Pharmacophore Model Refinement for 11β-Hydroxysteroid Dehydrogenase Inhibitors: Search for Modulators of Intracellular Glucocorticoid Concentrations

The analysis of pesticides and fungicides in the inhibition of human and rat placental 3β-hydroxysteroid dehydrogenase activity: mode of inhibition and mechanism

3β-Hydroxysteroid dehydrogenase/steroid Δ^5,4-isomerase 1 (3β-HSD1) plays a critical role in the biosynthesis of progesterone from pregnenolone in the human placenta to maintain normal pregnancy. Whether they inhibit placental 3β-HSD1 and mode of inhibition remains unclear. In this study, we screened 21 pesticides and fungicides in five classes to inhibit human 3β-HSD1 and compared them to rat homolog 3β-HSD4. 3β-HSD activity was measured by catalyzing pregnenolone to progesterone in the presence of NAD⁺. Of the 21 chemicals, azoles (difenoconazole), thiocarbamates (thiram and ferbam) and organochlorine (hexachlorophene) significantly inhibited human 3β-HSD1 with half maximal inhibitory concentration (IC₅₀) values of 2.77, 0.24, 0.68, and 17.96 μM, respectively. We also found that difenoconazole, ferbam and hexachlorophene are mixed/competitive inhibitors of 3β-HSD1 while thiram is a mixed/noncompetitive inhibitor. Docking analysis showed that difenoconazole and hexachlorophene bound steroid-binding site. Difenoconazole and hexachlorophene except thiram and ferbam also significantly inhibited rat 3β-HSD4 activity with IC₅₀ of 1.12 and 2.28µM, respectively. Thiram and ferbam significantly inhibited human 3β-HSD1 possibly by interfering with cysteine residues, while they had no effects on rat 3β-HSD4. In conclusion, some pesticides potently inhibit placental 3β-HSD, leading to the reduction of progesterone formation.

Combination of In Silico and In Vitro Screening to Identify Novel Glutamate Carboxypeptidase II Inhibitors

Glutamate carboxypeptidase II (GCPII) is a metalloprotease implicated in neurological diseases and prostate oncology. While several classes of potent GCPII-specific inhibitors exist, the development of novel active scaffolds with different pharmacological profiles remains a challenge. Virtual screening followed by in vitro testing is an effective means for the discovery of novel active compounds. Structure- and ligand-based pharmacophore models were created based on a dataset of known GCPII-selective ligands. These models were used in a virtual screening of the SPECS compound library (∼209.000 compounds). Fifty top-scoring virtual hits were further experimentally tested for their ability to inhibit GCPII enzymatic activity in vitro. Six hits were found to have moderate to high inhibitory potency with the best virtual hit, a modified xanthene, inhibiting GCPII with an IC50 value of 353 ± 24 nM. The identification of this novel inhibitory scaffold illustrates the applicability of pharmacophore-based modeling for the discovery of GCPII-specific inhibitors.

Pharmacophore-guided Virtual Screening to Identify New β3 -adrenergic Receptor Agonists

The β3 -adrenergic receptor (β3 -AR) is found in several tissues such as adipose tissue and urinary bladder. It is a therapeutic target because it plays a role in thermogenesis, lipolysis, and bladder relaxation. Two β3 -AR agonists are used clinically: mirabegron 1 and vibegron 2, which are indicated for overactive bladder syndrome. However, these drugs show adverse effects, including increased blood pressure in mirabegron patients. Hence, new β3 -AR agonists are needed as starting points for drug development. Previous pharmacophore modeling studies of the β3 -AR did not involve experimental in vitro validation. Therefore, this study aimed to conduct prospective virtual screening and confirm the biological activity of virtual hits. Ligand-based pharmacophore modeling was performed since no 3D structure of human β3 -AR is yet available. A dataset consisting of β3 -AR agonists was prepared to build and validate the pharmacophore models. The best model was employed for prospective virtual screening, followed by physicochemical property filtering and a docking evaluation. To confirm the activity of the virtual hits, an in vitro assay was conducted, measuring cAMP levels at the cloned β3 -AR. Out of 35 tested compounds, 4 compounds were active in CHO-K1 cells expressing the human β3 -AR, and 8 compounds were active in CHO-K1 cells expressing the mouse β3 -AR.

