We show that the binding of AR antagonists at the substrate binding pocket alter the dynamic fluctuations of H12, thereby disrupting the structural integrity of the agonistic conformation of AR. Two antagonists, enzalutamide and apalutamide, induce considerable structural changes to the agonist conformation of LBD, when bound close to H12 of AR LBD. When the antagonists bind to the pocket with different orientations having close contact with H11, no significant conformational changes were observed, suggesting the AR remains in the functionally activated (agonistic) state. The simulations on a drug resistance mutant F876L bound to enzalutamide demonstrated that the mutation stabilizes the agonistic conformation of AR LBD, which compromises the efficacy of the antagonists. Principal component analysis (PCA) of the structural fluctuations shows that the binding of enzalutamide and apalutamide induce conformational fluctuations in the AR, which are markedly different from those caused by the agonist as well as another antagonist, bicalutamide. These fluctuations could only be observed with the use of aMD.The androgen receptor (AR) is critical in the progression of prostate cancer (PCa). Small molecule antagonists that bind to the ligand binding domain (LBD) of the AR have been successful in treating PCa. However, the structural basis by which the AR antagonists manifest their therapeutic efficacy remains unclear, due to the lack of detailed structural information of the AR bound to the antagonists. We have performed accelerated molecular dynamics (aMD) simulations of LBDs bound to a set of ligands including a natural substrate (dihydrotestosterone), an agonist (RU59063) and three antagonists  as well as in the absence of ligand ( In the early phase of PCa, androgen ablation therapy or chemotherapy with antiandrogens are established as the standard therapies. However, castration-resistant prostate cancer (CRPC) occurs after a few years of the standard treatment, which becomes more aggressive and ultimately lethal5. CRPC is still predominantly dependent upon the AR signaling pathway for its progression. The cellular mechanisms to resist the existing therapies include the AR gene amplification, the AR mutations, the androgen-independent AR activation and the appearance of AR splice variants6. The second generation AR antagonists, enzalutamide (brand name: Xtandi) and apalutamide (brand name: Erleada) are approved for clinical use in CRPC, which show much improved therapeutic efficacy over the first generation antagonists8.Androgen Receptor (AR) is a ligand-activated transcription factor that regulates the expression of many target genes that are crucial for male sexual differentiation and development. The AR plays a critical role in the development and proliferation of prostate cancer (PCa)9. Upon binding of a ligand to the ligand binding pocket in the LBD, NRs undergo conformational changes that are associated with the activation function-2 (AF-2). Biochemical evidence suggests that upon binding of an agonist such as testosterone, the repositioning of H12 and the formation of hydrophobic cleft follow, that promote the interaction of the AR with the LXXLL motif of co-activators11. On the contrary, the binding of antagonists abolishes the recruitment of co-activators by preventing H12 from folding over into the space that facilitates AR interactions with co-activators. The structural details of several NR antagonists bound to the LBD of other NR have been reported. An estrogen receptor alpha (ERα) antagonist, raloxifene, induces repositioning of H12 by its bulky side chain and disrupts the overall surface topography of AF-212. Despite many attempts, the structural basis of the AR antagonism is not fully understood. There are over 90 x-ray crystal structures of the human AR LBD deposited in Protein Data Bank. However, all of the existing crystal structures represent functionally activated ARs either wild type bound to agonists or drug-resistant mutant ARs bound to antagonists , consists of two well-defined structural domains, a DNA binding domain and a C-terminal ligand binding domain (LBD); there is also a highly variable N-terminal domain. The LBD has been a major interest in the drug discovery efforts, where most ligands and cofactors bind to. proposed that the sulfonyl group of R-bicalutamide pushes W741 from its activating position, thereby achieving the observed antagonism. They also showed that the decreased activity of hydroxyflutamide (HFT) on wild type AR is caused by the weaker interaction between HFT and Met895 compared to T877A mutant AR23. Zhou and co-workers employed replica exchange molecular dynamics (REMD) simulations to study the structural changes of the WT and the T877A mutant ARs bound to HFT. Conformations of the H12 sampled from the REMD simulations were shown to have a correlation with the agonistic/antagonistic activities of the ligand24. By performing MD simulations and quantum mechanics (QM) calculations, Osguthorpe and Hagler demonstrated that bicalutamide can access an additional binding pocket (B-site) of the AR LBD, which results in the instability of H1225. Based on 10 ns MD simulations, Balbas et al. showed F876L mutation in AR allows enzalutamide to act as an agonist26. Xu et al. analyzed the structural stability of AR upon binding of dihydrotestosterone (DHT) and co-activator (SRC), based on 20 ns MD simulations27. Liu et al. demonstrated that the ligand binding affects the AR–coactivator interactions as well as the allosteric regulation pathway from ligands to co-activator, based on MD simulations and free energy calculations28. By using homology modeling, Wang et al. constructed a putative antagonistic structure of the AR and proposed the role of H12 in the molecular function of the AR29. Duan et al. carried out microsecond unbiased MD simulations as well as bias-exchange metadynamics MD simulations to quantify the conformational characteristics of antagonist bound AR LBD30. Recently, Hu et al. summarized the recent advances in the discovery of AR antagonists using computer-aided drug design approaches31. The computational analyses comprehensively support the mechanistic hypothesis that the AR antagonists cause conformational changes to the AR LBD, which can be correlated with the antagonism of the AR. However, to our best knowledge, the influences of the second-generation antagonists such as enzalutamide and apalutamide to the conformational dynamics of the AR LBDs have not been elucidated in detail. Moreover, a comprehensive comparison of the conformational changes in AR LBDs bound to the agonists, the first- and the second-generation antagonists has not been carried out. In addition, a structure activity-relationship that is readily applicable to the discovery of novel AR antagonists is in dire need for the rapid screening of next-generation AR antagonists.Computational studies have elucidated the molecular basis of AR antagonism. Bisson et alapo) using accelerated MD simulations. We found that the binding of agonists stabilizes the AR LBDs by retaining the agonistic conformation, that are in agreement with the previous experimental and computational results. The simulations revealed that the wild type AR bound to antagonist showed distinctive conformational dynamics depending on the initial pose and the nature of the antagonists. We explored two distinct binding poses of the N-methylbenzamide moiety of enzalutamide in the AR. The results show that the structural changes of α-helix 12 (H12) only occurs when the N-methylbenzamide of enzalutamide binds proximal to the H12, but not when it directs toward α-helix 11 (H11). We also found that the F876L mutation of the AR stabilizes the agonistic conformation, regardless of the conformation of enzalutamide in the binding pocket. Binding of bicalutamide to the wild type AR destabilizes the H1–H3 loop significantly, unlike the case of enzalutamide. The comparison of two distinct conformational changes induced by bicalutamide and enzalutamide suggested two different mode of antagonistic actions, in spite of their structural similarities. The finding suggested a new mechanistic scenario for AR antagonism caused by the second-generation antagonists, where a large conformational change in H12 directly hampers the binding of the cofactors to AR LBDs. Finally, we performed principal component analysis (PCA) on the conformational dynamics of the AR in the presence of agonists, antagonists, and the absence of ligand. The analysis suggested that one can correlate the efficacy of each ligand with the conformational fluctuation of LBD sampled from aMD simulations.Herein, we examined the detailed structural changes of the AR LBDs caused by the binding of antagonists by means of computational modeling and molecular dynamics (MD) simulations of the various ligands bound to the AR LBD. We have studied conformational dynamics on the LBDs of the wild type and F876L mutant AR in the presence of the first and the second-generation antagonists, the agonists, and in the absence of ligands  is a method to greatly enhance conformational sampling of biomolecules. Using aMD can facilitate conformational transitions of proteins over 500-fold than the conventional MD simulations. The aMD method has been successfully applied to study conformational dynamics of proteins and protein–ligand complexes36. We monitored the conformational changes of the AR LBD, particularly the pockets near H11 and H12 as well as the cofactor binding region near the ligand binding pocket during aMD simulations. To analyze further the structural fluctuations of the AR LBD simulated, principal component analyses (PCA) were employed to examine the relationships of conformations observed when agonists and antagonists bind to the AR38.We initially carried out 250 ns conventional MD simulations with the bound AR ligands. The backbone root-mean-square deviation (RMSD) variation of the AR bound to ENZ was small (< 2 Å), and there were no notable changes from the starting agonistic conformation Figure . As such16 and DHT19. The ligands were removed for the simulations of apo AR. We conducted constant temperature MD simulations of 250 ns duration and then commenced aMD simulations for 250 ns, starting from the last structure of the cMD simulations  were calculated after superimposing all the trajectories to the initial conformation  and a synthetic agonist (RU5) maintained the agonistic conformation after 250 ns of aMD simulations Figs.  and S2. conformation 1 and conformation 2, respectively. Starting from the two conformers, we explored the possible conformations of ENZ in the binding pocket of AR LBD based on the molecular docking simulations and the top scored binding pose of ENZ was selected as a starting structure for MD simulations. For conformation 1, the N-methylbenzamide tail stretches toward H11  that was co-crystallized with the AR LBD16 and that of BCA proposed by Osguthorpe and Hagler25. In the following, we demonstrate that the binding of ENZ in conformation 2 induces conformational changes in the surrounding regions of the binding pocket of AR LBD.We examined plausible binding modes of ENZ in the ligand binding pocket of AR, as high resolution structural information of the AR LBD bound to ENZ is not available. In the beginning, we explored viable conformers of ENZ by means of quantum mechanical calculations. As the head group (benzonitrile) and the core group (thiohydantoin) can rotate across the C–N bond connecting the two functional moieties, multiple energetic minimums were expected. Our quantum chemical calculations demonstrated that the two preferred conformers differ in Gibbs free energy by 1.5 kcal/mol Fig. , suggestconformation 2 large conformational changes of the AR occur as shown in Fig. conformation 2 increased to > 3 Å after 10 ns and reached to 4.5 Å in 50 ns. From aMD simulations using another crystal structure of AR LBD (PDB code: 2PIP), we also found the backbone RMSD increased to 2.5 Å  in the end of C-terminus, showed significant fluctuations in the course of aMD simulations , the angle began to decrease to 50° after 70 ns from the initiation of simulations, that coincided with the onset of increment in RMSD to > 3.0 Å.Figure  Figures  and S4.  