Subset Of Chemicals Research Articles

Chemistry domain of applicability evaluation against existing estrogen receptor high-throughput assay-based activity models.

The U. S. Environmental Protection Agency's Endocrine Disruptor Screening Program (EDSP) Tier 1 assays are used to screen for potential endocrine system-disrupting chemicals. A model integrating data from 16 high-throughput screening assays to predict estrogen receptor (ER) agonism has been proposed as an alternative to some low-throughput Tier 1 assays. Later work demonstrated that as few as four assays could replicate the ER agonism predictions from the full model with 98% sensitivity and 92% specificity. The current study utilized chemical clustering to illustrate the coverage of the EDSP Universe of Chemicals (UoC) tested in the existing ER pathway models and to investigate the utility of chemical clustering to evaluate the screening approach using an existing 4-assay model as a test case. Although the full original assay battery is no longer available, the demonstrated contribution of chemical clustering is broadly applicable to assay sets, chemical inventories, and models, and the data analysis used can also be applied to future evaluation of minimal assay models for consideration in screening. Chemical structures were collected for 6,947 substances via the CompTox Chemicals Dashboard from the over 10,000 UoC and grouped based on structural similarity, generating 826 chemical clusters. Of the 1,812 substances run in the original ER model, 1,730 substances had a single, clearly defined structure. The ER model chemicals with a clearly defined structure that were not present in the EDSP UoC were assigned to chemical clusters using a k-nearest neighbors approach, resulting in 557 EDSP UoC clusters containing at least one ER model chemical. Performance of an existing 4-assay model in comparison with the existing full ER agonist model was analyzed as related to chemical clustering. This was a case study, and a similar analysis can be performed with any subset model in which the same chemicals (or subset of chemicals) are screened. Of the 365 clusters containing >1ER model chemical, 321 did not have any chemicals predicted to be agonists by the full ER agonist model. The best 4-assay subset ER agonist model disagreed with the full ER agonist model by predicting agonist activity for 122 chemicals from 91 of the 321 clusters. There were 44 clusters with at least two chemicals and at least one agonist based upon the full ER agonist model, which allowed accuracy predictions on a per-cluster basis. The accuracy of the best 4-assay subset ER agonist model ranged from 50% to 100% across these 44 clusters, with 32 clusters having accuracy ≥90%. Overall, the best 4-assay subset ER agonist model resulted in 122 false-positive and only 2 false-negative predictions compared with the full ER agonist model. Most false positives (89) were active in only two of the four assays, whereas all but 11 true positive chemicals were active in at least three assays. False positive chemicals also tended to have lower area under the curve (AUC) values, with 110 out of 122 false positives having an AUC value below 0.214, which is lower than 75% of the positives as predicted by the full ER agonist model. Many false positives demonstrated borderline activity. The median AUC value for the 122 false positives from the best 4-assay subset ER agonist model was 0.138, whereas the threshold for an active prediction is 0.1. Our results show that the existing 4-assay model performs well across a range of structurally diverse chemicals. Although this is a descriptive analysis of previous results, several concepts can be applied to any screening model used in the future. First, the clustering of the chemicals provides a means of ensuring that future screening evaluations consider the broad chemical space represented by the EDSP UoC. The clusters can also assist in prioritizing future chemicals for screening in specific clusters based on the activity of known chemicals in those clusters. The clustering approach can be useful in providing a framework to evaluate which portions of the EDSP UoC chemical space are reliably covered by in silico and in vitro approaches and where predictions from either method alone or both methods combined are most reliable. The lessons learned from this case study can be easily applied to future evaluations of model applicability and screening to evaluate future datasets.

Sensitization Assessment of Extractables and Leachables in Pharmaceuticals: ELSIE Database Analysis.

