Biomedical subjects

Max Dobler

Publications and source records attributed to Max Dobler.

10 recordsLinked to original sources

PubMed2006

Crystal structure of homo-DNA and nature's choice of pentose over hexose in the genetic system.

An experimental rationalization of the structure type encountered in DNA and RNA by systematically investigating the chemical and physical properties of alternative nucleic acids has identified systems with a variety of sugar-phosphate backbones that are capable of Watson-Crick base pairing and in some cases cross-pairing with the natural nucleic acids. The earliest among the model systems tested to date, (4' --> 6')-linked oligo(2',3'-dideoxy-beta-d-glucopyranosyl)nucleotides or homo-DNA, shows stable self-pairing, but the pairing rules for the four natural bases are not the same as those in DNA. However, a complete interpretation and understanding of the properties of the hexapyranosyl (4' --> 6') family of nucleic acids has been impeded until now by the lack of detailed 3D-structural data. We have determined the crystal structure of a homo-DNA octamer. It reveals a weakly twisted right-handed duplex with a strong inclination between the hexose-phosphate backbones and base-pair axes, and highly irregular values for helical rise and twist at individual base steps. The structure allows a rationalization of the inability of allo-, altro-, and glucopyranosyl-based oligonucleotides to form stable pairing systems.

DNA↗

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

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↗