In Silico Models to Predict the Perturbation of Molecular Initiating Events Related to Thyroid Hormone Homeostasis.

Disturbance of the thyroid hormone homeostasis has been associated with adverse health effects such as goiters and impaired mental development in humans and thyroid tumors in rats. In vitro and in silico methods for predicting the effects of small molecules on thyroid hormone homeostasis are currently being explored as alternatives to animal experiments, but are still in an early stage of development. The aim of this work was the development of a battery of in silico models for a set of targets involved in molecular initiating events of thyroid hormone homeostasis: deiodinases 1, 2, and 3, thyroid peroxidase (TPO), thyroid hormone receptor (TR), sodium/iodide symporter, thyrotropin-releasing hormone receptor, and thyroid-stimulating hormone receptor. The training data sets were compiled from the ToxCast database and related scientific literature. Classical statistical approaches as well as several machine learning methods (including random forest, support vector machine, and neural networks) were explored in combination with three data balancing techniques. The models were trained on molecular descriptors and fingerprints and evaluated on holdout data. Furthermore, multi-task neural networks combining several end points were investigated as a possible way to improve the performance of models for which the experimental data available for model training are limited. Classifiers for TPO and TR performed particularly well, with F1 scores of 0.83 and 0.81 on the holdout data set, respectively. Models for the other studied targets yielded F1 scores of up to 0.77. An in-depth analysis of the reliability of predictions was performed for the most relevant models. All data sets used in this work for model development and validation are available in the Supporting Information.

KnowTox: pipeline and case study for confident prediction of potential toxic effects of compounds in early phases of development.

Risk assessment of newly synthesised chemicals is a prerequisite for regulatory approval. In this context, in silico methods have great potential to reduce time, cost, and ultimately animal testing as they make use of the ever-growing amount of available toxicity data. Here, KnowTox is presented, a novel pipeline that combines three different in silico toxicology approaches to allow for confident prediction of potentially toxic effects of query compounds, i.e. machine learning models for 88 endpoints, alerts for 919 toxic substructures, and computational support for read-across. It is mainly based on the ToxCast dataset, containing after preprocessing a sparse matrix of 7912 compounds tested against 985 endpoints. When applying machine learning models, applicability and reliability of predictions for new chemicals are of utmost importance. Therefore, first, the conformal prediction technique was deployed, comprising an additional calibration step and per definition creating internally valid predictors at a given significance level. Second, to further improve validity and information efficiency, two adaptations are suggested, exemplified at the androgen receptor antagonism endpoint. An absolute increase in validity of 23% on the in-house dataset of 534 compounds could be achieved by introducing KNNRegressor normalisation. This increase in validity comes at the cost of efficiency, which could again be improved by 20% for the initial ToxCast model by balancing the dataset during model training. Finally, the value of the developed pipeline for risk assessment is discussed using two in-house triazole molecules. Compared to a single toxicity prediction method, complementing the outputs of different approaches can have a higher impact on guiding toxicity testing and de-selecting most likely harmful development-candidate compounds early in the development process.

Computer-Aided Selective Optimization of Side Activities of Talinolol.

Selective optimization of side activities is a valuable source of novel lead structures in drug discovery. In this study, a computer-aided approach was used to deorphanize the pleiotropic cholesterol-lowering effects of the beta-blocker talinolol, which result from the inhibition of the enzyme soluble epoxide hydrolase (sEH). X-ray structure analysis of the sEH in complex with talinolol enables a straightforward optimization of inhibitory potency. The resulting lead structure exhibited in vivo activity in a rat model of diabetic neuropatic pain.

DrugBank screening revealed alitretinoin and bexarotene as liver X receptor modulators.

In silico screening of DrugBank database to detect liver X receptor (LXR) agonism of marketed drugs using a self-organizing map and successive LXR-Gal4 hybrid reporter gene assay evaluation in vitro discovered alitretinoin and bexarotene as partial liver X receptor agonists. Dose-response curves demonstrated that plasma concentrations observed in clinical trials are sufficient for LXR activation and thus could account for LXR-mediated side-effects such as hypercholesterolemia and hyperlipidemia. The discovered drugs are the first reported dual LXR/RXR agonists and can serve as lead structures for LXR and dual LXR/RXR modulator development.

