Predictive Modeling of Chemical Hazard by Integrating Numerical Descriptors of Chemical Structures and Short-term Toxicity Assay Data

Quantitative structure-activity relationship (QSAR) models are widely used for in silico prediction of in vivo toxicity of drug candidates or environmental chemicals, adding value to candidate selection in drug development or in a search for less hazardous and more sustainable alternatives for chemicals in commerce. The development of traditional QSAR models is enabled by numerical descriptors representing the inherent chemical properties that can be easily defined for any number of molecules; however, traditional QSAR models often have limited predictive power due to the lack of data and complexity of in vivo endpoints. Although it has been indeed difficult to obtain experimentally derived toxicity data on a large number of chemicals in the past, the results of quantitative in vitro screening of thousands of environmental chemicals in hundreds of experimental systems are now available and continue to accumulate. In addition, publicly accessible toxicogenomics data collected on hundreds of chemicals provide another dimension of molecular information that is potentially useful for predictive toxicity modeling. These new characteristics of molecular bioactivity arising from short-term biological assays, i.e., in vitro screening and/or in vivo toxicogenomics data can now be exploited in combination with chemical structural information to generate hybrid QSAR–like quantitative models to predict human toxicity and carcinogenicity. Using several case studies, we illustrate the benefits of a hybrid modeling approach, namely improvements in the accuracy of models, enhanced interpretation of the most predictive features, and expanded applicability domain for wider chemical space coverage

Trichloroethylene biotransformation and its role in mutagenicity, carcinogenicity and target organ toxicity

Metabolism is critical for the mutagenicity, carcinogenicity, and other adverse health effects of trichloroethylene (TCE). Despite the relatively small size and simple chemical structure of TCE, its metabolism is quite complex, yielding multiple intermediates and end-products. Experimental animal and human data indicate that TCE metabolism occurs through two major pathways: cytochrome P450 (CYP)-dependent oxidation and glutathione (GSH) conjugation catalyzed by GSH S-transferases (GSTs). Herein we review recent data characterizing TCE processing and flux through these pathways. We describe the catalytic enzymes, their regulation and tissue localization, as well as the evidence for transport and inter-organ processing of metabolites. We address the chemical reactivity of TCE metabolites, highlighting data on mutagenicity of these end-products. Identification in urine of key metabolites, particularly trichloroacetate (TCA), dichloroacetate (DCA), trichloroethanol and its glucuronide (TCOH and TCOG), and N-acetyl-S-(1,2-dichlorovinyl)-L-cysteine (NAcDCVC), in exposed humans and other species (mostly rats and mice) demonstrates function of the two metabolic pathways in vivo. The CYP pathway primarily yields chemically stable end-products. However, the GST pathway conjugate S-(1,2-dichlorovinyl)glutathione (DCVG) is further processed to multiple highly reactive species that are known to be mutagenic, especially in kidney where in situ metabolism occurs. TCE metabolism is highly variable across sexes, species, tissues and individuals. Genetic polymorphisms in several of the key enzymes metabolizing TCE and its intermediates contribute to variability in metabolic profiles and rates. In all, the evidence characterizing the complex metabolism of TCE can inform predictions of adverse responses including mutagenesis, carcinogenesis, and acute and chronic organ-specific toxicity

Addressing human variability in next-generation human health risk assessments of environmental chemicals

International audienceCharacterizing variability in the extent and nature of responses to environmental exposures is a critical aspect of human health risk assessment. Our goal was to explore how next-generation human health risk assessments may better characterize variability in the context of the conceptual framework for the source-to-outcome continuum. This review was informed by a National Research Council workshop titled 'Biological Factors that Underlie Individual Susceptibility to Environmental Stressors and Their Implications for Decision-Making.' We considered current experimental and in silico approaches, and emerging data streams (such as genetically defined human cells lines, genetically diverse rodent models, human omic profiling, and genome-wide association studies) that are providing new types of information and models relevant for assessing interindividual variability for application to human health risk assessments of environmental chemicals. One challenge for characterizing variability is the wide range of sources of inherent biological variability (e.g., genetic and epigenetic variants) among individuals. A second challenge is that each particular pair of health outcomes and chemical exposures involves combinations of these sources, which may be further compounded by extrinsic factors (e.g., diet, psychosocial stressors, other exogenous chemical exposures). A third challenge is that different decision contexts present distinct needs regarding the identification-and extent of characterization-of interindividual variability in the human population. Despite these inherent challenges, opportunities exist to incorporate evidence from emerging data streams for addressing interindividual variability in a range of decision-making contexts

A systematic approach for identifying and presenting mechanistic evidence in human health assessments

Clear documentation of literature search and presentation methodologies can improve transparency in chemical hazard assessments. We sought to improve clarity for the scientific support for cancer mechanisms of action using a systematic approach to literature retrieval, selection, and presentation of studies. The general question was “What are the mechanisms by which a chemical may cause carcinogenicity in the target tissue?” Di(2-ethylhexyl)phthalate was used as a case study chemical with a complex database of >3,000 publications. Relevant mechanistic events were identified from published reviews. The PubMed search strategy included relevant synonyms and wildcards for DEHP and its metabolites, mechanistic events, and species of interest. Tiered exclusion/inclusion criteria for study pertinence were defined, and applied to the retrieved literature. Manual curation was conducted for mechanistic events with large literature databases. Literature trees documented identification and selection of the literature evidence. The selected studies were summarized in evidence tables accompanied by succinct narratives. Primary publications were deposited into the Health and Environmental Research Online (http://hero.epa.gov/) database and identified by pertinence criteria and key terms to permit organized retrieval. This approach contributes to human health assessment by effectively managing a large volume of literature, improving transparency, and facilitating subsequent synthesis of information across studies

Physiologically Based Pharmacokinetic (PBPK) Modeling of Interstrain Variability in Trichloroethylene Metabolism in the Mouse

Background: Quantitative estimation of toxicokinetic variability in the human population is a persistent challenge in risk assessment of environmental chemicals. Traditionally, interindividual differences in the population are accounted for by default assumptions or, in rare cases, are based on human toxicokinetic data

Carcinogenicity of consumption of red and processed meat

In October, 2015, 22 scientists from ten countries met at the International Agency for Research on Cancer (IARC) in Lyon, France, to evaluate the carcinogenicity of the consumption of red meat and processed meat. These assessments will be published in volume 114 of the IARC Monographs