Computer-Driven Development of an in Silico Tool for Finding Selective Histone Deacetylase 1 Inhibitors

Sulfonamides as anticancer agents: A brief review on sulfonamide derivatives as inhibitors of various proteins overexpressed in cancer

Sulfonamides have gained prominence as versatile agents in cancer therapy, effectively targeting a spectrum of cancer-associated enzymes. This review provides an extensive exploration of their multifaceted roles in cancer biology. Sulfonamides exhibit adaptability by acting as tyrosine kinase inhibitors, disrupting pivotal signaling pathways in cancer progression. Moreover, they disrupt pH regulation mechanisms in cancer cells as carbonic anhydrase inhibitors, inhibiting growth, and survival. Sulfonamides also serve as aromatase inhibitors, interfering with estrogen synthesis in hormone-driven cancers. Inhibition of matrix metalloproteinases presents an opportunity to impede cancer cell invasion and metastasis. Additionally, their emerging role as histone deacetylase inhibitors offers promising prospects in epigenetic-based cancer therapies. These diverse roles underscore sulfonamides as invaluable tools for innovative anti-cancer treatments, warranting further exploration for enhanced clinical applications and patient outcomes.

Comprehensive screening of marine metabolites against class B1 metallo-β-lactamases of Klebsiella pneumoniae using two-pronged in silico approach

The emergence of antibiotic resistance is one of the major global threats in healthcare. Metallo-β-Lactamases (MBL) are a class of enzymes in bacteria that cleave β-lactam antibiotics and confer resistance. MBLs are further divided into subclasses B1, B2 and B3. Of these, subclasses B1-MBLs (including NDM-1, VIM-2 and IMP-1) constitute the clinically prevalent lactamases conferring resistance. To date, no effective drugs are available clinically against MBLs. In this work, we aim to identify potent inhibitors for the B1 subclass of MBL from available marine metabolites in Comprehensive Marine Natural Product database through integrated in silico approaches. We have used two methods, namely, the high-throughput strategy and the pharmacophore-based strategy to identify potential inhibitors from marine metabolites. High-throughput virtual screening identified N-methyl mycosporine-Ser, which had the highest binding affinity to NDM-1. The pharmacophore-based approach based on co-crystallized ligands identified makaluvic acid and didymellamide with higher binding affinity across B1-MBLs. Taking into account of the advantage of a pharmacophore model-based approach with higher binding affinity, we conclude that both makaluvic acid and didymellamide show potential broad-spectrum effects by binding to all three B1-MBL receptors. The study also indicates the need to take multiple in silico approaches to screen and identify novel inhibitors. Together, our study reveals promising inhibitors that can be identified from marine systems.Communicated by Ramaswamy H. Sarma.

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.

Identification of novel curcumin derivatives against pancreatic cancer: a comprehensive approach integrating 3D-QSAR pharmacophore modeling, virtual screening, and molecular dynamics simulations

Pancreatic cancer, known as the "silent killer," poses a daunting challenge in cancer therapy. The dysregulation of the PI3Kα signaling pathway in pancreatic cancer has attracted considerable interest as a promising target for therapeutic intervention. In this regard, the use of curcumin derivatives as inhibitors of PI3Kα has emerged, providing a novel and promising avenue for developing effective treatments for this devastating disease. Computational approaches were employed to explore this potential and investigate 58 curcumin derivatives with cytotoxic activity against the Panc-1 cell line. Our approach involved ligand-based pharmacophore modeling and atom-based 3D-QSAR analysis. The resulting QSAR model derived from the best-fitted pharmacophore hypothesis (AAHRR_1) demonstrated remarkable performance with high correlation coefficients (R2) of 0.990 for the training set and 0.977 for the test set. The cross-validation coefficient (Q2) of 0.971 also validated the model's predictive power. Tropsha's recommended criteria, including the Y-randomization test, were employed to ensure its reliability. Furthermore, an enrichment study was conducted to evaluate the model's performance in identifying active compounds. AAHRR_1 was used to screen a curated PubChem database of curcumin-related compounds. Two molecules (CID156189304 and CID154728220) exhibited promising pharmacokinetic properties and higher docking scores than Alpelisib, warranting further investigation. Extensive molecular dynamics simulations provided crucial insights into the conformational dynamics within the binding site, validating their stability and behavior. These findings contribute to our understanding of the potential therapeutic effectiveness of these compounds as PI3Kα inhibitors in pancreatic cancer.Communicated by Ramaswamy H. Sarma.

