An Integrated Workflow Assisted by In Silico Predictions To Expand the List of Priority Polycyclic Aromatic Compounds.

The limited information in existing mass spectral libraries hinders an accurate understanding of the composition, behavior, and toxicity of organic pollutants. In this study, a total of 350 polycyclic aromatic compounds (PACs) in 9 categories were successfully identified in fine particulate matter by gas chromatography high resolution mass spectrometry. Using mass spectra and retention indexes predicted by in silico tools as complementary information, the scope of chemical identification was efficiently expanded by 27%. In addition, quantitative structure-activity relationship models provided toxicity data for over 70% of PACs, facilitating a comprehensive health risk assessment. On the basis of extensive identification, the cumulative noncarcinogenic risk of PACs warranted attention. Meanwhile, the carcinogenic risk of 53 individual analogues was noteworthy. These findings suggest that there is a pressing need for an updated list of priority PACs for routine monitoring and toxicological research since legacy polycyclic aromatic hydrocarbons (PAHs) contributed modestly to the overall abundance (18%) and carcinogenic risk (8%). A toxicological priority index approach was applied for relative chemical ranking considering the environmental occurrence, fate, toxicity, and analytical availability. A list of 39 priority analogues was compiled, which predominantly consisted of high-molecular-weight PAHs and alkyl derivatives. These priority PACs further enhanced source interpretation, and the highest carcinogenic risk was attributed to coal combustion.

Machine Learning-Assisted QSAR Models on Contaminant Reactivity Toward Four Oxidants: Combining Small Data Sets and Knowledge Transfer.

To develop predictive models for the reactivity of organic contaminants toward four oxidants─SO4•-, HClO, O3, and ClO2─all with small sample sizes, we proposed two approaches: combining small data sets and transferring knowledge between them. We first merged these data sets and developed a unified model using machine learning (ML), which showed better predictive performance than the individual models for HClO (RMSEtest: 2.1 to 2.04), O3 (2.06 to 1.94), ClO2 (1.77 to 1.49), and SO4•- (0.75 to 0.70) because the model "corrected" the wrongly learned effects of several atom groups. We further developed knowledge transfer models for three pairs of the data sets and observed different predictive performances: improved for O3 (RMSEtest: 2.06 to 2.01)/HClO (2.10 to 1.98), mixed for O3 (2.06 to 2.01)/ClO2 (1.77 to 1.95), and unchanged for ClO2 (1.77 to 1.77)/HClO (2.1 to 2.1). The effectiveness of the latter approach depended on whether there was consistent knowledge shared between the data sets and on the performance of the individual models. We also compared our approaches with multitask learning and image-based transfer learning and found that our approaches consistently improved the predictive performance for all data sets while the other two did not. This study demonstrated the effectiveness of combining small, similar data sets and transferring knowledge between them to improve ML model performance.

Combined Micellar Liquid Chromatography Technique and QSARs Modeling in Predicting the Blood-Brain Barrier Permeation of Heterocyclic Drug-like Compounds.

The quantitative structure-activity relationship (QSAR) methodology was used to predict the blood-brain permeability (log BB) for 65 synthetic heterocyclic compounds tested as promising drug candidates. The compounds were characterized by different descriptors: lipophilicity, parachor, polarizability, molecular weight, number of hydrogen bond acceptors, number of rotatable bonds, and polar surface area. Lipophilic properties of the compounds were evaluated experimentally by micellar liquid chromatography (MLC). In the experiments, sodium dodecyl sulfate (SDS) as the effluent component and the ODS-2 column were used. Using multiple linear regression and leave-one-out cross-validation, we derived the statistically significant and highly predictive quantitative structure-activity relationship models. Thus, this study provides valuable information on the expected properties of the substances that can be used as a support tool in the design of new therapeutic agents.

Quantitative Retention (Structure)-Activity Relationships in Predicting the Pharmaceutical and Toxic Properties of Potential Pesticides.