Figures . From thconformation 2. In the initial agonistic structure, H12 is located in the close vicinity of the ligand binding pocket surrounding H4 and H11 (Cα distances of 7–11 Å from H4). During the aMD simulation, the N-methylbenzamide moiety of ENZ extends toward H12 that results in a large displacement of the helix from the initial position that covers the ligand binding pocket (Cα distances of 9–15 Å from H4). The bulky and hydrophobic side chains of H12  facing the binding pocket were influenced by ENZ. Repositioning of H12 causes a dynamic movement of the H11–H12 loop and displacement of F-domain composed of β-sheet as well as the flexible loop. In short, the binding of ENZ in conformation 2 repositions H12 by repulsive interactions with the bulky and hydrophobic side chains on H12.Figure 43. We performed aMD simulations of the AR F876L mutant in the presence of ENZ in conformation 1 and 2 and in the absence of the ligands, as to understand the origin of drug resistance caused by the mutation. For all simulations, we observed RMSDs < 2.5 Å from the initial agonistic structure , BCA and ENZ, respectivelye Figure A. Unlikes Figure B. The bi1 Figure C. Intere3, which is 36% larger than that of the wild type AR (74 ± 26 Å3), which were measured by POcket Volume MEasurer (POVME) program44. The increased volume may reduce the steric demand to accommodate the tail group of ENZ in conformation 2. In the F876L mutant, the gem-dimethyl of ENZ in conformation 2 is placed toward H3, H11 and the end of H6, resulting in a repositioning of benzamide tail of ENZ into H11  is a nonsteroidal AR antagonist known as the predecessor of ENZ. BCA has 5–8 times lower affinity to the AR than ENZ despite the structural similarity of the head group. The AR inhibitory mechanisms of BCA is primarily the blocking the binding of androgens to ARconformation 2 is 5.6 kcal/mol lower in Gibbs free energy than conformation 1. Of note, S1, the parent compound of BCA, was co-crystallized with the AR with the 4-fluorobenzenesulfonyl group positioned toward H1216. These findings strongly suggested that BCA binds to the AR in a similar orientation to that of S1. As such, in the following MD simulations, we considered the conformation BCA in the binding pocket of AR, which resembles that of S1 . Figure Based on our computational results, we concluded that the binding of BCA to the AR LBD inhibits the binding of androgens by inducing destabilization of H1–H3 loop and a tilt of H3 as well as nudging H12 outward, whereas ENZ and APL antagonize the AR by significantly displacing H12. The conformation of BCA is in a bent shape formed by chiral center bearing a methyl and a hydroxyl group, and a benzene sulfonyl moiety. By contrast, ENZ and APL have a thiohydantoin ring at the core that connects the benzyl (pyridyl) head and the benzamide tail group via two nitrogen atoms on the ring. The rigid core and extended tail of ENZ and APL enable the conformations of the ligands to maintain the linearity during the aMD simulations, which result in the repositioning of H12. From these observations, we speculate that the core of the two antagonists (ENZ and APL) can modulate conformational dynamics of the AR LBD, which can explain the improved efficacies of ENZ and APL to the wild type AR than BCA.apo. Prior to the computations of principal components, the backbone trajectories were aligned to an X-ray structure of the AR (PDB code: 2AXA), so as to remove the translation-rotational motions of the protein. We then calculated the projection of each aMD trajectory to the computed PCs, as shown in Fig. apo were captured by the second principal component (PC2); the fluctuations of AR bound to BCA were characterized by the third principal component (PC3).Figure The projection of aMD trajectories of drug resistant mutants bound to antagonists confirmed that the mutations minimizes the conformational changes in the agonistic conformation of AR. Using the computed PCs based on the aMD simulations of WT AR, we projected the backbone trajectories of W741L bound to BCA and F876L bound to ENZ Fig. b. The prapo.The direction of the backbone dynamics in each PC is also informative. Figure We demonstrate the utility of PCA as a measure of the structure–activity relationship (SAR) of a ligand of AR. We plot the projections of aMD trajectories of AR bound to an agonist (RU5) and an antagonist (APL), as shown in Fig. apo). We carried out > 250 ns aMD simulations of the ligand bound AR LBD complexes and the AR LBD in apo, initiated from available agonistic conformations of AR LBD. With the aMD simulations, we were able to expedite the conformational changes in the AR LBDs and to observe the influence of ligands on AR LBD that provides insights into how antagonists destabilize the AR LBD. A large and differential repositioning of H12 manifests the influence of ENZ and APL to the WT AR. The N-methylbenzamide tail of ENZ and APL is a salient structural element that induces repulsive interactions with the bulky and hydrophobic side chains of H12, leading to the destabilization of H12. The drug resistant mutations (F876L and W741L) suppress the influence of the antagonists by stabilizing the agonistic conformation of the AR. The simulations of BCA bound to AR show less pronounced repositioning of H12 but rather significant fluctuations in the H1–H3 loop. These distinct conformational changes caused by the first and the second generation AR antagonists were observed only from the aMD simulations, but not from conventional MD simulations. PCA results account for the conformational dynamics obtained from the aMD trajectories of the AR LBDs. These findings indicate that aMD simulations can guide future drug discovery of new-generation AR antagonists.We have explored the conformational dynamics of the AR bound to the agonists (DHT and RU5), the antagonists , and in the absence of the ligand  of the ligands of AR will need further validations, which are underway in our lab.Among the intriguing questions to be addressed in the future, the detailed interactions between the selective ligands and the AR LBDs are paramount. The detailed orientation of the tail group of ENZ and APL in the binding pocket of AR LBD remains elusive. Our computational analyses suggested the N-methylbenzamide group of ENZ and APL oriented toward H12 causes displacement of the helix, but we were not able to exclude the other possible orientation to occur in 46. The optimized ligands were docked to the each PDB structure using Autodock Vina that uses a scoring function combined knowledge-based potentials and empirical approach to rank ligands docked to protein structures. Protein receptors and ligands were prepared using default criteria from AutoDock Vina47. Receptors were treated as rigid, and the ligands were treated as a flexible molecule with active rotatable bonds. To perform molecular docking, all hydrogen atoms were added to each protein and ligand of which coordinate files were generated as PDBQT file by merging nonpolar hydrogen atoms of the AR and calculating Gasteiger charges. The search space for docking was determined based on the three dimensional dimension of the original ligand from the crystal structure. A grid box for binding site was set as 20, 24 and 20 in the three dimensions  using 1.0 Å grid spacing. The predicted binding affinity of an agonist (FHM) was − 10.6 kcal/mol when we used 2AXA as the agonistic conformation. In each docking experiment, the pose with best calculated binding affinity  was selected for further MD simulations. The docking simulations of the other ligands were performed with same condition as described above.The x-ray crystal structures of the AR LBD (2AXA and 2PIP) were retrieved from Protein Data Bank for the simulation. 2AXA is the structure of synthetic agonist (S1) bound wild type AR and 2PIP is co-crystalized with DHT and BF3 inhibitor (K10) and both were used as a starting structure. For the mutant AR, the residue 876 was mutated from phenylalanine to leucine using mutagenesis wizard in PyMOL. The structure of bicalutamide (BCA) was built based on the co-crystalized S1 by replacing the ether linkage with the sulfonyl group and the nitro group with cyano group. The other ligand structures were virtually generated since no structures of the AR bound to antagonists have been determined. Each ligand was optimized with density functional theory (DFT) at the B3LYP/6-31G* using Gaussian 09 software48. The Amber FF99SB force field was used for the proteins49. The generalized Amber Force Field (GAFF) was used to parameterize the ligands , enzalutamide (ENZ) and apalutamide (APL)). Partial atomic charges on each ligand were determined by the quantum mechanical (QM) calculations based on the Merz-Singh-Kollman algorithm using Gaussian 09. Initially, each ligand is docked in the ligand binding pocket manually. Each protein and ligand complex was immersed in a rectangular solvation box filled TIP3P water molecules, with a 15 Å margin from the solvation boundary50. Monovalent counter ions (Na+ or Cl−) were added to achieve electrostatic neutrality of the systems. Each solvated system consists of approximately 48,000 atoms. The second step of the preparatory steps was to equilibrate the solvated system. The equilibration steps were initiated by energy minimizations and then equilibrating each solvated system at 300 K using the NVT ensemble for 2 ns. Berendsen thermostat was used to control the temperature at the desired value, and then the isobaric ensemble (NPT) was used for next 4 ns. Extended system algorithm was used to control the system pressure at 1 atm. The coordinates of the carbon atoms were restrained during the first 6 ns equilibration period. A 250 ns equilibration period followed, without any restraint of the atomic coordinates. Langevin thermostat with a small friction coefficient (5 ps−1) and a Monte Carlo barostat were used to keep the temperature at 300 K, and the pressure at 1 atm throughout the equilibration period. For the entire equilibration steps, the time integration step was 2 fs. The SHAKE algorithm was employed to restrain all hydrogens to heavy atoms bond distances. The particle mesh EWALD method accelerated the computation of long-range electrostatic interactions.Molecular mechanics parameters were constructed using the Amber14 software packageAccelerated MD enhances the conformational sampling of the protein complex by adding a non-negative boost potential to the system. We initially carried out 250 ns MD simulations of the LBDs bound to the ligands. The backbone root-mean-square deviation (RMSD) from the initial conformation were < 2.0 Å Figure . Moreove51. The four control parameters of the aMDs were determined as following.52. We note that we have not attempted to construct potential energy profile of the conformational ensemble, as the original implementation of aMD methodology suffers from difficulty in reconstruction of the unbiased energy surface. In order to obtain proper conformational free energy surface of AR-antagonists systems, it is desirable to perform the other potential approaches such as Gaussian accelerated molecular dynamics (GaMD)53 for the future investigation.Once MD simulations were finished, trajectories were analyzed using the AmberTools14 software package. The backbone root-mean square deviations (RMSDs) between the simulated aMD trajectories and the initial PDB structures and the root-mean square fluctuations (RMSFs) per residue over the time were computed using VMD and plugin54. Specifically, the diagonalization of interatomic correlation matrix results in characteristic modes of conformation motions. Each mode represents an orthogonal direction of global conformational motion of a protein, with an eigenvalue that quantifies the relative significance of the motion. A few large eigenvalue modes are of specific interest, as they represent significant conformational changes during the simulations. To process the aMD trajectories for PCA, the 250 ns MD trajectories of the ARs bound to the agonists and antagonists and apo AR were reoriented and aligned to the initial x-ray structure of the AR. PCA were performed for Cα atoms on snapshots at 250 ps intervals. MDAnalysis software55 was used to compute the first three principal components.To understand conformational dynamics of AR bound to agonists and antagonists, principal component analysis (PCA) was performed with trajectories extracted from aMD simulations. With PCA, one can identify essential coordinates that describe the global motion of proteinsSupplementary Information.