Pyrazolo[1,5a]pyrimidines as a new class of FUSE binding protein 1 (FUBP1) inhibitors.

The transcriptional regulator FUSE binding protein 1 (FUBP1) is aberrantly upregulated in various malignancies, fulfilling its oncogenic role by the deregulation of critical genes involved in cell cycle control and apoptosis regulation. Thus, the pharmaceutical inhibition of this protein would represent an encouraging novel targeted chemotherapy. Here, we demonstrate the identification and initial optimization of a pyrazolo[1,5a]pyrimidine-based FUBP1 inhibitor derived from medium throughput screening, which interferes with the binding of FUBP1 to its single stranded target DNA FUSE. We were able to generate a new class of FUBP1 interfering molecules with in vitro and biological activity. In biophysical assays, we could show that our best inhibitor, compound 6, potently inhibits the binding of FUBP1 to the FUSE sequence with an IC50 value of 11.0µM. Furthermore, hepatocellular carcinoma cells exhibited sensitivity towards the treatment with compound 6, resulting in reduced cell expansion and induction of cell death. Finally, we provide insights into the corresponding SAR landscape, leading to a prospective enhancement in potency and cellular efficacy.

Emergence of succinate dehydrogenase inhibitor resistance of Pyrenophora teres in Europe.

BACKGROUND: Net blotch caused by Pyrenophora teres is an important disease of barley worldwide. In addition to strobilurins (quinone ouside inhibitors) and azoles (demethylation inhibitors), succinate dehydrogenase inhibitors (SDHIs) are very effective fungicides for net blotch control. Recently, SDHI-resistant isolates have been found in the field. Intensive sensitivity monitoring programmes across Europe were carried out to investigate the situation concerning SDHI resistance in P. teres. RESULTS: The first isolates with a lower sensitivity to SDHIs registered in barley were found in Germany in 2012 and carried the B-H277Y substitution in the succinate dehydrogenase enzyme. In 2013 and 2014, a significant increase in isolates with lower SDHI sensitivity was detected mainly in France and Germany, and the range of target-site mutations increased. Most of the resistant isolates carried the C-G79R substitution, which exhibits a strong impact on all SDHIs in microtitre tests. All SDHIs tested were shown to be cross-resistant. Other substitutions are gaining in importance, e.g. C-N75S in France and D-D145G in Germany. So far, no double mutants in SDH genes have been detected. Glasshouse tests showed that SDHI-resistant isolates were still controlled by the SDHI fluxapyroxad when applied preventively. To date, most isolates with C-G79R substitution have not simultaneously carried the F129L change in cytochrome b, which causes resistance towards QoI fungicides at low to moderate levels. CONCLUSION: Several target-site mutations in the genes of subunits SDH-B, SDH-C and SDH-D with different impact on SDHI fungicides were detected. The pattern of mutations varied from year to year and between different regions. Strict resistance management strategies are recommended to maintain SDHIs as effective tools for net blotch control, especially in areas with low frequencies of resistant isolates. © 2016 Society of Chemical Industry.

Synthetic cannabinoids: In silico prediction of the cannabinoid receptor 1 affinity by a quantitative structure-activity relationship model.

The number of new synthetic psychoactive compounds increase steadily. Among the group of these psychoactive compounds, the synthetic cannabinoids (SCBs) are most popular and serve as a substitute of herbal cannabis. More than 600 of these substances already exist. For some SCBs the in vitro cannabinoid receptor 1 (CB1) affinity is known, but for the majority it is unknown. A quantitative structure-activity relationship (QSAR) model was developed, which allows the determination of the SCBs affinity to CB1 (expressed as binding constant (Ki)) without reference substances. The chemically advance template search descriptor was used for vector representation of the compound structures. The similarity between two molecules was calculated using the Feature-Pair Distribution Similarity. The Ki values were calculated using the Inverse Distance Weighting method. The prediction model was validated using a cross validation procedure. The predicted Ki values of some new SCBs were in a range between 20 (considerably higher affinity to CB1 than THC) to 468 (considerably lower affinity to CB1 than THC). The present QSAR model can serve as a simple, fast and cheap tool to get a first hint of the biological activity of new synthetic cannabinoids or of other new psychoactive compounds.

