Showing papers in "Sar and Qsar in Environmental Research in 2006"

A conceptual framework for predicting the toxicity of reactive chemicals: modeling soft electrophilicity.

Abstract: Although the literature is replete with QSAR models developed for many toxic effects caused by reversible chemical interactions, the development of QSARs for the toxic effects of reactive chemicals lacks a consistent approach. While limitations exit, an appropriate starting-point for modeling reactive toxicity is the applicability of the general rules of organic chemical reactions and the association of these reactions to cellular targets of importance in toxicology. The identification of plausible "molecular initiating events" based on covalent reactions with nucleophiles in proteins and DNA provides the unifying concept for a framework for reactive toxicity. This paper outlines the proposed framework for reactive toxicity. Empirical measures of the chemical reactivity of xenobiotics with a model nucleophile (thiol) are used to simulate the relative rates at which a reactive chemical is likely to bind irreversibly to cellular targets. These measures of intrinsic reactivity serve as correlates to a variety of toxic effects; what's more they appear to be more appropriate endpoints for QSAR modeling than the toxicity endpoints themselves.

Decision threshold adjustment in class prediction

Abstract: Standard classification algorithms are generally designed to maximize the number of correct predictions (concordance). The criterion of maximizing the concordance may not be appropriate in certain applications. In practice, some applications may emphasize high sensitivity (e.g., clinical diagnostic tests) and others may emphasize high specificity (e.g., epidemiology screening studies). This paper considers effects of the decision threshold on sensitivity, specificity, and concordance for four classification methods: logistic regression, classification tree, Fisher's linear discriminant analysis, and a weighted k-nearest neighbor. We investigated the use of decision threshold adjustment to improve performance of either sensitivity or specificity of a classifier under specific conditions. We conducted a Monte Carlo simulation showing that as the decision threshold increases, the sensitivity decreases and the specificity increases; but, the concordance values in an interval around the maximum concordance are similar. For specified sensitivity and specificity levels, an optimal decision threshold might be determined in an interval around the maximum concordance that meets the specified requirement. Three example data sets were analyzed for illustrations.

SAR and QSAR modeling of endocrine disruptors

Abstract: A number of xenobiotics by mimicking natural hormones can disrupt crucial functions in wildlife and humans. These chemicals termed endocrine disruptors are able to exert adverse effects through a variety of mechanisms. Fortunately, there is a growing interest in the study of these structurally diverse chemicals mainly through research programs based on in vitro and in vivo experimentations but also by means of SAR and QSAR models. The goal of our study was to retrieve from the literature all the papers dealing with structure-activity models on endocrine disruptor xenobiotics. A critical analysis of these models was made focusing our attention on the quality of the biological data, the significance of the molecular descriptors and the validity of the statistical tools used for deriving the models. The predictive power and domain of application of these models were also discussed.

Validation of counter propagation neural network models for predictive toxicology according to the OECD principles: a case study

Abstract: The OECD has proposed five principles for validation of QSAR models used for regulatory purposes. Here we present a case study investigating how these principles can be applied to models based on Kohonen and counter propagation neural networks. The study is based on a counter propagation network model that has been built using toxicity data in fish fathead minnow for 541 compounds. The study demonstrates that most, if not all, of the OECD criteria may be met when modeling using this neural network approach.

Validation of a QSAR model for acute toxicity.

Abstract: In the present study, a quantitative structure – activity relationship (QSAR) model has been developed for predicting acute toxicity to the fathead minnow (Pimephales promelas), the aim being to demonstrate how statistical validation and domain definition are both required to establish model validity and to provide reliable predictions. A dataset of 408 heterogeneous chemicals was modelled by a diverse set of theoretical molecular descriptors by using multivariate linear regression (MLR) and Genetic Algorithm – Variable Subset Selection (GA-VSS). This QSAR model was developed to generate reliable predictions of toxicity for organic chemicals not yet tested, so particular emphasis was given to statistical validity and applicability domain. External validation was performed by using OECD Screening Information Data Set (SIDS) data for 177 High Production Volume (HPV) chemicals, and a good predictivity was obtained ( = 72.1). The model was evaluated according to the OECD principles for QSAR validation, and c...

Prediction of estrogenicity: validation of a classification model.