Quality by design is the foundation of the risk management framework for extractables and leachables (E&Ls) recommended by the Extractables and Leachables Safety Information Exchange (ELSIE). Following these principles during the selection of materials for pharmaceutical product development minimizes the presence of highly toxic substances and decreases the health risk of potential leachables in the drug product. Therefore, in the context of the broad arena of chemicals, it is important to distinguish E&Ls as a subset of chemicals and evaluate this relevant chemical space to derive appropriate analytical and safety thresholds. When considering the health hazards posed by E&Ls, one area presenting a challenge is understanding the sensitization potential and whether it poses a risk to patients. A dataset of E&Ls compiled by ELSIE (n = 466) was analyzed to determine the prevalence and potency of skin sensitizers in this chemical subset and explore a scientifically justified approach to the sensitization assessment of potential leachables in parenteral drug products. Approximately half of the compounds (56%, 259/466) had sensitization data recorded in the ELSIE database and of these, 20% (52/259) are potential skin sensitizers. Only 3% (8/259) of the E&L dataset with sensitization data were considered potent (strong or extreme) sensitizers following in silico analysis and expert review, illustrating that potent sensitizers are not routinely observed as leachables in pharmaceutical products. Our analysis highlights that in silico potency prediction and expert review are key tools during the sensitization assessment process for E&Ls. The results confirm where material selection is anticipated to mitigate the risk of presence of strong and/or extreme sensitizers (e.g., extractable testing via ISO 10993-10), and that implementing thresholds per ICH M7 and/or Masuda-Herrera et al. provides a reasonably conservative approach for establishing the analytical testing and safety thresholds.

A Combined Bayesian and Similarity-Based Approach for Predicting E. coli Biofilm Inhibition by Phenolic Natural Compounds.

Screening for biofilm inhibition by purified natural compounds is difficult due to compounds' chemical diversity and limited commercial availability, combined with time- and cost-intensiveness of the laboratory process. In silico prediction of chemical and biological properties of molecules is a widely used technique when experimental data availability is of concern. At the same time, the performance of predictive models directly depends on the amount and quality of experimental data. Driven by the interest in developing a model for prediction of the antibiofilm effect of phenolic natural compounds such as flavonoids, we performed experimental assessment of antibiofilm activity of 320 compounds from this subset of chemicals. The assay was performed once on two Escherichia coli strains on agar in 24-well microtiter plates. The inhibition was assessed visually by detecting morphological changes in macrocolonies. Using the data obtained, we subsequently trained a Bayesian logistic regression model for prediction of biofilm inhibition, which was combined with a similarity-based method in order to increase the overall sensitivity (at the cost of accuracy). The quality of the predictions was subsequently validated by experimental assessment in three independent experiments with two resistant E. coli strains of 23 compounds absent in the initial data set. The validation demonstrated that the model may successfully predict the targeted effect as compared to the baseline accuracy. Using a randomly selected database of commercially available natural phenolics, we obtained approximately 6.0% of active compounds, whereas using our prediction-based substance selection, the percentage of phenolics found to be active increased to 34.8%.

Predicting compound amenability with liquid chromatography-mass spectrometry to improve non-targeted analysis.

With the increasing availability of high-resolution mass spectrometers, suspect screening and non-targeted analysis are becoming popular compound identification tools for environmental researchers. Samples of interest often contain a large (unknown) number of chemicals spanning the detectable mass range of the instrument. In an effort to separate these chemicals prior to injection into the mass spectrometer, a chromatography method is often utilized. There are numerous types of gas and liquid chromatographs that can be coupled to commercially available mass spectrometers. Depending on the type of instrument used for analysis, the researcher is likely to observe a different subset of compounds based on the amenability of those chemicals to the selected experimental techniques and equipment. It would be advantageous if this subset of chemicals could be predicted prior to conducting the experiment, in order to minimize potential false-positive and false-negative identifications. In this work, we utilize experimental datasets to predict the amenability of chemical compounds to detection with liquid chromatography-electrospray ionization-mass spectrometry (LC-ESI-MS). The assembled dataset totals 5517 unique chemicals either explicitly detected or not detected with LC-ESI-MS. The resulting detected/not-detected matrix has been modeled using specific molecular descriptors to predict which chemicals are amenable to LC-ESI-MS, and to which form(s) of ionization. Random forest models, including a measure of the applicability domain of the model for both positive and negative modes of the electrospray ionization source, were successfully developed. The outcome of this work will help to inform future suspect screening and non-targeted analyses of chemicals by better defining the potential LC-ESI-MS detectable chemical landscape of interest.

