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Background Prescription opioids are powerful pain-reducing medications, but they may cause a variety of adverse effects. Long-term prescription opioid use (POU) is contributing to an opioid-related epidemic of addiction and death, and the scope of the opioid crisis continues to expand. As such, there is a need to identify the adverse effects associated with prescription opioid use (POU). Thousands of articles that focus on POU and its associated medical disorders have been published. However, it is time-consuming and labor-intensive to extract and understand the information of all POU-related published articles.Methods In this study, we applied the well-adapted topic modeling method, Latent Dirichlet Allocation (LDA), to perform text mining on POU-related literature. We compiled six large academic abstract datasets by searching PubMed using the Medical Subject Headings (MeSH): prescription opioid, codeine, morphine, hydrocodone, oxycodone, and methadone. We then applied topic modeling to identify topics and analyze topic similarities/differences in these six datasets. Word clouds and histograms were used to depict the distribution of vocabularies over each topic in which the most prevalent words conveyed a topic’s substance.Results The LDA topics recaptured the search keywords in PubMed, and further revealed relevant themes, such as patients, drugs, side effects, and association links between different POU and risk factors, such as gender and age. Moreover, based on the topic modeling results, TreeMap was used to fingerprint abstracts, which revealed the possibility of constructing a visualized literature index by combining topic modeling and visualization tools such as TreeMap. Meanwhile, while performing trend analysis to explore the prevalent topic dynamics in the POU-related literature, we found that an increasing trend in opioid prescription and its associated health risks are assessed as the most central issues.Conclusion The topic modeling results presented in this study not only convey an understandable and thematic structure of the POU literature, but also provide a means to discover which documents contain information about medical disorders associated with POU, thus, reducing the time and effort needed to review the literature for relevant articles. These results can be used as a preliminary study to systematically understand the risk factors related to increased POU-associated medical disorders.

Bisphenol A (BPA) replacement compounds are released to the environment and cause widespread human exposure. However, a lack of thorough safety evaluations on the BPA replacement compounds has raised public concerns. We assessed the endocrine disruption potential of BPA replacement compounds in the market to assist their safety evaluations. A literature search was conducted to ascertain the BPA replacement compounds in use. Available experimental estrogenic activity data of these compounds were extracted from the Estrogenic Activity Database (EADB) to assess their estrogenic potential. An in silico model was developed to predict the estrogenic activity of compounds lacking experimental data. Molecular dynamics (MD) simulations were performed to understand the mechanisms by which the estrogenic compounds bind to and activate the estrogen receptor (ER). Forty-five BPA replacement compounds were identified in the literature. Seven were more estrogenic and five less estrogenic than BPA, while six were nonestrogenic in EADB. A two-tier in silico model was developed based on molecular docking to predict the estrogenic activity of the 27 compounds lacking data. Eleven were predicted as ER binders and 16 as nonbinders. MD simulations revealed hydrophobic contacts and hydrogen bonds as the main interactions between ER and the estrogenic compounds.

A robust bioinformatics capability is widely acknowledged as central to realizing the promises of toxicogenomics. Successful application of toxicogenomic approaches, such as DNA microarrays, inextricably relies on appropriate data management, the ability to extract knowledge from massive amounts of data, and the availability of functional information for data interpretation. At the FDA's National Center for Toxicological Research (NCTR), we are developing a public microarray data management and analysis software, called ArrayTrack, that is also used in the routine review of genomic data submitted to the FDA. ArrayTrack stores a full range of information related to DNA microarrays and clinical and non-clinical studies as well as the digested data derived from proteomics and metabonomics experiments. In addition, ArrayTrack provides a rich collection of functional information about genes, proteins, and pathways drawn from various public biological databases for facilitating data interpretation. Many data analysis and visualization tools are available with ArrayTrack for individual platform data analysis, multiple omics data integration, and integrated analysis of omics data with study data. Importantly, gene expression data, functional information, and analysis methods are fully integrated so that the data analysis and interpretation process is simplified and enhanced. Using ArrayTrack, users can select an analysis method from the ArrayTrack tool box, apply the method to selected microarray data, and the analysis of results can be directly linked to individual gene, pathway, and Gene Ontology analysis. ArrayTrack is publicly available online ( http://www.fda.gov/nctr/science/centers/toxicoinformatics/ArrayTrack/index.htm ) and the prospective user can also request a local installation version by contacting the authors.

