Category analysis of the substituted anilines studied in a 28-day repeat-dose toxicity test conducted on rats: correlation between toxicity and chemical structure

In Silico Prediction of Organ Level Toxicity: Linking Chemistry to Adverse Effects

In silico methods to predict toxicity include the use of (Quantitative) Structure-Activity Relationships ((Q)SARs) as well as grouping (category formation) allowing for read-across. A challenging area for in silico modelling is the prediction of chronic toxicity and the No Observed (Adverse) Effect Level (NO(A)EL) in particular. A proposed solution to the prediction of chronic toxicity is to consider organ level effects, as opposed to modelling the NO(A)EL itself. This review has focussed on the use of structural alerts to identify potential liver toxicants. In silico profilers, or groups of structural alerts, have been developed based on mechanisms of action and informed by current knowledge of Adverse Outcome Pathways. These profilers are robust and can be coded computationally to allow for prediction. However, they do not cover all mechanisms or modes of liver toxicity and recommendations for the improvement of these approaches are given.

Hazard Evaluation Support System (HESS) for predicting repeated dose toxicity using toxicological categories

Repeated dose toxicity (RDT) is one of the most important hazard endpoints in the risk assessment of chemicals. However, due to the complexity of the endpoints associated with whole body assessment, it is difficult to build up a mechanistically transparent structure-activity model. The category approach, based on mechanism information, is considered to be an effective approach for data gap filling for RDT by read-across. Therefore, a library of toxicological categories was developed using experimental RDT data for 500 chemicals and mechanistic knowledge of the effects of these chemicals on different organs. As a result, 33 categories were defined for 14 types of toxicity, such as hepatotoxicity, hemolytic anemia, etc. This category library was then incorporated in the Hazard Evaluation Support System (HESS) integrated computational platform to provide mechanistically reasonable predictions of RDT values for untested chemicals. This article describes the establishment of a category library and the associated HESS functions used to facilitate the mechanistically reasonable grouping of chemicals and their subsequent read-across.

Categorization of nitrobenzenes for repeated dose toxicity based on adverse outcome pathways

Adoption of the data-gap filling method for complex endpoints such as repeated dose toxicity (RDT) and reproductive/developmental toxicity is one of the most important issues affecting international chemical management at present. A categorization method based on adverse outcome pathways (AOPs) has recently been investigated for such complex endpoints. In this paper, we report results of the categorization of nitrobenzenes for RDT based on the AOPs obtained by analysing the detailed RDT test reports for 24 different nitrobenzenes already evaluated. In most RDT testing of nitrobenzenes without hydroxyl groups or acid groups, findings related to haemolytic anaemia and liver effects were observed at low dosages. It was, therefore, possible to assume common AOPs for haemolytic anaemia and liver effects induced by these nitrobenzenes. As a result, a group of nitrobenzenes was defined as a single category for both haemolytic anaemia and liver effects, respectively, based on these AOPs.

In silico models for drug-induced liver injury--current status

INTRODUCTION

Drug-induced liver injury (DILI) is one of the most important reasons for drug attrition at both pre-approval and post-approval stages. Therefore, it is crucial to develop methods that will detect potential hepatotoxicity among drug candidates as early and quickly as possible. However, the complexity of hepatotoxicity endpoint makes it very difficult to predict. In addition, there is still a lack of sensitive and specific biomarkers for DILI that consequently leads to a scarcity of reliable hepatotoxic data, which are the key to any modelling approach.

AREAS COVERED

This review explores the current status of existing in silico models predicting hepatotoxicity. Over the past decade, attempts have been made to compile hepatotoxicity data and develop in silico models, which can be used as a first-line screening of drug candidates for further testing.

EXPERT OPINION

Most of the predictive methods discussed in this review are based on the structural properties of chemicals and do not take into account genetic and environmental factors; therefore, their predictions are still uncertain. To improve the predictability of in silico models for DILI, it is essential to better understand its mechanisms as well as to develop sensitive toxicogenomics biomarkers, which show relatively good differentiation between hepatotoxins and non-hepatotoxins.

Thesaurus for histopathological findings in publically available reports of repeated-dose oral toxicity studies in rats for 156 chemicals

Because histopathological findings are often conclusive indicators of the toxicities of chemicals, standardization of nomenclature and construction of a thesaurus for histopathological findings are important for the comparative evaluation of histopathological data from repeated-dose toxicity studies (RTS). However, terms for histopathological findings have not been standardized and different technical terms are used to indicate almost the same thing in RTS. The present study was conducted to construct an easy-to-use thesaurus for histopathological findings in order to facilitate hazard assessments of untested chemicals by the category approach using knowledge of the toxicity of analogue chemicals. We used reports of 28-day RTS, conducted on rats by gavage, which were posted on the websites of the National Institute of Health Sciences (NIHS) and the National Institute of Technology and Evaluation (NITE). The histopathological data were from 156 reports on RTS conducted by 13 institutions in Japan. As a result of this study, major parts of the thesaurus were devoted to the findings in the liver, kidney, stomach, adrenal, thyroid and testis; the first three organs are known to be the main targets of chemicals. We also decided that findings such as swelling and enlargement of hepatocytes should be categorized as synonyms for terms meaning hypertrophy. Our thesaurus will be helpful in assessing or screening new untested chemicals by the category approach using knowledge of the toxicities of analogues of the new chemical. The RTS database with this thesaurus will be made publically available in 2012.

The use of Mechanisms and Modes of Toxic Action in Integrated Testing Strategies: The Report and Recommendations of a Workshop held as part of the European Union OSIRIS Integrated Project

This report on The Potential of Mode of Action (MoA) Information Derived from Non-testing and Screening Methodologies to Support Informed Hazard Assessment, resulted from a workshop organised within OSIRIS (Optimised Strategies for Risk Assessment of Industrial Chemicals through Integration of Non-test and Test Information), a project partly funded by the EU Commission within the Sixth Framework Programme. The workshop was held in Liverpool, UK, on 30 October 2008, with 35 attendees. The goal of the OSIRIS project is to develop integrated testing strategies (ITS) fit for use in the REACH system, that would enable a significant increase in the use of non-testing information for regulatory decision making, and thus minimise the need for animal testing. One way to improve the evaluation of chemicals may be through categorisation by way of mechanisms or modes of toxic action. Defining such groups can enhance read-across possibilities and priority settings for certain toxic modes or chemical structures responsible for these toxic modes. Overall, this may result in a reduction of in vivo testing on organisms, through combining available data on mode of action and a focus on the potentially most-toxic groups. In this report, the possibilities of a mechanistic approach to assist in and guide ITS are explored, and the differences between human health and environmental areas are summarised.