Epigenetic Modulators as Potential Multi‑targeted Drugs Against Hedgehog Pathway for Treatment of Cancer

Anshika N. Singh1 · Neeti Sharma1


The Sonic hedgehog signalling is known to play a crucial role in regulating embryonic development, cancer stem cell maintenance and tissue patterning. Dysregulated hedgehog signalling has been reported to affect tumorigenesis and drug response in various human malignancies. Epigenetic therapy relying on DNA methyltransferase and Histone deacetylase inhibitors are being proposed as potential drug candidates considering their efficiency in preventing development of cancer progenitor cells, killing drug resistant cells and also dictating “on/off” switch of tumor suppressor genes and oncogenes. In this docking approach, epigenetic modulators were virtually screened for their efficiency in inhibiting key regulators of SHH pathway viz., sonic hedgehog, Smoothened and Gli using polypharmacological approach. The control drugs and epigenetic modulators were docked with PDB protein structures using AutoDock vina and further checked for their drug-likeness properties. Further molecular dynamics simulation using VMD and NAMD, and MMP/GBSA energy calculation were employed for verifying the stability and entropy of the ligand-receptor complex. EPZ-6438 and GSK 343 (EZH2 inhibi- tors), CHR 3996 and Mocetinostat (HDAC inhibitors), GSK 126 (HKMT inhibitor) and UNC 1215 (L3MBTL3 antagonist) exhibited multiple-targeted approach in modulating HH signalling. This is the first study to report these epigenetic drugs as potential multi-targeted hedgehog pathway inhibitors. Thus, epigenetic polypharmacology approach can be explored as a better alternative to challenges of acute long term toxicity and drug resistance occurring due to traditional single targeted chemotherapy in the future.
Keywords Polypharmacology · Epigenetic drugs · Hedgehog signalling · Cancer therapy
1 Introduction
According to current statistics given by the American Can- cer Society, almost 1,735,350 new cancer cases and 609,640 cancer mortalities are projected to occur in the United States in 2018 [1]. Although advancement in medical investiga- tions and increased understanding of the disease have led to improvement in cancer statistics; however, there still exists a huge lacuna in cancer treatment. Research has clearly established that complex diseases such as cancer result

from genetic mutations as well as environmental factors that lead to epigenetic changes in gene expressions [2]. Con- sequently, these changes bring about alterations in various cellular pathways which are known to play a crucial role in the initiation and progression of carcinogenesis such as cell cycle, embryogenesis, metastasis and apoptosis [3]. Thus regulation of these steps may lead to blockage in disease pro- gression and prolonged patient survival which are currently the main challenges of cancer treatment. Contrary to conven- tional therapy which generates toxicity and non-specific tar-

geting leading to damage of normal tissues, specific target-

Electronic supplementary material The online version of this article ( contains supplementary material, which is available to authorized users.
 Neeti Sharma [email protected]
1 Symbiosis School of Biological Sciences, Symbiosis International (Deemed University), Gram-Lavale, Taluka-Mulshi, Pune 412115, India

ing allows focused damage of subpopulation of cells which are directly involved in tumor progression [4, 5]. This has led to the emergence of a new generation of multi-targeted therapeutic drugs currently in their clinical, developmental phase wherein a single entity can act/target simultaneously at multiple molecular targets [6]. Among various signal- ling pathways, hedgehog (HH) signalling has been consid- ered as an attractive molecular target in cancer treatment

by multi-targeted therapeutic approach [7]. The hedgehog signalling is known to play a critical role in embryogenesis and other developmental processes including proliferation, survival, differentiation, and cancer stem cell maintenance [8, 9]. Altered HH signalling has been reported in various malignancies including medulloblastoma [10], leukemia [11], oral squamous cell carcinoma [12], pancreatic [13],
lung [14], breast [15], ovarian [16] and prostate cancer [17]. In a normal state, Hedgehog ligand activates signalling by binding to Patched-1 which is a transmembrane receptor. Consequently, this leads to reversal of Ptch-1 medicated inactivation of Smoothened, which transduces SHH signal- ling resulting in nuclear translocation of Gli (cytoplasmic transcription factors) which modulate gene expressions [8, 9]. Various hedgehog pathway inhibitors have been iden- tified and can be categorized into Hedgehog inhibitors, Smoothened inhibitors, and Gli protein inhibitors [7, 18–21]. Although, drugs such as Cyclopamine have reached clinical trials as Smoothened inhibitors, however, Gli inhibitors and Hedgehog pathway inhibitors still need to be explored at clinical levels.
Several naturally occurring dietary compounds such as Cyclopamine, Curcumin, Epigallocatechin-3-gallate, Gen- istein, Resveratrol, Zerumbone, Norcantharidin, and Arsenic Trioxide have shown promising results in inhibiting dys- regulated HH signaling [22]. However, these natural com- pounds lack in focused targeting in contrast to an epigenetic targeted approach which is being considered as a new way for cancer treatment. Since, Epigenetic changes are known to occur at the biochemical level and involve alteration of chromatin structures using methylation of DNA patterns, histone modifications and microRNA expressions and sub- sequently leading to activation/silencing of tumor suppressor and oncogenes [23, 24]. Epigenetic modifications are also reversible nuclear, chemical, enzymatic reactions contribut- ing to the onset and progression of tumors. Recent advances in bioinformatics and genome-wide analyses have provided considerable information on epigenetically dysregulated dis- ease-specific gene networks [25]. Hence, epigenetic therapy has gained significant attention from scientists and clinicians worldwide since these signatures are not only gene-specific but also disease-specific thereby making individually tai- lored patient-specific epigenetic therapy possible [26, 27]. Evidences have shown existing crosstalk between epigenetic signatures during disease initiation and progression [23, 24]; hence epigenetic modulators can work as good potential chemotherapeutic agents.
Hedgehog signalling has been frequently reported to be
epigenetically dysregulated involving crosstalks among mul- tiple biological networks which eventually increases the pos- sibility of drug resistance [7]. Thus we have based our strat- egy on network model which states that partial inhibition of several targets is more efficient than complete inhibition of

single target [27]. Considering the aforementioned epige- netic polypharmacology strategy, the aim of the study was designed to computationally investigate epigenetic modula- tors that can be developed as multi-targeting inhibitors of Hedgehog signalling (Fig. 1).

2 Methods and Materials
2.1 Selection of Receptor and Ligands

The epigenetic drugs in the study were retrieved from Human epigenetic drug database ( [28], HDAC inhibitor database ( [29] and human epigenetic enzyme and modulator database (http:// [30]. The controls used in the study were retrieved from Drug Path: a database for drug- induced pathways ( [31] and DGI db: Drug interaction database ( [32]. The standard drugs used in this analysis viz. Robot- kinin, GANT61 and Cyclopamine are the known standard inhibitors of Hedgehog, Gli and Smoothened [18, 33, 34].

2.2 Ligand Preparation

For ligand preparation, 3D structures of 100 epigenetic drugs and control drugs in SDF format were downloaded from Pubchem database ( [35]. The ligands were then converted to PDB format using Open Babel Convertor ( Page) [36]. The geometry of the ligands such as torsional angles, adjustment of rotatable bonds, the addition of pdbqt charges were then adjusted using ArgusLab (http://www. [37].

