Pharmacophore modeling and virtual screening studies to design some potential histone deacetylase inhibitors as new leads

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.

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.

NO-HDAC dual inhibitors

HDAC inhibitors and NO donors have both demonstrated independently broad therapeutic potential in a variety of diseases. Borretto et al. presented the topic of NO-HDAC dual inhibitors for the first time in 2013 as an attractive new topic. Here we collected the general structure of all synthesized NO-HDAC dual inhibitors, lead compounds, synthesis methods and biological features of the most potent dual NO-HDAC inhibitor in each category with the intention of assisting in the synthesis and optimization of new drug-like compounds for diverse diseases. Based on studies done so far, NO-HDAC dual inhibitors have displayed satisfactory results against wound healing (3), heart hypertrophy (3), inflammatory, cardiovascular, neuromuscular illnesses (11a-11e) and cancer (6a-6o, 9a-9d, 10a-10d, 16 and 17). NO-HDAC dual inhibitors can have high therapeutic potential for various diseases due to their new properties, NO properties, HDAC inhibitor properties and also due to the effects of NO on HDAC enzymes.

Discovery of novel hit compounds as potential HDAC1 inhibitors: The case of ligand- and structure-based virtual screening

Histone deacetylases (HDACs) as an important family of epigenetic regulatory enzymes are implicated in the onset and progression of carcinomas. As a result, HDAC inhibition has been proven as a compelling strategy for reversing the aberrant epigenetic changes associated with cancer. However, non-selective profile of most developed HDAC inhibitors (HDACIs) leads to the occurrence of various side effects, limiting their clinical utility. This evidence provides a solid ground for ongoing research aimed at identifying isoform-selective inhibitors. Among the isoforms, HDAC1 have particularly gained increased attention as a preferred target for the design of selective HDACIs. Accordingly, in this paper, we have developed a reliable virtual screening process, combining different ligand- and structure-based methods, to identify novel benzamide-based analogs with potential HDAC1 inhibitory activity. For this purpose, a focused library of 736,160 compounds from PubChem database was first compiled based on 80% structural similarity with four known benzamide-based HDAC1 inhibitors, Mocetinostat, Entinostat, Tacedinaline, and Chidamide. Our inclusive in-house 3D-QSAR model, derived from pharmacophore-based alignment, was then employed as a 3D-query to discriminate hits with the highest predicted HDAC1 inhibitory activity. The selected hits were subjected to subsequent structure-based approaches (induced-fit docking (IFD), MM-GBSA calculations and molecular dynamics (MD) simulation) to retrieve potential compounds with the highest binding affinity for HDAC1 active site. Additionally, in silico ADMET properties and PAINS filtration were also considered for selecting an enriched set of the best drug-like molecules. Finally, six top-ranked hit molecules, CID_38265326, CID_56064109, CID_8136932, CID_55802151, CID_133901641 and CID_18150975 were identified to expose the best stability profiles and binding mode in the HDAC1 active site. The IFD and MD results cooperatively confirmed the interactions of the promising selected hits with critical residues within HDAC1 active site. In summary, the presented computational approach can provide a set of guidelines for the further development of improved benzamide-based derivatives targeting HDAC1 isoform.

Pharmacophore-based drug design of AChE and BChE dual inhibitors as potential anti-Alzheimer's disease agents

For the Alzheimer's disease (AD) with complex pathogenesis, single target drugs represent one of the most effective therapeutic strategies in clinical. However, the traditional concept of "a disease, a target" is difficult to find very effective drugs, and multi-target drugs have already become new hot spot in drug development for this disease. In our present study, our efforts toward discovering new cholinesterase (ChE) inhibitors aided by computational methods will provide useful information as anti-AD agents in the future. The best 3D-QSAR acetylcholinesterase (AChE) and butyrylcholinesterase (BChE) inhibitors pharmacophore hypotheses Hypo1 A and Hypo1 B were generated and validated by HypoGen program in Discovery Studio 2016 based on the training set of flavonoids, and then they were used as 3D query for screening the ZINC database. Next, the hit molecules were then subjected to the ADMET and molecular docking study to prioritize the compounds. Finally, 6 compounds showed good estimated activities and promising ADMET properties. The result of best compound ZINC08751495 with AChE estimate activity (0.028), BChE estimate activity (1.55), AChE fit value (9.369), BChE fit value (8.415), AChE -CDOCKER ENERGY (30.22), BChE -CDOCKER ENERGY (33.13) has the potential for further development as a supplement to treat Alzheimer's disease.

