Biomedical subjects

Angelo Vedani

Publications and source records attributed to Angelo Vedani.

17 recordsLinked to original sources

PubMed2007

Simulating alpha/beta selectivity at the human thyroid hormone receptor: consensus scoring using multidimensional QSAR.

We present a consensus-scoring study on the human thyroid hormone receptor alpha and beta using two receptor-modeling concepts (software Quasar and Raptor) that are based on multidimensional QSAR and allow for the explicit simulation of induced fit. The binding mode of 82 agonists and indirect antagonists, spanning an activity range of seven orders of magnitude in K(i), was identified through flexible docking to the respective X-ray crystal structures (Yeti software) and represented by a 4D data set with up to four conformations per compound. The receptor surrogates for the thyroid alpha receptor converged at a cross-validated r(2) of 0.846/0.919 (64 training compounds; for Quasar and Raptor, respectively) and yielded a predictive r(2) of 0.812/0.814 (18 test compounds); the models for the thyroid beta receptor resulted in a cross-validated r(2) of 0.823/0.909 and a predictive r(2) of 0.665/0.796, respectively. Consensus was achieved as, on average, the calculated activities of the training set differ only by a factor of 2.2 in K(i) and those of the test set by a factor of 2.8 when predicted by Quasar and Raptor, respectively.

Binding Sites↗

PubMed2006

Prediction of small-molecule binding to cytochrome P450 3A4: flexible docking combined with multidimensional QSAR.

The inhibition of cytochrome P450 3A4 (CYP3A4) by small molecules is a major mechanism associated with undesired drug-drug interactions, which are responsible for a substantial number of late-stage failures in the pharmaceutical drug-development process. For a quantitative prediction of associated pharmacokinetic parameters, a computational model was developed that allows prediction of the inhibitory potential of 48 structurally diverse molecules. Based on the experimental structure of CYP3A4, possible binding modes were first sampled by using automated docking (Yeti software) taking protein flexibility into account. The results are consistent with both X-ray crystallographic data and data from metabolic studies. Next, an ensemble of energetically favorable orientations was composed into a 4D dataset for use as input for a multidimensional QSAR technique (Raptor software). A dual-shell binding-site model that allows an explicit induced fit was then generated by using hydrophobicity scoring and hydrogen-bond propensity. The simulation reached a cross-validated r2 value of 0.825 and a predictive r2 value of 0.659. On average, the predicted binding affinity of the training ligands deviates by a factor of 2.7 from the experiment; those of the test set deviate by a factor of 3.8 in Ki.

Cytochrome P-450 CYP3A↗

PubMed2006

The challenge of predicting drug toxicity in silico.

Poor pharmacokinetics, side effects and compound toxicity are frequent causes of late-stage failures in drug development. A safe in silico identification of adverse effects triggered by drugs and chemicals would be highly desirable as it not only bears economical potential but also spawns a variety of ecological benefits: sustainable resource management, reduction of animal models and possibly less risky clinical trials. In computer-aided drug discovery, both existing and hypothetical compounds may be studied; the methods are fast, reproducible, and typically based on human bioregulators, making the question of transferability obsolete. In the recent past, our laboratory contributed towards the development of in silico concepts (--> multi-dimensional QSAR) and validated a series of "virtual test kits" based on the oestrogen, androgen, thyroid, and aryl hydrocarbon receptor (endocrine disruption, receptor-mediated toxicity) as well as on the enzyme cytochrome P450 3A4 (metabolic transformations, drug-drug interactions). The test kits are based on the three-dimensional structure of their target protein (i.e. ER(alphabeta), AR, TR(alphabeta), CYP450) or a surrogate thereof (AhR) and were trained using a representative selection of 362 substances. Subsequent evaluation of 107 compounds different therefrom showed that binding affinities are predicted close to experimental uncertainty. These results suggest that our approach is suited for the in silico identification of adverse effects triggered by drugs and chemicals and encouraged us to compile an Internet Database for the virtual screening of drugs and chemicals for toxic effects.

Computational Biology↗

PubMed2005

Impact of induced fit on ligand binding to the androgen receptor: a multidimensional QSAR study to predict endocrine-disrupting effects of environmental chemicals.