Prostate cancer is the second most frequent cancer diagnosed in men worldwide. Localized disease can be successfully treated, but advanced cases are more problematic. After initial effectiveness of androgen deprivation therapy, resistance quickly occurs. Therefore, we aimed to investigate the role of Hedgehog-GLI (HH-GLI) signaling in sustaining androgen-independent growth of prostate cancer cells. We found various modes of HH-GLI signaling activation in prostate cancer cells depending on androgen availability. When androgen was not deprived, we found evidence of non-canonical SMO signaling through the SRC kinase. After short-term androgen deprivation canonical HH-GLI signaling was activated, but we found little evidence of canonical HH-GLI signaling activity in androgen-independent prostate cancer cells. We show that in androgen-independent cells the pathway ligand, SHH-N, non-canonically binds to the androgen receptor through its cholesterol modification. Inhibition of this interaction leads to androgen receptor signaling downregulation. This implies that SHH-N activates the androgen receptor and sustains androgen-independence. Targeting this interaction might prove to be a valuable strategy for advanced prostate cancer treatment. Also, other non-canonical aspects of this signaling pathway should be investigated in more detail and considered when developing potential therapies. With the aid of prostate-specific antigen (PSA) screening most prostate cancer cases are diagnosed and treated while the disease is still localized. However, there are still many patients that develop high-risk localized, locally advanced, or metastatic cancer2. Since androgen receptor (AR) signaling is the main pathway ensuring prostate cancer cell growth, androgen deprivation therapy (ADT) has been the standard treatment option for patients with advanced forms of prostate cancer. Even though this therapy is effective initially, most patients progress to a poor-prognosis stage called castration-resistant prostate cancer (CRPC). A major characteristic of this stage is that despite the low levels of androgen, AR signaling continues to be activated through alternative mechanisms and promotes the growth of CRPC3. Several AR-dependent and independent mechanisms leading to CRPC have been proposed. AR-dependent mechanisms include AR mutation or overexpression, expression of constitutively active AR splice variants, altered expression of AR co-regulators to non-canonical AR transactivation, all leading to increased AR activity4. Several AR-independent mechanisms involving signaling pathways able to bypass the AR, or the existence of AR-negative prostate cancer stem cells, ultimately leading to the development of CRPC5 have also been identified. To date none of these mechanisms can completely account for the progression to CRPC. Apparently, additional research is needed to elucidate the specificities of these mechanisms that could lead to the development of more effective treatment.Prostate cancer is the second most frequent cancer in men worldwide6. Aberrant activation in adult cells has been linked with the development of various tumors, including prostate cancer11. In mammals, signaling is initiated by the 50 kDa Hedgehog (HH) protein which serves as the pathway ligand. Mammals express three different Hedgehog ligands  consisting of an N-terminal signaling domain and a C-terminal autoprocessing domain. To become a fully active signaling molecule (HH-N), the C-terminal domain autocatalytically initiates cleavage of the protein and addition of a cholesterol moiety to the signaling domain’s C-terminus. Additionally, a palmitoyl group is added to the N-terminus of the signaling domain. Dual lipidation of the HH signaling peptide is essential both for its activity and the regulation of its spread through tissues13.The Hedgehog-GLI (HH-GLI) signaling pathway is involved in many aspects of embryonic development as well as stem cell maintenance and tissue homeostasis in adult organisms15.Binding of any of the Hedgehog ligands to the transmembrane receptor Patched1 (PTCH1) initiates the canonical pathway by releasing the co-receptor Smoothened (SMO), otherwise suppressed by PTCH1. A rapid translocation of SMO to the primary cilium follows, leading to the dissociation of the GLI transcription factors from their regulator Suppressor of Fused (SUFU). The GLI proteins translocate to the nucleus and promote target gene transcription. Mammals have three different GLI transcription factors: GLI1, GLI2 and GLI3. GLI2 and GLI3 harbor a N-terminal repressor domain and can act as both activators and repressors of the pathway, while GLI1, lacking this domain, acts only as a transcriptional activator. In absence of the HH signal, the GLI proteins remain bound to SUFU which enables their phosphorylation and subsequent degradation in the proteasome, leading to the formation of GLI2 and GLI3 repressors which block target gene activation16. It has been proposed that SMO can signal through G proteins leading to activation of downstream molecules such as SRC Family Kinases17. Also, activation of the GLI transcription factors by other signaling pathways can ensure pathway activity in many cancer types, independently of the upstream signaling cascade18. Interestingly, in prostate cancer cells a direct interaction between the AR and the GLI proteins has been found. This interaction is proposed to lead to activation of both AR and HH-GLI pathways, and subsequently to cell proliferation20.In recent years it has become evident that apart from the canonical Hedgehog signaling cascade, non-canonical signaling branches through PTCH1 or SMO existWe studied upstream HH-GLI signaling and its role in AR signaling and androgen resistance. We identified a potential new mechanism of AR activation by the HH-GLI ligand SHH-N in androgen-independent cells.GLI1, PTCH1, SHH as well as the AR target genes PSA and KLK2. The highest variability in gene expression levels among the cell lines was detected for SHH, PSA and KLK2, all androgen regulated genes , which was shown for our LNCaP-AI cell line when compared to wild type LNCaP cells (0.59) or short-term depleted cells LNCaP-D (0.43). Exogenously added DHT also had no significant effect on LNCaP-AI proliferation, as it did on wild type and LNCaP-D proliferation Figure . LNCaP c.76, whicPTCH1 expression in LNCaP cells and downregulated AR target genes, whereas GANT-61 had no effect on these pathways. In LNCaP-D cells there seemed to be a more pronounced effect of both compounds on HH-GLI pathway activity, but only PTCH1 downregulation by GANT-61 was statistically significant. On the other hand, both compounds significantly downregulated AR signaling. No effect of either cyclopamine or GANT-61 was seen in LNCaP-D + DHT or LNCaP-AI cells . Cyclopamine addition inhibited this phosphorylation, resulting in a pSRC (Tyr416) reduction, suggesting that SMO non-canonically activates SRC in wild type LNCaP cells Fig. D. Howeve21 where we found evidence of SHH and ERα interactions, we checked whether SHH-N could non-canonically bind the AR and contribute to its activity. We hypothesized that SHH-N could bind the AR through its cholesterol modification, since androgen is synthesized from cholesterol and they are structurally similar. Interestingly, we confirmed, by both co-immunoprecipitation and PLA, that a complex between SHH-N and AR forms, but only in androgen-independent LNCaP-AI cells. After cholesterol depletion with methyl-β-cyclodextrin this complex formation was reduced, indicating the interaction is cholesterol dependent. Also, when the androgen-independent cells were returned to non-CS-FBS RPMI medium, this interaction was inhibited, suggesting the affinity of AR for SHH-N is lower than its affinity for androgens  and cholesteroylated N-terminal domain of SHH were built Figure  and for MMGB > 100 ns values are − 96 kcal/mol and − 84 kcal/mol, respectively). Such result is in agreement with the population of the intermolecular, protein–protein hydrogen bonds  is calculated as the ratio of the number of trajectory frames containing hydrogen bond and the total number of frames. /N). In the case of residues forming multiple hydrogen bonds, a sum of these values is given, which allows values larger than 100%.24. However, this model was later reconsidered as it became evident that activation of SMO in a mouse model did not lead to neoplastic transformation of the prostate as it did of skin, brain and muscle tissue25. Also, Zhang et al. found no evidence of autocrine HH-GLI signaling in three prostate cancer cell lines, LNCaP, PC3 and 22RV1. They also found that cyclopamine inhibited cell proliferation but did not affect canonical HH-GLI pathway activity26. On the other hand, research on xenografts revealed paracrine HH-GLI signaling is more likely to support prostate cancer cell growth28. As SHH is an androgen repressed gene, it was found that prolonged exposure of LNCaP cells to androgen deprived conditions enables limited activation of autocrine HH-GLI signaling in response to HH ligands that are produced under these conditions, proposing a role of HH-GLI signaling in sustaining androgen-independent growth of prostate cancer cells29. Later on, a direct interaction between the HH-GLI and AR signaling pathways was found, suggesting a completely new, non-canonical function for the HH-GLI signaling pathway in prostate cancer. GLI2 binds the AR and this might be the basis for HH-GLI support of AR activity under androgen-deprived conditions19. Recently, this group discovered that GLI3 also forms a complex with the liganded AR. This prevents GLI3 processing and stabilizes it in its active conformation leading to upregulated transcription of GLI target genes20.The contribution of the HH-GLI signaling pathway to prostate cancer development as well as interactions of the HH-GLI and AR signaling pathways have been intensely investigated over the past two decades. Early research suggested autocrine HH-GLI signaling is key for sustaining prostate cancer growthGLI1 mRNA expression, that have also been found in clinical CRPC samples by Shaw et al.9. In addition to its increased growth rate in androgen deprived medium, these findings support the androgen independence of the LNCaP-AI cell line. Similar to previous findings, we showed that LNCaP cell proliferation is inhibited with cyclopamine, but canonical HH-GLI was only slightly affected. Only in LNCaP-D cells both HH-GLI and AR signaling pathways were downregulated