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Background: Histone deacetylase inhibitors (HDACIs) are important as anticancer agents. Objective: This study aimed to investigate some key structural features of HDACIs via the design, synthesis and biological evaluation of novel benzamide-based derivatives. Methods: Novel structures, designed using a molecular modification approach, were synthesized and biologically evaluated. Results: The results indicated that a subset of molecules with CH 3 /NH 2 at R 2 position possess selective antiproliferative activity. However, only those with an NH 2 group showed HDACI activity. Importantly, the shorter the molecule length, the stronger HDACI. Among all, 7j was the most potent HDAC1-3 inhibitor and antiproliferative compound. Conclusion: The results of the present investigation could provide valuable structural knowledge applicable for the development of the HDACIs and benzamide-based antiproliferative agents in the future.

During the past decades, histamine H 3 receptors have received widespread attention in pharmaceutical research due to their involvement in pathophysiology of several diseases such as neurodegenerative disorders. In this context, blocking of these receptors is of paramount importance in progression of such diseases. In the current investigation, novel histamine H 3 receptor ligands were designed by exploiting scaffold-hopping drug-design strategy. We inspected the designed molecules in terms of ADME properties, drug-likeness, as well as toxicity profiles. Additionally molecular docking and dynamics simulation studies were performed to predict binding mode and binding free energy calculations, respectively. Among the designed structures, we selected compound d2 and its demethylated derivative as examples for synthesis and affinity measurement. In vitro binding assays of the synthesized molecules demonstrated that d2 has lower binding affinity (K i  = 2.61 μM) in radioligand displacement assay to hH 3 R than that of demethylated form (K i  = 12.53 μM). The newly designed compounds avoid of any toxicity predictors resulted from extended in silico and experimental studies, can offer another scaffold for histamine H 3 R antagonists for further structure-activity relationship studies., (Copyright © 2021. Published by Elsevier Inc.)

Aldehyde oxidase (AO), an important enzyme in the biotransformation of drugs and xenobiotics, is inhibited by flavonoids. This enzyme can metabolize both aldehydes and N-heterocycles. In this work, a set of 15 flavonoids was assessed for inhibitory activity on the AO oxidation of vanillin as an aldehyde substrate. Spectrophotometrically determined IC50 values showed that myricetin, quercetin, and epicatechin were the most potent inhibitors. The results also revealed that the inhibition of vanillin oxidation by flavonoids was stronger than that of phenanthridine oxidation (an N-heterocyclic substrate) as reported previously. In order to investigate the important structural features responsible for the inhibitory effects of the studied flavonoids, a quantitative structure-activity relationship (QSAR) analysis was performed. This study showed that the size of the flavonoids was the most important factor inversely affecting their potencies. The QSAR model can be used more broadly to predict the AO inhibitory activity of flavonoid-like structures for application in food-drug and drug-drug interaction studies., (© 2014 WILEY-VCH Verlag GmbH & Co. KGaA, Weinheim.)

The application of QSAR analysis dates back a half-century ago and is currently continuously employed in any rational drug design. The multi-dimensional QSAR modeling can be a promising tool for researchers to develop reliable predictive QSAR models for designing novel compounds. In the present work, we studied inhibitors of human aldose reductase (AR) to generate multi-dimensional QSAR models using 3D- and 6D-QSAR methods. For this purpose, Pentacle and Quasar's programs were used to produce the QSAR models using corresponding dissociation constant (Kd) values. By inspecting the performance metrics of the generated models, we achieved similar results with comparable internal validation statistics. However, considering the externally validated values, 6D-QSAR models provide significantly better prediction of endpoint values. The obtained results suggest that the higher the dimension of the QSAR model, the higher the performance of the generated model. However, more studies are required to verify these outcomes. [ABSTRACT FROM AUTHOR]

