Orthogonal assay and QSAR modelling of Tox21 PPARγ antagonist in vitro high-throughput screening assay.

Disruption of signalling mediated by the nuclear receptor peroxisome proliferator-activated receptor gamma (PPARγ) is associated with risk of cancer, metabolic diseases, and endocrine disruption. The purpose of this study was to identify environmental chemicals acting as PPARγ antagonists. Data from the Tox21 PPARγ antagonism assay were replicated using a reporter system in HEK293 cells. Two quantitative structure-activity relationship (QSAR) models were developed, and five REACH-registered substances predicted positive were tested in vitro. Reporter assay results were consistent with Tox21 data since all conflicting results could be explained by assay interference. QSAR models showed good predictive performance, and follow-up experiments revealed two PPARγ antagonists out of three non-interfering chemicals. In conclusion, the developed QSAR models and follow-up experiments are important steps in the discovery of potential endocrine- and metabolism-disrupting chemicals.

New approach methods to improve human health risk assessment of thyroid hormone system disruption-a PARC project.

Current test strategies to identify thyroid hormone (TH) system disruptors are inadequate for conducting robust chemical risk assessment required for regulation. The tests rely heavily on histopathological changes in rodent thyroid glands or measuring changes in systemic TH levels, but they lack specific new approach methodologies (NAMs) that can adequately detect TH-mediated effects. Such alternative test methods are needed to infer a causal relationship between molecular initiating events and adverse outcomes such as perturbed brain development. Although some NAMs that are relevant for TH system disruption are available-and are currently in the process of regulatory validation-there is still a need to develop more extensive alternative test batteries to cover the range of potential key events along the causal pathway between initial chemical disruption and adverse outcomes in humans. This project, funded under the Partnership for the Assessment of Risk from Chemicals (PARC) initiative, aims to facilitate the development of NAMs that are specific for TH system disruption by characterizing in vivo mechanisms of action that can be targeted by in embryo/in vitro/in silico/in chemico testing strategies. We will develop and improve human-relevant in vitro test systems to capture effects on important areas of the TH system. Furthermore, we will elaborate on important species differences in TH system disruption by incorporating non-mammalian vertebrate test species alongside classical laboratory rat species and human-derived in vitro assays.

A method for in vitro data and structure curation to optimize for QSAR modelling of minimum absolute potency levels and a comparative use case.

Large screening programs such as the US Tox21 are releasing experimental in vitro results for many endpoints of relevance for human health. In (Q)SAR modelling, it is essential to clearly define the endpoint (OECD QSAR Validation Principle 1) and extract the most robust data points according to the definition. We have developed a comprehensive data curation procedure to interpret in vitro experimental data sets for (Q)SAR development, with modules for selecting actives according to quality of curve fittings, magnitude of activity and 'absolute' potency cut-offs, requiring non-cytotoxicity at activity concentration; extracting only very robust inactives; selecting only substances tested in high purity; and accounting for assay signal interference. A structure curation procedure with uniform representation of tautomeric classes of substances is also developed. The detailed method and a use case of modelling Tox21 data for an estrogen receptor α agonism assay with and without use of the method is presented.

Identification of substances with a carcinogenic potential in spray-formulated engine/brake cleaners and lubricating products, available in the European Union (EU) - based on IARC and EU-harmonised classifications and QSAR predictions.

Spray-formulated engine/brake cleaners and lubricating agents are widely used to maintain machines. The occupational exposure to their aerosols is evident. To assess the carcinogenic potential of these products, we identified such products available in the European Union (EU). We built a database with CAS numbers of 1) mono-constituent substances, and 2) multi-constituent-substances, and unknown-or-variable-composition,-complex-reaction-products-and-biological-materials (multi-constituent/UVCBs). The compositions of multi-constituent/UVCBs were unravelled with European Chemicals Agency (ECHA) registration dossiers. To identify carcinogenic potentials, we searched for 1) International Agency for Research on Cancer (IARC) classification; 2) Harmonised classifications in Annex VI to the EU classification, labelling and packaging (CLP) Regulation; and 3) whether they had a Danish Environmental Protection Agency advisory CLP self-classification based on quantitative structure-activity relationships (QSARs) for genotoxicity and carcinogenicity in the Danish (Q)SAR Database. In 82 products, we identified 332 mono-constituent substances and 44 multi-constituent/UVCBs. Six substances were either IARC 1 or 2B classified. Twelve mono-constituent substances and 22 multi-constituent/UVCBs had harmonised classifications as Carcinogenic Category 1A, 1B or 2, while nine substances fulfilled the QSAR-based advisory self-classification algorithms for mutagenicity or carcinogenicity. At the product level, 39 products contained substances of carcinogenic concern by either IARC, harmonised classification or QSAR. We conclude that in the investigated EU marketed spray-formulated engine/brake cleaners and lubricants, 24 of 332 mono-constituent substances and 28 of 44 multi-constituent/UVCBs had a carcinogenic potential. At the product level, 39 of 82 contained substances with an identified carcinogenic potential. Regulators and manufacturers can use this determination of carcinogenic potential to decrease occupational risk.