11β-hydroxysteroid dehydrogenases: A growing multi-tasking family

This review briefly addresses the history of the discovery and elucidation of the three cloned 11β-hydroxysteroid dehydrogenase (11βHSD) enzymes in the human, 11βHSD1, 11βHSD2 and 11βHSD3, an NADP⁺-dependent dehydrogenase also called the 11βHSD1-like dehydrogenase (11βHSD1L), as well as evidence for yet identified 11βHSDs. Attention is devoted to more recently described aspects of this multi-functional family. The importance of 11βHSD substrates other than glucocorticoids including bile acids, 7-keto sterols, neurosteroids, and xenobiotics is discussed, along with examples of pathology when functions of these multi-tasking enzymes are disrupted. 11βHSDs modulate the intracellular concentration of glucocorticoids, thereby regulating the activation of the glucocorticoid and mineralocorticoid receptors, and 7β-27-hydroxycholesterol, an agonist of the retinoid-related orphan receptor gamma (RORγ). Key functions of this nuclear transcription factor include regulation of immune cell differentiation, cytokine production and inflammation at the cell level. 11βHSD1 expression and/or glucocorticoid reductase activity are inappropriately increased with age and in obesity and metabolic syndrome (MetS). Potential causes for disappointing results of the clinical trials of selective inhibitors of 11βHSD1 in the treatment of these disorders are discussed, as well as the potential for more targeted use of inhibitors of 11βHSD1 and 11βHSD2.

Structure-based molecular modeling in SAR analysis and lead optimization

In silico methods like molecular docking and pharmacophore modeling are established strategies in lead identification. Their successful application for finding new active molecules for a target is reported by a plethora of studies. However, once a potential lead is identified, lead optimization, with the focus on improving potency, selectivity, or pharmacokinetic parameters of a parent compound, is a much more complex task. Even though in silico molecular modeling methods could contribute a lot of time and cost-saving by rationally filtering synthetic optimization options, they are employed less widely in this stage of research. In this review, we highlight studies that have successfully used computer-aided SAR analysis in lead optimization and want to showcase sound methodology and easily accessible in silico tools for this purpose.

Isolation, reactivity, pharmacological activities and total synthesis of hispanolone and structurally related diterpenes from Labiatae plants

Hispanolone is a furolabdane diterpene isolated from Ballota hispanica, whose natural product chemistry has been summarized and updated here, including several aspects associated with the isolation, structure determination, hemisynthesis, total synthesis, and pharmacology, and related hispanolone diterpenoids that have attracted the interest of different laboratories from diverse perspective and expertise in the last forty-two years.

Based on the Virtual Screening of Multiple Pharmacophores, Docking and Molecular Dynamics Simulation Approaches toward the Discovery of Novel HPPD Inhibitors

4-Hydroxyphenylpyruvate dioxygenase (HPPD) is an iron-dependent non-heme oxygenase involved in the catabolic pathway of tyrosine, which is an important enzyme in the transformation of 4-hydroxyphenylpyruvic acid to homogentisic acid, and thus being considered as herbicide target. Within this study, a set of multiple structure-based pharmacophore models for HPPD inhibitors were developed. The ZINC and natural product database were virtually screened, and 29 compounds were obtained. The binding mode of HPPD and its inhibitors obtained through molecular docking study showed that the residues of Phe424, Phe381, His308, His226, Gln307 and Glu394 were crucial for activity. Molecular-mechanics-generalized born surface area (MM/GBSA) results showed that the coulomb force, lipophilic and van der Waals (vdW) interactions made major contributions to the binding affinity. These efforts will greatly contribute to design novel and effective HPPD inhibitory herbicides.

