Combined pharmacophore modeling, 3D-QSAR and docking studies to identify novel HDAC inhibitors using drug repurposing

In silico approaches for drug repurposing in oncology: a scoping review

Introduction: Cancer refers to a group of diseases characterized by the uncontrolled growth and spread of abnormal cells in the body. Due to its complexity, it has been hard to find an ideal medicine to treat all cancer types, although there is an urgent need for it. However, the cost of developing a new drug is high and time-consuming. In this sense, drug repurposing (DR) can hasten drug discovery by giving existing drugs new disease indications. Many computational methods have been applied to achieve DR, but just a few have succeeded. Therefore, this review aims to show in silico DR approaches and the gap between these strategies and their ultimate application in oncology. Methods: The scoping review was conducted according to the Arksey and O'Malley framework and the Joanna Briggs Institute recommendations. Relevant studies were identified through electronic searching of PubMed/MEDLINE, Embase, Scopus, and Web of Science databases, as well as the grey literature. We included peer-reviewed research articles involving in silico strategies applied to drug repurposing in oncology, published between 1 January 2003, and 31 December 2021. Results: We identified 238 studies for inclusion in the review. Most studies revealed that the United States, India, China, South Korea, and Italy are top publishers. Regarding cancer types, breast cancer, lymphomas and leukemias, lung, colorectal, and prostate cancer are the top investigated. Additionally, most studies solely used computational methods, and just a few assessed more complex scientific models. Lastly, molecular modeling, which includes molecular docking and molecular dynamics simulations, was the most frequently used method, followed by signature-, Machine Learning-, and network-based strategies. Discussion: DR is a trending opportunity but still demands extensive testing to ensure its safety and efficacy for the new indications. Finally, implementing DR can be challenging due to various factors, including lack of quality data, patient populations, cost, intellectual property issues, market considerations, and regulatory requirements. Despite all the hurdles, DR remains an exciting strategy for identifying new treatments for numerous diseases, including cancer types, and giving patients faster access to new medications.

Lansoprazole as a potent HDAC2 inhibitor for treatment of colorectal cancer: An in-silico analysis and experimental validation

BACKGROUND

Histone deacetylase 2 (HDAC2), belonging to the class I HDAC family, holds significant therapeutic potential as a crucial target for diverse cancer types. As key players in the realm of epigenetic regulatory enzymes, histone deacetylases (HDACs) are intricately involved in the onset and progression of cancer. Consequently, pursuing isoform-specific inhibitors targeting histone deacetylases (HDACs) has garnered substantial interest in both biological and medical circles. The objective of the present investigation was to employ a drug repurposing approach to discover novel and potent HDAC2 inhibitors.

MATERIALS AND METHODS

In this study, our protocol is presented on virtual screening to identify novel potential HDAC2 inhibitors through 3D-QSAR, molecular docking, pharmacophore modeling, and molecular dynamics (MD) simulation. Afterward, In-vitro assays were employed to evaluate the cytotoxicity, apoptosis, and migration of HCT-116 cell lines under treatment of hit compound and valproic acid as a control inhibitor. The expression levels of HDAC2, TP53, BCL2, and BAX were evaluated by QRT-PCR.

RESULTS

RMSD, RMSF, H-bond, and DSSP analysis results confirmed that among bioinformatically selected compounds, lansoprazole exhibited the highest HDAC2 inhibitory potential. Experimental validation revealed that lansoprazole displayed significant antiproliferative activity. The determined IC50 value was 400 ± 2.36 μM. Furthermore, the apoptotic cells ratio concentration-dependently increased under Lansoprazole treatment. Results of the Scratch assay indicated that lansoprazole led to decreasing the migration of CRC cells. Finally, under Lansoprazole treatment the expression level of BCL2 and HDAC2 decreased and BAX and TP53 increased.

CONCLUSION

Taking together the results of the current study indicated that Lansoprazole as a novel HDAC2 inhibitor, could be used as a potential therapeutic agent for the treatment of CRC. Although, further experimental studies should be performed before using this compound in the clinic.

Pharmacophore-based virtual screening of ZINC database, molecular modeling and designing new derivatives as potential HDAC6 inhibitors

