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硅工具的及时需求，用于预测商业化学品的皮肤致敏效力。目前，还没有可靠和广泛适用的方法来替代昂贵和道德上不可行的皮肤致敏试验的体内筛选方法。皮肤致敏反应是由外源生物对关键生化靶点的共价修饰引起的。这些靶点已被确定为谷胱甘肽、含赖氨酸肽和Keap1蛋白的表面半胱氨酸残基。拟议研究的目标是(i)建立代表异种生物和生化目标之间共价相互作用的量子力学模型，以及（ii）结合皮肤生物利用度和潜在代谢物鉴定的支持模型，以组成皮肤致敏效力的综合模型。首先，计算皮肤致敏剂与生化靶标结合的反应能量。接下来，将开发基于属性的线性模型来解释皮肤的生物利用度。潜在的代谢物将使用现有的验证模型进行鉴定。预计最终的硅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性质为更安全的替代化学品的设计提供信息。该项目的更广泛的影响/商业潜力是为化妆品、消费化学品、药品、纺织和石油工业提供一种方法，以确定其产品线中引起人体皮肤过敏反应的化学物质，并用更安全的替代品取代它们。这个项目的成功完成也将给科学界带来不可估量的好处。应用计算化学家工具箱中的技术来建立预测毒理学模型，从而为设计更安全的化学品提供信息，这将是一个先例。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没有直接竞争对手的市场上处于独特的地位。目前的计算机毒性预测方法主要由统计模型主导，这些模型仅依赖于结构描述符和/或性质，因此受到有限的和经常不确定的适用领域的影响。提出的研究通过包括从分子相互作用的直接建模中获得的化学反应性数据来减轻这些缺陷。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.