Molecular mechanisms of posaconazole- and itraconazole-induced pseudohyperaldosteronism and assessment of other systemically used azole antifungals

Recent reports described cases of severe hypertension and hypokalemia accompanied by low renin and aldosterone levels during antifungal therapy with posaconazole and itraconazole. These conditions represent characteristics of secondary endocrine hypertension caused by mineralocorticoid excess. Different mechanisms can cause mineralocorticoid excess, including inhibition of the adrenal steroidogenic enzymes CYP17A1 and CYP11B1, inhibition of the peripheral cortisol oxidizing enzyme 11β-hydroxysteroid dehydrogenase type 2 (11β-HSD2) or direct activation of the mineralocorticoid receptor (MR). Compared to previous experiments revealing a threefold more potent inhibition of 11β-HSD2 by itraconazole than with posaconazole, the current study found sevenfold stronger CYP11B1 inhibition by posaconazole over itraconazole. Both compounds most potently inhibited CYP11B2. The major pharmacologically active itraconazole metabolite hydroxyitraconazole (OHI) resembled the effects of itraconazole but was considerably less active. Molecular modeling calculations assessed the binding of posaconazole, itraconazole and OHI to 11β-HSD2 and the relevant CYP enzymes, and predicted important interactions not formed by the other systemically used azole antifungals, thus providing an initial explanation for the observed inhibitory activities. Together with available clinical observations, the presented data suggest that itraconazole primarily causes pseudohyperaldosteronism through cortisol-induced MR activation due to 11β-HSD2 inhibition, and posaconazole by CYP11B1 inhibition and accumulation of the mineralocorticoids 11-deoxycorticosterone and 11-deoxycortisol because of hypothalamus-pituitary-adrenal axis (HPA) feedback activation. Therapeutic drug monitoring and introduction of upper plasma target levels may help preventing the occurrence of drug-induced hypertension and hypokalemia. Furthermore, the systemically used azole antifungals voriconazole, isavuconazole and fluconazole did not affect any of the mineralocorticoid excess targets, offering alternative therapeutic options.

Identification of the fungicide epoxiconazole by virtual screening and biological assessment as inhibitor of human 11β-hydroxylase and aldosterone synthase

Humans are constantly exposed to a multitude of environmental chemicals that may disturb endocrine functions. It is crucial to identify such chemicals and uncover their mode-of-action to avoid adverse health effects. 11β-hydroxylase (CYP11B1) and aldosterone synthase (CYP11B2) catalyze the formation of cortisol and aldosterone, respectively, in the adrenal cortex. Disruption of their synthesis by exogenous chemicals can contribute to cardio-metabolic diseases, chronic kidney disease, osteoporosis, and immune-related disorders. This study applied in silico screening and in vitro evaluation for the discovery of xenobiotics inhibiting CYP11B1 and CYP11B2. Several databases comprising environmentally relevant pollutants, chemicals in body care products, food additives and drugs were virtually screened using CYP11B1 and CYP11B2 pharmacophore models. A first round of biological testing used hamster cells overexpressing human CYP11B1 or CYP11B2 to analyze 25 selected virtual hits. Three compounds inhibited CYP11B1 and CYP11B2 with IC50 values below 3 μM. The most potent inhibitor was epoxiconazole (IC50 value of 623 nM for CYP11B1 and 113 nM for CYP11B2, respectively); flurprimidol and ancymidol were moderate inhibitors. In a second round, these three compounds were tested in human adrenal H295R cells endogenously expressing CYP11B1 and CYP11B2, confirming the potent inhibition by epoxiconazole and the more moderate effects by flurprimidol and ancymidol. Thus, the in silico screening, prioritization of chemicals for initial biological tests and use of H295R cells to provide initial mechanistic information is a promising strategy to identify potential endocrine disruptors inhibiting corticosteroid synthesis. A critical assessment of human exposure levels and in vivo evaluation of potential corticosteroid disrupting effects by epoxiconazole is required.

