Karen Blackburn

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.

A framework for using structural, reactivity, metabolic and physicochemical similarity to evaluate the suitability of analogs for SAR-based toxicological assessments

A systematic expert-driven process is presented for evaluating analogs for read across in SAR (structure activity relationship) toxicological assessments. The approach involves categorizing potential analogs based upon their degree of structural, reactivity, metabolic and physicochemical similarity to the chemical with missing toxicological data (target chemical). It extends beyond structural similarity, and includes differentiation based upon chemical reactivity and addresses the potential that an analog and target could show toxicologically significant metabolic convergence or divergence. In addition, it identifies differences in physicochemical properties, which could affect bioavailability and consequently biological responses observed in vitro or in vivo. The approach provides a stepwise decision tree for categorizing the suitability of analogs, which qualitatively characterizes the strength of the evidence supporting the hypothesis of similarity and level of uncertainty associated with their use for read across. The result is a comprehensive framework to apply chemical, biochemical and toxicological principles in a systematic manner to identify and evaluate factors that can introduce uncertainty into SAR assessments, while maximizing the appropriate use of all available data.

Tissue distribution of 20 nm, 100 nm and 1000 nm fluorescent polystyrene latex nanospheres following acute systemic or acute and repeat airway exposure in the rat.

Understanding tissue distribution and clearance of nanomaterials following different routes of exposure is needed for risk assessment. F344 female rats received single or multiple exposures to 20 nm, 100 nm or 1000 nm latex fluorospheres by intravenous (i.v.) injection or oral pharyngeal aspiration into the airways. The presence of fluorospheres in tissues was assessed up to 90-120 days after the final dose. Blood, perfusion fluid, bone marrow, brain, eyes, feces, gut, heart, kidney, liver, lung, muscle, skin, spleen, thymus, tongue, urine and uterus plus ovaries were collected for analysis. Liver, spleen and lung were the greatest tissue depots for all particles following i.v. injection. The proportion of 100 nm and 1000 nm but not 20 nm spheres significantly increased in the spleen over time. Lung was the greatest tissue depot for all particles following single or repeat airway exposure. Greater than 95% of 1000 nm spheres that were recovered were in the lung in contrast to 70-80% of 20 nm spheres or 89-95% of 100 nm spheres. All 3 sizes were found in gut or gut+feces 1-7 days after lung exposure. The thymus was the largest extra-pulmonary depot for the particles; up to 25% of recovered 20 nm particles were in the thymus up to 4 months after exposure compared to 6% of 100 nm particles and 1-3% of 1000 nm particles. A small proportion of 20 nm particles were detected in kidney following both acute and repeat airway exposure. Low numbers of particles were found in the circulation (blood, perfusion), bone marrow, brain, heart, liver and spleen but not in eye, muscle, skin, tongue, ovaries, uterus or urine. These data show that the tissue targets of nano- and micron-sized spheres are very similar whether exposure occurs systemically or via the airways while the proportion of particles in some tissues and tissue clearance varies based on particle size.

Framework for identifying chemicals with structural features associated with the potential to act as developmental or reproductive toxicants.

Developmental and reproductive toxicity (DART) end points are important hazard end points that need to be addressed in the risk assessment of chemicals to determine whether or not they are the critical effects in the overall risk assessment. These hazard end points are difficult to predict using current in silico tools because of the diversity of mechanisms of action that elicit DART effects and the potential for narrow windows of vulnerability. DART end points have been projected to consume the majority of animals used for compliance with REACH; thus, additional nonanimal predictive tools are urgently needed. This article presents an empirically based decision tree for determining whether or not a chemical has receptor-binding properties and structural features that are consistent with chemical structures known to have toxicity for DART end points. The decision tree is based on a detailed review of 716 chemicals (664 positive, 16 negative, and 36 with insufficient data) that have DART end-point data and are grouped into defined receptor binding and chemical domains. When tested against a group of chemicals not included in the training set, the decision tree is shown to identify a high percentage of chemicals with known DART effects. It is proposed that this decision tree could be used both as a component of a screening system to identify chemicals of potential concern and as a component of weight-of-evidence decisions based on structure-activity relationships (SAR) to fill data gaps without generating additional test data. In addition, the chemical groupings generated could be used as a starting point for the development of hypotheses for in vitro testing to elucidate mode of action and ultimately in the development of refined SAR principles for DART that incorporate mode of action (adverse outcome pathways).

Case studies to test: A framework for using structural, reactivity, metabolic and physicochemical similarity to evaluate the suitability of analogs for SAR-based toxicological assessments

A process for evaluating analogs for use in SAR (Structure-Activity Relationship) assessments was previously published (Wu et al. 2010). Subsequently, this process has been updated to include a decision tree for estrogen binding (from US EPA) and flags for developmental and reproductive toxicity (DART). This paper presents the results of blinded case studies designed to test this updated framework. The results of these case studies support the conclusion that the process outlined by Wu et al. (2010) can be successfully applied to develop surrogate values for risk assessment. The read across results generated by the process were shown to be protective when compared to the actual toxicity data. Successful application of the approach requires significant expertise as well as discipline to not overstep the boundaries of the defined analogs and the rating system. The end result of this rigor can be the inability to read across all endpoints for all chemicals resulting in data gaps that cannot be filled using read across, however, this reflects the current state of the science and is preferable to making non-protective decisions. Future work will be targeted towards expanding read across capabilities. Two examples of a broader category approach are also shown.

Chemical Safety Assessment Using Read-Across: Assessing the Use of Novel Testing Methods to Strengthen the Evidence Base for Decision Making.

