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.

Discovery of novel VEGFR2-TK inhibitors by phthalimide pharmacophore based virtual screening, molecular docking, MD simulation and DFT.

Currently, numerous potent chemotherapeutic agents are available in the market but most of them show poor pharmacokinetics, lethal effects and drug resistance during their enduring use. The increased cancer cases, deaths and need of better treatment stimulates us to give newer lifesaving anticancer drugs. The phthalimide derivatives are structurally diverse and exert potential anticancer activity. In this regard, the 3D QSAR Pharmacophore model was developed and validated using fifty-eight phthalimide derivatives. The validation parameters corroborated the reliability and statistical robustness of CEASER Hypo 1. Three databases-NCI Open, Drug Bank, and Asinex were submitted to ADMET and drug-like filtering; 117893 drug-like compounds were mapped on CEASER Hypo 1; and 362 hits with IC50 <1 µM were discovered. These hits were docked on VEGFR2-TK, and in the form of results fifteen hits exhibited greater affinity than sorafenib. The top lead ASN 03206926 was subjected for MD simulation (100 ns) and RMSD, Rg, RMSF, number of hydrogen bonds, and SASA verified that the complex was stable, rigid and highly compact. Results demonstrated GLU885, PHE918, CYS919, LYS920, HIS1026, CYS1045, ASP1046 are the essential residues for favourable interactions. The binding free energy calculations support the affinity and stability revealed by docking and MD simulation. The DFT calculations, negative binding energy and lower HOMO-LUMO band gap revealed that the process is spontaneous and ASN 03206926 is very reactive. Following extensive analysis we suggest that the ASN 03206926 might be employed as a new VEGFR2-TK inhibitor for the treatment of breast and VEGFR2-TK associated cancers.Communicated by Ramaswamy H. Sarma.

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.