2.3 Receptor Preparation

Based on previous literature, structures of Gli (2GLI) [38], Sonic hedgehog (3M1N) [39] and Smoothened (4O9R) [40] were retrieved from RCSB-PDB ( [41]. The structure of Gli (2GLI) is a five finger Gli/DNA com- plex, 3M1N is a crystal structure of human sonic hedgehog and 4O9R is a Smoothened–Cyclopamine complex struc- ture. The DNA molecule was then deleted from the 2GLI structure, and similarly, the ligand complex of Cyclopamine was removed from Smoothened 4O9R. The structures were then adjusted according to the bond angles, charges and tor- sional angles in the Argus Lab. The catalytic sites of the three receptors were identified using DogSite scorer (http:// [42]. After the minimization process, the grid box resolutions were set according to the active site residues calculated in DogSite scorer.


Fig. 1 Flowchart of methodology for the docking study

2.4 Molecular Docking Using AutoDock Vina

After receptor and ligand preparations, we opted for flexible docking approach in AutoDock Vina. Initially, docking was carried out for control drugs with the binding sites of their receptors, and the resulting interactions were compared with those calculated docking results of epigenetic drugs into the similar active sites using the same grid box dimensions. The threshold values were set according to the binding affinity energies of standard drugs. All of these docked representa- tions were rendered using the UCSF Chimera package from the Resource for Biocomputing, Visualization, and Informatics at the University of California, San Francisco (supported by NIH P41 RR-01081) [43].

2.5 Molecular Dynamics Simulation of the Docked Complexes

The molecular dynamic (MD) simulation of SHH key pro- teins with identified epigenetic modifiers was done by visual molecular dynamics (VMD) [44] and NAMD 2.7 (nano-scale molecular dynamics) with the CHARMM27 force field [45, 46]. Based on the identified protein–ligand complexes, we per- formed structural dynamics simulation on the selected lowest energy values and best docking complex. For ligand, Gasteiger partial charges were designated and non- polar hydrogen atoms were merged. The systems were energy minimized for 10,000 steps. Each system was annealed from 10 to 310 K through- out 60 ps. The systems were then equilibrated at 310 K using

Langevin thermostat with a coefficient of 5/ps in the isobaric- isothermal (NPT) ensemble for 2 ns. For simulations, a multi- ple time stepping algorithm was employed, where bonded and short-range bonded interactions were reviewed at every time step (2 ps) and also the electrostatic interactions were evalu- ated at every 2 time steps. For the short-range interactions a spherical cut-off of 10 Å was used [47]. All MD simulation results were used to initiate the functionally important residues and the binding free energies calculations. The protein–ligand complexes were also subjected to Root mean square fluctua- tions (RMSF) and Radius of Gyration (Rg) analysis for further validation of their structural flexibility.
3 Binding Free Energy Calculation
We have extracted the 100 models from the 10 ns MD tra- jectories to compute the binding force energies between our respective proteins, i.e., Shh, Smoothened and Gli with the six identified epigenetic modifiers using the molecular mechan- ics Poisson-Boltzmann solvent accessible surface area (MM- PBSA) method [47]. The total binding free energy can be calculated by
ΔGbind = ΔH − TΔS = ΔEMM + ΔGsol − TΔS
ΔEMM = ΔEinternal + ΔEelectrostatic + ΔEvdw
where ΔEMM is the change of molecular mechanical energy, ΔGsol is the solvation free energy, and TΔS considers the penalty of entropy. The ΔEMM includes non-bonded inter- action, Van der Waals ΔEvdW, and electrostatic ΔEele inter- actions, and local bonded interaction ΔEint. The last term, which is a sum of the bond, angle, and dihedral contribu- tions, is counteracted in a single trajectory approach. The solvation free energy ΔGsol contains electrostatic/polar (ΔGpolar) and nonpolar (ΔGnonpolar) terms. The nonpolar term can be further divided into the excluded volume contributed by repulsive interaction (ΔGpolar), and the attractive inter- action (ΔGdisper) aroused from the solute–solvent Van der Waals dispersion interaction. In the MM-PBSA calculation, a grid spacing of 0.5 Å was employed, and the relative die- lectric constant was set to 80.0 at the exterior and 2.0 at the interior of protein-epigenetic inhibitor complex. At last, free energy decomposition at residue level (ΔGper-decomp) also was carried out to provide detailed information on binding site and binding affinity.
3.1 Drug Likeness Calculation

Drugs scans were carried out using Molinspiration (http:// to determine whether the epi- genetic modulators fulfilled the drug likeness conditions.

Lipinski’s rule of five filters were applied for examin- ing drug-likeness attributes such as quantity of hydrogen acceptors (should not be more than 10), quantity of hydro- gen donors (should not be more than 5), molecular weight (should not be more than 500 Daltons) and partition coef- ficient log P (should not be less than 5) [48]. The smiles format for each of the identified multi-targeted epigenetic modulators were downloaded from Pubchem and uploaded in Molinspiration for analysis.

4 Results
4.1 Receptor Preparation

The 3D structures of Smoothened, Hedgehog, and Gli were downloaded from RCSB-PDB with PDB ID—2GLI1 (Gli), 3M1N (Sonic Hedgehog) and 4O9R (Smoothened). The receptors were then cleaned of any extra heteroatoms, water molecules and attached complexes in Argus Lab. The epi- genetic modulators included in this study were retrieved and collected from Human Epigenetic Drug Database, HDAC inhibitor database, and Human epigenetic enzyme and modulator database. The standard inhibitors of Hedgehog, Smoothened and Gli inhibitors used in this analysis were retrieved from DrugPath and DGIdb. The structures of both control drugs and epigenetic modulators were then down- loaded from Pubchem in SDF format and converted to 3D PDB format using Open Babel Convertor. The geometry of the ligands, i.e., torsion angles and extra molecules, hydro- gen bonds, rotatable bonds were then adjusted in AutoDock Vina.
4.2 Active Site Prediction

The binding site prediction was carried out using DogSite scorer to predict the catalytic sites of each of the receptor molecules viz. SHH, SMO, and GLI. Since, we wanted a multi-targeted approach for the modulators we have taken all the active sites of the receptors into consideration(Fig. 2, S1 Table). The binding grid dimensions were then set accord- ingly in the ArgusLab for flexible docking purpose.
4.3 Molecular Docking Using AutoDock Vina

Molecular docking was performed after preparation of ligands and receptors. The binding affinities were ranked according to the hydrogen bond interactions and interaction energies. Post molecular docking to evaluate the binding energies of control drugs with Smoothened, Gli, and SHH, we proceeded to identify epigenetic drugs which uniformly showed good binding energies with all three receptors (S2 Table). The threshold energies were set according to binding


