the rm2 metrics introduced by Roy and coworkers have been widely used by several groups of authors to ensure the close agreement of the predicted response data with the observed ones. However, none of the currently available and commonly used validation metrics provides any information regarding the rank-order predictions for the test set. Thus, to incorporate the concept of ranking order predictions while calculating the common validation metrics originally using the Pearson's correlation coefficient-based algorithm, the new rm2(rank) metric has been introduced in this work as a new variant of the rm2 series of metrics. The ability of this new metric to perform the rank-order prediction is determined based on its application in judging the quality of predictions of regression — based quantitative structure–activity/property relationship (QSAR/QSPR) models for four different data sets. The different validation metrics calculated in each case were compared for their ability to reflect the rank-order predictions based on their correlation with the conventional Spearman's rank correlation coefficient. Based on the results of the sum of ranking differences analysis performed using the Spearman's rank correlation coefficient as the reference, it was observed that the rm2(rank) metric exhibited the least difference in ranking from that of the reference metric. Thus, the close correlation of the rm2(rank) metric with the Spearman's rank correlation coefficient inferred that the new metric could aptly perform the rank-order prediction for the test data set and can be utilized as an additional validation tool, besides the conventional metrics, for assessing the acceptability and predictive ability of a QSAR/QSPR model.

The emergence of multidrug resistance of the currently available antimalarial drugs has led to the need of the discovery and development of new antimalarial compounds. In the present study, we have selected a series of 53 endochin analogs with antimalarial activity against the clinically relevant multidrug resistant malarial strain TM-90-C2B to develop robust QSAR models using different chemometric tools such as stepwise regression, factor analysis followed by multiple linear regression (FA-MLR), factor analysis followed by partial least squares (FA-PLS) analysis, genetic function approximation (GFA) and genetic partial least squares (G/PLS) techniques. We have tried to emphasize on importance of descriptor-thinning prior to feature selection step. For this purpose, we have partitioned the total pool of descriptors into three categories followed by running stepwise regression separately for all three categories of descriptors for selection of variables. Then we have applied genetic methods of model development on the selected pool of descriptors. We have validated the models using different validation parameters to check the statistical quality and reliability of the models. It has been found that models developed from variable selection by stepwise regression followed by GFA and G/PLS are the best two models. This study reflects the importance of descriptor-thinning and noise reduction prior to feature selection for the development of QSAR models. According to the best two models, we interpreted that substitutions with rotatable bond containing groups (e.g., long alkyl chains), limited volume of the molecules, absence of C—R group, presence of electronegative (like —Br) fragments, absence of cycles or rings in the molecules are important for the antimalarial activity. The developed models are believed to comply with the OECD guidelines for QSAR models. The results also confirmed that the QSAR models developed from the reduced pool of descriptors were better than those obtained from the entire pool of descriptors.