Grace Patlewicz

A roadmap for the development of alternative (non-animal) methods for systemic toxicity testing - t4 report

Systemic toxicity testing forms the cornerstone for the safety evaluation of substances. Pressures to move from traditional animal models to novel technologies arise from various concerns, including: the need to evaluate large numbers of previously untested chemicals and new products (such as nanoparticles or cell therapies), the limited predictivity of traditional tests for human health effects, duration and costs of current approaches, and animal welfare considerations. The latter holds especially true in the context of the scheduled 2013 marketing ban on cosmetic ingredients tested for systemic toxicity. Based on a major analysis of the status of alternative methods (Adler et al., 2011) and its independent review (Hartung et al., 2011), the present report proposes a roadmap for how to overcome the acknowledged scientific gaps for the full replacement of systemic toxicity testing using animals. Five whitepapers were commissioned addressing toxicokinetics, skin sensitization, repeated-dose toxicity, carcinogenicity, and reproductive toxicity testing. An expert workshop of 35 participants from Europe and the US discussed and refined these whitepapers, which were subsequently compiled to form the present report. By prioritizing the many options to move the field forward, the expert group hopes to advance regulatory science.

Read-across approaches--misconceptions, promises and challenges ahead.

Read-across is a data gap filling technique used within category and analogue approaches. It has been utilized as an alternative approach to address information requirements under various past and present regulatory programs such as the OECD High Production Volume Programme as well as the EUs Registration, Evaluation, Authorisation and restriction of CHemicals (REACH) regulation. Although read-across raises a number of expectations, many misconceptions still remain around what it truly represents; how to address its associated justification in a robust and scientifically credible manner; what challenges/issues exist in terms of its application and acceptance; and what future efforts are needed to resolve them. In terms of future enhancements, read-across is likely to embrace more biologically-orientated approaches consistent with the Toxicity in the 21st Century vision (Tox-21c). This Food for Thought article, which is notably not a consensus report, aims to discuss a number of these aspects and, in doing so, to raise awareness of the ongoing efforts and activities to enhance read-across. It also intends to set the agenda for a CAAT read-across initiative in 2014-2015 to facilitate the proper use of this technique.

Current and Future Perspectives on the Development, Evaluation, and Application of in Silico Approaches for Predicting Toxicity

Exploiting non-testing approaches to predict toxicity early in the drug discovery development cycle is a helpful component in minimizing expensive drug failures due to toxicity being identified in late development or even during clinical trials. Changes in regulations in the industrial chemicals and cosmetics sectors in recent years have prompted a significant number of advances in the development, application, and assessment of non-testing approaches, such as (Q)SARs. Many efforts have also been undertaken to establish guiding principles for performing read-across within category and analogue approaches. This review offers a perspective, as taken from these sectors, of the current status of non-testing approaches, their evolution in light of the advances in high-throughput approaches and constructs such as adverse outcome pathways, and their potential relevance for drug discovery. It also proposes a workflow for how non-testing approaches could be practically integrated within testing and assessment strategies.

Compilation of historical local lymph node data for evaluation of skin sensitization alternative methods.

Background: Within the toxicology community, considerable effort is directed toward the development of alternative methods for skin sensitization testing. The availability of high‐quality, relevant, and reliable in vivo data regarding skin sensitization is essential for the effective evaluation of alternative methodologies. Ideally, data derived from humans would be the most appropriate source because the test methods are attempting to predict a toxicologic effect in humans. Unfortunately, insufficient human data of the necessary quality are available, so it is necessary to rely on the best available animal data. In recent years, the local lymph node assay (LLNA) has emerged as a practical option for assessing the skin sensitization potential of chemicals. In addition to accurately identifying skin sensitizers, the LLNA can also provide a reliable measure of relative sensitization potency, information that is pivotal to the successful management of human health risks. Objective: To provide a database of robust in vivo data to calibrate, evaluate, and eventually validate new approaches for skin sensitization testing. Methods: LLNA data derived from previously conducted studies were compiled from the published literature and unpublished sources. Results: We provide a database that comprises LLNA data on 211 individual chemicals. This extensive chemical data set encompasses both the chemical and biologic diversity of known chemical allergens. To cover the range of relative allergenic potencies, the data set includes data on 13 extreme, 21 strong, 69 moderate, and 66 weak contact allergens, classified according to each allergens mathematically estimated concentration of chemical required to induce a threefold stimulation index. In addition, there are also 42 chemicals that are considered to be nonsensitizers. In terms of chemical diversity, the database contains data pertaining to the chemical classes represented by aldehydes, ketones, aromatic amines, quinones, and acrylates, as well as compounds that have different reactivity mechanisms. In addition to two‐dimensional chemical structures, the physicochemical parameters included are log Kp, log KO/W, and molecular weight. Conclusions: The list of chemicals contained in the data set represents both the chemical and biologic diversity that is known to exist for chemical allergens and non‐allergens. It is anticipated that this database will help accelerate the development, evaluation, and eventual validation of new approaches to skin sensitization assessment.

