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PubMed2005

In silico modelling--pharmacophores and hERG channel models.

In computational drug design, modelling studies are undertaken following two main strategies that depend on which information is available. If experimental data exist only for the molecules displaying the biological property of interest, a so-called ligand-based approach is taken; if information is available on the macromolecular target(s) of the compounds (e.g. proteins' 3D structures), target-based studies can be carried out. Recently, in the field of hERG K+-channel blocking drugs, pharmacophoric (ligand-based) studies started appearing aimed at determining the physicochemical features associated with the channel block, and also at predicting the hERG blocking potential of compounds. However, partial homology models (target-based) of the hERG channel have also been built and used as working tools to interpret electrophysiological and mutagenesis studies. Here, we review some of the ligand- and target-based in silico studies carried out on hERG, focusing on both their main characteristics and their meaning. In addition, we discuss some methodological aspects of the computational work that in our opinion should be considered, in view of the construction of reliable models possibly able to predict the functional behaviour of the channel system and the blocking potential of drugs.

Amino Acid Sequence↗
PubMedDate not supplied

Development and evaluation of an in silico model for hERG binding.

It has been recognized that drug-induced QT prolongation is related to blockage of the human ether-a-go-go-related gene (hERG) ion channel. Therefore, it is prudent to evaluate the hERG binding of active compounds in early stages of drug discovery. In silico approaches provide an economic and quick method to screen for potential hERG liability. A diverse set of 90 compounds with hERG IC(50) inhibition data was collected from literature references. Fragment-based QSAR descriptors and three different statistical methods, support vector regression, partial least squares, and random forests, were employed to construct QSAR models for hERG binding affinity. Important fragment descriptors relevant to hERG binding affinity were identified through an efficient feature selection method based on sparse linear support vector regression. The support vector regression predictive model built upon selected fragment descriptors outperforms the other two statistical methods in this study, resulting in an r(2) of 0.912 and 0.848 for the training and testing data sets, respectively. The support vector regression model was applied to predict hERG binding affinities of 20 in-house compounds belonging to three different series. The model predicted the relative binding affinity well for two out of three compound series. The hierarchical clustering and dendrogram results show that the compound series with the best prediction has much higher structural similarity and more neighbors of training compounds than the other two compound series, demonstrating the predictive scope of the model. The combination of a QSAR model and postprocessing analysis, such as clustering and visualization, provides a way to assess the confidence level of QSAR prediction results on the basis of similarity to the training set.

Cell Line↗
PubMed2005

Predictive in silico modeling for hERG channel blockers.

hERG-mediated sudden death as a side effect of non-antiarrhythmic drugs has been receiving increased regulatory attention. Perhaps owing to the unique shape of the ligand-binding site and its hydrophobic character, the hERG channel has been shown to interact with pharmaceuticals of widely varying structure. Several in silico approaches have attempted to predict hERG channel blockade. Some of these approaches are aimed primarily at filtering out potential hERG blockers in the context of virtual libraries, others involve understanding structure-activity relationships governing hERG-drug interactions. This review summarizes the most recent efforts in this emerging field.

Cation Transport Proteins↗
PubMed2006

In silico modeling of protein tyrosine phosphatase 1B inhibitors with cellular activity.

Protein tyrosine phosphatase 1B (PTP1B) is a potential drug target for the treatment of Type 2 diabetes and obesity. The design of PTP1B inhibitors as therapeutic agents has been hampered mostly owing to their poor cell permeability and oral bioavailability. In the present study, we investigated the cellular activity of PTP1B inhibitors in relation to the 3D structure using classical VolSurf analysis. A model based on the VolSurf descriptors for a set of 80 compounds of PTP1B inhibitors, half of which display cellular activity, was analyzed using the principal components analysis (PCA) approach. The PCA model was applied to predict the cellular activities of an external data set of 40 PTP1B inhibitors and satisfactory results were obtained. Further partial least squares (PLS) analysis revealed useful information about the behavior of the Volsurf descriptors in predicting the cell permeability and pharmacokinetic properties of PTP1B inhibitors. In silico ADME studies provide a valuable tool in the development of effective PTP1B inhibitors as drug candidates.