Spotlight onto surfactant\u2013steam\u2013bitumen interfacial behavior via molecular dynamics simulation

Heavy oil and bitumen play a vital role in the global energy supply, and to unlock such resources, thermal methods, e.g., steam injection, are applied. To improve the performance of these methods, different additives, such as air, solvents, and chemicals, can be used. As a subset of chemicals, surfactants are one of the potential additives for steam-based bitumen recovery methods. Molecular interactions between surfactant/steam/bitumen have not been addressed in the literature. This paper investigates molecular interactions between anionic surfactants, steam, and bitumen in high-temperature and high-pressure conditions. For this purpose, a real Athabasca oil sand composition is employed to assess the phase behavior of surfactant/steam/bitumen under in-situ steam-based bitumen recovery. Two different asphaltene architectures, archipelago and Island, are used to examine the effect of asphaltene type on bitumen's interfacial behavior. The influence of having sulfur heteroatoms in a resin structure and a benzene ring's effect in an anionic surfactant structure on surfactant–steam–bitumen interactions are investigated systematically. The outputs are supported by different analyses, including radial distribution functions (RDFs), mean squared displacement (MSD), radius of gyration, self-diffusion coefficient, solvent accessible surface area (SASA), interfacial thickness, and interaction energies. According to MD outputs, adding surfactant molecules to the steam phase improved the interaction energy between steam and bitumen. Moreover, surfactants can significantly improve steam emulsification capability by decreasing the interfacial tension (IFT) between bitumen and the steam phase. Asphaltene architecture has a considerable effect on the interfacial behavior in such systems. This study provides a better and more in-depth understanding of surfactant–steam–bitumen systems and spotlights the interactions between bitumen fractions and surfactant molecules under thermal recovery conditions.

Characterizing the effects of missing data and evaluating imputation methods for chemical prioritization applications using ToxPi

BackgroundThe Toxicological Priority Index (ToxPi) is a method for prioritization and profiling of chemicals that integrates data from diverse sources. However, individual data sources (“assays”), such as in vitro bioassays or in vivo study endpoints, often feature sections of missing data, wherein subsets of chemicals have not been tested in all assays. In order to investigate the effects of missing data and recommend solutions, we designed simulation studies around high-throughput screening data generated by the ToxCast and Tox21 programs on chemicals highlighted by the Agency for Toxic Substances and Disease Registry’s (ATSDR) Substance Priority List (SPL), which helps prioritize environmental research and remediation resources.ResultsOur simulations explored a wide range of scenarios concerning data (0-80% assay data missing per chemical), modeling (ToxPi models containing from 160-700 different assays), and imputation method (k-Nearest-Neighbor, Max, Mean, Min, Binomial, Local Least Squares, and Singular Value Decomposition). We find that most imputation methods result in significant changes to ToxPi score, except for datasets with a small number of assays. If we consider rank change conditional on these significant changes to ToxPi score, we find that ranks of chemicals in the minimum value imputation, SVD imputation, and kNN imputation sets are more sensitive to the score changes.ConclusionsWe found that the choice of imputation strategy exerted significant influence over both scores and associated ranks, and the most sensitive scenarios were those involving fewer assays plus higher proportions of missing data. By characterizing the effects of missing data and the relative benefit of imputation approaches across real-world data scenarios, we can augment confidence in the robustness of decisions regarding the health and ecological effects of environmental chemicals

Inferring transcription factor activity from microarray data reveals novel targets for toxicological investigations