A Stepwise Approach for Defining the Applicability Domain of SAR and QSAR Models

A stepwise approach for determining the model applicability domain is proposed. Four stages are applied to account for the diversity and complexity of the current SAR/QSAR models, reflecting their mechanistic rationality (including metabolic activation of chemicals) and transparency. General parametric requirements are imposed in the first stage, specifying in the domain only those chemicals that fall in the range of variation of the physicochemical properties of the chemicals in the training set. The second stage defines the structural similarity between chemicals that are correctly predicted by the model. The structural neighborhood of atom-centered fragments is used to determine this similarity. The third stage in defining the domain is based on a mechanistic understanding of the modeled phenomenon. Here, the model domain combines the reliability of specific reactive groups hypothesized to cause the effect and the domain of explanatory variables determining the parametric requirements in order for functional groups to elicit their reactivity. Finally, the reliability of simulated metabolism (metabolites, pathways, and maps) is taken into account in assessing the reliability of predictions, if metabolic activation of chemicals is a part of the (Q)SAR model. Some of the stages of the proposed approach for defining the model domain can be eliminated depending on the availability and quality of the experimental data used to derive the model, the specificity of (Q)SARs, and the goals of their ultimate application. The performance of the proposed definition of the model domain is tested using several examples of (Q)SARs that have been externally validated, including models for predicting acute toxicity, skin sensitization, and biodegradation. The results clearly showed that credibility in predictions of QSAR models for chemicals belonging to their domain is much higher than for chemicals outside this domain.

An evaluation of the implementation of the Cramer classification scheme in the Toxtree software

Risk assessment for most human health effects is based on the threshold of a toxicological effect, usually derived from animal experiments. The Threshold of Toxicological Concern (TTC) is a concept that refers to the establishment of a level of exposure for all chemicals below which there would be no appreciable risk to human health. When carefully applied, the TTC concept can provide a means of waiving testing based on knowledge of exposure limits. Two main approaches exist; the first of these is a General Threshold of Toxicological Concern; the second approach is a TTC in relation to structural information and/or toxicological data of chemicals. The structural scheme most routinely used is that of Cramer and co-workers from 1978. Recently this scheme was encoded into a software program called Toxtree, specifically commissioned by the European Chemicals Bureau (ECB). Here we evaluate two published datasets using Toxtree to demonstrate its concordance and highlight potential software modifications. The results were promising with an overall good concordance between the reported classifications and those generated by Toxtree. Further evaluation of these results highlighted a number of inconsistencies which were examined in turn and rationalised as far as possible. Improvements for Toxtree were proposed where appropriate. Notable of these is a necessity to update the lists of common food components and normal body constituents as these accounted for the majority of false classifications observed. Overall Toxtree was found to be a useful tool in facilitating the systematic evaluation of compounds through the Cramer scheme.

The Role of the European Chemicals Bureau in Promoting the Regulatory Use of (Q)SAR Methods

Under the proposed REACH (Registration, Evaluation and Authorisation of CHemicals) legislation, (Q)SAR models and grouping methods (chemical categories and read across approaches) are expected to play a significant role in prioritising industrial chemicals for further assessment, and for filling information gaps for the purposes of classification and labelling, risk assessment and the assessment of persistent, bioaccumulative and toxic (PBT) chemicals. The European Chemicals Bureau (ECB), which is part of the European Commissions Joint Research Centre (JRC), has a well-established role in providing independent scientific and technical advice to European policy makers. The ECB also promotes consensus and capacity building on scientific and technical matters among stakeholders in the Member State authorities and industry. To promote the availability and use of (Q)SARs and related estimation methods, the ECB is carrying out a range of activities, including applied research in computational toxicology, the assessment of (Q)SAR models and methods, the development of technical guidance documents and computational tools, and the organisation of training courses. This article provides an overview of ECB activities on computational toxicology, which are intended to promote the development, validation, acceptance and use of (Q)SARs and related estimation methods, both at the European and international levels. †Presented at the 12th International Workshop on Quantitative Structure-Activity Relationships in Environmental Toxicology (QSAR2006), 8–12 May 2006, Lyon, France.