Mushroom Tyrosinase-Based Enzyme Inhibition Assays Are Not Suitable for Bioactivity-Guided Fractionation of Extracts

Tyrosinase (Tyr) catalyzes the rate-limiting step of melanogenesis in human skin and is thus the main target for treating pigmentation disorders today. This has led to an increased research interest in Tyr inhibitors during the last decades, with a frequent focus on polyphenols. In the early stages of drug discovery, it is typical to avoid the high costs of human Tyr by using the more economic mushroom tyrosinase (mh-Tyr). Since some polyphenols are accepted as substrates by mh-Tyr, the present study aimed to more generally investigate this enzyme's specificity toward polyphenols and to discuss its significance in the context of bioactivity-guided fractionation. Mh-Tyr substrates can change the sample color during an inhibition assay, leading to unreliable inhibition constants or to the discontinuation of a bioactivity-guided fractionation campaign. A data set of 56 natural products was investigated and classified into assay interferers (AIs) and noninterferers, using a spectrophotometric and an LC-ESIHRMS assay. Based on these experimental findings, structure-activity relationships defining AIs were deduced and implemented into an in silico tool that will allow for rapid prescreening in the future. We anticipate that these results will aid in the search for new Tyr inhibitors and contribute to the understanding of this enzyme, as well as its optimal use in pharmacological research.

A Strength-Weaknesses-Opportunities-Threats (SWOT) Analysis of Cheminformatics in Natural Product Research

Cheminformatics-based techniques, such as molecular modeling, docking, virtual screening, and machine learning, are well accepted for their usefulness in drug discovery and development of therapeutically relevant small molecules. Although delayed by several decades, their application in natural product research has led to outstanding findings. Combining information obtained from different sources, i.e., virtual predictions, traditional medicine, structural, biochemical, and biological data, and handling big data effectively will open up new possibilities, but also challenges in the future. Strategies and examples will be presented on how to integrate cheminformatics in pharmacognostic workflows to benefit from these two highly complementary disciplines toward streamlining experimental efforts. While considering their limits and pitfalls and by exploiting their potential, computer-aided strategies should successfully guide future studies and thereby augment our knowledge of bioactive natural lead structures.

Drug target prediction using chem- and bioinformatics

The biological pre-validation of natural products (NPs) and their underlying frameworks ensures an unrivaled source of inspiration for chemical probe and drug design. However, the poor knowledge of their drug target counterparts critically hinders the broader exploration of NPs in chemical biology and molecular medicine. Cutting-edge algorithms now provide powerful means for the target deconvolution of phenotypic screen hits and generate motivated research hypotheses. Herein, we present recent progress in artificial intelligence applied to target identification that may accelerate future NP-inspired molecular medicine.

Pharmacophore Modeling and in Silico/in Vitro Screening for Human Cytochrome P450 11B1 and Cytochrome P450 11B2 Inhibitors

Cortisol synthase (CYP11B1) is the main enzyme for the endogenous synthesis of cortisol and its inhibition is a potential way for the treatment of diseases associated with increased cortisol levels, such as Cushing's syndrome, metabolic diseases, and delayed wound healing. Aldosterone synthase (CYP11B2) is the key enzyme for aldosterone biosynthesis and its inhibition is a promising approach for the treatment of congestive heart failure, cardiac fibrosis, and certain forms of hypertension. Both CYP11B1 and CYP11B2 are structurally very similar and expressed in the adrenal cortex. To facilitate the identification of novel inhibitors of these enzymes, ligand-based pharmacophore models of CYP11B1 and CYP11B2 inhibition were developed. A virtual screening of the SPECS database was performed with our pharmacophore queries. Biological evaluation of the selected hits lead to the discovery of three potent novel inhibitors of both CYP11B1 and CYP11B2 in the submicromolar range (compounds 8–10), one selective CYP11B1 inhibitor (Compound 11, IC₅₀ = 2.5 μM), and one selective CYP11B2 inhibitor (compound 12, IC₅₀ = 1.1 μM), respectively. The overall success rate of this prospective virtual screening experiment is 20.8% indicating good predictive power of the pharmacophore models.

