A Minireview of Available Skin Sensitization (Q)SARs/Expert Systems

Abstract

This minireview describes the state-of-the-art of available (Q)SARs/expert systems for skin sensitization and evaluates their utility for potential regulatory use. It is one of a series of minireviews in this journal and is based on an in-depth review performed by the European Chemicals Bureau (ECB) of the European Commissions Joint Research Centre (JRC) in support of the development of technical guidance for the implementation of the Registration, Evaluation, and Authorization of CHemicals (REACH) legislation. Most existing 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 global QSARs have been recently characterized in accordance with the OECD validation principles and shown to be of limited use for regulatory applications. These have been evaluated in more detail elsewhere. Expert systems capable of predicting skin sensitization are discussed briefly. The strong mechanistic understanding of skin sensitization has facilitated the development of relative alkylation index (RAI) models. 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 regulatory context. These are now referred to as Quantitative Mechanistic Models (QMM). Whilst hydrophobicity is conveniently modelled using log P, reactivity is often not easily determined from chemical structure. Initiatives are in progress to generate the necessary reactivity data for reactions relevant to skin sensitization but more resources are still required. A strategy of how information from using the RAI approach can be used in the evaluation of skin sensitization potential is described. This is the subject of on-going and future work.

Fragment-based prediction of skin sensitization using recursive partitioning

“…DPSA-3 and WPSA-3 encode the features responsible for polar interactions between sensitizer and [37] phtalic anhydride [16], oxazolone and alkyl azlactones, which can be regarded as aza-analogues of cyclic anhydrides [12]. X=halogen or other leaving group [7] 2-bromotetradecanoic acid [12], 1-bromohexadecane [12], 1-bromotridecane [12] c Although the reactivity of these compounds toward DNA and proteins is not doubtful, the underlying mechanistic chemistry is quite complex [38]. In this table, only one possibility was listed for protein binding protein, which indicate that non-covalent interactions are also important for skin sensitization.…”

“…SAR approaches are useful for toxicity evaluation because they can not only be used for toxicity prediction, but also provide insights into toxicity mechanisms [7,8]. However, there are two main factors that may cause difficulty in the models' interpretation.…”

Perspectives on Non-Animal Alternatives for Assessing Sensitization Potential in Allergic Contact Dermatitis

“…37,55,59,120,160 Various companies have also been developed that provide QSAR based models as listed in Table 2.…”

Machine learning of chemical reactivity from databases of organic reactions

“…Prediction of reactivity is needed in pharmaceutical R&D innovation processes, or for the prioritization of experimental tests in risk assessment of chemicals, namely in relation with the EU REACH [4] legislation. Furthermore, the legislative trend for the abolition of animal testing of cosmetic products [5] is demanding alternative evaluation procedures [6,11]. For the assessment of skin sensitization, in vitro reactivity tests, [7][8][9][10] as well as QSARs have been proposed.…”