Fig. 2 3D structures of SHH, Gli and SMO with their binding pockets (P1-Pn: represents the binding pockets of the proteins)
affinity energies of the control drugs with respect to each of their individual receptors: Smoothened, Gli, and Shh. The threshold energies according to respective receptors were then decided as SHH: − 9 kcal/mol (Robotkinin: − 9.3 kcal/ mol), Gli: − 7 kcal/mol (GANT 61: − 7.3 kcal/mol) and SMO: − 8 kcal/mol (Cyclopamine: − 8.9 kcal/mol). The binding affinities of multi-targeted epigenetic drugs with Smoothened, Gli, and SHH were then compared with the threshold binding affinities set according to the controls. The six epigenetic drugs we identified were Tazemetostat, GSK 343, CHR 3996, Mocetinostat, GSK 126 and UNC 1215. These epigenetic drugs exhibited good binding affin- ity energies with each of the three receptors, i.e., Hedgehog, Smoothened and Gli (Table 1). The docked complex of the identified multi-targeted epigenetic drugs with the key pro- teins were then visualized in UCSF chimera (Fig. 3).
4.4 Molecular Dynamics Simulation

To account for the stability of the protein and identified epi- genetic modifiers and to determine the binding affinity of

the inhibitors with the key SHH proteins, a 10 ns molecular dynamics simulation of the docked complexes was carried out. Binding mode analysis revealed that the binding modes obtained after MD simulation were more or less similar to that obtained post-docking using AutoDock Vina. Presence of a large number of H bond acceptors, H bond donors, as well as hydrophobic groups in the ligands, account for the stability of the ligands inside the binding pockets of the three proteins. Based on the RMSD of the ligand–protein com- plex, it was observed that the identified multi-targeted epi- genetic drugs maintained their interaction with the proteins (Shh, Gli, Smo) with low RMSD fluctuations (Fig. 4). Cal- culations were done with the NAMD molecular dynamics software using the CHARMM parameters. The geometries of the proteins and six epigenetic inhibitors were fully opti- mized, and their electrostatic potentials were obtained using a single-point calculation. Subsequently, their partial charges were obtained by the restrained electrostatic potential. All MD simulations were performed in the NVT ensemble (temperature equal to 310 K). In electrostatic interactions, Atom-based truncation was undertaken and also the switch
Table 1 Identified multi- targeted epigenetic drugs and their binding affinity energies

Epigenetic modulators Binding affinity (kcal/mol) (Smoothened)

Binding affinity (kcal/mol) (Gli)

Binding affinity (kcal/mol) (Hedgehog)

with Smoothened, Gli and


GSK 126 − 8.4 − 7.8 − 9.9
CHR 3996 − 8.5 − 7 − 9.8
EPZ 6438 − 8.9 − 7.7 − 9.4
Mocetinostat − 8.6 − 8.1 − 9.3
GSK 343 − 8.2 − 7.3 − 9.7
UNC 1215 − 8.6 − 7.2 − 9.4

Fig. 3 1a Best docking conformation of CHR-3996 and GLI (bind- ing affinity: − 7 kcal/mol); 1b best docking conformation of EPZ- 6438 and GLI (binding affinity: − 7.7 kcal/mol); 1c best docking conformation of GSK 126 and GLI (binding affinity: − 7.8 kcal/mol); 1d best docking conformation of GSK 343 and GLI (binding affin- ity: − 7.3 kcal/mol); 1e best docking conformation of Mocetinostat and GLI (binding affinity: − 8.1 kcal/mol); 1f best docking confor- mation of UNC-125 and GLI (binding affinity: − 7.2 kcal/mol); 2a best docking conformation of CHR-3996 and SHH (binding affin- ity: − 9.8 kcal/mol); 2b best docking conformation of EPZ-6438 and SHH (binding affinity: − 9.4 kcal/mol); 2c best docking confor- mation of GSK 126 and SHH (binding affinity: − 9.9 kcal/mol); 2d
best docking conformation of GSK 343 and SHH (binding affinity:
– 9.7 kcal/mol); 2e best docking conformation of Mocetinostat and SHH (binding affinity: − 9.3 kcal/mol); 2f best docking conforma- tion of UNC-125 and SHH (binding affinity: − 9.4 kcal/mol). 3a Best docking conformation of CHR-3996 and SMO (binding affin- ity: − 8.5 kcal/mol); 3b best docking conformation of EPZ-6438 and SMO (binding affinity: − 8.9 kcal/mol); 3c best docking confor- mation of GSK 126 and SMO (binding affinity: − 8.4 kcal/mol); 3d best docking conformation of GSK 343 and SMO (binding affinity:
– 8.2 kcal/mol); 3e best docking conformation of Mocetinostat and SMO (binding affinity: − 8.6 kcal/mol); 3f best docking conformation of UNC-125 and SMO (binding affinity: − 8.6 kcal/mol)
Van der Waals functions were also used with a 1.8 nm cutoff for atom-pair lists. The complex structures were minimized for 20,000 conjugate gradient steps. The minimized com- plex structures were then subjected to a 10 ns isothermal, constant volume MD simulation. Table 2 lists the binding free energies resulted from the MM/PBSA of the three SHH proteins (SHH, Smo and Gli) with the six epigenetic modi- fiers. The stability of the potential of selected epigenetic modifiers in inhibiting key Hedgehog proteins—SHH, Smo and Gli were further demonstrated by performing dynamic simulations of the protein–ligand complex by considering their dynamic trajectories i.e., root mean square deviation(RMSD), root mean square fluctuations (RMSF) and radius of gyration (Rg). The simulation results were analyzed for the six epigenetic modifiers, i.e., Tazemetostat, GSK 343, CHR 3996, Mocetinostat, GSK 126 and UNC 1215 in com- plex with respective proteins—SHH, Smo and Gli. The increasing RMSD trend of the inhibitors was observed for each of the inhibitors in initial stages during the simula- tion period (0–400 ns). Initially, GSK 126 and GSK 343 showed higher RMSD variations during (0–600 ns) while CHR 3996 and EPZ 6438 depicted comparatively lesser fluc- tuations post-docking simulations in each of the three pro- tein–ligand complex (Fig. 4). Although, with progression, in

Fig. 4 a The RMSD of the identified multi-targeted epigenetic modi- fiers viz., CHR-3996, EPZ- 6438, GSK-126, GSK 343, Mocetinostat, UNC-1215 in the active site of GLI. b The RMSD of the identified multi-targeted epigenetic modifiers viz., CHR-3996, EPZ- 6438,

Table 2 The approximate binding (ΔGb) free energies of identified multi-targeted epigenetic drugs with Smoothened, Gli and Hedgehog calculated by MM/PBSA method post MD simulation (kcal/mol)

GSK-126, GSK 343, Mocetinostat, UNC-1215 in the active site of SMO. c The RMSD of the identified multi-targeted epigenetic modi- fiers viz., CHR-3996, EPZ- 6438, GSK-126, GSK 343, Mocetinostat,
UNC-1215 in the active site of SHH

time each of the protein–ligand complexes exhibited RMSD values < 2.0 Å and hence showed that the six epigenetic modifiers tend to achieve stable conformation with fewer

Epigenetic modula- tors

Binding affinity (kcal/ mol) (Smooth-

Binding affinity (kcal/ mol)

Binding affin- ity (kcal/mol) (Hedgehog)

fluctuations and minimal changes in their RMSD with time.
A comprehensive analysis of the RMSF profiles as shown in Fig. 5, depicted that all the residues in the binding sites of the proteins had RMSF values in the range of 0.2 Å and
0.8 Å which indicate that all the ligands remained close to their initial binding sites after MD simulations. Although, GSK 126, GSK 343 and EPZ 6438 in complex with the three proteins showed high fluctuations in the initial residues rang- ing from 0.2 ± 1.4 Å, however eventually the three inhibitors

UNC 1215 − 12.4 − 10.65 − 13.5

along with Mocetinostat, UNC 1215 and CHR 3996 reached an equilibrium stage of minimum residual fluctuations in their structures and thus implying towards them acquiring stable conformational levels in their docked protein–ligand complex.