The micellar liquid chromatography technique and quantitative retention (structure)-activity relationships method were used to predict properties of carbamic and phenoxyacetic acids derivatives, newly synthesized in our laboratory and considered as potential pesticides. Important properties of the test substances characterizing their potential significance as pesticides as well as threats to humans were considered: the volume of distribution, the unbonded fractions, the blood-brain distribution, the rate of skin and cell permeation, the dermal absorption, the binding to human serum albumin, partitioning between water and plants' cuticles, and the lethal dose. Pharmacokinetic and toxicity parameters were predicted as functions of the solutes' lipophilicities and the number of hydrogen bond donors, the number of hydrogen bond acceptors, and the number of rotatable bonds. The equations that were derived were evaluated statistically and cross-validated. Important features of the molecular structure influencing the properties of the tested substances were indicated. The QSAR models that were developed had high predictive ability and high reliability in modeling the properties of the molecules that were tested. The investigations highlighted the applicability of combined chromatographic technique and QS(R)ARs in modeling the important properties of potential pesticides and reducing unethical animal testing.

Rate Constants and Mechanisms for Reactions of Bromine Radicals with Trace Organic Contaminants.

Bromine radicals can pose great impacts on the photochemical transformation of trace organic contaminants in natural and engineered waters. However, the reaction kinetics and mechanisms involved are barely known. In this work, second-order reaction rate constants with Br• and Br2•- were determined for 70 common trace organic contaminants and for 17 model compounds using laser flash photolysis and steady-state competition kinetics. The kBr• values ranged from <108 to (2.86 ± 0.31) × 1010 M-1 s-1 and the kBr2•- values from <105 to (1.18 ± 0.09) × 109 M-1 s-1 at pH 7.0. Six quantitative structure-activity relationships were developed, which allow predicting additional unknown kBr• and kBr2•- values. Single-electron transfer was shown to be a favored pathway for the reactions of Br• and Br2•- with trace organic contaminants, and this was supported by transient spectroscopy and quantum chemical calculations. This study is essential in advancing the scientific understanding of halogen radical-involved chemistry in contaminant transformation.

Predicting Pharmacokinetic Properties of Potential Anticancer Agents via Their Chromatographic Behavior on Different Reversed Phase Materials.

The Quantitative Structure-Activity Relationship (QSAR) methodology was used to predict biological properties, i.e., the blood-brain distribution (log BB), fraction unbounded in the brain (fu,brain), water-skin permeation (log Kp), binding to human plasma proteins (log Ka,HSA), and intestinal permeability (Caco-2), for three classes of fused azaisocytosine-containing congeners that were considered and tested as promising drug candidates. The compounds were characterized by lipophilic, structural, and electronic descriptors, i.e., chromatographic retention, topological polar surface area, polarizability, and molecular weight. Different reversed-phase liquid chromatography techniques were used to determine the chromatographic lipophilicity of the compounds that were tested, i.e., micellar liquid chromatography (MLC) with the ODS-2 column and polyoxyethylene lauryl ether (Brij 35) as the effluent component, an immobilized artificial membrane (IAM) chromatography with phosphatidylcholine column (IAM.PC.DD2) and chromatography with end-capped octadecylsilyl (ODS) column using aqueous solutions of acetonitrile as the mobile phases. Using multiple linear regression, we derived the statistically significant quantitative structure-activity relationships. All these QSAR equations were validated and were found to be very good. The investigations highlight the significance and possibilities of liquid chromatographic techniques with three different reversed-phase materials and QSARs methods in predicting the pharmacokinetic properties of our important organic compounds and reducing unethical animal testing.

Development of a generic lifelong physiologically based biokinetic model for exposome studies.

