R. Woodrow Setzer

Estimating toxicity-related biological pathway altering doses for high-throughput chemical risk assessment.

We describe a framework for estimating the human dose at which a chemical significantly alters a biological pathway in vivo, making use of in vitro assay data and an in vitro-derived pharmacokinetic model, coupled with estimates of population variability and uncertainty. The quantity we calculate, the biological pathway altering dose (BPAD), is analogous to current risk assessment metrics in that it combines dose-response data with analysis of uncertainty and population variability to arrive at conservative exposure limits. The analogy is closest when perturbation of a pathway is a key event in the mode of action (MOA) leading to a specified adverse outcome. Because BPADs are derived from relatively inexpensive, high-throughput screening (HTS) in vitro data, this approach can be applied to high-throughput risk assessments (HTRA) for thousands of data-poor environmental chemicals. We envisage the first step of HTRA to be an assessment of in vitro concentration-response relationships across biologically important pathways to derive biological pathway altering concentrations (BPAC). Pharmacokinetic (PK) modeling is then used to estimate the in vivo doses required to achieve the BPACs in the blood at steady state. Uncertainty and variability are incorporated in both the BPAC and the PK parameters and then combined to yield a probability distribution for the dose required to perturb the critical pathway. We finally define the BPADL as the lower confidence bound of this pathway-altering dose. This perspective outlines a framework for using HTRA to estimate BPAD values; provides examples of the use of this approach, including a comparison of BPAD values with published dose-response data from in vivo studies; and discusses challenges and alternative formulations.

High-throughput models for exposure-based chemical prioritization in the ExpoCast project.

The United States Environmental Protection Agency (U.S. EPA) must characterize potential risks to human health and the environment associated with manufacture and use of thousands of chemicals. High-throughput screening (HTS) for biological activity allows the ToxCast research program to prioritize chemical inventories for potential hazard. Similar capabilities for estimating exposure potential would support rapid risk-based prioritization for chemicals with limited information; here, we propose a framework for high-throughput exposure assessment. To demonstrate application, an analysis was conducted that predicts human exposure potential for chemicals and estimates uncertainty in these predictions by comparison to biomonitoring data. We evaluated 1936 chemicals using far-field mass balance human exposure models (USEtox and RAIDAR) and an indicator for indoor and/or consumer use. These predictions were compared to exposures inferred by Bayesian analysis from urine concentrations for 82 chemicals reported in the National Health and Nutrition Examination Survey (NHANES). Joint regression on all factors provided a calibrated consensus prediction, the variance of which serves as an empirical determination of uncertainty for prioritization on absolute exposure potential. Information on use was found to be most predictive; generally, chemicals above the limit of detection in NHANES had consumer/indoor use. Coupled with hazard HTS, exposure HTS can place risk earlier in decision processes. High-priority chemicals become targets for further data collection.

Activity profiles of 309 ToxCast™ chemicals evaluated across 292 biochemical targets☆

Understanding the potential health risks posed by environmental chemicals is a significant challenge elevated by the large number of diverse chemicals with generally uncharacterized exposures, mechanisms, and toxicities. The present study is a performance evaluation and critical analysis of assay results for an array of 292 high-throughput cell-free assays aimed at preliminary toxicity evaluation of 320 environmental chemicals in EPAs ToxCast™ project (Phase I). The chemicals (309 unique, 11 replicates) were mainly precursors or the active agent of commercial pesticides, for which a wealth of in vivo toxicity data is available. Biochemical HTS (high-throughput screening) profiled cell and tissue extracts using semi-automated biochemical and pharmacological methodologies to evaluate a subset of G-protein coupled receptors (GPCRs), CYP450 enzymes (CYPs), kinases, phosphatases, proteases, HDACs, nuclear receptors, ion channels, and transporters. The primary screen tested all chemicals at a relatively high concentration 25 μM concentration (or 10 μM for CYP assays), and a secondary screen re-tested 9132 chemical-assay pairs in 8-point concentration series from 0.023 to 50 μM (or 0.009-20 μM for CYPs). Mapping relationships across 93,440 chemical-assay pairs based on half-maximal activity concentration (AC50) revealed both known and novel targets in signaling and metabolic pathways. The primary dataset, summary data and details on quality control checks are available for download at http://www.epa.gov/ncct/toxcast/.

