Dataset Information

Identification of Novel 3-Hydroxy-pyran-4-One Derivatives as Potent HIV-1 Integrase Inhibitors Using in silico Structure-Based Combinatorial Library Design Approach.

ABSTRACT: 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.

SUBMITTER: Sirous H

PROVIDER: S-EPMC6700280 | biostudies-literature | 2019

REPOSITORIES: biostudies-literature

ACCESS DATA

Publications

Identification of Novel 3-Hydroxy-pyran-4-One Derivatives as Potent HIV-1 Integrase Inhibitors Using <i>in silico</i> Structure-Based Combinatorial Library Design Approach.

Sirous Hajar H Chemi Giulia G Gemma Sandra S Butini Stefania S Debyser Zeger Z Christ Frauke F Saghaie Lotfollah L Brogi Simone S Fassihi Afshin A Campiani Giuseppe G Brindisi Margherita M

Frontiers in chemistry 20190813

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 <i>in silico</i> structure-based combinatorial library designing approach. This method allowed us to combine a combinatorial library design and side chain hopping with Qua ...[more]

PMID: 31457006

Similar Datasets

Project description:Introduction: Cancer is the second most prevalent cause of mortality in the world, despite the availability of several medications for cancer treatment. Therefore, the cancer research community emphasized on computational techniques to speed up the discovery of novel anticancer drugs. Methods: In the current study, QSAR-based virtual screening was performed on the Zinc15 compound library (271 derivatives of methotrexate (MTX) and phototrexate (PTX)) to predict their inhibitory activity against dihydrofolate reductase (DHFR), a potential anticancer drug target. The deep learning-based ADMET parameters were employed to generate a 2D QSAR model using the multiple linear regression (MPL) methods with Leave-one-out cross-validated (LOO-CV) Q2 and correlation coefficient R2 values as high as 0.77 and 0.81, respectively. Results: From the QSAR model and virtual screening analysis, the top hits (09, 27, 41, 68, 74, 85, 99, 180) exhibited pIC50 ranging from 5.85 to 7.20 with a minimum binding score of -11.6 to -11.0 kcal/mol and were subjected to further investigation. The ADMET attributes using the message-passing neural network (MPNN) model demonstrated the potential of selected hits as an oral medication based on lipophilic profile Log P (0.19-2.69) and bioavailability (76.30% to 78.46%). The clinical toxicity score was 31.24% to 35.30%, with the least toxicity score (8.30%) observed with compound 180. The DFT calculations were carried out to determine the stability, physicochemical parameters and chemical reactivity of selected compounds. The docking results were further validated by 100 ns molecular dynamic simulation analysis. Conclusion: The promising lead compounds found endorsed compared to standard reference drugs MTX and PTX that are best for anticancer activity and can lead to novel therapies after experimental validations. Furthermore, it is suggested to unveil the inhibitory potential of identified hits via in-vitro and in-vivo approaches.

Dataset Information

Identification of Novel 3-Hydroxy-pyran-4-One Derivatives as Potent HIV-1 Integrase Inhibitors Using in silico Structure-Based Combinatorial Library Design Approach.

Publications

Identification of Novel 3-Hydroxy-pyran-4-One Derivatives as Potent HIV-1 Integrase Inhibitors Using <i>in silico</i> Structure-Based Combinatorial Library Design Approach.

Similar Datasets

OmicsDI is part of the ELIXIR infrastructure

Tweets