Profiling withanolide A for therapeutic targets in neurodegenerative diseases.

To identify new potential therapeutic targets for neurodegenerative diseases, we initiated activity-based protein profiling studies with withanolide A (WitA), a known neuritogenic constituent of Withania somnifera root with unknown mechanism of action. Molecular probes were designed and synthesized, and led to the discovery of the glucocorticoid receptor (GR) as potential target. Molecular modeling calculations using the VirtualToxLab predicted a weak binding affinity of WitA for GR. Neurite outgrowth experiments in human neuroblastoma SH-SY5Y cells further supported a glucocorticoid-dependent mechanism, finding that WitA was able to reverse the outgrowth inhibition mediated by dexamethasone (Dex). However, further GR binding and transactivation assays found no direct interference of WitA. Further molecular modeling analysis suggested that WitA, although forming several contacts with residues in the GR binding pocket, is lacking key stabilizing interactions as observed for Dex. Taken together, the data suggest that WitA-dependent induction of neurite outgrowth is not through a direct effect on GR, but might be mediated through a closely related pathway. Further experiments should evaluate a possible role of GR modulators and/or related signaling pathways such as ERK, Akt, NF-κB, TRα, or Hsp90 as potential targets in the WitA-mediated neuromodulatory effects.

In Silico Prediction of the Toxic Potential of Lupeol.

Lupeol is a natural triterpenoid found in many plant species such as mango. This compound is the principal active component of many traditional herbal medicines. In the past decade, a considerable number of publications dealt with lupeol and its analogues due to the interest in their pharmacological activities against cancer, inflammation, arthritis, diabetes, and heart disease. To identify further potential applications of lupeol and its analogues, it is necessary to investigate their mechanisms of action, particularly their interaction with off-target proteins that may trigger adverse effects or toxicity. In this study, we simulated and quantified the interaction of lupeol and 11 of its analogues toward a series of 16 proteins known or suspected to trigger adverse effects employing the VirtualToxLab. This software provides a thermodynamic estimate of the binding affinity, and the results were challenged by molecular-dynamics simulations, which allow probing the kinetic stability of the underlying protein-ligand complexes. Our results indicate that there is a moderate toxic potential for lupeol and some of its analogues, by targeting and binding to nuclear receptors involved in fertility, which could trigger undesired adverse effects.

VirtualToxLab: Exploring the Toxic Potential of Rejuvenating Substances Found in Traditional Medicines.

Docking and quantifying the binding of small molecules to the 3D structure of a macromolecular bioregulator by computational techniques is a typical task in R&D aimed at the design and optimization of medically or otherwise active compounds. Much less known is the fact that these methods can be successfully applied for the purpose of toxicity prediction-for example, detecting a compound's potential binding to so-called "off-targets" already at the preclinical stage. In this chapter, we provide an overview of such a computational approach, discuss its strengths and weaknesses, and include a case study-focused on natural compounds present in traditional medicines.

Molecular mechanisms of endocrine and metabolic disruption: An in silico study on antitrypanosomal natural products and some derivatives.

The VirtualToxLab is an in silico technology for estimating the toxic potential - endocrine and metabolic disruption, as well as aspects of carcinogenicity and cardiotoxicity - of drugs, chemicals and natural products. The technology is based on an automated protocol that simulates and quantifies the binding of small molecules towards a series of currently 16 proteins, known or suspected to trigger adverse effects. The simulations are conducted at the atomic level and explicitly allow for a mechanistic interpretation of the results (in real-time 3D/4D), thereby complying with the Setubal principles put forward in 2002 for computational approaches to toxicology. Moreover, the underlying "ab-initio" protocol is independent from any training data and makes the approach universal with respect to the applicability domain. The VirtualToxLab runs in client-server mode and is freely available to academic and non-profit organizations. As the underlying technology yields a thermodynamic estimate of the binding affinity, the associated ligand-protein complexes have been challenged by molecular-dynamics simulations to probe their kinetic stability. Human African trypanosomiasis is a neglected tropical disease caused by two subspecies of Trypanosoma brucei. The control of this parasitic infection relies on a few chemotherapeutic agents, most of which were discovered decades ago and pose many challenges including adverse side effects, poor efficacy, and the occurrence of drug resistances. Natural products, on the other hand, offer a high potential for the discovery of new drug leads due to their chemical diversity. In this in silico study, we analyze a series of 89 natural products and derivatives displaying anti-trypanosomal activity for their potential to trigger adverse effects. Our results indicate a moderate potential for a number of those compounds to bind to nuclear receptors and thereby ease the development of endocrine disregulation. A few others would seem to inhibit enzymes of the cytochrome P450 family and, hence, sustain drug-drug interactions.

