Study Published in Nature Digital Medicine Shows Potential of Machine Learning and Augmented Reality-based Digital Biomarkers in Alzheimer’s Detection

Optimizing The Use of Digital Biomarkers in Clinical Trials for Alzheimer’s

March 31, 2022Neelem Sheikh

Alzheimer’s disease clinical research is rapidly shifting towards decentralized clinical trials, particularly as pharmaceutical companies and researchers continue to navigate new challenges stemming from the COVID-19 pandemic. As this shift progresses, the method for data collection, subject screening, and subject monitoring must align with the needs of this digital transformation.

The future of Alzheimer’s disease clinical research will likely rely heavily on digital adoptions—namely, digital biomarkers—to not only meet the need for decentralized and/or digitized clinical trials but also to provide new, more powerful insights via high-density data from sensors, wearables, and app-based technologies.

Below, we take a closer look at the utilization of digital biomarkers in clinical trials for Alzheimer’s disease.

Digital Biomarkers in Clinical Trials for Alzheimer’s Disease

Pharmaceutical companies face many challenges and barriers that contribute to expensive, prolonged, or failed Alzheimer’s disease clinical trials. Alzheimer’s disease drug trials have had a 99% failure rate—but this doesn’t necessarily mean all of the failed drugs were ineffective. Many experts believe that these failures stem largely from narrow-natured, unreliable data originating from non-ecologically valid assessment methods.

While an incredibly promising Alzheimer’s drug development pipeline is emerging, methods for subject screening (diagnostic tools) and subject monitoring (cognitive assessment tools) for clinical trials remain outdated and insufficient. The use of such tools can skyrocket subject recruitment costs, significantly prolong trial timelines, and contribute to clinical trial failures. 

Digital biomarkers have the power to transform the face of Alzheimer’s disease clinical trials, improving cost efficiency, speed, and success. Currently, there are two primary use cases for digital biomarkers in clinical trials for Alzheimer’s disease

  1. Improving subject screening and selection 
  2. Improving longitudinal monitoring of subjects 

Subject Screening and Selection

Screening and selecting the “right” patients for Alzheimer’s clinical trials is currently extremely cost-inefficient due to a lack of precision diagnosis. To determine subject eligibility, many Alzheimer’s drug clinical trials require expensive positron emission (PET) scans or invasive cerebrospinal fluid analyses as a part of the key inclusion criteria to establish protein levels, such as beta-amyloid and tau. With thousands of potential subjects, the screening and selection process can drive clinical trial expenses through the roof. 

Furthermore, with many Alzheimer’s trials, pharmaceutical companies need to understand where in the disease course each potential subject is, as their drug may only be effective at a specific phase or severity of the disease. Digital biomarkers, along with strong analytical tools, have the potential to be utilized to determine precisely where on the Alzheimer’s disease continuum a given patient lies. This method can serve as an initial tool to narrow down the subject pool, filtering out ineligible subjects before completing costly imaging or diagnostic procedures needed for key inclusion criteria. This will ensure pharmaceutical companies are selecting the right patients for their trials while greatly reducing costs associated with subject selection.

Longitudinal Monitoring of Trial Subjects

Promptly reaching the all-important go/no-go decision, particularly in early phase Alzheimer’s clinical trials, is an ongoing challenge for pharmaceutical companies. At the end of the day, the longer a trial runs, the more money spent. 

Understanding at a highly granular level how an individual’s Activities of Daily Living (ADLs) are changing is arguably the most compelling method for monitoring brain function in Alzheimer’s disease patients. However, longitudinally assessing the effect of Alzheimer’s disease-modifying drugs on trial subjects is currently largely dependent on costly and time-consuming imaging and outdated, unreliable cognitive assessment tools.

Confidently reaching a go/no-go decision as early as possible can help achieve proof of concept more rapidly and at lower costs—and digital biomarkers may very well be the means to achieve this. 

While traditional endpoints and traditional biomarkers are still fundamental and necessary for trials, digital endpoints and digital biomarkers in clinical trials provide pharmaceutical companies with the granular, supplemental, and contextual information needed to arrive at a go/no-go decision more confidently and efficiently.

Altoida: Improving and Accelerating Neurological Disease Drug Development

At Altoida, we are dedicated to providing a reliable, accessible, and affordable solution for Alzheimer’s disease clinical trials. Here is what sets us apart from traditional and digitized cognitive assessment tools:

Clinical Trial Needs

What Traditional Tests Offer

What Our Competition Offers

What Altoida Offers

Screen and select subjects for a clinical trial based on the presence of pathology

Up to 64% accuracy for Alzheimer's diagnosis; high-level assessment

Up to 64% accuracy for Alzheimer's diagnosis; high-level assessment

94% accuracy for Alzheimer's diagnosis; precisely place patients at a phase on the disease continuum

Monitor patients to assess drug efficacy and reach a go/no-go decision

Noisy data; only test cognition

Noisy data; only test cognition

Sensitive, granular data on cognition and function from a test that uses augmented reality

Altoida’s mission is to accelerate and improve drug development, neurological disease research, and patient care. To learn more about our precision-neurology platform and app-based medical device, contact us!

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