Pharmacophore and docking-based virtual screening approach for the design of new dual inhibitors of Janus kinase 1 and Janus kinase 2

Putative dual inhibitors of Janus kinase 1 and 3 (JAK1/3): Pharmacophore based hierarchical virtual screening

Janus kinase 1 and 3 are non-receptor protein tyrosine kinases, involved in the regulation of various cytokines implicated in the pathogenesis of autoimmune and inflammatory disease conditions. Thus, they serve as therapeutic targets for the designing of multi-targeted agents for the treatment of inflammatory-mediated pathological conditions. In the present study, diverse inhibitors of JAK1 and JAK3 were considered for the development of ligand-based pharmacophore models, followed by docking analysis to design putative dual inhibitors. The pharmacophore models were generated in PHASE 3.4, and top five models for each target were selected on the basis of survival minus inactive score. The best model for JAK1 (AAADH.25) and JAK3 (ADDRR.142) were selected corresponding to the highest value of Q²_test. Both models were employed for the screening of a PHASE database, and subsequently, the retrieved hits were filtered employing molecular docking in JAK1 and JAK3 proteins. The stable interactions between retrieved hits and proteins were confirmed using molecular dynamics simulations. Finally, ADME properties of screened dual inhibitors displaying essential interactions with both proteins were calculated. Thus, the new leads obtained in this way may be prioritized for experimental validation as potential novel therapeutic agents in the treatment of various autoimmune and inflammatory disorders related to JAK1 and JAK3.

PASS Targets: Ligand-based multi-target computational system based on a public data and naïve Bayes approach

Estimation of interactions between drug-like compounds and drug targets is very important for drug discovery and toxicity assessment. Using data extracted from the 19th version of the ChEMBL database ( https://www.ebi.ac.uk/chembl ) as a training set and a Bayesian-like method realized in PASS software ( http://www.way2drug.com/PASSOnline ), we developed a computational tool for the prediction of interactions between protein targets and drug-like compounds. After training, PASS Targets became able to predict interactions of drug-like compounds with 2507 protein targets from different organisms based on analysis of structure-activity relationships for 589,107 different chemical compounds. The prediction accuracy, estimated as AUC ROC calculated by the leave-one-out cross-validation and 20-fold cross-validation procedures, was about 96%. Average AUC ROC value was about 90% for the external test set from approximately 700 known drugs interacting with 206 protein targets.