SETDB1 as a cancer target: challenges and perspectives in drug design.

Genome stability is governed by chromatin structural dynamics, which modify DNA accessibility under the influence of intra- and inter-nucleosomal contacts, histone post-translational modifications (PTMs) and variations, besides the activity of ATP-dependent chromatin remodelers. These are the main ways by which chromatin dynamics are regulated and connected to nuclear processes, which when dysregulated can frequently be associated with most malignancies. Recently, functional crosstalk between histone modifications and chromatin remodeling has emerged as a critical regulatory method of transcriptional regulation during cell destiny choice. Therefore, improving therapeutic outcomes for patients by focusing on epigenetic targets dysregulated in malignancies should help prevent cancer cells from developing resistance to anticancer treatments. For this reason, SET domain bifurcated histone lysine methyltransferase 1 (SETDB1) has gained a lot of attention recently as a cancer target. SETDB1 is a histone lysine methyltransferase that plays an important role in marking euchromatic and heterochromatic regions. Hence, it promotes the silencing of tumor suppressor genes and contributes to carcinogenesis. Some studies revealed that SETDB1 was overexpressed in various human cancer types, which enhanced tumor growth and metastasis. Thus, SETDB1 appears to be an attractive epigenetic target for new cancer treatments. In this review, we have discussed the effects of its overexpression on the progression of tumors and the development of inhibitor drugs that specifically target this enzyme.

Convergent QSAR Models for the Prediction of Cruzain Inhibitors.

Chagas disease is a parasitosis caused by Trypanosoma cruzi. Cruzain, the major cysteine protease from T. cruzi, is an excellent therapeutic target in the search for antichagasic drugs. It is important in the role of cell invasion, replication, differentiation, and metabolism of the parasite. In this work, we developed and assessed multiple quantitative structure-activity relationship (QSAR) models for a set of 61 cruzain inhibitors. These models include two-dimensional (2D) QSAR, three-dimensional (3D) QSAR, such as comparative molecular field analysis (CoMFA) and comparative molecular similarity indices analysis (CoMSIA), and Hologram QSAR (HQSAR). In total, we generated 10 major and 114 minor model variations. Molecular docking was used to successfully align the molecules. All CoMFA and CoMSIA models, which incorporate multiple fields, demonstrated robustness in our analysis. Steric fields exhibited satisfactory convergence in the contour maps, while the electrostatic field converged into a single small region. The HQSAR model taking into consideration only Atoms and Connectivity, with fragment sizes ranging from two to five atoms, was considered the best of the HQSAR variations, despite exhibiting a higher level of deviance. In total, 78 model variations meet the minimum requirements to be considered acceptable. We found that using as few as five descriptors it is possible to obtain robust results with 2D-QSAR. Models such as Random Forest, Tree Ensemble, Linear Regression, and HQSAR are recommended for working with large data sets, while the 3D-QSAR models are intended to study the geometry of the ligands, to optimize them into new and better performing antichagasics. Virtual Screening of a set of hydrazones, guided by the top-performing models, identified promising candidates for experimental validation. Among them, dv007 and dv015 exhibited consistently high predicted pIC50 values (7.26 and 7.24, respectively), making them compelling candidates for further drug development.

Trypanosoma cruzi Sirtuin 2 as a Relevant Druggable Target: New Inhibitors Developed by Computer-Aided Drug Design.

Trypanosoma cruzi, the etiological agent of Chagas disease, relies on finely coordinated epigenetic regulation during the transition between hosts. Herein we targeted the silent information regulator 2 (Sir2) enzyme, a NAD+-dependent class III histone deacetylase, to interfere with the parasites' cell cycle. A combination of molecular modelling with on-target experimental validation was used to discover new inhibitors from commercially available compound libraries. We selected six inhibitors from the virtual screening, which were validated on the recombinant Sir2 enzyme. The most potent inhibitor (CDMS-01, IC50 = 40 µM) was chosen as a potential lead compound.

Machine learning methods applied for the prediction of biological activities of triple reuptake inhibitors.

