Exploring fingerprints for antidiabetic therapeutics related to peroxisome proliferator-activated receptor gamma (PPARγ) modulators: A chemometric modeling approach.

This study demonstrated the correlation of molecular structures of Peroxisome proliferator-activated receptor gamma (PPARγ) modulators and their biological activities. Bayesian classification, and recursive partitioning (RP) studies have been applied to a dataset of 323 PPARγ modulators with diverse scaffolds. The results provide a deep insight into the important sub-structural features modulating PPARγ. The molecular docking analysis again confirmed the significance of the identified sub-structural features in the modulation of PPARγ activity. Molecular dynamics simulations further underscored the stability of the complexes formed by investigated modulators with PPARγ. Overall, the integration of many computational approaches unveiled key structural motifs essential for PPARγ modulatory activity that will shed light on the development of effective modulators in the future.

Identification of structural fingerprints among natural inhibitors of HDAC1 to accelerate nature-inspired drug discovery in cancer epigenetics.

Histone deacetylase 1 (HDAC1), a class I HDAC enzyme, is crucial for histone modification. Currently, it is emerged as one of the important biological targets for designing small molecule drugs through cancer epigenetics. Along with synthetic inhibitors different natural inhibitors are showing potential HDAC1 inhibitions. In order to gain insights into the relationship between the molecular structures of the natural inhibitors and HDAC1, different molecular modelling techniques (Bayesian classification, recursive partitioning, molecular docking and molecular dynamics simulations) have been applied on a dataset of 155 HDAC1 nature-inspired inhibitors with diverse scaffolds. The Bayesian study showed acceptable ROC values for both the training set and test sets. The Recursive partitioning study produced decision tree 1 with 6 leaves. Further, molecular docking study was processed for generating the protein ligand complex which identified some potential amino acid residues such as F205, H28, L271, P29, F150, Y204 for the binding interactions in case of natural inhibitors. Stability of these HDAC1-natutal inhibitors complexes has been also evaluated by molecular dynamics simulation study. The current modelling study is an attempt to get a deep insight into the different important structural fingerprints among different natural compounds modulating HDAC1 inhibition.Communicated by Ramaswamy H. Sarma.

Unveiling critical structural features for effective HDAC8 inhibition: a comprehensive study using quantitative read-across structure-activity relationship (q-RASAR) and pharmacophore modeling.

Histone deacetylases constitute a group of enzymes that participate in several biological processes. Notably, inhibiting HDAC8 has become a therapeutic strategy for various diseases. The current inhibitors for HDAC8 lack selectivity and target multiple HDACs. Consequently, there is a growing recognition of the need for selective HDAC8 inhibitors to enhance the effectiveness of therapeutic interventions. In our current study, we have utilized a multi-faceted approach, including Quantitative Structure-Activity Relationship (QSAR) combined with Quantitative Read-Across Structure-Activity Relationship (q-RASAR) modeling, pharmacophore mapping, molecular docking, and molecular dynamics (MD) simulations. The developed q-RASAR model has a high statistical significance and predictive ability (Q2F1:0.778, Q2F2:0.775). The contributions of important descriptors are discussed in detail to gain insight into the crucial structural features in HDAC8 inhibition. The best pharmacophore hypothesis exhibits a high regression coefficient (0.969) and a low root mean square deviation (0.944), highlighting the importance of correctly orienting hydrogen bond acceptor (HBA), ring aromatic (RA), and zinc-binding group (ZBG) features in designing potent HDAC8 inhibitors. To confirm the results of q-RASAR and pharmacophore mapping, molecular docking analysis of the five potent compounds (44, 54, 82, 102, and 118) was performed to gain further insights into these structural features crucial for interaction with the HDAC8 enzyme. Lastly, MD simulation studies of the most active compound (54, mapped correctly with the pharmacophore hypothesis) and the least active compound (34, mapped poorly with the pharmacophore hypothesis) were carried out to validate the observations of the studies above. This study not only refines our understanding of essential structural features for HDAC8 inhibition but also provides a robust framework for the rational design of novel selective HDAC8 inhibitors which may offer insights to medicinal chemists and researchers engaged in the development of HDAC8-targeted therapeutics.