Abstract: (Q)SAR models can be used to reduce animal testing as well as to minimise the testing costs. In particular, classification models have been widely used for estimating endpoints with binary activity. The aim of the present study was to develop and validate a classification-based quantitative structure-activity relationship (QSAR) model for endocrine disruption, based on interpretable mechanistic descriptors related to estrogenic gene activation. The model predicts the presence or absence of estrogenic activity according to a pre-defined cut-off in activity as determined in a recombinant yeast assay. The experimental data was obtained from the literature. A two-descriptor classification model was developed that has the form of a decision tree. The predictivity of the model was evaluated by using an external test set and by taking into account the limitations associated with the applicability domain (AD) of the model. The AD was determined as coverage of the model descriptor space. After removing the compoun...

QSPR of the bioconcentration factors of non-ionic organic compounds in fish using extended topochemical atom (ETA) indices.

Abstract: Bioconcentration refers to the absorption or uptake of a chemical from the media to an organism's tissues leading to greater concentration in tissues than that in the surrounding environment. Considering the importance of bioconcentration from the viewpoint of ecological safety assessment, a QSPR study was conducted based upon log BCF of 122 non-ionic organic compounds in fish using the recently introduced extended topochemical atom (ETA) indices. In deriving the models, principal component factor analysis (FA) followed by multiple linear regression (MLR), stepwise regression, partial least squares (PLS) and principal component regression analysis (PCRA) were applied as statistical tools. This was repeated with non-ETA (topological and physicochemical) descriptors and a combination set including both the ETA and non-ETA descriptors. The ETA indices suggested negative contributions of functionalities of nitro, amino and hydroxy substructures and positive contributions of branching, volume and functionality...

QSAR approach for mixture toxicity prediction using independent latent descriptors and fuzzy membership functions.

Abstract: The principle of using a singe model to predict the toxicity of mixtures of chemicals based on the characterisation of the degrees of similarity and dissimilarity of the constituent chemicals using descriptors has been demonstrated in a previous work. The current study introduces a feature extraction technique, independent component analysis, to the method to remove the correlations and dependencies between descriptors and reduce the dimension prior to similarity and dissimilarity calculations. In addition, a goal attainment multi-objective optimisation technique is used for the determination of the fuzzy membership function parameters. For three mixtures, which include a new mixture and two previously studied mixtures that all inhibit reproduction (via different mechanisms of action) in green freshwater algae scenedesmus vacuolatus, the approach showed better or equivalent prediction performance than either concentration addition or independent action models. Unlike QSARs for pure compounds that require ...

A model validation and consensus building environment.

Abstract: Over half of the failures in drug development are due to problems with the absorption, distribution, metabolism, excretion, and toxicity, or ADME/Tox properties of a candidate compound. The utilization of in silico tools to predict ADME/Tox and physicochemical properties holds great potential for reducing the attrition rate in drug research and development, as this technology can prioritize candidate compounds in the pharmaceutical R&D pipeline. However, a major concern surrounding the use of in silico ADME/Tox technology is the reliability of the property predictions. Bio-Rad Laboratories, Inc. has created a computational environment that addresses these concerns. This environment is referred to as KnowItAll. Within this platform are encoded a number of ADME/Tox predictors, the ability to validate these predictors with/without in-house data and models, as well as build a 'consensus' model that may be a much better model than any of the individual predictive model. The KnowItAll system can handle two types of predictions: real number and categorical classification.

Quantitative structure-toxicity relationships (QSTRs): a comparative study of various non linear methods. General regression neural network, radial basis function neural network and support vector machine in predicting toxicity of nitro- and cyano- aromatics to Tetrahymena pyriformis.

Abstract: Prediction of toxicity of 203 nitro- and cyano-aromatic chemicals to Tetrahymena pyriformis was carried out by radial basis function neural network, general regression neural network and support vector machine, in non-linear response surface methodology. Toxicity was predicted from hydrophobicity parameter (log Kow) and maximum superdelocalizability (Amax). Special attention was drawn to prediction ability and robustness of the models, investigated both in a leave-one-out and 10-fold cross validation (CV) processes. The influence that the corresponding changes in the learning sets during these CV processes could have on a common external test set including 41 compounds was also examined. This allowed us to establish the stability of the models. The non linear results slightly outperform (as expected) multilinear relationships (MLR) and also favourably compete with various other non linear approaches recently proposed by Ren (J. Chem. Inf. Comput. Sci., 43 1679 (2003)). § Presented at CMTPI 2005: Computati...

3D-QSAR study on heterocyclic topoisomerase II inhibitors using CoMSIA.