and this effect was reversed by DHT, indicating canonical pathway activation under androgen deprived conditions. Similar results were obtained for GANT-61, but the effect on cell proliferation was minor. On the other hand, the HH-GLI and AR signaling pathways were not affected by cyclopamine or GANT-61 in androgen-independent LNCaP-AI cells. Interestingly, in LNCaP cells we observed that SMO signals non-canonically through the SRC kinase, as pSRC (Tyr416) levels were reduced after cyclopamine treatment. Also, cyclopamine downregulated AR signaling in these cells. It has previously been shown that SMO, as a GPCR like protein, can non-canonically signal through G-proteins that activate the SRC Family Kinases31. Since it is known that the SRC kinase promotes AR transactivation function33 this could be the mechanism through which upstream HH-GLI signaling sustains AR signaling and contributes to prostate cancer cell growth. The lack of canonical HH-GLI signaling in these cells and our previous findings of potential SHH-N and ERα interactions21, motivated us to investigate whether SHH-N can non-canonically bind and activate the AR in prostate cancer cells. Indeed, we showed that SHH-N binds the AR, but only in androgen-independent LNCaP-AI cells. This is a cholesterol-dependent interaction since depletion of cholesterol decreased complex formation. Molecular modelling study revealed molecular details of this interaction. It showed that SHH-N is able to bind both, the wild-type AR and the mutated T878A form expressed in LNCaP cells. The cholesterol molecule attached to the SHH-N C-terminal tightly binds into the ligand binding site of AR while the two proteins pair to each other by their complementary polar regions forming stable complexes. MMGBSA energies suggest that SHH-N binds to the wild-type AR with slightly higher affinity than to the T878A mutant. This could mean that in androgen-independent cells harboring wild type AR this interaction could be even more prominent. This newly identified interaction between SHH-N and AR is one potential mechanism of sustaining androgen-independent growth of prostate cancer cells and could represent a new therapeutic target. Our results suggest this interaction is specific to androgen-independent cells as they grow in conditions where no androgen is present and need alternative modes of AR activation for survival. So, under these conditions AR cannot be activated by androgen, but SHH-N could take its place through its cholesterol moiety. An upregulation of SHH-N and DHH-N expression in the epithelial compartment of hormone-treated and hormone-refractory prostate cancer samples was associated with the highest risk of biological recurrence34, which supports the role of the Hedgehog ligand as a key player in androgen-independent growth. Since cholesterol is the key of SHH-N binding to the AR, a possible means of targeting this interaction could involve cholesterol depletion. Statins, that have been widely used in the clinic as cholesterol-lowering drugs, could potentially have an effect in this case. Epidemiological studies reported a reduced risk of advanced prostate cancer in patients who were taking statins, but no significant results were observed on overall incidence of prostate cancer35. This study is in concordance with our finding where SHH-N supports only androgen-independent cell growth and therefore this therapy could prove efficient only for CRPC that relies on the SHH-N–AR interaction, or in combination with ADT for advanced prostate cancer to prevent the interaction from occurring and enabling androgen-independent growth. However, a recent study reported pro-tumorigenic effects of low-dose statins in prostate cancer36 and therefore caution is advised and more detailed research of the effect of statins on different stages of prostate cancer should be conducted.In this paper we aimed to investigate the role of HH-GLI signaling in androgen-independent growth of prostate cancer cells in more detail. After establishing the androgen independent cell line LNCaP-AI we observed an upregulation in PTCH1 protein expression, as well as upregulated In conclusion, we would like to emphasize the importance of HH-GLI signaling in prostate cancer. Even though it might seem its role is contradictory, deeper insights reveal that its role may not be as straightforward as in other types of cancer. It is becoming more and more evident that canonical HH-GLI signaling might not be the leading cause of prostate cancer development, rather that it is essential for survival of prostate cancer cells under androgen deprived conditions. Also, our findings and those of other groups, highlight the ability of certain HH-GLI components to non-canonically interact with the AR and activate it, which present mechanisms of sustaining androgen-independent growth. As for the SHH-N–AR interaction that we identified, the extent of this interaction in clinical samples should be evaluated and it remains to be investigated whether its inhibition in advanced prostate cancer and CRPC could be relevant for successful treatment.37. LNCaP cells (passages < 40) were maintained in RPMI-1640 medium supplemented with 10% fetal bovine serum , 2 mM l-glutamine and 1000 U/mL penicillin/streptomycin. To investigate HH-GLI signaling in androgen-independent growth, LNCaP cells were cultured in RPMI-1640 supplemented with charcoal-stripped FBS (CS-FBS), l-glutamine and penicillin/streptomycin for 5 days (LNCaP-D). To check if the observed effects after short term androgen deprivation are androgen specific, 5 nM dihydrotestosterone  was added to the medium (LNCaP-D + DHT). Androgen-independent cells (LNCaP-AI) were generated by growing LNCaP cells in RPMI-1640 supplemented with charcoal-stripped FBS (CS-FBS), l-glutamine and penicillin/streptomycin > 10 months. To confirm that LNCaP-AI cells have indeed become independent of androgen, growth rates of cell lines in androgen depleted conditions were calculated as the growth for 5 days in depleted medium/growth in complete medium9.In this study the prostate cancer cell line LNCaP, obtained from the ATCC  was used, which is known to express the mutated AR (p.T878A)For gene and protein expression experiments, 24 h after seeding cells were treated with 10 µM SMO inhibitor cyclopamine  or 20 µM GLI inhibitor GANT-61 (SelleckChem) for 48 h. For cholesterol depletion, LNCaP-AI cells were treated with 1 mM methyl-β-cyclodextrin (Sigma) for 24 h. All experiments were performed at least in duplicate.38. The cells were treated in quadruplicates for 72 h with 0.5–5 µM cyclopamine or 5–30 µM GANT-61. All experiments were performed in triplicate.Cell proliferation and viability were determined using MTT assay as previously describedRNA from cells was extracted using Nucleozol reagent (Macherey–Nagel) according to manufacturer’s instructions. 1 µg of RNA was reverse transcribed into cDNA using the High-Capacity cDNA Reverse Transcription Kit (Thermo Fisher). Gene expression was analyzed on the CFX96 real-time PCR machine (Bio-Rad) using the SsoAdvanced Universal SYBR Green Supermix (Bio-Rad) and the primers listed in Table 38. Protein concentration was determined using the Pierce BCA Protein Assay Kit (Thermo Fisher). Primary antibodies against AR , SHH , PTCH1 , GLI1 , GLI3 , SRC  and pSRC (Tyr416)  were used. β-ACTIN  was used as loading control.Proteins were extracted using RIPA buffer supplemented with protease inhibitors (Roche). For analysis of phosphorylated proteins phosphatase inhibitors (Roche) were also added as described previously38. For SHH co-IP 5 µg of SHH antibody  was used per sample. An irrelevant IgG antibody, known not to have any interactions with the AR, was used as negative control.For co-immunoprecipitation experiments Protein G coated Dynabeads (Thermo Fisher) were used according to manufacturer’s instructions as previously describedFor PLA cells were seeded on Milli EZSlide 8-well glass slides (Merck). 24 h after seeding the cells were fixed with 4% paraformaldehyde and permeabilized with methanol. Epitope retrieval was performed using sodium citrate buffer, pH 6.0. Blocking and further steps of the assay were performed using Duolink PLA Technology (Sigma Aldrich) reagents as per manufacturer’s instructions: Duolink PLA Probes anti-rabbit PLUS (DUO92002), anti-mouse MINUS (DUO92004), Duolink detection agent Red (DUO92008), Duolink wash buffers (DUO82049-4L). The samples were incubated with the primary antibodies against SHH  and AR  in a humidity chamber at + 4 °C overnight. LNCaP-AI cells were also used for negative controls: in one negative control no primary antibodies were used, as for the second, instead of the anti-SHH antibody, anti-γ-tubulin was used that should not interact with the AR. The next day the cells were mounted in Prolong Gold Antifade mounting medium with DAPI (Thermo Fisher). Confocal images were obtained with the Leica SP8 X FLIM confocal microscope (Leica Microsystems). The cell nuclei are stained blue with the DAPI stain, whereas the points of SHH-N and AR interactions are shown as red dots in the cells.39: the X-ray structure of human AR ligand binding domain in complex with dihydrotestosterone, PDB code 2AM9  and the C-terminally cholesteroylated N-terminal domain of SHH determined by cryo electron microscope .The AR-SHH-cholesterol complex was built using structures available in PDB40 water. The obtained complexes were parametrized by the AmberTools16 module tleap using ff14SB force field41. Cholesterol was parametrized within the gaff force field42 and AM1-BCC charges were assigned. For the zinc cation, nonbonded parameters developed by us were used43. For each complex two independent 100 ns long molecular dynamic (MD) simulations were performed . Finally, the AR-SHH-cholesterol binding free energies were calculated using single trajectory MMGBSA approach on 20 ns and 40 ns long intervals sampled throughout the trajectory sampled during 100 ns of MD simulation. The calculations were performed by MMPBSA.py script available within the Amber software suite version 16 (https://ambermd.org/)44.The complexes between both the wild type AR and its T878A mutant were built  and Prism 8 (GraphPad Software).Supplementary Information.


