Cogstate Webinar Series

Featured Speaker

• Paul Maruff, Chief Science Officer
• Edward Bartolic, Senior Science Director

Overview

Drug development strategies for early Alzheimer’s disease (AD) require clinical endpoints that are sensitive to the subtlest cognitive changes that may be occurring at even preclinical stages. Commonly used measures of cognition in clinical trials of treatments for Alzheimer’s disease, such as the Alzheimer’s Disease Assessment Scale-Cognitive subscale (ADAS-Cog) and the Clinical Dementia Rating (CDR), have inadequate measurement properties (e.g., ceiling effects, practice effects, restricted range, and/or skewed distributions), which render them insensitive to subtle change.

The most common approach to defining novel endpoints has been to assemble composite measures by combining tests, or specific items of tests, which assess domains of cognitive function that have historically been shown to be most sensitive to change in studies of early clinical populations (i.e., mild cognitive impairment or prodromal AD) or in observational studies of preclinical, “at-risk” samples.

One such composite measure, the Alzheimer’s Disease Cooperative Study- Preclinical Alzheimer Cognitive Composite (ADCS-PACC), was based on retrospective analyses of datasets from 3 longitudinal, observational studies of cognitively normal elderly individuals. These studies were the Australian Imaging, Biomarkers, and Lifestyle (AIBL) Flagship Study of Aging, the Alzheimer’s Disease Neuroimaging Study (ADNI), and the ADCS Prevention Instrument (ADCS-PI) study. Those members of the study samples with risk factors for Alzheimer’s disease, such as positive brain amyloid (AIBL, ADNI) or ApoE4 allele ( ADCS-PI), showed the greatest decline relative to members without risk factors on measures of episodic memory (delayed word recall and delayed story recall), executive functioning, and orientation.

This webinar will discuss the PACC component tests, challenges to implementation and possible solutions and possible alternative component measures.