One endocrine disruption mechanism is through binding to nuclear receptors such as the androgen receptor (AR) and estrogen receptor (ER) in target cells. The concentration of a chemical in serum is important for its entry into the target cells to bind the receptors, which is regulated by the serum proteins. Human sex hormone-binding globulin (SHBG) is the major transport protein in serum that can bind androgens and estrogens and thus change a chemical's availability to enter the target cells. Sequestration of an androgen or estrogen in the serum can alter the chemical elicited AR- and ER-mediated responses. To better understand the chemical-induced endocrine activity, we developed a competitive binding assay using human pregnancy plasma and measured the binding to the human SHBG for 125 structurally diverse chemicals, most of which were known to bind AR and ER. Eighty seven chemicals were able to bind the human SHBG in the assay, whereas 38 chemicals were nonbinders. Binding data for human SHBG are compared with that for rat α-fetoprotein, ER and AR. Knowing the binding profiles between serum and nuclear receptors will improve assessment of a chemical's potential for endocrine disruption. The SHBG binding data reported here represent the largest data set of structurally diverse chemicals tested for human SHBG binding. Utilization of the SHBG binding data with AR and ER binding data could enable better evaluation of endocrine disrupting potential of chemicals through AR- and ER-mediated responses since sequestration in serum could be considered.

We present primary results from the Sequencing Quality Control (SEQC) project, coordinated by the US Food and Drug Administration. Examining Illumina HiSeq, Life Technologies SOLiD and Roche 454 platforms at multiple laboratory sites using reference RNA samples with built-in controls, we assess RNA sequencing (RNA-seq) performance for junction discovery and differential expression profiling and compare it to microarray and quantitative PCR (qPCR) data using complementary metrics. At all sequencing depths, we discover unannotated exon-exon junctions, with >80% validated by qPCR. We find that measurements of relative expression are accurate and reproducible across sites and platforms if specific-filters are used. In contrast, RNA-seq and microarrays do not provide accurate absolute measurements, and gene-specific biases are observed for all examined platforms, including qPCR. Measurement performance depends on the platform and data analysis pipeline, and variation is large for transcript-level profiling. The complete SEQC data sets, comprising >100 billion reads (10Tb), provide unique resources for evaluating RNA-seq analyses for clinical and regulatory settings.

The estrogen receptors (ERs) are a group of versatile receptors. They regulate an enormity of processes starting in early life and continuing through sexual reproduction, development, and end of life. This review provides a background and structural perspective for the ERs as part of the nuclear receptor superfamily and discusses the ER versatility and promiscuity. The wide repertoire of ER actions is mediated mostly through ligand-activated transcription factors and many DNA response elements in most tissues and organs. Their versatility, however, comes with the drawback of promiscuous interactions with structurally diverse exogenous chemicals with potential for a wide range of adverse health outcomes. Even when interacting with endogenous hormones, ER actions can have adverse effects in disease progression. Finally, how nature controls ER specificity and how the subtle differences in receptor subtypes are exploited in pharmaceutical design to achieve binding specificity and subtype selectivity for desired biological response are discussed. The intent of this review is to complement the large body of literature with emphasis on most recent developments in selective ER ligands.