We investigated the influence of induced fit of the androgen receptor binding pocket on free energies of ligand binding. On the basis of a novel alignment procedure using flexible docking, molecular dynamics simulations, and linear-interaction energy analysis, we simulated the binding of 119 molecules representing six compound classes. The superposition of the ligand molecules emerging from the combined protocol served as input for Raptor, a receptor-modeling tool based on multidimensional QSAR allowing for ligand-dependent induced fit. Throughout our study, protein flexibility was explicitly accounted for. The model converged at a cross-validated r(2) = 0.858 (88 training compounds) and yielded a predictive r(2) = 0.792 (26 test compounds), thereby predicting the binding affinity of all compounds close to their experimental value. We then challenged the model by testing five molecules not belonging to compound classes used to train the model: the IC(50) values were predicted within a factor of 4.5 compared to the experimental data. The demonstrated predictivity of the model suggests that our approach may well be beneficial for both drug discovery and the screening of environmental chemicals for endocrine-disrupting effects, a problem that has recently become a cause for concern among scientists, environmental advocates, and politicians alike.

Benzhydryl Compounds↗

PubMed2005

In silico prediction of harmful effects triggered by drugs and chemicals.

While the computer-assisted discovery and optimization of drug candidates based on the known three-dimensional structure of the macromolecular target (structure-based design) or a binding-site surrogate (receptor modeling) is doubtless one of the more potent approaches in rational drug design, the simulation and quantification of side effects triggered by drugs and chemicals are still in their infancy. Major obstacles include the often not available 3D structure of the molecular target, the low specificity of the involved bioregulators and the identification of the controlling metabolic pathways. In the recent past, our laboratory has explored concepts allowing to simulate receptor-mediated toxic phenomena by developing algorithms, allowing to construct realistic 3D binding-site surrogates of receptors known or assumed triggering adverse effects and validating them against large batches of molecular data. The underlying technology (software Quasar and Raptor, respectively) specifically allows for induced fit, solvation phenomena and entropic effects. It has been applied to various systems both of pharmacological and toxicological interest including the neurokinin-1, chemokine-3, bradykinin B(2), steroid, 5 HT(2A), aryl hydrocarbon, estrogen and androgen receptor, respectively. In this account, we describe the design of a virtual laboratory allowing for a reliable estimation of harmful effects triggered by drugs, chemicals and their metabolites in silico. In the recent past, the Biographics Laboratory 3R has compiled a 3D database including the surrogates of three major receptor systems known to mediate adverse effects (the aryl hydrocarbon, the estrogen and the androgen receptor, respectively) and validated them against a total of 345 compounds (drugs, chemicals, toxins) using multidimensional QSAR technologies. Within this pilot project, we could demonstrate that our virtual laboratory is able to both recognize toxic compounds substantially different from those used in the training set as well as to classify harmless compounds as being nontoxic. This suggests that our approach may be used for the prediction of adverse effects of drug molecules and chemicals. It is the aim to provide cost-covering access to this technology--particularly to universities, hospitals and regulatory bodies--as it bears a significant potential to recognize hazardous compounds early in the development process and hence improve resource and waste management as well as reduce animal testing. The Biographics Laboratory 3R is a non-profit-oriented organization aimed at reducing animal experimentation in the biomedical sciences by computational approaches (cf. http://www.biograf.ch).

Computer Simulation↗

PubMed2005

Combining protein modeling and 6D-QSAR. Simulating the binding of structurally diverse ligands to the estrogen receptor.

We present a concept for the in silico simulation of adverse effects triggered by drugs and chemicals. The underlying philosophy combines flexible docking (software Yeti) for the identification of the binding mode(s) and 6D-QSAR (software Quasar) for their quantification. The results obtained for 106 diverse molecules binding to the estrogen receptor (q2 = 0.903; p2 = 0.885) suggest that our approach is suitable for the identification of an endocrine-disrupting potential associated with drugs and chemicals.

Binding Sites↗

PubMed2005

Novel ligands for the chemokine receptor-3 (CCR3): a receptor-modeling study based on 5D-QSAR.

