In silico soil degradation and ecotoxicity analysis of veterinary pharmaceuticals on terrestrial species: first report.

With the aim of persistence property analysis and ecotoxicological impact of veterinary pharmaceuticals on different terrestrial species, different classes of veterinary pharmaceuticals (n = 37) with soil degradation property (DT50) were gathered and subjected to QSAR and q-RASAR model development. The models were developed from 2D descriptors under organization for economic cooperation and development guidelines with the application of multiple linear regressions along with genetic algorithm. All developed QSAR and q-RASAR were statistically significant (Internal = R2adj: 0.721-0.861, Q2LOO: 0.609-0.757, and external = Q2Fn = 0.597-0.933, MAEext = 0.174-0.260). Further, the leverage approach of applicability domain assured the model's reliability. The veterinary pharmaceuticals with no experimental values were classified based on their persistence level. Further, the terrestrial toxicity analysis of persistent veterinary pharmaceuticals was done using toxicity prediction by computer assisted technology and in-house built quantitative structure toxicity relationship models to prioritize the toxic and persistent veterinary pharmaceuticals. This study will be helpful in estimation of persistence and toxicity of existing and upcoming veterinary pharmaceuticals.

Integrated predictive QSAR, Read Across, and q-RASAR analysis for diverse agrochemical phytotoxicity in oat and corn: A consensus-based approach for risk assessment and prioritization.

In the modern fast-paced lifestyle, time-efficient and nutritionally rich foods like corn and oat have gained popularity for their amino acids and antioxidant contents. The increasing demand for these cereals necessitates higher production which leads to dependency on agrochemicals, which can pose health risks through residual present in the plant products. To first report the phytotoxicity for corn and oat, our study employs QSAR, quantitative Read-Across and quantitative RASAR (q-RASAR). All developed QSAR and q-RASAR models were equally robust (R2 = 0.680-0.762, Q2Loo = 0.593-0.693, Q2F1 = 0.680-0.860) and find their superiority in either oat or corn model, respectively, based on MAE criteria. AD and PRI had been performed which confirm the reliability and predictability of the models. The mechanistic interpretation reveals that the symmetrical arrangement of electronegative atoms and polar groups directly influences the toxicity of compounds. The final phytotoxicity and prioritization are performed by the consensus approach which results into selection of 15 most toxic compounds for both species.

In silico Strategy: A Promising Implement in the Development of Multitarget Drugs against Neurodegenerative Diseases.

Multi-target drug development (MTDD) is the demand of the recent era, especially in the case of multi-factorial conditions such as cancer, depression, neurodegenerative diseases (NDs), etc. The MTDD approaches have many advantages; avoidance of drug-drug interactions, predictable pharmacokinetic profile, and less drug resistance. The wet lab practice in MTDD is very challenging for the researchers, and the chances of late-stage failure are obvious. Identification of an appropriate target (Target fishing) is another challenging task in the development of multi-target drugs. The in silico tools will be one of the promising tools in the MTDD for the NDs. Therefore the outlook of the review comprises a short description of NDs, target associated with different NDs, in silico studies so far done for MTDD for various NDs. The main thrust of this review is to explore the present and future aspects of in silico tools used in MTDD for different NDs in combating the challenge of drug development and the application of various in silico tools to solve the problem of target fishing.

1,3,4-Oxadiazole: An Emerging Scaffold to Inhibit the Thymidine Phosphorylase as an Anticancer Agent.