Fig. 5 a The MD simulation (RMSF analysis) of protein ligand com- plex of identified multi-targeted epigenetic modifiers viz., CHR-3996, EPZ- 6438, GSK-126, GSK 343, Mocetinostat, UNC-1215 with GLI

for 10 ns. b The MD simulation (RMSF analysis) of protein ligand complex of identified multi-targeted epigenetic modifiers viz., CHR-

3996, EPZ- 6438, GSK-126, GSK 343, Mocetinostat, UNC-1215
with SMO for 10 ns. c The MD simulation (RMSF analysis) of pro- tein ligand complex of identified multi-targeted epigenetic modifi- ers viz., CHR-3996, EPZ- 6438, GSK-126, GSK 343, Mocetinostat, UNC-1215 with SHH for 10 ns

Additionally, the radius of gyration (Rg) was deter- mined for ligand bound Ca backbone. The radius of gyra- tion depicts the compactness of protein while protein fold- ing and unfolding during the 10 ns simulations (Fig. 6). The depicted results showed that compounds CHR 3996 and Mocetinostat showed many static variations and con- stant Rg values at 1.0–1.5 Å throughout the simulation period of 0–1200 ns. The other epigenetic modifiers such as UNC 1215 showed comparatively higher ranges of radius of gyrations ranging from 1 to 2.5 Å in some cases, however subsequently the ligands reached an equilibrium stage in all the three proteins. Therefore these results fur- ther depicted that these six epigenetic modifiers could

form relatively stable conformations with the complex after initial 500 ns.
4.5 Drug Likeness Calculations

During the initial part of the study, we docked various com- mercially available drugs in the identified binding sites of the three receptors viz. SHH, SMO, and GLI to draw a com- parison between the binding energies of the epigenetic mod- ulators. Molecular properties of the identified multi-targeted epigenetic modulators were then investigated using Molin- spiration software to satisfy whether they fulfilled Lipin- ski’s rule of five, which is crucial for rational drug designing and also to determine their bioactivity score. Our analysis

Fig. 6 a The MD simulation (radius of gyration analysis) of protein ligand complex of identified multi-targeted epigenetic modifiers viz., CHR-3996, EPZ- 6438, GSK-126, GSK 343, Mocetinostat, UNC-

1215 with GLI for 10 ns. b The MD simulation (radius of gyration analysis) of protein ligand complex of identified multi-targeted epi-

genetic modifiers viz., CHR-3996, EPZ- 6438, GSK-126, GSK 343, Mocetinostat, UNC-1215 with SMO for 10 ns. c The MD simulation (radius of gyration analysis) of protein ligand complex of identified multi-targeted epigenetic modifiers viz., CHR-3996, EPZ-6438, GSK- 126, GSK 343, Mocetinostat, UNC-1215 with SHH for 10 ns

Table 3 Table depicting

Modulators miLogP TPSA Natoms MW nON nOHNH Violation Nrotb Volume

Lipinski’s rule of five filters of
shortlisted epigenetic drugs EPZ 6438 4.32 86.9 42 572.75 8 2 1 9 551.96
CHR 3996 0.78 103.27 29 394.41 8 3 0 5 335.7
GSK 126 4.00 95.05 39 526.68 8 3 1 7 502.21
Mocetinostat 2.94 105.82 30 396.45 7 4 0 6 357.68
GSK 343 3.82 99.15 40 541.7 9 2 1 8 515.23
UNC 1215 3.44 59.12 39 529.73 7 1 1 6 516.07

showed CHR 3996 and Mocetinostat did not exhibit any vio- lations, however, EPZ-6438, GSK 126, GSK 343 and UNC 1215 showed one violation however, according to Lipinski’s rule of five no more than one violation is allowed hence, they can also be used for further clinical investigation (Table 3). Further, we also calculated the bioactivity score for the iden- tified epigenetic modulators using Molinspiration (Table 4).

Hence these drugs fulfilled the drug likeness criteria of Lipinski’s rule of five. The reported drugs viz. EPZ-6438 (Tazemetostat) and GSK 343 (Enhancer of Zeste homolog 2 (EZH2) inhibitors), CHR 3996 and Mocetinostat (His- tone deacetylase (HDAC) inhibitors), GSK 126 (Histone lysine methyltransferase inhibitor) and UNC 1215 (Inhibi- tor for the methyl-lysine (Kme) reading function of Lethal

Table 4 Bioactivity score of identified shortlisted epigenetic drugs


(3) malignant brain tumor-like protein 3 (L3MBTL3)) are already in the process of clinical trials for cancer treatment (S3 Table). Our results thus revealed the promising nature of these epigenetic drugs in inhibiting multitargets (SHH, Smo and Gli) during carcinogenesis.

5 Discussion
Epigenetic polypharmacology refers to the application of epigenetic drugs which can modulate multiple key proteins in disease-related metabolic networks simultaneously via DNA methylation and Histone deacetylation [49]. The multi- targeted epigenetic drugs can maintain a balance between the expressions of various proto-oncogenes and tumor sup- pressor genes which may be contributing towards tumor ini- tiation and progression, rather than complete suppression of single target which may affect a specific pathway drastically [50]. Thus in contrast to single target approach, which com- pletely inhibits the expression of a particular gene/protein and it’s possible interactions with other metabolic pathways, a multi-targeted approach focuses on partial inhibition of several key targets thereby regulating a particular metabolic network as desired [51]. The multi-targeted drug designing hence can lead to increased therapeutic efficacy in diseases wherein an acquired resistance against traditional chemo- therapeutic drugs may develop [49–51].
Several in silico drug interaction databases are available commercially which aim to provide thorough knowledge related to not only molecular pathways and crystallographic structures of drugs and proteins but also binding targets, chemical properties such as ADMET, IC50 prediction and similar crucial information shedding light on possible drug- target interactions [52]. Databases and software such as DRUGBANK [53], STITCH [54], BindingDB [55], dbEM
and DrugPath help in the prediction of possible targets of a known drug or designing a pharmacological multi-targeted approach against a particular molecular pathway. In our analysis, we identified six epigenetic modulators viz. EPZ 6438, GSK 343, CHR 3996, Mocetinostat, GSK 126 and UNC 1215 as potential multi-targeted therapeutic agents