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.

Advances in structure-activity relationships of HDAC inhibitors as HIV latency-reversing agents.

INTRODUCTION: HIV-infected cells may rebound due to the existence of the silent HIV-infected memory CD4+ T cells (HIV latency). This HIV latency makes the disease almost incurable. In latency, the integrated proviral DNA of HIV is transcriptionally silenced partly due to the activity of histone deacetylases (HDACs). Hence, inhibition of HDAC is considered a prime target for HIV latency reversal. AREAS COVERED: A brief biology and function of HDACs have been discussed to identify key points to design HDAC inhibitors (HDACis). This article summarizes recent achievements in the development of HDACis to achieve HIV latency reversal. Structure-activity relationships (SARs) of some series of compounds were also explored. EXPERT OPINION: Depletion of the HIV reservoir is the only way to end this deadly epidemic. HDACis are latency-reversing agents (LRA) that can be used to 'shock' the latently infected CD4+ T cells to induce them to produce viral proteins. It is interesting to note that HDAC3, which is extensively expressed in resting T cells, is specifically preferred by benzamide-containing HDACis for inhibition. Thus, the benzamide class of compounds should be explored. Nevertheless, more data on selective HDAC inhibition is needed for further development of HDACis in HIV latency reversal.

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.

Exploring molecular fingerprints of different drugs having bile interaction: a stepping stone towards better drug delivery.

Bile acids are amphiphilic substances produced naturally in humans. In the context of drug delivery and dosage form design, it is critical to understand whether a drug interacts with bile inside the gastrointestinal (GI) tract or not. This study focuses on the identification of structural fingerprints/features important for bile interaction. Molecular modelling methods such as Bayesian classification and recursive partitioning (RP) studies are executed to find important fingerprints/features for the bile interaction. For the Bayesian classification study, the ROC score of 0.837 and 0.950 are found for the training set and the test set compounds, respectively. The fluorine-containing aliphatic/aromatic group, the branched chain of the alkyl group containing hydroxyl moiety and the phenothiazine ring etc. are identified as good fingerprints having a positive contribution towards bile interactions, whereas, the bad fingerprints such as free carboxylate group, purine, and pyrimidine ring etc. have a negative contribution towards bile interactions. The best tree (tree ID: 1) from the RP study classifies the bile interacting or non-interacting compounds with a ROC score of 0.941 for the training and 0.875 for the test set. Additionally, SARpy and QSAR-Co analyses are also been performed to classify compounds as bile interacting/non-interacting. Moreover, forty-six recently FDA-approved drugs have been screened by the developed SARpy and QSAR-Co models to assess their bile interaction properties. Overall, this attempt may facilitate the researchers to identify bile interacting/non-interacting molecules in a faster way and help in the design of formulations and target-specific drug development.

Exploring structural requirements of HDAC10 inhibitors through comparative machine learning approaches.

Histone deacetylase (HDAC) inhibitors are in the limelight of anticancer drug development and research. HDAC10 is one of the class-IIb HDACs, responsible for cancer progression. The search for potent and effective HDAC10 selective inhibitors is going on. However, the absence of human HDAC10 crystal/NMR structure hampers the structure-based drug design of HDAC10 inhibitors. Different ligand-based modeling techniques are the only hope to speed up the inhibitor design. In this study, we applied different ligand-based modeling techniques on a diverse set of HDAC10 inhibitors (n = 484). Machine learning (ML) models were developed that could be used to screen unknown compounds as HDAC10 inhibitors from a large chemical database. Moreover, Bayesian classification and Recursive partitioning models were used to identify the structural fingerprints regulating the HDAC10 inhibitory activity. Additionally, a molecular docking study was performed to understand the binding pattern of the identified structural fingerprints towards the active site of HDAC10. Overall, the modeling insight might offer helpful information for medicinal chemists to design and develop efficient HDAC10 inhibitors.

A review of MMP-2 structures and binding mode analysis of its inhibitors to strategize structure-based drug design.