Transcription factors (TFs) are important modulators of the inducible portion of the transcriptome, and therefore relevant in the context of exposure to exogenous compounds. Current approaches to predict the activity of TFs in biological systems are usually restricted to a few entities at a time due to low-throughput techniques targeting a limited fraction of annotated human TFs. Therefore, high-throughput alternatives may help to identify new targets of mechanistic and predictive value in toxicological investigations. In this study, we inferred the activity multiple TFs using publicly available microarray data from primary human hepatocytes exposed to hundreds of chemicals and evaluated these molecular profiles using multiple correspondence analysis. Our results demonstrate that the lowest dose and latest exposure time (24h) in a subset of chemicals generates a signature indicative of carcinogenicity possibly due to DNA-damaging properties. Furthermore, profiles from the earliest exposure time (2h) and highest dose creates clusters of chemicals implicated in the development of diverse forms of drug-induced liver injury (DILI). Both approaches yielded a number of TFs with similar activity across groups of chemicals, including TFs known in toxicological responses such as AhR, NFE2L2 (Nrf2), NF-κB and PPARG. FOXM1, IRF1 and E2F4 were some of the TFs identified that may be relevant in genotoxic carcinogenesis. SMADs (SMAD1, SMAD2, SMAD5) and KLF5 were identified as some of potentially new TFs whose inferred activities were linked to acute and progressive outcomes in DILI. In conclusion this study offers a novel mechanistic approach targeting TF activity during chemical exposure.

Dataset for human sensitivity to chemicals during development of motor function.

The authors reviewed human data related to motor development following exposure to a subset of chemicals thoroughly reviewed in Agency for Toxic Substances and Disease Registry (ATSDR) Toxicological Profiles and Addenda. The resulting dataset includes the following variables and confounders: chemical name, exposure route, exposure duration and frequency, study design, cohort name and/or geographic location, sex of cohort subjects, NOAEL, and LOAEL. This data summary can help validate motor development outcomes observed in animal exposure studies; it can also aid in determining whether these outcomes and corresponding exposure windows are relevant to humans.

Windows of sensitivity to toxic chemicals in the motor effects development

Many chemicals currently used are known to elicit nervous system effects. In addition, approximately 2000 new chemicals introduced annually have not yet undergone neurotoxicity testing. This review concentrated on motor development effects associated with exposure to environmental neurotoxicants to help identify critical windows of exposure and begin to assess data needs based on a subset of chemicals thoroughly reviewed by the Agency for Toxic Substances and Disease Registry (ATSDR) in Toxicological Profiles and Addenda. Multiple windows of sensitivity were identified that differed based on the maturity level of the neurological system at the time of exposure, as well as dose and exposure duration. Similar but distinct windows were found for both motor activity (GD 8–17 [rats], GD 12–14 and PND 3–10 [mice]) and motor function performance (insufficient data for rats, GD 12–17 [mice]). Identifying specific windows of sensitivity in animal studies was hampered by study designs oriented towards detection of neurotoxicity that occurred at any time throughout the developmental process. In conclusion, while this investigation identified some critical exposure windows for motor development effects, it demonstrates a need for more acute duration exposure studies based on neurodevelopmental windows, particularly during the exposure periods identified in this review.

Simulations of atmospheric OH, O<sub>3</sub> and NO<sub>3</sub> reactivities within and above the boreal forest