Enzyme Inhibitors↗
PubMed2006

In silico modeling of non-linear drug absorption for the P-gp substrate talinolol and of consequences for the resulting pharmacodynamic effect.

PURPOSE: The aim of the present work was to demonstrate P-glycoprotein's involvement in the non-linear talinolol pharmacokinetics using an advanced compartment and transit model (ACAT) and to compare the results predicted from the model to the finding of a phase I dose escalation study with oral talinolol doses increasing from 25 to 400 mg. MATERIALS AND METHODS: Besides minimum input parameters for the compound (pKa(s), solubility at one or more pH's, Peff, doses, formulation, diffusivity), physiological and pharmacokinetic properties, transporter data are included in these predictions. The simulations assumed higher expression levels in lower gastrointestinal regions, in particular in the colon, which is in accordance with the results of intestinal rat perfusion studies and intestinal distribution data from rats, catfishes, micropigs and humans reported in the literature. Optimized values for P-glycoprotein (P-gp) Km and Vmax were used for the final simulation results and for a stochastic virtual trial with 12 patients. RESULTS: Talinolol, a P-gp substrate, exhibits non-linear dose AUC relationship after administration of 25, 50, 100 and 400 mg immediate-release tablets. This dose dependency is due to a decrease of efflux transport caused by saturation of P-gp by talinolol. It was found that oral bioavailability increases after administration of higher doses of talinolol. The predicted bioavailability of the p.o. 25, 50, 100 and 400 mg doses of talinolol was 64, 76, 85, 94%, respectively. Pharmacokinetic parameters (AUC, Cmax) from in silico simulations are within acceptable range comparing with data, observed in vivo. However, the in vitro value of Km for talinolol's interactions with P-gp could not be used in the simulation and still reproduce the observed non-linear dose dependence. For each of the four doses, GastroPlus was used to model pharmacodynamic (PD) response and to optimize the values of CLe, Emax, and EC5o with the effect compartment linked indirectly to the central compartment. For all simulations, EC50 was 114 nM and E0 was 83 bpm. CONCLUSION: Comparison between the results of the in vivo study and the in silico simulations determined the quality and reliability of the in silico predictions and demonstrate the simulation of dose dependent absorption. In contrast to previous simulation work for the non-linear dose dependence of interaction with intestinal transporters or enterocyte metabolism, optimized Km and Vmax values were required to reproduce the clinically observed non-linear dose dependence. The model developed may be useful in the prediction of absorption of other P-gp substrates including pharmacodynamic consequences.

ATP Binding Cassette Transporter, Subfamily B, Mem↗
PubMed2006

Machine learning techniques for in silico modeling of drug metabolism.

The computational assessment of drug metabolism has gained considerable interest in pharmaceutical research. Amongst others, machine learning techniques have been employed to model relationships between the chemical structure of a compound and its metabolic fate. Examples for these techniques, which were originally developed in fields far from drug discovery, are artificial neural networks or support vector machines. This paper summarizes the application of various machine learning techniques to predict the interaction between organic molecules and metabolic enzymes. More complex endpoints such as metabolic stability or in vivo clearance will also be addressed. It is shown that the prediction of metabolic endpoints with machine learning techniques has made considerable progress over the past few years. Depending on the procedure used, either classification or quantitative prediction is possible for even large and diverse compound sets. Together with the expanding experimental data basis, these in silico methods have become valuable tools in the drug discovery and development process.

Artificial Intelligence↗
PubMedDate not supplied

In silico modelling of hazard endpoints: current problems and perspectives.