Asthma-inducing potential of 28 substances in spray cleaning products-Assessed by quantitative structure activity relationship (QSAR) testing and literature review.

Exposure to spray cleaning products constitutes a potential risk for asthma induction. We set out to review whether substances in such products are potential inducers of asthma. We identified 101 spray cleaning products for professional use. Twenty-eight of their chemical substances were selected. We based the selection on (a) positive prediction for respiratory sensitisation in humans based on quantitative structure activity relationship (QSAR) in the Danish (Q)SAR Database, (b) positive QSAR prediction for severe skin irritation in rabbits and (c) knowledge on the substances' physico-chemical characteristics and toxicity. Combining the findings in the literature and QSAR predictions, we could group substances into four classes: (1) some indication in humans for asthma induction: chloramine, benzalkonium chloride; (2) some indication in animals for asthma induction: ethylenediaminetetraacetic acid (EDTA), citric acid; (3) equivocal data: hypochlorite; (4) few or lacking data: nitriloacetic acid, monoethanolamine, 2-(2-aminoethoxy)ethanol, 2-diethylaminoethanol, alkyldimethylamin oxide, 1-aminopropan-2-ol, methylisothiazolinone, benzisothiazolinone and chlormethylisothiazolinone; three specific sulphonates and sulfamic acid, salicylic acid and its analogue sodium benzoate, propane-1,2-diol, glycerol, propylidynetrimethanol, lactic acid, disodium malate, morpholine, bronopol and benzyl alcohol. In conclusion, we identified an asthma induction potential for some of the substances. In addition, we identified major knowledge gaps for most substances. Thus, more data are needed to feed into a strategy of safe-by-design, where substances with potential for induction of asthma are avoided in future (spray) cleaning products. Moreover, we suggest that QSAR predictions can serve to prioritise substances that need further testing in various areas of toxicology.

Organophosphate ester flame retardants have antiandrogenic potential and affect other endocrine related endpoints in vitro and in silico.

Organophosphate ester flame retardants (OPFRs) are used to prevent ignition and spreading of fire. They are present in various human matrices suggesting adult, fetal, and neonate exposure. Endocrine related effects have been observed in vivo, but information at the molecular level is lacking for some OPFRs. Also, a better understanding of potential contribution from chemical substructures is needed. The aim of this study was to screen OPFRs for endocrine disruptive potential in vitro and in silico. We selected eleven substances to represent some OPFRs with 1) little information on endocrine activity and others to represent 2) varied chemical substructures. We used in vitro assays for androgen receptor (AR), aryl hydrocarbon receptor (AhR), and Nrf2 activity, effects on steroidogenesis, and transthyretin (TTR) binding, as well as in silico models covering estrogen, thyroid, and CYP3A4 induction related endpoints. Ten OPFRs affected AR and AhR activity, seven affected TTR binding, and five affected 17ß-estradiol levels. Several substances had IC50-values below 10 µM and exhibited efficacious effects. These included TPHP, CDP, TMPP, TIPPP, and EHDPP for AR antagonism, suggesting that the degree of arylation and the size of the substance can play a role for the activity. Chlorinated OPFRs had low/no effect on TTR binding. No clear trend was observed for AhR and steroidogenesis, but all arylated OPFRs were predicted to have alert for estrogen receptor binding in an in silico model with metabolism simulator included. Collectively, our data suggest that OPFRs have endocrine disruptive potential warranting further studies to enable human risk assessment.

Removing Critical Gaps in Chemical Test Methods by Developing New Assays for the Identification of Thyroid Hormone System-Disrupting Chemicals-The ATHENA Project.