The current study within the frame of the HEALS project aims at the development of a lifelong physiologically based biokinetic (PBBK) model for exposome studies. The aim was to deliver a comprehensive modelling framework for addressing a large chemical space. Towards this aim, the delivered model can easily adapt parameters from existing ad-hoc models or complete the missing compound specific parameters using advanced quantitative structure activity relationship (QSAR). All major human organs are included, as well as arterial, venous, and portal blood compartments. Xenobiotics and their metabolites are linked through the metabolizing tissues. This is mainly the liver, but also other sites of metabolism might be considered (intestine, brain, skin, placenta) based on the presence or not of the enzymes involved in the metabolism of the compound of interest. Each tissue is described by three mass balance equations for (a) red blood cells, (b) plasma and interstitial tissue and (c) cells respectively. The anthropometric parameters of the models are time dependent, so as to provide a lifetime internal dose assessment, as well as to describe the continuously changing physiology of the mother and the developing fetus. An additional component of flexibility is that the biokinetic processes that relate to metabolism are related with either Michaelis-Menten kinetics, as well as intrinsic clearance kinetics. The capability of the model is demonstrated in the assessment of internal exposure and the prediction of expected biomonitored levels in urine for three major compounds within the HEALS project, namely bisphenol A (BPA), Bis(2-ethylhexyl) phthalate (DEHP) and cadmium (Cd). The results indicated that the predicted urinary levels fit very well with the ones from human biomonitoring (HBM) studies; internal exposure to plasticizers is very low (in the range of ng/L), while internal exposure to Cd is in the range of µg/L.

Predicting the Blood-Brain Barrier Permeability of New Drug-Like Compounds via HPLC with Various Stationary Phases.

The permeation of the blood-brain barrier is a very important consideration for new drug candidate molecules. In this research, the reversed-phase liquid chromatography with different columns (Purosphere RP-18e, IAM.PC.DD2 and Cosmosil Cholester) was used to predict the penetration of the blood-brain barrier by 65 newly-synthesized drug-like compounds. The linear free energy relationships (LFERs) model (log BB = c + eE + sS + aA + bB + vV) was established for a training set of 23 congeneric biologically active azole compounds with known experimental log BB (BB = Cblood/Cbrain) values (R2 = 0.9039). The reliability and predictive potency of the model were confirmed by leave-one-out cross validation as well as leave-50%-out cross validation. Multiple linear regression (MLR) was used to develop the quantitative structure-activity relationships (QSARs) to predict the log BB values of compounds that were tested, taking into account the chromatographic lipophilicity (log kw), polarizability and topological polar surface area. The excellent statistics of the developed MLR equations (R2 > 0.8 for all columns) showed that it is possible to use the HPLC technique and retention data to produce reliable blood-brain barrier permeability models and to predict the log BB values of our pharmaceutically important molecules.

The utility of QSARs in predicting acute fish toxicity of pesticide metabolites: A retrospective validation approach.

The European Plant Protection Products Regulation 1107/2009 requires that registrants establish whether pesticide metabolites pose a risk to the environment. Fish acute toxicity assessments may be carried out to this end. Considering the total number of pesticide (re-) registrations, the number of metabolites can be considerable, and therefore this testing could use many vertebrates. EFSA's recent "Guidance on tiered risk assessment for plant protection products for aquatic organisms in edge-of-field surface waters" outlines opportunities to apply non-testing methods, such as Quantitative Structure Activity Relationship (QSAR) models. However, a scientific evidence base is necessary to support the use of QSARs in predicting acute fish toxicity of pesticide metabolites. Widespread application and subsequent regulatory acceptance of such an approach would reduce the numbers of animals used. The work presented here intends to provide this evidence base, by means of retrospective data analysis. Experimental fish LC50 values for 150 metabolites were extracted from the Pesticide Properties Database (http://sitem.herts.ac.uk/aeru/ppdb/en/atoz.htm). QSAR calculations were performed to predict fish acute toxicity values for these metabolites using the US EPA's ECOSAR software. The most conservative predicted LC50 values generated by ECOSAR were compared with experimental LC50 values. There was a significant correlation between predicted and experimental fish LC50 values (Spearman rs = 0.6304, p < 0.0001). For 62% of metabolites assessed, the QSAR predicted values are equal to or lower than their respective experimental values. Refined analysis, taking into account data quality and experimental variation considerations increases the proportion of sufficiently predictive estimates to 91%. For eight of the nine outliers, there are plausible explanation(s) for the disparity between measured and predicted LC50 values. Following detailed consideration of the robustness of this non-testing approach, it can be concluded there is a strong data driven rationale for the applicability of QSAR models in the metabolite assessment scheme recommended by EFSA. As such there is value in further refining this approach, to improve the method and enable its future incorporation into regulatory guidance and practice.