PENG: a neural gas-based approach for pharmacophore elucidation. method design, validation, and virtual screening for novel ligands of LTA4H.

The pharmacophore concept is commonly employed in virtual screening for hit identification. A pharmacophore is generally defined as the three-dimensional arrangement of the structural and physicochemical features of a compound responsible for its affinity to a pharmacological target. Given a number of active ligands binding to a particular target in the same manner, it can reasonably be assumed that they have some shared features, a common pharmacophore. We present a growing neural gas (GNG)-based approach for the extraction of the relevant features which we called PENG (pharmacophore elucidation by neural gas). Results of retrospective validation indicate an acceptable quality of the generated models. Additionally a prospective virtual screening for leukotriene A4 hydrolase (LTA4H) inhibitors was performed. LTA4H is a bifunctional zinc metalloprotease which displays both epoxide hydrolase and aminopeptidase activity. We could show that the PENG approach is able to predict the binding mode of the ligand by X-ray crystallography. Furthermore, we identified a novel chemotype of LTA4H inhibitors.

VAMMPIRE-LORD: a web server for straightforward lead optimization using matched molecular pairs.

VAMMPIRE-LORD (lead optimization by rational design) describes an innovative strategy to improve the binding affinity of a defined lead compound using 3D matched molecular pairs (3D-MMPs). 3D-MMPs are defined as pairs of molecules that differ in exactly one structural transformation and have a known bioactive conformation. We developed a novel atom-pair descriptor (LORD_FP) that represents the ligand-as well as the receptor environment-of a chemical transformation and built a predictive model based on 17602 3D-MMPs. We demonstrate that the created model is able to extrapolate the knowledge of a chemical transformation and the associated effect on ligand affinity to any similar system. VAMMPIRE-LORD was implemented as a web server that guides the user step-by-step through the optimization process of a defined lead compound.

Multi-dimensional target profiling of N,4-diaryl-1,3-thiazole-2-amines as potent inhibitors of eicosanoid metabolism.

Eicosanoids like leukotrienes and prostaglandins play a considerable role in inflammation. Produced within the arachidonic acid (AA) cascade, these lipid mediators are involved in the pathogenesis of pain as well as acute and chronic inflammatory diseases like rheumatoid arthritis and asthma. With regard to the lipid cross-talk within the AA pathway, a promising approach for an effective anti-inflammatory therapy is the development of inhibitors targeting more than one enzyme of this cascade. Within this study, thirty N-4-diaryl-1,3-thiazole-2-amine based compounds with different substitution patterns were synthesized and tested in various cell-based assays to investigate their activity and selectivity profile concerning five key enzymes involved in eicosanoid metabolism (5-, 12-, 15-lipoxygenase (LO), cyclooxygenase-1 and -2 (COX-1/-2)). With compound 7, 2-(4-phenyl)thiazol-2-ylamino)phenol (ST-1355), a multi-target ligand targeting all tested enzymes is presented, whereas compound 9, 2-(4-(4-chlorophenyl)thiazol-2-ylamino)phenol (ST-1705), represents a potent and selective 5-LO and COX-2 inhibitor with an IC50 value of 0.9 ± 0.2 µM (5-LO) and a residual activity of 9.1 ± 1.1% at 10 µM (COX-2 product formation). The promising characteristics and the additional non-cytotoxic profile of both compounds reveal new lead structures for the treatment of eicosanoid-mediated diseases.

VAMMPIRE: a matched molecular pairs database for structure-based drug design and optimization.

Structure-based optimization to improve the affinity of a lead compound is an established approach in drug discovery. Knowledge-based databases holding molecular replacements can be supportive in the optimization process. We introduce a strategy to relate the substitution effect within matched molecular pairs (MMPs) to the atom environment within the cocrystallized protein-ligand complex. Virtually Aligned Matched Molecular Pairs Including Receptor Environment (VAMMPIRE) database and the supplementary web interface ( http://vammpire.pharmchem.uni-frankfurt.de ) provide valuable information for structure-based lead optimization.

Argyreia nervosa (Burm. f.): receptor profiling of lysergic acid amide and other potential psychedelic LSD-like compounds by computational and binding assay approaches.

