U.S. patent application number 16/077766 was filed with the patent office on 2019-02-07 for diagnosing mild cognitive impairment (mci), predicting alzheimer's disease (ad) dementia onset, and screening and monitoring agents for treating mci or preventing dementia onset.
The applicant listed for this patent is THE WEST VIRGINIA UNIVERSITY BOARD OF GOVERNORS ON BEHALF OF WEST VIRGINIA UNIVERSITY. Invention is credited to Daniel L. Alkon, Florin V. Chirila.
Application Number | 20190041404 16/077766 |
Document ID | / |
Family ID | 58361076 |
Filed Date | 2019-02-07 |
United States Patent
Application |
20190041404 |
Kind Code |
A1 |
Chirila; Florin V. ; et
al. |
February 7, 2019 |
DIAGNOSING MILD COGNITIVE IMPAIRMENT (MCI), PREDICTING ALZHEIMER'S
DISEASE (AD) DEMENTIA ONSET, AND SCREENING AND MONITORING AGENTS
FOR TREATING MCI OR PREVENTING DEMENTIA ONSET
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.
Inventors: |
Chirila; Florin V.;
(Morgantown, WV) ; Alkon; Daniel L.; (Chevy Chase,
MD) |
|
Applicant: |
Name |
City |
State |
Country |
Type |
THE WEST VIRGINIA UNIVERSITY BOARD OF GOVERNORS ON BEHALF OF WEST
VIRGINIA UNIVERSITY |
Morgantown |
WV |
US |
|
|
Family ID: |
58361076 |
Appl. No.: |
16/077766 |
Filed: |
February 22, 2017 |
PCT Filed: |
February 22, 2017 |
PCT NO: |
PCT/US2017/018810 |
371 Date: |
August 14, 2018 |
Related U.S. Patent Documents
|
|
|
|
|
|
Application
Number |
Filing Date |
Patent Number |
|
|
62298182 |
Feb 22, 2016 |
|
|
|
Current U.S.
Class: |
1/1 |
Current CPC
Class: |
G01N 2800/2821 20130101;
G16H 50/20 20180101; G01N 2800/60 20130101; G16B 5/00 20190201;
G01N 33/6896 20130101; G16B 45/00 20190201 |
International
Class: |
G01N 33/68 20060101
G01N033/68; G06F 19/26 20060101 G06F019/26; G06F 19/12 20060101
G06F019/12; G16H 50/20 20060101 G16H050/20 |
Claims
1. A method of diagnosing Mild Cognitive Impairment (MCI) in a
subject comprising: (a) obtaining one or more cells from the
subject; (b) determining an output signal of one or more diagnostic
biomarkers using the one or more cells from the subject, wherein
the diagnostic biomarker is chosen from AD Index Biomarker,
Morphometric Imaging Biomarker and PKC Epsilon Biomarker; and (c)
comparing the output signal determined in step (b) to output
signals of the diagnostic biomarker for age-matched control (AC)
cells and for AD cells, wherein MCI is indicated in the subject if
the output signal determined in step (b) is less than the lowest
output signal for the AD cells but greater than the highest output
signal for the AC cells.
2. The method of claim 1, further comprising predicting the time to
clinical onset of AD dementia, comprising: (1) plotting the output
signals of the diagnostic biomarker for the AD cells as a function
of their AD duration, wherein each AD duration is the age
difference between the AD subject's age at the time of clinical
onset of AD and the AD subject's age at the time of collecting one
or more cells for generating the output signals of the diagnostic
biomarker; (2) fitting a function to the plotted output signals of
step (1); and (3) if MCI is indicated in step (c), predicting the
time to clinical onset of AD dementia by inputting into the fit
function the output signal of the diagnostic biomarker determined
in step (b) and determining the time to clinical onset of AD
dementia.
3. The method of claim 2, wherein the fit function is a linear
function.