Abstract: Selective topoisomerase II (Topo II) inhibitors have interested to a great extent for the design of new antitumoral compounds in recent years. Comparative molecular similarity indices analysis (CoMSIA) was performed on a series of previously synthesized benzoxazole, benzimidazole, and oxazolo(4,5-b)pyridine derivatives as eukaryotic Topo II inhibitors. A training set of 16 heterocyclic compounds was used to establish the CoMSIA model. They were constructed and geometrically optimized using SYBYL v7.0. The predictive ability of the model was assessed using a test set of 7 compounds. The best model has demonstrated a good fit having r2 value of 0.968 and cross-validated coefficient q2 value as 0.562 including steric and hydrophobic fields. The hydrophobic interactions showed a dominant role for increasing Topo II inhibitor activity and hydrophilic substituent was found more important than hydrophobic one on the 5 or 6 position of benzazole moiety. The model obtained from the present study can be useful for ...

Novel numerical and graphical representation of DNA sequences and proteins

Abstract: We have introduced novel numerical and graphical representations of DNA, which offer a simple and unique characterization of DNA sequences. The numerical representation of a DNA sequence is given as a sequence of real numbers derived from a unique graphical representation of the standard genetic code. There is no loss of information on the primary structure of a DNA sequence associated with this numerical representation. The novel representations are illustrated with the coding sequences of the first exon of β-globin gene of half a dozen species in addition to human. The method can be extended to proteins as is exemplified by humanin, a 24-aa peptide that has recently been identified as a specific inhibitor of neuronal cell death induced by familial Alzheimer's disease mutant genes.

Metabolic activation of chemicals: in-silico simulation.

Abstract: The role of metabolism in prioritising chemicals according to their potential adverse health effects is extremely important given the fact that innocuous parents can be transformed into toxic metabolites. Our recent efforts in simulating metabolic activation of chemicals are reviewed in this work. The application of metabolic simulators to predict biodegradation (microbial degradation pathways), bioaccumulation (fish liver metabolism), skin sensitisation (skin metabolism), mutagenicity (rat liver S-9 metabolism) are discussed. The ability of OASIS approach to predict metabolism (toxicokinetics) and toxicity (toxicodynamics) of chemicals resulting from their metabolic activation in a single modelling platform is an important advantage of the method. It allows prioritisation of chemicals due to predicted toxicity of their metabolites.

A molecular structure based model for predicting surface tension of organic compounds.

Abstract: A Quantitative Structure-Property Relationship (QSPR) model for the prediction of surface tension of organic compounds was derived from a data set of 320 chemicals including N, O, F, Cl, Br, and/or S atoms and covering a range of about 14–45 dyn cm−1. The model, only involving six molecular descriptors obtained solely from the chemical structures, yielded an r 2 of 0.96. Its predictive capability was estimated from an external test set containing 55 structures not considered in the training set (r 2 = 0.94). It was shown that the selected molecular descriptors presented a physical meaning corresponding to the different intermolecular interactions occurring in the bulk solution. The model is applicable to a wider variety of compounds, includes less parameters and correlates better than other QSPR models reported in literature.

A combined QSAR and partial order ranking approach to risk assessment

Abstract: QSAR generated data appear as an attractive alternative to experimental data as foreseen in the proposed new chemicals legislation REACH. A preliminary risk assessment for the aquatic environment can be based on few factors, i.e. the octanol-water partition coefficient (Kow ), the vapour pressure (VP) and the potential biodegradability of the compound in combination with the predicted no-effect concentration (PNEC) and the actual tonnage in which the substance is produced. Application of partial order ranking, allowing simultaneous inclusion of several parameters leads to a mutual prioritisation of the investigated substances, the prioritisation possibly being further analysed through the concept of linear extensions and average ranks. The ranking uses endpoint values (log Kow and log VP) derived from strictly linear ‘noise-deficient’ QSAR models as input parameters. Biodegradation estimates were adopted from the BioWin module of the EPI Suite. The population growth impairment of Tetrahymena pyriformis wa...

QSPRs for the prediction of photodegradation half-life of PCBs in n-hexane

Abstract: By partial least squares (PLS) regression analysis, a quantitative structure-property relationship (QSPR) model was developed for photodegradation half-life (t1/2) of polychlorinated biphenyls (PCBs) in n-hexane solution under UV irradiation. Quantum chemical descriptors computed by PM3 Hamiltonian were used as predictor variables. The cross-validated value for the optimal QSPR model was 0.589, indicating good predictive capability for log t1/2 values of PCBs in n-hexane. The QSPR results show that standard heat of formation (DeltaHf), total energy (TE), and molecular weight (Mw) have dominant effect on t1/2 values of PCBs in n-hexane. Increasing DeltaHf and TE values or decreasing Mw values of the PCBs leads to decrease of log t1/2 values. In addition, increasing the largest negative atomic charge on a carbon atom and dipole moment of the PCBs leads to decrease of log t1/2 values.