against Hedgehog signalling pathways. These drugs were seen to have good binding affinity energies with the key regulators of SHH signalling pathways, i.e., SHH, SMO, and GLI. The molecular dynamic simulation studies and free binding energy analysis was further carried out to calculate and validate the binding energies of small molecules (epige- netic modifiers viz., GSK 126,CHR 3996, EPZ 6438, Moce- tinostat, GSK 343 and UNC 1215) and the macromolecules (Shh, Smo and Gli). The binding energy was determined using the values of the gas-phase electrostatic energy (Eele), van der Waals (EvdW), polar (Gpolar) and nonpolar (Gnonpolar) constituent of the proteins and ligands complex. The results as shown in Table 2, depicted that Mocetinostat possessed the free binding energy of − 9.1 kcal/mol when docked with Smoothened, − 9.5 kcal/mol in complex with Gli and
10.1 kcal/mol with Shh respectively.
Similarly, GSK 126 exhibited free energy scores of
11.7 kcal/mol, − 12.9 kcal/mol and − 10.6 kcal/mol when docked with Smoothened, Gli, and Hedgehog. CHR 3996 exhibited the best binding free energy of − 13.6 kcal/mol when docked with Smoothened. Epigenetic modifiers viz., GSK 343 and UNC 1215 also exhibited good free energies post-docking with Smoothened, Gli, and Shh as depicted in Table 2. These results further strengthened the output of the docking analysis where these compounds showed good binding affinity energies with the proteins when compared to standard drugs. The Van der Waals interaction (ΔEvdW) and the electrostatic interaction (ΔEele) in the complex were seen to be favorable for binding, while the polar and the nonpolar solvation terms show unfavorable contributions. Hence overall, the binding free energy calculation indicates that hydrophobic interaction together with hydrogen bonding dominates the binding of epigenetic modifiers to the hedge- hog key proteins viz., Shh, Smo and Gli. Interactions viz., van der Waals and electrostatic interaction (majorly from hydrogen bonding) show favorable contributions, while the solvation component showed unfavorable contribution in the formation of these protein–ligand complexes.
EPZ-6438 (Tazemetostat) identified in our study has
reached clinical trials as a crucial EZH2 inhibitor for the treatment of various malignancies including Synovial

sarcoma [56], INI-1 negative tumors [57] and Rhabdoid Tumors [58]. EZH2, a component of Polycomb Repressive Complex 2 (PRC2) which belongs to the class of histone methyltransferases (HMTs), is overexpressed/mutated in a variety of cancer cells and plays a key role in tumor cell proliferation and also associated with poor cancer prognosis [59]. Studies have shown EPZ-6438 specifically inhibiting EZH2 which prevents the methylation of histone H3 lysine 27 (H3K27) and hence epigenetically regulates transcription [60]. Differential expression of EZH2 has been reported in various cancers including myeloid neoplasia [61], myelod- ysplastic syndrome [62] and some forms of lymphoma [63]. GSK 343, another selective and S-adenosyl methio- nine competitive EZH2 inhibitor identified in our study is being widely studied at both in vitro and in vivo levels as a potential chemotherapeutic agent against several cancers including glioblastoma [64], endometrial cancer [65] oro- pharyngeal cancer [66] and cervical cancer [67]. Reports have shown EZH2 inhibitor GSK 343 can suppress cancer stem cell-like phenotype and even reverse epithelial to mes-
enchymal transition in glioma cells [68].
One of the most widely studied EZH2 inhibitor GSK 126 was seen to exhibit multi-targeted approach against SHH pathway in our analysis. Several reports have shown GSK 126 leading to a decrease in global methylation of histone H3 on lysine 27 and also activation of PRC2 target genes [69].GSK126 is also known to suppress cell migration and angiogenesis via down-regulating Vascular Endothelial Growth Factor-A (VEGF-A) [70] and also potent effects on growth, cell cycle arrest and apoptosis via EZH2 inhibi- tion at in vitro levels in Prostate cancer [71], acute myeloid leukaemia [72], colorectal cancer [73] and small cell lung cancer [74]. GSK126 was also seen to effectively inhibit the proliferation of EZH2 mutant Diffuse large B cell lymphoma (DLBCL) cell lines and even markedly inhibit the growth of EZH2 mutant DLBCL xenografts in mice [75].
Mocetinostat (commercially named as MGCD0103), a benzamide histone deacetylase inhibitor is being clinically tested for various cancers including Hodgkin’s lymphoma [76], follicular lymphoma [77] and acute myelogenous leu- kemia [78].
CHR-3996, a second generation hydroxamic acid-based inhibitor of histone deacetylase is being explored for its anti- neoplastic activity by scientists worldwide [79–81]. CHR 3996, as HDAC inhibitor, brings about accumulation of acetylated histones, induction of chromatin remodelling and eventually selective transcription of tumor suppressor genes [79]. Hence, CHR-3996 can inhibit tumor cell proliferation and even induce apoptosis in some cases [80]. Studies have shown CHR-3996 to upregulate HSP 70 and downregulate anti-apoptotic BCL-2 gene at in vitro levels [81].
Another multi-targeted epigenetic drug identified in our analysis was UNC 1215 which acts as a potent and selective

probe for methyllysine (Kme) reading function of L3MBTL3 which is a member of the malignant brain tumor (MBT) family of chromatin interacting transcriptional repressors [82]. UNC 1215 has been reported to affect the interaction of Kme dependent L3MBTL3 with BCLAF1 and eventu- ally play a regulatory role in DNA damage repair and also apoptosis in some cases. L3MBTL3 is known to play an important role in hematopoiesis and has been reported to be differentially expressed in various cancers [82–84]. As a selective inhibitor of L3MBTL3, UNC 1215 has demon- strated high inhibitory activity via antagonizing the mono and dimethyl lysine reading functions of L3MBTL3 [84].
Our analysis also identified several other epigenetic mod- ulators such as Decitabine (5-Aza-2′-dC) [85], Trichostatin A [86], Resveratrol [87], Butyric acid [88], AR-42 [89] and Genistein [90] (S2 Table) which are currently under clini- cal trials for various cancers however, their role as potential multi-targeted chemotherapeutic epigenetic modulators still need to be explored.

6 Conclusion
Growing evidence have shown the initiation and progres- sion of complex diseases such as cancer can be attributed to multiple genetic and epigenetic aberrations, wherein multi- targeted approach or combination therapy have shown better efficiency in controlling dysregulated disease-related molec- ular pathways and also drug resistance issues. The present study focuses on analyzing an in silico strategy to identify multi-targeted epigenetic drugs to overcome dysregulated SHH signalling. Epigenetic modulators viz. Tazemetostat and GSK 343 (EZH2 inhibitors), CHR 3996 and Moceti- nostat (HDAC inhibitors), GSK 126 (Histone lysine methyl- transferase inhibitor) and UNC 1215 (L3MBTL3 antagonist) were seen to exhibit good binding affinity with key regula- tors of SHH pathway viz. SHH, SMO, and Gli.
Further molecular dynamics simulations (root mean square deviation (RMSD), root mean square fluctuations (RMSF) and radius of gyration (Rg) and MM/PBSA calcu- lations supported the stability of these identified drugs when docked with SHH key proteins. Predicted ADMET proper- ties further proved that these drugs could act as potential chemotherapeutic agents by modulating SHH pathways in the future. Hence, with the advancement in understanding of epigenetic targets and their involvement in the biologi- cal network, epigenetic polypharmacology will eventually be successful in impacting the complex system of human biology thus leading to better disease treatments in the near future. On the whole, our study revealed that the identified epigenetic drugs viz EPZ-6438 (Tazemetostat), GSK 343, CHR 3996, Mocetinostat, GSK 126 and UNC 1215 could be promising candidates as multi-targeted epigenetic drugs

against Hedgehog signalling. Further, in vitro and in vivo studies, as well as clinical trials, are warranted to investigate the therapeutic potential of these identified epigenetic drugs.