Towards a nanospecific approach for risk assessment.

In the current paper, a new strategy for risk assessment of nanomaterials is described, which builds upon previous project outcomes and is developed within the FP7 NANoREG project. NANoREG has the aim to develop, for the long term, new testing strategies adapted to a high number of nanomaterials where many factors can affect their environmental and health impact. In the proposed risk assessment strategy, approaches for (Quantitative) Structure Activity Relationships ((Q)SARs), grouping and read-across are integrated and expanded to guide the user how to prioritise those nanomaterial applications that may lead to high risks for human health. Furthermore, those aspects of exposure, kinetics and hazard assessment that are most likely to be influenced by the nanospecific properties of the material under assessment are identified. These aspects are summarised in six elements, which play a key role in the strategy: exposure potential, dissolution, nanomaterial transformation, accumulation, genotoxicity and immunotoxicity. With the current approach it is possible to identify those situations where the use of nanospecific grouping, read-across and (Q)SAR tools is likely to become feasible in the future, and to point towards the generation of the type of data that is needed for scientific justification, which may lead to regulatory acceptance of nanospecific applications of these tools.

An integrated QSAR-PBK/D modelling approach for predicting detoxification and DNA adduct formation of 18 acyclic food-borne α,ß-unsaturated aldehydes.

Acyclic α,ß-unsaturated aldehydes present in food raise a concern because the α,ß-unsaturated aldehyde moiety is considered a structural alert for genotoxicity. However, controversy remains on whether in vivo at realistic dietary exposure DNA adduct formation is significant. The aim of the present study was to develop physiologically based kinetic/dynamic (PBK/D) models to examine dose-dependent detoxification and DNA adduct formation of a group of 18 food-borne acyclic α,ß-unsaturated aldehydes without 2- or 3-alkylation, and with no more than one conjugated double bond. Parameters for the PBK/D models were obtained using quantitative structure-activity relationships (QSARs) defined with a training set of six selected aldehydes. Using the QSARs, PBK/D models for the other 12 aldehydes were defined. Results revealed that DNA adduct formation in the liver increases with decreasing bulkiness of the molecule especially due to less efficient detoxification. 2-Propenal (acrolein) was identified to induce the highest DNA adduct levels. At realistic dietary intake, the predicted DNA adduct levels for all aldehydes were two orders of magnitude lower than endogenous background levels observed in disease free human liver, suggesting that for all 18 aldehydes DNA adduct formation is negligible at the relevant levels of dietary intake. The present study provides a proof of principle for the use of QSAR-based PBK/D modelling to facilitate group evaluations and read-across in risk assessment.

Hazard assessment of nitrosamine and nitramine by-products of amine-based CCS: alternative approaches.

Carbon capture and storage (CCS) technologies are considered vital and economic elements for achieving global CO2 reduction targets, and is currently introduced worldwide (for more information on CCS, consult for example the websites of the International Energy Agency (http://www.iea.org/topics/ccs/) and the Global CCS Institute (http://www.globalccsinstitute.com/)). One prominent CCS technology, the amine-based post-combustion process, may generate nitrosamines and their related nitramines as by-products, the former well known for their potential mutagenic and carcinogenic properties. In order to efficiently assess the carcinogenic potency of any of these by-products this paper reviews and discusses novel prediction approaches consuming less time, money and animals than the traditionally applied 2-year rodent assay. For this, available animal carcinogenicity studies with N-nitroso compounds and nitramines have been used to derive carcinogenic potency values, that were subsequently used to assess the predictive performance of alternative prediction approaches for these chemicals. Promising cancer prediction models are the QSARs developed by the Helguera group, in vitro transformation assays, and the in vivo initiation-promotion, and transgenic animal assays. All these models, however, have not been adequately explored for this purpose, as the number of N-nitroso compounds investigated is yet too limited, and therefore further testing with relevant N-nitroso compounds is needed.

