(Q)SARs are models that are 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 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), or they are based on expert judgment and take the form of SARs describing reactive chemistry (e.g. Derek for Windows) or they are 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 been largely limited to those models developed for environmental properties. Such models have proved useful in the prioritization of chemicals and the filling data gaps in risk assessment rather than as stand alone replacements for animal tests (Cronin, 2003). 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 characterised by numerical representations called descriptors and statistical techniques are then applied to derive an algorithm which 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 in vivo 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 show 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.
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 else 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 (Roberts, et al., 2007a; Patlewicz, 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 which can be applied in a risk assessment context. These are now referred to as Quantitative Mechanistic Models (QMM). A strategy of how information from using the RAI approach can be used in the evaluation of skin sensitization potential has been described in more detail elsewhere (Aptula & Roberts, 2006; Roberts, et al., 2007b; Patlewicz, et al., 2007b).
|