Major scientific hurdles in the acceptance of quantitative structure-activity relationships (QSAR) for regulatory purposes have been identified. First, when quantifying important features of chemical structure complexities of molecular structure have often been ignored. More mechanistic modelling of chemical structure should proceed on two fronts: by developing a more in-depth understanding and representation of the multiple states possible for a single chemical by achieving greater rigor in understanding of conformational flexibility of chemicals; and, by considering families of activated metabolites that are derived in biological systems from an initial chemical substrate. Second, QSAR research is severely limited by the lack of systematic databases for important risk assessment endpoints, and despite many decades of research, the ability to cluster reactive chemicals by common toxicity pathways is in its infancy. Finally, computational tools are lacking for defining where a specific QSAR is applicable within the domain (universe) of chemical structures that are to be regulated. This paper describes some of the approaches being taken to address these needs. Applications of some of these new approaches are demonstrated for the prediction of chemical mutagenicity, where considerations of both molecular flexibility and metabolic activation improved the QSAR predictability and interpretations. Lastly, the applicability domain for a specific QSAR predicting estrogen receptor binding is presented in the context of a mechanistically-defined chemical structure space for large heterogeneous chemical datasets of regulatory concern. A strategic approach is discussed to selecting chemicals for model improvement and validation until regulatory acceptance criteria for risk assessment applications are met.

Endpoint Determination↗
PubMed2006

Joining high-throughput technology with in silico modelling advances genome-wide screening towards targeted discovery.

Genome research has entered the functional evaluation phase now and high-throughput (HT) methods provide an enormous amount of raw data for that purpose. However, functional verification still requires experimental regimens not suitable for application in a HT style, requiring an efficient discovery and selection process pinpointing biological mechanisms and processes for subsequent targeted verification. Regulatory networks and underlying molecular mechanisms can now be deduced through the interpretation of HT-data in the context of biologic knowledge. Computational models of promoter structures are suitable for genome-wide searches and a number of recent examples demonstrate their usefulness in prediction and selection of functional targets for experimental verification.

Computer Simulation↗
PubMed2004

The CyberCell Database (CCDB): a comprehensive, self-updating, relational database to coordinate and facilitate in silico modeling of Escherichia coli.

The CyberCell Database (CCDB: http://redpoll. pharmacy.ualberta.ca/CCDB) is a comprehensive, web-accessible database designed to support and coordinate international efforts in modeling an Escherichia coli cell on a computer. The CCDB brings together both observed and derived quantitative data from numerous independent sources covering many aspects of the genomic, proteomic and metabolomic character of E.coli (strain K12). The database is self-updating but also supports 'community' annotation, and provides an extensive array of viewing, querying and search options including a powerful, easy-to-use relational data extraction system.

Computational Biology↗
PubMed2006

In silico models of acute inflammation in animals.

Trauma and hemorrhagic shock elicit an acute inflammatory response, predisposing patients to sepsis, organ dysfunction, and death. Few approved therapies exist for these acute inflammatory states, mainly due to the complex interplay of interacting inflammatory and physiological elements working at multiple levels. Various animal models have been used to simulate these phenomena, but these models often do not replicate the clinical setting of multiple overlapping insults. Mathematical modeling of complex systems is an approach for understanding the interplay among biological interactions. We constructed a mathematical model using ordinary differential equations that encompass the dynamics of cells and cytokines of the acute inflammatory response, as well as global tissue dysfunction. The model was calibrated in C57Bl/6 mice subjected to (1) various doses of lipopolysaccharide (LPS) alone, (2) surgical trauma, and (3) surgery + hemorrhagic shock. We tested the model's predictive ability in scenarios on which it had not been trained, namely, (1) surgery +/- hemorrhagic shock + LPS given at times after the beginning of surgical instrumentation, and (2) surgery + hemorrhagic shock + bilateral femoral fracture. Software was created that facilitated fitting of the mathematical model to experimental data, as well as for simulation of experiments with various inflammatory challenges and associated variations (gene knockouts, inhibition of specific cytokines, etc.). Using this software, the C57Bl/6-specific model was recalibrated for inflammatory analyte data in CD14-/- mice and was used to elucidate altered features of inflammation in these animals. In other experiments, rats were subjected to surgical trauma +/- LPS or to bacterial infection via fibrin clots impregnated with various inocula of Escherichia coli. Mathematical modeling may provide insights into the complex dynamics of acute inflammation in a manner that can be tested in vivo using many fewer animals than has been possible previously.