Discriminating Agonist from Antagonist Ligands of the Nuclear Receptors Using Different Chemoinformatics Approaches

Nuclear receptors (NRs) constitute an important class of therapeutic targets. During the last 4 years, we tackled the pharmacological profile assessment of NR ligands for which we constructed the NRLiSt BDB. We evaluated and compared the performance of different virtual screening approaches: mean of molecular descriptor distribution values, molecular docking and 3D pharmacophore models. The simple comparison of the distribution profiles of 4885 molecular descriptors between the agonist and antagonist datasets didn't provide satisfying results. We obtained an overall good performance with the docking method we used, Surflex-Dock which was able to discriminate agonist from antagonist ligands. But the availability of PDB structures in the "pharmacological-profile-to-predict-bound-state" (agonist-bound or antagonist-bound) and the availability of enough ligands of both pharmacological profiles constituted limits to generalize this protocol for all NRs. Finally, the 3D pharmacophore modeling approach, allowed us to generate selective agonist pharmacophores and selective antagonist pharmacophores that covered more than 99 % of the whole NRLiSt BDB. This study allowed a better understanding of the pharmacological modulation of NRs with small molecules and could be extended to other therapeutic classes.

Virtual screening applications in short-chain dehydrogenase/reductase research

Several members of the short-chain dehydrogenase/reductase (SDR) enzyme family play fundamental roles in adrenal and gonadal steroidogenesis as well as in the metabolism of steroids, oxysterols, bile acids, and retinoids in peripheral tissues, thereby controlling the local activation of their cognate receptors. Some of these SDRs are considered as promising therapeutic targets, for example to treat estrogen-/androgen-dependent and corticosteroid-related diseases, whereas others are considered as anti-targets as their inhibition may lead to disturbances of endocrine functions, thereby contributing to the development and progression of diseases. Nevertheless, the physiological functions of about half of all SDR members are still unknown. In this respect, in silico tools are highly valuable in drug discovery for lead molecule identification, in toxicology screenings to facilitate the identification of hazardous chemicals, and in fundamental research for substrate identification and enzyme characterization. Regarding SDRs, computational methods have been employed for a variety of applications including drug discovery, enzyme characterization and substrate identification, as well as identification of potential endocrine disrupting chemicals (EDC). This review provides an overview of the efforts undertaken in the field of virtual screening supported identification of bioactive molecules in SDR research. In addition, it presents an outlook and addresses the opportunities and limitations of computational modeling and in vitro validation methods.

Inhibition of 11β-hydroxysteroid dehydrogenase 2 by the fungicides itraconazole and posaconazole