4. The method of claim 1, further comprising predicting the time to
clinical onset of AD dementia, comprising: (1) plotting the output
signals of the diagnostic biomarker for the AD cells as a function
of their AD duration, wherein each AD duration is the age
difference between an AD subject's age at the time of clinical
onset of AD and the AD subject's age at the time of collecting one
or more cells for generating the output signals of the diagnostic
biomarker; (2) plotting the output signals of the diagnostic
biomarker for the AC cells as a function of their age difference,
wherein each age difference is the difference between an AC
subject's age at the time of collecting one or more cells for
generating the output signals of the diagnostic biomarker and the
age of the oldest AC subject at the time of collecting one or more
cells for generating the output signals of the diagnostic
biomarker; (3) fitting a function to the plotted output signals of
steps (1) and (2); and (4) if MCI is indicated in step (c),
predicting the time to clinical onset of AD dementia by inputting
into the fit function the output signal of the diagnostic biomarker
determined in step (b) and determining the time to clinical onset
of AD dementia.
5. The method of claim 4, wherein the fit function is a logistic
function.
6. The method of claim 1, further comprising monitoring the
progression of MCI, comprising repeating steps (a) through (c) at
one or more subsequent points in time, wherein the subject has
progressed toward the clinical onset of AD dementia if the output
signals determined in step (b) above have increased over time.
7. The method of claim 1, wherein the output signals of the
diagnostic biomarker for AC cells and AD cells in step (c) are
average output signals.
8. The method of claim 1, wherein the one or more cells are
peripheral cells.
9. The method of claim 8, wherein the peripheral cells are skin
fibroblast cells.
10. The method of claim 1, wherein the subject displays no
phenotypic symptoms of AD.
11. A method of screening for a compound useful for treating MCI or
preventing the clinical onset of AD dementia comprising: (a)
obtaining one or more cells from a subject; (b) determining an
output signal of one or more diagnostic biomarkers using the one or
more cells from the subject, wherein the diagnostic biomarker is
chosen from AD Index Biomarker, Morphometric Imaging Biomarker and
PKC Epsilon Biomarker; (c) comparing the output signal determined
in step (b) to output signals of the diagnostic biomarker for AC
cells and for AD cells, wherein MCI is indicated in the subject if
the output signal determined in step (b) is less than the lowest
output signal for the AD cells but greater than the highest output
signal for the AC cells; (d) if MCI is indicated in step (c),
determining the output signal of the diagnostic biomarker in step
(b) after contacting cells from the subject with a compound for an
initial time period and/or for an ongoing time period; and (e)
comparing the output signal determined in step (d) to the output
signal determined in step (b), wherein the test compound is
indicated as useful for the treatment of MCI or the prevention of
the clinical onset of AD dementia if the output signal determined
in step (d) is less than the output signal determined in step
(b).
12. A method of evaluating or monitoring the therapeutic benefit of
an agent for treating MCI or preventing the clinical onset of AD
dementia in a subject comprising: (a) obtaining one or more cells
from a subject; (b) determining an output signal of one or more
diagnostic biomarkers using the one or more cells from the subject,
wherein the diagnostic biomarker is chosen from AD Index Biomarker,
Morphometric Imaging Biomarker and PKC Epsilon Biomarker; (c)
comparing the output signal determined in step (b) to output
signals of the diagnostic biomarker for AC cells and for AD cells,
wherein MCI is indicated in the subject if the output signal
determined in step (b) is less than the lowest output signal for
the AD cells but greater than the highest output signal for the AC
cells; (d) if MCI is indicated in step (c), determining the output
signal of the diagnostic biomarker in step (b) using one or more
cells from the subject after initial, ongoing, and/or cessation of
treatment with an agent; and (e) comparing the output signal
determined in step (d) to the output signal determined in step (b),
wherein the agent is indicated as providing therapeutic benefit for
treating MCI or preventing the clinical onset of AD dementia in the
subject if the output signal determined in step (d) is equal to or
less than the output signal determined in step (b).
Description
[0001] This application claims priority to U.S. Provisional
Application 62/298,182, filed Feb. 22, 2016, the entire contents of
which are incorporated herein by reference.
[0002] Alzheimer's disease (AD) is a neurodegenerative disorder
generally characterized by the progressive decline of mental
functioning. More specifically, AD is characterized clinically by
the progressive loss of memory, cognition, reasoning, judgment, and
emotional stability that gradually leads to profound mental
deterioration and, ultimately, death. Although there are many
hypotheses for the possible mechanisms of AD, one central theory is
that the excessive formation and accumulation of toxic beta-amyloid
(A.beta.) peptides either directly or indirectly affects a variety
of cellular events and leads to neuronal damage and cell death.