A multi-scenario risk assessment strategy applied to mixtures of chemicals of emerging concern in the River Aconcagua basin in Central Chile.

Environmental risk assessments strategies that account for the complexity of exposures are needed in order to evaluate the toxic pressure of emerging chemicals, which also provide suggestions for risk mitigation and management, if necessary. Currently, most studies on the co-occurrence and environmental impacts of chemicals of emerging concern (CECs) are conducted in countries of the Global North, leaving massive knowledge gaps in countries of the Global South. In this study, we implement a multi-scenario risk assessment strategy to improve the assessment of both the exposure and hazard components in the chemical risk assessment process. Our strategy incorporates a systematic consideration and weighting of CECs that were not detected, as well as an evaluation of the uncertainties associated with Quantitative Structure-Activity Relationships (QSARs) predictions for chronic ecotoxicity. Furthermore, we present a novel approach to identifying mixture risk drivers. To expand our knowledge beyond well-studied aquatic ecosystems, we applied this multi-scenario strategy to the River Aconcagua basin of Central Chile. The analysis revealed that the concentrations of CECs exceeded acceptable risk thresholds for selected organism groups and the most vulnerable taxonomic groups. Streams flowing through agricultural areas and sites near the river mouth exhibited the highest risks. Notably, the eight risk drivers among the 153 co-occurring chemicals accounted for 66-92 % of the observed risks in the river basin. Six of them are pesticides and pharmaceuticals, chemical classes known for their high biological activity in specific target organisms.

Dimethylaminopyridine derivatives of lupane triterpenoids cause mitochondrial disruption and induce the permeability transition.

Triterpenoids are a large class of naturally occurring compounds, and some potentially interesting as anticancer agents have been found to target mitochondria. The objective of the present work was to investigate the mechanisms of mitochondrial toxicity induced by novel dimethylaminopyridine (DMAP) derivatives of pentacyclic triterpenes, which were previously shown to inhibit the growth of melanoma cells in vitro. MCF-7, Hs 578T and BJ cell lines, as well as isolated hepatic mitochondria, were used to investigate direct mitochondrial effects. On isolated mitochondrial hepatic fractions, respiratory parameters, mitochondrial transmembrane electric potential, induction of the mitochondrial permeability transition (MPT) pore and ion transport-dependent osmotic swelling were measured. Our results indicate that the DMAP triterpenoid derivatives lead to fragmentation and depolarization of the mitochondrial network in situ, and to inhibition of uncoupled respiration, induction of the permeability transition pore and depolarization of isolated hepatic mitochondria. The results show that mitochondrial toxicity is an important component of the biological interaction of DMAP derivatives, which can explain the effects observed in cancer cells.

The OSIRIS Weight of Evidence approach: ITS mutagenicity and ITS carcinogenicity.

Risk assessment of chemicals usually implies data evaluation of in vivo tests in rodents to conclude on their hazards. The FP7 European project OSIRIS has developed integrated testing strategies (ITS) for relevant toxicological endpoints to avoid unnecessary animal testing and thus to reduce time and costs. This paper describes the implementation of ITS mutagenicity and carcinogenicity in the public OSIRIS webtool. The data requirements of REACH formed the basis for these ITS. The main goal was to implement procedures to reach a conclusion on the adequacy and validity of available data. For the mutagenicity ITS a quantitative Weight of Evidence approach based on Bayesian statistics was developed and implemented. The approach allows an overall quality assessment of all available data for the five types of mutagenicity data requirements: in vitro bacterial mutagenicity, in vitro and in vivo chromosome aberration, in vitro and in vivo mammalian mutagenicity. For the carcinogenicity ITS a tool was developed to evaluate the quality of studies not conforming (entirely) to guidelines. In a tiered approach three quality aspects are assessed: documentation (reliability), study design (adequacy) and scope of examination (validity). The quality assessment is based on expert and data driven quantitative Weight of Evidence.