To date, many HDAC6 inhibitors have been identified and developed but none is clinically approved as of now. Through this study, we aim to obtain novel HDAC6 selective inhibitors and provide new insights into the detailed structural design of potential HDAC6 inhibitors. A HypoGen-based 3D QSAR HDAC6 pharmacophore was built and used as a query model to screen approximately 8 million ZINC database compounds. First, the ZINC Database was filtered using ADMET, followed by pharmacophore-based library screening. Using fit value and estimated activity cutoffs, a final set of 54 ZINC hits was obtained that were further investigated using molecular docking with the crystal structure of human histone deacetylase 6 catalytic domain 2 in complex with Trichostatin A (PDB ID: 5EDU). Through detailed in silico screening of the ZINC database, we shortlisted three hits as the lead molecules for designing novel HDAC6 inhibitors with better efficacy. Docking with 5EDU, followed by ADMET and TOPKAT analysis of modified ZINC hits provided 9 novel potential HDAC6 inhibitors that possess better docking scores and 2D interactions as compared to the control ZINC hit molecules. Finally, a 50 ns MD analysis run followed by Protein-Ligand Interaction Energy (PLIE) analysis of the top scored hits provided a novel molecule N1 that showed promisingly similar results to that of Ricolinostat (a known HDAC6 inhibitor). The comparable result of the designed hits to established HDAC6 inhibitors suggests that these compounds might prove to be successful HDAC6 inhibitors in future. Designed novel hits that might act as good HDAC6 inhibitors derived from ZINC database using combined molecular docking and modeling approaches.

Molecular modeling strategy for detailing the primary mechanism of action of copanlisib to PI3K: combined ligand-based and target-based approach

Since dysregulation of the phosphatidylinositol 3-kinase (PI3K) signaling pathway is associated with the pathogenesis of cancer, inflammation, and autoimmunity, PI3K has emerged as an attractive target for drug development. Although copanlisib is the first pan-PI3K inhibitor to be approved for clinical use, the precise mechanism by which it acts on PI3K has not been fully elucidated. To reveal the binding mechanisms and structure-activity relationship between PI3K and copanlisib, a comprehensive modeling approach that combines 3D-quantitative structure-activity relationship (3D-QSAR), pharmacophore model, and molecular dynamics (MD) simulation was utilized. Initially, the structure-activity relationship of copanlisib and its derivatives were explored by constructing a 3D-QSAR. Then, the key chemical characteristics were identified by building common feature pharmacophore models. Finally, MD simulations were performed to elucidate the important interactions between copanlisib and different PI3K subtypes, and highlight the key residues for tight-binding inhibitors. The present study uncovered the principal mechanism of copanlisib's action on PI3K at the theoretical level, and these findings might provide guidance for the rational design of pan-PI3K inhibitors.Communicated by Ramaswamy H. Sarma.

Virtual screening and structure-activity relationship study of novel BTK inhibitors in Traditional Chinese Medicine for the treatment of rheumatoid arthritis

Bruton tyrosine kinase (BTK) is a known drug target for the treatment of autoimmune diseases, including rheumatoid arthritis (RA). In this study, a series of 1-amino-1H-imidazole-5-carboxamide derivatives with good inhibitory activity against BTK were selected to explore the structure-activity relationships of these BTK inhibitors (BTKIs). Furthermore, we concentrated on 182 prescriptions of Traditional Chinese Medicine with therapeutic effects on RA. 54 herbs with a frequency of ≥10 were counted to establish a database containing 4027 ingredients for virtual screening. Five compounds with relatively higher docking scores and better absorption, distribution, metabolism, elimination and toxicity (ADMET) parameters were then selected for higher precision docking. The results demonstrated that the potentially active molecules form hydrogen bond interactions with the hinge region residues Met477, Glu475, glycine-rich P-loop residue Val416, Lys430 and DFG motif Asp539. In particular, they also interact with the key residues Thr474 and Cys481 of BTK. The molecular dynamics (MD) results demonstrated that all five compounds above could bind with BTK stably as its cognate ligand in dynamic conditions. This work identified several potential BTKIs using a computer-aided drug design approach and may provide crucial information for developing novel BTKIs.Communicated by Ramaswamy H. Sarma.

3D-ALMOND-QSAR Models to Predict the Antidepressant Effect of Some Natural Compounds

The current treatment of depression involves antidepressant synthetic drugs that have a variety of side effects. In searching for alternatives, natural compounds could represent a solution, as many studies reported that such compounds modulate the nervous system and exhibit antidepressant effects. We used bioinformatics methods to predict the antidepressant effect of ten natural compounds with neuroleptic activity, reported in the literature. For all compounds we computed their drug-likeness, absorption, distribution, metabolism, excretion (ADME), and toxicity profiles. Their antidepressant and neuroleptic activities were predicted by 3D-ALMOND-QSAR models built by considering three important targets, namely serotonin transporter (SERT), 5-hydroxytryptamine receptor 1A (5-HT1A), and dopamine D2 receptor. For our QSAR models we have used the following molecular descriptors: hydrophobicity, electrostatic, and hydrogen bond donor/acceptor. Our results showed that all compounds present drug-likeness features as well as promising ADME features and no toxicity. Most compounds appear to modulate SERT, and fewer appear as ligands for 5-HT1A and D2 receptors. From our prediction, linalyl acetate appears as the only ligand for all three targets, neryl acetate appears as a ligand for SERT and D2 receptors, while 1,8-cineole appears as a ligand for 5-HT1A and D2 receptors.