[0003] AD is a progressive disorder with a mean duration of around
8-15 years between onset of clinical symptoms and death. AD is
believed to represent the seventh most common medical cause of
death and affects about 5 million people in the United States.
[0004] The value of diagnostic biomarkers derives from their
ability to monitor disease progression and remission, as well as
their predictive accuracy before the onset of the disease.
Detection of AD even before its onset, could provide important
opportunities for prevention and/or planning therapeutic
strategies. For example, Mild Cognitive Impairment has been
characterized as a decline in cognition that is greater than the
level expected for an individual's age and education level but that
does not interfere notably with activities of daily life. It
represents an intermediate stage between the expected cognitive
changes of normal aging and the earliest clinical manifestations of
dementia. MCI increases the risk of developing Alzheimer disease.
In the early stages of AD, however, within four years from the
dementia onset, clinical diagnosis has a limited rate of success.
Furthermore, clinical diagnostic accuracy before dementia onset has
not been previously validated. Thus, there is a need to develop
improved diagnostic and predictive capabilities for AD. In
particular, there is a need to develop methods to detect the
signature of AD before the clinical onset of dementia.
[0005] The methods of the present disclosure address these needs by
providing for methods of diagnosing MCI, of monitoring the
progression of MCI, and of predicting the time to clinical onset of
AD dementia. The present disclosure is also directed to 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.
[0006] In one aspect of the present disclosure, a method of
diagnosing MCI in a subject comprises:
(a) obtaining one or more cells from the subject; (b) determining
an output signal of one or more diagnostic biomarkers using the one
or more cells from the subject, wherein the diagnostic biomarker is
chosen from AD Index Biomarker, Morphometric Imaging Biomarker and
PKC Epsilon Biomarker; and (c) comparing the output signal
determined in step (b) to output signals of the diagnostic
biomarker for age-matched control (AC) cells and for AD cells,
wherein MCI is indicated in the subject if the output signal
determined in step (b) is less than the lowest output signal for
the AD cells but greater than the highest output signal for the AC
cells.
[0007] In another aspect, a method of monitoring the progression of
MCI in a subject comprises:
(a) obtaining one or more cells from the subject; (b) determining
an output signal of one or more diagnostic biomarkers using the one
or more cells from the subject, wherein the diagnostic biomarker is
chosen from AD Index Biomarker, Morphometric Imaging Biomarker and
PKC Epsilon Biomarker; (c) comparing the output signal determined
in step (b) to output signals of the diagnostic biomarker for AC
cells and for AD cells, wherein MCI is indicated in the subject if
the output signal determined in step (b) is less than the lowest
output signal for the AD cells but greater than the highest output
signal for the AC cells; and (d) repeating steps (a) through (c) at
one or more subsequent points in time, wherein the subject has
progressed toward the clinical onset of AD dementia if the output
signals determined in step (b) have increased over time.
[0008] In another aspect, a method of predicting the time to
clinical onset of AD dementia in a subject comprises:
(a) obtaining one or more cells from the subject; (b) determining
an output signal of one or more diagnostic biomarkers using the one
or more cells from the subject, wherein the diagnostic biomarker is
chosen from AD Index Biomarker, Morphometric Imaging Biomarker and
PKC Epsilon Biomarker; (c) comparing the output signal determined
in step (b) to output signals of the diagnostic biomarker for AC
cells from a group of AC subjects and to output signals of the
diagnostic biomarker for AD cells from a group of AD subjects,
wherein MCI is indicated in the subject if the output signal
determined in step (b) is less than the lowest output signal for
the AD cells but greater than the highest output signal for the AC
cells; (d) plotting the output signals of the diagnostic biomarker
for the AD cells as a function of their AD duration, wherein each
AD duration is the age difference between the AD subject's age at
the time of clinical onset of AD and the AD subject's age at the
time of collecting one or more cells for generating the output
signals of the diagnostic biomarker; (e) fitting a function to the
plotted output signals of step (d); and (f) if MCI is indicated in
step (c), predicting the time to clinical onset of AD dementia by
inputting into the fit function the output signal of the diagnostic
biomarker determined in step (b) and determining the time to
clinical onset of AD dementia.
