Tuberculosis (TB) is one of the top 10 causes of death worldwide. This scenario is further complicated by the insurgence of multidrug-resistant (MDR) and extensively drug-resistant (XDR) TB. The identification of appropriate drugs with multi-target affinity profiles is considered to be a widely accepted strategy to overcome the rapid development of resistance. The aim of this study was to discover Food and Drug Administration (FDA)-approved drugs possessing antimycobacterial activity, potentially coupled to an effective multi-target profile. An integrated screening platform was implemented based on computational procedures (high-throughput docking techniques on the target enzymes peptide deformylase and Zmp1) and in vitro phenotypic screening assays using two models to evaluate the activity of the selected drugs against Mycobacterium tuberculosis (Mtb), namely, growth of Mtb H37Rv and of two clinical isolates in axenic media, and infection of peripheral blood mononuclear cells with Mtb. Starting from over 3000 FDA-approved drugs, we selected 29 marketed drugs for submission to biological evaluation. Out of 29 drugs selected, 20 showed antimycobacterial activity. Further characterization suggested that five drugs possessed promising profiles for further studies. Following a repurposing strategy, by combining computational and biological efforts, we identified marketed drugs with relevant antimycobacterial profiles.

The development of a novel comprehensive approach for the prediction of hERG activity is herein presented. Software Phase has been used to derive a 3D-QSAR model, employing as alignment rule a common pharmacophore built on a subset of 22 highly active compounds (threshold Ki: 50 nM) against hERG K+ channel. Five features comprised the pharmacophore: two aromatic rings (R1 and R2), one hydrogen-bond acceptor (A), one hydrophobic site (H), and one positive ionizable function (P). The sequential 3D-QSAR model developed with a set of 421 compounds (randomly divided in training and test set) yielded a test set (Q2) equal to 0.802 and proved to be predictive with respect to an external test set of 309 compounds that were not used to generate the model (r2ext_ts = 0.86). Furthermore, the model was submitted to an in silico validation for assessing the reliability of the approach, by applying a decoys set, evaluating the Güner and Henry score (GH) and the Enrichment Factor (EF), and by using the ROC curve analysis. The outcome demonstrated the high predictive power of the inclusive 3D-QSAR model developed for the hERG K+ channel blockers, confirming the fundamental validity of the chosen approach for obtaining a fast proprietary cardiotoxicity predictive tool to be employed for rationally designing compounds with reduced hERG K+ channel activity at the early steps of the drug discovery trajectory.

We describe herein the development and experimental validation of a computational protocol for optimizing a series of 3-hydroxy-pyran-4-one derivatives as HIV integrase inhibitors (HIV INIs). Starting from a previously developed micromolar inhibitors of HIV integrase (HIV IN), we performed an in-depth investigation based on an in silico structure-based combinatorial library designing approach. This method allowed us to combine a combinatorial library design and side chain hopping with Quantum Polarized Ligand Docking (QPLD) studies and Molecular Dynamics (MD) simulation. The combinatorial library design allowed the identification of the best decorations for our promising scaffold. The resulting compounds were assessed by the mentioned QPLD methodology using a homology model of full-length binary HIV IN/DNA for retrieving the best performing compounds acting as HIV INIs. Along with the prediction of physico-chemical properties, we were able to select a limited number of drug-like compounds potentially displaying potent HIV IN inhibition. From this final set, based on the synthetic accessibility, we further shortlisted three representative compounds for the synthesis. The compounds were experimentally assessed in vitro for evaluating overall HIV-1 IN inhibition, HIV-1 IN strand transfer activity inhibition, HIV-1 activity inhibition and cellular toxicity. Gratifyingly, all of them showed relevant inhibitory activity in the in vitro tests along with no toxicity. Among them HPCAR-28 represents the most promising compound as potential anti-HIV agent, showing inhibitory activity against HIV IN in the low nanomolar range, comparable to that found for Raltegravir, and relevant potency in inhibiting HIV-1 replication and HIV-1 IN strand transfer activity. In summary, our results outline HPCAR-28 as a useful optimized hit for the potential treatment of HIV-1 infection by targeting HIV IN.