Discovery of newer pyrazole derivatives with potential anti-tubercular activity via 3D-QSAR based pharmacophore modelling, virtual screening, molecular docking and molecular dynamics simulation studies

Tuberculosis is one of the leading causes of death of at least one million people annually. The deadliest infectious disease has caused more than 120 million deaths in humans since 1882. The cell wall structure of Mycobacterium tuberculosis is important for survival in the host environment. InhA is the foremost target for the development of novel anti-tubercular agents. Therefore, we report pharmacophore-based virtual screening (ZINC and ASINEX databases) and molecular docking study (PDB Code: 4TZK) to identify and design potent inhibitors targeting to InhA. A five-point pharmacophore model AADHR_1 (with R2 = 0.97 and Q2 = 0.77) was developed by using 47 compounds with its reported MIC values. Further, to identify and design potent hit molecules based on lead identification and modification, generated hypothesis employed for virtual screening using ZINC and ASINEX databases. Predicted pyrazole derivatives further gauged for drug likeliness and docked against enoyl acyl carrier protein reductase to categorize the essential amino acid interactions to the active site of the enzyme. Structure elucidation of these synthesized compounds was carried out using IR, MS, 1H-NMR and 13C-NMR spectroscopy. Amongst all the synthesized compounds, some of the compounds 5a, 5c, 5d and 5e were found to be potent with their MIC ranging from 2.23 to 4.61 µM. Based on preliminary anti-tubercular activity synthesized potent molecules were further assessed for MDR-TB, XDR-TB and cytotoxic study.

Identification of novel and potent dual-targeting HDAC1/SPOP inhibitors using structure-based virtual screening, molecular dynamics simulation and evaluation of in vitro and in vivo antitumor activity

Cancer is one of the important factors threatening human health. Hence, it is essential to create novel potent drugs to treat it. Due to the strong correlation among histone deacetylase1 (HDAC1), speckle-type POZ protein (SPOP) and cancers, dual inhibition of HDAC1 and SPOP may be a promising strategy for cancer treatment. In this study, we successfully identified four potential dual-targeting HDAC1/SPOP candidate compounds with structure-based virtual screening. In vitro inhibition experiments confirmed that the four compounds had dual inhibitory effects on HDAC1 and SPOP. Among them, compound HS-2 had a stronger inhibitory effect on HDAC1 and SPOP than the positive controls. Further molecular dynamics simulations indicated that HS-2 could stably bind to HDAC1 and SPOP. In addition, MTT assay indicated that HS-2 inhibited the growth of tumor cells in the micromolar range. In vivo evaluation showed that HS-2 could obviously inhibit the growth of tumor in nude mice without obvious toxicity. These findings suggest that HS-2 is a novel and potent dual-targeting HDAC1/SPOP inhibitor for cancer treatment.

Development of New Inhibitors of HDAC1-3 Enzymes Aided by In Silico Design Strategies

Histone deacetylases (HDACs) are overexpressed in cancer, and their inhibition shows promising results in cancer therapy. In particular, selective class I HDAC inhibitors such as entinostat are proposed to be more beneficial in breast cancer treatment. Computational drug design is an inevitable part of today's drug discovery projects because of its unequivocal role in saving time and cost. Using three HDAC inhibitors trichostatin, vorinostat, and entinostat as template structures and a diverse fragment library, all synthetically accessible compounds thereof (∼3200) were generated virtually and filtered based on similarity against the templates and PAINS removal. The 298 selected structures were docked into the active site of HDAC I and ranked using a calculated binding affinity. Top-ranking structures were inspected manually, and, considering the ease of synthesis and drug-likeness, two new structures (3a and 3b) were proposed for synthesis and biological evaluation. The synthesized compounds were purified to a degree of more than 95% and structurally verified using various methods. The designed compounds 3a and 3b showed 65-80 and 5% inhibition on HDAC 1, 2, and 3 isoforms at a concentration of 10 μM, respectively. The novel compound 3a may be used as a lead structure for designing new HDAC inhibitors.