Toxicogenomics (TGx) endeavors to elucidate the underlying molecular mechanisms through exploring gene expression profiles in response to toxic substances. Recently, RNA-Seq is increasingly regarded as a more powerful alternative to microarrays in TGx studies. However, realizing RNA-Seq's full potential requires novel approaches to extracting information from the complex TGx data. Considering read counts as the number of times a word occurs in a document, gene expression profiles from RNA-Seq are analogous to a word by document matrix used in text mining. Topic modeling aiming at to discover the latent structures in text corpora would be helpful to explore RNA-Seq based TGx data. In this study, topic modeling was applied on a typical RNA-Seq based TGx data set to discover hidden functional modules. The RNA-Seq based gene expression profiles were transformed into "documents", on which latent Dirichlet allocation (LDA) was used to build a topic model. We found samples treated by the compounds with the same modes of actions (MoAs) could be clustered based on topic similarities. The topic most relevant to each cluster was identified as a "marker" topic, which was interpreted by gene enrichment analysis with MoAs then confirmed by compound and pathways associations mined from literature. To further validate the "marker" topics, we tested topic transferability from RNA-Seq to microarrays. The RNA-Seq based gene expression profile of a topic specifically associated with peroxisome proliferator-activated receptors (PPAR) signaling pathway was used to query samples with similar expression profiles in two different microarray data sets, yielding accuracy of about 85%. This proof-of-concept study demonstrates the applicability of topic modeling to discover functional modules in RNA-Seq data and suggests a valuable computational tool for leveraging information within TGx data in RNA-Seq era.

Endocrine-active chemicals can potentially have adverse effects on both humans and wildlife. They can interfere with the body's endocrine system through direct or indirect interactions with many protein targets. Estrogen receptors (ERs) are one of the major targets, and many endocrine disruptors are estrogenic and affect the normal estrogen signaling pathways. However, ERs can also serve as therapeutic targets for various medical conditions, such as menopausal symptoms, osteoporosis, and ER-positive breast cancer. Because of the decades-long interest in the safety and therapeutic utility of estrogenic chemicals, a large number of chemicals have been assayed for estrogenic activity, but these data exist in various sources and different formats that restrict the ability of regulatory and industry scientists to utilize them fully for assessing risk-benefit. To address this issue, we have developed an Estrogenic Activity Database (EADB; http://www.fda.gov/ScienceResearch/BioinformaticsTools/EstrogenicActivityDatabaseEADB/default.htm) and made it freely available to the public. EADB contains 18,114 estrogenic activity data points collected for 8212 chemicals tested in 1284 binding, reporter gene, cell proliferation, and in vivo assays in 11 different species. The chemicals cover a broad chemical structure space and the data span a wide range of activities. A set of tools allow users to access EADB and evaluate potential endocrine activity of chemicals. As a case study, a classification model was developed using EADB for predicting ER binding of chemicals.

A robust bioinformatics capability is widely acknowledged as central to realizing the promises of toxicogenomics. Successful application of toxicogenomic approaches, such as DNA microarrays, inextricably relies on appropriate data management, the ability to extract knowledge from massive amounts of data, and the availability of functional information for data interpretation. At the FDA's National Center for Toxicological Research (NCTR), we are developing a public microarray data management and analysis software, called ArrayTrack, that is also used in the routine review of genomic data submitted to the FDA. ArrayTrack stores a full range of information related to DNA microarrays and clinical and non-clinical studies as well as the digested data derived from proteomics and metabonomics experiments. In addition, ArrayTrack provides a rich collection of functional information about genes, proteins, and pathways drawn from various public biological databases for facilitating data interpretation. Many data analysis and visualization tools are available with ArrayTrack for individual platform data analysis, multiple omics data integration, and integrated analysis of omics data with study data. Importantly, gene expression data, functional information, and analysis methods are fully integrated so that the data analysis and interpretation process is simplified and enhanced. Using ArrayTrack, users can select an analysis method from the ArrayTrack tool box, apply the method to selected microarray data, and the analysis of results can be directly linked to individual gene, pathway, and Gene Ontology analysis. ArrayTrack is publicly available online ( http://www.fda.gov/nctr/science/centers/toxicoinformatics/ArrayTrack/index.htm ) and the prospective user can also request a local installation version by contacting the authors.

