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.

Modelling lethality and teratogenicity of zebrafish (Danio rerio) due to ß-lactam antibiotics employing the QSTR approach.

Nowadays, ß-lactam antibiotics are one of the most consumed OTC (over-the-counter) medicines in the world. Its frequent use against several infectious diseases leads to the development of antibiotic resistance. Another unavoidable risk factor of ß-lactam antibiotics is environmental toxicity. Numerous terrestrial as well as aquatic species have suffered due to the excessive use of these pharmaceuticals. In this present study, we have performed a toxicity assessment employing a novel in silico technique like quantitative structure-toxicity relationships (QSTRs) to explore toxicity against zebrafish (Danio rerio). We have developed single as well as inter-endpoint QSTR models for the ß-lactam compounds to explore important structural attributes responsible for their toxicity, employing median lethal (LC50) and median teratogenic concentration (TC50) as the endpoints. We have shown how an inter-endpoint model can extrapolate unavailable endpoint values with the help of other available endpoint values. To verify the models' robustness, predictivity, and goodness-of-fit, several universally popular metrics for both internal and external validation were extensively employed in model validation (single endpoint models: r2 = 0.631 - 0.75, Q2F1 = 0.607 - 0.684; inter-endpoint models: r2 = 0.768 - 0.84, Q2F1 = 0.678 - 0.76). Again, these models were engaged in the prediction of these two responses for a true external set of ß-lactam molecules without response values to prove the reproducibility of these models.