Understanding Failure and Improving Treatment Using HDAC Inhibitors for Prostate Cancer

Novel treatment regimens are required for castration-resistant prostate cancers (CRPCs) that become unresponsive to standard treatments, such as docetaxel and enzalutamide. Histone deacetylase (HDAC) inhibitors showed promising results in hematological malignancies, but they failed in solid tumors such as prostate cancer, despite the overexpression of HDACs in CRPC. Four HDAC inhibitors, vorinostat, pracinostat, panobinostat and romidepsin, underwent phase II clinical trials for prostate cancers; however, phase III trials were not recommended due to a majority of patients exhibiting either toxicity or disease progression. In this review, the pharmacodynamic reasons for the failure of HDAC inhibitors were assessed and placed in the context of the advancements in the understanding of CRPCs, HDACs and resistance mechanisms. The review focuses on three themes: evolution of androgen receptor-negative prostate cancers, development of resistance mechanisms and differential effects of HDACs. In conclusion, advancements can be made in this field by characterizing HDACs in prostate tumors more extensively, as this will allow more specific drugs catering to the specific HDAC subtypes to be designed.

Discovery of a Potent PLK1-PBD Small-Molecule Inhibitor as an Anticancer Drug Candidate through Structure-Based Design

Polo-box domain of polo-like kinase 1 (PLK1-PBD) has a pivotal role in cell proliferation and could be implicated as a potential anticancer target. Although some small-molecule inhibitors have been developed, their clinical application has been restricted by the poor selectivity. Therefore, there is an urgent need to develop effective PLK1-PBD inhibitors. Herein, we have developed a virtual screening protocol to find PLK1-PBD inhibitors by using combination of structure-based pharmacophore modeling and molecular docking. This protocol was successfully applied to screen PLK1-PBD inhibitors from specs database. MTT assay indicated that five screened hits suppressed the growth of HeLa cells. Particularly, hit-5, as a selective PLK1 inhibitor targeting PLK1-PBD, significantly inhibited the progression of HeLa cells-derived xenograft, with no obvious side effects. This work demonstrates that hit-5 may be a potential anticancer agent.

Rational Drug Design of Antineoplastic Agents Using 3D-QSAR, Cheminformatic, and Virtual Screening Approaches

Background:

Computer-Aided Drug Design has strongly accelerated the development of novel antineoplastic agents by helping in the hit identification, optimization, and evaluation.

Results:

Computational approaches such as cheminformatic search, virtual screening, pharmacophore modeling, molecular docking and dynamics have been developed and applied to explain the activity of bioactive molecules, design novel agents, increase the success rate of drug research, and decrease the total costs of drug discovery. Similarity, searches and virtual screening are used to identify molecules with an increased probability to interact with drug targets of interest, while the other computational approaches are applied for the design and evaluation of molecules with enhanced activity and improved safety profile.

Conclusion:

In this review are described the main in silico techniques used in rational drug design of antineoplastic agents and presented optimal combinations of computational methods for design of more efficient antineoplastic drugs.

Big Data and Artificial Intelligence Modeling for Drug Discovery

Due to the massive data sets available for drug candidates, modern drug discovery has advanced to the big data era. Central to this shift is the development of artificial intelligence approaches to implementing innovative modeling based on the dynamic, heterogeneous, and large nature of drug data sets. As a result, recently developed artificial intelligence approaches such as deep learning and relevant modeling studies provide new solutions to efficacy and safety evaluations of drug candidates based on big data modeling and analysis. The resulting models provided deep insights into the continuum from chemical structure to in vitro, in vivo, and clinical outcomes. The relevant novel data mining, curation, and management techniques provided critical support to recent modeling studies. In summary, the new advancement of artificial intelligence in the big data era has paved the road to future rational drug development and optimization, which will have a significant impact on drug discovery procedures and, eventually, public health.

In Silico Identification of Potential Inhibitor Against a Fungal Histone Deacetylase, RPD3 from Magnaporthe Oryzae

Histone acetylation and deacetylation play an essential role in the epigenetic regulation of gene expression. Histone deacetylases (HDAC) are a group of zinc-binding metalloenzymes that catalyze the removal of acetyl moieties from lysine residues from histone tails. These enzymes are well known for their wide spread biological effects in eukaryotes. In rice blast fungus, Magnaporthe oryzae, MoRPD3 (an ortholog of Saccharomyces cerevisiae Rpd3) was shown to be required for growth and development. Thus in this study, the class I HDAC, MoRpd3 is considered as a potential drug target, and its 3D structure was modelled and validated. Based on the model, a total of 1880 compounds were virtually screened (molecular docking) against MoRpd3 and the activities of the compounds were assessed by docking scores. The in silico screening suggested that [2-[[4-(2-methoxyethyl) phenoxy] methyl] phenyl] boronic acid (−8.7 kcal/mol) and [4-[[4-(2-methoxyethyl) phenoxy] methyl] phenyl] boronic acid (−8.5 kcal/mol) are effective in comparison to trichostatin A (−7.9 kcal/mol), a well-known general HDAC inhibitor. The in vitro studies for inhibition of appressorium formation by [2-[[4-(2-methoxyethyl) phenoxy] methyl] phenyl] boronic acid has resulted in the maximum inhibition at lower concentrations (1 μM), while the trichostatin A exhibited similar levels of inhibition at 1.5 μM. These findings thus suggest that 3D quantitative structure activity relationship studies on [2-[[4-(2-methoxyethyl) phenoxy] methyl] phenyl] boronic acid compound can further guide the design of more potential and specific HDAC inhibitors.

