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BACKGROUND: Endocrine disrupting chemicals (EDCs) are xenobiotics that mimic the interaction of natural hormones and alter synthesis, transport, or metabolic pathways. The prospect of EDCs causing adverse health effects in humans and wildlife has led to the development of scientific and regulatory approaches for evaluating bioactivity. This need is being addressed using high-throughput screening (HTS) in vitro approaches and computational modeling. OBJECTIVES: In support of the Endocrine Disruptor Screening Program, the U.S. Environmental Protection Agency (EPA) led two worldwide consortiums to virtually screen chemicals for their potential estrogenic and androgenic activities. Here, we describe the Collaborative Modeling Project for Androgen Receptor Activity (CoMPARA) efforts, which follows the steps of the Collaborative Estrogen Receptor Activity Prediction Project (CERAPP). METHODS: The CoMPARA list of screened chemicals built on CERAPP's list of 32,464 chemicals to include additional chemicals of interest, as well as simulated ToxCast (TM) metabolites, totaling 55,450 chemical structures. Computational toxicology scientists from 25 international groups contributed 91 predictive models for binding, agonist, and antagonist activity predictions. Models were underpinned by a common training set of 1,746 chemicals compiled from a combined data set of 11 ToxCast (TM)/Tox21 HTS in vitro assays. RESULTS: The resulting models were evaluated using curated literature data extracted from different sources. To overcome the limitations of single-model approaches, CoMPARA predictions were combined into consensus models that provided averaged predictive accuracy of approximately 80% for the evaluation set. DISCUSSION: The strengths and limitations of the consensus predictions were discussed with example chemicals; then, the models were implemented into the free and open-source OPERA application to enable screening of new chemicals with a defined applicability domain and accura

BACKGROUND: Endocrine disrupting chemicals (EDCs) are xenobiotics that mimic the interaction of natural hormones and alter synthesis, transport, or metabolic pathways. The prospect of EDCs causing adverse health effects in humans and wildlife has led to the development of scientific and regulatory approaches for evaluating bioactivity. This need is being addressed using high-throughput screening (HTS) in vitro approaches and computational modeling. OBJECTIVES: In support of the Endocrine Disruptor Screening Program, the U.S. Environmental Protection Agency (EPA) led two worldwide consortiums to virtually screen chemicals for their potential estrogenic and androgenic activities. Here, we describe the Collaborative Modeling Project for Androgen Receptor Activity (CoMPARA) efforts, which follows the steps of the Collaborative Estrogen Receptor Activity Prediction Project (CERAPP). METHODS: The CoMPARA list of screened chemicals built on CERAPP’s list of 32,464 chemicals to include additional chemicals of interest, as well as simulated ToxCastTM metabolites, totaling 55,450 chemical structures. Computational toxicology scientists from 25 international groups contributed 91 predictive models for binding, agonist, and antagonist activity predictions. Models were underpinned by a common training set of 1,746 chemicals compiled from a combined data set of 11 ToxCastTM/Tox21 HTS in vitro assays. RESULTS: The resulting models were evaluated using curated literature data extracted from different sources. To overcome the limitations of single-model approaches, CoMPARA predictions were combined into consensus models that provided averaged predictive accuracy of approximately 80% for the evaluation set. DISCUSSION: The strengths and limitations of the consensus predictions were discussed with example chemicals; then, the models were implemented into the free and open-source OPERA application to enable screening of new chemicals with a defined applicability domain and accuracy ass

Investigative Toxicology describes the de-risking and mechanistic elucidation of toxicities, supporting early safety decisions in the pharmaceutical industry. Recently, Investigative Toxicology has contributed to a shift in pharmaceutical toxicology, from a descriptive to an evidence-based, mechanistic discipline. This was triggered by high costs and low throughput of Good Laboratory Practice in vivo studies, and increasing demands for adhering to the 3R (Replacement, Reduction and Refinement) principles of animal welfare. Outside the boundaries of regulatory toxicology, Investigative Toxicology has the flexibility to embrace new technologies, enhancing translational steps from in silico, in vitro to in vivo mechanistic understanding to eventually predict human response. One major goal of Investigative Toxicology is improving preclinical decisions, which coincides with the concept of animal-free safety testing. Currently, compounds under preclinical development are being discarded due to the use of inappropriate animal models. Progress in Investigative Toxicology could lead to humanized in vitro test systems and the development of medicines less reliant on animal tests. To advance this field a group of 14 European-based leaders from the pharmaceutical industry founded the Investigative Toxicology Leaders Forum (ITLF), an open, non-exclusive and pre-competitive group that shares knowledge and experience. The ITLF collaborated with the Centre for Alternatives to Animal Testing Europe (CAAT-Europe) to organize an "Investigative Toxicology Think-Tank", which aimed to enhance the interaction with experts from academia and regulatory bodies in the field. Summarizing the topics and discussion of the workshop, this article highlights Investigative Toxicology's position by identifying key challenges and perspectives.

The bile salt export pump (BSEP) is an ABC-transporter expressed at the canalicular membrane of hepatocytes. Its physiological role is to expel bile salts into the canaliculi from where they drain into the bile duct. Inhibition of this transporter may lead to intrahepatic cholestasis. Predictive computational models of BSEP inhibition may allow for fast identification of potentially harmful compounds in large databases. This article presents a predictive in silico model based on physicochemical descriptors that is able to flag compounds as potential BSEP inhibitors. This model was built using a training set of 670 compounds with available BSEP inhibition potencies. It successfully predicted BSEP inhibition for two independent test sets and was in a further step used for a virtual screening experiment. After in vitro testing of selected candidates, a marketed drug, bromocriptin, was identified for the first time as BSEP inhibitor. This demonstrates the usefulness of the model to identify new BSEP inhibitors and therefore potential cholestasis perpetrators.

