Non-animal Methods for Toxicity Testing


Last Updated: April 12, 2013
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(Q)SARs are models aimed at predicting the physicochemical and biological properties of molecules. A structure-activity relationship (SAR) is a (qualitative) association between a chemical substructure and the potential of a chemical containing the substructure to exhibit a certain biological effect (e.g. a (eco)toxicological effect), whereas a (quantitative) structure-activity relationship ((Q)SAR) is a statistically established correlation relating a quantitative parameter(s) derived from chemical structure or determined by experimental chemistry to exhibit a quantitative measure of biological activity. Expert systems are built upon experimental toxicity results with rules derived from the data. The rules may be based on statistical inference and take the form of (Q)SARs (e.g. TOPKAT), they may be based on expert judgment and take the form of SARs describing reactive chemistry (e.g. Derek for Windows), or they may be a hybrid of the two (e.g. TIMES). The benefits of using (Q)SAR approaches include their relative low cost, speed, and potential to minimize animal testing.

The Organisation for Economic Co-operation and Development (OECD) has described a (Q)SAR as "a quantitative (mathematical) relationship between a numerical measure of chemical structure, and/or a physicochemical property, and an effect/activity [that] often take[s] the form of regression equations, and can make predictions of effects/activities that are either on a continuous scale or on a categorical scale.... In many cases, (Q)SARs are quantitative models of key mechanistic processes which result in the measured activity of the chemicals" (OECD, 2007).

Uptake of (Q)SARs has remained largely limited to those models developed for properties such as aquatic toxicity, physicochemical properties or environmental fate. Such (Q)SAR models have proved particularly useful in prioritizing chemicals. More recently under regulatory programs such as REACH, they have been used extensively to fill data gaps for hazard characterization. (Q)SARs for human health effects remain best used as part of weight of evidence assessment rather than as standalone replacements for animal tests (Patlewicz, et al., 2011). This background article will provide a brief introduction to (Q)SAR models developed for the assessment of human health toxicity endpoints. In addition, most of the Toxicity Endpoints & Tests sections of AltTox discuss endpoint-specific (Q)SAR applications.

In general, the process of (Q)SAR development may be described by a series of steps. A set of chemicals with corresponding biological activity data are collected. The chemicals are characterized by numerical representations called descriptors, and statistical techniques are typically then applied to derive an algorithm that relates the relevant chemical information to biological activity. Access to good quality data is obviously a critical requirement for (Q)SAR development. As noted by Schultz & Seward (2000), the development of useful (Q)SARs for ecotoxicity endpoints resulted from the availability of sufficient data for the construction and validation of the computational models. (Q)SAR models for large scale screening of chemicals and pharmaceuticals for mutagenic potential have also been developed, aided by the underlying microbial mutagenicity data (Contrera, et al., 2005).

However, when it comes to developing databases of human toxicity endpoints for (Q)SAR models, the amount and quality of the data needed for model building are often insufficient. Collecting additional whole animal toxicity data is not always feasible or practical. Mechanistic differences between the test system and the human species are an additional factor to consider. Human data would be most useful but is often not available. Efforts directed toward model building based on molecular toxicological endpoints are now being explored as a promising way of providing a sufficient amount of quantifiable and reliable data for developing human-predictive models. Predictive models based on (Q)SARs that use these types of validated surrogate endpoints will also have to take account of biokinetics and metabolism effects (Schultz & Seward, 2000).

Skin sensitization is one human health-effect endpoint where (Q)SAR models have shown promise. Gerberick, et al. (2005) compiled a database of quality in vivo mouse local lymph node assay (LLNA) data on 211 chemicals for the purpose of accelerating the development and validation of new skin sensitization approaches. This was followed up with a second compilation by some of the same authors (Kern et al., 2011).

Many of the existing (Q)SAR models fall into one of two main categories: either they are local in nature, usually specific to a chemical class or reaction chemical mechanism, or they are global in form, derived empirically using statistical methods. Some of these global (Q)SARs were recently characterized and shown to be of limited value in safety assessment (Patlewicz, et al., 2007a; Roberts, et al., 2007a).

The strong mechanistic understanding of skin sensitization has facilitated the development of Relative Alkylation Index (RAI) models (Roberts & Williams, 1982). The RAI approach, relying on reactivity and hydrophobicity as the key parameters, actually appears to be the most promising means of deriving robust and mechanistically interpretable models that can be applied in a risk assessment context. These are now referred to as Quantitative Mechanistic Models (QMM). A strategy of how information from the RAI approach can be used in the evaluation of skin sensitization potential has been described in more detail elsewhere (Aptula & Roberts, 2006; Patlewicz, et al., 2007b; Roberts, et al., 2007b).

With the evolution of the AOP framework, the nature of QSARs will likely change. Instead of relating chemical descriptors to an ultimate adverse outcome such as skin sensitization, it is feasible that next generation QSARs will likely be developed to characterize MIEs or other upstream key events. Indeed the construct of AOPs has already seen a shift in how expert systems undergo refinement. The hybrid expert system TIMES has used the AOP framework to develop new models to predict in vivo genotoxicity through an understanding of metabolism as well as the scope and performance of current in vitro genotoxicity assays (Mekenyan et al., 2012). TIMES has also exploited the concept of common MIEs to propose refinements in its genotoxicity models based on insights derived from skin sensitization data (Mekenyan et al., 2010).


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