On the Brink of Change
How Academic Data and Machine Learning Can Revolutionize Alzheimer's Drug Development
White Paper
Nov 20, 2018


Alzheimer’s disease is both a devastating degenerative brain disorder and the most common type of dementia. About 5.7 million Americans live with Alzheimer’s today and a new person is diagnosed with the disease every 65 seconds (1, 2). Elderly Americans are more afraid of developing Alzheimer’s or dementia (35%) than cancer (34%) and for good reason. The prognosis is not good, but the future is hopeful when human science meets data science. When scientific expertise and advances in data analytics and innovative technologies—like predictive analytics and machine learning—come together, we can ask better questions and extract more meaningful insights about Alzheimer’s disease, while proactively creating a more accurate and predictable picture of the patient pipeline, identifying patients earlier in the diagnosis, optimizing study planning and speeding time to market.

In this white paper, you will:

  • Hear about current industry challenges in Alzheimer’s drug development and glean insights as to why we must explore new clinical approaches
  • Understand the importance of machine learning and predictive analytics for identifying non-diagnosed prodromal Alzheimer’s disease patients—and why we must tap into this unexplored general population to bring about real advancement
  • Learn why innovative approaches to Alzheimer’s drug development are not only necessary, but how they will positively impact the future of Alzheimer’s disease for patients and the medical community alike

Challenges and Considerations of Clinical Development in Alzheimer’s Disease

More than 100 Alzheimer’s agents have failed clinical trials since 1998, and early Alzheimer’s trials have a high screen failure rate of about 75%. Only five agents have ever been approved: tacrine (later withdrawn for safety), donepezil, rivastigmine, galantamine, and memantine. Unfortunately, they are only able to provide a moderate symptomatic relief with no impact of disease progression.

Dozens of unsuccessful trials have provided some lessons, which are important to understand since at least 112 potential agents to treat Alzheimer’s and its symptoms are currently in clinical trials.

First, for drug development efforts, it is critical to target Alzheimer’s pathology as early as possible before the onset of dementia to lessen the disease’s effects. Amyloid deposits and other brain changes associated with Alzheimer’s appear more than 20 years before the onset of clinical symptoms. As per Alzheimer’s Association, earlier diagnosis (even with no disease-modification treatment yet available) may also save $7.9 trillion in healthcare costs in the US alone (1, 2).

Second, it is critical to enroll a well-defined patient population using biomarker confirmation of diagnosis.

In addition, because most agents currently under trial are monoclonal antibodies (mAbs), the blood–brain barrier poses a substantial challenge. These challenges translate into four concerns:

  • The generally low penetration of mAbs into the brain requires elevated dosing, which raises safety issues.
  • Trial participants must be stratified by apolipoprotein E (APOE4) for safety management. Between 10% and 15% of the population is APOE4+, which increases the risk for developing Alzheimer’s and lowers the age of onset.
  • Dose titration to mitigate amyloidrelated imaging abnormalities is a particular concern for APOE4+ individuals.
  • The primary outcome measure, the clinical dementia rating (CDR) scale, must be protected by using blinded raters.

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