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

开发用于精准医学的分子级皮肤状况诊断

• 批准号: 10600694
• 负责人: Stacy D. Sherrod
• 金额: $ 27.58万
• 依托单位: FINALLY, LLC
• 依托单位国家: 美国
• 财政年份: 2022
• 资助国家: 美国
• 起止时间: 2022-09-20 至 2024-08-31
• 项目状态: 已结题
• 来源: https://reporter.nih.gov/project-details/10600694
• 关键词: Academy; Acne; Address; Affect; American; Area; Area Under Curve; Artificial Intelligence; Atopic Dermatitis; Big Data; Biocompatible Materials; Biological Assay; Biological Markers; Biology; Caring; Chronic; Clinical; Collection; Complementary Health; Complex; Data; Data Set; Dermatologic; Dermatologist; Dermatology; Development; Diagnosis; Diagnostic; Diagnostic Reagent Kits; Disease; Disease Management; Economic Burden; Ensure; Fish Oils; Frustration; Future; Health Care Costs; Heterogeneity; Hippophae; Home; Human; Individual; Innovation Corps; Instruction; Interview; Lesion; Measures; Methods; Modeling; Molecular; Monitor; Nature; Oils; Patients; Pattern; Phase; Physicians; Prediction of Response to Therapy; Process; Proteins; Quality of life; Recommendation; Recording of previous events; Reporting; Sampling; Services; Ships; Skin; Standardization; Stratum corneum; Technology; Testing; Training; Treatment Efficacy; Treatment outcome; Universities; Validation; Visual; Vitamin E; accurate diagnosis; base; clinical diagnostics; conventional therapy; cost; deep neural network; diagnostic biomarker; diagnostic technologies; diagnostic tool; disability; evidence base; feature selection; flexibility; home test; improved; individual patient; innovation; large scale data; liquid chromatography mass spectrometry; machine learning algorithm; machine learning prediction; people of color; personalized diagnostics; personalized medicine; precision medicine; predicting response; predictive marker; prevent; programs; recruit; sample collection; service delivery; skin disorder; skin lesion; small molecule; success; telehealth; treatment planning; treatment response

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.

项目摘要 美国皮肤科学会报告说，四分之一的美国人（约8450万）受到皮肤的影响 疾病。皮肤病是全球疾病的第四个主要原因，会对生活质量产生重大影响，并且 每年花费约750亿美元来治疗。皮肤病特应特应性皮炎（AD）通常由 使用临床病史和体格检查特征的从业者；但是，由于对 基于复杂皮肤病变的各种病理生理机制，疾病管理仍然遵循 “一定大小”的范式。缺乏基于证据的个性化或精确药物导致差 治疗结果和患者挫败感。该提议的核心目标是发展 分子水平的皮肤评估平台将允许循证诊断皮肤状况以及 提供有关疾病状态的病理生理机制的补充信息以帮助 从业者选择治疗和监测治疗进展。最终产品皮肤评估 平台包括：1）标准化样品收集套件，可轻松，无创收集 通过胶带绑带中的患者角膜层中的材料，以及2）阐明生​​物标志物数据的管道 由液相色谱 - 质谱法（LC-MS/MS）分析和大数据人工智能组成 方法（即深神经网络等）。测试可以通过邮件运送并在家完成， 允许该技术用于远程皮肤科护理并扩大对小组的访问 历史上服务不足。成功完成第一阶段将提供使用皮肤生物标志物的原则证明 用于预测在家中收集的样品中的特征性皮炎。在AIM 1中，我们将验证我们的样本收集 旨在维修房屋样品收集的鲁棒性。在对25个人的研究中，我们将评估 从未经培训（在家）样品收集与经过训练（办公室内）样品收集的数据质量 通过评估这些样品之间检测到的化合物的蛋白质含量和相似性。在AIM 2中， 我们将在一项针对75个健康（对照）和75个人（患者）的研究中确定AD的预测性生物标志物 被诊断为AD。特征选择和机器学习预测分析将用于确定小 与AD相关的分子生物标志物，成功将以90％的预测能力（面积下 在隔离的交叉验证数据集上设置的生物标志物的曲线（AUC）≥0.90。这些研究将证明 概念证明并通过识别诊断，监测和预测来证明产品可行性 与AD和AD疗法相关的皮肤生物标志物，对房屋样品进行了关键的分析验证 用户收集套件，并增加了未来II期计划的成功，该计划的重点是临床验证 用于使用已识别的生物标志物进行AD中的治疗预测和有效性监测。这项技术将 通过提供准确的诊断和分子级信息来指导皮肤病学护理 治疗建议和通过精确医学进行监测。