Sharon B. Stuard

Toward good read-across practice (GRAP) guidance

Summary Grouping of substances and utilizing read-across of data within those groups represents an important data gap filling technique for chemical safety assessments. Categories/analogue groups are typically developed based on structural similarity and, increasingly often, also on mechanistic (biological) similarity. While read-across can play a key role in complying with legislation such as the European REACH regulation, the lack of consensus regarding the extent and type of evidence necessary to support it often hampers its successful application and acceptance by regulatory authorities. Despite a potentially broad user community, expertise is still concentrated across a handful of organizations and individuals. In order to facilitate the effective use of read-across, this document presents the state of the art, summarizes insights learned from reviewing ECHA published decisions regarding the relative successes/pitfalls surrounding read-across under REACH, and compiles the relevant activities and guidance documents. Special emphasis is given to the available existing tools and approaches, an analysis of ECHAs published final decisions associated with all levels of compliance checks and testing proposals, the consideration and expression of uncertainty, the use of biological support data, and the impact of the ECHA Read-Across Assessment Framework (RAAF) published in 2015.

Development of a physiologically-based pharmacokinetic model of 2-phenoxyethanol and its metabolite phenoxyacetic acid in rats and humans to address toxicokinetic uncertainty in risk assessment ☆

2-Phenoxyethanol (PhE) has been shown to induce hepatotoxicity, renal toxicity, and hemolysis at dosages ≥ 400 mg/kg/day in subchronic and chronic studies in multiple species. To reduce uncertainty associated with interspecies extrapolations and to evaluate the margin of exposure (MOE) for use of PhE in cosmetics and baby products, a physiologically-based pharmacokinetic (PBPK) model of PhE and its metabolite 2-phenoxyacetic acid (PhAA) was developed. The PBPK model incorporated key kinetic processes describing the absorption, distribution, metabolism and excretion of PhE and PhAA following oral and dermal exposures. Simulations of repeat dose rat studies facilitated the selection of systemic AUC as the appropriate dose metric for evaluating internal exposures to PhE and PhAA in rats and humans. Use of the PBPK model resulted in refinement of the total default UF for extrapolation of the animal data to humans from 100 to 25. Based on very conservative assumptions for product composition and aggregate product use, model-predicted exposures to PhE and PhAA resulting from adult and infant exposures to cosmetic products are significantly below the internal dose of PhE observed at the NOAEL dose in rats. Calculated MOEs for all exposure scenarios were above the PBPK-refined UF of 25.

Induction of mouse cytochrome P450 2B enzymes by amine metabolites of musk xylene: contribution of microsomal enzyme induction to the hepatocarcinogenicity of musk xylene.

Musk xylene (MX) is a synthetic nitromusk perfume ingredient that, although uniformly negative in genotoxicity testing, causes liver tumors in B6C3F1 mice. MX is also capable of inducing cytochrome P450 enzymes in a manner similar to that of phenobarbital (PB), which suggests that epigenetic mechanisms may be involved in the carcinogenic response. At the same time, MX is metabolized in vivo by nitroreduction, a reaction catalyzed by intestinal flora that yields aromatic amine metabolites. These amine metabolites are also capable of inactivating CYP2B10, the major cytochrome P450 enzyme induced by MX treatment. In the study reported here, the monoamine metabolites of MX, o‐ and p‐NH2‐MX, were evaluated for their potential to induce CYP2B10 and CYP1A2 mRNAs. Northern blot analyses indicated that both amines markedly induced CYP2B10 mRNA, whereas CYP1A2 mRNA, the enzyme implicated in the bioactivation of aromatic amines and frequently induced by aromatic amines, was induced only slightly, a response that was not different from that seen with PB. Induction of CYP2B10 mRNA suggested that the amine metabolites may contribute to the enzyme induction profile seen with MX treatment. To test this hypothesis, mice were treated with broad‐spectrum antibiotics (neomycin, tetracycline, and bacitracin) to eliminate the intestinal flora and prevent formation of o‐ and p‐NH2‐MX. In antibiotic‐treated mice treated with MX (200 mg/kg) for 4 d, no evidence of microsomal enzyme induction was observed, including no increases in liver weight, total cytochrome P450 content, or CYP2B protein levels. These results indicate that the amine metabolites of MX are responsible for the enzyme induction seen after MX administration. Thus, the biochemical and molecular effects of amine metabolites of MX are markedly different from those of other aromatic amines but very similar to those of PB. Therefore, it appears that MX is a non‐genotoxic chemical that may cause mouse liver tumors in a manner analogous to that of PB. Mol. Carcinog. 20:308–316, 1997.

A strategy for safety assessment of chemicals with data gaps for developmental and/or reproductive toxicity

Alternative methods for full replacement of in vivo tests for systemic endpoints are not yet available. Read across methods provide a means of maximizing utilization of existing data. A limitation for the use of read across methods is that they require analogs with test data. Repeat dose data are more frequently available than are developmental and/or reproductive toxicity (DART) studies. There is historical precedent for using repeat dose data in combination with a database uncertainty factor (UF) to account for missing DART data. We propose that use of the DART decision tree (Wu et al., 2013), in combination with a database UF, provides a path forward for DART data gap filling that better utilizes all of the data. Our hypothesis was that chemical structures identified by the DART tree as being related to structures with known DART toxicity would potentially have lower DART NOAELs compared to their respective repeat dose NOAELs than structures that lacked this association. Our analysis supports this hypothesis and as a result also supports that the DART decision tree can be used as part of weight of evidence in the selection of an appropriate DART database UF factor.