Diagnosing mild cognitive impairment (MCI), predicting alzheimer's disease (AD) dementia onset, and screening and monitoring agents for treating mci or preventing dementia onset

Inventors

Chirila, Florin V. • Alkon, Daniel L.

Abstract

Methods of detecting the signature of Alzheimer's disease before the clinical onset of the disease are disclosed, such as methods of diagnosing Mild Cognitive Impairment (MCI), monitoring the progress of MCI, and predicting the time to clinical onset of AD dementia. The methods use a Biomarker Severity Score, which corresponds to output signals of one or more biomarkers chosen from AD Index, Morphometric Imaging, and PKC Epsilon Biomarkers. Also disclosed are methods of screening for a compound useful for treating MCI or for preventing the clinical onset of AD dementia, as well as methods of evaluating or monitoring the therapeutic benefit of an agent for treating MCI or preventing the clinical onset of AD dementia.

Core Innovation

The disclosure provides biomarker-based methods for predicting the time to clinical onset of Alzheimer's disease (AD) dementia in a test subject having Mild Cognitive Impairment (MCI). Cells obtained from the test subject are used to determine an output signal of an AD Index Biomarker based on levels of phosphorylated extracellular signal-regulated kinase 1 (pERK1) and phosphorylated extracellular signal-regulated kinase 2 (pERK2). The AD Index Biomarker output is produced by calculating a first pERK1/pERK2 ratio in a first portion of cells treated with a Protein Kinase C (PKC) activator, calculating a second pERK1/pERK2 ratio in a second portion of cells left uncontacted with the PKC activator, and subtracting the second ratio from the first ratio.

The methods further place the determined AD Index Biomarker output on a graph to predict clinical onset timing. The graph includes first AD Index Biomarker values from AD-afflicted subjects and second AD Index Biomarker values from age-matched control subjects. The graph uses age difference in years as the x-axis and AD Index values from 0-100 as the y-axis, and the graph includes an inflection point between the first and second AD Index Biomarker values. Prediction of time to clinical onset is based on the position of the output signal relative to the inflection point.

The disclosure also supports longitudinal monitoring and additional biomarker outputs within the overall framework. In particular, progression of MCI is monitored by repeating the steps for determining AD Index Biomarker output and assessing progression toward clinical onset when output signals increase over time. A Morphometric Imaging Biomarker output is additionally determined using cultured aggregate metrics, including calculating an average aggregate area per aggregate count (A/N) and using ln(A/N) as part of the biomarker output signal.

Claims Coverage

Independent claim clm-00001 covers a method for predicting the time to clinical onset of AD dementia in an MCI test subject by generating an AD Index Biomarker output from PKC activator-dependent pERK1/pERK2 ratios and mapping the output onto an age-difference graph with an inflection point, relative to AD-afflicted versus age-matched control values.

Across the independent claim, the coverage centers on producing an AD Index Biomarker output from PKC activator-treated versus untreated cell portions using pERK1/pERK2 ratios, constructing an age-difference graph separating AD-afflicted and age-matched control AD Index values with an inflection point, and predicting the time to clinical onset of AD dementia based on the output signal’s position relative to that inflection point.

Stated Advantages

Documented Applications

No documented applications found