Discovery of Novel PRMT5 Inhibitors by Virtual Screening and Biological Evaluations

As an important epigenetics related enzyme, protein arginine methyltransferase 5 (PRMT5) has been confirmed as an anticancer therapeutic target in recent years. Among all the reported PRMT5 inhibitors, two small molecules (GSK-3326595 and JNJ-64619178) are currently being assessed in clinical trial. In this study, 40 PRMT5 inhibitor candidates were purchased from SPECS database supplier according to the pharmacophore and molecular docking based virtual screening results. Alpha linked immunosorbent assay (LISA) methylation assay was performed to test their inhibitory activity against PRMT5. The in vitro enzymatic assay results indicated that four compounds (2, 4, 10 and 37) showed PRMT5 inhibitory activity, while 4 and 10 displayed the most potent activity with IC₅₀ values of 8.1 ± 1.1 and 6.5 ± 0.6 µM, respectively. The inhibitory activity results of 20 extra analogs of 4 further confirmed the potency of this scaffold. As expected, compounds 4 and 10 exhibited moderate anti-proliferative activity against mantle cell lymphoma Jeko-1 and leukemia cell MV4-11. Besides, Western blot assay results showed that 4 could reduce the H4R3me2s level in a dose-dependent manner, indicating that it could inhibit the activity of PRMT5 in cellular context. Detailed interactions between 4 and PRMT5 were characterized by binding mode analysis through molecular docking. The compounds discovered in this study will inspire medicinal chemists to further explore this series of PRMT5 inhibitors.

Discovery of Potential Inhibitors of Squalene Synthase from Traditional Chinese Medicine Based on Virtual Screening and In Vitro Evaluation of Lipid-Lowering Effect

Squalene synthase (SQS), a key downstream enzyme involved in the cholesterol biosynthetic pathway, plays an important role in treating hyperlipidemia. Compared to statins, SQS inhibitors have shown a very significant lipid-lowering effect and do not cause myotoxicity. Thus, the paper aims to discover potential SQS inhibitors from Traditional Chinese Medicine (TCM) by the combination of molecular modeling methods and biological assays. In this study, cynarin was selected as a potential SQS inhibitor candidate compound based on its pharmacophoric properties, molecular docking studies and molecular dynamics (MD) simulations. Cynarin could form hydrophobic interactions with PHE54, LEU211, LEU183 and PRO292, which are regarded as important interactions for the SQS inhibitors. In addition, the lipid-lowering effect of cynarin was tested in sodium oleate-induced HepG2 cells by decreasing the lipidemic parameter triglyceride (TG) level by 22.50%. Finally. cynarin was reversely screened against other anti-hyperlipidemia targets which existed in HepG2 cells and cynarin was unable to map with the pharmacophore of these targets, which indicated that the lipid-lowering effects of cynarin might be due to the inhibition of SQS. This study discovered cynarin is a potential SQS inhibitor from TCM, which could be further clinically explored for the treatment of hyperlipidemia.

Identification of potential isoform-selective histone deacetylase inhibitors for cancer therapy: a combined approach of structure-based virtual screening, ADMET prediction and molecular dynamics simulation assay

Histone deacetylases (HDACs) have gained increased attention as targets for anticancer drug design and development. HDAC inhibitors have proven to be effective for reversing the malignant phenotype in HDAC-dependent cancer cases. However, lack of selectivity of the many HDAC inhibitors in clinical use and trials contributes to toxicities to healthy cells. It is believed that, the continued identification of isoform-selective inhibitors will eliminate these undesirable adverse effects - a task that remains a major challenge to HDAC inhibitor designs. Here, in an attempt to identify isoform-selective inhibitors, a large compound library containing 2,703,000 compounds retrieved from Otava database was screened against class I HDACs by exhaustive approach of structure-based virtual screening using rDOCK and Autodock Vina. A total of 41 compounds were found to show high-isoform selectivity and were further redocked into their respective targets using Autodock4. Thirty-six compounds showed remarkable isoform selectivity and passed drug-likeness and absorption, distribution, metabolism, elimination and toxicity prediction tests using ADMET Predictor™ and admetSAR. Furthermore, to study the stability of ligand binding modes, 10 ns-molecular dynamics (MD) simulations of the free HDAC isoforms and their complexes with respective best-ranked ligands were performed using nanoscale MD software. The inhibitors remained bound to their respective targets over time of the simulation and the overall potential energy, root-mean-square deviation, root-mean-square fluctuation profiles suggested that the detected compounds may be potential isoform-selective HDAC inhibitors or serve as promising scaffolds for further optimization towards the design of selective inhibitors for cancer therapy.