To report the first use of a novel projected augmented reality (AR) system in open sinonasal tumor resections in preclinical models and to compare the AR approach with an advanced intraoperative navigation (IN) system.Four tumor models were created. Five head and neck surgeons participated in the study performing virtual osteotomies. Unguided, AR, IN, and AR + IN simulations were performed. Statistical comparisons between approaches were obtained. Intratumoral cut rate was the main outcome. The groups were also compared in terms of percentage of intratumoral, close, adequate, and excessive distances from the tumor. Information on a wearable gaze tracker headset and NASA Task Load Index questionnaire results were analyzed as well.A total of 335 cuts were simulated. Intratumoral cuts were observed in 20.7%, 9.4%, 1.2,% and 0% of the unguided, AR, IN, and AR + IN simulations, respectively (p < 0.0001). The AR was superior than the unguided approach in univariate and multivariate models. The percentage of time looking at the screen during the procedures was 55.5% for the unguided approaches and 0%, 78.5%, and 61.8% in AR, IN, and AR + IN, respectively (p < 0.001). The combined approach significantly reduced the screen time compared with the IN procedure alone.We reported the use of a novel AR system for oncological resections in open sinonasal approaches, with improved margin delineation compared with unguided techniques. AR improved the gaze-toggling drawback of IN. Further refinements of the AR system are needed before translating our experience to clinical practice. The complex anatomy and close proximity of critical structures in the sinonasal region represent a major challenge for surgeons when treating advanced tumors in this location, and incomplete resections are not uncommon, both in open and endoscopic approaches , 2. IntrOur group has recently published an advanced IN system for open sinonasal approaches during the resection of locally aggressive cancers . This teAugmented reality (AR) uses visual inputs to enhance the user’s natural vision and therefore can integrate navigation information onto the surgical field . This feThe objective of this study was to report the first use of a novel AR system in open sinonasal tumor resections in preclinical models and to compare an AR approach with an advanced IN navigation system.®) and a moldable material (Play-Doh®) mixed with acrylic glue were employed to build four locally advanced sinonasal tumor models  scans acquired 3D images of the skull models . Tumors An in-house navigation software package, GTx-Eyes, processed and displayed the CBCT images . This soThe AR system was composed of a portable high-definition projector , a stereoscopic infrared camera , a USB 2.0M pixel generic camera , and a laptop computer . A custom-made 3D printed case was fabricated to anchor a four-sphere reference tool and contain the other elements of the AR system Figure 3The AR system was registered into a single coordinate system by pairing correspondent landmarks using fiducial markers identifiable in both the images and projection surface Figure 4vs. navigation monitor) during surgical tasks. This headset was used solely for gaze-tracking and did not project any information. The eye tracker  consists of two cameras: one trained on the eye and the other, a “world camera”, recording the individual’s field of view. A series of computer vision algorithms are applied to the input from the eye camera to reliably detect the pupil throughout the eye’s range of motion. A calibration step provides a triangulating mapping function between the pupil and world cameras, which enables the user’s gaze to be precisely tracked  and to provide a 1-cm margin from the tumor along the plane trajectory. Instead of cutting the skulls, virtual cuts were performed in order to allow the reutilization of the models. This involved recording the osteotome position and orientation in the navigation software after the surgeon placed the osteotome in a certain direction and provided confirmation of obtaining the proposed cut. The analysis was performed on the virtual cutting trajectory after all the simulations were completed.Four procedures were performed: 1) Unguided using axial, sagittal, coronal images; 2) Guided -AR-; 3) Guided -IN-; and 4) Guided -AR and IN-. This last group was possible, as both systems are contained in the same platform software and can be used simultaneously. Analysis of cutting planes was performed using MATLAB software. An area of 4 cm × 2 cm  along the longitudinal axis of the cut was isolated from each plane. The minimal distance with respect to the tumor surface was calculated for each point making up the isolated area and reproduced as a distribution of distances shown as a 4 cm × 2 cm color scaled image. Distance from the tumor surface was classified as “intratumoral” when ≤0 mm, “close” when >0 mm and ≤5 mm, “adequate” when >5 mm and ≤15 mm, and “excessive” when >15 mm. The percentages of points at intratumoral, close, adequate, and excessive distances were calculated for each simulation plane.The gaze-tracking system was calibrated to each participant, and it was used in all the simulations. The eye-tracking data were analyzed to identify each time point the participants switched their gaze between the navigation monitor and the surgical field. The metric reported is the percentage of total study time spent looking at the navigation monitor.Finally, a NASA Task Load Index (NASA-TLX) questionnaire that measures the workload of a task was completed by the participants to evaluate the different approaches . This qu® . Simulations were grouped into four categories: unguided, AR, IN, and AR + IN. Rate of intratumoral virtual cuts was the main outcome and was assessed with the Fisher’s exact test. Multivariable analysis adjusting for surgeon and tumor was performed through logistic regression analysis. The groups were also compared in terms of percentage of intratumoral, close, adequate, and excessive distances from the tumor and duration of the simulations through the bilateral Kruskal–Wallis test and Steel–Dwass–Critchlow–Fligner post-hoc test. The Kruskal–Wallis test was also employed to analyze the gaze-tracking outcomes and the NASA-TLX scores. Level of significance was set at 0.05 for all statistical tests.Statistical analysis was run through XLSTATA total of 335 cuts were simulated. Intratumoral cuts were observed in 20.7%, 9.4%, 1.2%, and 0% of the unguided, AR, IN, and AR + IN simulations, respectively (p < 0.0001). Univariate analysis comparing different procedures with AR showed that this technology improved margins with respect to unguided simulations. The advanced IN approach reduced the intratumoral cut rates compared with AR, and the combination of AR and IN did not significantly decrease the intratumoral cut rate compared with IN alone (p = 0.51). These differences were also seen in a multivariate model adjusted for tumor and surgeon Table 1.The percentage of points forming the simulation planes was also registered. We observed that only the advanced IN system and the combined approach significantly decreased the percentage of intratumoral (p < 0.0001) and close margin points (p = 0.008) compared with the unguided resections . There were no differences between the AR-, IN-, and AR + IN-guided procedures. The percentage of time looking at the screen during the procedures was 55.5% for the unguided approaches and 0%, 78.5%, and 61.8% in AR, IN, and AR + IN, respectively (p < 0.001). Adding the AR technology to the combined approach significantly reduced the screen time compared with the advanced IN procedures alone  were incomplete, being posterior and superior margins the most commonly involved . In a biCurrently, IN is employed in many centers in endoscopic sinonasal procedures –29. By p®, Microsoft Corporation, Redmond) and other head-mounted displays (HMDs), which might be cumbersome especially in long procedures, and preclude the use of loupes/headlights. There are literature reports about HMD limitations including heaviness of the devices, breaches in patient privacy/information, battery life, potential lag time secondary to preoperative image processing, and the potential of signal interferences of wireless Internet or Bluetooth connections that may cause intermittent data transmission of image , including the Kevin and Sandra Sullivan Chair in Surgical Oncology, the Myron and Berna Garron Fund, the Strobele Family Fund, and the RACH Fund.The authors declare that the research was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest.All claims expressed in this article are solely those of the authors and do not necessarily represent those of their affiliated organizations, or those of the publisher, the editors and the reviewers. Any product that may be evaluated in this article, or claim that may be made by its manufacturer, is not guaranteed or endorsed by the publisher.


In addition to nasal symptoms, patients with allergic rhinitis (AR) often experience mental and psychological disorders such as depression. Depression not only makes the treatment of AR more difficult and expensive but also poses a serious impact on the patients' daily activities and quality of life, thus bringing additional burden to the families and the society. Here we systematically review the recent research advances in the correlation between AR and depression, analyze the possible causes and mechanisms of depression in AR, summarize the current diagnosis and treatment strategies, and provide our insights into the AR-related depression; in addition, we introduce briefly the basic research status on AR-related depression. We hope that this review article will provide evidence for future studies. Allergic rhinitis (AR), a common chronic inflammatory disease of the upper respiratory tract, is associated with immunoglobulin E (IgE)-mediated immune-inflammatory response , involviAn increasing number of clinical and epidemiological investigations have found a close correlation between depression and AR ; howeverAs shown in As shown in Two major factors are involved in the depression caused by AR treatment: (a) excessive concern about the side effects of the treatments; in particular, about 66% of AR patients developed depressive symptoms after long-term use of glucocorticoids ; and (b)In terms of genetic susceptibility, some Chinese authors  suggesteFinally and most interestingly, immune-inflammatory response may also play a key role. As is known, AR essentially is an IgE-mediated immune-inflammatory response triggered by allergens. Many studies have suggested that peripheral inflammatory signals or cytokines  in the nose can enter the central system through the possible “neural pathways ,” “cellular pathways,” and “humoral