OpenVirtualToxLab--a platform for generating and exchanging in silico toxicity data.

The VirtualToxLab is an in silico technology for estimating the toxic potential--endocrine and metabolic disruption, some aspects of carcinogenicity and cardiotoxicity--of drugs, chemicals and natural products. The technology is based on an automated protocol that simulates and quantifies the binding of small molecules towards a series of currently 16 proteins, known or suspected to trigger adverse effects: 10 nuclear receptors (androgen, estrogen α, estrogen ß, glucocorticoid, liver X, mineralocorticoid, peroxisome proliferator-activated receptor γ, progesterone, thyroid α, thyroid ß), four members of the cytochrome P450 enzyme family (1A2, 2C9, 2D6, 3A4), a cytosolic transcription factor (aryl hydrocarbon receptor) and a potassium ion channel (hERG). The toxic potential of a compound--its ability to trigger adverse effects--is derived from its computed binding affinities toward these very proteins: the computationally demanding simulations are executed in client-server model on a Linux cluster of the University of Basel. The graphical-user interface supports all computer platforms, allows building and uploading molecular structures, inspecting and downloading the results and, most important, rationalizing any prediction at the atomic level by interactively analyzing the binding mode of a compound with its target protein(s) in real-time 3D. Access to the VirtualToxLab is available free of charge for universities, governmental agencies, regulatory bodies and non-profit organizations.

Exploring the free-energy landscape of carbohydrate-protein complexes: development and validation of scoring functions considering the binding-site topology.

Carbohydrates play a key role in a variety of physiological and pathological processes and, hence, represent a rich source for the development of novel therapeutic agents. Being able to predict binding mode and binding affinity is an essential, yet lacking, aspect of the structure-based design of carbohydrate-based ligands. We assembled a diverse data set comprising 273 carbohydrate-protein crystal structures with known binding affinity and evaluated the prediction accuracy of a large collection of well-established scoring and free-energy functions, as well as combinations thereof. Unfortunately, the tested functions were not capable of reproducing binding affinities in the studied complexes. To simplify the complex free-energy surface of carbohydrate-protein systems, we classified the studied proteins according to the topology and solvent exposure of the carbohydrate-binding site into five distinct categories. A free-energy model based on the proposed classification scheme reproduced binding affinities in the carbohydrate data set with an r(2) of 0.71 and root-mean-squared-error of 1.25 kcal/mol (N = 236). The improvement in model performance underlines the significance of the differences in the local micro-environments of carbohydrate-binding sites and demonstrates the usefulness of calibrating free-energy functions individually according to binding-site topology and solvent exposure.

Migration of cyclo-diBA from coatings into canned food: method of analysis, concentration determined in a survey and in silico hazard profiling.