Novel HDAC8 inhibitors: A multi-computational approach

Abnormal HDAC function triggers irregular gene transcription that hampers the essential cellular activities leading to tumour activation and progression. HDAC inhibition has, therefore, been reported as a potential target for cancer treatment. In the present study, a sequential computational framework was carried out to discover newer lead compounds, namely HDAC8 inhibitors for cancer therapy. Pharmacophoric hypotheses were generated based on hydroxamic acid derivatives reported earlier for HDAC inhibition. The model AAADR.122, demonstrated statistical significance (r² = 0.93, Q² = 0.81) and proved robust on validation with a cross-validated correlation coefficient of 0.89. It was utilized to arrive at novel hits through a virtual screening workflow. The specificity of the process was enhanced further by analysing the crucial interactions of the ligands with key catalytic residues, achieved by induced fit docking (PDB ID: 1T64). On assessment, the filtered leads displayed optimal drug like features. Investigations using density functional theory (DFT) also facilitated the recognition of molecular spots in the leads beneficial for HDAC8 interaction. Overall, two leads were proposed for HDAC8 inhibition with potential anti-cancer activity.

An Olefin Cross-Metathesis Approach to Depudecin and Stereoisomeric Analogues

A new total synthesis of the natural product (-)-depudecin, a unique and unexplored histone deacetylase (HDAC) inhibitor, is reported. A key feature of the synthesis is the utilization of an olefin cross-metathesis strategy, which provides for an efficient and improved access to natural depudecin, compared with our previous linear synthesis. Featured by its brevity and convergency, our developed synthetic strategy was applied to the preparation of the 10-epi derivative and the enantiomer of depudecin, which represent interesting stereoisomeric analogues for structure-activity relationship studies.

Mechanistic Insights into the Binding of Class IIa HDAC Inhibitors toward Spinocerebellar Ataxia Type-2: A 3D-QSAR and Pharmacophore Modeling Approach

Spinocerebellar ataxia (SCA-2) type-2 is a rare neurological disorder among the nine polyglutamine disorders, mainly caused by polyQ (CAG) trinucleotide repeats expansion within gene coding ataxin-2 protein. The expanded trinucleotide repeats within the ataxin-2 protein sequesters transcriptional cofactors i.e., CREB-binding protein (CBP), Ataxin-2 binding protein 1 (A2BP1) leading to a state of hypo-acetylation and transcriptional repression. Histone de-acetylases inhibitors (HDACi) have been reported to restore transcriptional balance through inhibition of class IIa HDAC's, that leads to an increased acetylation and transcription as demonstrated through in-vivo studies on mouse models of Huntington's. In this study, 61 di-aryl cyclo-propanehydroxamic acid derivatives were used for developing three dimensional (3D) QSAR and pharmacophore models. These models were then employed for screening and selection of anti-ataxia compounds. The chosen QSAR model was observed to be statistically robust with correlation coefficient (r²) value of 0.6774, cross-validated correlation coefficient (q²) of 0.6157 and co-relation coefficient for external test set (pred_r²) of 0.7570. A high F-test value of 77.7093 signified the robustness of the model. Two potential drug leads ZINC 00608101 (SEI) and ZINC 00329110 (ACI) were selected after a coalesce procedure of pharmacophore based screening using the pharmacophore model ADDRR.20 and structural analysis using molecular docking and dynamics simulations. The pharmacophore and the 3D-QSAR model generated were further validated for their screening and prediction ability using the enrichment factor (EF), goodness of hit (GH), and receiver operating characteristics (ROC) curve analysis. The compounds SEI and ACI exhibited a docking score of −10.097 and −9.182 kcal/mol, respectively. An evaluation of binding conformation of ligand-bound protein complexes was performed with MD simulations for a time period of 30 ns along with free energy binding calculations using the g_mmpbsa technique. Prediction of inhibitory activities of the two lead compounds SEI (7.53) and ACI (6.84) using the 3D-QSAR model reaffirmed their inhibitory characteristics as potential anti-ataxia compounds.

Discovery of Potential Inhibitors of Aldosterone Synthase from Chinese Herbs Using Pharmacophore Modeling, Molecular Docking, and Molecular Dynamics Simulation Studies

Aldosterone synthase (CYP11B2) is a key enzyme for the biosynthesis of aldosterone, which plays a significant role for the regulation of blood pressure. Excess aldosterone can cause the dysregulation of the renin-angiotensin-aldosterone system (RAAS) and lead to hypertension. Therefore, research and development of CYP11B2 inhibitor are regarded as a novel approach for the treatment of hypertension. In this study, the pharmacophore models of CYP11B2 inhibitors were generated and the optimal model was used to identify potential CYP11B2 inhibitors from the Traditional Chinese Medicine Database (TCMD, Version 2009). The hits were further refined by molecular docking and the interactions between compounds and CYP11B2 were analyzed. Compounds with high Fitvalue, high docking score, and expected interactions with key residues were selected as potential CYP11B2 inhibitors. Two most promising compounds, ethyl caffeate and labiatenic acid, with high Fitvalue and docking score were reserved for molecular dynamics (MD) study. All of them have stability of ligand binding which suggested that they might perform the inhibitory effect on CYP11B2. This study provided candidates for novel drug-like CYP11B2 inhibitors by molecular simulation methods for the hypertension treatment.