Major depressive disorder (MDD) is characterized by a series of disabling symptoms like anhedonia, depressed mood, lack of motivation for daily tasks and self-extermination thoughts. The monoamine deficiency hypothesis states that depression is mainly caused by a deficiency of monoamine at the synaptic cleft. Thus, major efforts have been made to develop drugs that inhibit serotonin (SERT), norepinephrine (NET) and dopamine (DAT) transporters and increase the availability of these monoamines. Current gold standard treatment of MDD uses drugs that target one or more monoamine transporters. Triple reuptake inhibitors (TRIs) can target SERT, NET, and DAT simultaneously, and are believed to have the potential to be early onset antidepressants. Quantitative structure-activity relationship models were developed using machine learning algorithms in order to predict biological activities of a series of triple reuptake inhibitor compounds that showed in vitro inhibitory activity against multiple targets. The results, using mostly interpretable descriptors, showed that the internal and external predictive ability of the models are adequate, particularly of the DAT and NET by Random Forest and Support Vector Machine models. The current work shows that models developed from relatively simple, chemically interpretable descriptors can predict the activity of TRIs with similar structure in the applicability domain using ML methods.Communicated by Ramaswamy H. Sarma.

Aporphine and isoquinoline derivatives block glioblastoma cell stemness and enhance temozolomide cytotoxicity.

Glioblastoma (GBM) is the most aggressive and common primary malignant brain tumor with limited available therapeutic approaches. Despite improvements in therapeutic options for GBM patients, efforts to develop new successful strategies remain as major unmet medical needs. Based on the cytotoxic properties of aporphine compounds, we evaluated the biological effect of 12 compounds obtained through total synthesis of ( ±)-apomorphine hydrochloride (APO) against GBM cells. The compounds 2,2,2-trifluoro-1-(1-methylene-3,4-dihydroisoquinolin-2(1H)-yl)ethenone (A5) and ( ±)-1-(10,11-dimethoxy-6a,7-dihydro-4H-dibenzo[de,g]quinolin-6(5H)-yl)ethenone (C1) reduced the viability of GBM cells, with 50% inhibitory concentration ranging from 18 to 48 µM in patient-derived GBM cultures. Our data show that APO, A5 or C1 modulate the expression of DNA damage and apoptotic markers, impair 3D-gliomasphere growth and reduce the expression of stemness markers. Potential activity and protein targets of A5, C1 or APO were predicted in silico based on PASS and SEA software. Dopamine receptors (DRD1 and 5), CYP2B6, CYP2C9 and ABCB1, whose transcripts were differentially expressed in the GBM cells, were among the potential A5 or C1 target proteins. Docking analyses (HQSAR and 3D-QSAR) were performed to characterize possible interactions of ABCB1 and CYP2C9 with the compounds. Notably, A5 or C1 treatment, but not temozolomide (TMZ), reduced significantly the levels of extracellular ATP, suggesting ABCB1 negative regulation, which was correlated with stronger cytotoxicity induced by the combination of TMZ with A5 or C1 on GBM cells. Hence, our data reveal a potential therapeutic application of A5 and C1 as cytotoxic agents against GBM cells and predicted molecular networks that can be further exploited to characterize the pharmacological effects of these isoquinoline-containing substances.

Hydroquinone Exposure Worsens Rheumatoid Arthritis through the Activation of the Aryl Hydrocarbon Receptor and Interleukin-17 Pathways.

Rheumatoid arthritis (RA) development is strongly associated with cigarette smoke exposure, which activates the aryl hydrocarbon receptor (AhR) as a trigger for Th17 inflammatory pathways. We previously demonstrated that the exposure to hydroquinone (HQ), one of the major compounds of cigarette tar, aggravates the arthritis symptomatology in rats. However, the mechanisms related to the HQ-related RA still remain elusive. Cell viability, cytokine secretion, and gene expression were measured in RA human fibroblast-like synoviocytes (RAHFLS) treated with HQ and stimulated or not with TNF-α. Antigen-induced arthritis (AIA) was also elicited in wild type (WT), AhR -/- or IL-17R -/- C57BL/6 mice upon daily exposure to nebulized HQ (25ppm) between days 15 to 21. At day 21, mice were challenged with mBSA and inflammatory parameters were assessed. The in vitro HQ treatment up-regulated TNFR1, TNFR2 expression, and increased ROS production. The co-treatment of HQ and TNF-α enhanced the IL-6 and IL-8 secretion. However, the pre-incubation of RAHFLS with an AhR antagonist inhibited the HQ-mediated cell proliferation and gene expression profile. About the in vivo approach, the HQ exposure worsened the AIA symptoms (edema, pain, cytokines secretion and NETs formation) in WT mice. These AIA effects were abolished in HQ-exposed AhR -/- and IL-17R -/- animals though. Our data demonstrated the harmful HQ influence over the onset of arthritis through the activation and proliferation of synoviocytes. The HQ-related RA severity was also associated with the activation of AhR and IL-17 pathways, highlighting how cigarette smoke compounds can contribute to the RA progression.