pathways,” causing neuroinflammation, oxidative stress, and neurotransmitter disturbances in the brain, ultimately leading to depression in individuals with AR –31. NotaThe diagnosis of depression is often missed, even in specialist institutions and clinics . There iAlthough there is a lack of guidelines and consensus on the treatment of depression in AR, some clinical studies have found that untreated depression exacerbates the symptoms of AR, suggesting that the proper management of depression is important for the comprehensive treatment of AR patients . ClinicaThe number of AR patients is increasing as we are being exposed to increasingly polluted environments . MeanwhiX-CS: conception and design. CR: administrative support. YZ, W-BZ, and H-RW: provision of study materials. W-BZ, H-RW, and Y-KM: collection and assembly of data. CR and X-CS: data analysis and interpretation. All authors: manuscript writing and final approval of manuscript.This study was supported by National Natural Science Foundation of China (Grant No. 82071021), the Key Research and Development Program of Shandong Province (Major Science and Technology Innovation Project) (Grant No. 2020CXGC011302), the Key Project of Shandong Provincial Natural Science Foundation (Grant No. ZR2020KH024), and the Natural Science Foundation of Shandong Province, China (Grant No. ZR2020MH177).The authors declare that the research was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest.All claims expressed in this article are solely those of the authors and do not necessarily represent those of their affiliated organizations, or those of the publisher, the editors and the reviewers. Any product that may be evaluated in this article, or claim that may be made by its manufacturer, is not guaranteed or endorsed by the publisher.


The tested compounds showed promising anti-proliferative activities against both cell lines. The most active ten candidates  were further evaluated for their effect on the gene expression levels of Bax as an apoptotic marker and Bcl-2 as an anti-apoptotic one. Moreover, they were evaluated for their ability to inhibit histone deacetylase  activities. Compound 6c achieved the best cytotoxic activities on both HepG-2 and HuH-7 cell lines with IC50 values of 1.53 and 3.06 µM, respectively, and also it showed the most inhibitory activities on HDAC1, HDAC4, and HDAC6 with IC50 values of 1.76, 1.39, and 3.46 µM, respectively, compared to suberoylanilide hydroxamic acid (SAHA) as a reference drug . Furthermore, it achieved a more characteristic arrest in the growth of cell population of HepG-2 at both G0/G1 and S phases with 1.23-, and 1.18-fold, respectively, compared to that of the control, as determined by cell cycle analysis. Also, compound 6c showed a marked elevation in the AnxV-FITC apoptotic HepG-2 cells percentage in both early and late phases increasing the total apoptosis percentage by 9.98-, and 10.81-fold, respectively, compared to the control. Furthermore, docking studies were carried out to identify the proposed binding mode of the synthesized compounds towards the prospective target (HDAC4). In silico ADMET and toxicity studies revealed that most of the synthesized compounds have accepted profiles of drug-likeness with low toxicity. Finally, an interesting SAR analysis was concluded to help the future design of more potent HDACIs in the future by medicinal chemists.Guided by the structural optimization principle and the promising anticancer effect of the quinoxaline nucleus, a new series of novel HDAC inhibitors were designed and synthesized. The synthesized compounds were designed to bear the reported pharmacophoric features of the HDAC inhibitors in addition to an extra moiety to occupy the non-used vacant deep pocket of the HDAC receptor. The newly prepared compounds were evaluated for their  The produced resistance arises from synergistic interactions among diverse mechanisms of chemoresistance (MOC) in which about 100 genes are involved .A very important pathway to fight cancer has appeared after the discovery of tumor angiogenesis by Judah Folkman approximately 50 years ago . DiffereHypoxia-inducible factor-α (HIF-1α), a central regulator of oxygen detection and adaptation at the cellular level, and its transcriptional activity are the key mediator of VEGF activity. Both HIF-1α and VEGF are crucial to angiogenesis and can be regulated by post-translational modifications (PTMs), including acetylation by histone acetyltransferases (HATs) and deacetylation by histone deacetylases (HDACs). Subsequently, many studies indicated HDAC inhibitors (HDACIs) as promising antiangiogenic compounds and recommended them as an effective class of anticancer therapeutics .The first HDACI approved by the FDA for the treatment of cutaneous T-cell lymphoma is vorinostat, followed by depsipeptide for the same purpose . This waI (linomide) is a quinoline derivative immunostimulant by increasing natural killer (NK) cell activity and macrophage cytotoxicity. It also inhibits angiogenesis and reduces the secretion of TNF-α. It was indicated for the treatment of some cancer types and autoimmune diseases, such as multiple sclerosis, and prevention of autoimmune diabetes mellitus . Phase III clinical trials for MS started in December 2007 and showed a slow progression of disability and reduction in the rate of relapse in patients with relapsing-remitting multiple sclerosis  role has promised to be new hope for the treatment of various malignancies . Thus, tiscovery  due to iiscovery . So, our2+-binding group, ii) an amide linker which occupies the linker region between the zinc-binding region and the hydrophobic tail region and contains one H-bond donor that forms an H-bond with the crucial amino acid His132, iii) a terminal hydrophobic group which protrudes to outside, similar to that of the co-crystallized inhibitor, and helps in the stabilization of the remaining part of the compound  a heterocyclic aromatic cap that occupies the narrow tubular pocket of HDAC and containing at least one H-bond acceptor. This H-bond acceptor constitutes the Zncompound .Interestingly, the newly synthesized quinoxaline candidates were found to form nearly the same binding mode compared to the co-crystallized inhibitor (TSA) with an additional extra positioning of the quinoxaline moiety inside the narrow tubular pocket of HDAC, which provides a more promising fitting as the N1 of quinoxaline, and it was found to be the zinc-binding group, as described in In continuation of our previous works , that pr6a-l, 7a,b, 8, and 9) and 2-methoxyquinoxaline . The choice of quinoxaline was based on some bio-isosteric considerations: i) the bicyclic structure of quinazoline core which occupies the narrow tubular pocket of HDAC superior to the co-crystallized inhibitor  or ethylidenehydrazine . The third position was the terminal hydrophobic group. We used many different aromatic moieties to play the role of the terminal hydrophobic group. The wide variety of modifications enabled us to study the SAR of these compounds as effective anticancer agents with potential HDAC inhibitory activities, which is considered a crucial objective of our work. All modification pathways and molecular design rational were illustrated and summarized in The first position was the heterocyclic aromatic cap. Two different substituted quinoxaline moieties were used as follows: 2-chloroquinoxaline  theAlso, to explore and emphasize the mechanism of action of the synthesized compounds, molecular docking studies were conducted to understand the expected binding interactions of the target compounds with HDAC active sites. For the same purpose, other studies regarding the ability of the most active compounds to induce apoptosis and arrest cell cycle growth have been done.o-phenylenediamine was treated with oxalic acid in the presence of 4 N HCl to afford 2,3--quinoxalinedione 3. The latter was treated with thionyl chloride in the presence of a catalytic amount of DMF to produce 2,3- dichloroquinoxaline 4  of quinoxaline-based HDACIs, five series: 50 values of the newly synthesized compounds on both HepG-2 and HuH-7 liver cancer cell lines revealed the following interesting results. Compounds of 6 series showed the best promising cytotoxic activities with IC50 ranging from 1.53 to 18.70 µM against the two cell lines except for compound 6i with a 3-NO2 side chain . Compound 6c with a 4-Cl side chain showed the best cytotoxic activities against both cell lines among all the synthesized series .Analyzing the IC10 series incorporating 2-OCH3 group at 2-position of quinoxaline nucleus showed a decrease in the cytotoxic activities ranging from 5.56 to 65.46 µM against the two cell lines, except for compound 10h incorporating 2-OH group in the side chain which elevated greatly its cytotoxic activity (IC50 = 5.56 and 6.72 µM) against both HepG-2 and HuH-7 cell lines, respectively.On the other hand, the derivatives of 7 series showed variable anticancer activities. On the one hand, compound 7b incorporates 2-OH . On the other hand, the unsubstituted member 7a showed decreased activities against the two cell lines .The two compounds of the 8 made it superior to compound 9 incorporating isatin moiety against the two cell lines. Compound 8  was 2.98-fold more active than compound 9 .The presence of 2-hydroxy-1-naphthyl moiety in the side chain of compound 8  makes it more active than the corresponding member 11 incorporating 2-OCH3 . Compound 8 showed increase activities by 4.76-fold against the HepG-2 cell line and 3.30-fold against the HuH-7 cell line. Contrary to the above, the insertion of the 2-OCH3 group in quinoxaline moiety of compound 12  make it more active than the corresponding member 9 incorporating 2-Cl  Compound 12 showed increase activities by 5.81-fold against the HepG-2 cell line and 5.33-fold against the HuH-7 cell line.Insertion of the 2-Cl group in quinoxaline moiety of compound 6c, 6d, 6f, 6g, 6k, 6l, 7b, 8, 10h, 12) were further evaluated for their ability to inhibit histone deacetylase activities   . The HDAd HDAC6) . Moreove6c incorporating 4-Cl benzylidene side chain was further evaluated through cell cycle analysis in HepG-2 cell line. Such a test was carried out to detect the exact phase at which cell cycle arrest takes place. The HepG-2 cells were treated with compound 6c at a concentration of 1.53 µM equal to its IC50, and its impact on the different phases of cell growth was recorded. Treatment of HepG-2 cells with 6c showed a significant decline