Virtual screening and experimental validation of novel histone deacetylase inhibitors

Background

Histone deacetylases (HDACs) are promising therapeutic targets for the treatment of cancer, diabetes and other human diseases. HDAC inhibitors, as a new class of potential therapeutic agents, have attracted a great deal of interest for both research and clinical applications. Increasing efforts have been focused on the discovery of HDAC inhibitors and some HDAC inhibitors have been approved for use in cancer therapy. However, most HDAC inhibitors, including the clinically approved agents, do not selectively inhibit the deacetylase activity of class I and II HDAC isforms, and many suffer from metabolic instability. This study aims to identify new HDAC inhibitors by using a high-throughput virtual screening approach.

Methods

An integration of in silico virtual screening and in vitro experimental validation was used to identify novel HDAC inhibitors from a chemical database.

Results

A virtual screening workflow for HDAC inhibitors were created by integrating ligand- and receptor- based virtual screening methods. Using the virtual screening workflow, 22 hit compounds were selected and further tested via in vitro assays. Enzyme inhibition assays showed that three of the 22 compounds had HDAC inhibitory properties. Among these three compounds, ZINC12555961 significantly inhibited HDAC activity. Further in vitro experiments indicated that ZINC12555961 can selectively inhibit proliferation and promote apoptosis of cancer cells.

Conclusions

In summary, our study presents three new and potent HDAC inhibitors and one of these HDAC inhibitors shows anti-proliferative and apoptosis-inducing activity against various cancer cell lines. These results suggest that the developed virtual screening workflow can provide a useful source of information for the screening and validation of new HDAC inhibitors. The new-found HDAC inhibitors are worthy to further and more comprehensive investigations.

Electronic supplementary material

The online version of this article (doi:10.1186/s40360-016-0075-8) contains supplementary material, which is available to authorized users.

Exploration of Novel Inhibitors for Class I Histone Deacetylase Isoforms by QSAR Modeling and Molecular Dynamics Simulation Assays

Histone deacetylases (HDAC) are metal-dependent enzymes and considered as important targets for cell functioning. Particularly, higher expression of class I HDACs is common in the onset of multiple malignancies which results in deregulation of many target genes involved in cell growth, differentiation and survival. Although substantial attempts have been made to control the irregular functioning of HDACs by employing various inhibitors with high sensitivity towards transformed cells, limited success has been achieved in epigenetic cancer therapy. Here in this study, we used ligand-based pharmacophore and 2-dimensional quantitative structure activity relationship (QSAR) modeling approaches for targeting class I HDAC isoforms. Pharmacophore models were generated by taking into account the known IC50 values and experimental energy scores with extensive validations. The QSAR model having an external R2 value of 0.93 was employed for virtual screening of compound libraries. 10 potential lead compounds (C1-C10) were short-listed having strong binding affinities for HDACs, out of which 2 compounds (C8 and C9) were able to interact with all members of class I HDACs. The potential binding modes of HDAC2 and HDAC8 to C8 were explored through molecular dynamics simulations. Overall, bioactivity and ligand efficiency (binding energy/non-hydrogen atoms) profiles suggested that proposed hits may be more effective inhibitors for cancer therapy.

Computer-aided Molecular Design of Compounds Targeting Histone Modifying Enzymes

Growing evidences show that epigenetic mechanisms play crucial roles in the genesis and progression of many physiopathological processes. As a result, research in epigenetic grew at a fast pace in the last decade. In particular, the study of histone post-translational modifications encountered an extraordinary progression and many modifications have been characterized and associated to fundamental biological processes and pathological conditions. Histone modifications are the catalytic result of a large set of enzyme families that operate covalent modifications on specific residues at the histone tails. Taken together, these modifications elicit a complex and concerted processing that greatly contribute to the chromatin remodeling and may drive different pathological conditions, especially cancer. For this reason, several epigenetic targets are currently under validation for drug discovery purposes and different academic and industrial programs have been already launched to produce the first pre-clinical and clinical outcomes. In this scenario, computer-aided molecular design techniques are offering important tools, mainly as a consequence of the increasing structural information available for these targets. In this mini-review we will briefly discuss the most common types of known histone modifications and the corresponding operating enzymes by emphasizing the computer-aided molecular design approaches that can be of use to speed-up the efforts to generate new pharmaceutically relevant compounds.