[0009] In another aspect, a method of predicting the time to
clinical onset of AD dementia in a subject comprises:
(a) obtaining one or more cells from the subject; (b) determining
an output signal of one or more diagnostic biomarkers using the one
or more cells from the subject, wherein the diagnostic biomarker is
chosen from AD Index Biomarker, Morphometric Imaging Biomarker and
PKC Epsilon Biomarker; (c) comparing the output signal determined
in step (b) to output signals of the diagnostic biomarker for AC
cells from a group of AC subjects and to output signals of the
diagnostic biomarker for AD cells from a group of AD subjects,
wherein MCI is indicated in the subject if the output signal
determined in step (b) is less than the lowest output signal for
the AD cells but greater than the highest output signal for the AC
cells; (d) plotting the output signals of the diagnostic biomarker
for the AD cells as a function of their AD duration, wherein each
AD duration is the age difference between an AD subject's age at
the time of clinical onset of AD and the AD subject's age at the
time of collecting one or more cells for generating the output
signals of the diagnostic biomarker; (e) plotting the output
signals of the diagnostic biomarker for the AC cells as a function
of their age difference, wherein each age difference is the
difference between an AC subject's age at the time of collecting
one or more cells for generating the output signals of the
diagnostic biomarker and the age of the oldest AC subject at the
time of collecting one or more cells for generating the output
signals of the diagnostic biomarker; (f) fitting a function to the
plotted output signals of steps (d) and (e); and (g) if MCI is
indicated in step (c), predicting the time to clinical onset of AD
dementia by inputting into the fit function the output signal of
the diagnostic biomarker determined in step (b) and determining the
time to clinical onset of AD dementia.
[0010] In another aspect, a method of screening for a compound
useful for treating MCI or preventing the clinical onset of AD
dementia comprises:
(a) obtaining one or more cells from a subject; (b) determining an
output signal of one or more diagnostic biomarkers using the one or
more cells from the subject, wherein the diagnostic biomarker is
chosen from AD Index Biomarker, Morphometric Imaging Biomarker and
PKC Epsilon Biomarker; (c) comparing the output signal determined
in step (b) to output signals of the diagnostic biomarker for AC
cells and for AD cells, wherein MCI is indicated in the subject if
the output signal determined in step (b) is less than the lowest
output signal for the AD cells but greater than the highest output
signal for the AC cells; (d) if MCI is indicated in step (c),
determining the output signal of the diagnostic biomarker in step
(b) after contacting cells from the subject with a compound for an
initial time period and/or for an ongoing time period; and (e)
comparing the output signal determined in step (d) to the output
signal determined in step (b), wherein the test compound is
indicated as useful for the treatment of MCI or the prevention of
the clinical onset of AD dementia if the output signal determined
in step (d) is less than the output signal determined in step
(b).
[0011] In another aspect, a method of evaluating or monitoring the
therapeutic benefit of an agent for treating MCI or preventing the
clinical onset of AD dementia in a subject comprises:
(a) obtaining one or more cells from a subject; (b) determining an
output signal of one or more diagnostic biomarkers using the one or
more cells from the subject, wherein the diagnostic biomarker is
chosen from AD Index Biomarker, Morphometric Imaging Biomarker and
PKC Epsilon Biomarker; (c) comparing the output signal determined
in step (b) to output signals of the diagnostic biomarker for AC
cells and for AD cells, wherein MCI is indicated in the subject if
the output signal determined in step (b) is less than the lowest
output signal for the AD cells but greater than the highest output
signal for the AC cells; (d) if MCI is indicated in step (c),
determining the output signal of the diagnostic biomarker in step
(b) using one or more cells from the subject after initial,
ongoing, and/or cessation of treatment with an agent; and (e)
comparing the output signal determined in step (d) to the output
signal determined in step (b), wherein the agent is indicated as
providing therapeutic benefit for treating MCI or preventing the
clinical onset of AD dementia in the subject if the output signal
determined in step (d) is equal to or less than the output signal
determined in step (b).
BRIEF DESCRIPTION OF THE FIGURES
[0012] FIGS. 1A and 1B show a correlation between the loss of
synapses and the Mini-Mental State Examination (MMSE) score in MCI
patients based on the average of the total number of synapses in
the outer molecular layer of the hippocampal dentate gyms and the
average MMSE score for three populations: Age-matched controls
(AC), MCI patients, and AD patients. In particular, FIG. 1A shows
the total number of synapses.times.10.sup.10 in the outer molecular
layer of the hippocampal dentate gyrus (closed triangles and the
left y-scale) and the MMSE score (open triangles and the right
y-scale). FIG. 1B uses the same values as in FIG. 1A and scales
them between 0 and 100, showing a severity score for total number
of synapses and the MMSE score. The curves are the best-fit
logistic functions.