Small-molecule inhibitors of breast cancer-related targets: Potential therapeutic agents for breast cancer

Despite dramatic advances in cancer research and therapy, breast cancer remains a tricky health problem and represents a top biomedical research priority. Nowadays, breast cancer is still the leading cause of malignancy-related deaths in women, and incidence and mortality rates of it are expected to increase significantly the next years. Currently more and more researchers are interested in the study of breast cancer by its arising in young women. The common treatment options of breast cancer are chemotherapy, immunotherapy, hormone therapy, surgery, and radiotherapy. Most of them require chemical agents, such as PARP inhibitors, CDK4/6 inhibitors, and HER2 inhibitors. Recent studies suggest that some targets or pathways, including BRD4, PLK1, PD-L1, HDAC, and PI3K/AKT/mTOR, are tightly related to the occurrence and development of breast cancer. This article reviews the interplay between these targets and breast cancer and summarizes the progress of current research on small molecule inhibitors of these anti-breast cancer targets. The review aims to provide structural and theoretical basis for designing novel anti-breast cancer agents.

Identification of the natural compound inhibitors against Plasmodium falciparum plasmepsin-II via common feature based screening and molecular dynamics simulations

Malaria is counted amongst the deadly disease caused by Plasmodium falciparum. Recently, plasmepsin-II enzyme has gained much importance as an attractive drug target for the exploration of antimalarials. Therefore, the common feature pharmacophore models were generated from the crystallized complexes of the plasmepsin-II proteome. These models were subjected to a series of validation procedures, i.e. test set and Güner Henry studies to enlist the representative models. The selected representative hypotheses incorporating the most essential chemical features (common ZHHA) were screened against the natural product database to retrieve the potential candidates. To ensure the selection of the drug-like candidates, prior to screening, filtering steps (Drug-likeness and ADMET filters) were employed on the selected database. To study the interaction pattern of the candidates within the protein, these molecules were advanced to the molecular docking studies. Subsequently, based on the selected cut-off criteria obtained via redocking of the reference (4Z22), 15 compounds showed higher docking score (> -16.05 kcal/mol), and displayed the presence of hydrogen bonding with the crucial amino acids, i.e. Asp34 and Asp214. Further, the stability of the docked molecules was scrutinized via molecular dynamics simulations, and the results were compared with the reference compound 4Z22. All the docked compounds showed stable dynamics behaviour. Thus, in the present contribution, the combination of screening and stability procedures resulted in the identification of 15 hits that can serve as a new chemical space in the designing of the novel antimalarials.Communicated by Ramaswamy H. Sarma.

Dual inhibitors of SARS-CoV-2 proteases: pharmacophore and molecular dynamics based drug repositioning and phytochemical leads

SARS-related coronaviruses poses continual threat to humanity by rapidly mutating and emerging as severe pandemic outbreaks, including the current nCoV-19 pandemic. Hence a rapid drug repositioning and lead identification strategy are required to mitigate these outbreaks. We report a pharmacophore and molecular dynamics-based approach for drug repositioning and lead identification against dual targets (3CLp and PLp) of SARS-CoV-2. The pharmacophore model of 3CLp inhibitors was apolar with two aromatic and two H-bond acceptors, whereas that of PLp was relatively polar, bearing one aromatic and three H-bond acceptors. Pharmacophore-based virtual screening yielded six existing FDA-approved drugs and twelve natural products with both the pharmacophoric features. Among them are nelfinavir, tipranavir and licochalcone-D, which has shown better binding characteristics with both the proteases compared to lopinavir. The molecular dynamics revealed that the connecting loop (residues 176–199) of 3CLp is highly flexible, and hence, inhibitors should avoid high-affinity interactions with it. Lopinavir, due to its high affinity with the loop region, exhibited unstable binding. Further, the van der Waals size of the 3CLp inhibitors positively correlated with their binding affinity with 3CLp. However, the van der Waals size of a ligand should not cross a threshold of 572ų, beyond which the ligands are likely to make high-affinity interaction with the loop and suffer unstable binding as observed in the case of lopinavir. Similarly, the total polar surface area of the ligands were found to be negatively correlated with their binding affinity with PLp.

Identification of HDAC6 selective inhibitors: pharmacophore based virtual screening, molecular docking and molecular dynamics simulation

HDAC6 regulates the expression and activity of various tumor-related proteins, but currently there is no selective inhibitor targeting HDAC6 for clinical application. In order to discover novel HDAC6 inhibitors, virtual screening methods comprised of pharmacophore based virtual screening, molecular docking and molecular dynamics (MD) simulations were employed. 15 molecules were obtained after virtual screening. After in vitro bioassays, two of the hits showed inhibition activity against HDAC6, among which the inhibition activity of G1 to HDAC6 reached 81% at concentration of 20 μM. In addition, the inhibitory activity against HDAC1 and HDAC10 demonstrated that G1 and G10 were highly selective to HDAC6. The analysis of the binding modes of G1 and G10 provides a reference for further development of highly active HDAC6 inhibitors. Communicated by Ramaswamy H. Sarma.