A Review: In – Silico Approaches in Predictive Toxicology

This article deals with the in – silico techniques for predicting the toxicity of chemical compounds. Toxicology is the branch of biology that deals with the study of adverse effect of chemical substances on the living organisms and the practice of treating and preventing such adverse effects. Predicting toxicity of a new drug to be produced is the first aim of preclinical trials. It is achieved by in-silico methods. There are several in - silico technique softwares which are used for the prediction of ADME and hence toxicity of drugs. In – silico methods involves the use of various softwares to calculate and then predict the toxicity of a compound by first determining its structural and pharmacokinetic and pharmacodynamic properties and then it correlates this information with already existing drugs and molecules and thus gives us conclusion. The article focuses on QSAR and its techniques, HQSAR, several other methods like structural alerts and rule-based models, chemical category and read across model, dose and time response model, virtual ligand screening, docking, 3D pharmacophore mapping, simulation approaches, PKPD models and several other approaches like bioinformatics. After reviewing and studying various in silico techniques the conclusion comes out to be that, in-silico methods of predictive toxicology are more better than in-vitro and in-vivo methods since they are much more safe (as animals are not harmed), economic, fast and accurate w.r.to, results/output in predicting toxicity of compounds by computational methods and hence are widely used in the production of new drug for accessing its toxicity

Structure-Based Virtual Screening of Ultra-Large Library Yields Potent Antagonists for a Lipid GPCR

Cysteinyl leukotriene G protein-coupled receptors, CysLT1R and CysLT2R, regulate bronchoconstrictive and pro-inflammatory effects and play a key role in allergic disorders, cardiovascular diseases, and cancer. CysLT1R antagonists have been widely used to treat asthma disorders, while CysLT2R is a potential target against uveal melanoma. However, very few selective antagonist chemotypes for CysLT receptors are available, and the design of such ligands has proved to be challenging. To overcome this obstacle, we took advantage of recently solved crystal structures of CysLT receptors and an ultra-large Enamine REAL library, representing a chemical space of 680 M readily available compounds. Virtual ligand screening employed 4D docking models comprising crystal structures of CysLT1R and CysLT2R and their corresponding ligand-optimized models. Functional assessment of the candidate hits yielded discovery of five novel antagonist chemotypes with sub-micromolar potencies and the best Ki = 220 nM at CysLT1R. One of the hits showed inverse agonism at the L129Q constitutively active mutant of CysLT2R, with potential utility against uveal melanoma.

VIRTUAL SCREENING TO IDENTIFY POTENTIAL INHIBITOR OF ARGINASE OF LEISHMANIASIS

Leishmaniasis is caused by Leishmania protozoan parasites transmitted by the female phlebotomine sandfly. The current treatment regimen of leishmaniasis is not up to the mark and there is a huge scope of improvement. Hence, the need to approve new drugs, a complete understanding of the pathophysiology of the parasite is required. Since polyamines are required by the parasites for their infection cycle, inhibitors of polyamine pathway can be targeted by the new drugs for restricting the infection. In Leishmania, the polyamine biosynthetic pathway comprises of four compounds: arginase (ARG), ornithine decarboxylase (ODC), spermidine synthase (SPD), and S-adenosylmethionine decarboxylase (ADOMETDC). To identify these novel medicines like compounds, a structure-based screening system was utilized against downloaded drug-like compounds. In total, 1279 compounds were downloaded from the ZINC database dependent on the properties like the known inhibitor nor-NOHA [N(omega)- hydroxy-nor-L-arginase]. Virtual-ligand screening approaches were applied to identify drug-related like compounds utilizing sub-atomic docking program AutoDockVina in PyRx 0.8, and five best novel medication like compounds were chosen and their hydrogen ties with the receptor were decided.ZINC84057569, ZINC87440467, ZINC04617649, ZINC33978586, ZINC01677572 and ZINC35794928, ZINC33978586, ZINC84057569. ZINC53751324, ZINC00204059 were finalized as inhibitors against Human Arginase I and L. mexicana arginase individually contrasted with nor-NOHA, based on their binding efficiency. These inhibitors may become the base for formulating new drugs against Leishmania's, focusing on both the protein for example Human Arginase I and L. mexicana arginase. Keywords: Leishmaniasis, Human Arginase I, Leishmania mexicana arginase, nor-NOHA, Polyamine pathway, Molecular docking, Virtual screening.