Comparative modeling and benchmarking data sets for human histone deacetylases and sirtuin families

Histone deacetylases (HDACs) are an important class of drug targets for the treatment of cancers, neurodegenerative diseases, and other types of diseases. Virtual screening (VS) has become fairly effective approaches for drug discovery of novel and highly selective histone deacetylase inhibitors (HDACIs). To facilitate the process, we constructed maximal unbiased benchmarking data sets for HDACs (MUBD-HDACs) using our recently published methods that were originally developed for building unbiased benchmarking sets for ligand-based virtual screening (LBVS). The MUBD-HDACs cover all four classes including Class III (Sirtuins family) and 14 HDAC isoforms, composed of 631 inhibitors and 24609 unbiased decoys. Its ligand sets have been validated extensively as chemically diverse, while the decoy sets were shown to be property-matching with ligands and maximal unbiased in terms of "artificial enrichment" and "analogue bias". We also conducted comparative studies with DUD-E and DEKOIS 2.0 sets against HDAC2 and HDAC8 targets and demonstrate that our MUBD-HDACs are unique in that they can be applied unbiasedly to both LBVS and SBVS approaches. In addition, we defined a novel metric, i.e. NLBScore, to detect the "2D bias" and "LBVS favorable" effect within the benchmarking sets. In summary, MUBD-HDACs are the only comprehensive and maximal-unbiased benchmark data sets for HDACs (including Sirtuins) that are available so far. MUBD-HDACs are freely available at http://www.xswlab.org/ .

Ligand Based Pharmacophore Modeling and Virtual Screening Studies to Design Novel HDAC2 Inhibitors

Histone deacetylases 2 (HDAC2), Class I histone deacetylase (HDAC) family, emerged as an important therapeutic target for the treatment of various cancers. A total of 48 inhibitors of two different chemotypes were used to generate pharmacophore model using 3D QSAR pharmacophore generation (HypoGen algorithm) module in Discovery Studio. The best HypoGen model consists of four pharmacophore features namely, one hydrogen bond acceptor (HBA), and one hydrogen donor (HBD), one hydrophobic (HYP) and one aromatic centres, (RA). This model was validated against 20 test set compounds and this model was utilized as a 3D query for virtual screening to validate against NCI and Maybridge database and the hits further screened by Lipinski's rule of 5, and a total of 382 hit compounds from NCI and 243 hit compounds from Maybridge were found and were subjected to molecular docking in the active site of HDAC2 (PDB: 3MAX). Finally eight hit compounds, NSC108392, NSC127064, NSC110782, and NSC748337 from NCI database and MFCD01935795, MFCD00830779, MFCD00661790, and MFCD00124221 from Maybridge database, were considered as novel potential HDAC2 inhibitors.

A steered molecular dynamics mediated hit discovery for histone deacetylases

The inhibitors of class I histone deacetylases (HDACIs) have gained significant interest in cancer therapeutics. Virtual high throughput screening (vHTS) is one of the popular approaches used in the identification of novel scaffolds of HDACIs. However, an accurate description of ligand-protein flexibilities in the vHTS remains challenging. In this work, we implement an integrated approach, which combines the vHTS with the 'state-of-the-art' steered molecular dynamics (SMD). This approach serves as an efficient tool to identify potential hits and characterize their binding potencies against the class I HDACs in a flexible solvent environment. A hybrid pharmacophore-based and structure-based vHTS method identifies the hits with more favourable physico-chemical features against the class I HDACs. Our pharmacophore-based screening enhanced the quality of the vHTS outcomes. Further, the molecular interactions between the hits and the HDACs are investigated using the SMD-driven force profiles, which in turn resulted in filtering the hits with higher binding potencies against the HDACs. Our results, therefore, reveal that vHTS and SMD can be a complementary and effective analytical tool for accelerating the hit identification phase in structure-based drug design.

Dual binding site and selective acetylcholinesterase inhibitors derived from integrated pharmacophore models and sequential virtual screening

In this study, we have employed in silico methodology combining double pharmacophore based screening, molecular docking, and ADME/T filtering to identify dual binding site acetylcholinesterase inhibitors that can preferentially inhibit acetylcholinesterase and simultaneously inhibit the butyrylcholinesterase also but in the lesser extent than acetylcholinesterase. 3D-pharmacophore models of AChE and BuChE enzyme inhibitors have been developed from xanthostigmine derivatives through HypoGen and validated using test set, Fischer's randomization technique. The best acetylcholinesterase and butyrylcholinesterase inhibitors pharmacophore hypotheses Hypo1_A and Hypo1_B, with high correlation coefficient of 0.96 and 0.94, respectively, were used as 3D query for screening the Zinc database. The screened hits were then subjected to the ADME/T and molecular docking study to prioritise the compounds. Finally, 18 compounds were identified as potential leads against AChE enzyme, showing good predicted activities and promising ADME/T properties.