Next-generation sequencing (NGS) technologies have provided researchers with vast possibilities in various biological and biomedical research areas. Efficient data mining strategies are in high demand for large scale comparative and evolutional studies to be performed on the large amounts of data derived from NGS projects. Topic modeling is an active research field in machine learning and has been mainly used as an analytical tool to structure large textual corpora for data mining.We report a novel procedure to analyse NGS data using topic modeling. It consists of four major procedures: NGS data retrieval, preprocessing, topic modeling, and data mining using Latent Dirichlet Allocation (LDA) topic outputs. The NGS data set of the Salmonella enterica strains were used as a case study to show the workflow of this procedure. The perplexity measurement of the topic numbers and the convergence efficiencies of Gibbs sampling were calculated and discussed for achieving the best result from the proposed procedure.The output topics by LDA algorithms could be treated as features of Salmonella strains to accurately describe the genetic diversity of fliC gene in various serotypes. The results of a two-way hierarchical clustering and data matrix analysis on LDA-derived matrices successfully classified Salmonella serotypes based on the NGS data. The implementation of topic modeling in NGS data analysis procedure provides a new way to elucidate genetic information from NGS data, and identify the gene-phenotype relationships and biomarkers, especially in the era of biological and medical big data.The implementation of topic modeling in NGS data analysis provides a new way to elucidate genetic information from NGS data, and identify the gene-phenotype relationships and biomarkers, especially in the era of biological and medical big data.

Flavonoids are frequently used as dietary supplements in the absence of research evidence regarding health benefits or toxicity. Furthermore, ingested doses could far exceed those received from diet in the course of normal living. Some flavonoids exhibit binding to estrogen receptors (ERs) with consequential vigilance by regulatory authorities at the U.S. EPA and FDA. Regulatory authorities must consider both beneficial claims and potential adverse effects, warranting the increases in research that has spanned almost two decades. Here, we report pathway enrichment of 14 targets from the Comparative Toxicogenomics Database (CTD) and the Herbal Ingredients' Targets (HIT) database for 22 flavonoids that bind ERs. The selected flavonoids are confirmed ER binders from our earlier studies, and were here found in mainly involved in three types of biological processes, ER regulation, estrogen metabolism and synthesis, and apoptosis. Besides cancers, we conjecture that the flavonoids may affect several diseases via apoptosis pathways. Diseases such as amyotrophic lateral sclerosis, viral myocarditis and non-alcoholic fatty liver disease could be implicated. More generally, apoptosis processes may be importantly evolved biological functions of flavonoids that bind ERs and high dose ingestion of those flavonoids could adversely disrupt the cellular apoptosis process.

Background Both adolescent substance use and adolescent depression are major public health problems, and have the tendency to co-occur. Thousands of articles on adolescent substance use or depression have been published. It is labor intensive and time consuming to extract huge amounts of information from the cumulated collections. Topic modeling offers a computational tool to find relevant topics by capturing meaningful structure among collections of documents. Methods In this study, a total of 17,723 abstracts from PubMed published from 2000 to 2014 on adolescent substance use and depression were downloaded as objects, and Latent Dirichlet allocation (LDA) was applied to perform text mining on the dataset. Word clouds were used to visually display the content of topics and demonstrate the distribution of vocabularies over each topic. Results The LDA topics recaptured the search keywords in PubMed, and further discovered relevant issues, such as intervention program, association links between adolescent substance use and adolescent depression, such as sexual experience and violence, and risk factors of adolescent substance use, such as family factors and peer networks. Using trend analysis to explore the dynamics of proportion of topics, we found that brain research was assessed as a hot issue by the coefficient of the trend test. Conclusions Topic modeling has the ability to segregate a large collection of articles into distinct themes, and it could be used as a tool to understand the literature, not only by recapturing known facts but also by discovering other relevant topics. Electronic supplementary material The online version of this article (doi:10.1186/s12889-016-2932-1) contains supplementary material, which is available to authorized users.

