SBIR Phase I: Quantum Mechanical Predictive Tools for Identification and Redesign of Skin Sensitizing Chemicals

这个小型企业创新研究（SBIR）第一阶段项目解决了开发新的和强大的计算机工具的及时需要，用于预测商业化学品的皮肤致敏效力。 目前，对于昂贵且伦理上不可行的皮肤致敏试验体内筛选方法，没有可靠且广泛适用的替代方法。皮肤致敏反应是由异生物质共价修饰关键生化靶点引发的。这些目标已被确定为谷胱甘肽，含赖氨酸的肽和Keap 1蛋白的表面半胱氨酸残基。拟议研究的目标是（i）建立代表异生物质和生化靶标之间共价相互作用的量子力学模型，以及（ii）纳入皮肤生物利用度和潜在代谢物鉴定的支持模型，以构成皮肤致敏效力的综合模型。首先，将计算与皮肤致敏剂与生化靶标结合相关的反应能量学。接下来，将开发基于属性的线性模型来解释皮肤生物利用度。将使用现有经验证的模型鉴别潜在代谢物。预计最终的计算机模拟工具将根据化学结构预测皮肤致敏效力，并根据化学反应性和物理特性设计更安全的替代化学品。该项目的更广泛影响/商业潜力是为化妆品、消费品、药品、纺织和石油工业，具有识别其产品线中诱导人类皮肤致敏反应的化学品的手段，替代品.该项目的成功完成也将为科学界带来不可估量的利益。将为应用计算化学家工具箱中的技术建立能够为更安全化学品的设计提供信息的预测毒理学模型开创先例。这种计算毒理学和计算化学的新型协同方法使我们的计算机工具在市场上处于独特的地位，没有直接的竞争对手。目前的毒性预测方法在硅片上占主导地位的统计模型，仅依赖于结构描述符和/或属性，并随后遭受有限的，往往是不确定的适用性领域。拟议的研究通过包括从分子相互作用的直接建模获得的化学反应性数据来弥补这些不足。拟议研究的社会影响在于向消费者提供更安全的产品，并减少对体内筛选方法所需的动物试验的依赖。

This Small Business Innovation Research (SBIR) Phase I project addresses the timely need to develop new and robust in silico tools for the prediction of skin sensitization potency of commercial chemicals. Currently, there are no reliable and broadly applicable alternatives to the costly and ethically unfeasible in vivo screening methods for skin sensitization testing. Skin sensitization response is triggered by covalent modification of key biochemical targets by the xenobiotic. These targets have been identified as glutathione, lysine-containing peptides and surface cysteine residues of the Keap1 protein. The objectives of the proposed research are to (i) build quantum-mechanical models representative of covalent interactions between the xenobiotic and biochemical targets, and (ii) incorporate supporting models for skin bioavailability and identification of potential metabolites to compose a comprehensive model of skin sensitization potency. First, reaction energetics associated with binding of skin sensitizers to the biochemical targets will be calculated. Next, property-based linear models will be developed to account for skin bioavailability. Potential metabolites will be identified using existing validated models. The final in silico tool is anticipated to predict skin sensitization potency based on a chemical structure and inform design of safer alternative chemicals based on chemical reactivity and physical properties.The broader impact/commercial potential of this project is to provide the cosmetics, consumer chemicals, pharmaceuticals, textile and petroleum industries with a means of identifying chemicals in their product lines that induce human skin sensitization response and replacing them with safer alternatives. Successful completion of this project will also bring immeasurable benefits to the scientific community. A precedent will be set for applying techniques from the computational chemist's toolbox for building predictive toxicology models capable of informing the design of safer chemicals. This novel synergistic approach to computational toxicology and computational chemistry places our in silico tool in a unique position in the market with no direct competitors. Current approaches for toxicity predictions in silico are dominated by statistical models that rely solely on structural descriptors and/or properties, and subsequently suffer from limited and often uncertain applicability domains. The proposed research alleviates these deficiencies by including chemical reactivity data obtained from direct modeling of molecular interactions. The societal impact of the proposed research is in the safer product delivered to the consumer and in the reduced dependence on animal testing required by in vivo screening methods.