Cyclo-diBA, the cyclic product formed from bisphenol A and bisphenol A diglycidyl ether during production of epoxy resins, was measured in canned food using reversed phase HPLC with fluorescence detection. Half (9 of 17) of the samples of canned fish in oil collected in April 2010 contained cyclo-diBA with an average concentration of 1025 µg/kg and a maximum of 1980 µg/kg. In September 2012, cyclo-diBA was detectable (>25 µg/kg) in merely 13 from 44 such products; the average concentration in these was 807 µg/kg and the maximum now reached 2640 µg/kg. Fish in brine contained far less cyclo-diBA. The majority of the canned meat products contained cyclo-diBA at a mean concentration of 477 µg/kg and a maximum of 1050 µg/kg. All prepared meals, such as ravioli or soups, contained cyclo-diBA, with a mean at 287 µg/kg. In canned tomatoes, peas and other vegetables in water or fruits in syrup, no cyclo-diBA was detected (<25 µg/kg). Since no experimental toxicity data are available except for its cytotoxicity, an in silico hazard profiling was performed. Cyclo-diBA seems to be stable and of low reactivity. There is indication for considerable oral bioavailability and for the potential to accumulate in the human body. Cyclo-diBA can be metabolized into cyclic and acyclic compounds. Based on SAR assessment for cyclo-diBA and read-across from BADGE to linear cyclo-diBA metabolites, genotoxic effects are improbable. Specific binding of cyclo-diBA to nuclear receptors, such as ERß, can be predicted, indicating a potential endocrine-disrupting potency. The limit by the EFSA guidelines of 50 µg/person/d for compounds shown not to be genotoxic as well as the TTC-based Cramer structural class III value of 90 µg/person/d could be exceeded several fold by high consumers of canned fish in oil with high brand loyalty. As a consequence, risk reduction measures were taken.

A molecular-modeling toolbox aimed at bridging the gap between medicinal chemistry and computational sciences.

In the current era of high-throughput drug discovery and development, molecular modeling has become an indispensable tool for identifying, optimizing and prioritizing small-molecule drug candidates. The required background in computational chemistry and the knowledge of how to handle the complex underlying protocols, however, might keep medicinal chemists from routinely using in silico technologies. Our objective is to encourage those researchers to exploit existing modeling technologies more frequently through easy-to-use graphical user interfaces. In this account, we present two innovative tools (which we are prepared to share with academic institutions) facilitating computational tasks commonly utilized in drug discovery and development: (1) the VirtualDesignLab estimates the binding affinity of small molecules by simulating and quantifying their binding to the three-dimensional structure of a target protein; and (2) the MD Client launches molecular dynamics simulations aimed at exploring the time-dependent stability of ligand-protein complexes and provides residue-based interaction energies. This allows medicinal chemists to identify sites of potential improvement in their candidate molecule. As a case study, we present the application of our tools towards the design of novel antagonists for the FimH adhesin.

VirtualToxLab - a platform for estimating the toxic potential of drugs, chemicals and natural products.

The VirtualToxLab is an in silico technology for estimating the toxic potential (endocrine and metabolic disruption, some aspects of carcinogenicity and cardiotoxicity) of drugs, chemicals and natural products. The technology is based on an automated protocol that simulates and quantifies the binding of small molecules towards a series of proteins, known or suspected to trigger adverse effects. The toxic potential, a non-linear function ranging from 0.0 (none) to 1.0 (extreme), is derived from the individual binding affinities of a compound towards currently 16 target proteins: 10 nuclear receptors (androgen, estrogen α, estrogen ß, glucocorticoid, liver X, mineralocorticoid, peroxisome proliferator-activated receptor γ, progesterone, thyroid α, and thyroid ß), four members of the cytochrome P450 enzyme family (1A2, 2C9, 2D6, and 3A4), a cytosolic transcription factor (aryl hydrocarbon receptor) and a potassium ion channel (hERG). The interface to the technology allows building and uploading molecular structures, viewing and downloading results and, most importantly, rationalizing any prediction at the atomic level by interactively analyzing the binding mode of a compound with its target protein(s) in real-time 3D. The VirtualToxLab has been used to predict the toxic potential for over 2500 compounds: the results are posted on http://www.virtualtoxlab.org. The free platform - the OpenVirtualToxLab - is accessible (in client-server mode) over the Internet. It is free of charge for universities, governmental agencies, regulatory bodies and non-profit organizations.

AcquaAlta: a directional approach to the solvation of ligand-protein complexes.