RNA-Seq has been increasingly used for the quantification and characterization of transcriptomes. The ongoing development of the technology promises the more accurate measurement of gene expression. However, its benefits over widely accepted microarray technologies have not been adequately assessed, especially in toxicogenomics studies. The goal of this study is to enhance the scientific community's understanding of the advantages and challenges of RNA-Seq in the quantification of gene expression by comparing analysis results from RNA-Seq and microarray data on a toxicogenomics study. A typical toxicogenomics study design was used to compare the performance of an RNA-Seq approach (Illumina Genome Analyzer II) to a microarray-based approach (Affymetrix Rat Genome 230 2.0 arrays) for detecting differentially expressed genes (DEGs) in the kidneys of rats treated with aristolochic acid (AA), a carcinogenic and nephrotoxic chemical most notably used for weight loss. We studied the comparability of the RNA-Seq and microarray data in terms of absolute gene expression, gene expression patterns, differentially expressed genes, and biological interpretation. We found that RNA-Seq was more sensitive in detecting genes with low expression levels, while similar gene expression patterns were observed for both platforms. Moreover, although the overlap of the DEGs was only 40-50%, the biological interpretation was largely consistent between the RNA-Seq and microarray data. RNA-Seq maintained a consistent biological interpretation with time-tested microarray platforms while generating more sensitive results. However, there is clearly a need for future investigations to better understand the advantages and limitations of RNA-Seq in toxicogenomics studies and environmental health research.

Gene expression data from microarrays are being applied to predict preclinical and clinical endpoints, but the reliability of these predictions has not been established. In the MAQC-II project, 36 independent teams analyzed six microarray data sets to generate predictive models for classifying a sample with respect to one of 13 endpoints indicative of lung or liver toxicity in rodents, or of breast cancer, multiple myeloma or neuroblastoma in humans. In total, >30,000 models were built using many combinations of analytical methods. The teams generated predictive models without knowing the biological meaning of some of the endpoints and, to mimic clinical reality, tested the models on data that had not been used for training. We found that model performance depended largely on the endpoint and team proficiency and that different approaches generated models of similar performance. The conclusions and recommendations from MAQC-II should be useful for regulatory agencies, study committees and independent investigators that evaluate methods for global gene expression analysis.

The discordance in results of independent genome-wide association studies (GWAS) indicates the potential for Type I and Type II errors. We assessed the repeatibility of current Affymetrix technologies that support GWAS. Reasonable reproducibility was observed for both raw intensity and the genotypes/copy number variants. We also assessed consistencies between different SNP arrays and between genotype calling algorithms. We observed that the inconsistency in genotypes was generally small at the specimen level. To further examine whether the differences from genotyping and genotype calling are possible sources of variation in GWAS results, an association analysis was applied to compare the associated SNPs. We observed that the inconsistency in genotypes not only propagated to the association analysis, but was amplified in the associated SNPs. Our studies show that inconsistencies between SNP arrays and between genotype calling algorithms are potential sources for the lack of reproducibility in GWAS results.

Quality control of a microarray experiment has become an important issue for both research and regulation. External RNA controls (ERCs), which can be either added to the total RNA level (tERCs) or introduced right before hybridization (cERCs), are designed and recommended by commercial microarray platforms for assessment of performance of a microarray experiment. However, the utility of ERCs has not been fully realized mainly due to the lack of sufficient data resources. The US Food and Drug Administration (FDA)-led community-wide Microarray Quality Control (MAQC) study generates a large amount of microarray data with implementation of ERCs across several commercial microarray platforms. The utility of ERCs in quality control by assessing the ERCs’ concentration–response behavior was investigated in the MAQC study. In this work, an ERC-based correlation analysis was conducted to assess the quality of a microarray experiment. We found that the pairwise correlations of tERCs are sample independent, indicating that the array data obtained from different biological samples can be treated as technical replicates in analysis of tERCs. Consequently, the commonly used quality control method of applying correlation analysis on technical replicates can be adopted for assessing array performance based on different biological samples using tERCs. The proposed approach is sensitive to identifying outlying assays and is not dependent on the choice of normalization method.