The protease enzyme, matrix metalloproteinase-2 (MMP-2) has been a target of choice for the drug development due to its multi-façade involvement in numerous diseased conditions including cancer. To find a selective MMP-2 inhibitor several computational strategies are employed in its design and discovery. In these strategies, protein structure of MMP-2 is an inevitable part to formulate effective structure-based drug design (SBDD) of selective MMP-2 inhibitors. In the present communication, several crystal structures of MMP-2 have been analyzed with different statistical parameters and their implementations in SBDD of inhibitors are scrutinized. In addition, binding mode analyses of various classes of inhibitors are discussed to pinpoint the effective design of selective inhibitors by maximizing its interaction with the MMP-2 enzyme binding site. This may provide a crucial insight for exploring the numerous possibilities for SBDD of MMP-2 inhibitors to accelerate anticancer drug discovery efforts.

Recent trends in fragment-based anticancer drug design strategies against different targets: A mini-review.

Cancer is a rapidly growing disease in modern society. Chemotherapy is the first choice for cancer treatment. Design and development of new chemotherapeutic drugs by targeting specific proteins are put down by a high attrition rate at different stages. Fragment-based drug design (FBDD) is one of the successful structure-based drug design processes to avoid attrition-related problems. This review highlighted the computational and experimental FBDD techniques used to design molecules with anticancer properties. This study describes FBBD strategies for different targets like aurora kinase, phosphoinositide-dependent protein kinase-1 (PDK1), signal transducer and activator of transcription 3 (STAT3), myeloid cell leukemia-1 (Mcl-1), tankyrase (TNKS), choline kinase, protein kinase, tyrosine kinase and lysine-specific demethylase 1 (LSD1) which are vital targets for cancer treatments. This review will enrich the scientific community to understand the fragment-based design strategies for finding suitable leads over high throughput screening (HTS) in the future.

Host P2X7R-p38MAPK axis mediated intra-macrophage leishmanicidal activity of Spergulin-A.

Current drugs are inefficient for the treatment of visceral leishmaniasis an immunosuppressive ailment caused by Leishmania donovani. Regrettably, there is no plant-origin antileishmanial drug present. P2X7R is constitutively present on macrophage surfaces and can be a putative therapeutic target in intra-macrophage pathogens with function attributes towards inflammation, host cell apoptosis, altered redox, and phagolysosomal maturation by activating p38MAPK. Here we demonstrated that the initial interaction of Spergulin-A (Sp A), a triterpenoid saponin with RAW 264.7 macrophages was mediated through P2X7R involving the signaling cascade intermediates Ca++, p38MAPK, and NF-κß. Phospho (P)-p38MAPK involvement is shown to have specific and firm importance in leishmanial killing with increased NF-κßp65. Phago-lysosomal maturation by Sp A also campaigns for another contribution of P2X7R. In vivo evaluation of the anti-leishmanial activity of Sp A was monitored through expression analyses of P2X7R, P-p38MAPK, and NF-κßp65 in murine spleen and bone-marrow macrophages and supported Sp A being a natural compound of leishmanicidal functions which acted through the P2X7R-p38MAPK axis.

Updated chemical scaffolds of ABCG2 inhibitors and their structure-inhibition relationships for future development.

ATP-binding cassette (ABC) transporters are pivotal for cell detoxification and survival. Overexpression of ABC transporter in tumor cells lead to chemoresistance through the efflux of chemotherapeutic agents. P-glycoprotein (Pgp/ABCB1), multidrug resistance protein 1 (MRP1/ABCC1) and breast cancer resistance protein (BCRP/ABCG2) are the major ABC transporters involved in multidrug resistance (MDR) of cancer cells against anticancer drugs. ABCG2 is one of the major transporters involved in the efflux of different cytotoxic agents. Hence, inhibition of ABCG2-mediated transport is considered a prime target to resist MDR of cancer cells. Here, brief structural biology and functions of ABCG2 were discussed with the aim to identify key pharmacophoric elements to design potent and selective as well as non-toxic ABCG2 inhibitors. Structure-inhibition relationships (SIRs) of the earlier reported compounds were also explored. Taken together, this study offers insight for further development of ABCG2 inhibitors.