The test methods that currently exist for the identification of thyroid hormone system-disrupting chemicals are woefully inadequate. There are currently no internationally validated in vitro assays, and test methods that can capture the consequences of diminished or enhanced thyroid hormone action on the developing brain are missing entirely. These gaps put the public at risk and risk assessors in a difficult position. Decisions about the status of chemicals as thyroid hormone system disruptors currently are based on inadequate toxicity data. The ATHENA project (Assays for the identification of Thyroid Hormone axis-disrupting chemicals: Elaborating Novel Assessment strategies) has been conceived to address these gaps. The project will develop new test methods for the disruption of thyroid hormone transport across biological barriers such as the blood-brain and blood-placenta barriers. It will also devise methods for the disruption of the downstream effects on the brain. ATHENA will deliver a testing strategy based on those elements of the thyroid hormone system that, when disrupted, could have the greatest impact on diminished or enhanced thyroid hormone action and therefore should be targeted through effective testing. To further enhance the impact of the ATHENA test method developments, the project will develop concepts for better international collaboration and development in the area of thyroid hormone system disruptor identification and regulation.

CoMPARA: Collaborative Modeling Project for Androgen Receptor Activity.

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™ 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™/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 assessment. This implementation was used to screen the entire EPA DSSTox database of ∼875,000 chemicals, and their predicted AR activities have been made available on the EPA CompTox Chemicals dashboard and National Toxicology Program's Integrated Chemical Environment. https://doi.org/10.1289/EHP5580.

QSAR modelling of a large imbalanced aryl hydrocarbon activation dataset by rational and random sampling and screening of 80,086 REACH pre-registered and/or registered substances.

The Aryl hydrocarbon receptor (AhR) plays important roles in many normal and pathological physiological processes, including endocrine homeostasis, foetal development, cell cycle regulation, cellular oxidation/antioxidation, immune regulation, metabolism of endogenous and exogenous substances, and carcinogenesis. An experimental data set for human in vitro AhR activation comprising 324,858 substances, of which 1,982 were confirmed actives, was used to test an in-house-developed approach to rationally select Quantitative Structure-Activity Relationship (QSAR) training set substances from an unbalanced data set. In the first iteration, active and inactive substances were selected by random to make QSAR models. Then, more inactive substances were added to the training set in two further iterations based on incorrect or out-of-domain predictions to produce larger models. The resulting 'rational' model, comprising 832 actives and four times as many inactives, i.e. 3,328, was compared to a model with a training set of same size and proportion of inactives chosen entirely by random. Both models underwent robust cross-validation and external validation showing good statistical performance, with the rational model having external validation sensitivity of 85.1% and specificity of 97.1%, compared to the random model with sensitivity 89.1% and specificity 91.3%. Furthermore, we integrated the training sets for both models with the 93 external validation test set actives and 372 randomly selected inactives to make two final models. They also underwent external validations for specificity and cross-validations, which confirmed that good predictivity was maintained. All developed models were applied to predict 80,086 EU REACH substances. The rational and random final models had 63.1% and 56.9% coverage of the REACH set, respectively, and predicted 1,256 and 3,214 substances as actives. The final models as well as predictions for AhR activation for 650,000 substances will be published in the Danish (Q)SAR Database and can, for example, be used for priority setting, in read-across predictions and in weight-of-evidence assessments of chemicals.

Facilitated subcutaneous immunoglobulin administration (fSCIg): a new treatment option for patients with secondary immune deficiencies.

The number of patients with secondary immune deficiencies (SID) is on the rise, mostly since the arrival on the market of novel targeted therapies that have increased the survival rates of patients with hematological malignancies. The recent changes in the SID landscape have brought with them new and diverse medical needs that treatments for SID management should strive to meet. In this special report, we study the opportunities provided by facilitated subcutaneous immunoglobulin administration (fSCIg) to treat patients for whom the conventional routes (intravenous and subcutaneous) are sub-optimal. Experts in the treatment of SID describe real-life cases from their daily practice, in which fSCIg has led to reducing the burden of treatment and increasing the treatment satisfaction.

10% liquid human immunoglobulin (KIOVIG(®)) for immunomodulation in autoimmune disorders.