Abstract. Using the 1-D atmospheric chemistry transport model SOSAA, we have investigated the atmospheric reactivity of a boreal forest ecosystem during the HUMPPA-COPEC-10 campaign (summer 2010, at SMEAR~II in southern Finland). For the very first time, we present vertically resolved model simulations of the NO3 and O3 reactivity (R) together with the modelled and measured reactivity of OH. We find that OH is the most reactive oxidant (R ∼ 3 s-1) followed by NO3 (R ∼ 0.07 s-1) and O3 (R ∼ 2 × 10-5s-1). The missing OH reactivity was found to be large in accordance with measurements (∼ 65%) as would be expected from the chemical subset described in the model. The accounted OH radical sinks were inorganic compounds (∼ 41%, mainly due to reaction with CO), emitted monoterpenes (∼ 14%) and oxidised biogenic volatile organic compounds (∼ 44%). The missing reactivity is expected to be due to unknown biogenic volatile organic compounds and their photoproducts, indicating that the true main sink of OH is not expected to be inorganic compounds. The NO3 radical was found to react mainly with primary emitted monoterpenes (∼ 60%) and inorganic compounds (∼ 37%, including NO2). NO2 is, however, only a temporary sink of NO3 under the conditions of the campaign (with typical temperatures of 20–25 °C) and does not affect the NO3 concentration. We discuss the difference between instantaneous and steady-state reactivity and present the first boreal forest steady-state lifetime of NO3 (113 s). O3 almost exclusively reacts with inorganic compounds (∼ 91%, mainly NO, but also NO2 during night) and less with primary emitted sesquiterpenes (∼ 6%) and monoterpenes (∼ 3%). When considering the concentration of the oxidants investigated, we find that OH is the oxidant that is capable of removing organic compounds at a faster rate during daytime, whereas NO3 can remove organic molecules at a faster rate during night-time. O3 competes with OH and NO3 during a short period of time in the early morning (around 5 a.m. local time) and in the evening (around 7–8 p.m.). As part of this study, we developed a simple empirical parameterisation for conversion of measured spectral irradiance into actinic flux. Further, the meteorological conditions were evaluated using radiosonde observations and ground-based measurements. The overall vertical structure of the boundary layer is discussed, together with validation of the surface energy balance and turbulent fluxes. The sensible heat and momentum fluxes above the canopy were on average overestimated, while the latent heat flux was underestimated.

Immunotoxicology of Inhaled Compounds—Assessing Risks of Local Immune Suppression and Hypersensitivity

Immunotoxic risks associated with inhaled chemicals include (1) suppression of local defenses resulting in increased risk of pulmonary infection and (2) sensitization of the immune system resulting in respiratory hypersensitivity, including allergic asthma. Under laboratory conditions, environmentally relevant levels of many air pollutants enhance susceptibility to bacterial pneumonia in mice. Because these compounds do not necessarily affect systemic immune responses, conventional testing for immune suppression does not adequately predict this risk. The underlying mechanism is suppression of alveolar macrophage (AM) phagocytic activity that ordinarily clears these organisms from the lung. Hence, the most practical way to test for this risk is to assess AM function in bronchoalveolar lavage fluid. Methods for assessing chemicals for the potential to cause allergic asthma are not as clear-cut. Low-molecular-weight chemicals are not physically large enough to initiate an immune response on their own; to produce sensitization they must be reactive enough to complex with protein. Structure–activity approaches and testing in vitro for reactivity with protein have been proposed for use in hazard identification. Diisocyanates and acid anhydrides are structures associated with occupational asthma. These types of chemical allergen appear to be a subset of chemicals that test positive in contact sensitivity assays, such as the local lymph node assay (LLNA). It has been proposed that “asthmagens” can be further distinguished from other types of sensitizers by the potential to induce T helper 2-mediated responses. Several approaches have been proposed but none have been widely accepted. This article has been reviewed by the National Health and Environmental Effects Research Laboratory, U.S. Environmental Protection Agency, and approved for publication. Approval does not signify that the contents necessarily reflects the views and policies of the agency, nor does mention of trade names or commercial products constitute endorsement or recommendation for use.

Indicators for Chemicals: Sources, impacts and policy performance (5 pp)