Insights into Newly Approved Drugs from a Medicinal Chemistry Perspective.

The development of new drugs is becoming notably harder each decade. To overcome the present pitfalls in the drug development pipeline, such as those related to potency, selectivity, or absorption, distribution, metabolism, excretion and toxicity properties, medicinal chemistry strategies need to be in continuous evolution and need to become even more multidisciplinary. In this review, we present how structure-based, ligand-based, and fragment-based drug design (SBDD, LBDD, and FBDD, respectively) and their respective techniques were used for the design and optimization of successful cases of New Molecular Entities (NMEs) approved by the Food and Drug Administration (FDA).

Design, synthesis and biological activity of novel substituted 3-benzoic acid derivatives as MtDHFR inhibitors.

The enzyme dihydrofolate reductase from M.tuberculosis (MtDHFR) has a high unexploited potential to be a target for new drugs against tuberculosis (TB), due to its importance for pathogen survival. Preliminary studies have obtained fragment-like molecules with low affinity to MtDHFR which can potentially become lead compounds. Taking this into account, the fragment MB872 was used as a prototype for analogue development by bioisosterism/retro-bioisosterism, which resulted in 20 new substituted 3-benzoic acid derivatives. Compounds were active against MtDHFR, with IC50 values ranging from 7 to 40 µM, where compound 4e not only had the best inhibitory activity (IC50 = 7 µM), but also was 71-fold more active than the original fragment MB872. The 4e inhibition kinetics indicated an uncompetitive mechanism, which was supported by molecular modeling which suggested that the compounds can access an independent backpocket from the substrate and competitive inhibitors. Thus, based on these results, substituted 3-benzoic acid derivatives have strong potential to be developed as novel MtDHFR inhibitors and also anti-TB agents.

QSAR studies on the human sirtuin 2 inhibition by non-covalent 7,5,2-anilinobenzamide derivatives.

Sirtuin 2 is a key enzyme in gene expression regulation that is often associated with tumor proliferation control and therefore is a relevant anticancer drug target. Anilinobenzamide derivatives have been discussed as selective sirtuin 2 inhibitors and can be developed further. In the present study, hologram and three-dimensional quantitative structure-activity relationship (HQSAR and 3D-QSAR) analyses were employed for determining structural contributions of a compound series containing human sirtuin-2-selective inhibitors that were then correlated with structural data from the literature. The final QSAR models were robust and predictive according to statistical validation (q2 and r2pred values higher than 0.85 and 0.75, respectively) and could be employed further to generate fragment contribution and contour maps. 3D-QSAR models together with information about the chemical properties of sirtuin 2 inhibitors can be useful for designing novel bioactive ligands.Communicated by Ramaswamy H. Sarma.

Inhibition of Porcine Aminopeptidase M (pAMP) by the Pentapeptide Microginins.

Aminopeptidase M (AMP) inhibition is of interest for several diseases, such as highly vascularized cancer types. AMP can be inhibited by linear pentapeptides isolated from Microcystis aeruginosa LTPNA08 (MG7XX). Porcine AMP inhibition-a model for human AMP-activity was spectrophotometrically measured by the formation of p-nitroanilide from L-leucine-p-nitroanilide substrate by AMP. AMP inhibition by MG770 exhibited comparable inhibition levels to amastatin (IC50 values: 1.20 ± 0.1 µM and 0.98 ± 0.1 µM, respectively), while MG756 was slightly less potent (with IC50 values of 3.26 ± 0.5 µM). Molecular modelling suggests a potential binding mode, based on the interaction with the Zn2+ cofactor, where MG770's extra methyl group contributes to the disturbance of the Zn2+ cofactor complex and highlights the importance of hydrophobicity for the site.