Water molecules mediating polar interactions in ligand-protein complexes can substantially contribute to binding affinity and specificity. To account for such water molecules in computer-aided drug design, we performed an extensive search in the Cambridge Structural Database (CSD) to identify the geometrical criteria defining interactions of water molecules with ligand and protein. In addition, with ab initio calculations the propensity of ligand hydration was evaluated. Based on this information, we developed an algorithm (AcquaAlta) to reproduce water molecules bridging polar interactions between ligand and protein moieties. This approach was validated with 20 crystal structures and yielded a match of 76% between experimental and calculated water positions. When water molecules establishing only weak interactions with the protein were neglected, the match could be improved to 88%. Supported by a pharmacophore-based alignment tool, the solvation algorithm was then applied to the docking of oligopeptides to the periplasmic oligopeptide binding protein A (OppA). Calculated waters based on the crystal poses matched an average of 66% of the experimental waters. With water molecules calculated based on the docked ligands, the average match with the experimental waters dropped to 53%.

From the ganglioside GQ1balpha to glycomimetic antagonists of the myelin-associated glycoprotein (MAG).

The tetrasaccharide 4, a substructure of ganglioside GQ1balpha, shows a remarkable affinity for the myelin-associated glycoprotein (MAG) and was therefore selected as starting point for a lead optimization program. In our search for structurally simplified and pharmacokinetically improved mimics of 4, antagonists with modifications of the core disaccharide Galbeta(1-3)GalNAc, as well as the terminal alpha(2-3)- and the internal alpha(2-6)-linked neuraminic acid were synthesized and tested in target-based binding assays. Compared to the reference tetrasaccharide 4, the most potent antagonist 17 exhibits a 360-fold improved affinity. Furthermore, pharmacokinetic parameters such as stability in the cerebrospinal fluid, logD and permeation through the BBB indicate the drug-like properties of antagonist 17.

Probing small-molecule binding to cytochrome P450 2D6 and 2C9: An in silico protocol for generating toxicity alerts.

Drug metabolism, toxicity, and their interaction profiles are major issues in the drug-discovery and lead-optimization processes. The cytochromes P450 (CYPs) 2D6 and 2C9 are enzymes involved in the oxidative metabolism of a majority of marketed drugs. Therefore, the prediction of the binding affinity towards CYP2D6 and CYP2C9 would be beneficial for identifying cytochrome-mediated adverse effects triggered by drugs or chemicals (e.g., toxic reactions, drug-drug, and food-drug interactions). By identifying the binding mode by using pharmacophore prealignment, automated flexible docking, and by quantifying the binding affinity by multidimensional QSAR (mQSAR), we validated a model family of 56 compounds (46 training, 10 test) and 85 compounds (68 training, 17 test) for CYP2D6 and CYP2C9, respectively. The correlation with the experimental data (cross-validated r²=0.811 for CYP2D6 and 0.687 for CYP2C9) suggests that our approach is suited for predicting the binding affinity of compounds towards CYP2D6 and CYP2C9. The models were challenged by Y-scrambling and by testing an external dataset of binding compounds (15 compounds for CYP2D6 and 40 for CYP2C9). To assess the probability of false-positive predictions, datasets of nonbinders (64 compounds for CYP2D6 and 56 for CYP2C9) were tested by using the same protocol. The two validated mQSAR models were subsequently added to the VirtualToxLab (VTL, http://www.virtualtoxlab.org).

Design, synthesis, biological evaluation, and modeling of a non-carbohydrate antagonist of the myelin-associated glycoprotein.

Broad modifications of various positions of the minimal natural epitope recognized by the myelin-associated glycoprotein (MAG), a blocker of regeneration of neurite injuries, produced sialosides with nanomolar affinities. However, important pharmacokinetic issues, for example, the metabolic stability of these sialosides, remain to be addressed. For this reason, the novel non-carbohydrate mimic 3 was designed and synthesized from (-)-quinic acid. For the design of 3, previously identified beneficial modifications of side chains of Neu5Ac were combined with the replacement of the ring oxygen by a methylene group and the substitution of the C(4)-OH by an acetamide. Although docking experiments to a homology model of MAG revealed that mimic 3 forms all but one of the essential hydrogen bonds identified for the earlier reported lead 2, its affinity was substantially reduced. Extensive molecular-dynamics simulation disclosed that the missing hydrogen bond of the former C(8)-OH leads to a change of the orientation of the side chain. As a consequence, an important hydrophobic contact is compromised leading to a loss of affinity.