Prediction of tissue: air partition coefficients — theoretical vs . experimental methods

Abstract: Predictive QSAR models for rat and human tissue : air partition coefficients, namely blood : air, fat : air, brain : air, liver : air, muscle : air, and kidney : air were developed utilizing experimentally determined partition coefficients for 131 chemicals obtained from the literature and molecular descriptors based solely on chemical structure. The descriptors were partitioned into four hierarchical classes, including topostructural, topochemical, 3-dimensional, and ab initio quantum chemical. Three types of regression methodologies — ridge regression, principal components regression, and partial least squares regression — were used comparatively in the development of the structure-based models. In addition to the structure-based models, ordinary least squares regression was used to develop comparative models based on experimentally determined properties including saline : air and olive oil : air partition coefficients. The results of the study indicate that many of the structure-based models are compar...

Ecotoxicity prediction by adaptive fuzzy partitioning: comparing descriptors computed on 2D and 3D structures.

Abstract: Classification models were established on four endpoints, i.e. trout, daphnia, quail and bee, including from 100 to 300 pesticides subdivided into 3 toxicity classes. For each species, five separate sets of molecular descriptors, computed by several software, were compared, including parameters related to 2D or 3D structures. The most relevant descriptors were selected with help of a procedure based on genetic algorithms. Then, structure-activity relationships were built by Adaptive Fuzzy Partition (AFP), a recursive partitioning method derived from Fuzzy Logic concepts. Globally, satisfactory results were obtained for each animal species. The best cross-validation and test set scores reached values of about 70–75%. More important, the relationships derived from the descriptors calculated from 2D structures were superior or similar to those computed from 3D structures. These results underline that the long computational time employed to compute 3D descriptors is often useless to improve the prediction abi...

Computer-aided molecular design of highly potent HIV-1 RT inhibitors: 3D QSAR and molecular docking studies of efavirenz derivatives.

Abstract: Ligand- and structure-based design approaches have been applied to an extended series of 74 efavirenz compounds effectively inhibiting wild type (WT) and mutant type (K103N) HIV-1 reverse transcriptase (RT). For ligand-based approach, three dimensional quantitative structure-activity relationship (3D-QSAR) methods, comparative molecular field analysis (CoMFA) and comparative similarity indices analysis (CoMSIA), were performed. The starting geometry of efavirenz was obtained from X-ray crystallographic data. The efavirenz derivatives were constructed and fully optimized by ab-initio molecular orbital method at HF/3-21G level. Reliable QSAR models for high predictive abilities were developed. Regarding WT and K103N inhibitions, CoMFA models with r2/cv = 0.651 and 0.678 and CoMSIA models with r2/cv = 0.662 and 0.743 were derived, respectively. The interpretation obtained from the models highlights different structural requirements for inhibition of WT and K103N HIV-1 RT. To elucidate potential binding modes of efavirenz derivatives in the binding pocket of WT and K103N HIV-1 RT, structure-based approach based on computational docking studies of selected efavirenz compounds were performed by using GOLD and FlexX programs. The results derived from docking analysis give additional information and further probe the inhibitor-enzyme interactions. The correlation of the results obtained from 3D QSAR and docking models validate each other and lead to better understanding of the structural requirements for the activity. Therefore, these integrated results are informative to provide key features and a helpful guideline for novel compound design active against HIV-1 RT.

Induction of decision trees using genetic programming for modelling ecotoxicity data: adaptive discretization of real-valued endpoints.

Abstract: Recent literature has demonstrated the applicability of genetic programming to induction of decision trees for modelling toxicity endpoints. Compared with other decision tree induction techniques that are based upon recursive partitioning employing greedy searches to choose the best splitting attribute and value at each node that will necessarily miss regions of the search space, the genetic programming based approach can overcome the problem. However, the method still requires the discretization of the often continuous-valued toxicity endpoints prior to the tree induction. A novel extension of this method, YAdapt, is introduced in this work which models the original continuous endpoint by adaptively finding suitable ranges to describe the endpoints during the tree induction process, removing the need for discretization prior to tree induction and allowing the ordinal nature of the endpoint to be taken into account in the models built.