ETHNOPHARMACOLOGICAL RELEVANCE: The convolvulacea Argyreia nervosa (Burm. f.) is well known as an important medical plant in the traditional Ayurvedic system of medicine and it is used in numerous diseases (e.g. nervousness, bronchitis, tuberculosis, arthritis, and diabetes). Additionally, in the Indian state of Assam and in other regions Argyreia nervosa is part of the traditional tribal medicine (e.g. the Santali people, the Lodhas, and others). In the western hemisphere, Argyreia nervosa has been brought in attention as so called "legal high". In this context, the seeds are used as source of the psychoactive ergotalkaloid lysergic acid amide (LSA), which is considered as the main active ingredient. AIM OF THE STUDY: As the chemical structure of LSA is very similar to that of lysergic acid diethylamide (LSD), the seeds of Argyreia nervosa (Burm. f.) are often considered as natural substitute of LSD. In the present study, LSA and LSD have been compared concerning their potential pharmacological profiles based on the receptor binding affinities since our recent human study with four volunteers on p.o. application of Argyreia nervosa seeds has led to some ambiguous effects. MATERIAL AND METHODS: In an initial step computer-aided in silico prediction models on receptor binding were employed to screen for serotonin, norepinephrine, dopamine, muscarine, and histamine receptor subtypes as potential targets for LSA. In addition, this screening was extended to accompany ergotalkaloids of Argyreia nervosa (Burm. f.). In a verification step, selected LSA screening results were confirmed by in vitro binding assays with some extensions to LSD. RESULTS: In the in silico model LSA exhibited the highest affinity with a pKi of about 8.0 at α1A, and α1B. Clear affinity with pKi>7 was predicted for 5-HT1A, 5-HT1B, 5-HT1D, 5-HT6, 5-HT7, and D2. From these receptors the 5-HT1D subtype exhibited the highest pKi with 7.98 in the prediction model. From the other ergotalkaloids, agroclavine and festuclavine also seemed to be highly affine to the 5-HT1D-receptor with pKi>8. In general, the ergotalkaloids of Argyreia nervosa seem to prefer serotonin and dopamine receptors (pKi>7). However, with exception of ergometrine/ergometrinine only for 5-HT3A, and histamine H2 and H4 no affinities were predicted. Compared to LSD, LSA exhibited lower binding affinities in the in vitro binding assays for all tested receptor subtypes. However, with a pKi of 7.99, 7.56, and 7.21 a clear affinity for 5-HT1A, 5-HT2, and α2 could be demonstrated. For DA receptor subtypes and the α1-receptor the pKi ranged from 6.05 to 6.85. CONCLUSION: Since the psychedelic activity of LSA in the recent human study was weak and although LSA from Argyreia nervosa is often considered as natural exchange for LSD, LSA should not be regarded as LSD-like psychedelic drug. However, vegetative side effects and psychotropic effects may be triggered by serotonin or dopamine receptor subtypes.

Exploring the chemical space of multitarget ligands using aligned self-organizing maps.

Design of multitarget drugs and polypharmacological compounds has become popular during the past decade. However, the main approach to design such compounds is to link two selective ligands via a flexible linker. Although such chimeric ligands often have reasonable potency in vitro, the in vivo efficacy is low due to high molecular weight, low ligand efficiency, and poor pharmacokinetic profile. We developed an unprecedented in silico approach for fragment-based design of multitarget ligands. It relies on superposition of the chemical spaces related to the affinity on single targets represented by self-organizing maps. We used this approach for screening of molecular fragments, which bind to the enzymes 5-lipoxygenase (5-LO) and soluble epoxide hydrolase (sEH). Using STD-NMR and activity-based assays, we were able to identify fragments binding to both targets. Furthermore, we were able to expand one of the fragments to a potent dual inhibitor bearing a reasonable molecular weight (MW = 446) and high affinity to both targets (IC50 of 0.03 µM toward 5-LO and 0.17 µM toward sEH).

Evaluation of structure-derived pharmacophore of soluble epoxide hydrolase inhibitors by virtual screening.