Intravenous immunoglobulins have been used to treat autoimmune disorders (ADs) for over 50 years. The etiologies of various ADs are not fully understood and although intravenous immunoglobulin treatment has proved its immunomodulatory properties, the roles of proposed mechanisms of action also remain a matter of speculation. A systemic search of the literature regarding KIOVIG(®) (Baxalta US, Inc., MA, USA) use in clinical trials on patients with ADs and a detailed review of retrieved articles revealed eight relevant publications. These articles reported KIOVIG use in multifocal motor neuropathy, chronic inflammatory demyelinating polyneuropathy, idiopathic thrombocytopenic purpura, Kawasaki disease, Guillain-Barré syndrome and other autoimmune and neurologic disorders and showed that KIOVIG is an effective, safe and well-tolerated treatment in the studied populations. Nevertheless, further studies on larger patient cohorts are needed.

CERAPP: Collaborative Estrogen Receptor Activity Prediction Project.

BACKGROUND: Humans are exposed to thousands of man-made chemicals in the environment. Some chemicals mimic natural endocrine hormones and, thus, have the potential to be endocrine disruptors. Most of these chemicals have never been tested for their ability to interact with the estrogen receptor (ER). Risk assessors need tools to prioritize chemicals for evaluation in costly in vivo tests, for instance, within the U.S. EPA Endocrine Disruptor Screening Program. OBJECTIVES: We describe a large-scale modeling project called CERAPP (Collaborative Estrogen Receptor Activity Prediction Project) and demonstrate the efficacy of using predictive computational models trained on high-throughput screening data to evaluate thousands of chemicals for ER-related activity and prioritize them for further testing. METHODS: CERAPP combined multiple models developed in collaboration with 17 groups in the United States and Europe to predict ER activity of a common set of 32,464 chemical structures. Quantitative structure-activity relationship models and docking approaches were employed, mostly using a common training set of 1,677 chemical structures provided by the U.S. EPA, to build a total of 40 categorical and 8 continuous models for binding, agonist, and antagonist ER activity. All predictions were evaluated on a set of 7,522 chemicals curated from the literature. To overcome the limitations of single models, a consensus was built by weighting models on scores based on their evaluated accuracies. RESULTS: Individual model scores ranged from 0.69 to 0.85, showing high prediction reliabilities. Out of the 32,464 chemicals, the consensus model predicted 4,001 chemicals (12.3%) as high priority actives and 6,742 potential actives (20.8%) to be considered for further testing. CONCLUSION: This project demonstrated the possibility to screen large libraries of chemicals using a consensus of different in silico approaches. This concept will be applied in future projects related to other end points. CITATION: Mansouri K, Abdelaziz A, Rybacka A, Roncaglioni A, Tropsha A, Varnek A, Zakharov A, Worth A, Richard AM, Grulke CM, Trisciuzzi D, Fourches D, Horvath D, Benfenati E, Muratov E, Wedebye EB, Grisoni F, Mangiatordi GF, Incisivo GM, Hong H, Ng HW, Tetko IV, Balabin I, Kancherla J, Shen J, Burton J, Nicklaus M, Cassotti M, Nikolov NG, Nicolotti O, Andersson PL, Zang Q, Politi R, Beger RD, Todeschini R, Huang R, Farag S, Rosenberg SA, Slavov S, Hu X, Judson RS. 2016. CERAPP: Collaborative Estrogen Receptor Activity Prediction Project. Environ Health Perspect 124:1023-1033; http://dx.doi.org/10.1289/ehp.1510267.

Raspberry ketone in food supplements--High intake, few toxicity data--A cause for safety concern?

Raspberry ketone (4-(4-hydroxyphenyl)-2-butanone) is marketed on the Internet as a food supplement. The recommended intake is between 100 and 1400 mg per day. The substance is naturally occurring in raspberries (up to 4.3 mg/kg) and is used as a flavouring substance. Toxicological studies on raspberry ketone are limited to acute and subchronic studies in rats. When the lowest recommended daily dose of raspberry ketone (100 mg) as a food supplement is consumed, it is 56 times the established threshold of toxicological concern (TTC) of 1800 µg/day for Class 1 substances. The margin of safety (MOS) based on a NOAEL of 280 mg/kg bw/day for lower weight gain in rats is 165 at 100 mg and 12 at 1400 mg. The recommended doses are a concern taking into account the TTC and MOS. Investigations of raspberry ketone in quantitative structure-activity relationship (QSAR) models indicated potential cardiotoxic effects and potential effects on reproduction/development. Taking into account the high intake via supplements, the compound's toxic potential should be clarified with further experimental studies. In UK the pure compound is regarded as novel food requiring authorisation prior to marketing but raspberry ketone is not withdrawn from Internet sites from this country.