Ranking of concern, based on environmental indexes, for pharmaceutical and personal care products: an application to the Spanish case.

A wide range of Pharmaceuticals and Personal Care Products (PPCPs) are present in the environment, and many of their adverse effects are unknown. The emergence of new compounds or changes in regulations have led to dynamical studies of occurrence, impact and treatment, which consider geographical areas and trends in consumption and innovation in the pharmaceutical industry. A Quantitative study of Structure-Activity Relationship ((Q)SAR) was performed to assess the possible adverse effects of ninety six PPCPs and metabolites with negligible experimental data and establish a ranking of concern, which was supported by the EPA EPI Suite™ interface. The environmental and toxicological indexes, the persistence (P), the bioaccumulation (B), the toxicity (T) (extensive) and the occurrence in Spanish aquatic environments (O) (intensive) were evaluated. The most hazardous characteristics in the largest number of compounds were generated by the P index, followed by the T and B indexes. A high number of metabolites has a concern score equal to or greater than their parent compounds. Three PBT and OPBT rankings of concern were proposed using the total and partial ranking method (supported by a Hasse diagram) by the Decision Analysis by Ranking Techniques (DART) tool, which was recently recommended by the European Commission. An analysis of the sensibility of the relative weights of these indexes has been conducted. Hormones, antidepressants (and their metabolites), blood lipid regulators and all of the personal care products considered in this study were at the highest levels of risk according to the PBT and OPBT total rankings. Furthermore, when the OPBT partial ranking was performed, X-ray contrast media, H2 blockers and some antibiotics were included at the highest level of concern. It is important to improve and incorporate useful indexes for the predicted environmental impact of PPCPs and metabolites and thus focus experimental analysis on the compounds that require urgent attention.

Quantitative structure activity relationships (QSARs) and machine learning models for abiotic reduction of organic compounds by an aqueous Fe(II) complex.

Due to the increasing diversity of organic contaminants discharged into anoxic water environments, reactivity prediction is necessary for chemical persistence evaluation for water treatment and risk assessment purposes. Almost all quantitative structure activity relationships (QSARs) that describe rates of contaminant transformation apply only to narrowly-defined, relatively homogenous families of reactants (e.g., dechlorination of alkyl halides). In this work, we develop predictive models for abiotic reduction of 60 organic compounds with diverse reducible functional groups, including nitroaromatic compounds (NACs), aliphatic nitro-compounds (ANCs), aromatic N-oxides (ANOs), isoxazoles (ISXs), polyhalogenated alkanes (PHAs), sulfoxides and sulfones (SOs), and others. Rate constants for their reduction were measured using a model reductant system, Fe(II)-tiron. Qualitatively, the rates followed the order NACs > ANOs ≈ ISXs ≈ PHAs > ANCs > SOs. To develop QSARs, both conventional chemical descriptor-based and machine learning (ML)-based approaches were investigated. Conventional univariate QSARs based on a molecular descriptor ELUMO (energy of the lowest-unoccupied molecular orbital) gave good correlations within classes. Multivariate QSARs combining ELUMO with Abraham descriptors for physico-chemical properties gave slightly improved correlations within classes for NCs and NACs, but little improvement in correlation within other classes or among classes. The ML model obtained covers reduction rates for all classes of compounds and all of the conditions studied with the prediction accuracy similar to those of the conventional QSARs for individual classes (r2 = 0.41-0.98 for univariate QSARs, 0.71-0.94 for multivariate QSARs, and 0.83 for the ML model). Both approaches required a scheme for a priori classification of the compounds for model training. This work offers two alternative modeling approaches to comprehensive abiotic reactivity prediction for persistence evaluation of organic compounds in anoxic water environments.

Development and evaluation of two-parameter linear free energy models for the prediction of human skin permeability coefficient of neutral organic chemicals.