Assessing Susceptibility from Early-Life Exposure to Carcinogens

Cancer risk assessment methods currently assume that children and adults are equally susceptible to exposure to chemicals. We reviewed available scientific literature to determine whether this was scientifically supported. We identified more than 50 chemicals causing cancer after perinatal exposure. Human data are extremely limited, with radiation exposures showing increased early susceptibility at some tumor sites. Twenty-seven rodent studies for 18 chemicals had sufficient data after postnatal and adult exposures to quantitatively estimate potential increased susceptibility from early-life exposure, calculated as the ratio of juvenile to adult cancer potencies for three study types: acute dosing, repeated dosing, and lifetime dosing. Twelve of the chemicals act through a mutagenic mode of action. For these, the geometric mean ratio was 11 for lifetime exposures and 8.7 for repeat exposures, with a ratio of 10 for these studies combined. The geometric mean ratio for acute studies is 1.5, which was influenced by tissue-specific results [geometric mean ratios for kidney, leukemia, liver, lymph, mammary, nerve, reticular tissue, thymic lymphoma, and uterus/vagina > 1 (range, 1.6–8.1); forestomach, harderian gland, ovaries, and thyroid < 1 (range, 0.033–0.45)]. Chemicals causing cancer through other modes of action indicate some increased susceptibility from postnatal exposure (geometric mean ratio is 3.4 for lifetime exposure, 2.2 for repeat exposure). Early exposures to compounds with endocrine activity sometimes produce different tumors after exposures at different ages. These analyses suggest increased susceptibility to cancer from early-life exposure, particularly for chemicals acting through a mutagenic mode of action.

Integrated Model of Chemical Perturbations of a Biological Pathway Using 18 In Vitro High-Throughput Screening Assays for the Estrogen Receptor

We demonstrate a computational network model that integrates 18 in vitro, high-throughput screening assays measuring estrogen receptor (ER) binding, dimerization, chromatin binding, transcriptional activation, and ER-dependent cell proliferation. The network model uses activity patterns across the in vitro assays to predict whether a chemical is an ER agonist or antagonist, or is otherwise influencing the assays through a manner dependent on the physics and chemistry of the technology platform (assay interference). The method is applied to a library of 1812 commercial and environmental chemicals, including 45 ER positive and negative reference chemicals. Among the reference chemicals, the network model correctly identified the agonists and antagonists with the exception of very weak compounds whose activity was outside the concentration range tested. The model agonist score also correlated with the expected potency class of the active reference chemicals. Of the 1812 chemicals evaluated, 111 (6.1%) were predicted to be strongly ER active in agonist or antagonist mode. This dataset and model were also used to begin a systematic investigation of assay interference. The most prominent cause of false-positive activity (activity in an assay that is likely not due to interaction of the chemical with ER) is cytotoxicity. The model provides the ability to prioritize a large set of important environmental chemicals with human exposure potential for additional in vivo endocrine testing. Finally, this model is generalizable to any molecular pathway for which there are multiple upstream and downstream assays available.

Application of the Margin of Exposure (MoE) approach to substances in food that are genotoxic and carcinogenic: EXAMPLE: Acrylamide (CAS No. 79-06-1)

Acrylamide (CH(2)CHCONH(2), CAS Registry No. 79-06-1) is an industrial chemical used since the 1950s as a chemical intermediate in the production of polyacrylamides, which are used as flocculants for clarifying drinking-water and other industrial applications. The neurotoxicity of acrylamide in humans is well known from occupational and accidental exposures. In addition, experimental studies with acrylamide in animals have shown reproductive, genotoxic and carcinogenic properties. Acrylamide may be formed when foods, particularly those that are high in carbohydrates and low in protein, are subjected to high temperatures during cooking or other thermal processing.

Shape and steepness of toxicological dose–response relationships of continuous endpoints

Abstract A re-analysis of a large number of historical dose–response data for continuous endpoints indicates that an exponential or a Hill model with four parameters adequately describes toxicological dose–responses. No exceptions were found for the datasets considered, which related to a wide variety of endpoints and to both in vivo and in vitro studies of various types. For a given endpoint/study type dose–response shapes were found to be homogenous among chemicals in the in vitro studies considered, while a mild among-chemical variation in the steepness parameter seemed to be present in the in vivo studies. Our findings have various practical consequences. For continuous endpoints, model selection in the BMD approach is not a crucial issue. The often applied approach of using constraints on the model parameters to prevent “infinite” slopes at dose zero in fitting a model is not in line with our findings, and appears to be unjustified. Instead, more realistic ranges of parameter values could be derived from re-analyses of large numbers of historical dose–response datasets in the same endpoint and study type, which could be used as parameter constraints in future individual datasets. This approach will be particularly useful for weak datasets (e.g. few doses, much scatter). In addition, this approach may open the way to use fewer animals in future studies. In the discussion, we argue that distinctions between linear, sub/supralinear or thresholded dose–response shapes, based on visual inspection of plots, are not biologically meaningful nor useful for risk assessment.