in the cell population at both G0/G1 and S phases with 36.75% (1.23-fold) and 30.32% (1.18-fold), respectively, compared to that of the control which was 45.40 and 35.95%  and late apoptosis  phases  phases . This re6c, 6d, 6f, 6g, 6k, 6l, 7b, 8, 10h, and 12 were utilized in this test.To prove that the synthesized compounds exert their cytotoxic effects through driving cells to apoptosis, the effect of most active compounds against the level of Bax (as an apoptotic marker) and Bcl-2 (as an anti-apoptotic marker) was evaluated. Compounds 6c among all of the tested derivatives achieved the highest increase in the level of Bax protein (274.3 pg/ml) and the lowest decrease in the level of Bcl-2 protein (1.27 pg/ml), compared to the control (24.37 and 5.04 pg/ml), respectively , compared to the control (24.37 pg/ml). On the other hand, all the tested compounds were able to decrease the level of the anti-apoptotic Bcl-2 protein by a range from 2.91 to 1.27 pg/ml (from 1.73 to 3.96-fold decrease), compared to the control (5.04 pg/ml). Again, compound ectively .Docking studies of the synthesized compounds were carried out to rationalize the obtained biological results and to understand the proposed binding mode of such compounds with the prospective target (HDAC). Trichostatin A (TSA) as an HDAC inhibitor was used as a reference drug in the docking studies.At first, a validation process was performed for the target receptor by running a redocking process for only the co-crystallized inhibitor, and a low RMSD value indicated the valid performance (RMSD = 0.57) .2+ metal in a similar bidentate manner through its terminal charged and carbonyl oxygen atoms with 1.93 and 2.51 Å, respectively. Moreover, it formed one ionic bond through its charged oxygen atom with His131 with 3.17 Å, and two hydrogen bonds with Tyr297 and His132 with 2.67 and 3 Å through the carbonyl oxygen and amidic NH groups, respectively. Finally, it formed a pi-H interaction through its phenyl group with His170 with 4.74 Å. It was noted that the hydroxamic acid group at the end of the aliphatic chain of TSA fitted the polar bottom part of the pocket, leaving its hydrophobic moiety outside . It recorded a binding score of −11.95 kcal/mol and an RMSD of 1.23 Å. It bound Zn outside .Interestingly, the synthesized derivatives were found to form nearly the same binding mode compared to the co-crystallized inhibitor (TSA) with an additional extra positioning of the quinoxaline moiety inside the narrow tubular pocket of HDAC, which provides a more promising fitting as the N1 of quinoxaline was found to be the zinc-binding group.6K, as a representative example, was fitted inside the deep pocket through its quinoxaline ring with a binding score of − 6.46 kcal/mol and RMSD of 0.77 Å. It bound Zn2+ metal in a unidentate manner through its N1 atom of quinoxaline moiety with 2.63 Å. Furthermore, it formed a hydrogen bond with His132 at 2.96 Å through its linker NH group of the hydrazide moiety  to carrier proteins in the blood (The cytochrome P450 2D6 (CYP2D6) model predicts CYP2D6 enzyme inhibition using a 2D chemical structure as input. CYP2D6 inhibition experiment is required as part of the regulatory procedures in the drug discovery and development process . All thehe blood . All comhe blood .Toxicity prediction was carried out for the synthesized compounds based on the validated and constructed models in Discovery Studio software .in silico low adverse effects and toxicity against the tested models. Concerning FDA rodent carcinogenicity, all the tested compounds were predicted to be non-carcinogenic except compounds 8, 10f, and 12 which were single carcinogens. For the carcinogenic potency TD50 rat model, the tested compounds showed TD50 values ranging from 1.797 to 52.581 mg/kg body weight/day. Regarding the rat maximum tolerated dose model, the compounds showed maximum tolerated dose with a range of 0.082–0.760 g/kg body weight. Additionally, all compounds were non-toxic against the developmental toxicity potential model except compound 6h. For the rat oral LD50 model, all compounds showed low oral LD50 values (from 0.102 to 1.109 mg/kg body weight/day). For the rat chronic LOAEL model, the compounds showed LOAEL values ranging from 0.055 to 0.413 g/kg body weight. Moreover, all compounds were predicted to be mild and non-irritant against ocular irritancy and skin irritancy models, respectively, as represented in It is worth mentioning that most compounds showed 50 values towards liver cancer cell lines (HepG-2 and HuH-7) showed the following interesting results:Studying the structure-activity relationship of our newly synthesized tested quinoxaline candidates according to their IC6) with different substituted benzylidene side chains were found to exert the most promising cytotoxic activityranging from very strong to strong (except for 3-NO2 one). Besides, 2-methoxy quinoxaline compound (12) with isatin side chain maintains a very strong cytotoxic activity as well. Moreover, 2-chloro quinoxaline derivative with a 2-hydroxy-1-naphthaldehyde side chain (8) showed a very strong anticancer activity on both cell lines which exceeds that of some derivatives of (6). Furthermore, the introduction of a 2-hydroxy acetophenone side chain (7b) maintains the very strong cytotoxic activity of 2-chloro quinoxaline moiety in contrast to the plain acetophenone derivative (7a) which decreased the cytotoxic effect greatly. Also, the presence of an isatin side chain on 2-chloro quinoxaline (9) achieved a strong anti-proliferative effect. On the other hand, the analog of the previously mentioned compound (8) with a very strong cytotoxic activity, 2-methoxy quinoxaline derivative (11), showed only a strong cytotoxic activity. Finally, most of the 2-methoxy quinoxaline derivatives (10) compared to their 2-chloro analogs showed weak anti-proliferative activities except those with 2-OH, 4-NO2 and 2-OCH3 benzylidene side chains which showed very strong, strong, and moderate cytotoxic activities, respectively  were further evaluated as HDAC1, HDAC4, and HDAC6 inhibitors and revealed IC50 values ranging from 1.39 to 7.21 µM, compared to the reference drug, suberoylanilide hydroxamic acid (SAHA). Moreover, the most active compound 6c with a 4-Cl benzylidene side chain was further evaluated through cell cycle analysis in HepG-2 cell line and showed a significant decline in the cell population at both G0/G1 and S phases with 36.75% (1.23-fold) and 30.32% (1.18-fold), respectively, compared to that of the control which was 45.40 and 35.95%. Furthermore, it was subjected to annexin V-based flow cytometry assay and achieved an increase in the total apoptosis percentage by 9.98-, and 10.81-fold, respectively, compared to the control. Finally, the aforementioned most active ten compounds greatly elevated the level of proapoptotic Bax protein by a range from 6.74- to 11.25-fold increase, compared to the control. On the other hand, they were able to decrease the level of the anti-apoptotic Bcl-2 protein by a range from 1.73- to 3.96-fold decrease, compared to the control. Again, compound 6c among all of the tested derivatives achieved the highest increase in the level of Bax protein (274.3 pg/ml) and the lowest decrease in the level of Bcl-2 protein (1.27 pg/ml), compared to the control (24.37 and 5.04 pg/ml), respectively. Interestingly, docking studies revealed nearly the same binding mode compared to the co-crystallized inhibitor (TSA) with an additional extra positioning of the quinoxaline moiety inside the narrow tubular pocket of HDAC, which may explain the reason behind the previously discussed promising biological results, and confirm our proposed mechanism of action for them as HDACIs. Also, the SAR studies of our diverse synthesized derivatives based on their biological results may give a clear spot on the essential pharmacophoric features required for targeting HDAC as well in the future by medicinal chemists.Twenty-seven new quinoxaline derivatives were synthesized with different substitutions to study their SAR as promising anticancer candidates targeting the HDAC enzymes . Twenty compounds showed cytotoxic effects ranging from very strong to moderate against two liver cancer cell lines (HepG-2 and HuH-7). Then, the most active ten compounds -Quinoxalinedione 3, 2,3- dichloroquinoxaline 4, 2-chloro-3-hydrazinylquinoxaline 5 were obtained according to the reported procedures. The mass spectra were recorded on Varian MAT 311-A (70 e.v.) and Direct Inlet unit (DI-50) of SHIMADZU GC/MSeQP5050A. The reactions were monitored by thin-layer chromatography (TLC) using TLC sheets precoated with UV fluorescent silica gel Merck 60 F254 plates and were visualized using a UV lamp and different solvents as mobile phases. 2,3- and appropriate carbonyl derivatives (0.002 mol), namely, benzaldehyde, 2-chlorobenzaldehyde, 4-chlorobenzaldehyde, 2,4-dichlorobenzaldehyde, 2,6-dichlorobenzaldehyde, 2-methoxybbenzaldehyde, 3,4-dimethoxybenzaldehyde, 3,4,5-trimethoxybenzaldehyde, 3-nitrobenzaldehyde, 4-nitrobenzaldehyde, 2-hydroxybenzaldehyde, acetophenone, 2-hydroxyacetophenone, 2-hydroxy-1-naphthaldehyde, and isatin were refluxed in absolute ethanol (25 ml) in the presence of a catalytic amount of glacial acetic acid for the appropriate time, and the reaction was followed up by TLC. The mixture was cooled, and the formed solid product was filtered, dried, and crystallized from ethanol to afford the corresponding compounds 6a-l, 7a,b, 8, and 9, respectively.Equimolar amounts of compound ; m. p. = 228–230°C; IR : 3,265 (NH), 3,056 (CH aromatic), 1,607 (C=N); 1H NMR  δ 10.99 , 8.62 , 8.32–7.63 , 7.51 , 7.16 ; 13C NMR  δ 156.44 , 146.82 (NCH), 140.42 , 135.44 (Ar-C), 133.53 (Ar-C), 130.85(Ar-C), 129.13(Ar-C), 128.78 (Ar-C), 127.37 (Ar-C), 126.02 (Ar-C), 125.31(Ar-C), 123.05 (Ar-C), 115.36 (Ar-C); C15H11ClN4 (282.73).Canary yellow crystal ; m. p. = 224–226°C; IR : 3,361 (NH), 3,006 (CH aromatic), 1,610 (C=N); 1H NMR  δ 11.36 , 9.07 , 8.61 , 8.11 , 7.58 , 7.56–7.49 , 7.46 , 7.24–7.12 ; C15H10Cl2N4 (317.17). 