Background Safety assessment for repeated dose toxicity is one of the largest challenges in the process to replace animal testing. This is also one of the proof of concept ambitions of SEURAT-1, the largest ever European Union research initiative on alternative testing, co-funded by the European Commission and Cosmetics Europe. This review is based on the discussion and outcome of a workshop organized on initiative of the SEURAT-1 consortium joined by a group of international experts with complementary knowledge to further develop traditional read-across and include new approach data. Objectives The aim of the suggested strategy for chemical read-across is to show how a traditional read-across based on structural similarities between source and target substance can be strengthened with additional evidence from new approach data—for example, information from in vitro molecular screening, “-omics” assays and computational models—to reach regulatory acceptance. Methods We identified four read-across scenarios that cover typical human health assessment situations. For each such decision context, we suggested several chemical groups as examples to prove when read-across between group members is possible, considering both chemical and biological similarities. Conclusions We agreed to carry out the complete read-across exercise for at least one chemical category per read-across scenario in the context of SEURAT-1, and the results of this exercise will be completed and presented by the end of the research initiative in December 2015. Citation Berggren E, Amcoff P, Benigni R, Blackburn K, Carney E, Cronin M, Deluyker H, Gautier F, Judson RS, Kass GE, Keller D, Knight D, Lilienblum W, Mahony C, Rusyn I, Schultz T, Schwarz M, Schüürmann G, White A, Burton J, Lostia AM, Munn S, Worth A. 2015. Chemical safety assessment using read-across: assessing the use of novel testing methods to strengthen the evidence base for decision making. Environ Health Perspect 123:1232–1240; http://dx.doi.org/10.1289/ehp.1409342

Quantitative risk assessment for the induction of allergic contact dermatitis: uncertainty factors for mucosal exposures

The quantitative risk assessment (QRA) paradigm has been extended to evaluating the risk of induction of allergic contact dermatitis from consumer products. Sensitization QRA compares product‐related, topical exposures to a safe benchmark, the sensitization reference dose. The latter is based on an experimentally or clinically determined ‘no observable adverse effect level’ (NOAEL) and further refined by incorporating ‘sensitization uncertainty factors’ (SUFs) that address variables not adequately reflected in the data from which the threshold NOAEL was derived. A critical area of uncertainty for the risk assessment of oral care or feminine hygiene products is the extrapolation from skin to mucosal exposures. Most sensitization data are derived from skin contact, but the permeability of vulvovaginal and oral mucosae is greater than that of keratinized skin. Consequently, the QRA for some personal products that are exposed to mucosal tissue may require the use of more conservative SUFs. This article reviews the scientific basis for SUFs applied to topical exposure to vulvovaginal and oral mucosae. We propose a 20‐fold range in the default uncertainty factor used in the contact sensitization QRA when extrapolating from data derived from the skin to situations involving exposure to non‐keratinized mucosal tissue.

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.

Structure activity relationship (SAR) toxicological assessments: The role of expert judgment

ABSTRACT Structure activity relationships (SAR) and read‐across are widely used animal alternative approaches for filling toxicological data gaps. A framework describing the use of expert judgment in evaluating analogs for SAR has been published and widely cited, however, reliance on expert judgment can introduce inconsistent results across experts and hinder transparency. Here we explore the use of a quantitative similarity score between an analog and a Structure of Interest (SOI) to see if these scores correlate with the expert judgement‐based suitability rankings. We find these global similarity scores representing a “whole‐molecule” view of similarity to be insensitive to differences in local structure which may be important for toxicity, and, therefore, cannot be substituted for expert judgement‐based similarity rankings. In this paper, we suggest that the next step in the progression of SAR approaches retains the insights from expert judgment, but facilitates consistency and transparency through the development of rating “rules”. This report outlines and defines analog rating rules for several compound categories. While not comprehensive, the exercises demonstrate the development of rules for categories with a large spread in molecular weight and alkyl chain length and explains the advantages that we see in this approach compared to relying solely on a computational approach or an unstructured expert judgement approach. These rules may be incorporated into analog searching work flows to define boundaries for analogs “suitable” for read‐across. HighlightsExpert judgment in analog selection for read‐across can introduce inconsistent results across experts and hinder transparency.Quantitative similarity scores do not correlate with expert judgement‐based similarity rankings.Rating “rules” can be developed to standardize expert‐judgment based analog rankings.

Use of Read-Across and Computer-Based Predictive Analysis for the Safety Assessment of PEG Cocamines

In the European Union animal testing has been eliminated for cosmetic ingredients while the US Cosmetic Ingredient Review Expert Panel may request data from animal studies. The use of read-across and predictive toxicology provides a path for filling data gaps without additional animal testing. The PEG cocamines are tertiary amines with an alkyl group derived from coconut fatty acids and two PEG chains of varying length. Toxicology data gaps for the PEG cocamines can be addressed by read-across based on structure-activity relationship using the framework described by Wu et al. (2010) for identifying suitable structural analogs. Data for structural analogs supports the conclusion that the PEG cocamines are non-genotoxic and not expected to exhibit systemic or developmental/reproductive toxicity with use in cosmetics. Due to lack of reliable dermal sensitization data for suitable analogs, this endpoint was addressed using predictive software (TIMES SS) as a first step (Laboratory of Mathematical Chemistry). The prediction for PEG cocamines was the same as that for PEGs, which have been concluded to not present a significant concern for dermal sensitization. This evaluation for PEG cocamines demonstrates the utility of read-across and predictive toxicology tools to assess the safety of cosmetic ingredients.