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-? (A?) in brain interstitium and aggregation of the microtubule protein tau in neurofibrillary tangles in neurons. The development of anti-A? 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-A? 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-A? 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 A? 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.
From 2002 to 2012, 99.6% of the clinical trials of disease-modifying treatments for Alzheimer?s Disease have failed. We propose to develop a detailed computational model of Alzheimer?s Disease that maximize our learning from past clinical failures, and guides future clinical trials to increase their success rate. If successful, this work will have a significant impact on the costs and time to develop novel therapeutics in this critical area of unmet medical need and may help to bring better drugs to patients.