Funding This work was supported by grants from the Symbiosis Cen- tre for Research and Innovation (SCRI) and Symbiosis School of Bio- logical Sciences (SSBS); Symbiosis International (Deemed University) (SIU), Lavale, Pune, India and Senior Research INSPIRE Fellowship (SRF) from the Department of Science and Technology, Government of India, to Miss Anshika Nikita Singh.

Compliance with Ethical Standards

Conflict of interest All authors declare that they have no conflict of interest.
Research Involving Human Participants and/or Animals This article does not contain any studies with human participants or animals per- formed by any of the authors.
1. Siegel RL, Miller KD, Jemal A (2018) Cancer statistics. CA Can- cer J Clin 68:7–30
2. You J, Jones P (2012) Cancer genetics and epigenetics: two sides of the same coin? Cancer Cell 22:9–20
3. Hanahan D, Weinberg RA (2011) Hallmarks of cancer: the next generation. Cell 144:646–674
4. Padma VV (2015) An overview of targeted cancer therapy. Bio- medicine 5:19
5. Tsuruo T, Naito M, Fujita TN, Mashima T, Sakamoto H, Haga N et al (2003) Molecular targeting therapy of cancer: drug resist- ance, apoptosis and survival signal. Cancer Sci 94:15–21
6. Lu JJ, Pan W, Hu YJ, Wang YT (2012) Multi-target drugs: the trend of drug research and development. PLoS ONE 7:e40262
7. Mayank VJ (2015) Molecular docking study of natural alkaloids as multi-targeted hedgehog pathway inhibitors in cancer stem cell therapy. Comput Biol Chem 5:737–742
8. Hanna A, Shevde LA (2016) Hedgehog signaling: modulation of cancer properties and tumor mircroenvironment. Mol Cancer 15:1–14
9. Briscoe J, Thérond PP (2013) The mechanisms of Hedgehog sig- nalling and its roles in development and disease. Nat Rev Mol Cell Biol 14:418–431
10. Markant SL, Esparza LA, Sun J, Barton KL, McCoig LM, Grant GA et al (2013) Targeting sonic hedgehog-associated medullab- lastoma through inhibition of Aurora and Polo-like kinases. Can- cer Res 73:6310–6322
11. Aberger F, Hutterer E, Sternberg C, Del Burgo PJ, Hartmann TN (2017) Acute myeloid leukemia—strategies and challenges for tar- geting oncogenic Hedgehog/GLI signaling. Cell Commun Signal 15:1–11
12. Srinath S, Iyengar A, Mysorekar V (2016) Sonic hedgehog in oral squamous cell carcinoma: an immunohistochemical study. J Oral Maxillofac Pathol 20:377
13. Gu D, Schlotman KE, Xie J (2016) Deciphering the role of hedge- hog signaling in pancreatic cancer. J. Biomed Res 30:353–360
14. Abe Y, Tanaka N (2016) The Hedgehog signaling networks in lung cancer: the mechanisms and roles in tumor progression

and implications for cancer therapy. Biomed Res Int. https://doi. org/10.1155/2016/7969286
15. Habib JG, O’Shaughnessy JA (2016) The hedgehog pathway in triple-negative breast cancer. Cancer Med 5:2989–3006
16. Zeng C, Chen T, Zhang Y, Chen Q (2017) Hedgehog signaling pathway regulates ovarian cancer invasion and migration via adhe- sion molecule CD24. J Cancer 8:786–792
17. Gonnissen A, Isebaert S, Haustermans K (2013) Hedgehog signal- ing in prostate cancer and its therapeutic implication. Int J Mol Sci 14:13979–14007
18. Peukert S, Miller-Moslin K (2010) Small-molecule inhibitors of the hedgehog signaling pathway as cancer therapeutics. Chem Med Chem 5:500–512
19. Sheikh A, Alvi AA, Aslam HM, Haseeb A (2012) Hedgehog path- way inhibitors—current status and future prospects. Infect Agents Cancer 7:29
20. Rimkus TK, Carpenter RL, Qasem S, Chan M, Lo HW (2016) Targeting the sonic hedgehog signaling pathway: review of smoothened and GLI inhibitors. Cancers 8:22
21. Jäger T, Ocker M, Kiesslich T, Neureiter E, Neureiter D (2017) Thoughts on investigational hedgehog pathway inhibitors for the treatment of cancer. Expert Opin Investig Drugs 26:133–136
22. Huang YC, Chao KSC, Liao HF, Chen YJ (2013) Targeting sonic hedgehog signaling by compoundsand derivatives from natural products. Evid Based Complement Altern Med 2013:2013
23. Esteller M (2008) Molecular origins of cancer epigenetics in can- cer. N Engl J Med 358:1148–1159
24. Sharma S, Kelly TK, Jones PA (2009) Epigenetics in cancer. Car- cinogenesis 31:27–36
25. deLera AR, Ganesan A (2016) Epigenetic polypharmacology: from combination therapy to multitargeted drugs. Clin Epigenet 8:1–21
26. Benedetti R, Conte M, Iside C, Altucci L (2015) Epigenetic-based therapy: from single- to multi-target approaches. Int J Biochem Cell Biol 69:121–131
27. Csermely P, Ágoston V, Pongor S (2005) The efficiency of multi- target drugs: the network approach might help drug design. Trends Pharmacol Sci 26:178–182
28. Qi Y, Wang D, Wang D, Jin T, Yang L, Wu H et al (2016) HEDD: the human epigenetic drug database. Database 2016:1–10
29. Murugan K, Sangeetha S, Ranjitha S, Vimala A, Al-Sohaibani S, Rameshkumar G (2015) HDACiDB: a database for histone dea- cetylase inhibitors. Drug Des Dev Ther 9:2257–2264
30. Huang Z, Jiang H, Liu X et al (2012) HEMD: an integrated tool of human epigenetic enzymes and chemical modulators for thera- peutics. PLoS ONE 7:1–6
31. Zeng H, Qiu C, Cui Q (2015) Drug-Path: a database for drug- induced pathways. Database 2015:1–4
32. Wagner AH, Coffman AC, Ainscough BJ, Spies NC, Skidmore ZL, Campbell KM et al (2016) DGIdb 2.0: mining clinically rele- vant drug-gene interactions. Nucleic Acids Res 44:D1036–D1044
33. Agyeman A, Jha BK, Mazumdar T, Houghton JA (2014) Mode and specificity of binding of the small molecule GANT61 to GLI determines inhibition of GLI-DNA binding. Oncotarget 5:4492
34. Chen JK, Taipale J, Cooper MK, Beachy PA (2002) Inhibition of Hedgehog signaling by direct binding of cyclopamine to Smooth- ened. Genes Dev 16:2743–2748
35. Kim S, Thiessen PA, Bolton EE, Chen J, Fu G, Gindulyte A et al (2016) PubChem substance and compound databases. Nucleic Acids Res 44:D1202–D1213
36. O’Boyle NM, Banck M, James CA, Morley C, Vandermeersch T, Hutchison GR (2011) Open Babel: an open chemical toolbox. J Cheminform 3:1–14
37. Thompson MA (2004)Molecular docking using ArgusLab, an efficient shape-based search algorithm and the AScore scoring function. In: ACS Meeting, Philadelphia