Different types of indicators have been developed to describe the impact of chemicals on society and environment. Due to the high number of substances and their different types of use, most of these indicators are directed to specific areas of interest--regarding workplace safety, environmental health or consumer health. They address a specific subset of chemicals and can be used for monitoring enterprise-specific, national or international management measures. A survey of existing indicators for chemicals has shown that indicators already exist for a remarkable number of problem fields. As soon as the release and the environmental fate of chemicals are taken into account, the complexity of the approaches increases considerably. The distinction between indicators for drivers, pressures, state, impacts and responses, as proposed by the European Environmental Agency, supports the identification of proper indicators for a specific type of problem. No single indicator exists which is able to cover the whole range of chemicals and their applications. Several indicator approaches cover at least a subset of the most relevant substances. If they are intended to be used for European monitoring, robust data must be provided by EU Member States. Chemicals in enterprises (ancillary inputs as well as process chemicals) are an important element of in-plant material flow management--in terms of occupational safety and health as well as environmental protection. Existing indicators for hazardous chemicals can be a valuable tool for process and product refinement regarding hazardous chemicals, especially for enterprises. Indicators for production and impact of chemicals, as well as policy performance indicators, are essential elements in order to monitor the management of chemicals. They have to be established for the national and for the EU level.

Ds-optimal designs for studying combinations of chemicals using multiple fixed-ratio ray experiments

Detecting and characterizing interactions among chemicals is an important environmental issue. Traditional factorial designs become infeasible as the number of compounds under study increases. Ray designs, which reduce the amount of experimental effort, can be considered when interest is restricted to relevant mixing ratios. Simultaneous tests for departure from additivity across multiple fixed-ratio rays in the presence and absence of single chemical data have been developed. Tests for characterizing interactions among subsets of chemicals at relevant mixing ratios have also been developed. Of primary importance are precise estimates for the parameters associated with these hypotheses. Since the hypotheses of interest are stated in terms of subsets of parameters, we have developed a methodology for finding Ds-optimal designs, which are associated with the minimum generalized variance of subsets of the parameter vector, along fixed-ratio rays. We illustrate these methods by characterizing the interactions of five organophosphorus pesticides (full-ray) as well as a subset of pesticides (reduced-ray) on a measure of motor activity. Copyright © 2004 John Wiley & Sons, Ltd.

Overview of Hepatotoxicity

This overview presents some of the salient features of liver biology and physiology that form the basis for a variety of experimental tools that are used for the study of chemical histopathology. Prevalence and risk of heptatoxicity are examined among a subset of chemicals from the EPA IRIS database. The goal is to provide an objective assessment of the magnitude of the problem of chemically-induced hepatotoxicity and contextual background against which the diversity of responses of the liver to toxicants and the analytical methods available for their study can be appreciated.

The Precautionary Principle and Chemical Regulation in the U.S.

Although the statutory goals for chemical regulation are consistent with the precau tionary principle, the current U.S. regulatory program governing synthetic chemicals generally adopts little precautionary controls for the manufacture of most chemicals. For the vast majority of chemicals in use, current law places the burden of producing scientific evidence on the regulatory agency, which actually may serve to discourage companies from testing the safety of their chemicals, since the results could then be used against them in regulatory proceedings. By contrast, for a small subset of chemicals — new chemicals that belong to suspect categories — regulatory controls are quite precautionary. The result of this schizophrenic approach to chemical regulation is a regulatory system that is characterized by the absence of preventative regulation for most existing chemicals, an inequitable barrier to entry for newer safer chemicals, and a lack of information upon which to understand the safety of most chemicals in the U.S. Informal reforms of the current regulatory program are already underway to provide a more consistent and precautionary approach to chemical regulation, although to fully advance the dual goals of regulatory consistency and precaution in the regulation of chemicals, legislative action is necessary.

Using Short-Term Tests to Predict Carcinogenic Activity in the Long-Term Bioassay

A method for classifying chemicals with respect to carcinogenic potential based on short-term test results is presented. The method utilizes the logistic regression model to translate results from short-term toxicity assays into predictions of the likelihood that a chemical will be carcinogenic if tested in a long-term bioassay. The proposed method differs from previous approaches in two ways. First, statistical confidence limits on probabilities of cancer rather than central estimates of those probabilities are used for classification. Second, the method does not classify all chemicals in a data base with respect to carcinogenic potential. Instead, it identifies chemicals with highest and lowest likelihood of testing positive for carcinogenicity in the bioassay. A subset of chemicals with intermediate likelihood of being positive remains unclassified, and will require further testing, perhaps in a long-term bioassay. Two data bases of binary short-term and long-term test results from the literature are used to illustrate and evaluate the proposed procedure. A cross-validation analysis of one of the data sets suggests that, for a sufficiently rich data base of chemicals, the development of a robust predictive system to replace the bioassay for some unknown chemicals is a realistic goal.

