First report on pesticide sub-chronic and chronic toxicities against dogs using QSAR and chemical read-across.

Excessive use of chemicals is the outcome of the industrialization of agricultural sectors which leads to disturbance of ecological balance. Various agrochemicals are widely used in agricultural fields, urban green areas, and to protect from various pest-associated diseases. Due to their long-term health and environmental hazards, chronic toxicity assessment is crucial. Since in vivo and in vitro toxicity assessments are costly, lengthy, and require a large number of animal experiments, in silico toxicity approaches are better alternatives to save time, cost, and animal experimentation. We have developed the first regression-based 2D-QSAR models using different sub-chronic and chronic toxicity data of pesticides against dogs employing 2D descriptors. From the statistical results (ntrain=53-62, r2 = 0.614 to 0.754, QLOO2 = 0.501 to 0.703 and QF12 = 0.531 to 0.718, QF22=0.523-0.713), it was concluded that the models are robust, reliable, interpretable, and predictive. Similarity-based read-across algorithm was also used to improve the predictivity (QF12=0.595-0.813,QF22=0.573-0.809) of the models. 5132 chemicals obtained from the CPDat and 1694 pesticides obtained from the PPDB database were also screened using the developed models, and their predictivity and reliability were checked. Thus, these models will be helpful for eco-toxicological data-gap filling, toxicity prediction of untested pesticides, and development of novel, safer & eco-friendly pesticides.

q-RASTR modelling for prediction of diverse toxic chemicals towards T. pyriformis.

A series of diverse organic compounds impose serious detrimental effects on the health of living organisms and the environment. Determination of the structural aspects of compounds that impart toxicity and evaluation of the same is crucial before public usage. The present study aims to determine the structural characteristics of compounds for Tetrahymena pyriformis toxicity using the q-RASTR (Quantitative Read Across Structure-Toxicity Relationship) model. It was developed using RASTR and 2-D descriptors for a dataset of 1792 compounds with defined endpoint (pIGC50) against a model organism, T. pyriformis. For the current study, the whole dataset was divided based on activity/property into the training and test sets, and the q-RASTR model was developed employing six descriptors (three latent variables) having r2, Q2F1 and Q2 values of 0.739, 0.767, and 0.735, respectively. The generated model was thoroughly validated using internationally recognized internal and external validation criteria to assess the model's dependability and predictability. It was highlighted that high molecular weight, aromatic hydroxyls, nitrogen, double bonds, and hydrophobicity increase the toxicity of organic compounds. The current study demonstrates the applicability of the RASTR algorithm in QSTR model development for the prediction of toxic chemicals (pIGC50) towards T. pyriformis.

QSAR assessment of aquatic toxicity potential of diverse agrochemicals.

The fast-increasing number of commercially produced chemicals challenges the experimental ecotoxicity assessment methods, which are costly, time-consuming, and dependent on the sacrifice of animals. In this regard, Quantitative Structure-Property/Activity Relationships (QSPR/QSAR) have led the way in developing ecotoxicity assessment models. In this study, QSAR models have been developed using the pEC50 values of 82 diverse agrochemicals or agro-molecules against a planktonic crustacean Daphnia magna with easily interpretable 2D descriptors. Moreover, a link among octanol-water partition coefficient (KOW), bio-concentration factor (BCF), and critical body residue (CBR) has been addressed, and their imputation for the prediction of the toxicity endpoint (EC50) has been done with an objective of the advanced exploration of several ecotoxicological parameters for toxic chemicals. The developed partial least squares (PLS) models were validated rigorously and proved to be robust, sound, and immensely well-predictive. The final Daphnia toxicity model derived from experimental derived properties along with computed descriptors emerged better in statistical quality and predictivity than those obtained solely from computed descriptors. Additionally, the pEC50 and other important properties (log KOW, log BCF, and log CBR) for a set of external agro-molecules, not employed in model development, were predicted to show the predictive ability of the models.

Electrochemical deposition of three-dimensional platinum nanoflowers for high-performance polymer electrolyte fuel cells.

In the present work, the three-dimensional ultra-fine platinum nanoflowers are directly deposited on carbon-coated gas diffusion layer electrode (C-GDL) by a single-step electrodeposition method towards the application of polymer electrolyte fuel cells. The surface morphology, particle size distribution, crystallinity, and chemical oxidation state of platinum nanoflowers are examined using various techniques. The morphological features of the Pt nanostructures are highly influenced by the difference in current density. Notabely, the Pt nanospheres converts into three-dimensional nanoflower with an increase in current density from -1.6 to -32 mA cm-2. Electrodeposited Pt catalyst on C-GDL as the cathode catalyst was fabricated and steady-state polarization studies were carried out. Mainly, the fuel cell performance is analysed considering the electrodeposited Pt morphology. Among the prepared electrocatalysts, the nanoflower shaped Pt catalyst exhibit a high peak power density of 660 mW cm-2 at 0.6 V in PEFC.

Exploring 2D-QSAR for prediction of beta-secretase 1 (BACE1) inhibitory activity against Alzheimer's disease.

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.

Diacerein-mediated inhibition of IL-6/IL-6R signaling induces apoptotic effects on breast cancer.

Interleukin-6 (IL-6) signaling network has been implicated in oncogenic transformations making it attractive target for the discovery of novel cancer therapeutics. In this study, potent antiproliferative and apoptotic effect of diacerein were observed against breast cancer. In vitro apoptosis was induced by this drug in breast cancer cells as verified by increased sub-G1 population, LIVE/DEAD assay, cell cytotoxicity and presence of terminal deoxynucleotidyl transferase dUTP nick end labeling (TUNEL)-positive cells, as well as downregulation of antiapoptotic proteins Bcl-2 and Bcl-xL and upregulation of apoptotic protein Bax. In addition, apoptosis induction was found to be caspase dependent. Further molecular investigations indicated that diacerein instigated apoptosis was associated with inhibition of IL-6/IL-6R autocrine signaling axis. Suppression of STAT3, MAPK and Akt pathways were also observed as a consequence of diacerein-mediated upstream inhibition of IL-6/IL-6R. Fluorescence study and western blot analysis revealed cytosolic accumulation of STAT3 in diacerein-treated cells. The docking study showed diacerein/IL-6R interaction that was further validated by competitive binding assay and isothermal titration calorimetry. Most interestingly, it was found that diacerein considerably suppressed tumor growth in MDA-MB-231 xenograft model. The in vivo antitumor effect was correlated with decreased proliferation (Ki-67), increased apoptosis (TUNEL) and inhibition of IL-6/IL-6R-mediated STAT3, MAPK and Akt pathway in tumor remnants. Taken together, diacerein offered a novel blueprint for cancer therapy by hampering IL-6/IL-6R/STAT3/MAPK/Akt network.