Dye-specific bias effects, commonly observed in the two-color microarray platform, are normally corrected using the dye swap design. This design, however, is relatively expensive and labor-intensive. We propose a self-self hybridization design as an alternative to the dye swap design. In this design, the treated and control samples are labeled with Cy5 and Cy3 (or Cy3 and Cy5), respectively, without dye swap, along with a set of self-self hybridizations on the control sample. We compare this design with the dye swap design through investigation of mouse primary hepatocytes treated with three peroxisome proliferator-activated receptor-alpha (PPARalpha) agonists at three dose levels. Using Agilent's Whole Mouse Genome microarray, differentially expressed genes (DEG) were determined for both the self-self hybridization and dye swap designs. The DEG concordance between the two designs was over 80% across each dose treatment and chemical. Furthermore, 90% of DEG-associated biological pathways were in common between the designs, indicating that biological interpretations would be consistent. The reduced labor and expense for the self-self hybridization design make it an efficient substitute for the dye swap design. For example, in larger toxicogenomic studies, only about half the chips are required for the self-self hybridization design compared to that needed in the dye swap design.

Single-nucleotide polymorphisms (SNPs) determined based on SNP arrays from the international HapMap consortium (HapMap) and the genetic variants detected in the 1000 genomes project (1KGP) can serve as two references for genomewide association studies (GWAS). We conducted comparative analyses to provide a means for assessing concerns regarding SNP array-based GWAS findings as well as for realistically bounding expectations for next generation sequencing (NGS)-based GWAS. We calculated and compared base composition, transitions to transversions ratio, minor allele frequency and heterozygous rate for SNPs from HapMap and 1KGP for the 622 common individuals. We analysed the genotype discordance between HapMap and 1KGP to assess consistency in the SNPs from the two references. In 1KGP, 90.58% of 36,817,799 SNPs detected were not measured in HapMap. More SNPs with minor allele frequencies less than 0.01 were found in 1KGP than HapMap. The two references have low disc ordance (generally smaller than 0.02) in genotypes of common SNPs, with most discordance from heterozygous SNPs. Our study demonstrated that SNP array-based GWAS findings were reliable and useful, although only a small portion of genetic variances were explained. NGS can detect not only common but also rare variants, supporting the expectation that NGS-based GWAS will be able to incorporate a much larger portion of genetic variance than SNP arrays-based GWAS.

Background Topic modelling is an active research field in machine learning. While mainly used to build models from unstructured textual data, it offers an effective means of data mining where samples represent documents, and different biological endpoints or omics data represent words. Latent Dirichlet Allocation (LDA) is the most commonly used topic modelling method across a wide number of technical fields. However, model development can be arduous and tedious, and requires burdensome and systematic sensitivity studies in order to find the best set of model parameters. Often, time-consuming subjective evaluations are needed to compare models. Currently, research has yielded no easy way to choose the proper number of topics in a model beyond a major iterative approach. Methods and results Based on analysis of variation of statistical perplexity during topic modelling, a heuristic approach is proposed in this study to estimate the most appropriate number of topics. Specifically, the rate of perplexity change (RPC) as a function of numbers of topics is proposed as a suitable selector. We test the stability and effectiveness of the proposed method for three markedly different types of grounded-truth datasets: Salmonella next generation sequencing, pharmacological side effects, and textual abstracts on computational biology and bioinformatics (TCBB) from PubMed. Conclusion The proposed RPC-based method is demonstrated to choose the best number of topics in three numerical experiments of widely different data types, and for databases of very different sizes. The work required was markedly less arduous than if full systematic sensitivity studies had been carried out with number of topics as a parameter. We understand that additional investigation is needed to substantiate the method's theoretical basis, and to establish its generalizability in terms of dataset characteristics.

