Binary quantitative activity-activity relationship (QAAR) studies to explore selective HDAC8 inhibitors: In light of mathematical models, DFT-based calculation and molecular dynamic simulation studies

Identification of structural features of surface modifiers in engineered nanostructured metal oxides regarding cell uptake through ML-based classification

Nanoparticles (NPs) are considered as versatile tools in various fields including medicine, electronics, and environmental science. Understanding the structural aspects of surface modifiers in nanoparticles that govern their cellular uptake is crucial for optimizing their efficacy and minimizing potential cytotoxicity. The cellular uptake is influenced by multiple factors, namely, size, shape, and surface charge of NPs, as well as their surface functionalization. In the current study, classification-based ML models (i.e., Bayesian classification, random forest, support vector classifier, and linear discriminant analysis) have been developed to identify the features/fingerprints that significantly contribute to the cellular uptake of ENMOs in multiple cell types, including pancreatic cancer cells (PaCa2), human endothelial cells (HUVEC), and human macrophage cells (U937). The best models have been identified for each cell type and analyzed to detect the structural fingerprints/features governing the cellular uptake of ENMOs. The study will direct scientists in the design of ENMOs of higher cellular uptake efficiency for better therapeutic response.

Unraveling HDAC11: Epigenetic orchestra in different diseases and structural insights for inhibitor design

Histone deacetylase 11 (HDAC11), a member of the HDAC family, has emerged as a critical regulator in numerous physiological as well as pathological processes. Due to its diverse roles, HDAC11 has been a focal point of research in recent times. Different non-selective inhibitors are already approved, and research is going on to find selective HDAC11 inhibitors. The objective of this review is to comprehensively explore the role of HDAC11 as a pivotal regulator in a multitude of physiological and pathological processes. It aims to delve into the intricate details of HDAC11's structural and functional aspects, elucidating its molecular interactions and implications in different disease contexts. With a primary focus on elucidating the structure-activity relationships (SARs) of HDAC11 inhibitors, this review also aims to provide a holistic understanding of how its molecular architecture influences its inhibition. Additionally, by integrating both established knowledge and recent research, the review seeks to contribute novel insights into the potential therapeutic applications of HDAC11 inhibitors. Overall, the scope of this review spans from fundamental research elucidating the complexities of HDAC11 biology to the potential of targeting HDAC11 in therapeutic interventions.

Density functional theory (DFT) studies in HDAC-based chemotherapeutics: Current findings, case studies and future perspectives

Density Functional Theory (DFT) is a quantum chemical computational method used to predict and analyze the electronic properties of atoms, molecules, and solids based on the density of electrons rather than wavefunctions. It provides insights into the structure, bonding, and behavior of different molecules, including those involved in the development of chemotherapeutic agents, such as histone deacetylase inhibitors (HDACis). HDACs are a wide group of metalloenzymes that facilitate the removal of acetyl groups from acetyl-lysine residues situated in the N-terminal tail of histones. Abnormal HDAC recruitment has been linked to several human diseases, especially cancer. Therefore, it has been recognized as a prospective target for accelerating the development of anticancer therapies. Researchers have studied HDACs and its inhibitors extensively using a combination of experimental methods and diverse in-silico approaches such as machine learning and quantitative structure-activity relationship (QSAR) methods, molecular docking, molecular dynamics, pharmacophore mapping, and more. In this context, DFT studies can make significant contribution by shedding light on the molecular properties, interactions, reaction pathways, transition states, reactivity and mechanisms involved in the development of HDACis. This review attempted to elucidate the scope in which DFT methodologies may be used to enhance our comprehension of the molecular aspects of HDAC inhibitors, aiding in the rational design and optimization of these compounds for therapeutic applications in cancer and other ailments. The insights gained can guide experimental efforts toward developing more potent and selective HDAC inhibitors.

The Histone Deacetylase Family: Structural Features and Application of Combined Computational Methods

Histone deacetylases (HDACs) are crucial in gene transcription, removing acetyl groups from histones. They also influence the deacetylation of non-histone proteins, contributing to the regulation of various biological processes. Thus, HDACs play pivotal roles in various diseases, including cancer, neurodegenerative disorders, and inflammatory conditions, highlighting their potential as therapeutic targets. This paper reviews the structure and function of the four classes of human HDACs. While four HDAC inhibitors are currently available for treating hematological malignancies, numerous others are undergoing clinical trials. However, their non-selective toxicity necessitates ongoing research into safer and more efficient class-selective or isoform-selective inhibitors. Computational methods have aided the discovery of HDAC inhibitors with the desired potency and/or selectivity. These methods include ligand-based approaches, such as scaffold hopping, pharmacophore modeling, three-dimensional quantitative structure-activity relationships, and structure-based virtual screening (molecular docking). Moreover, recent developments in the field of molecular dynamics simulations, combined with Poisson-Boltzmann/molecular mechanics generalized Born surface area techniques, have improved the prediction of ligand binding affinity. In this review, we delve into the ways in which these methods have contributed to designing and identifying HDAC inhibitors.

