Pharmacophore-based virtual screening for identifying β5 subunit inhibitor of 20S proteasome

Exploring EZH2-Proteasome Dual-Targeting Drug Discovery through a Computational Strategy to Fight Multiple Myeloma

Multiple myeloma is an incurable plasma cell neoplastic disease representing about 10-15% of all haematological malignancies diagnosed in developed countries. Proteasome is a key player in multiple myeloma and proteasome inhibitors are the current first-line of treatment. However, these are associated with limited clinical efficacy due to acquired resistance. One of the solutions to overcome this problem is a polypharmacology approach, namely combination therapy and multitargeting drugs. Several polypharmacology avenues are currently being explored. The simultaneous inhibition of EZH2 and Proteasome 20S remains to be investigated, despite the encouraging evidence of therapeutic synergy between the two. Therefore, we sought to bridge this gap by proposing a holistic in silico strategy to find new dual-target inhibitors. First, we assessed the characteristics of both pockets and compared the chemical space of EZH2 and Proteasome 20S inhibitors, to establish the feasibility of dual targeting. This was followed by molecular docking calculations performed on EZH2 and Proteasome 20S inhibitors from ChEMBL 25, from which we derived a predictive model to propose new EZH2 inhibitors among Proteasome 20S compounds, and vice versa, which yielded two dual-inhibitor hits. Complementarily, we built a machine learning QSAR model for each target but realised their application to our data is very limited as each dataset occupies a different region of chemical space. We finally proceeded with molecular dynamics simulations of the two docking hits against the two targets. Overall, we concluded that one of the hit compounds is particularly promising as a dual-inhibitor candidate exhibiting extensive hydrogen bonding with both targets. Furthermore, this work serves as a framework for how to rationally approach a dual-targeting drug discovery project, from the selection of the targets to the prediction of new hit compounds.

In silico modeling and in vitro activity of vitexin and isovitexin against SGLT2

The homology model of hSGLT2 (human sodium dependent glucose co-transporter 2) was used as a target for diabetes mellitus. Molecular docking and dynamics simulations were carried out on vitexin- and isovitexin-SGLT2 complexes with dapagliflozin as positive control. The results show that both vitexin and isovitexin have weaker binding energies compared to dapagliflozin, indicating that both ligands may exhibit weak anti-diabetic effects through inhibiting SGLT2. The poor binding mode of vitexin and isovitexin may be responsible for their weak anti-diabetic effect. These results are in accordance with the inhibitory activity against hSGLT2 in vitro test with the inhibitory rate 26.3% of vitexin and 11.2% of isovitexin at the dose of 10[Formula: see text][Formula: see text]mol[Formula: see text][Formula: see text][Formula: see text]L[Formula: see text]. The results of calculation and in vitro test may explain the possible inhibiting mechanism of vitexin and isovitexin against SGLT2, and therefore enhance our understanding of the structure-activity relationships of SGLT2 inhibitors.

Turning to Computer-aided Drug Design in the Treatment of Diffuse Large B-cell Lymphoma: Has it been Helpful?

INTRODUCTION

Amidst the numerous effective therapeutic options available for the treatment of Diffuse Large B-cell Lymphoma (DLBCL), about 30-40% of patients treated with first-line chemoimmunotherapy still experience a relapse or refractory DLBCL. This has necessitated a continuous search for new therapeutic agents to augment the existing therapeutic arsenal.

METHODS

The dawn of Computer-Aided Drug Design (CADD) in the drug discovery process has accounted for persistency in the application of computational approaches either alone or in combinatorial strategies with experimental methods towards the identification of potential hit compounds with high therapeutic efficacy in abrogating DLBCL.

RESULTS

This review showcases the interventions of structure-based and ligand-based computational approaches which have led to the identification of numerous small molecule inhibitors against implicated targets in DLBCL therapy, even though many of these potential inhibitors are piled-up awaiting further experimental validation and exploration.

CONCLUSION

We conclude that a successful and a conscious amalgamation of CADD and experimental approaches could pave the way for the discovery of the next generation potential leads in DLBCL therapy with improved activities and minimal toxicities.