We recently reported the development of a receptor-modeling concept based on 5D-QSAR (quantitative structure-activity relationships) and which explicitly allows for the simulation of induced fit. In this account, we report its utilization toward the design of novel compounds able to inhibit the chemokine receptor-3 (CCR3). The study was based on a total of 141 compounds, representing four different substance classes. Using the Quasar software, we built two receptor surrogates that yielded a cross-validated r(2) value of 0.950/0.861 and a predictive r(2) of 0.879/0.798, respectively. The model was then employed to predict the activity of 58 hypothetical compounds featuring two variation patterns: lipophilic substitutions and amphiphilic H-bond acceptors. Eleven of the proposed ligands show a calculated binding affinity lower than any compound within the training set; the most potent candidate molecule is expected to bind at an IC(50) of 0.3 nM.

Benzene Derivatives↗

PubMed2005

Virtual test kits for predicting harmful effects triggered by drugs and chemicals mediated by specific proteins.

Poor pharmacokinetics and toxicity are not only frequent causes of late-stage failures in drug development but also a source for unnecessary animal tests. In drug discovery and for the assessment of the toxic potential of chemicals, in silico techniques are nowadays considered as valuable alternatives to in vivo approaches. Based on a receptor-modelling concept developed at our laboratory (multidimensional QSAR), we have developed and validated virtual test kits for the estrogen, androgen and aryl hydrocarbon receptor (endocrine disruption), for cytochrome P450 3A4 (metabolic transformations) and most recently for the thyroid receptor. These surrogates have been tested against a total of 430 compounds and are able to predict the binding affinity close to the experimental uncertainty. These results suggest that our approach is suited for the in silico identification of adverse effects triggered by drugs and chemicals. Consequently, we are prepared to offer a free testing to selected academic institutions and non-profit oriented organisations.

Cytochrome P-450 CYP3A↗

PubMed2004

Raptor: combining dual-shell representation, induced-fit simulation, and hydrophobicity scoring in receptor modeling: application toward the simulation of structurally diverse ligand sets.

We present a novel receptor-modeling approach (software Raptor) based on multidimensional quantitative structure-activity relationships (QSARs). To accurately predict relative free energies of ligand binding, it is of utmost importance to simulate induced fit. In Raptor, we explicitly and anisotropically allow for this phenomenon by a dual-shell representation of the receptor surrogate. In our concept, induced fit is not limited to steric aspects but includes the variation of the physicochemical fields along with it. The underlying scoring function for evaluating ligand-receptor interactions includes directional terms for hydrogen bonding and hydrophobicity and thereby treats solvation effects implicitly. This makes the approach independent from a partial-charge model and, as a consequence, allows one to smoothly model ligand molecules binding to the receptor with different net charges. We have applied the new concept toward the estimation of ligand-binding energies associated with the chemokine receptor-3 (50 ligands: r(2) = 0.965; p(2) = 0.932), the bradykinin B(2) receptor (52 ligands: r(2) = 0.949; p(2) = 0.859), and the estrogen receptor (116 ligands: r(2) = 0.908; p(2) = 0.907), respectively.

Binding Sites↗

PubMed2003

Internet laboratory for predicting harmful effects triggered by drugs and chemicals--a progress report.

The main objective of our institution is to establish a virtual laboratory on the Internet to allow for a reliable in silico estimation of harmful effects triggered by drugs, chemicals and their metabolites. In the past two years, we have compiled a pilot system including the 3D models of five receptors known to mediate adverse effects (the Ah, 5HT(2A), cannabinoid, GABA(A), and estrogen receptor, respectively) and tested them against 280 compounds (drugs, chemicals, toxins). Within this set-up we could demonstrate that our concept is able to both recognise toxic compounds substantially different from those used in the training set as well as to classify harmless compounds clearly as being non-toxic at low-level doses. This suggests that our approach can be used for the prediction of adverse effects of drug molecules and chemicals. It is the aim to provide free access to this 3D data base, particularly to universities, hospitals and regulatory bodies as it bears a significant potential to recognise hazardous compounds early in the development process and withdraw them from the evaluation pipeline. Hence, for substances recognised as hazardous in silico, subsequent toxicity tests involving animal models become obsolete.

Animal Testing Alternatives↗

PubMed2002

5D-QSAR: the key for simulating induced fit?