HDAC9 as a Privileged Target: Reviewing its Role in Different Diseases and Structure-activity Relationships (SARs) of its Inhibitors

HDAC9 is a histone deacetylase enzyme belonging to the class IIa of HDACs which catalyses histone deacetylation. HDAC9 inhibit cell proliferation by repairing DNA, arresting the cell cycle, inducing apoptosis, and altering genetic expression. HDAC9 plays a significant part in human physiological system and are involved in various type of diseases like cancer, diabetes, atherosclerosis and CVD, autoimmune response, inflammatory disease, osteoporosis and liver fibrosis. This review discusses the role of HDAC9 in different diseases and structure-activity relationships (SARs) of various hydroxamate and non-hydroxamate-based inhibitors. SAR of compounds containing several scaffolds have been discussed in detail. Moreover, structural requirements regarding the various components of HDAC9 inhibitor (cap group, linker and zinc-binding group) has been highlighted in this review. Though, HDAC9 is a promising target for the treatment of a number of diseases including cancer, a very few research are available. Thus, this review may provide useful information for designing novel HDAC9 inhibitors to fight against different diseases in the future.

In the quest for histone deacetylase inhibitors: current trends in the application of multilayered computational methods

Histone deacetylase (HDAC) inhibitors have gained attention over the past three decades because of their potential in the treatment of different diseases including various forms of cancers, neurodegenerative disorders, autoimmune, inflammatory diseases, and other metabolic disorders. To date, 5 HDAC inhibitor drugs are marketed for the treatment of hematological malignancies and several drug-candidate HDAC inhibitors are at different stages of clinical trials. However, due to the toxic side effects of these drugs resulting from the lack of target selectivity, active studies are ongoing to design and develop either class-selective or isoform-selective inhibitors. Computational methods have aided the discovery of HDAC inhibitors with the desired potency and/or selectivity. These methods include ligand-based approaches such as scaffold hopping, pharmacophore modeling, three-dimensional quantitative structure-activity relationships (3D-QSAR); and structure-based virtual screening (molecular docking). The current trends involve the application of the combination of these methods and incorporating molecular dynamics simulations coupled with Poisson-Boltzmann/molecular mechanics generalized Born surface area (MM-PBSA/MM-GBSA) to improve the prediction of ligand binding affinity. This review aimed at understanding the current trends in applying these multilayered strategies and their contribution to the design/identification of HDAC inhibitors.

Are inhibitors of histone deacetylase 8 (HDAC8) effective in hematological cancers especially acute myeloid leukemia (AML) and acute lymphoblastic leukemia (ALL)?

Histone deacetylase 8 (HDAC8) aberrantly deacetylates histone and non-histone proteins. These include structural maintenance of chromosome 3 (SMC3) cohesin protein, retinoic acid induced 1 (RAI1), p53, etc and thus, regulating diverse processes such as leukemic stem cell (LSC) transformation and maintenance. HDAC8, one of the crucial HDACs, affects the gene silencing process in solid and hematological cancer progressions especially on acute myeloid leukemia (AML) and acute lymphoblastic leukemia (ALL). A specific HDAC8 inhibitor PCI-34051 showed promising results against both T-cell lymphoma and AML. Here, we summarize the role of HDAC8 in hematological malignancies, especially in AML and ALL. This article also introduces the structure/function of HDAC8 and a special attention has been paid to address the HDAC8 enzyme selectivity issue in hematological cancer especially against AML and ALL.

Evaluation of apigenin-based biflavonoid derivatives as potential therapeutic agents against viral protease (3CLpro) of SARS-CoV-2 via molecular docking, molecular dynamics and quantum mechanics studies

Severe Acute Respiratory Syndrome Coronavirus 2 (SARS-CoV-2) is the causative agent of the pandemic COVID-19 disease that affects human respiratory function. Despite the scientific progression made in the development of the vaccine, there is an urgent need for the discovery of antiviral drugs for better performance at different stages of SARS-CoV-2 reproduction. The main protease (Mpro or 3CLpro) plays a pivotal role in the life cycle of the virus, making it an attractive target for the development of antiviral agents effective against the new strains of coronaviruses (CoVs). In this study, a series of apigenin-based natural biflavonoid derivatives as potential inhibitors of coronaviruses 3CLpro was investigated by in silico approaches. For this purpose, the molecular docking was performed to analyze the interaction of the natural biflavonoids with SARS-Cov-2 main protease and for further investigation, docking to the 3CLpro of SARS-CoV and MERS-CoV. Based on docking scores and comparison with the reference inhibitors (ritonavir and lopinavir), more than half of the biflavonoids had strong interactions with the residues of the binding pocket of the coronaviruses 3CLpro and exhibited better binding affinities toward the main protease than ritonavir and lopinavir. The top biflavonoids were further explored through molecular dynamics simulation, binding free energy calculation and residual energy contributions estimated by the MM-PBSA. Also, drug likeness property investigation by Swiss ADME tools and density functional theory (DFT) calculations were performed. The results confirmed that the 3CLpro-amentoflavone, 3CLpro-bilobetin, 3CLpro-ginkgetin, and 3CLpro-sotetsuflavone complexes possess a large amount of dynamic properties such as high stability, significant binding energy and fewer conformation fluctuations. Also, the pharmacokinetics and drug-likeness studies and HOMO-LUMO and DFT descriptor values indicated a promising result of the selected natural biflavonoids. Overall findings indicate that the apigenin-based biflavonoids may inhibit COVID-19 by significant interactions in the binding pocket and those results can pave the way in drug discovery although the effectiveness of these bioactive compounds should be further validated by in-vitro and in-vivo investigations. Communicated by Ramaswamy H. Sarma.