A fragment-based structural analysis of MMP-2 inhibitors in search of meaningful structural fragments.

The matrix metalloproteinase family of Zn2+-dependent metalloenzymes are a group of proteases that possess the ability to degrade the extracellular matrix (ECM) and thus are involved in different biological processes. Being one of the prime members of this family, the matrix metalloproteinase-2 (MMP-2) is associated with several pathophysiological conditions including cancer, tumor progression, invasion, and metastasis. Due to its contribution toward human pathophysiology, MMP-2 is in the limelight of anticancer drug development research for more than three decades. Our study deals with a combination of classification-based modeling approaches with fragment-based data mining techniques on a large and diverse set of MMP-2 inhibitors. This has enabled the identification of important structural fragments/features of MMP-2 inhibitors essential for their potency and efficacy. Significantly, molecular properties such as AlogP, MW, hydrogen bond features, and the molecular fractional polar surface area can be utilized to discriminate the potent MMP-2 inhibitors from the lesser active/inactive ones. From this extensive study, it can be concluded that the hydroxamate and carboxylic acid groups as suitable zinc-binding groups (ZBGs) for potent MMP-2 inhibition. This study also concludes the importance of solvent accessibility, hydrogen bond donor and acceptor groups as well as the bulky aromatic groups for better interactions inside the MMP-2 active site. Meanwhile, the similarities of this current study with our previous observations validate the outcomes of this current study. Hence, these findings may provide useful information toward the development of effective MMP-2 inhibitors in the future.

A critical analysis of urea transporter B inhibitors: molecular fingerprints, pharmacophore features for the development of next-generation diuretics.

Urea transporter is a membrane transport protein. It is involved in the transferring of urea across the cell membrane in humans. Along with urea transporter A, urea transporter B (UT-B) is also responsible for the management of urea concentration and blood pressure of human. The inhibitors of urea transporters have already generated a huge attention to be developed as alternate safe class of diuretic. Unlike conventional diuretics, these inhibitors are suitable for long-term therapy without hampering the precious electrolyte imbalance in the human body. In this study, UT-B inhibitors were analysed by using multi-chemometric modelling approaches. The possible pharmacophore features along with favourable and unfavourable sub-structural fingerprints for UT-B inhibition are extracted. This information will guide the medicinal chemist to design potent UT-B inhibitors in future.

How the structural properties of the indole derivatives are important in kinase targeted drug design?: A case study on tyrosine kinase inhibitors.

Kinases are considered as important signalling enzymes that illustrate 20% of the druggable genome. Human kinase family comprises >500 protein kinases and about 20 lipid kinases. Protein kinases are responsible for the mechanism of protein phosphorylation. These are necessary for regulation of various cellular activities including proliferation, cell cycle, apoptosis, motility, growth, differentiation, etc. Their deregulation leads to disruption of many cellular processes leading to different diseases most importantly cancer. Thus, kinases are considered as valuable targets in different types of cancer as well as other diseases. Researchers around the world are actively engaged in developing inhibitors based on distinct chemical scaffolds. Indole represents as a versatile scaffold in the naturally occurring and bioactive molecules. It is also used as a privileged scaffold for the target-based drug design against different diseases. This present article aim to review the applications of indole scaffold in the design of inhibitors against different tyrosine kinases such as epidermal growth factor receptors (EGFRs), vascular endothelial growth factor receptors (VEGFRs), platelet-derived growth factor receptors (PDGFRs), etc. Important structure activity relationships (SARs) of indole derivatives were discussed. The present work is an attempt to summarize all the crucial structural information which is essential for the development of indole based tyrosine kinase inhibitors with improved potency.

Exploring naphthyl derivatives as SARS-CoV papain-like protease (PLpro) inhibitors and its implications in COVID-19 drug discovery.