A chemical dataset for evaluation of alternative approaches to skin-sensitization testing.

Allergic contact dermatitis resulting from skin sensitization is a common occupational and environmental health problem. In recent years, the local lymph node assay (LLNA) has emerged as a practical option for assessing the skin‐sensitization potential of chemicals. In addition to accurate identification of skin sensitizers, the LLNA can also provide a reliable measure of relative sensitization potency, information that is pivotal in successful management of human health risks. However, even with the significant animal welfare benefits provided by the LLNA, there is interest still in the development of non‐animal test methods for skin sensitization. Here, we provide a dataset of chemicals that have been tested in the LLNA and the activity of which correspond with what is known of their potential to cause skin sensitization in humans. It is anticipated that this will be of value to other investigators in the evaluation and calibration of novel approaches to skin‐sensitization testing. The materials that comprise this dataset encompass both the chemical and biological diversity of known chemical allergens and provide also examples of negative controls. It is hoped that this dataset will accelerate the development, evaluation and eventual validation of new approaches to skin‐sensitization testing.

Ranking of hair dye substances according to predicted sensitization potency: quantitative structure–activity relationships

Allergic contact dermatitis following the use of hair dyes is well known. Many chemicals are used in hair dyes and it is unlikely that all cases of hair dye allergy can be diagnosed by means of patch testing with p‐phenylenediamine (PPD). The objectives of this study are to identify all hair dye substances registered in Europe and to provide their tonnage data. The sensitization potential of each substance was then estimated by using a quantitative structure–activity relationship (QSAR) model and the substances were ranked according to their predicted potency. A cluster analysis was performed in order to help select a number of chemically diverse hair dye substances that could be used in subsequent clinical work. Various information sources, including the Inventory of Cosmetics Ingredients, new regulations on cosmetics, data on total use and ChemId (the Chemical Search Input website provided by the National Library of Medicine), were used in order to identify the names and structures of the hair dyes. A QSAR model, developed with the help of experimental local lymph node assay data and topological sub‐structural molecular descriptors (TOPS‐MODE), was used in order to predict the likely sensitization potential. Predictions for sensitization potential were made for the 229 substances that could be identified by means of a chemical structure, the majority of these hair dyes (75%) being predicted to be strong/moderate sensitizers. Only 22% were predicted to be weak sensitizers and 3% were predicted to be extremely weak or non‐sensitizing. Eight of the most widely used hair dye substances were predicted to be strong/moderate sensitizers, including PPD – which is the most commonly used hair dye allergy marker in patch testing. A cluster analysis by using TOPS‐MODE descriptors as inputs helped us group the hair dye substances according to their chemical similarity. This would facilitate the selection of potential substances for clinical patch testing. A patch‐test series with potent, frequently used, substances representing various chemical clusters is suggested. This may prove useful in diagnosing PPD‐negative patients with symptoms of hair dye allergy and would provide some clinical validation of the QSAR predictions.

Skin-sensitization structure-activity relationships for aldehydes

A selection of 17 aldehydes (13 sensitizing and 4 non‐sensitizing), all of which possessed a benzene ring, were evaluated using structure‐activity relationships (SARs). The sensitizing compounds were classified as strong, moderate or weak skin sensitizers on the basis of in vivo data. The aldehydes were grouped into 4 distinct subcategories of functionally related aldehydes that were termed aryl‐substituted aliphatic, aryl, aryl with special features (that can undergo metabolism) and α,β‐unsaturated aldehydes. It was observed that a structure‐activity relationship could be derived for a subset of aldehydes that could react via the same chemical mechanism. This further supports the view that applying knowledge on reaction mechanisms to develop SAR models can provide a more accurate means of investigating and predicting the sensitization potential of structurally and functionally related chemicals.