Impaired 11β-hydroxysteroid dehydrogenase type 2 (11β-HSD2)-dependent cortisol inactivation can lead to electrolyte dysbalance, hypertension and cardiometabolic disease. Furthermore, placental 11β-HSD2 essentially protects the fetus from high maternal glucocorticoid levels, and its impaired function has been associated with altered fetal growth and a higher risk for cardio-metabolic diseases in later life. Despite its important role, 11β-HSD2 is not included in current off-target screening approaches. To identify potential 11β-HSD inhibitors among approved drugs, a pharmacophore model was used for virtual screening, followed by biological assessment of selected hits. This led to the identification of several azole fungicides as 11β-HSD inhibitors, showing a significant structure-activity relationship between azole scaffold size, 11β-HSD enzyme selectivity and inhibitory potency. A hydrophobic linker connecting the azole ring to the other, more polar end of the molecule was observed to be favorable for 11β-HSD2 inhibition and selectivity over 11β-HSD1. The most potent 11β-HSD2 inhibition, using cell lysates expressing recombinant human 11β-HSD2, was obtained for itraconazole (IC₅₀ 139±14nM), its active metabolite hydroxyitraconazole (IC₅₀ 223±31nM) and posaconazole (IC₅₀ 460±98nM). Interestingly, experiments with mouse and rat kidney homogenates showed considerably lower inhibitory activity of these compounds towards 11β-HSD2, indicating important species-specific differences. Thus, 11β-HSD2 inhibition by these compounds is likely to be overlooked in preclinical rodent studies. Inhibition of placental 11β-HSD2 by these compounds, in addition to the known inhibition of cytochrome P450 enzymes and P-glycoprotein efflux transport, might contribute to elevated local cortisol levels, thereby affecting fetal programming.

Discriminating agonist and antagonist ligands of the nuclear receptors using 3D-pharmacophores

Nuclear receptors (NRs) constitute an important class of therapeutic targets. We evaluated the performance of 3D structure-based and ligand-based pharmacophore models in predicting the pharmacological profile of NRs ligands using the NRLiSt BDB database. We could generate selective pharmacophores for agonist and antagonist ligands and we found that the best performances were obtained by combining the structure-based and the ligand-based approaches. The combination of pharmacophores that were generated allowed to cover most of the chemical space of the NRLiSt BDB datasets. By screening the whole NRLiSt BDB on our 3D pharmacophores, we demonstrated their selectivity towards their dedicated NRs ligands. The 3D pharmacophores herein presented can thus be used as a predictor of the pharmacological activity of NRs ligands.Graphical AbstractUsing a combination of structure-based and ligand-based pharmacophores, agonist and antagonist ligands of the Nuclear Receptors included in the NRLiSt BDB database could be separated.

Mechanisms of interaction between persistent organic pollutants (POPs) and CYP2B6: An in silico approach

Human Cytochrome P450s (CYP450) are a group of heme-containing metalloenzymes responsible for recognition and metabolism of numerous xenobiotics, including drugs and environmental contaminants. CYP2B6, a member of CYP450, is well known for being a highly inducible and polymorphic enzyme and for its important role in the oxidative metabolism of environmental pollutants, such as the Polybrominated Diphenyl Ethers (PBDEs) and Polychlorinated Biphenyls (PCBs). However the mechanisms of interaction of PBDEs and PCBs with CYP2B6 is not entirely known. In this work, a computational approach was carried out to study the interactions of 41 POPs (17 PBDEs, 17 PCBs, and 7 Dioxins) with four CYP2B6 protein structures downloaded from PDB data base (PDB: 3UA5, 3QOA, 3QU8 and 4I91) using molecular docking protocols with AutoDock Vina. The best binding affinity values (kcal/mol) were obtained for PBDE-99 (-8.5), PCB-187 (-9.6), and octachloro-dibenzo-dioxin (-9.8) that can be attributed to the hydrophobic interactions with important residues, such as Phe-363, in the catalytic site of CYP2B6. Molecular docking validation revealed the best values for PDB: 3UA5 (R = 0.622, p = 0.001) demonstrating the reliability of molecular docking predictions. The information obtained in this work can be useful in evaluating the modes of interaction of xenobiotic compounds with the catalytic site of CYP2B6 and provide insights on the important role of these enzymes in the metabolism of potentially toxic compounds in humans.

Counting on natural products for drug design

Natural products and their molecular frameworks have a long tradition as valuable starting points for medicinal chemistry and drug discovery. Recently, there has been a revitalization of interest in the inclusion of these chemotypes in compound collections for screening and achieving selective target modulation. Here we discuss natural-product-inspired drug discovery with a focus on recent advances in the design of synthetically tractable small molecules that mimic nature's chemistry. We highlight the potential of innovative computational tools in processing structurally complex natural products to predict their macromolecular targets and attempt to forecast the role that natural-product-derived fragments and fragment-like natural products will play in next-generation drug discovery.