13C NMR  δ 152.16, 145.05, 142.73, 140.94, 134.08, 133.63, 132.84, 131.34, 130.38, 129.27, 127.83, 127.34, 125.60, 123.30, 115.48.Bright yellow crystal ; m. p. = 184–186°C; 1H NMR  δ 10.82 , 8.61 , 7.87 , 7.75 , 7.61 , 7.52 , 7.40 ; 13C NMR  13C NMR  δ 145.05 , 144.83, (NCH), 138.41 , 136.33 (Ar-C), 134.96 (Ar-C), 134.08 (Ar-C), 131.06 (Ar-C), 129.55 (Ar-C), 129.29 (Ar-C), 128.06 (Ar-C), 125.40 (Ar-C).; C15H10Cl2N4 (317.17).Orang powder 1H NMR  δ 11.50 , 9.03 , 8.12 , 7.89–7.83 , 7.75 , 7.58 ; 13C NMR  δ 145.27 , 142.25 , 140.93 (NCH), 137.54 (Ar-C), 136.71 (Ar-C), 135.09 (Ar-C), 134.02 (Ar-C), 131.62 (Ar-C), 131.20 (Ar-C), 129.86 (Ar-C), 128.47 (Ar-C), 128.03 (Ar-C), 126.94 (Ar-C).; C15H9Cl3N4 (351.62).Yellow crystal ; m. p. = 182–184°C; 1H NMR  δ 11.50 , 8.83 , 7.87 , 7.72 , 7.59 , 7.50–7.42 ; 13C NMR  δ 13C NMR  δ 145.37 , 142.69 , 140.88 , 137.58 (NCH), 136.59 (Ar-C), 134.44 (Ar-C), 131.60 (Ar-C), 131.15 (Ar-C), 129.52 (Ar-C), 128.01 (Ar-C), 127.02 (Ar-C); C15H9Cl3N4 (351.62).Orange crystal ; m. p. = 220–222°C; 1): 3,369 (NH), 3,015 (CH aromatic), 2,952 , 1,627 (C=N); 1H NMR  δ 10.95 , 8.82 , 8.39 , 8.24 , 7.51 , 7.46 , 7.32 , 7.13 , 7.07–7.03 , 3.89 ; 13C NMR  δ 158.51 , 151.40 , 149.37 , 140.31 (NCH), 133.88 , 132.30 (Ar-C), 128.58 (Ar-C), 127.73 (Ar-C), 124.77 (Ar-C), 123.75 (Ar-C), 123.36 (Ar-C), 122.73 (Ar-C), 120.93 (Ar-C), 115.21 (Ar-C), 112.18 (Ar-C), 56.17 (OCH3).; MS (m/z): 313 , 308.42 ; C16H13ClN4O (312.76).Canary yellow crystal ; m. p. = 210–212°C; IR : 3,292 (NH), 3,053 (CH aromatic), 2,967 ; 1H NMR  δ 10.89 , 8.56 , 7.86–7.66 , 7.66–7.45 , 7.39 , 7.08 , 3.89 , 3.84 ; 13C NMR  δ 151.42 , 149.66 , 145.24 (NCH), 144.73 , 131.04 (Ar-C), 128.08 (Ar-C), 127.87 (Ar-C), 125.98 (Ar-C), 124.58 (Ar-C), 122.96 (Ar-C), 116.42 (Ar-C), 111.74 (Ar-C), 109.84 (Ar-C), 56.20 (OCH3), 56.07 (OCH3).; C16H13ClN4O (312.76).Orange powder ; m. p. = 164–166°C; IR : 3,275 (NH), 3,065 (CH aromatic), 2,998 , 1,607 (C=N); 1H NMR  δ 11.2 , 8.60 , 8.00–7.51 , 7.52–6.94 , 3.89 , 3.74 ; MS (m/z): 372 ; C18H17ClN4O3 (372.81); 13C NMR  δ 153.65 , 145.03 , 144.82 , 131.05 (Ar-C), 130.66 (Ar-C), 128.10 (Ar-C), 106.03 (Ar-C), 105.46 (Ar-C), 60.65 (OCH3), 56.50 (OCH3).Orange powder ; m. p. = 210–212°C; IR : 3,260 (NH), 3,066 (CH aromatic), 1,615 (C=N); 1H NMR  δ 11.47 , 8.75 , 8.34  , 8.02 , 7.89 , 7.75 , 7.68–7.20 ; C15H10ClN5O2 (327.73); 13C NMR  δ 155.63 , 147.95 (Ar-C C3 of benzene), 145.20 (NCH), 141.46 , 140.88 (Ar-C), 137.63 (Ar-C) , 136.73 (Ar-C), 131.22 (Ar-C), 129.70 (Ar-C), 128.15 (Ar-C), 127.06 (Ar-C), 124.60 (Ar-C), 115.95 (Ar-C).Orange crystal ; m. p. = 214–216°C; IR : 3,308 )NH), 3,100 (CH aromatic), 1,615  δ 11.40 , 8.77 , 8.58 , 8.37–8.26 , 8.19 , 7.96–7.85 , 7.84–7.70 , 7.68–7.14 ; 13C NMR  δ 155.83 , 148.78 (Ar-C C4 of benzene), 145.34 (NCH), 140.92 , 137.54 (Ar-C), 136.96 (Ar-C), 134.95 (Ar-C), 133.67 (Ar-C), 131.22 (Ar-C), 128.03 (Ar-C), 127.05 (Ar-C), 124.41 (Ar-C), 121.17 (Ar-C).; C15H10ClN5O2 (327.73).Yellow crystal ; m. p. = 231–232°C; IR : 3,337 (NH), 3,055 (CH aromatic), 1,618 (C=N); 1H NMR  δ 11.74 , 11.51 , 8.82 , 8.06–7.38 , 7.33 , 7.04–6.89 ; 13C NMR  δ 157.92 , 144.88 , 131.66 (Ar-C), 131.23 (Ar-C), 130.28 (Ar-C), 128.07 (Ar-C), 126.39 (Ar-C), 126.12 (Ar-C), 119.79 (Ar-C), 119.44 (Ar-C), 116.96 (Ar-C); C15H11ClN4O (298.73).Yellow crystal ; m. p. = 225–227°C; IR : 3,386 (OH), 3,239 (NH), 3,050 (CH aromatic), 2,892 , 1,610 (C=N); 1H NMR  δ 11.00 , 8.51 , 7.87 , 7.81 , 7.60 , 7.55–7.47 , 7.14 , 6.81 , 6.79 , 3.03 , 3.01 .; C17H16ClN5 (325.80); 13C NMR  δ 158.56 , 152.44 , 148.99 (NCH), 145.46 , 141.38 (Ar-C), 136.73 (Ar-C), 133.09 (Ar-C), 130.66 (Ar-C), 128.87 (Ar-C), 128.08 (Ar-C), 126.61 (Ar-C), 122.37 (Ar-C), 115.44 (Ar-C), 111.97 (Ar-C), 41.30 (NCH3).Yellow crystal ; m. p. = 202–204°C; IR : 3,424 (NH), 3,020 (CH aromatic), 1,603 (C=N); 1H NMR  δ 11.10 , 8.29 , 8.02 , 7.76–7.61 , 7.56–7.42 , 7.40–6.98 , 2.67 ; MS (m/z): 298 , 297 (87.40%), 296 ; C16H13ClN4 (296.76); 13C NMR  δ 139.10 , 132.64 (NHC), 131.50 , 130.87 (Ar-C), 130.00 (Ar-C), 129.30 (Ar-C), 126.25 (Ar-C), 126.00 (Ar-C), 119.92 (Ar-C), 115.60 (Ar-C), 26.94 (CCH3).Dark red crystal ; m. p. = 272–274°C; IR  δ 13.33 , 9.97 , 7.69 , 7.48 , 7.41 , 7.32 , 7.23–7.12 , 7.06–6.79 , 2.69 ; 13C NMR  δ 166.14 , 159.12 , 144.94 (NCH), 137.10 , 133.04(Ar-C), 131.35(Ar-C), 129.52(Ar-C), 128.64 (Ar-C), 126.28 (Ar-C), 125.89 (Ar-C), 123.32 (Ar-C), 120.09 (Ar-C), 119.09 (Ar-C), 117.78 (Ar-C), 115.76 (Ar-C), 15.60 (CCH3). MS (m/z): 313 , 312 , 205 ; C16H13ClN4O (312.76).Yellow crystal ; m. p. = 248–250°C; 1): 3,342 (NH), 3,058 (CH aromatic), 1,656 (C=N); 1H NMR  δ 13.18 , 11.58 , 9.76 , 8.20 , 8.01–7.86 , 7.78 , 7.65 , 7.59 , 7.44 , 7.29 ; C19H13ClN4O (348.79); 13C NMR  δ 158.19 , 146.46 , 144.82 , 140.94 (NCH), 137.49 (Ar-C), 136.52 (Ar-C), 132.80 (Ar-C), 132.09 (Ar-C), 131.33(Ar-C), 129.47(Ar-C), 128.16 (Ar-C), 126.88 (Ar-C), 124.01 (Ar-C), 120.87 (Ar-C), 119.56 (Ar-C), 109.33 (Ar-C).Yellow crystal ; m. p. = 218–220°C; IR : 3,171 )NH), 3,062 (CH aromatic), 1715 (C=O), 1,616 (C=N); 1H NMR  δ 12.35 , 10.72 , 8.52 , 7.86 , 7.71 , 7.58 , 7.37 , 7.07 , 6.91 ; 13C NMR  δ 165.85 , 148.07 , 146.52 , 144.26 , 133.23 (Ar-C), 131.99 (Ar-C), 131.55 (Ar-C), 128.34 (Ar-C), 124.58 (Ar-C), 122.52 (Ar-C), 118.02 (Ar-C), 117.08 (Ar-C), 110.69 (Ar-C). C16H10ClN5O (323.74).Red powder ; m. p. > 300°C; IR  and sodium methoxide (0.02 mol) were refluxed in methanol (25 ml) for the appropriate time, and the reaction was followed up by TLC. The mixture was cooled then poured into water (50 ml). The formed precipitated was filtered, dried, and crystallized in ethanol to afford the corresponding compounds 10, 11, and 12, respectively.An equimolar of compounds 1H NMR  δ 11.16 , 8.54 , 7.94–7.74 , 7.68 , 7.53 , 7.49–7.20 , 4.11 ; 13C NMR  δ 134.33 (Ar-C), 129.34 (Ar-C), 127.19 (Ar-C), 126.49 (Ar-C), 54.44 (OCH3); C16H13ClN4O (312.76).Red orange powder ; m. p. = 184–186°C; 1H NMR  δ 11.42 , 8.85 , 8.16 , 7.71 , 7.64 , 7.54 , 7.43 , 7.34 , 4.10 ; 13C NMR  δ 129.73 (Ar-C), 128.33 (Ar-C), 126.46 (Ar-C), 54.40 (OCH3); MS (m/z): 346 , 331 ; C16H12Cl2N4O (347.20).Yellow crystal ; m. p. = 232–234°C 1H NMR  δ 11.39 , 8.70 , 7.65 , 7.59 , 7.45 , 7.39 , 4.13 ; 13C NMR  δ 141.35 , 136.41 (NCH), 134.41 , 131.86 (Ar-C), 131.36 (Ar-C), 129.45 (Ar-C), 128.06 (Ar-C), 127.20 (Ar-C), 126.50 (Ar-C), 125.27 (Ar-C), 54.49 (OCH3); C16H12Cl2N4O (347.20).Faint yellow crystal ; m. p. = 240–242°C; 1): 3,449 (NH), 3,021 (CH aromatic), 2,935 , 1,656 (C=N); 1H NMR  δ 9.04 , 8.66 , 8.12 , 7.95 , 7.84 , 7.73 , 7.55 , 7.45 , 7.14 , 7.08 , 3.93 , 3.79 ; MS (m/z): 308 , 91 ; C17H16N4O2 (308.34); 13C NMR  δ 158.05 , 137.69 (NCH), 131.39 , 129.43 (Ar-C), 126.32 (Ar-C), 125.97 (Ar-C), 123.03 (Ar-C), 122.62 (Ar-C), 121.24 (Ar-C), 112.32 (Ar-C), 56.22 (OCH3).Orange yellow powder ; m. p. = 225–227°C; IR : 3,419 (NH), 3,060 (CH aromatic), 2,941 , 1,661 (C=N); 1H NMR  δ 12.95 , 8.65 , 8.01 , 7.85–7.69 , 7.54 , 7.46 , 7.38 , 7.10 , 4.17 , 3.92 , 3.85 ; 13C NMR  δ 149.69 , 149.60 (NCH), 145.19 , 128.02 (Ar-C), 127.59 (Ar-C), 127.25 (Ar-C), 126.48 (Ar-C), 126.03 (Ar-C), 124.52 (Ar-C), 123.92 (Ar-C), 123.75 (Ar-C), 111.88 (Ar-C), 110.33 (Ar-C), 109.96 (Ar-C), 56.35 (OCH3), 56.26 (OCH3), 56.13 (OCH3); C18H18N4O3 (338.37).Faint yellow crystal ; m. p. = 235–237°C; IR : 3,421 (NH), 3,025 (CH aromatic), 2,940, 1,657 (C=N); 1H NMR  δ 10.96 , 8.43 , 7.67–7.50 , 7.38 , 7.31–7.20 , 7.09 , 4.07 , 3.88 , 3.72 ; 13C NMR  δ 153.62 , 126.7 (Ar-C), 126.33 (Ar-C), 106.45 (Ar-C), 104.72 (Ar-C), 60.62 (OCH3), 56.45 (OCH3). 54.14 (OCH3); C19H20N4O4 (368.39).Faint yellow crystal ; m. p. = 256–258°C; IR ; m. p. = 228–230°C; IR : 3,310 )NH), 3,079 (CH aromatic), 2,991 , 1,623 (C=N); 1H NMR  δ 11.38 , 8.68 , 8.58 , 8.27 , 7.88–7.73 , 7.45 , 4.14 ; 13C NMR  δ 148.77 , 143.70 , 137.24 (NCH), 133.47 (Ar-C), 130.92 (Ar-C), 127.28 (Ar-C), 126.54 (Ar-C), 124.66 (Ar-C), 123.63 (Ar-C), 123.33 (Ar-C), 54.53 (OCH3). C16H13N5O3 (323.31).Yellow crystal 1H NMR  δ 12.91 , 11.43 , 8.89 , 7.83 , 7.76 , 7.71 , 7.53 , 7.45 , 7.34 , 7.03 , 6.94 , 4.15 ; C16H14N4O2 (294.31).Yellow crystal ; m. p. = 247–249°C; 1): 3,185 (NH), 3,042 (CH aromatic), 2,941 , 1,616 (C=N); 1H NMR  δ 10.77 , 8.41 , 7.70–7.52 , 7.40 , 7.28 , 6.79 , 4.08 , 3.00 ; 13C NMR  δ 152.14 , 141.00 , 133.22 (Ar-C), 129.49 (Ar-C), 128.06 (Ar-C), 127.03 (Ar-C), 126.39 (Ar-C), 122.77 (Ar-C), 112.27 (Ar-C), 54.35 (OCH3), 41.57 (NH3); C18H19N5O (321.38).Yellow crystal ; m. p. = 214–216°C; IR : 3,423 (NH), 3,053 (CH aromatic), 2,941 , 1,663 (C=N); 1H NMR  δ 12.71 , 9.67 , 8.29 , 8.05–7.77 , 7.73 , 7.64 , 7.53 , 7.45 , 7.29 , 4.19 ; 13C NMR  δ 158.18 , 140.39  , 132.03 , 129.44, 128.44 (Ar-C), 128.19 (Ar-C), 127.54 (Ar-C), 126.68 (Ar-C), 125.87 (Ar-C), 124.34 (Ar-C), 124.00 (Ar-C), 121.24 (Ar-C), 119.47 (Ar-C), 116.00 (Ar-C), 109.52 (Ar-C), 54.74 (OCH3).; MS (m/z): 345 , 344 , 332 ; C20H16N4O2 (344.37).Orange powder ; m. p. = 153–155°C; IR  δ 12.17 , 10.75 , 8.49 , 7.85 , 7.70 , 7.58 , 7.35 , 7.06 , 6.91 , 4.13 ; C17H13N5O2 (319.32).Yellow crystal ; m. p. = 242–244°C; Experimental protocols applied for our newly synthesized compounds in the different biological assays were provided in detail in the 6, 7, 8, 9, and 10 were evaluated for their potential anti-proliferative activity against two liver cancer cell lines (HepG-2 and HuH-7) obtained from the American Type Culture Collection. Cytotoxicity was assessed following the SRB colorimetric assay protocol  were further assayed for their deacetylase enzymes inhibitory activities  based on a homogeneous fluorescence release assay, as discussed before  following the manufacture’s protocol. The previous compound was analyzed by FACS as we previously described  and the housekeeping gene (GAPDH) in HepG-2 cells was performed as well. The method was performed in detail as previously explained   receptor was performed, and the co-crystallized inhibitor, trichostatin A (TSA), was used as a reference standard. Using MOE 2019.0102 drug design software , the bin 2.00 Å) . The pro 2.00 Å) . The dea 2.00 Å) . Validat 2.00 Å) . All of ADMET descriptors  of the synthesized compounds were determined using Discovery studio 4.0. i) Blood-brain barrier penetration predicts blood-brain barrier penetration of a molecule. ii) Intestinal absorption predicts human intestinal absorption (HIA) after oral administration. iii) Aqueous solubility predicts the solubility of each compound in the water at 25°C. iv) CYP2D6 binding predicts cytochrome P450 2D6 enzyme inhibition. v) Plasma protein binding predicts the fraction of drug bound to plasma proteins in the blood . Discove50 which predicts the tumorigenic dose rate 50 (TD50) of a chemical in a rodent chronic exposure toxicity test of carcinogenic potency  in the toxicity test of a chemical  protocol as follows: i) FDA rodent carcinogenicity which computes the probability of a submitted chemical structure being a carcinogen, ii) carcinogenic potency TD potency , iii) ra potency , iv) dev potency , v) rat chemical , vi) ratchemical , vii) occhemical , viii) schemical .