Identification of novel leads as potent inhibitors of HDAC3 using ligand-based pharmacophore modeling and MD simulation

In the landscape of epigenetic regulation, histone deacetylase 3 (HDAC3) has emerged as a prominent therapeutic target for the design and development of candidate drugs against various types of cancers and other human disorders. Herein, we have performed ligand-based pharmacophore modeling, virtual screening, molecular docking, and MD simulations to design potent and selective inhibitors against HDAC3. The predicted best pharmacophore model ‘Hypo 1’ showed excellent correlation (R² = 0.994), lowest RMSD (0.373), lowest total cost value (102.519), and highest cost difference (124.08). Hypo 1 consists of four salient pharmacophore features viz. one hydrogen bond acceptor (HBA), one ring aromatic (RA), and two hydrophobic (HYP). Hypo 1 was validated by Fischer's randomization with a 95% of confidence level and the external test set of 60 compounds with a good correlation coefficient (R² = 0.970). The virtual screening of chemical databases, drug-like properties calculations followed by molecular docking resulted in identifying 22 representative hit compounds. Performed 50 ns of MD simulations on top three hits were retained the salient π-stacking, Zn²⁺ coordination, hydrogen bonding, and hydrophobic interactions with catalytic residues from the active site pocket of HDAC3. Total binding energy calculated by MM-PBSA showed that the Hit 1 and Hit 2 formed stable complexes with HDAC3 as compared to reference TSA. Further, the PLIP analysis showed a close resemblance between the salient pharmacophore features of Hypo 1 and the presence of molecular interactions in co-crystallized FDA-approved drugs. We conclude that the screened hit compounds may act as potent inhibitors of HDAC3 and further preclinical and clinical studies may pave the way for developing them as effective therapeutic agents for the treatment of different cancers and neurodegenerative disorders.

Computational Approaches: Drug Discovery and Design in Medicinal Chemistry and Bioinformatics

To date, computational approaches have been recognized as a key component in drug design and discovery workflows [...].

Epigenetic Target Fishing with Accurate Machine Learning Models

Epigenetic targets are of significant importance in drug discovery research, as demonstrated by the eight approved epigenetic drugs for treatment of cancer and the increasing availability of chemogenomic data related to epigenetics. This data represents many structure-activity relationships that have not been exploited thus far to develop predictive models to support medicinal chemistry efforts. Herein, we report the first large-scale study of 26 318 compounds with a quantitative measure of biological activity for 55 protein targets with epigenetic activity. We built predictive models with high accuracy for small molecules' epigenetic target profiling through a systematic comparison of the machine learning models trained on different molecular fingerprints. The models were thoroughly validated, showing mean precisions of up to 0.952 for the epigenetic target prediction task. Our results indicate that the models reported herein have considerable potential to identify small molecules with epigenetic activity. Therefore, our results were implemented as a freely accessible web application.

Development of In Vitro Corneal Models: Opportunity for Pharmacological Testing

The human eye is a specialized organ with a complex anatomy and physiology, because it is characterized by different cell types with specific physiological functions. Given the complexity of the eye, ocular tissues are finely organized and orchestrated. In the last few years, many in vitro models have been developed in order to meet the 3Rs principle (Replacement, Reduction and Refinement) for eye toxicity testing. This procedure is highly necessary to ensure that the risks associated with ophthalmic products meet appropriate safety criteria. In vitro preclinical testing is now a well-established practice of significant importance for evaluating the efficacy and safety of cosmetic, pharmaceutical, and nutraceutical products. Along with in vitro testing, also computational procedures, herein described, for evaluating the pharmacological profile of potential ocular drug candidates including their toxicity, are in rapid expansion. In this review, the ocular cell types and functionality are described, providing an overview about the scientific challenge for the development of three-dimensional (3D) in vitro models.

Recent progress on cheminformatics approaches to epigenetic drug discovery

The ability of epigenetic markers to affect genome function has enabled transformative changes in drug discovery, especially in cancer and other emerging therapeutic areas. Concordant with the introduction of the term 'epi-informatics', the size of the epigenetically relevant chemical space has grown substantially and so did the number of applications of cheminformatic methods to epigenetics. Recent progress in epi-informatics has improved our understanding of the structure-epigenetic activity relationships and boosted the development of models predicting novel epigenetic agents. Herein, we review the advances in computational approaches to drug discovery of small molecules with epigenetic modulation profiles, summarize the current chemogenomics data available for epigenetic targets, and provide a perspective on the greater utility of biomedical knowledge mining as a means to advance the epigenetic drug discovery.