38. Pavletich N, Pabo C (1993) Crystal structure of a five-finger GLI-DNA complex: new perspectives on zinc fingers. Science 261:1701–1707
39. Pepinsky RB, Rayhorn P, Day ES, Dergay A, Williams KP, Galdes A et al (2000) Mapping sonic hedgehog-receptor interactions by steric interference. J Biol Chem 275:10995–11001
40. Weierstall U, James D, Wang C, White TA, Dingjie W, Wei L et al (2014) Lipidic cubic phase injector facilitates membrane protein serial femtosecond crystallography. Nat Commun 5:3309
41. Berman HM, Westbrook J, Feng Z, Gilliland G, Bhat TN, Weis- sig H et al (2000) The Protein Data Bank. Nucleic Acids Res 28:235–242
42. Volkamer A, Kuhn D, Rippmann F, Rarey M (2012) Dogsites- corer: a web server for automatic binding site prediction, analysis and druggability assessment. Bioinformatics 28:2074–2075
43. Pettersen EF, Goddard TD, Huang CC, Couch GS, Greenb- latt DM, Meng EC (2004) UCSF Chimera—a visualization system for exploratory research and analysis. J Comput Chem 25(13):1605–1612
44. Humphrey W, Dalke A, Schulten K (1996) VMD—visual molecu- lar dynamics. J Mol Graph 14:33–38
45. Philips JC, Braun R, Wang W, Gumbart J, Tajkhorshid E, Villa E et al (2005) Scalable molecular dynamics with NAMD. J Comput Chem 26:1781–1802
46. Brooks BR, Brooks CL III, Mackerell AD, Nilsson L, Petrella RJ, Roux B et al (2009) CHARMM: the biomolecular simulation program. J Comput Chem 30:1545–1615
47. Cui F, Yang K, Li Y (2015) Investigate the binding of catechins to trypsin using docking and molecular dynamics simulation. PLoS ONE 10(5):e0125848
48. Lipinski CA, Lombardo F, Dominy BW, Feeney PJ (1997) Experi- mental and computational approaches to estimate solubility and permeability in drug discovery and development settings. Adv Drug Deliv Rev 23:3–25
49. Talevi A (2015) Multi-target pharmacology: possibilities and limi- tations of the “skeleton key approach” from a medicinal chemist perspective. Front Pharmacol 6:205
50. Srinivas Reddy AS, Zhang S (2013) Polypharmacology: drug discovery for the future. Expert Rev Clin Pharmacol 6:41–47
51. Franci G, Miceli M, Altucci L (2010) Targeting epigenetic net- works with polypharmacology: a new avenue to tackle cancer. Epigenomics 2:731–742
52. Zhang W, Pei J, Lai L (2017) Computational multitarget drug design. J ChemInf Model 57:403–412
53. Wishart DS, Knox C, Guo AC, Cheng D, Shrivastava S, Tzur D et al (2008) DrugBank: a knowledgebase for drugs, drug actions and drug targets. Nucleic Acids Res 36:901–906
54. Kuhn M, von MeringC Campillos M, Jensen LJ, Bork P (2008) STITCH: interaction networks of chemicals and proteins. Nucleic Acids Res 36:684–688
55. Liu T, Lin Y, Wen X, Jorissen RN, Gilson MK (2007) BindingDB: a web-accessible database of experimentally determined protein- ligand binding affinities. Nucleic Acids Res 35:198–201
56. Schoffski P, Agulnik M, Stacchiotti S, Davis LE, Villalobos VM, Italiano A (2017) Phase 2 multicenter study of the EZH2 inhibitor tazemetostat in adults with synovial sarcoma (NCT02601950). J Clin Oncol 35:11057
57. Agulnik M, Tannir NM, Pressey JG, Gounder MM, Cote GM, Roche M et al (2016) A phase II, multicenter study of the EZH2 inhibitor tazemetostat in adult subjects with INI1-negative tumors or relapsed/refractory synovial sarcoma. J Clin Oncol. https://doi. org/10.1200/JCO.2016.34.15_suppl.TPS11071
58. Knutson SK, Warholic NM, Wigle TJ, Klaus CR, Allain CJ, Rai- mondi A et al (2013) Durable tumor regression in genetically altered malignant rhabdoid tumors by inhibition of methyltrans- ferase EZH2. Proc Natl Acad Sci USA 110:7922–7927