In this journal we recently reported the development and the validation of a four-dimensional (4D)-QSAR (quantitative structure-activity relationships) concept, allowing for multiple conformation, orientation, and protonation state representation of ligand molecules. While this approach significantly reduces the bias with selecting a bioactive conformer, orientation, or protonation state, it still requires a "sophisticated guess" about manifestation and magnitude of the associated local induced fit-the adaptation of the receptor binding pocket to the individual ligand topology. We have therefore extended our concept (software Quasar) by an additional degree of freedom--the fifth dimension--allowing for a multiple representation of the topology of the quasi-atomistic receptor surrogate. While this entity may be generated using up to six different induced-fit protocols, we demonstrate that the simulated evolution converges to a single model and that 5D-QSAR--due to the fact that model selection may vary throughout the entire simulation--yields less biased results than 4D-QSAR where only a single induced- fit model can be evaluated at a time. Using two bioregulators (the neurokinin-1 receptor and the aryl hydrocarbon receptor), we compare the results obtained with 4D- and 5D-QSAR. The NK-1 receptor system (represented by a total of 65 antagonist molecules) converges at a cross-validated r2 of 0.870 and a predictive r2 of 0.837; the corresponding values for the Ah receptor system (represented by a total of 131 dibenzodioxins, dibenzofurans, biphenyls, and polyaromatic hydrocarbons) are 0.838 and 0.832, respectively. The results indicate that the formal investment of additional computer time is well-returned both in quantitative and in qualitative values: less-biased boundary conditions, healthier (i.e., less inbred) model populations, and more accurate predictions of new compounds.

Ligands↗

PubMed1998

[Ochratoxins: Molecular strategies for developing an antidote]

Ochratoxin A (OcA) is a prominent member of a group of mycotoxins which display nephrotoxic, genotoxic, teratogenic, carcinogenic and immunosuppressive effects and which have also been linked to Balkan Endemic Nephropathy. The toxicity of OcA is thought to be primarily due to its inhibition of phenylalanine-t-RNA synthetase, a phenylalanine-metabolizing enzyme. Based on the three-dimensional structure of phenylalanine-t-RNA synthetase, we have analyzed its interactions with OcA by means of molecular-dynamical simulations and identified three quite different binding modes, all of which suggest an affinity only in the millimolar range. This would seem to be in conflict with toxicological findings frequently cited in textbooks but is in agreement with recent in vitro studies on purified phenylalanine-t-RNA synthetase, which also exclude this enzyme as the main target for OcA action. In vivo, OcA binds preferentially to serum albumin, a plasma protein, with a corresponding effect on its toxicokinetics (retention). Antagonizing this effect would lead to an enhanced elimination rate, thereby reducing all adverse effects of OcA, as has been demonstrated using albumin-deficient mice. Based on the three-dimensional structure of serum albumin, we have simulated its interaction with OcA. The long-term goal is the animal-free identification of a synthetic antagonist with an affinity between that of the endogenous ligands (e.g. billirubin) and OcA. Such a substance could - by reducing the retention time of the toxin in the body - potentially eliminate all toxic effects of OcA.

Journal Article↗

PubMed1997

[Target proteins and mechanisms of ochratoxin toxicity. A contribution to the identification of potential ochratoxin antagonists]

Ochratoxins are mycotoxins released by moulds on grain, peanuts and vegetables. Toxicological investigations have shown that ochratoxin A displays nephrotoxic, genotoxic, teratogenic, cancerogenic and immunosuppressive effects. Increased blood levels observed in humans would seem to suggest a link to a kidney desease (Balcan Endemic Nephropaty) frequently observed in the Balkan countries. The adverse effects of ochratoxin A are mainly associated with its impact on phenylalanine-metabolizing enzymes. Based on the three-dimensional structure of phenylalanine-t-RNA-synthetase, its interactions with ochratoxins are analyzed as well as with Aspartam. In animal models, Aspartam has been shown to almost fully prevent toxic effects of ochratoxin A. The topology of the binding site of phenylalanine-t-RNA-synthetase would seem to be favorable towards a few affinity-enhancing modifications of the Aspartame molecule. A known molecular mechanism is a prerequisite for a systematic search of antagonizing substances for toxins. Based on a receptor structure, binding properties of such drugs can be identified and optimized using computer-aided drug design. Susequently, only the most potent candidate structures must be subjected to a determination of their biological activity, which can lead to a significant reduction of substances to be tested in vivo. Such experiments are particularly stressful as the animals must be intoxicated beforehand. The extent of an antagonistic impact on humans suffering from a chronical ochratoxin A intoxication must be subject of clinical studies.

