Preclinical Alzheimer’s Disease

Clinical trials in preclinical Alzheimer’s disease (AD) require different scales and operational approaches than standard dementia studies. Your study needs increased sensitivity to capture the earliest disease-related changes and establish treatment effects of investigational drugs.

Cogstate has substantial expertise in preclinical AD digital and rater-administered cognitive assessments (CDR, PACC, etc.) to help your team gather reliable data to answer study questions.

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Improve Strategic Study Design

Clinical measurement in preclinical AD presents unique trial challenges. Alzheimer’s disease progresses along a disease continuum with an extended preclinical stage defined by individuals showing amyloid positivity but with normal functional status.

In clinical trials for preclinical AD, the participant characteristics, outcome measures, assessment procedures, and context of assessments require substantially different strategies to those in symptomatic AD. Many scales commonly used to measure cognitive change in other neurodegenerative conditions lack the sensitivity to capture subtle changes associated with preclinical AD so it’s important to carefully consider endpoint approaches.

Cogstate has the hands-on experience to support the unique approaches necessary to reduce patient and site burden and capture quality data in your preclinical AD trial.

Implement Robust Endpoint Data Quality Programs

From helping you establish rater qualification criteria, to tailored certification pathways that credit the experience of expert raters, to central monitoring programs that target clinician reviews where they’re needed most, Cogstate supports all aspects of rater training and endpoint data quality programs. Studies also benefit from our global network of 180 highly trained expert clinicians who provide assessment support in 40+ languages.

We have extensive experience with the assessments commonly used in preclinical AD trials: Category Fluency, DDST, FCSRT, Logical Memory Test, MMSE, Paragraph Recall, Rey Auditory Verbal Learning Test, Trail Making, and more.

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Leverage Sensitive and Reliable Digital Assessments

Based on decades of experience in cognitive science, Cogstate developed a digital PACC that substitutes conventional paper scales with digital scales that measure the same domains of cognition.

This digital PACC has been shown to be valid for clinical trials and has demonstrated validity for use in in-clinic assessment contexts as well as for remote, supervised administration contexts.

All Cogstate tests are culture- and education-neutral and designed for repeated administration with minimal practice or learning effects, making them ideal for use in global clinical trials.

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Cogstate Digital PACC

Cogstate Tests:• International Shopping List (Verbal Learning)
• Continuous Paired Associate Learning (Paired Associate Learning)
• International Daily Symbol Substitution - Medicines (Processing Speed)
Length:Approximately 15 minutes
Administration:Standardized
Data Processing and Scoring:Automated
Application:Phase I-IV
Culture and Language Neutral:Yes

Experience Supporting Preclinical AD Trials

Cogstate has substantial experience in preclinical AD, including:

  • The only vendor to support central rating of a primary endpoint in a pivotal phase 3 preclinical AD trial.
  • Trained hundreds of raters to administer CDR and PACC scales (in addition to other relevant scales) and monitored these assessments to capture and address common administration errors.
  • Developed a digital PACC that is validated for in-clinic assessment as well as for remote, administration. Data shared at CTAD, AAIC, and AD/PD highlight the validity, equivalency, and sensitivity.

Cogstate understands your preclinical AD patient population is typically younger, active, and busy. Low-burden and/or at-home testing can increase the likelihood that individuals will participate in trials; these methods can also help address the FDA mandate for increased enrollment of diverse patient populations.

How can we help you optimize the measurement of brain health?

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