Biased Agonist TRV027 Determinants in AT1R by Molecular Dynamics Simulations.

Functional selectivity is a phenomenon observed in G protein-coupled receptors in which intermediate active-state conformations are stabilized by mutations or ligand binding, resulting in different sets of signaling pathways. Peptides capable of selectively activating ß-arrestin, known as biased agonists, have already been characterized in vivo and could correspond to a new therapeutic approach for treatment of cardiovascular diseases. Despite the potential of biased agonism, the mechanism involved in selective signaling remains unclear. In this work, molecular dynamics simulations were employed to compare the conformational profile of the angiotensin II type 1 receptor (AT1R) crystal bound to angiotensin II, bound to the biased ligand TRV027, and in the apo form. Our results show that both ligands induce changes near the NPxxY motif in transmembrane domain 7 that are related to receptor activation. However, the biased ligand does not cause the rotamer toggle alternative positioning and displays an exclusive hydrogen-bonding pattern. Our work sheds light on the biased agonism mechanism and will help in the future design of novel biased agonists for AT1R.

Targeting the Plasmodium falciparum plasmepsin V by ligand-based virtual screening.

Malaria is a devastating disease depending only on chemotherapy as treatment. However, medication is losing efficacy, and therefore, there is an urgent need for the discovery of novel pharmaceutics. Recently, plasmepsin V, an aspartic protease anchored in the endoplasmaic reticulum, was demonstrated as responsible for the trafficking of parasite-derived proteins to the erythrocytic surface and further validated as a drug target. In this sense, ligand-based virtual screening has been applied to design inhibitors that target plasmepsin V of P. falciparum (PMV). After screening 5.5 million compounds, four novel plasmepsin inhibitors have been identified which were subsequently analyzed for the potency at the cellular level. Since PMV is membrane-anchored, the verification in vivo by using transgenic PMV overexpressing P. falciparum cells has been performed in order to evaluate drug efficacy. Two lead compounds, revealing IC50 values were 44.2 and 19.1 µm, have been identified targeting plasmepsin V in vivo and do not significantly affect the cell viability of human cells up to 300 µm. We herein report the use of the consensus of individual virtual screening as a new technique to design new ligands, and we propose two new lead compounds as novel protease inhibitors to target malaria.

Identification, Characterization and Molecular Modelling Studies of Schistosoma mansoni Dihydrofolate Reductase Inhibitors: From Assay Development to Hit Identification.

INTRODUCTION: Schistosoma mansoni is responsible for virtually all reported cases of schistosomiasis in Latin America and the emergence of praziquantel- and oxaminiquine-resistant strains makes it urgent to develop new schistosomicide agents. Dihydrofolate reductases (DHFR) from bacteria and protozoan parasites are considered validated macromolecular targets for this goal, but S. mansoni DHFR (SmDHFR) has been largely overlooked. To fill this gap in knowledge, the present work describes optimized conditions to carry out thermal shift assays with SmDHFR, as well as a balanced kinetic assay that supports 2,4-diaminopyrimidine derivatives as SmDHFR inhibitors. The most potent inhibitor (2a) shows a large shift of the melting temperature (ΔTm = + 8 ± 0,21 ºC) and a low micromolar IC50 value (12 ± 2,3 µM). Both thermal shift and classical kinectic assay suggest that 2a binds to the substrate binding site (competitive inhibition mechanism). This information guided docking and molecular dynamics studies that probed 2a interaction profile towards SmDHFR. CONCLUSION: In conclusion, this work not only provides standardized assay conditions to identify SmDHFR inhibitors, but also describes the binding profile of the first low micromolar inhibitor of this macromolecular target.

Advances and Challenges in Drug Design of PPARδ Ligands.