Low molecular weight antagonists of the myelin-associated glycoprotein: synthesis, docking, and biological evaluation.

The injured adult mammalian central nervous system is an inhibitory environment for axon regeneration due to specific inhibitors, among them the myelin-associated glycoprotein (MAG), a member of the Siglec family (sialic-acid binding immunoglobulin-like lectin). In earlier studies, we identified the lead structure 5, which shows a 250-fold improved in vitro affinity for MAG compared to the tetrasaccharide binding epitope of GQ1balpha (1), the best physiological MAG ligand described so far. By modifying the 2- and 5-position, the affinity of 5 could be further improved to the nanomolar range (-->19a). Docking studies to a homology model of MAG allowed the rationalization of the experimental binding properties. Finally, pharmacokinetic parameters (stability in the cerebrospinal fluid, logD and permeation through the BBB) indicate the drug-like properties of the high-affinity antagonist 19a.

Probing Small-Molecule Binding to the Liver-X Receptor: A Mixed-Model QSAR Study.

The LXR model has been added in the VirtualToxLab, a fully automated technology that allows for the identification of the endocrine-disrupting potential of drugs, chemicals and natural products. This protocol has then been applied to screen a series of 161 natural compounds probing their binding to the LXR. The results of the simulation were compared with experimental data (where available) and suggest that the LXR model can be applied to predict the binding affinity of existing or hypothetical compounds for screening purposes. The binding of 52 ligands towards the liver X receptors (LXRs) was identified trough docking to the three-dimensional protein structure and quantified by multidimensional QSAR (mQSAR), an approach referred to as 'mixed-model QSAR'. The model was validated by the prediction of 17 external compounds (oxysterols) present neither in the training nor in the test set. The robustness of the model was verified by consensus scoring using a conceptually different methodology, and chance correlation was ruled out by a series of scramble tests.

In silico toxicology in drug discovery - concepts based on three-dimensional models.

Animal testing is still compulsory worldwide, for the approval of drugs and chemicals produced in large quantities. Computer-assisted (in silico) technologies are considered to be efficient alternatives to in vivo experiments, and are therefore endorsed by many regulatory agencies, e.g. for use in the European REACH initiative. Advantages of in silico methods include: the possible study of hypothetical compounds; their low cost; and the fact that such virtual experiments are typically based on human data, thus making the question of interspecies transferability obsolete. Since the mid-1990s, computer-based technologies have become an indispensable tool in drug discovery - used primarily to identify small molecules displaying a stereospecific and selective binding to a regulatory macromolecule. Since toxic effects are still responsible for some 20% of the late-stage failures, there is a continuing need for in silico concepts which can be used to estimate a compound's ADMET (adsorption, distribution, metabolism, elimination, toxicity) properties - in particular, toxicity. The aim of this paper is to provide an insight into computational technologies that allow for the prediction of toxic effects triggered by pharmaceuticals. As most adverse and toxic effects are mediated by unwanted interactions with macromolecules involved in biological regulatory systems, we have focused on methodologies that are based on three-dimensional models of small molecules binding to such entities, and discuss the results at the molecular level.

VirtualToxLab - in silico prediction of the toxic (endocrine-disrupting) potential of drugs, chemicals and natural products. Two years and 2,000 compounds of experience: a progress report.