Exploration of structural and physicochemical requirements and search of virtual hits for aminopeptidase N inhibitors

Aminopeptidase N (APN) inhibitors have been reported to be effective in treating of life threatening diseases including cancer. Validated ligand- and structure-based pharmacophore mapping approaches were combined with Bayesian modeling and recursive partitioning to identify structural and physicochemical requirements for highly active APN inhibitors. Based on the assumption that ligand- and structure-based pharmacophore models are complementary, the efficacy of 'multiple pharmacophore screening' for filtering true positive virtual hits was investigated. These multiple pharmacophore screening methods were utilized to search novel virtual hits for APN inhibition. The number of hits was refined and reduced by recursive partitioning, drug-likeliness, pharmacokinetic property prediction, and comparative molecular-docking studies. Four compounds were proposed as the potential virtual hits for APN enzyme inhibition.

Pharmacophore and QSAR studies to design novel histone deacetylase 2 inhibitors

One pharmacophore model and three quantitative structure-activity relationship models were developed on a series of benzimidazole and imidazole inhibitors of histone deacetylase 2. The goodness of hit score value of the best pharmacophore model was 0.756, which indicated that it is reliable to be used for virtual screening. The built pharmacophore model was used to search the NCI database. The hit compounds were subjected to molecular docking. The results showed that 25 compounds had high scores and strong interactions with histone deacetylase 2. In three-dimensional quantitative structure-activity relationship studies, good predictive models were obtained using comparative molecular field analysis, comparative molecular similarity indices analysis, and Topomer comparative molecular field analysis. Some putative active compounds were proposed based on compound no. 41. Twenty-six compounds had high scores and good interactions when they were docking into histone deacetylase 2.

Identification of novel, less toxic PTP-LAR inhibitors using in silico strategies: pharmacophore modeling, SADMET-based virtual screening and docking

Human leukocyte antigen-related (PTP-LAR) is a receptor-like transmembrane phosphatase and a potential target for diabetes, obesity and cancer. In the present study, a sequence of in silico strategies (pharmacophore mapping, a 3D database searching, SADMET screening, and docking and toxicity studies) was performed to identify eight novel nontoxic PTP-LAR inhibitors. Twenty different pharmacophore hypotheses were generated using two methods; the best (hypothesis 2) consisted of three hydrogen-bond acceptor (A), one ring aromatic (R), and one hydrophobic aliphatic (Z) features. This hypothesis was used to screen molecules from several databases, such as Specs, IBS, MiniMaybridge, NCI, and an in-house PTP inhibitor database. In order to overcome the general bioavailability problem associated with phosphatases, the hits obtained were filtered by Lipinski's rule of five and SADMET properties and validated by molecular docking studies using the available crystal structure 1LAR. These docking studies suggested the ligand binding pattern and interactions required for LAR inhibition. The docking analysis also revealed that sulfonylurea derivatives with an isoquinoline or naphthalene scaffold represent potential LAR drugs. The screening protocol was further validated using ligand pharmacophore mapping studies, which showed that the abovementioned interactions are indeed crucial and that the screened molecules can be presumed to possess potent inhibitory activities.

Computer- and structure-based lead design for epigenetic targets

The term epigenetics is defined as inheritable changes that influence the outcome of a phenotype without changes in the genome. Epigenetics is based upon DNA methylation and posttranslational histone modifications. While there is much known about reversible acetylation as a posttranslational modification, research on reversible histone methylation is still emerging, especially with regard to drug discovery. As aberrant epigenetic modifications have been linked to many diseases, inhibitors of histone modifying enzymes are very much in demand. This article will summarize the progress on small molecule epigenetic inhibitors identified by structure- and computer-based approaches.

Strategies in developing promising histone deacetylase inhibitors

Histone deacetylases (HDACs) are a family of enzymes that have been of interest in drug discovery for more than 30 years. Inhibitors of HDACs are potential therapeutics for various diseases, such as neurodegenerative diseases, inflammation, viral infection, and especially cancer. Most HDAC inhibitors (HDACi) are designed for cancer therapy. In 2006, suberoylanilide hydroxamic acid was approved by the US Food and Drug Administration for once-daily oral treatment of advanced cutaneous T-cell lymphoma. In the meantime, there have been aggressive efforts to bring HDACi to the market for every major tumor type, either as a single therapy or in combination, and a number of compounds are currently undergoing clinical trials. Multiple strategies have been applied to the rational design of drugs targeting HDACs by taking advantage of the new developments in proteomics, chemogenomics, cheminformatics, and computational chemistry/biology. Herein, we review the current methods successfully used in developing novel HDACi.