BACKGROUND: Peroxisome proliferator-activated receptors (PPAR) are nuclear receptors activated by endogenous fatty acids and prostaglandins that are classified into three types: α, γ and δ, which have different functions and tissue distribution. PPAR modulators have been exploited to the treatment of important metabolic diseases, such as type 2 diabetes mellitus and metabolic syndrome, which are considered relevant epidemic diseases currently. Along the last decades, several studies have reported structural differences between the three PPAR subtypes associated with the discovery of selective ligands, dual and pan-agonists. Nowadays, there are several approved drugs that activate PPARα (fibrates) and PPARγ (glitazones), but up to now there is none clinically used drug targeting PPARδ. Additionally, several side-effects associated with the use of PPARα and γ agonists are reported by regulatory agencies, which do not indicate anymore their use as first-line drugs. OBJECTIVE: A significant new market has grown in the last years, focusing on the development of new PPARδ agonists as drug candidates to treat metabolic diseases and, in this sense, this study proposes to review the structural requirements to achieve selective PPARδ activation, as well to discuss the most relevant agonists in clinical trials, providing information on the current phase in the drug discovery and design targeting PPARδ. CONCLUSION: Several PPARδ ligands with high potency were reported in the literature and were designed or discovered by a combination of experimental and computational approaches. Furthermore, the reported importance of pockets and individual residues at PPARδ binding site as well as the importance of substituent and some physicochemical properties that could help to design of new classes of agonists.

The anti-inflammatory and immunomodulatory potential of braylin: Pharmacological properties and mechanisms by in silico, in vitro and in vivo approaches.

Braylin belongs to the group of natural coumarins, a group of compounds with a wide range of pharmacological properties. Here we characterized the pharmacological properties of braylin in vitro, in silico and in vivo in models of inflammatory/immune responses. In in vitro assays, braylin exhibited concentration-dependent suppressive activity on activated macrophages. Braylin (10-40 µM) reduced the production of nitrite, IL-1ß, TNF-α and IL-6 by J774 cells or peritoneal exudate macrophages stimulated with LPS and IFN-γ. Molecular docking calculations suggested that braylin present an interaction pose to act as a glucocorticoid receptor ligand. Corroborating this idea, the inhibitory effect of braylin on macrophages was prevented by RU486, a glucocorticoid receptor antagonist. Furthermore, treatment with braylin strongly reduced the NF-κB-dependent transcriptional activity on RAW 264.7 cells. Using the complete Freund's adjuvant (CFA)-induced paw inflammation model in mice, the pharmacological properties of braylin were demonstrated in vivo. Braylin (12.5-100 mg/kg) produced dose-related antinociceptive and antiedematogenic effects on CFA model. Braylin did not produce antinociception on the tail flick and hot plate tests in mice, suggesting that braylin-induced antinociception is not a centrally-mediated action. Braylin exhibited immunomodulatory properties on the CFA model, inhibiting the production of pro-inflammatory cytokines IL-1ß, TNF-α and IL-6, while increased the anti-inflammatory cytokine TGF-ß. Our results show, for the first time, anti-inflammatory, antinociceptive and immunomodulatory effects of braylin, which possibly act through the glucocorticoid receptor activation and by inhibition of the transcriptional activity of NF-κB. Because braylin is a phosphodiesterase-4 inhibitor, this coumarin could represent an ideal prototype of glucocorticoid receptor ligand, able to induce synergic immunomodulatory effects.

Fragment-based drug discovery as alternative strategy to the drug development for neglected diseases.

Neglected diseases (NDs) affect large populations and almost whole continents, representing 12% of the global health burden. In contrast, the treatment available today is limited and sometimes ineffective. Under this scenery, the Fragment-Based Drug Discovery emerged as one of the most promising alternatives to the traditional methods of drug development. This method allows achieving new lead compounds with smaller size of fragment libraries. Even with the wide Fragment-Based Drug Discovery success resulting in new effective therapeutic agents against different diseases, until this moment few studies have been applied this approach for NDs area. In this article, we discuss the basic Fragment-Based Drug Discovery process, brief successful ideas of general applications and show a landscape of its use in NDs, encouraging the implementation of this strategy as an interesting way to optimize the development of new drugs to NDs.