Thymidine phosphorylase (TP), also referred to as "platelet-derived endothelial cell growth factor" is crucial to the pyrimidine salvage pathway. TP reversibly transforms thymidine into thymine and 2-deoxy-D-ribose-1-phosphate (dRib-1-P), which further degraded to 2-Deoxy-D-ribose (2DDR), which has both angiogenic and chemotactic activity. In several types of human cancer such as breast and colorectal malignancies, TP is abundantly expressed in response to biological disturbances like hypoxia, acidosis, chemotherapy, and radiation therapy. TP overexpression is highly associated with angiogenic factors such as vascular endothelial growth factor (VEGF), interleukins (ILs), matrix metalloproteases (MMPs), etc., which accelerate tumorigenesis, invasion, metastasis, immune response evasion, and resistant to apoptosis. Hence, TP is recognized as a key target for the development of new anticancer drugs. Heterocycles are the primary structural element of most chemotherapeutics. Even 75% of nitrogen-containing heterocyclic compounds are contributing to the pharmaceutical world. To create the bioactive molecule, medicinal chemists are concentrating on nitrogen-containing heterocyclic compounds such as pyrrole, pyrrolidine, pyridine, imidazole, pyrimidines, pyrazole, indole, quinoline, oxadiazole, benzimidazole, etc. The Oxadiazole motif stands out among all of them due to its enormous significance in medicinal chemistry. The main thrust area of this review is to explore the synthesis, SAR, and the significant role of 1,3,4-oxadiazole derivatives as a TP inhibitor for their chemotherapeutic effects.

Aquatic toxicity prediction of diverse pesticides on two algal species using QSTR modeling approach.

With the aim of identification of toxic nature of the diverse pesticides on the aquatic compartment, a large dataset of pesticides (n = 325) with experimental toxicity data on two algal test species (Pseudokirchneriella subcapitata (PS) (synonym: Raphidocelis subcapitata, Selenastrum capricornutum) and Scenedemus subspicatus (SS)) was gathered and subjected to quantitative structure toxicity relationship (QSTR) analysis to predict aquatic toxicity of pesticides. The QSTR models were developed by multiple linear regressions (MLRs), and the genetic algorithm (GA) was used for the variable selection. The developed GA-MLR models were statistically robust enough internally (Q2LOO = 0.620-0.663) and externally (Q2Fn = 0.693-0.868, CCCext = 0.843-0.877). The leverage approach of applicability domain (AD) and prediction reliability indicator assured the reliability of the developed models. The mechanistic interpretation highlighted that the presence of SO2, F and aromatic rings influenced the toxicity of pesticides towards PS species while the presence of alkyl, alkyl halide, aromatic rings and carbonyl was responsible for the toxicity of pesticides towards SS species. Additionally, we have reported the application of developed models to pesticides without experimental value and the cumulative toxicity of pesticides on the aquatic environment by using principal component analysis (PCA). The reliable prediction and prioritization of toxic compounds from the developed models will be useful in the aquatic toxicity assessment of pesticides.

Pharmacophore based virtual screening of cholinesterase inhibitors: search of new potential drug candidates as antialzheimer agents.

Alzheimer's disease (AD) is a distinctive medical condition characterized by loss of memory, orientation, and cognitive impairments, which is an exceptionally universal form of neurodegenerative disease. The statistical data suggested that it is the 3rd major cause of death in older persons. Butyrylcholinesterase (BChE) and acetylcholinesterase (AChE) inhibitors play a vital role in the treatment of AD. Coumarins, natural derivatives, are reported as cholinesterase inhibitors and emerges as a promising scaffold for design of ligands targeting enzymes and pathological alterations related to AD. In this regard, the 3D QSAR pharmacophore models were developed for coumarin scaffold containing BChE and AChE inhibitors. Several 3D QSAR pharmacophore models were developed with FAST, BEST, and CEASER methods, and finally, statistically robust models (based on correlation coefficient, cost value, and RMSE value) were selected for further analysis for both targets. The important features ((HBA 1, HBA 2, HY, RA (BChE) HBA 1, HBA 2, HY, PI, (AChE)) were identified for good inhibitory activity of coumarin derivatives. Finally, the selected models were applied to various database compounds to find potential BChE and AChE inhibitors, and we found 13 for BChE and 1 potent compound for AChE with an estimated activity of IC50 < 10 µM. Further, the Lipinski filters, and ADMET analysis supports the selected compounds to become a drug candidate. These selected BChE and AChE inhibitors can be used in the treatment of AD. Supplementary Information: The online version contains supplementary material available at 10.1007/s40203-022-00133-1.

