Enoch Steven James, Cronin Mark Timothy David, Schultz Terry Wayne, Madden Judith Clare
School of Pharmacy and Chemistry, Liverpool John Moores University, Byrom Street, Liverpool, L3 3AF, England.
Chem Res Toxicol. 2008 Feb;21(2):513-20. doi: 10.1021/tx700322g. Epub 2008 Jan 12.
Read across is a powerful tool to predict toxicity from structure: It relies on "obvious" chemical similarities to allow for interpolation of activity. This study has extended the read across concept within a known mechanism of action to be quantitative. The chemicals that have been chosen are skin sensitizers and are considered to elicit this response by direct interaction through a direct-acting Michael type addition electrophilic mechanism of action. The Michael addition domain is well-defined for skin sensitizers; however, developing quantitative models for predicting potency within the domain has proven to be difficult. This study highlights the ability of an electrophilicity index (omega) to be used as a measure of similarity for sensitizing chemicals acting through the Michael addition mechanism. The index is shown to offer a chemically interpretable qualitative ranking of the chemicals within the Michael acceptor domain, enabling potentially nonsensitizing and extremely sensitizing chemicals to be easily identified. This study also demonstrates the utility of omega to make predictions of skin sensitization using a mechanism-based read across model. Predictions were made for 19 chemicals within the Michael acceptor domain, with the majority being in good agreement with the experimentally determined values. The mechanism-based read across predictions are in keeping with the OECD principles of transparency and simplicity for quantitative structure-activity relationships and are likely to be of significant benefit to regulators and risk assessors.
它依靠“明显的”化学相似性来实现活性的内插。本研究在已知作用机制范围内扩展了类推法概念，使其具有定量性。所选择的化学物质是皮肤致敏剂，被认为是通过直接作用的迈克尔型加成亲电作用机制进行直接相互作用来引发这种反应的。对于皮肤致敏剂，迈克尔加成域已得到明确界定；然而，事实证明，在该域内开发用于预测效力的定量模型具有难度。本研究强调了亲电性指数（ω）用作通过迈克尔加成机制起作用的致敏化学物质相似性度量的能力。该指数能够对迈克尔受体域内的化学物质进行具有化学可解释性的定性排序，从而能够轻松识别潜在的非致敏和极致敏化学物质。本研究还展示了ω在使用基于机制的类推模型预测皮肤致敏方面的效用。对迈克尔受体域内的19种化学物质进行了预测，大多数预测结果与实验测定值高度吻合。基于机制的类推预测符合经合组织关于定量构效关系的透明度和简单性原则，可能会对监管机构和风险评估人员有极大帮助。
