Optimizing Immunosuppression Drug Dosing via Phenotypic Precision Medicine

通过表型精准医学优化免疫抑制药物剂量

• 批准号: 9767781
• 负责人: ALI ZARRINPAR
• 金额: $ 19.42万
• 依托单位: UNIVERSITY OF FLORIDA
• 依托单位国家: 美国
• 财政年份: 2018
• 资助国家: 美国
• 起止时间: 2018-09-01 至 2021-08-31
• 项目状态: 已结题
• 来源: https://reporter.nih.gov/project-details/9767781
• 关键词: Acute; Affect; Algorithms; Area Under Curve; Blood; Calcineurin inhibitor; Cancer Model; Clinical; Clinical Data; Clinical Trials; Combination Medication; Communicable Diseases; Data; Disease; Dose; Drug Combinations; Drug Kinetics; Ensure; Generations; Genetic; Goals; Human; Immune response; Immunologic Markers; Immunosuppression; In Vitro; Incidence; Individual; Infection; Knowledge; Lead; Maintenance; Malignant Neoplasms; Maps; Measurement; Measures; Methods; Monitor; Mutation; Nature; Organ; Organ Transplantation; Outcome; Output; Patient-Focused Outcomes; Patients; Pharmaceutical Preparations; Pharmacotherapy; Phenotype; Physicians; Physiological; Physiology; Population; Procedures; Process; Randomized Clinical Trials; Randomized Controlled Trials; Regimen; Renal function; Safety; Solid; Surface; System; Tacrolimus; Techniques; Testing; Therapeutic; Time; Toxic effect; Transplant Recipients; Transplantation; Treatment Protocols; appropriate dose; arm; base; biological systems; comparative efficacy; computational platform; dose individualization; first-in-human; human study; improved; in vivo Model; individual variation; liver function; liver transplantation; novel drug combination; participant safety; patient subsets; patient variability; personalized medicine; post-transplant; precision medicine; predictive modeling; prospective; racial disparity; response; risk minimization; side effect; standard of care; trend; trial comparing; visual map

PROJECT SUMMARY The authors have developed a computational platform to rapidly identify optimal drug and dose combinations from the innumerable possibilities. By testing this technique termed Phenotypic Personalized Medicine (PPM) in a diverse number of experimental systems representing different diseases, they have found that the response of biological systems to drugs can be described by a low order, smooth multidimensional surface. The main consequence of this is that optimal drug combinations can be found in a small number of tests. This input–output relationship is always based on experimental data, not modeling, and it would lead to a straightforward solution for handling human diversity in drug dosing needs, among other clinical problems. They will test the hypothesis that PPM can be developed and validated for clinical use by conducting a prospective clinical trial to compare the feasibility and efficacy of this approach to standard of care physician dosing. This group has previously used PPM-based optimization to find novel drug combinations in in vitro and in vivo models of cancer and infection. In a first-in-human study, they recently compared 4 PPM-dosed patients and 4 control (standard of care dosed) patients. They calculated the tacrolimus dosing regimen using the PPM process and showed significant improvement in variability and a trend toward improved efficacy. For this application, they aim to show in a clinical trial, that PPM is more effective than unaided physician dosing. This will allow the generation of data to justify a multi-center confirmatory study and to explore a wider array of clinical outcomes to optimize.

项目摘要 作者开发了一个计算平台，可以快速识别最佳药物和剂量组合 从无数的可能性。通过测试这种被称为表型个性化医学（PPM）的技术， 在代表不同疾病的各种实验系统中，他们发现， 生物系统对药物的反应可以用低阶、光滑的多维表面来描述。 这样做的主要结果是，最佳药物组合可以在少量的测试中找到。这 投入产出关系总是基于实验数据，而不是建模，这将导致 这是一种简单的解决方案，用于处理药物给药需求的人类多样性以及其他临床问题。 他们将通过进行一项研究来测试PPM可以开发和验证用于临床用途的假设 一项前瞻性临床试验，旨在比较该方法与标准治疗医生的可行性和有效性 剂量。该小组先前使用基于PPM的优化来在体外发现新的药物组合， 癌症和感染的体内模型。 在一项首次人体研究中，他们最近比较了4例PPM给药患者和4例对照（标准治疗剂量） 患者他们使用PPM过程计算了他克莫司的给药方案， 变异性的改善和功效改善的趋势。对于这个应用程序，他们的目标是在一个 在临床试验中，PPM比独立的医生给药更有效。这将允许生成数据， 证明多中心验证性研究的合理性，并探索更广泛的临床结局以优化。