In silico selectivity modeling of pyridine and pyrimidine based CYP11B1 and CYP11B2 inhibitors: A case study.

DESIGN: of selective drug candidates for highly structural similar targets is a challenging task for researchers. The main objective of this study was to explore the selectivity modeling of pyridine and pyrimidine scaffold towards the highly homologous targets CYP11B1 and CYP11B2 enzymes by in silico (Molecular docking and QSAR) approaches. In this regard, a big dataset (n = 228) of CYP11B1 and CYP11B2 inhibitors were gathered and classified based on heterocyclic ring and the exhaustive analysis was carried out for pyridine and pyrimidinescaffolds. The LibDock algorithm was used to explore the binding pattern, screening, and identify the structural feature responsible for the selectivity of the ligands towards the studied targets. Finally, QSAR analysis was done to explore the correlation between various binding parameters and structural features responsible for the inhibitory activity and selectivity of the ligands in a quantitative way. The docking and QSAR analysis clearly revealed and distinguished the importance of structural features, functional groups attached for CYP11B2 and CYP11B1 selectivity for pyridine and pyrimidine analogs. Additionally, the docking analysis highlighted the differentiating amino acids residues for selectivity for ligands for each of the enzymes. The results obtained from this research work will be helpful in designing the selective CYP11B1/CYP11B2 inhibitors.

The modulatory role of prime identified compounds in Geophila repens in mitigating scopolamine-induced neurotoxicity in experimental rats of Alzheimer's disease via attenuation of cholinesterase, ß-secretase, MAPt levels and inhibition of oxidative stress imparts inflammation.

ETHNOPHARMACOLOGICAL RELEVANCE: Geophila repens (L.) I.M. Johnst (Rubiaceae) is a small perennial creeper native to India, China, and other countries in Southeast Asia. The hot decoction of leaves is used orally for memory enhancing by the local folk of Andhra Pradesh, India. The ethnomedicinal claim of G. repens as memory enhancer was initially studied by the authors. Results demonstrated the important antioxidant and anticholinesterase activities of isolated molecule Pentylcurcumene and bioactive hydroalcohol extract of leaves of G. repens (GRHA). AIM OF THE STUDY: Based on the previous findings, additional research is needed to examine the efficacy of GRHA for memory enhancing properties. We therefore investigated the modulatory role of prime identified compounds in GRHA in mitigating scopolamine-induced neurotoxicity in experimental rats of Alzheimer's disease (AD) via attenuation of cholinesterase, ß-secretase, MAPt levels and inhibition of oxidative stress imparts inflammation. METHODS: Scopolamine (3 mg/kg) induced experimental rats of AD were treated with GRHA (300, 400 mg/kg) for 14 days. During the experimental period, elevated T-maze and locomotion-activity were performed to assess learning and memory efficacy of GRHA. At the end of the experiment, biochemical, neurochemical, neuroinflammation and histopathological observation of brain cortex were examined. GC-MS/MS analysis reported 31 compounds, among them 8 bioactive compounds possess antioxidant, neuroinflammation, neuroprotective activities, and were considered for docking analysis towards cholinesterase, ß-secretase activities in AD. RESULTS: GRHA 400 significantly improved learning and memory impairment with the improvement of oxidative stress (MDA, SOD, GSH, CAT), DNA damage (8-OHdG), neurochemical (AChE, BuChE, BACE1, BACE2, MAPt), neuroinflammation (IL-6, TNF-α) markers in neurotoxic rats. Docking studies of 8 compounds demonstrated negative binding energies for cholinesterase and ß-secretase indicating high affinity for target enzymes in AD. Test results were corroborated by the improvement of cellular tissue architecture of brain cortex in AD rats. CONCLUSION: Synergistic action of genistin, quercetin-3-D-galactoside, 9,12,15-octadecatrienoic-acid methyl-ester, phytol, retinal, stigmasterol, n-hexadecanoic acid, ß-sitosterol in GRHA restores memory-deficits via attenuation of cholinesterase, ß-secretase, MAPt level and inhibition of oxidative-stress imparts inflammation in AD.