Novel coronavirus disease 2019 (COVID-19) emerges as a serious threat to public health globally. The rapid spreading of COVID-19, caused by severe acute respiratory syndrome (SARS) coronavirus 2 (SARS-CoV-2), proclaimed the multitude of applied research needed not only to save the human health but also for the environmental safety. As per the recent World Health Organization reports, the novel corona virus may never be wiped out completely from the world. In this connection, the inhibitors already designed against different targets of previous human coronavirus (HCoV) infections will be a great starting point for further optimization. Pinpointing biochemical events censorious to the HCoV lifecycle has provided two proteases: a papain-like protease (PLpro) and a 3C-like protease (3CLpro) enzyme essential for viral replication. In this study, naphthyl derivatives inhibiting PLpro enzyme were subjected to robust molecular modelling approaches to understand different structural fingerprints important for the inhibition. Here, we cover two main aspects such as (a) exploration of naphthyl derivatives by classification QSAR analyses to find important fingerprints that module the SARS-CoV PLpro inhibition and (b) implications of naphthyl derivatives against SARS-CoV-2 PLpro enzyme through detailed ligand-receptor interaction analysis. The modelling insights will help in the speedy design of potent broad spectrum PLpro inhibitors against infectious SARS-CoV and SARS-CoV-2 in the future.

Synthesis, anticancer activity, SAR and binding mode of interaction studies of substituted pentanoic acids: part II.

Aim: Our previous results suggest that phenyl/naphthylacetyl pentanoic acid derivatives may exhibit dual MMP-2 and HDAC8 inhibitory activities and show effective cytotoxic properties. Methodology: Here, 13 new compounds (C1-C13) were synthesized and characterized. Along with these new compounds, 16 previously reported phenyl/napthylacetyl pentanoic acid derivatives (C14-C29) were biologically evaluated. Results: Compounds C6 and C27 showed good cytotoxicity against leukemia cell line Jurkat E6.1. The mechanisms of cytotoxicity of these compounds were confirmed by DNA deformation assay and reactive oxygen species assay. MMP-2 and HDAC8 expression assays suggested the dual inhibiting property of these two compounds. These findings were supported by results of molecular docking studies. In silico pharmacokinetic properties showed compounds C6 and C27 have high gastrointestinal absorption. Conclusion: This study highlights the action of phenyl/naphthylacetyl pentanoic acid derivatives as anticancer agents.

Exploring sodium glucose cotransporter (SGLT2) inhibitors with machine learning approach: A novel hope in anti-diabetes drug discovery.

Conventional anti-diabetes agents exhibit some undesirable side effects. Recently, lactic acidosis and/or bladder cancer were also reported with the use of these agents. Hence, there is an urgent need for alternative anti-diabetes in order to reduce/avoid the unwanted effects. In this scenario sodium glucose cotransporter 2 (SGLT2) inhibitors has already been established as an important class of anti-diabetic drug. The search for new generation SGLT2 inhibitors with high affinity is still an ongoing process. Here, we aim to develop computational models to predict the SGLT2 inhibitory activity of small molecules based on chemical structures. This work provides in-silico analysis to propose possible fragment/fingerprint identification recommended for SGLT2 inhibitors. Up-to-our knowledge, this study is an initiative to propose fingerprints responsible for SGLT2 inhibition. Furthermore, we used nine different algorithms to build machine learning (ML) models that could be used to prioritize compounds as SGLT2 inhibitors from large libraries. The best performing ML models were applied to virtually screen a large collection of FDA approved drugs. The best predicted compounds have been recommended to be biologically investigated in future in order to identify next generation SGLT2 inhibitors with different chemical structure.

Ligand-based design of anticancer MMP2 inhibitors: a review.

MMP2, a Zn2+-dependent metalloproteinase, is related to cancer and angiogenesis. Inhibition of this enzyme might result in a potential antimetastatic drug to leverage the anticancer drug armory. In silico or computer-aided ligand-based drug design is a method of rational drug design that takes multiple chemometrics (i.e., multi-quantitative structure-activity relationship methods) into account for virtually selecting or developing a series of probable selective MMP2 inhibitors. Though existing matrix metalloproteinase inhibitors have shown plausible pan-matrix metalloproteinase (MMP) activity, they have resulted in various adverse effects leading to their being rescinded in later phases of clinical trials. Therefore a review of the ligand-based designing methods of MMP2 inhibitors would result in an explicit route map toward successfully designing and synthesizing novel and selective MMP2 inhibitors.