Development of a molecular-level skin condition diagnostic for precision medicine

项目总结 美国皮肤病学会报告称，四分之一的美国人(约8450万人)受到皮肤的影响 疾病。皮肤病是全球第四大致残原因，严重影响生活质量， 每年的治疗费用约为750亿美元。特应性皮炎(AD)等皮肤病通常由 使用临床病史和体检特征的从业者；然而，由于对 复杂皮肤损伤背后的不同病理生理机制，疾病治疗仍然遵循 “一刀切”的模式。这种缺乏基于证据的个性化或精确医学导致了糟糕的 治疗结果和患者的挫折感。这项提议的中心目标是发展一种 分子级别的皮肤评估平台，将允许基于证据的皮肤状况诊断以及 提供有关疾病状态的病理生理机制的补充信息以帮助 医生在选择治疗和监测治疗进展方面的作用。最终产品皮肤评估 平台包括：1)标准化样本收集工具包，允许轻松、非侵入性地收集 通过磁带剥离患者角质层的材料，以及2)用于阐明生物标记物数据的管道 由液质联用(LC-MS/MS)分析和大数据人工智能组成 方法(即，深度神经网络等)。考试可以通过邮寄并在家中完成， 允许将该技术用于远程皮肤病护理，并扩大对群体的访问 从历史上看，服务不足。第一阶段的成功完成将为使用皮肤生物标志物提供原则证明 用于预测在家中收集的样本中的特应性皮炎。在目标1中，我们将验证我们收集的样本 验证居家样本收集的稳健性的过程。在一项对25个人的研究中，我们将评估 从未经培训(在家)样本采集与经过培训(办公室内)样本采集获得的数据质量 通过评估这些样品之间检测到的蛋白质含量和化合物的相似性。在目标2中， 我们将在75名健康人(对照组)和75名个体(患者)的研究中确定AD的预测生物标记物 被诊断为阿尔茨海默病。特征选择和机器学习预测分析将用于确定较小的 与AD相关的分子生物标志物，成功将被衡量为90%的预测能力(以下面积 在隔离的交叉验证数据集上设置的生物标记物的曲线(AuC)≥为0.90。这些研究将证明 通过确定诊断、监控和预测来验证概念并证明产品的可行性 与阿尔茨海默病和阿尔茨海默病治疗相关的皮肤生物标志物，为家庭样本提供关键的分析验证 由用户收集试剂盒，并增加以临床验证为重点的未来第二阶段计划的成功 将已确定的生物标志物用于AD的治疗预测和疗效监测。这项技术将 通过提供准确的诊断和分子水平的信息来指导皮肤科护理，从而带来革命性的变化 通过精准医学提供治疗建议和监测。

PROJECT SUMMARY The American Academy of Dermatology reports that 1 in 4 Americans (~84.5 million) are impacted by skin disease. Skin disease is the fourth leading cause of disability worldwide, significantly impacts quality of life, and costs ~$75 billion annually to treat. Skin conditions like atopic dermatitis (AD) are commonly diagnosed by practitioners using clinical history and physical exam features; however, because of limited understanding of the diverse pathophysiological mechanisms that underlie complex skin lesions, disease management still follows a ‘one-size-fits-all’ paradigm. This lack of evidence-based personalization or precision medicine leads to poor treatment outcomes and patient frustration. The central objective of this proposal is the development of a molecular-level skin assessment platform that will allow evidence-based diagnosis of skin conditions as well as the delivery of supplementary information on the pathophysiological mechanisms of the disease state to aid practitioners in choosing treatments and monitoring treatment progress. The final product skin assessment platform includes: 1) a standardized sample collection kit which allows for easy, non-invasive collection of material from a patient’s stratum corneum via tape-stripping, and 2) a pipeline to elucidate biomarker data consisting of liquid chromatography-mass spectrometry (LC-MS/MS) analysis and big data artificial intelligence approaches (i.e., deep neural networks, etc.). The test can be shipped through the mail and completed at home, allowing for the technology to be used for remote dermatological care and expanding access to groups historically underserved. Successful completion of Phase I will provide proof-of-principle of using skin biomarkers for prediction of atopic dermatitis in samples collected at-home. In Aim 1, we will validate our sample collection process to verity the robustness of at-home sample collection. In a study of 25 individuals, we will assess the quality of data obtained from untrained (at-home) sample collection versus trained (in-office) sample collection through assessing the protein content and similarity of compounds detected between these samples. In Aim 2, we will identify predictive biomarkers of AD in a study of 75 healthy (control) and 75 individuals (patients) diagnosed with AD. Feature selection and machine learning prediction analysis will be used to determine small molecule biomarkers associated with AD, and success will be measured as 90% predictive ability (area under curve (AUC) ≥ 0.90) of the biomarker set on an isolated cross validation dataset. These studies will demonstrate proof of concept and prove product feasibility through the identification of diagnostic, monitoring and predictive skin biomarkers associated with AD and AD therapy, provide critical analytical validation of the at-home sample collection kit by users, and increase the success of a future Phase II program focused on the clinical validation for the use of identified biomarkers for treatment predictions and efficacy monitoring in AD. This technology will revolutionize dermatological care by providing accurate diagnoses and molecular-level information to guide treatment recommendations and monitoring through precision medicine.