[0013] FIG. 2A shows a linear dependence of the AD Index Biomarker
on the MMSE score for AC cells (squares) and AD cells (circles).
FIG. 2B shows normalized output signals for AC (squares) and AD
(circles) cells as a function of AD duration for the AD cells and
age difference for the AC cells. AD duration for the AD cells was
the age difference between an AD subject's age at the time of
clinical onset of AD and the AD subject's age at the time of
collecting one or more cells for generating the output signals of
the diagnostic biomarker. Age difference for the AC cells was the
difference between an AC subject's age at the time of collecting
one or more cells for generating the output signals of the
diagnostic biomarker and the age at the time of collecting one or
more cells for generating the output signals of the diagnostic
biomarker of the oldest AC subject in the AC group. The age
difference for the AC group was plotted to the left of the AD onset
as negative age differences.
[0014] FIGS. 3A and 3B show Severity Scores for the Morphometric
Imaging Biomarker and the PKC Epsilon Biomarker as a function of AD
duration; FIG. 3A plots the output signals of the Morphometric
Imaging Biomarker as a function of AD duration (The minimum and
maximum values for Ln(A/N) were scaled between 0 and 100) and FIG.
3B plots the output signals of the PKC Epsilon Biomarker as a
function of AD duration (The minimum and maximum values for S/I
were called between 0 and 100.). Each plot includes a linear fit
line.
[0015] FIG. 4A shows normalized output signals for the Morphometric
Imaging Biomarker as a function of AD duration for the AD cells and
age difference for the AC cells. FIG. 4B shows normalized output
signals for the PKC Epsilon Biomarker as a function of AD duration
for the AD cells and age difference for the AC cells. The age
difference for the AC group was plotted to the left of the AD onset
line in both figures. The error bars are standard errors of the
mean. The vertical arrows indicate the gap between the AC and AD
groups. Patients with a Severity Score within this gap are MCI
patients.
[0016] FIG. 5A is similar to FIG. 4A but further illustrates the
predictive value of Morphometric Imaging Biomarker applying the
linear dependence of the AD group. FIG. 5B is similar to FIG. 4B
but further illustrates the predictive value of the PKC Epsilon
Biomarker following the linear dependence of the AD group.
[0017] FIGS. 6A, 6B, and 6C illustrate the predictive value of the
Morphometric, PKC Epsilon, and AD Index Biomarkers, respectively,
applying a logistic fit function rather than linear.
[0018] FIG. 7A shows the significant overlap in the Severity Score
of the three biomarkers. FIG. 7B shows the significant overlap for
the average for the total number of synapses in the outer molecular
layer of the hippocampal dentate gyrus and the average MMSE score
for the three populations, AC, MCI, and AD.
[0019] FIGS. 8A, 8B, and 8C shows logistic fit curves for ranked
output signals for the Morphometric Imaging, PKC Epsilon, and AD
Index Biomarkers, respectively. The MCI patients are located in the
gap between the AC and AD groups of patients as indicated by the
horizontal arrows and thick lines. FIG. 8D summarizes FIGS. 8A, 8B,
and 8C (Morphometric Imaging--red; PKC Epsilon--green; AD
Index--blue).
DESCRIPTION
[0020] As used herein, the singular forms "a," "an," and "the"
include plural reference.
[0021] As used herein, "protein kinase C activator" or "PKC
activator" refers to a substance that increases the rate of the
reaction catalyzed by PKC. PKC activators can be non-specific or
specific activators. A specific activator activates one PKC
isoform, e.g., PKC-.epsilon. (epsilon), to a greater detectable
extent than another PKC isoform.
[0022] The term "subject" or "subjects" used herein is
non-limiting. It refers to humans, but can also include other
mammals, such as mice, rats, monkeys, and apes.