Pharmacophore Models and Pharmacophore-Based Virtual Screening: Concepts and Applications Exemplified on Hydroxysteroid Dehydrogenases

Computational methods are well-established tools in the drug discovery process and can be employed for a variety of tasks. Common applications include lead identification and scaffold hopping, as well as lead optimization by structure-activity relationship analysis and selectivity profiling. In addition, compound-target interactions associated with potentially harmful effects can be identified and investigated. This review focuses on pharmacophore-based virtual screening campaigns specifically addressing the target class of hydroxysteroid dehydrogenases. Many members of this enzyme family are associated with specific pathological conditions, and pharmacological modulation of their activity may represent promising therapeutic strategies. On the other hand, unintended interference with their biological functions, e.g., upon inhibition by xenobiotics, can disrupt steroid hormone-mediated effects, thereby contributing to the development and progression of major diseases. Besides a general introduction to pharmacophore modeling and pharmacophore-based virtual screening, exemplary case studies from the field of short-chain dehydrogenase/reductase (SDR) research are presented. These success stories highlight the suitability of pharmacophore modeling for the various application fields and suggest its application also in futures studies.

Pharmacophore modeling for COX-1 and -2 inhibitors with LigandScout in comparison to Discovery Studio

BACKGROUND

Pharmacophore modeling has become an integrated tool in drug discovery. However, no prospective study compares the performance of the available software.

METHODS

The two widely used pharmacophore modeling and screening software programs Discovery Studio and LigandScout were used to generate, validate, and prospectively apply COX-1 and -2 models. Selected virtual hits were tested in cell-free enzymatic assays. The correct retrieval of active compounds was compared.

RESULTS

In the enzymatic testing, 10.5% of the tested hits for COX-2 and 6.6% of the predicted compounds for COX-1 were active. To directly compare the two models, both based on the same PDB entry, were selected for virtual screening. The two programs yielded vastly different hit lists, but both predicted active compounds.

CONCLUSION

To obtain a comprehensive selection of active compounds, more than one program should be used for modeling.

Computational fishing of new DNA methyltransferase inhibitors from natural products

DNA methyltransferase inhibitors (DNMTis) have become an alternative for cancer therapies. However, only two DNMTis have been approved as anticancer drugs, although with some restrictions. Natural products (NPs) are a promising source of drugs. In order to find NPs with novel chemotypes as DNMTis, 47 compounds with known activity against these enzymes were used to build a LDA-based QSAR model for active/inactive molecules (93% accuracy) based on molecular descriptors. This classifier was employed to identify potential DNMTis on 800 NPs from NatProd Collection. 447 selected compounds were docked on two human DNA methyltransferase (DNMT) structures (PDB codes: 3SWR and 2QRV) using AutoDock Vina and Surflex-Dock, prioritizing according to their score values, contact patterns at 4 Å and molecular diversity. Six consensus NPs were identified as virtual hits against DNMTs, including 9,10-dihydro-12-hydroxygambogic, phloridzin, 2',4'-dihydroxychalcone 4'-glucoside, daunorubicin, pyrromycin and centaurein. This method is an innovative computational strategy for identifying DNMTis, useful in the identification of potent and selective anticancer drugs.