High Throughput Heuristics for Prioritizing Human Exposure to Environmental Chemicals

The risk posed to human health by any of the thousands of untested anthropogenic chemicals in our environment is a function of both the hazard presented by the chemical and the extent of exposure. However, many chemicals lack estimates of exposure intake, limiting the understanding of health risks. We aim to develop a rapid heuristic method to determine potential human exposure to chemicals for application to the thousands of chemicals with little or no exposure data. We used Bayesian methodology to infer ranges of exposure consistent with biomarkers identified in urine samples from the U.S. population by the National Health and Nutrition Examination Survey (NHANES). We performed linear regression on inferred exposure for demographic subsets of NHANES demarked by age, gender, and weight using chemical descriptors and use information from multiple databases and structure-based calculators. Five descriptors are capable of explaining roughly 50% of the variability in geometric means across 106 NHANES chemicals for all the demographic groups, including children aged 6-11. We use these descriptors to estimate human exposure to 7968 chemicals, the majority of which have no other quantitative exposure prediction. For thousands of chemicals with no other information, this approach allows forecasting of average exposure intake of environmental chemicals.

Modeling Single and Repeated Dose Pharmacokinetics of PFOA in Mice

Perfluorooctanoic acid (PFOA) displays complicated pharmacokinetics in that serum concentrations indicate long half-lives despite which steady state appears to be achieved rapidly. In this study, serum and tissue concentration time-courses were obtained for male and female CD1 mice after single, oral doses of 1 and 10 mg/kg of PFOA. When using one- and two-compartment models, the pharmacokinetics for these two dosages are not consistent with serum time-course data from female CD1 mice administered 60 mg/kg, or with serum concentrations following repeated daily doses of 20 mg/kg PFOA. Some consistency between dose regimens could be achieved using the saturable resorption model of Andersen et al. In this model PFOA is cleared from the serum into a filtrate compartment from which it is either excreted or resorbed into the serum by a process presumed transporter mediated with a Michaelis-Menten form. Maximum likelihood estimation found a transport maximum of T(m) = 860.9 (1298.3) mg/l/h and half-maximum concentration of K(T) = 0.0015 (0.0022) mg/l where the estimated standard errors (in parentheses) indicated large uncertainty. The estimated rate of flow into and out of the filtrate compartment, 0.6830 (1.0131) l/h was too large to be consistent with a biological interpretation. For these model parameters a single dose greater than 40 mg/kg, or a daily dose in excess of 5 mg/kg were necessary to observe nonlinear pharmacokinetics for PFOA in female CD1 mice. These data and modeling analyses more fully characterize PFOA in mice for purposes of estimating internal exposure for use in risk assessment.

The antimutagenic effect of vanillin and cinnamaldehyde on spontaneous mutation in Salmonella TA104 is due to a reduction in mutations at GC but not AT sites.

Vanillin (VAN) and cinnamaldehyde (CIN) are dietary antimutagens that, when added to assay plates, reduced the spontaneous mutant frequency in Salmonella typhimurium strain TA104 (hisG428, rfa, uvrB, pKM101) by 50%. To date, no study has demonstrated whether or not the antimutagenic effects of an agent are due to a reduction in all classes of mutations or to a reduction in selective classes of mutations. To explore this issue, we have determined the spontaneous mutation spectrum in TA104 as well as the mutation spectrum after treatment of cells with antimutagens at concentrations that produced approximately a 50% reduction in mutant frequency but only a 10% reduction in survival. Statistical analysis revealed no significant difference between the mutation spectra of VAN- and CIN-treated cells. Relative to untreated cells, treatment with either VAN or CIN produced a significant reduction in mutations at GC sites, whereas neither compound produced a significant reduction in mutations at AT sites. Antimutagenesis experiments in hisG428 strains of Salmonella with varying DNA repair backgrounds showed that VAN and CIN require SOS repair genes to produce an antimutagenic effect against spontaneous mutagenesis. Studies evaluating the effect of VAN and CIN on growth rate showed that neither compound suppressed growth relative to untreated cells. To our knowledge, this is the first study to examine if an antimutagen reduced all or just some classes of mutations that were available for reduction.