The VirtualToxLab is an in silico tool for predicting the toxic (endocrine-disrupting) potential of drugs, chemicals and natural products. It is based on a fully automated protocol and calculates the binding affinity of any molecule of interest towards a series of 12 proteins, known or suspected to trigger adverse effects and estimates the resulting toxic potential. In contrast to other approaches in the field, the technology allows to rationalize a prediction at the molecular level by interactively analyzing the binding mode of the tested compound with any target protein in 3D. The technology is accessible over the Internet (via a secure SSH protocol) and available for any science-oriented organization. The toxic potential - a complex value derived from the individual binding affinities, their standard deviation and the quality of the underlying model (number and ratio of training and test compounds, activity range covered) - of existing and hypothetical compounds is estimated by simulating and quantifying their interactions towards a series of macromolecular targets at the molecular level using automated flexible docking combined with multidimensional QSAR (mQSAR). Currently, those targets comprise 12 proteins: the androgen, aryl hydrocarbon, estrogen alpha/beta, glucocorticoid, mineralocorticoid, thyroid alpha/beta liver X and the peroxisome proliferator-activated receptor gamma as well as the enzymes cytochrome P450 3A4 (CYP 3A4) and 2A13 (CYP 2A13). Up to date, the technology has been used to predict the toxic potential for more than 2,000 drugs, chemicals and natural compounds. All results are posted in the Internet - in this account, a few will be discussed in detail with reference to the molecular mechanisms triggering the adverse effect.

Design, synthesis and evaluation of monovalent ligands for the asialoglycoprotein receptor (ASGP-R).

A series of novel aryl-substituted triazolyl D-galactosamine derivatives was synthesized as ligands for the carbohydrate recognition domain of the major subunit H1 (H1-CRD) of the human asialoglycoprotein receptor (ASGP-R). The compounds were biologically evaluated with a newly developed competitive binding assay, surface plasmon resonance and by a competitive NMR binding experiment. With compound 1b, a new ligand with a twofold improved affinity to the best so far known D-GalNAc was identified. This small, drug-like ligand can be used as targeting device for drug delivery to hepatocytes.

Mixed-model QSAR at the human mineralocorticoid receptor: predicting binding mode and affinity of anabolic steroids.

We present a computational study on the human mineralocorticoid receptor (hMR) that is based on multi-dimensional quantitative structure-activity relationships (mQSAR). Therein, we identified the binding mode of 48 steroid and non-steroid homologues by flexible docking to the crystal structure (software Yeti) and quantified it using 6D-QSAR (software Quasar). The receptor surrogate, evolved using a genetic algorithm, converged at a cross-validated r2 of 0.810, and yielded a predictive r2 of 0.661. The model was challenged by a series of scramble tests and by consensus scoring (software Raptor: r2=0.844, predictive r(2)=0.620). The model was then employed to predict the binding affinity of 26 anabolic steroids, demonstrating to which extent they might disrupt the endocrine system via binding to the hMR. The model for the hMR was added to the VirtualToxLab, a technology developed by the Biographics Laboratory 3R, allows the identification of the endocrine-disrupting potential of drugs, chemicals and natural products in silico.

Examination of the biological role of the alpha(2-->6)-linked sialic acid in gangliosides binding to the myelin-associated glycoprotein (MAG).

The tetrasaccharide 1, a substructure of ganglioside GQ1b alpha, shows a remarkable affinity for the myelin-associated glycoprotein (MAG) and was therefore selected as starting point for a lead optimization program. In our search for structurally simplified and pharmacokinetically improved mimics of 1, modifications of the core disaccharide, the alpha(2-->3)- and the alpha(2-->6)-linked sialic acid were synthesized. Biphenylmethyl and (S)-lactate were identified as suitable replacements for the alpha(2-->6)-linked sialic acid. Combined with a core modification and the earlier found aryl amide substituent in the 9-position of the alpha(2-->3)-linked sialic acid, high affinity MAG antagonists were identified. All mimics were tested in a competitive target-based binding assay, providing relative inhibitory potencies (rIP). Compared to the reference tetrasaccharide 1, the rIPs of the most potent antagonists 59 and 60 are enhanced nearly 400-fold. Their K(D)s determined in surface plasmon resonance experiments are in the low micromolar range. These results are in semiquantitative agreement with molecular modeling studies. This new class of glycomimetics will allow to validate the role of MAG in the axon regeneration process.