Structural e-bioinformatics and drug design†

Abstract: Nowadays the in silico scenario for drug design is totally dependent on structural biology and structural bioinformatics. A myriad of free bioinformatics applications and services have been posted on the web. This mini-review mentions web sites that are useful in structure-based drug design. The information is given in a logical manner, following the drug design process i.e. characterization of a protein target, modelling the protein using sequence homology, optimization of the protein structure and finally docking of small ligands into the active site.

Complexity of chemical graphs in terms of size, branching, and cyclicity.

Abstract: Chemical graph complexity depends on many factors, but the main ones are size, branching, and cyclicity. Some molecular descriptors embrace together all these three parameters, which cannot then be disentangled. The topological index J (and its refinements that include accounting for bond multiplicity and the presence of heteroatoms) was designed to compensate in a significant measure for graph size and cyclicity, and therefore it contains information mainly on branching. In order to separate these factors, two new indices (F and G) related with J are proposed, which allow to group together graphs with the same size into families of constitutional formulas differing in their branching and cyclicity. A comparison with other topological indices revealed that a few other topological indices vary similarly with index G, notably DN2S(4) among the triplet indices, and TOTOP among the indices contained in the Molconn-Z program. This comparison involved all possible chemical graphs (i.e. connected planar graphs w...

Elucidation of chiral recognition processes of macrocyclic antibiotic vancomycin

Abstract: A theoretical investigation was carried out on the retention and separation of enantiomeric molecules including nonsteroidal anti-inflammatory drugs, anti-neoplastic compounds and N-derivatized amino acids by capillary electrophoresis using macrocyclic antibiotics, a new class of chiral selectors, as stationary phase. Firstly docking methods were used to study the enantiorecognition in chiral electrophoresis. The molecular dynamics simulations of the two diastereoisomer complexes were then performed in order to understand how these antibiotics recognize the enantiomers. Another approach was applied in this study to establish a quantitative structure-enantioselectivity relationship (QSER) model, able to describe the resolution of a series of chiral compounds in capillary electrophoresis using vancomycin as the resolving agent.

Quantitative structure-property relationship studies on electrochemical degradation of substituted phenols using a support vector machine.

Abstract: A quantitative structure-property relationship (QSPR) model has been developed for the electrochemical degradation of substituted phenols using a support vector machine (SVM). Thirty descriptors, including quantum chemical parameters, steric effect descriptors and half wave potential (E 1/2), were used for describing twelve substituted phenols, including mono- and multi-substituent phenols. A leave-one-out (LOO) cross validation procedure resulted in the selection of three descriptors, the total of electron and nuclear energies of the two-center terms for the carbon–chlorine or carbon–nitrogen bond (TE2), the net atomic charges on the chlorine or nitrogen (q x), and the largest negative atomic charge on an atom (q −). The model based on SVM yielded a Q 2 value of 0.892, indicating a high predictive ability. Compared with models developed with partial least squares (PLS) and multiple linear regression (MLR), where Q 2 were 0.804 and 0.799 respectively, SVM showed higher performances.

Homology model of the rainbow trout estrogen receptor (rtERalpha) and docking of endocrine disrupting chemicals (EDCs).

Abstract: A model for rainbow trout (Oncorhynchus mykiss) estrogen receptor (rtERα) was built by homology with the human estrogen receptor (hERα). A high level of sequence conservation between the two receptors was found with 64% and 80% of identity and similarity, respectively. Selected endocrine disrupting chemicals were docked into the ligand binding domain (LBD) of rtERα and the corresponding free binding energies Δ(ΔGbind) values were calculated. A Quantitative Structure-Activity Relationship (QSAR) model between the relative binding affinity data and the Δ(ΔGbind) values was derived in order to predict which further organic pollutants are likely to bind to rtERα. ‖Presented at CMTPI 2005: Computational Methods in Toxicology and Pharmacology Integrating Internet Resources (Shanghai, China, October 29–November 1, 2005).

Optimal neighbor selection in molecular similarity: comparison of arbitrary versus tailored prediction spaces†

Abstract: Three classes of arbitrary quantitative molecular similarity analysis (QMSA) methods have been computed using atom pairs (APs), topological indices (TIs), and principal components (PCs) derived from topological indices. Tailored QMSA models have been developed from TIs selected through ridge regression. K-nearest neighbor (kNN) based estimation has been applied to all of the methods to estimate normal vapor pressure (p vap) and water solubility (sol) for a set of 194 chemicals. Results show that the tailored QMSA methods are superior to arbitrary similarity methods in estimating both of these properties for the given set of chemicals. †Presented at CMTPI 2005: Computational Methods in Toxicology and Pharmacology Integrating Internet Resources (Shanghai, China, October 29–November 1, 2005).