Docking-enabled pharmacophore model for histone deacetylase 8 inhibitors and its application in anti-cancer drug discovery

Zinc-dependent histone deacetylase 8 removes the epsilon-acetyl groups present in the N-terminal lysine residues of histone proteins, thereby restricting various transcription factors from being expressed. Inhibition of this enzyme has been reported to be a novel strategy in cancer treatment. To identify novel and diverse leads for use in potent histone deacetylase 8 inhibitor design, a pharmacophore model showing high correlation between experimental and estimated activities was generated using the best conformations of training set compounds from molecular docking experiments. The best pharmacophore model was validated using four different strategies and then used in database screening for novel virtual leads. Hit compounds were selected and subjected to molecular docking using GOLD. The top-scored compound was further optimized for improved binding. The optimization step led to a new set of compounds with both improved binding at the active site and estimated activities. The identified virtual leads could be used for designing potent histone deacetylase 8 inhibitors as anti-cancer therapeutics.

Identifying Novel Type ZBGs and Nonhydroxamate HDAC Inhibitors Through a SVM Based Virtual Screening Approach

Histone deacetylase inhibitors (HDACi) have been successfully used for the treatment of cancers and other diseases. Search for novel type ZBGs and development of non-hydroxamate HDACi has become a focus in current research. To complement this, it is desirable to explore a virtual screening (VS) tool capable of identifying different types of potential inhibitors from large compound libraries with high yields and low false-hit rates similar to HTS. This work explored the use of support vector machines (SVM) combined with our newly developed putative non-inhibitor generation method as such a tool. SVM trained by 702 pre-2008 hydroxamate HDACi and 64334 putative non-HDACi showed good yields and low false-hit rates in cross-validation test and independent test using 220 diverse types of HDACi reported since 2008. The SVM hit rates in scanning 13.56 M PubChem and 168K MDDR compounds are comparable to HTS rates. Further structural analysis of SVM virtual hits suggests its potential for identification of non-hydroxamate HDACi. From this analysis, a series of novel ZBG and cap groups were proposed for HDACi design.

Inside HDAC with HDAC inhibitors

Histone deacetylase inhibitors are a large group of diverse molecules intrinsically able to inhibit cell proliferation in various cancer cell lines. Their apoptotic effects have been linked to the modulation in the expression of several regulatory tumor suppressor genes caused by the modified status of histone acetylation, a key event in chromatin remodelling. As the initial histone deacetylase activity of HDAC has been extended to other proteins, the possible other biological mechanisms modified by HDAC inhibitor treatments are still to be clarified. The need for HDAC isoform selective inhibitors is an important issue to serve this goal. This review discusses the approaches proposed by several research groups working on the synthesis of HDAC inhibitors, based on modelling studies and the way these findings were used to obtain new HDAC inhibitors with possible isoform selectivity.

PhDD: a new pharmacophore-based de novo design method of drug-like molecules combined with assessment of synthetic accessibility

This account describes a new pharmacophore-based de novo design method of drug-like molecules (PhDD). The method PhDD first generates a set of new molecules that completely conform to the requirements of a given pharmacophore model, followed by a series of assessments to the generated molecules, including assessments of drug-likeness, bioactivity, and synthetic accessibility. PhDD is tested on three typical examples, namely, pharmacophore hypotheses of histone deacetylase (HDAC), cyclin-dependent kinase 2 (CDK2) and HIV-1 integrase (IN) inhibitors. The test results demonstrate that PhDD is able to generate molecules with novel structures but having similar biological functions with existing inhibitors. The validity of PhDD together with its ability of assessing synthetic accessibility makes it a useful tool in rational drug design.

Investigation on the isoform selectivity of histone deacetylase inhibitors using chemical feature based pharmacophore and docking approaches

A three dimensional (3D) chemical feature based pharmacophore model was developed for selective histone deacetylase 1 (HDAC1) inhibitors, which provides an efficient way to discuss the isoform selectivity of HDAC inhibitors. In contrast to the classical pan-HDAC pharmacophore, two hydrophobic features (HY and HYAr2) were found in the chemical feature based pharmacophore model, which might be responsible for the selectivity of HDAC1 inhibitions. Molecular docking also highlighted the two hydrophobic features, which are located in the internal cavity adjacent to the active site. The results contribute to our understanding of the molecular mechanism underlying the selectivity of HDAC1 inhibitors and suggest a possible target region to design novel selective HDAC1 inhibitors.

Knowledge based identification of MAO-B selective inhibitors using pharmacophore and structure based virtual screening models

Monoamine Oxidase B interaction with known ligands was investigated using combined pharmacophore and structure based modeling approach. The docking results suggested that the pharmacophore and docking models are in good agreement and are used to identify the selective MAO-B inhibitors. The best model, Hypo2 consists of three pharmacophore features, i.e., one hydrogen bond acceptor, one hydrogen bond donor and one ring aromatic. The Hypo2 model was used to screen an in-house database of 80,000 molecules and have resulted in 5500 compounds. Docking studies were performed, subsequently, on the cluster representatives of 530 hits from 5500 compounds. Based on the structural novelty and selectivity index, we have suggested 15 selective MAO-B inhibitors for further synthesis and pharmacological screening.