The experimental values of skin permeability coefficients, required for dermal exposure assessment, are not readily available for many chemicals. The existing estimation approaches are either less accurate or require many parameters that are not readily available. Furthermore, current estimation methods are not easy to apply to complex environmental mixtures. We present two models to estimate the skin permeability coefficients of neutral organic chemicals. The first model, referred to here as the 2-parameter partitioning model (PPM), exploits a linear free energy relationship (LFER) of skin permeability coefficient with a linear combination of partition coefficients for octanol-water and air-water systems. The second model is based on the retention time information of nonpolar analytes on comprehensive two-dimensional gas chromatography (GC × GC). The PPM successfully explained variability in the skin permeability data (n = 175) with R2 = 0.82 and root mean square error (RMSE) = 0.47 log unit. In comparison, the US-EPA's model DERMWIN™ exhibited an RMSE of 0.78 log unit. The Zhang model-a 5-parameter LFER equation based on experimental Abraham solute descriptors (ASDs)-performed slightly better with an RMSE value of 0.44 log unit. However, the Zhang model is limited by the scarcity of experimental ASDs. The GC × GC model successfully explained the variance in skin permeability data of nonpolar chemicals (n = 79) with R2 = 0.90 and RMSE = 0.23 log unit. The PPM can easily be implemented in US-EPA's Estimation Program Interface Suite (EPI Suite™). The GC × GC model can be applied to the complex mixtures of nonpolar chemicals.

An automated framework for compiling and integrating chemical hazard data.

Comparative chemical hazard assessment, which compares hazards for several endpoints across several chemicals, can be used for a variety of purposes including alternatives assessment and the prioritization of chemicals for further assessment. A new framework was developed to compile and integrate chemical hazard data for several human health and ecotoxicity endpoints from public online sources including hazardous chemical lists, Globally Harmonized System hazard codes (H-codes) or hazard categories from government health agencies, experimental quantitative toxicity values, and predicted values using Quantitative Structure-Activity Relationship (QSAR) models. QSAR model predictions were obtained using EPA's Toxicity Estimation Software Tool. Java programming was used to download hazard data, convert data from each source into a consistent score record format, and store the data in a database. Scoring criteria based on the EPA's Design for the Environment Program Alternatives Assessment Criteria for Hazard Evaluation were used to determine ordinal hazard scores (i.e., low, medium, high, or very high) for each score record. Different methodologies were assessed for integrating data from multiple sources into one score for each hazard endpoint for each chemical. The chemical hazard assessment (CHA) Database developed in this study currently contains more than 990,000 score records for more than 85,000 chemicals. The CHA Database and the methods used in its development may contribute to several cheminformatics, public health, and environmental activities.

Extending the concept of predicting fish acute toxicity in vitro to the intestinal cell line RTgutGC.

Testing chemicals for fish acute toxicity is a legal requirement in many countries as part of environmental risk assessment. To reduce the numbers of fish used, substantial efforts have been focussed on alternative approaches. Prominently, the cell viability assay with the rainbow trout (Oncorhynchus mykiss) gill cell line, RTgill-W1, has been recognized, owing to its high predictive power and robustness. Like gills, the intestine is considered a major site of chemical uptake and biotransformation but, in contrast to gills, is expected to be exposed to rather hydrophobic chemicals, which enter the fish via food. In the present study, we therefore aimed to extend the cell bioassay to the rainbow trout epithelial cell line from intestine, RTgutGC. Using 16 hydrophobic and volatile chemicals from the fragrance palette, we showed that also the RTgutGC cell line can be used to predict fish acute toxicity of chemicals and yields intra-laboratory variability in line with other bioassays. By comparing the RTgutGC toxicity to a study employing the RTgill-W1 assay on the same group of chemicals, a fragrance specific relationship was established which reflects an almost perfect 1:1 relationship between in vitro and in vivo toxicity results. Thus, both cell lines can be used to predict fish acute toxicity, either by using the obtained in vivo-in vitro relationship or by taking the in vitro results at face value. We moreover demonstrate the derivation of non-toxic concentrations for downstream applications which rely on a healthy cell state, such as the assessment of biotransformation or chemical transfer.