QSAR modeling is a method for predicting properties, e.g. the solubility or toxicity, of chemical compounds using machine learning techniques. QSAR is in widespread use within the pharmaceutical industry to prioritize compounds for experimental testing or to alert for potential toxicity during the drug discovery process. However, the confidence or reliability of predictions from a QSAR model are difficult to accurately assess. We frame the application of QSAR to preclinical drug development in an off-line inductive conformal prediction framework and apply it prospectively to historical data collected from four different assays within AstraZeneca over a time course of about five years. The results indicate weakened validity of the conformal predictor due to violations of the randomness assumption. The validity can be strengthen by adopting semi-off-line conformal prediction. The non-randomness of the data prevents exactly valid predictions but comparisons to the results of a traditional QSAR procedure applied to the same data indicate that conformal predictions are highly useful in the drug discovery process.

Medicine is an increasingly data intensive discipline, with a growing need to link individual patient health records to rapidly changing research knowledge for better differential diagnosis, prognosis, and prediction of treatment response. Equally, biomedical research will gain massively from the integrative analysis of clinical and multi-omics information. Capitalizing on these opportunities must be guided by a precise understanding of many complex issues related to the integration of large amounts of diverse information. Partly this involves overcoming barriers between different disciplines, such as biology, medicine and computer sciences – implying a key role for ‘knowledge engineers’. This paper summarizes recent expert debates on such matters, leading to suggestions for concrete actions that should improve and better synergize both research and healthcare.

Feature selection is an important part of contemporary QSAR analysis. In a recently published paper, we investigated the performance of different feature selection methods in a large number of in silico experiments conducted using real QSAR datasets. However, an interesting question that we did not address is whether certain feature selection methods are better than others in combination with certain learning methods, in terms of producing models with high prediction accuracy. In this report we extend our work from the previous investigation by using four different feature selection methods (wrapper, ReliefF, MARS, and elastic nets), together with eight learners (MARS, elastic net, random forest, SVM, neural networks, multiple linear regression, PLS, kNN) in an empirical investigation to address this question. The results indicate that state-of-the-art learners (random forest, SVM, and neural networks) do not gain prediction accuracy from feature selection, and we found no evidence that a certain feature selection is particularly well-suited for use in combination with a certain learner.

Variable selection is important in QSAR modeling since it can improve model performance and transparency, as well as reduce the computational cost of model fitting and predictions. Which variable selection methods that perform well in QSAR settings is largely unknown. To address this question we, in a total of 1728 benchmarking experiments, rigorously investigated how eight variable selection methods affect the predictive performance and transparency of random forest models fitted to seven QSAR datasets covering different endpoints, descriptors sets, types of response variables, and number of chemical compounds. The results show that univariate variable selection methods are suboptimal and that the number of variables in the benchmarked datasets can be reduced with about 60?% without significant loss in model performance when using multivariate adaptive regression splines MARS and forward selection.

Bioclipse Decision Support (DS) is a system capable of building predictive models of any collection of SAR data, and making them available in a simple user interface based on Bioclipse (www.bioclipse.net). The method is fast and uses Faulon Signatures as chemical descriptors together with a Support Vector Machine algorithm for QSAR model building. A key feature is the capability to visualize and interpret results by highlighting the substructures which contributed most to the prediction. This, together with very fast predictions, allows for editing chemical structures with instantly updated results. We here present the results from applying Bioclipse Decision Support to several open QSAR data sets, including endpoints from OpenTox and PubChem. The results show how to extract data from the sources and to build models which can be integrated with user specific models.

Chemical liabilities, such as adverse effects and toxicity, have a major impact on today's drug discovery process. In silk prediction of chemical liabilities is an important approach which can reduce costs and animal testing by complementing or replacing in vitro and in vivo liability models. There is a lack of integrated, extensible decision support systems for chemical liability assessment which run quickly and have easily interpretable results. Here we present a method which integrates similarity searches, structural alerts, and QSAR models which all are available from the Bioclipse workbench. Emphasis has been placed on interpretation of results, and substructures which are important for predictions are highlighted in the original chemical structures. This allows for interactively changing chemical structures with instant visual feedback and can be used for hypothesis testing of single chemical structures as well as compound collections. The system has a clear separation between methods and data, and the extensible architecture enables straightforward extension via addition of more plugins (such as new data sets and computational models). We demonstrate our method on three important safety end points: mutagenicity, carcinogenicity, and aryl hydrocarbon receptor (AhR) activation. Bioclipse and the decision support implementation are free, open source, and available from http://www.bioclipse.net/decision-support.

A thorough comparison between different QSAR modeling strategies is presented. The comparison is conducted for local versus global modeling strategies, risk assessment, and computational cost. The strategies are implemented using random forests, support vector machines, and partial least squares. Results are presented for simulated data, as well as for real data, generally indicating that a global modeling strategy is preferred over a local strategy. Furthermore, the results also show that there is an pronounced risk and a comparatively high computational cost when using the local modeling strategies.

Background: Predicting metabolic sites is important in the drug discovery process to aid in rapid compound optimisation. No interactive tool exists and most of the useful tools are quite expensive.Results: Here a fast and reliable method to analyse ligands and visualise potential metabolic sites is presented which is based on annotated metabolic data, described by circular fingerprints. The method is available via the graphical workbench Bioclipse, which is equipped with advanced features in cheminformatics.Conclusions: Due to the speed of predictions (less than 50 ms per molecule), scientists can get real time decision support when editing chemical structures. Bioclipse is a rich client, which means that all calculations are performed on the local computer and do not require network connection. Bioclipse and MetaPrint2D are free for all users, released under open source licenses, and available from http://www.bioclipse.net.