Over the last decade, the introduction of microarray technology has had a profound impact on gene expression research. The publication of studies with dissimilar or altogether contradictory results, obtained using different microarray platforms to analyze identical RNA samples, has raised concerns about the reliability of this technology. The MicroArray Quality Control (MAQC) project was initiated to address these concerns, as well as other performance and data analysis issues. Expression data on four titration pools from two distinct reference RNA samples were generated at multiple test sites using a variety of microarray-based and alternative technology platforms. Here we describe the experimental design and probe mapping efforts behind the MAQC project. We show intraplatform consistency across test sites as well as a high level of interplatform concordance in terms of genes identified as differentially expressed. This study provides a resource that represents an important first step toward establishing a framework for the use of microarrays in clinical and regulatory settings.

Current advances in genomics, proteomics, and metabonomics would result in a constellation of benefits in human health. Classification applying supervised learning methods to omics data as one of the molecular classification approaches has enjoyed its growing role in clinical application. However, the utility of a molecular classifier will not be fully appreciated unless its quality is carefully validated. A clinical omics data is usually noisy with the number of independent variables far more than the number of subjects and, possibly, with a skewed subject distribution. Given that, the consensus approach holds an advantage over a single classifier. Thus, the focus of this review is mainly placed on how validating a molecular classifier using Decision Forest (DF), a robust consensus approach. We recommended that a molecular classifier has to be assessed with respect to overall prediction accuracy, prediction confidence and chance correlation, which can be readily achieved in DF. The commonalities and differences between external validation and cross-validation are also discussed for perspective use of these methods to validate a DF classifier. In addition, the advantages of using consensus approaches for identification of potential biomarkers are also rationalized. Although specific DF examples are used in this review, the provided rationales and recommendations should be equally applicable to other consensus methods.

Recent progress in combinatorial chemistry and parallel synthesis has radically changed the approach to drug discovery in the pharmaceutical industry. At present, thousands of compounds can be made in a short period, creating a need for fast and effective in silico methods to select the most promising lead candidates. Decision forest is a novel pattern recognition method, which combines the results of multiple distinct but comparable decision tree models to reach a consensus prediction. In this article, a decision forest model was developed using a structurally diverse training data set containing 232 compounds whose estrogen receptor binding activity was tested at the U.S. Food and Drug Administration (FDA)'s National Center for Toxicological Research (NCTR). The model was subsequently validated using a test data set of 463 compounds selected from the literature, and then applied to a large data set with 57,145 compounds as a screening example. The results show that the decision forest method is a fast, reliable and effective in silico approach, which could be useful in drug discovery.

This is the 52nd report of a series of workshops organised by the European Centre for the Validation of Alternative Methods (ECVAM). The main objective of ECVAM, as defined in 1993 by its Scientific Advisory Committee, is to promote the scientific and regulatory acceptance of alternative methods which are of importance to the biosciences, and that reduce, refine or replace the use of laboratory animals. The ECVAM workshop on the quantitative structure-activity relationship applicability domain was held at ECVAM on 29 September–1 October 2004, under the chairmanship of Andrew Worth. The workshop was attended by experts from academia, industry, international organisations and regulatory authorities. The aim of the workshop was to review the state of the art of methods for identifying the domain of applicability of structure-activity relationships (SARs) and quantitative structure-activity relationships (QSARs), collectively referred to as (Q)SARs. The report is intended to provide a source of input to the development of an OECD Guidance Document on (Q)SAR Validation. The report also makes recommendations for further research needed to understand and apply the concept of the (Q)SAR applicability domain (AD).