Physicochemical properties/descriptors governing the solubility and partitioning of chemicals in water-solvent-gas systems. Part 1. Partitioning between octanol and air

Abstract: QSPR analyses of a data set containing experimental partition coefficients in the three systems octanol-water, water-gas, and octanol-gas for 98 chemicals have shown that it is possible to calculate any partition coefficient in the system 'gas phase/octanol/water' by three different approaches: (1) from experimental partition coefficients obtained in the corresponding two other subsystems. However, in many cases these data may not be available. Therefore, a solution may be approached (2), a traditional QSPR analysis based on e.g. HYBOT descriptors (hydrogen bond acceptor and donor factors, SigmaCa and SigmaCd, together with polarisability alpha, a steric bulk effect descriptor) and supplemented with substructural indicator variables. (3) A very promising approach which is a combination of the similarity concept and QSPR based on HYBOT descriptors. In this approach observed partition coefficients of structurally nearest neighbours of a compound-of-interest are used. In addition, contributions arising from differences in alpha, SigmaCa, and SigmaCd values between the compound-of-interest and its nearest neighbour(s), respectively, are considered. In this investigation highly significant relationships were obtained by approaches (1) and (3) for the octanol/gas phase partition coefficient (log Log).

Collection and preparation of molecular databases for virtual screening

Abstract: Drug discovery and development research is undergoing a paradigm shift from a linear and sequential nature of the various steps involved in the drug discovery process of the past to the more parallel approach of the present, due to a lack of sufficient correlation between activities estimated by in vitro and in vivo assays. This is attributed to the non-drug-likeness of the lead molecules, which has often been detected at advanced drug development stages. Thus a striking aspect of this paradigm shift has been early/parallel in silico prioritization of drug-like molecular databases (also database pre-processing), in addition to prioritizing compounds with high affinity and selectivity for a protein target. In view of this, a drug-like database useful for virtual screening has been created by prioritizing molecules from 36 catalog suppliers, using our recently derived binary QSAR based drug-likeness model as a filter. The performance of this model was assessed by a comparative evaluation with respect to com...

Structure–activity relationships for flow cytometric data of smaller polycyclic aromatic hydrocarbons

Abstract: Using flow cytometry, select polycyclic aromatic hydrocarbons (PAHs) were evaluated for induction of apoptosis in human monocytic THP-1 cells. Based on structure, the PAHs were divided into linear and bay-region-containing compounds. Except for fluorene, the linear PAHs failed to induce apoptosis; all of the bay-region-containing PAHs induce apoptosis. The relationship that a bay-region is required to induce apoptosis is supported by results for benzo[a]pyrene (positive) and 2-methylanthracene (negative). The data for bay-region containing, four-ringed PAH compounds reveal that possessing a linear-region of more than two rings diminishes the ability of a PAH to induce apoptosis. Owing to the steric interactions of the hydrogen atoms of the methyl group and those on the ring carbons, 1-methylanthracene does not have a true bay-region. However, the methyl group substituted in the 1-position does confer a bay-like conformation, which may explain its activity in contrast to its parent derivative anthracene an...

Integration of genomic data for pharmacology and toxicology using Internet resources.

Abstract: Genome based technologies such as sequencing and gene expression profiling using microarrays are creating massive amounts of data. Results from these studies have provided unique insights into targets, biochemical pathways, and biological systems affected by drug or xenobiotic chemical treatments. Moreover, these genomic technologies offer the potential to identify biomarkers for pharmacological development or toxicological prediction. Nonetheless, microarray studies involving a single compound produce useful although limited data. To gain further power from these individual studies, the ability to combine datasets through integration schemes has become imperative. In the current study, we describe and analyze currently available Internet resources designed to address this problem. Many functionalities, such as ability to cross reference orthologous genes across species or to combine same technology platform data, are present in these resources. Nonetheless, these resources are limited in the number of technology platforms they can support. While the ability to integrate all currently existing gene expression datasets remains enigmatic, the current tools provide a partial solution that may still yield unique insights into the affects of exogenous molecules at the level of gene expression.