An integrated approach towards the discovery of novel non-nucleoside Leishmania major pteridine reductase 1 inhibitors.

Despite the fact that Leishmania ssp are pteridine auxotrophs, Dihydrofolate Reductase-Thymidylate Synthase (DHFR-TS) inhibitors are ineffective against Leishmania major. On the other hand Pteridine Reductase 1 (PTR1) inhibitors proved to be lethal to the parasite. Aiming at identifying hits that lie outside the chemical space of known PTR1 inhibitors, pharmacophore models that differentiate true-binders from decoys and explain the structure-activity relationships of known inhibitors were employed to virtually screen the lead-like subset of ZINC database. This approach leads to the identification of Z80393 (IC50 = 32.31 ± 1.18 µM), whose inhibition mechanism was investigated by Thermal Shift Assays. This experimental result supports a competitive mechanism and was crucial to establish the docking search space as well as select the best pose, which was then investigated by molecular dynamics studies that corroborate the hit putative binding profile towards LmPTR1. The information gathered from such studies shall be useful to design more potent non-nucleoside LmPTR1 inhibitors.

Studies of Staphylococcus aureus FabI inhibitors: fragment-based approach based on holographic structure-activity relationship analyses.

AIM: FabI is a key enzyme in the fatty acid metabolism of Gram-positive bacteria such as Staphylococcus aureus and is an established drug target for known antibiotics such as triclosan. However, due to increasing antibacterial resistance, there is an urgent demand for new drug discovery. Recently, aminopyridine derivatives have been proposed as promising competitive inhibitors of FabI. METHODS: In the present study, holographic structure-activity relationship (HQSAR) analyses were employed for determining structural contributions of a series containing 105 FabI inhibitors. RESULTS & CONCLUSION: The final HQSAR model was robust and predictive according to statistical validation (q2 and r2pred equal to 0.696 and 0.854, respectively) and could be further employed to generate fragment contribution maps. Then, final HQSAR model together with FabI active site information can be useful for designing novel bioactive ligands.

Studies of Staphylococcus aureus FabI inhibitors: fragment-based approach based on holographic structure-activity relationship analyses.

AIM: FabI is a key enzyme in the fatty acid metabolism of Gram-positive bacteria such as Staphylococcus aureus and is an established drug target for known antibiotics such as triclosan. However, due to increasing antibacterial resistance, there is an urgent demand for new drug discovery. Recently, aminopyridine derivatives have been proposed as promising competitive inhibitors of FabI. METHODS: In the present study, holographic structure-activity relationship (HQSAR) analyses were employed for determining structural contributions of a series containing 105 FabI inhibitors. RESULTS & CONCLUSION: The final HQSAR model was robust and predictive according to statistical validation (q2 and r2pred equal to 0.696 and 0.854, respectively) and could be further employed to generate fragment contribution maps. Then, final HQSAR model together with FabI active site information can be useful for designing novel bioactive ligands.

Use of machine learning approaches for novel drug discovery.

INTRODUCTION: The use of computational tools in the early stages of drug development has increased in recent decades. Machine learning (ML) approaches have been of special interest, since they can be applied in several steps of the drug discovery methodology, such as prediction of target structure, prediction of biological activity of new ligands through model construction, discovery or optimization of hits, and construction of models that predict the pharmacokinetic and toxicological (ADMET) profile of compounds. AREAS COVERED: This article presents an overview on some applications of ML techniques in drug design. These techniques can be employed in ligand-based drug design (LBDD) and structure-based drug design (SBDD) studies, such as similarity searches, construction of classification and/or prediction models of biological activity, prediction of secondary structures and binding sites docking and virtual screening. EXPERT OPINION: Successful cases have been reported in the literature, demonstrating the efficiency of ML techniques combined with traditional approaches to study medicinal chemistry problems. Some ML techniques used in drug design are: support vector machine, random forest, decision trees and artificial neural networks. Currently, an important application of ML techniques is related to the calculation of scoring functions used in docking and virtual screening assays from a consensus, combining traditional and ML techniques in order to improve the prediction of binding sites and docking solutions.