Diversity analysis of 14 156 molecules tested by the National Cancer Institute for anti-HIV activity using the quantitative structure-activity relational expert system MCASE.

Using the MCASE program, a procedure to analyze the diversity of the large amount of available HIV-1 antiviral data was proposed. A subset of 1 819 chemicals was logically selected from the original 14 156 chemicals tested by NCI. This subset of chemicals was shown to contain most of the structural and the functional information of the original database. A full analysis of the 1 819 chemicals by the MCASE program produced a correlation between chemical structures and HIV antiviral activity. In our model, 74 fragments were identified as being responsible for all the chemical's HIV antiviral activity. These fragments may be related to different inhibiting mechanisms, some known and some probably still unknown. The expert system resulting from this analysis can be used to predict the activity of new chemicals and to design new agents that can target multiple enzymes. This was shown to be the case by using the model to predict the activity of 10 diverse chemicals whose activities were not known at the time of model development. Of these, 8 were predicted in agreement with experimental observations. As far as we can tell, this is probably the first project ever to attempt to create a quantitative model of activity for such a massive database of diverse chemicals.

Relevance of risk assessment to exposed communities.

Current environmental policy tends to evaluate potential, theoretical exposure to health risks by evaluating one chemical or hazard at a time. Risk assessment techniques used by the U. S. Environmental Protection Agency (EPA) and other government agencies do not evaluate the cumulative impact of exposure to environmental contaminants. This problem is of particular significance to low-income and minority populations who tend to live in neighborhoods and work in locations that involve exposure to pollutants in air, water, and workplace activity. Certain areas within the border typify this lifestyle. The problem is further complicated by the fact that EPA operates separate programs for different "media." Exposure patterns in the border suggest the need for a cross-media pollution prevention approach. Minority recruitment into health research, a coordination of research approaches and dollars, and new resources for effective monitoring of minority communities could provide a basic assessment of the risks and their sources. Further research into the cumulative impacts of prevalent subsets of chemicals is also needed. Recent efforts in the Great Lakes may provide a model for this type of regional, cross-border effort.

Electrophilicity as measured by Ke: molecular determinants, relationship with other physical-chemical and quantum mechanical parameters, and ability to predict rodent carcinogenicity

This paper analyzes electrophilicity data as measured by the Ke system for 205 chemicals including both rodent carcinogens and non-carcinogens. Multivariate statistical methods were used. The analysis identified atoms and substructures contributing to electrophilicity, and permitted to establish a theoretical method by which the Ke value (electrophilicity) of chemicals can be easily estimated. In a subset of chemicals, the Ke parameter was compared with other physical-chemical and quantum mechanical properties: Ke appeared to be mostly correlated with the energy of the lowest unoccupied molecular orbital and with the absolute electronegativity. The role of Ke in structure-activity studies was also investigated; in particular, a comparative analysis of the performance of Ke, Salmonella typhimurium and Ashby's structural alerts in predicting carcinogenicity was carried out. The Ke system performed better than the other systems. However, because of the many different mechanisms underlying carcinogenesis, the Ke system cannot predict the potential carcinogenicity of all kinds of chemicals. It is concluded that the main role of Ke in risk assessment consists in producing a probabilistic estimate of the rodent carcinogenicity of the chemicals: e.g. a chemical with Ke higher than 3.0 x 10(12) M-1S-1 has nearly 80% probability of being a carcinogen. Such a probability estimate can be used to rank the chemicals in a priority scale for subsequent and more detailed studies, either theoretical or experimental. In view of this, the role of our method for estimating Ke is particularly important: it gives rapidly and at no cost a chemical classification for risk assessment and priority setting.