Use of category approaches, read-across and (Q)SAR: general considerations.

Regulation for nanomaterials is urgently needed, and the drive to adopt an intelligent testing strategy is evident. Such a strategy will not only provide economic benefits but will also reduce moral and ethical concerns arising from animal testing. For regulatory purposes, such an approach is promoted by REACH, particularly the use of quantitative structure-activity relationships [(Q)SAR] as a tool for the categorisation of compounds according to their physicochemical and toxicological properties. In addition to compounds, (Q)SAR has also been applied to nanomaterials in the form of nano(Q)SAR. Although (Q)SAR in chemicals is well established, nano(Q)SAR is still in early stages of development and its successful uptake is far from reality. This article aims to identify some of the pitfalls and challenges associated with nano-(Q)SARs in relation to the categorisation of nanomaterials. Our findings show clear gaps in the research framework that must be addressed if we are to have reliable predictions from such models. Three major barriers were identified: the need to improve quality of experimental data in which the models are developed from, the need to have practical guidelines for the development of the nano(Q)SAR models and the need to standardise and harmonise activities for the purpose of regulation. Of these three, the first, i.e. the need to improve data quality requires immediate attention, as it underpins activities associated with the latter two. It should be noted that the usefulness of data in the context of nano-(Q)SAR modelling is not only about the quantity of data but also about the quality, consistency and accessibility of those data.

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Nano(Q)SAR: Challenges, pitfalls and perspectives.

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 stra...

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

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.

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

https://hal-ineris.archives-ouvertes.fr/ineris-01862412/document

Prediction of the thermal decomposition of organic peroxides by validated QSPR models.

Legislation such as REACH strongly advocates the use of alternative approaches including in vitro, (Q)SARs, and chemical categories as a means to satisfy the information requirements for risk assessment. One of the most promising alternative approaches is that of chemical categories, where the underlying hypothesis is that the compounds within the category are similar and therefore should have similar biological activities. The challenge lies in characterizing the chemicals, understanding the mode/mechanism of action for the activity of interest and deriving a way of relating these together to form inferences about the likely activity outcomes. (Q)SARs are underpinned by the same hypothesis but are packaged in a more formalized manner. Since the publication of the White Paper for REACH, there have been a number of efforts aimed at developing tools, approaches and techniques for (Q)SARs and read-across for regulatory purposes. While technical guidance is available, there still remains little practical guid...

C.A. Bellin

Papers

Non-testing approaches under REACH--help or hindrance? Perspectives from a practitioner within industry.