Identification of new dual spleen tyrosine kinase (Syk) and phosphoionositide-3-kinase δ (PI3Kδ) inhibitors using ligand and structure-based integrated ideal pharmacophore models

Combination of bendamustine-azacitidine against Syk target of breast cancer: an in silico study

Breast cancer (BC) is the most serious and second leading cause of death in women worldwide. When breast cancer is diagnosed and treated early, the chance of long-term survival is up to 90%. On the other hand, 90% of BC patient deaths are due to metastasis and a lack of effective early diagnosis. The existing conventional chemotherapy provides negative feedback due to transportation barriers towards the action sites, multidrug resistance, poor bio-availability, non-specific delivery and systemic side effects on the healthy tissue. Syk protein Kinase has been reported in BC, as a tumor modulator, providing a pro-survival signal and also by restricting epithelial-mesenchymal transition, enhancing cell-cell interactions and inhibiting migration. In the present study, we explored the possibility of targeting BC by attenuating Syk protein Kinase. Hence, we have conjugated the hydrophobic Bendamustine (BEN) and hydrophilic Azacitidine (AZA) anticancer drugs to evaluate their efficacy against BC. The native drugs (BEN and AZA) and designed drug-drug conjugate (BEN-AZA) were docked with Syk protein. Then, the docked complex was performed for Binding Free Energy and Molecular Dynamics Simulations. Furthermore, DFT and ADME properties were carried out. The results revealed that the designed drug-drug conjugate has a better docking score, ΔGbind and admirable stability throughout the simulation when compared with native drugs. In DFT and ADME analyses, the designed drug-drug conjugate has shown good stereo electronic features and pharmaceutical relevant parameters than that of native drugs. The overall results suggested that the designed drug-drug conjugate may be a suitable candidate for BC treatment.Communicated by Ramaswamy H. Sarma.

Development of Multi-Target Chemometric Models for the Inhibition of Class I PI3K Enzyme Isoforms: A Case Study Using QSAR-Co Tool

The present work aims at establishing multi-target chemometric models using the recently launched quantitative structure–activity relationship (QSAR)-Co tool for predicting the activity of inhibitor compounds against different isoforms of phosphoinositide 3-kinase (PI3K) under various experimental conditions. The inhibitors of class I phosphoinositide 3-kinase (PI3K) isoforms have emerged as potential therapeutic agents for the treatment of various disorders, especially cancer. The cell-based enzyme inhibition assay results of PI3K inhibitors were curated from the CHEMBL database. Factors such as the nature and mutation of cell lines that may significantly alter the assay outcomes were considered as important experimental elements for mt-QSAR model development. The models, in turn, were developed using two machine learning techniques as implemented in QSAR-Co: linear discriminant analysis (LDA) and random forest (RF). Both techniques led to models with high accuracy (ca. 90%). Several molecular fragments were extracted from the current dataset, and their quantitative contributions to the inhibitory activity against all the proteins and experimental conditions under study were calculated. This case study also demonstrates the utility of QSAR-Co tool in solving multi-factorial and complex chemometric problems. Additionally, the combination of different in silico methods employed in this work can serve as a valuable guideline to speed up early discovery of PI3K 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.