Human immunoglobulin (KIOVIG®/GAMMAGARD LIQUID®) for immunodeficiency and autoimmune diseases: an observational cohort study.

AIM: To document the therapeutic efficacy and safety of Human Normal Immunoglobulin 10% Liquid (KIOVIG(®)/GAMMAGARD LIQUID(®) [IVIG 10%]) under clinical routine conditions. PATIENTS & METHODS: Subjects received IVIG 10% according to the prescribing information and were followed for 6 ± 1 weeks to 12 ± 2 months depending on indication. Efficacy, adverse events, infusion rates and duration and dose were recorded. RESULTS: Overall efficacy of IVIG 10% was rated as good or very good by the investigator in 81.8% of subjects; overall tolerability was good or very good in 87.5%. One serious adverse drug reaction (ADR) occurred (urticaria); no severe ADRs occurred. CONCLUSION: In this observational study, the efficacy and safety of IVIG 10% in routine clinical practice was similar to that previously reported in clinical studies.

QSAR screening of 70,983 REACH substances for genotoxic carcinogenicity, mutagenicity and developmental toxicity in the ChemScreen project.

The ChemScreen project aimed to develop a screening system for reproductive toxicity based on alternative methods. QSARs can, if adequate, contribute to the evaluation of chemical substances under REACH and may in some cases be applied instead of experimental testing to fill data gaps for information requirements. As no testing for reproductive effects should be performed in REACH on known genotoxic carcinogens or germ cell mutagens with appropriate risk management measures implemented, a QSAR pre-screen for 70,983 REACH substances was performed. Sixteen models and three decision algorithms were used to reach overall predictions of substances with potential effects with the following result: 6.5% genotoxic carcinogens, 16.3% mutagens, 11.5% developmental toxicants. These results are similar to findings in earlier QSAR and experimental studies of chemical inventories, and illustrate how QSAR predictions may be used to identify potential genotoxic carcinogens, mutagens and developmental toxicants by high-throughput virtual screening.

hERG blocking potential of acids and zwitterions characterized by three thresholds for acidity, size and reactivity.

Ionization is a key factor in hERG K(+) channel blocking, and acids and zwitterions are known to be less probable hERG blockers than bases and neutral compounds. However, a considerable number of acidic compounds block hERG, and the physico-chemical attributes which discriminate acidic blockers from acidic non-blockers have not been fully elucidated. We propose a rule for prediction of hERG blocking by acids and zwitterionic ampholytes based on thresholds for only three descriptors related to acidity, size and reactivity. The training set of 153 acids and zwitterionic ampholytes was predicted with a concordance of 91% by a decision tree based on the rule. Two external validations were performed with sets of 35 and 48 observations, respectively, both showing concordances of 91%. In addition, a global QSAR model of hERG blocking was constructed based on a large diverse training set of 1374 chemicals covering all ionization classes, externally validated showing high predictivity and compared to the decision tree. The decision tree was found to be superior for the acids and zwitterionic ampholytes classes.

Accessing and using chemical databases.

Computer-based representation of chemicals makes it possible to organize data in chemical databases-collections of chemical structures and associated properties. Databases are widely used wherever efficient processing of chemical information is needed, including search, storage, retrieval, and dissemination. Structure and functionality of chemical databases are considered. The typical kinds of information found in a chemical database are considered-identification, structural, and associated data. Functionality of chemical databases is presented, with examples of search and access types. More details are included about the OASIS database and platform and the Danish (Q)SAR Database online. Various types of chemical database resources are discussed, together with a list of examples.

QSAR model for human pregnane X receptor (PXR) binding: screening of environmental chemicals and correlations with genotoxicity, endocrine disruption and teratogenicity.