The soluble epoxide hydrolase (sEH) is an enzyme located downstream of the CYP 450 branch of the arachidonic acid cascade and can be linked to a number of indications, including cardiovascular disorders, diabetes and inflammatory processes. Numerous inhibitors (sEHI) have been reported, mostly based on urea or amide scaffolds. The search for valid bioisosteric replacements is an ongoing challenge in the discovery of sEHI. We developed a receptor-based pharmacophore model on the basis of 13 crystal structures of the sEH and performed a virtual screening for novel compounds. The virtual screening hits were verified in vitro proving the basic applicability of the model and leading to novel non-urea sEHI.

Investigation of imatinib and other approved drugs as starting points for antidiabetic drug discovery with FXR modulating activity.

A self-organizing map (SOM) is a virtual screening method used for correlation of molecular structure and potential biological activity on a certain target and offers a way to represent multi-dimensional data of large databases in a two-dimensional space. Large databases, for example the DrugBank database, provide information about biological activity and chemical structure of small molecules and are widely used in drug development for identification of new lead structures. The farnesoid X receptor (FXR) is a ligand activated transcription factor involved in key regulation mechanisms within glucose and lipid homeostasis. Although FXR became an established target in drug development for diseases associated with lipid, glucose or hepatic disorders during the last decade, none of the developed compounds have reached later phases of clinical development so far. We used a SOM trained with known FXR ligands to screen the DrugBank database for potential ligands for FXR. In this article, we report the successful identification of six approved drugs out of the Drugbank as FXR modulators (ketoconazole, pentamidine, dobutamine, imatinib, papaverine and montelukast) by using a SOM for screening of the DrugBank database. We show FXR modulation by selected compounds in a full length FXR transactivation assay and modulation of a FXR target gene by imatinib.

Molecular characterization of EP6--a novel imidazo[1,2-a]pyridine based direct 5-lipoxygenase inhibitor.

5-Lipoxygenase (5-LO) is a crucial enzyme of the arachidonic acid (AA) cascade and catalyzes the formation of bioactive leukotrienes (LTs) which are involved in inflammatory diseases and allergic reactions. The pathophysiological effects of LTs are considered to be prevented by 5-LO inhibitors. In this study we present cyclohexyl-[6-methyl-2-(4-morpholin-4-yl-phenyl)-imidazo[1,2-a]pyridin-3-yl]-amine (EP6), a novel imidazo[1,2-a]pyridine based compound and its characterization in several in vitro assays. EP6 suppresses 5-LO activity in intact polymorphonuclear leukocytes with an IC(50) value of 0.16µM and exhibits full inhibitory potency in cell free assays (IC(50) value of 0.05µM for purified 5-LO). The efficacy of EP6 was not affected by the redox tone or the concentration of exogenous AA, characteristic drawbacks known for the class of nonredox-type 5-LO inhibitors. Furthermore, EP6 suppressed 5-LO activity independently of the cell stimulus or the activation pathway of 5-LO contrary to what is known for some nonredox-type inhibitors. Using molecular modeling and site-directed mutagenesis studies, we were able to derive a feasible binding region within the C2-like domain of 5-LO that can serve as a new starting point for optimization and development of new 5-LO inhibitors targeting this site. EP6 has promising effects on cell viability of tumor cells without mutagenic activity. Hence the drug may possess potential for intervention with inflammatory and allergic diseases and certain types of cancer including leukemia.

Dual-target virtual screening by pharmacophore elucidation and molecular shape filtering.

Dual-target inhibitors gained increased attention in the past years. A novel in silico approach was employed for the discovery of dual 5-lipoxygenase/soluble epoxide hydrolase inhibitors. The ligand-based approach uses excessive pharmacophore elucidation and pharmacophore alignment in conjunction with shape-based scoring. The virtual screening results were verified in vitro, leading to nine novel inhibitors including a dual-target compound.

Computational tools for polypharmacology and repurposing.

Most drugs act on a multitude of targets rather than on one single target. Polypharmacology, an upcoming branch of pharmaceutical science, deals with the recognition of these off-target activities of small chemical compounds. Due to the high amount of data to be processed, application of computational methods is indispensable in this area. This review summarizes the most important in silico approaches for polypharmacology. The described methods comprise network pharmacology, machine learning techniques and chemogenomic approaches. The use of these methods for drug repurposing as a branch of drug discovery and development is discussed. Furthermore, a broad range of prospective applications is summarized to give the reader an overview of possibilities and limitations of the described techniques.