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.

项目概要 美国皮肤病学会报告称，四分之一的美国人（约 8450 万）受到皮肤病的影响 疾病。皮肤病是全球第四大残疾原因，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) 等皮肤病通常通过以下方式诊断： 执业医师使用临床病史和体检特征；但由于了解有限 复杂皮肤病变的病理生理机制多种多样，疾病管理仍然遵循 “一刀切”范式。缺乏基于证据的个性化或精准医学导致了不良的结果 治疗结果和患者的挫败感。该提案的中心目标是开发一个 分子级皮肤评估平台，可对皮肤状况进行循证诊断 提供有关疾病状态病理生理机制的补充信息以提供帮助 医生选择治疗方法和监测治疗进展。最终产品肤质评估 平台包括：1) 标准化样本采集套件，可轻松、非侵入性地采集样本 通过胶带剥离从患者角质层获取材料，以及 2) 阐明生物标志物数据的管道 由液相色谱-质谱（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）。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群体的访问范围 历史上服务不足。第一阶段的成功完成将为使用皮肤生物标志物提供原理证明 用于预测在家收集的样本中的特应性皮炎。在目标 1 中，我们将验证我们的样本收集 验证家庭样本采集稳健性的过程。在一项针对 25 人的研究中，我们将评估 从未经训练的（家庭）样本收集与经过训练的（办公室）样本收集获得的数据质量 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， 我们将在一项针对 75 名健康人（对照）和 75 名个体（患者）的研究中确定 AD 的预测生物标志物 诊断患有AD。将使用特征选择和机器学习预测分析来确定小 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（下面积） 孤立的交叉验证数据集上生物标志物集的曲线（AUC）≥0.90）。这些研究将证明 通过诊断、监测和预测的识别来进行概念验证并证明产品的可行性 与 AD 和 AD 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 用户收集套件，并提高未来专注于临床验证的第二阶段计划的成功 使用已识别的生物标志物进行 AD 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信息来指导皮肤病护理发生革命性变化 通过精准医疗提出治疗建议和监测。