Development of a robust quantitative systems pharmacology model of amyloid beta and tau pathways for clinical trial design and decision making in Alzheimer’s Disease and Dementia.

开发强大的β淀粉样蛋白和tau蛋白通路定量系统药理学模型，用于阿尔茨海默病和痴呆症的临床试验设计和决策。

• 批准号: 9466332
• 负责人: Lore Gruenbaum
• 金额: $ 18.84万
• 依托单位: APPLIED BIOMATH, LLC
• 依托单位国家: 美国
• 财政年份: 2017
• 资助国家: 美国
• 起止时间: 2017-09-15 至 2018-03-31
• 项目状态: 已结题
• 来源: https://reporter.nih.gov/project-details/9466332
• 关键词: Alzheimer' s Disease; Alzheimer' s disease model; American; Amyloid beta-Protein; Area; Binding; Biological; Biological Markers; Biology; Biotechnology; Brain; Brain imaging; Clinical; Clinical Data; Clinical Pathways; Clinical Pharmacology; Clinical Research; Clinical Trials; Clinical Trials Design; Combined Modality Therapy; Computer Simulation; Data; Data Reporting; Decision Making; Dementia; Development; Disease; Disease Progression; Dose; Drug Exposure; Drug Industry; Drug effect disorder; Failure; Functional disorder; Future; Goals; Health Care Costs; Intellectual Property; Investments; Knowledge; Lead; Learning; Medical; Microtubule Proteins; Modeling; Modification; Mutation; Neurofibrillary Tangles; Neurons; Patients; Pharmaceutical Preparations; Pharmacologic Substance; Pharmacology; Pharmacotherapy; Phase; Physiological; Plasma; Positioning Attribute; Positron-Emission Tomography; Property; Protein Precursors; Regimen; Research; Senile Plaques; Speed; Symptoms; System; Techniques; Testing; Therapeutic; Time; Work; autosomal dominant mutation; beta-site APP cleaving enzyme 1; clinical biomarkers; clinical decision-making; clinical predictors; cost; design; drug candidate; head-to-head comparison; improved; inhibitor/antagonist; interest; next generation; novel; novel therapeutics; patient population; pre-clinical; preclinical development; predicting response; presenilin-1; presenilin-2; research and development; response; response biomarker; simulation; success; tau Proteins; tool; treatment effect; trial design; virtual

Project Summary/Abstract In 2016 there were an estimated 5.4 million Americans living with Alzheimer's Disease (AD), at an annual healthcare cost of $236 billion (Alzheimer’s Association 2016). AD is characterized by accumulation of insoluble forms of amyloid-β (Aβ) in brain interstitium and aggregation of the microtubule protein tau in neurofibrillary tangles in neurons. The development of anti-Aβ therapies is the leading effort for pharmaceutical industry for disease modification. However, despite significant effort in research and development, from 2002 to 2012, 99.6% of the clinical trials of disease-modifying treatments for Alzheimer’s Disease have failed. What is worse is the fact that in most cases it could not be conclusively decided whether these trials failed due to: the overall trial design, the specific mechanism of action of the drug candidate, or the invalidity of the underlying scientific hypothesis (Karran 2014; Toyn 2014). As a consequence of the failure of anti-Aβ trials, there has been a shift to conduct studies with subjects at earlier stages of disease before clinical symptoms appear as well as a new focus on anti-tau therapies. For the 75 anti-Aβ drug candidates that currently remain in Phase I to III clinical trials (as well as those in preclinical development) and all the anti-tau therapies under development, there is an urgent need for tools and techniques that improve clinical decision making, and decrease the extremely high failure rate. We propose to build a computational quantitative systems pharmacology (QSP) model for Abeta and tau disease-related biology that captures their changes with different disease states sporadic AD patients and in the context of the autosomal dominant mutations in ADAD patients. This model will integrate data and scientific knowledge gained in the past . The model will also include drug disposition and distribution into the brain and mechanisms of action for the selected drugs with parameters calibrated against their clinical data. This platform model can be expanded in the future to include additional drug(s) of interest to pharmaceutical developers. We anticipate the Aβ and Tau platform model will be used by pharmaceutical and biotech companies currently engaged in, or contemplating clinical trials in AD to inform the design of future clinical trials, Develop a quantitative mechanistic understanding of past clinical failures, Inform preclinical and clinical biomarker strategies. The ultimate goal is to help speed the delivery of novel therapeutics for AD patients.

项目总结/摘要 2016年，估计有540万美国人患有阿尔茨海默病（AD），每年的医疗费用 2360亿美元（阿尔茨海默氏症协会2016）。AD的特征是不溶性淀粉样蛋白-β的积累 在神经元中，微管蛋白tau在神经元缠结中的聚集。的 抗A β疗法的开发是制药业用于疾病修饰的主要努力。然而，在这方面， 尽管在研究和开发方面做出了重大努力，但从2002年到2012年，99.6%的 阿尔茨海默病的疾病改善治疗已经失败。更糟糕的是，在大多数情况下，它可能 无法最终确定这些试验失败的原因是否是：总体试验设计， 候选药物或潜在科学假设的无效性（Karran 2014; Toyn 2014）。因此 在抗A β试验失败的情况下，已经有一种转变，即在疾病的早期阶段进行研究， 出现了临床症状以及对抗tau治疗的新关注。对于目前正在研究的75种抗A β药物候选药物， 仍处于I期至III期临床试验（以及临床前开发）中，所有抗tau治疗均在 因此，迫切需要改善临床决策并减少并发症的工具和技术。 极高的失败率。 我们建议建立一个与Abeta和tau疾病相关的计算定量系统药理学（QSP）模型 生物学，捕捉他们的变化与不同的疾病状态，散发性AD患者和在常染色体的背景下， ADAD患者中的显性突变。该模型将整合过去获得的数据和科学知识。的 模型还将包括药物在大脑中的处置和分布以及所选药物的作用机制 根据他们的临床数据校准参数。这种平台模式将来可以扩展到包括 药物开发人员感兴趣的其他药物。 我们预计A β和Tau平台模型将被目前从事的制药和生物技术公司使用 在AD临床试验中，或考虑进行AD临床试验，为未来临床试验的设计提供信息， 了解过去的临床失败，告知临床前和临床生物标志物策略。最终目的是帮助 加速为AD患者提供新的治疗方法。