Accessing biological actions of Ganoderma secondary metabolites by in silico profiling

The species complex around the medicinal fungus Ganoderma lucidum Karst. (Ganodermataceae) is widely known in traditional medicines, as well as in modern applications such as functional food or nutraceuticals. A considerable number of publications reflects its abundance and variety in biological actions either provoked by primary metabolites, such as polysaccharides, or secondary metabolites, such as lanostane-type triterpenes. However, due to this remarkable amount of information, a rationalization of the individual Ganoderma constituents to biological actions on a molecular level is quite challenging. To overcome this issue, a database was generated containing meta-information, i.e., chemical structures and biological actions of hitherto identified Ganoderma constituents (279). This was followed by a computational approach subjecting this 3D multi-conformational molecular dataset to in silico parallel screening against an in-house collection of validated structure- and ligand-based 3D pharmacophore models. The predictive power of the evaluated in silico tools and hints from traditional application fields served as criteria for the model selection. Thus, the focus was laid on representative druggable targets in the field of viral infections (5) and diseases related to the metabolic syndrome (22). The results obtained from this in silico approach were compared to bioactivity data available from the literature. 89 and 197 Ganoderma compounds were predicted as ligands of at least one of the selected pharmacological targets in the antiviral and the metabolic syndrome screening, respectively. Among them only a minority of individual compounds (around 10%) has ever been investigated on these targets or for the associated biological activity. Accordingly, this study discloses putative ligand target interactions for a plethora of Ganoderma constituents in the empirically manifested field of viral diseases and metabolic syndrome which serve as a basis for future applications to access yet undiscovered biological actions of Ganoderma secondary metabolites on a molecular level.

Methods for generating and applying pharmacophore models as virtual screening filters and for bioactivity profiling

Biological effects of small molecules in an organism result from favorable interactions between the molecules and their target proteins. These interactions depend on chemical functionalities, bonds, and their 3D-orientations towards each other. These 3D-arrangements of chemical functionalities that make a small molecule active towards its target can be described by pharmacophore models. In these models, chemical functionalities are represented as so-called features. Commonly, they are obtained either from a set of active compounds or directly from the observed protein-ligand interactions as present in X-ray crystal structures, NMR structures, or docking poses. In this review, we explain the basics of pharmacophore modeling including dataset generation, 3D-representations and conformational analysis of small molecules, pharmacophore model construction, model validation, and its benefits to virtual screening and other applications.

Experimentally validated HERG pharmacophore models as cardiotoxicity prediction tools

The goal of this study was to design, experimentally validate, and apply a virtual screening workflow to identify novel hERG channel blockers. The hERG channel is an important antitarget in drug development since cardiotoxic risks remain as a major cause of attrition. A ligand-based pharmacophore model collection was developed and theoretically validated. The seven most complementary and suitable models were used for virtual screening of in-house and commercially available compound libraries. From the hit lists, 50 compounds were selected for experimental validation through bioactivity assessment using patch clamp techniques. Twenty compounds inhibited hERG channels expressed in HEK 293 cells with IC50 values ranging from 0.13 to 2.77 μM, attesting to the suitability of the models as cardiotoxicity prediction tools in a preclinical stage.

Ligand-based pharmacophore modeling and virtual screening for the discovery of novel 17β-hydroxysteroid dehydrogenase 2 inhibitors

17β-Hydroxysteroid dehydrogenase 2 (17β-HSD2) catalyzes the inactivation of estradiol into estrone. This enzyme is expressed only in a few tissues, and therefore its inhibition is considered as a treatment option for osteoporosis to ameliorate estrogen deficiency. In this study, ligand-based pharmacophore models for 17β-HSD2 inhibitors were constructed and employed for virtual screening. From the virtual screening hits, 29 substances were evaluated in vitro for 17β-HSD2 inhibition. Seven compounds inhibited 17β-HSD2 with low micromolar IC50 values. To investigate structure-activity relationships (SAR), 30 more derivatives of the original hits were tested. The three most potent hits, 12, 22, and 15, had IC50 values of 240 nM, 1 μM, and 1.5 μM, respectively. All but 1 of the 13 identified inhibitors were selective over 17β-HSD1, the enzyme catalyzing conversion of estrone into estradiol. Three of the new, small, synthetic 17β-HSD2 inhibitors showed acceptable selectivity over other related HSDs, and six of them did not affect other HSDs.