The profound impact of in silico studies for a fast-paced drug discovery pipeline is undeniable for pharmaceutical community. The rational design of novel drug candidates necessitates considering optimization of their different aspects prior to synthesis and biological evaluations. The affinity prediction of small ligands to target of interest for rank-ordering the potential ligands is one of the most routinely used steps in the context of virtual screening. So, the end-point methods were employed for binding free energy estimation focusing on evaluating simulation time effect. Then, a set of human aldose reductase inhibitors were selected for molecular dynamics (MD)-based binding free energy calculations. A total of 100 ns MD simulation time was conducted for the ligand-receptor complexes followed by prediction of binding free energies using MM/PB(GB)SA and LIE approaches under different simulation time. The results revealed that a maximum of 30 ns simulation time is sufficient for determination of binding affinities inferred from steady trend of squared correlation values (R2) between experimental and predicted Δ G as a function of MD simulation time. In conclusion, the MM/PB(GB)SA algorithms performed well in terms of binding affinity prediction compared to LIE approach. The results provide new insights for large-scale applications of such predictions in an affordable computational cost. [ABSTRACT FROM AUTHOR]

Purpose: Implication of protein-protein interactions (PPIs) in development of many diseases such as cancer makes them attractive for therapeutic intervention and rational drug design. RON (Recepteur d'Origine Nantais) tyrosine kinase receptor has gained considerable attention as promising target in cancer therapy. The activation of RON via its ligand, macrophage stimulation protein (MSP) is the most common mechanism of activation for this receptor. The aim of the current study was to perform in silico alanine scanning mutagenesis and to calculate binding energy for prediction of hot spots in proteinprotein interface between RON and MSPβ chain (MSPβ). Methods: In this work the residues at the interface of RON-MSPβ complex were mutated to alanine and then molecular dynamics simulation was used to calculate binding free energy. Results: The results revealed that Gln193, Arg220, Glu287, Pro288, Glu289and His424 residues from RON and Arg521, His528, Ser565, Glu658 and Arg683 from MSPβ may play important roles in protein-protein interaction between RON and MSP. Conclusion: Identification of these RON hot spots is important in designing anti-RON drugs when the aim is to disrupt RON-MSP interaction. In the same way, the acquired information regarding the critical amino acids of MSPβ can be used in the process of rational drug design for developing MSP antagonizing agents, the development of novel MSP mimicking peptides where inhibition of RON activation is required and the design of experimental site directed mutagenesis studies. [ABSTRACT FROM AUTHOR]

Histone deacetylases (HDACs) are one of the essential epigenetic targets in cancer treatment. These enzymes play key roles in post-translation modification (PTM) and gene expression, and consequently, their inhibitors are about to find their place in pharmacotherapy. Most of the currently approved HDAC inhibitors (HDACIs) are wide-spectrum with poor clinical outcomes and numerous side effects. Therefore, new generations of HDAC-based chemotherapeutics with better clinical outcomes are emerging, e.g., isoform-selective inhibitors, multitargeted HDACIs, as well as HDAC degraders. The review intended to introduce drug design approaches which were used for designing novel agents which can be beneficial in the process of finding new and more effective HDACI-based therapeutics. PubMed and other databases were searched for literature regarding the structure-function of HDAC isoforms, and strategies used to design HDAC inhibitors. Also, all clinical trials available from the ClinicalTrials site for years 2021–2022 were investigated. It is expected that the future of drug discovery projects in HDAC field will concentrate mostly on issues such as isoform-selectivity, multitargeted HDAC inhibitors and HDAC degraders. Deeper knowledge of the 3D structure of HDACs complexed with inhibitors and extensive delineation of biological roles of HDACs are needed for efficient investigations leading to the discovery of novel potent inhibitors. Histone deacetylases (HDACs) are one of the important epigenetic targets in cancer treatment drug discovery. Comprehending the structure of HDAC isoforms along with applied drug design strategies can inspire new ideas. [Display omitted] [ABSTRACT FROM AUTHOR]