Animals↗
PubMed2004

Genome-scale in silico models of E. coli have multiple equivalent phenotypic states: assessment of correlated reaction subsets that comprise network states.

The constraint-based analysis of genome-scale metabolic and regulatory networks has been successful in predicting phenotypes and useful for analyzing high-throughput data sets. Within this modeling framework, linear optimization has been used to study genome-scale metabolic models, resulting in the enumeration of single optimal solutions describing the best use of the network to support growth. Here mixed-integer linear programming was used to calculate and study a subset of the alternate optimal solutions for a genome-scale metabolic model of Escherichia coli (iJR904) under a wide variety of environmental conditions. Analysis of the calculated sets of optimal solutions found that: (1) only a small subset of reactions in the network have variable fluxes across optima; (2) sets of reactions that are always used together in optimal solutions, correlated reaction sets, showed moderate agreement with the currently known transcriptional regulatory structure in E. coli and available expression data, and (3) reactions that are used under certain environmental conditions can provide clues about network regulatory needs. In addition, calculation of suboptimal flux distributions, using flux variability analysis, identified reactions which are used under significantly more environmental conditions suboptimally than optimally. Together these results demonstrate the utilization of reactions in genome-scale models under a variety of different growth conditions.

Aerobiosis↗
PubMed2006

In silico model as a tool for interpretation of intestinal infection studies.

In nutrition research the number of human in vivo experiments is limited because of the many restrictions and the high costs of testing in humans. Up to now predictive computer models aiming to enhance research have been rare or too complex, with many nonmeasurable adjustable parameters. This study aimed to develop a basic physicochemical computer model for a first quantitative interpretation of results obtained from in vivo intestinal experiments with bacteria. This new modeling approach is validated with results obtained from gut infection studies in vivo. The design of the model is described, and its ability to reproduce experimental data is evaluated. The model predictions are compared with new experimental data. The phenomena that take place in the gastrointestinal tract are summarized by model constants for growth, adherence, and release of bacteria. Although the model is far from describing all details and many processes in the intestine are combined, the model calculation results lead to reasonable conclusions and interesting hypotheses. One of these hypotheses concluded from the model outcomes is that Escherichia coli bacteria have a much lower intestinal growth rate in humans than in rats. Extra laboratory validation experiments proved the reliability of this hypothesis predicted by the model. In addition, the known protective effect of dietary calcium and detrimental effect of clindamycin on the growth and adherence of Salmonella bacteria could be quantified. From these results it is clear that the model enhances the interpretation of in vivo gastrointestinal experiments and will facilitate research trajectories towards new functional foods that improve resistance to pathogenic bacteria in humans.

Animals↗
PubMed2006

In silico modelling of hormone response elements.

BACKGROUND: An important step in understanding the conditions that specify gene expression is the recognition of gene regulatory elements. Due to high diversity of different types of transcription factors and their DNA binding preferences, it is a challenging problem to establish an accurate model for recognition of functional regulatory elements in promoters of eukaryotic genes. RESULTS: We present a method for precise prediction of a large group of transcription factor binding sites - steroid hormone response elements. We use a large training set of experimentally confirmed steroid hormone response elements, and adapt a sequence-based statistic method of position weight matrix, for identification of the binding sites in the query sequences. To estimate the accuracy level, a table of correspondence of sensitivity vs. specificity values is constructed from a number of independent tests. Furthermore, feed-forward neural network is used for cross-verification of the predicted response elements on genomic sequences. CONCLUSION: The proposed method demonstrates high accuracy level, and therefore can be used for prediction of hormone response elements de novo. Experimental results support our analysis by showing significant improvement of the proposed method over previous HRE recognition methods.

Binding Sites↗