59. Shen JK, Cote GM, Gao Y, Choy E, Mankin HJ, Hornicek FJ et al (2016) Targeting EZH2-mediated methylation of H3K27 inhibits proliferation and migration of Synovial Sarcoma in vitro. Sci Rep 6:srep25239
60. Wen S, Wang J, Liu P, Li Y, Lu W, Hu Y et al (2018) Novel combination of histone methylation modulators with therapeutic synergy against acute myeloid leukemia in vitro and in vivo. Can- cer Lett 28:35–45
61. Nikoloski G, Langemeijer SMC, Kuiper RP, Knops R, Massop M, Tonnissen ERLTM et al (2010) Somatic mutations of the histone methyltransferase gene EZH2 in myelodysplastic syndromes. Nat Genet 42:665–667
62. Knutson SK, Kawano S, Minoshima Y, Warholic NM, Huang KC, Xiao Y et al (2014) Selective inhibition of EZH2 by EPZ-6438 leads to potent antitumor activity in EZH2-mutant non-Hodgkin Lymphoma. Mol Cancer Ther 13:842–854
63. deVries NA, Hulsman D, Akhtar W, de Jong J, Miles DC, Blom M et al (2015) Prolonged EZH2 depletion in glioblastoma causes a robust switch in cell fate resulting in tumor progression. Cell Rep 10:383–397
64. Ihira K, Dong P, Xiong Y, Watari H, Konno Y, Hanley SJ et al (2017) EZH2 inhibition suppresses endometrial cancer progres- sion via miR-361/Twist axis. Oncotarget 8:13509–13520
65. IdrisS Lindsay C, Kostiuk M, Andrews C, Cote DW, O’Connell DA et al (2016) Investigation of EZH2 pathways for novel epige- netic treatment strategies in oropharyngeal cancer. J Otolaryngol Head Neck Surg 45:54
66. Ding M, Zhang H, Li Z, Wang C, Chen J, Shi L et al (2015) The polycomb group protein enhancer of zeste 2 is a novel thera- peutic target for cervical cancer. Clin Exp Pharmacol Physiol 42:458–464
67. Yu T, Wang Y, Hu Q, Wu W, Wu Y, Wei W et al (2017) The EZH2 inhibitor GSK343 suppresses cancer stem-like phenotypes and reverses mesenchymal transition in glioma cells. Oncotarget 8:98348–98359
68. Geng J, Li X, Zhou Z, Wu CL, Dai M, Bai X (2015) EZH2 pro- motes tumor progression via regulating VEGF-A/AKT signaling in non-small cell lung cancer. Cancer Lett 359:275–287
69. Chen YT, Zhu F, Lin WR, Ying RB, Yang YP, Zeng LH (2016) The novel EZH2 inhibitor, GSK126, suppresses cell migration and angiogenesis via down-regulating VEGF-A. Cancer Chemother Pharmacol 77:757–765
70. Lin W, Chen Y, Zeng L, Ying R, Zhu F (2016) Effect of a novel EZH2 inhibitor GSK126 on prostate cancer cells. Zhejiang Da XueBao Yi Xue Ba 45:356–363
71. Swords RT, Perez A, Rodriguez A, Watts JM, Schenk T, Vargas F et al (2015) In acute myeloid leukemia (AML), targeting the histone methyltransferase EZH2 promotes differentiation, impairs clonogenic survival and augments the anti-leukemic effects of the retinoid, all-trans-retinoic acid (ATRA). Blood 126:3786
72. Koppens MAJ, Bounova G, Cornelissen-steijger P (2016) Large variety in a panel of human colon cancer organoids in response to EZH2 inhibition. Oncotarget 1:1–5
73. Sato T, Kaneda A, Tsuji S, Isagawa T, Yamaoto S, Fujita T et al (2013) PRC2 overexpression and PRC2-target gene repression relating to poorer prognosis in small cell lung cancer. Sci Rep 3:1–9
74. McCabe MT, Ott HM, GanjiG Korenchuk S, Thompson C, Van Aller GS et al (2012) EZH2 inhibition as a therapeutic strategy for lymphoma with EZH2-activating mutations. Nature 492:108–112
75. Younes A, Oki Y, Bociek RG, Kuruvilla J, Fanale M, Neelapu S et al (2011) Phase II study of mocetinostat (MGCD0103) in patients with relapsed and refractory classical Hodgkin lym- phoma. Lancet Oncol 12:1221–1228
76. Batlevi CL, Crump M, Andreadis C, Rizzieri D, Assouline SE, Fox S et al (2017) A phase 2 study of mocetinostat, a histone

deacetylase inhibitor, in relapsed or refractory lymphoma. Br J Haematol 178:434–441
77. Fredly H, Gjertsen BT, Bruserud Ø (2013) Histone deacetylase inhibition in the treatment of acute myeloid leukemia: the effects of valproic acid on leukemic cells, and the clinical and experimen- tal evidence for combining valproic acid with other antileukemic agents. Clin Epigenetics 5:1–13
78. Moffat D, Patel S, Day F, Belfield A, Donald A, Rowlands M et al (2010) Discovery of 2-(6-{[6-fluoroquinolin-2-yl)methyl] amino} bicyclo[3.1.0]hex-3-yl)-N-hydroxypyrimidine-5-carboxamide (CHR-3996), a class I selective orally active histone deacetylase inhibitor. J Med Chem 53:8663–8678
79. Smith EM, Zhang L, Walker BA, Davenport EL, Li Aronson, Krige D et al (2015) The combination of HDAC and amino peptidase inhibitors is highly synergistic in myeloma and leads to disruption of the NF κB signalling pathway. Oncotarget 6:17314–17327
80. Banerji U, Van Doorn L, Papadatos-PastosD Kristeleit R, Debnam P, Tall M et al (2012) A phase I pharmacokinetic and pharma- codynamic study of CHR-3996, an oral class I selective histone deacetylase inhibitor in refractory solid tumors. Clin Cancer Res 18:2687–2694
81. Milosevich N, Hof F (2016) Chemical inhibitors of epigenetic methyllysine reader proteins. Biochemistry 55:1570–1583
82. Xu T, Park S, Giaimo BD, Hall D, Ferrante F, Ho DM et al (2017) RBPJ/CBF1 interacts with L3MBTL3/MBT1 to promote repres- sion of Notch signaling via histone demethylase KDM1A/LSD1. EMBO J 36(21):3232–3249
83. Ning B, Li W, Zhao W, Wang R (2015) Targeting epigenetic regulations in cancer. Acta Biochim Biophys Sin (Shanghai) 48:97–109
84. James LI, Korboukh VK, Krichevsky L, Baughman BM, Herold JM, Norris JL et al (2013) Small-molecule ligandsof methyl-lysine

binding proteins: optimization of selectivity for L3MBTL3. J Med Chem 56:7358–7371
85. Kantarjian HM, Thomas XG, Dmoszynska A, Wierzbowska A, Mazur G, Mayer J et al (2012) Multicenter, randomized, open- label, phase III trial of decitabine versus patient choice, with phy- sician advice, of either supportive care or low-dose cytarabine for the treatment of older patients with newly diagnosed acute myeloid leukemia. J Clin Oncol 30:2670–2677
86. Mottamal M, Zheng S, Huang TL, Wang G (2015) Histone deacet- ylase inhibitors in clinical studies as templates for new anticancer agents. Molecules 20:3898–3941
87. Tomé-Carneiro J, Larrosa M, González-Sarrías A, Tomás-Bar- berán F, García-Conesa M, Espín J (2013) Resveratrol and clinical trials: the crossroad from in vitro studies to human evidence. Curr Pharm Des 19:6064–6093
88. Borycka-Kiciak K, Banasiewicz T, Rydzewska G (2017) Butyric acid-a well known molecule revisited. Prz Gastroenterol 12:83–89
89. Sborov DW, Canella A, Hade EM, Mo X, Khountham S, Wang J et al (2017) A phase I trial of the HDAC inhibitor AR-42 in patients with multiple myeloma and T- and B- cell lymphomas. Leul Lymphoma 58:2310–2318
90. Lazarevic B, Boezelijn G, Diep LM, Kvernrod K, Ogren O, Ram- berg H et al (2011) Efficacy and safety of short-term genistein intervention in patients with localized prostate cancer prior to radical prostatectomy: a randomized, placebo-controlled, double blind Phase 2 clinical trials. Nutr Cancer 63:889–898
Publisher’s Note Springer Nature remains neutral with regard Mocetinostat to jurisdictional claims in published maps and institutional affiliations.

Leave a Reply

Your email address will not be published. Required fields are marked *


You may use these HTML tags and attributes: <a href="" title=""> <abbr title=""> <acronym title=""> <b> <blockquote cite=""> <cite> <code> <del datetime=""> <em> <i> <q cite=""> <strike> <strong>