The pregnane X receptor (PXR) has a key role in regulating the metabolism and transport of structurally diverse endogenous and exogenous compounds. Activation of PXR has the potential to initiate adverse effects, causing drug-drug interactions, and perturbing normal physiological functions. Therefore, identification of PXR ligands would be valuable information for pharmaceutical and toxicological research. In the present study, we developed a quantitative structure-activity relationship (QSAR) model for the identification of PXR ligands using data based on a human PXR binding assay. A total of 631 molecules, representing a variety of chemical structures, constituted the training set of the model. Cross-validation of the model showed a sensitivity of 82%, a specificity of 85%, and a concordance of 84%. The developed model provided knowledge about molecular descriptors that may influence the binding of molecules to PXR. The model was used to screen a large inventory of environmental chemicals, of which 47% was found to be within domain of the model. Approximately 35% of the chemicals within domain were predicted to be PXR ligands. The predicted PXR ligands were found to be overrepresented among chemicals predicted to cause adverse effects, such as genotoxicity, teratogenicity, estrogen receptor activation and androgen receptor antagonism compared to chemicals not causing these effects. The developed model may be useful as a tool for predicting potential PXR ligands and for providing mechanistic information of toxic effects of chemicals.

Identification of cytochrome P450 2D6 and 2C9 substrates and inhibitors by QSAR analysis.

This paper presents four new QSAR models for CYP2C9 and CYP2D6 substrate recognition and inhibitor identification based on human clinical data. The models were used to screen a large data set of environmental chemicals for CYP activity, and to analyze the frequency of CYP activity among these compounds. A large fraction of these chemicals were found to be CYP active, and thus potentially capable of affecting human physiology. 20% of the compounds within applicability domain of the models were predicted to be CYP2C9 substrates, and 17% to be inhibitors. The corresponding numbers for CYP2D6 were 9% and 21%. Where the majority of CYP2C9 active compounds were predicted to be both a substrate and an inhibitor at the same time, the CYP2D6 active compounds were primarily predicted to be only inhibitors. It was demonstrated that the models could identify compound classes with a high occurrence of specific CYP activity. An overrepresentation was seen for poly-aromatic hydrocarbons (group of procarcinogens) among CYP2C9 active and mutagenic compounds compared to CYP2C9 inactive and mutagenic compounds. The mutagenicity was predicted with a QSAR model based on Ames in vitro test data.

Representation of chemical information in OASIS centralized 3D database for existing chemicals.

The present inventory of existing chemicals in regulatory agencies in North America and Europe, encompassing the chemicals of the European Chemicals Bureau (EINECS, with 61 573 discrete chemicals); the Danish EPA (159 448 chemicals); the U.S. EPA (TSCA, 56 882 chemicals; HPVC, 10 546 chemicals) and pesticides' active and inactive ingredients of the U.S. EPA (1379 chemicals); the Organization for Economic Cooperation and Development (HPVC, 4750 chemicals); Environment Canada (DSL, 10851 chemicals); and the Japanese Ministry of Economy, Trade, and Industry (16811), was combined in a centralized 3D database for existing chemicals. The total number of unique chemicals from all of these databases exceeded 185 500. Defined and undefined chemical mixtures and polymers are handled, along with discrete (hydrolyzing and nonhydrolyzing) chemicals. The database manager provides the storage and retrieval of chemical structures with 2D and 3D data, accounting for molecular flexibility by using representative sets of conformers for each chemical. The electronic and geometric structures of all conformers are quantum-chemically optimized and evaluated. Hence, the database contains over 3.7 million 3D records with hundreds of millions of descriptor data items at the levels of structures, conformers, or atoms. The platform contains a highly developed search subsystem--a search is possible on Chemical Abstracts Service numbers; names; 2D and 3D fragment searches; structural, conformational, or atomic properties; affiliation in other chemical databases; structure similarity; logical combinations; saved queries; and search result exports. Models (collections of logically related descriptors) are supported, including information on a model's author, date, bioassay, organs/tissues, conditions, administration, and so forth. Fragments can be interactively constructed using a visual structure editor. A configurable database browser is designed for the inspection and editing of all types of data items. Database statistics are maintained on the number and quality of structures, conformers, and descriptors. Reports can be generated presenting any chosen subset of structures and descriptors into different formats suitable for inclusion into documents. In addition to fixed report formats, there is a powerful report template designer module with a visual report template editor to produce a customized page layout. The database is compatible at the import/export level with SDF, MOL, SMILES, and other known formats. The precalculated centralized 3D database could be useful for quantitative structure-activity relationship developers avoiding the time-consuming and cumbersome 3D calculation phase of model development.