Journal Article↗

PubMed1996

[Orchratoxin A and B: A three-dimensional molecular model for a mechanistic explanation of their toxicity]

Ochratoxins are toxic substances produced and released by Aspergillus alutaceum and other molds. So far, it was generally believed that the inhibition of the enzyme phenylalanin-t-RNA-synthetase was responsible for the toxicity of ochratoxin A. Most recent in vitro results, however, suggest a non-competitive mechanism with respect to phenylalanin and, consequently, the existence of a proper ochratoxin receptor. Based on the structure ochratoxin A and B, mellein and phenylalanine, we have generated a three-dimensional molecular model for the binding site of a putative ochratoxin receptor by means of pseudoreceptor modeling. The model consists of twelve amino-acid residues. In this model, the high affinity (and, hence, the receptor-mediated toxicity) of ochratoxin A and B is explained by a hydrogen-bond network - a network that is not possible with both mellein and phenylalanine as substrates. Consequently, these compounds do not exhibit toxic effects at comparable concentrations. The relevance of the model is supported by the quantitative prediction of the binding affinity of three test compounds.

Journal Article↗

PubMed1994

[Pseudoreceptor modeling - a tool in the pharmacological screening process]

Pseudoreceptor Modeling, a new concept within the field of Computer-Aided Drug Design, allows the reconstruction of the three-dimensional structure of an unknown bioregulator based on the structures of its ligands (known bioactive compounds). It combines the present techniques but significantly extends their possibilities by the generation of an explicit receptor model. This model may subsequently be used for the qualitative prediction of the binding strength of novel drug molecules. The relevance of pseudoreceptor modeling for reducing and replacing animal models is given by the fact that the technique can be applied under circumstances where little or no information is available on the true biological receptor, a situation where up to date only in vivo techniques were promising. As a first technique, pseudoreceptor modeling allows the screening of potential drug molecules of an unknown bioregulator ex vivo.

Journal Article↗

PubMed1991

[Computer-Aided Drug Design: An Alternative to Animal Testing in the Pharmacological Screening]

By means of graphical and numerical simulation of the binding of a biological key molecule to a receptor, Computer-Aided Drug Design (CADD) allows the semi-quantitative prediction of the activity of potential drug molecules. New drugs can be tailor-made for a specific receptor without animal testing; substances with little or no pharmacological activity are unambiguously identified and removed from the evaluation process before preclinical "in vivo" tests become necessary. Two methods, direct CADD and Receptor Mapping are presented.

Journal Article↗

PubMedDate not supplied

Combining 4D pharmacophore generation and multidimensional QSAR: modeling ligand binding to the bradykinin B2 receptor.

We recently reported the development of two receptor-modeling concepts (software Quasar and Raptor) based on multidimensional quantitative structure-activity relationships (QSAR) and allowing for the explicit simulation of induced fit. As the identification of the bioactive configuration of ligand molecules in such studies is all but unambiguous, each compound may be represented by an ensemble of different conformations, orientations, stereoisomers, and protonation states, leading to a 4D data set. In this account, we present a novel technology (software Symposar) allowed to automatically generate a 4D pharmacophore as input for multidimensional QSAR. Symposar aligns ligands utilizing fuzzylike 2D-subfeature mapping and, subsequently, a Monte Carlo search on a 3D similarity grid. The two-step concept (4D pharmacophore generation and quantification of ligand binding by multidimensional QSAR) was applied to 186 compounds binding to the bradykinin B2 receptor. The prediction of their binding affinity by means of the Quasar and Raptor technologies allowed for consensus scoring and generated topologically and quantitatively consistent receptor models. These converged at a cross-validated r2 of 0.752 and 0.815 and yielded a predictive r2 of 0.784 and 0.853 for a test set (for Quasar and Raptor, respectively).

Binding Sites↗