In silico local QSAR modeling of bioconcentration factor of organophosphate pesticides.

The persistent and accumulative nature of the pesticide of indiscriminate use emerged as ecotoxicological hazards. The bioconcentration factor (BCF) is one of the key elements for environmental assessments of the aquatic compartment. Limitations of prediction accuracy of global model facilitate the use of local predictive models in toxicity modeling of emerging compounds. The BCF data of diverse organophosphate (n = 55) was collected from the Pesticide Properties Database and used as a model data set in the present study to explore physicochemical properties and structural alert concerning BCF. The structures were downloaded from Pubchem, ChemSpider database. Two splitting techniques (biological sorting and structure-based) were used to divide the whole dataset into training and test set compounds. The QSAR study was carried out with two-dimensional descriptors (2D) calculated from PaDEL by applying genetic algorithm (GA) as chemometric tools using QSARINS software. The models were statistically robust enough both internally as well as externally (Q2: 0.709-0.722, Q2 Ext: 0.717-0.903, CCC: 0.857-0.880). Overall molecular mass, presence of fused, and heterocyclic ring with electron-withdrawing groups affect the BCF value. The developed models reflected extended applicability domain (AD) and reliable predictions than the reported models for the studied chemical class. Finally, predictions of unknown organophosphate pesticides and the toxic nature of unknown organophosphate pesticides were commented on. These findings may be useful for the scientific community in prioritizing high potential pesticides of organophosphate class.

Predictive classification-based QSTR models for toxicity study of diverse pesticides on multiple avian species.

Protection and restoration of different endangered bird species from pesticide exposure is crucial from the point of safety assessment of ecosystem. Toxicity predictions or risk assessment of pesticides by chemometric tools is one of the challenging fields in recent era. In the present study, classification-based quantitative structure toxicity relationship (QSTR) models were developed for a large dataset (516) of diverse pesticides on multiple avian species mallard duck, bobwhite quail, and zebra finch according to the Organization for Economic Co-operation and Development guidelines. The QSTR models were developed by linear discriminant analysis method with genetic algorithm for feature selection from 2D descriptors using QSAR-Co software. Different statistical metrics assured the reliability and robustness of the developed models. External compound prediction highlighted predictive nature of the models. The mechanistic interpretation suggested that presence of phosphate, halogens (Cl, Br), ether linkage, and NCOO influence the avian toxicity. Furthermore, model reliability was checked by the application of the standardization approach of the applicability domain (AD). Finally, the developed models provided a priori toxic and non-toxic classification for unknown pesticides (inside AD), with particular emphasis on organophosphate pesticides. The interspecies toxicity correlation and predictions encouraged for their further applicability for the fulfilment of data gaps in vital missing species.

Acute Rat and Mouse Oral Toxicity Determination of Anticholinesterase Inhibitor Carbamate Pesticides: A QSTR Approach.

The toxic potentials of carbamates to human and non target organisms are of public concern in relation to society and ecosystem for their unregulated and indiscriminate use. No computational study was found on rat and mouse oral toxicity for carbamate pesticides. In this context, carbamate pesticides were collected from ChemIDplus databases for the modeling study. A series of local QSTR model for both rat and mouse oral toxicity of carbamate derivatives were developed according to OECD principle from 2D descriptors by using Genetic Algorithm (GA) as feature selection chemometric tools using QSARINS software. All the models indicate the importance of auto correlation descriptors related to charge, I-State, atom type E state for fragment -O- in relation to acute mammalian toxicity. Reliability of predictions of the models was verified by applicability domain (AD) and prediction reliability index. Finally developed models were applied to unknown carbamate pesticides to evaluate their predictions and AD. The toxic nature of the prioritized compounds with structural alerts were commented in a consensus way. Additional toxicity-toxicity relationship studies (QTTR) between these two responses with similar findings promoted further application of QTTR models in absence of one response. These findings may help the scientific community in prioritizing potentially hazardous pesticides of carbamate and related classes.