[0023] The only pathologic hallmark of the autopsy AD brain that is
closely correlated with the extent of cognitive impairment is the
loss of synapses. Scheff et al., "Hippocampal synaptic loss in
early Alzheimer's disease and mild cognitive impairment," Neurobiol
Aging 27(10):1372-84 (2006); Masliah et al., "Physical basis of
cognitive alterations in Alzheimer's disease: synapse loss is the
major correlate of cognitive impairment." Ann Neurol. Oct;
30(4):572-80 (1991). Amyloid plaques are not closely correlated
with the degree of cognitive deficits. The total number of
synapses, however, are closely correlated with cognitive
performance in life. Id. Many patients with impairment of cognition
that have not reached the level required for clinical diagnosis of
dementia have been classified as having Mild Cognitive Impairment.
Scheff et al., Neurobiol Aging 27(10):1372-84. A significant
proportion (approximately 60%) of MCI patients progress to a
diagnosis of AD. Many MCI patients have no plaques, but do show a
significant loss of synapses that closely correlate with the
cognitive deficits (see FIGS. 1A and 1B). Id. Furthermore all three
populations, Age-matched Controls (AC), MCI patients, and AD
patients show a correlation between the total number of synapses
and the results of the MMSE, which is a widely used tool for
cognitive screening (see FIGS. 1A and 1B). Id. These collective
clinical and pathological findings suggest that the synaptic loss
associated with AD has already begun before the onset of AD
dementia.
[0024] One cause of the Alzheimer's disease synaptic loss is the
pathological reduction of synaptogenic PKC.epsilon. isozymes and
their downstream synaptogenic substrates, such as brain-derived
neurotrophic factor. Hongpaisan et al., "PKC.epsilon. Activation
Prevents Synaptic Loss, A.beta. Elevation, and Cognitive Deficits
in Alzheimer's Disease Transgenic Mice," J. Neuroscience,
31(2):630-643 (2011); Khan et al., "PKC.epsilon. Deficits in
Alzheimer's Disease Brains and Skin Fibroblasts," Journal of
Alzheimer's Disease, 2015;43(2):491-509. The reduction of
PKC.alpha. and .epsilon. occurs in association with elevation of
soluble beta amyloid protein (A.beta.), but before the appearance
of the amyloid plaques or neuronal loss. Id.
[0025] Three biomarkers for AD--PKC.epsilon. Biomarker, AD Index
Biomarker, and the Morphometric Imaging Biomarker--are related to
synaptic formation, and were found to increase in abnormality as AD
progresses. Khan et al., Journal of Alzheimer's Disease,
2015;43(2):491-509. All three biomarkers have also been found to
correlate with brain changes at autopsy that identifies the AD
pathologic diagnosis. The present inventors have developed a
Biomarker Severity Score, which corresponds to output signals of a
respective biomarker for Age-matched controls (AC) and AD patients.
The output signals can, but need not, be normalized, e.g. scaled
between 0 and 100%. In one embodiment, the Biomarker Severity Score
is represented as a continuous logistic fit function on normalized
values (between 0 and 100%) of the output signals for a respective
biomarker. The present inventors have discovered that these
biomarkers, using the Biomarker Severity Score, can detect the
signature of AD before the clinical onset of dementia, such as
years before clinical onset, and can be used to diagnose MCI,
monitor the progression of MCI, and predict the time to clinical
onset of AD dementia. Using the Biomarker Severity Score, the
inventors have also discovered 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.
[0026] In one aspect, a method of diagnosing MCI in a subject
comprises:
(a) obtaining one or more cells from the subject; (b) determining
an output signal of one or more diagnostic biomarkers using the one
or more cells from the subject, wherein the diagnostic biomarker is
chosen from AD Index Biomarker, Morphometric Imaging Biomarker and
PKC Epsilon Biomarker; and (c) comparing the output signal
determined in step (b) to output signals of the diagnostic
biomarker for AC cells from a group of AC subjects and to output
signals of the diagnostic biomarker for AD cells from a group of AD
subjects, wherein MCI is indicated in the subject if the output
signal determined in step (b) is less than the lowest output signal
for the AD cells but greater than the highest output signal for the
AC cells.
[0027] The method may further comprise predicting the time to
clinical onset of AD dementia, comprising:
(1) plotting the output signals of the diagnostic biomarker for the
AD cells as a function of their AD duration, wherein each AD
duration is the age difference between the AD subject's age at the
time of clinical onset of AD and the AD subject's age at the time
of collecting one or more cells for generating the output signals
of the diagnostic biomarker; (2) fitting a function to the plotted
output signals of step (1); and (3) if MCI is indicated in step
(c), predicting the time to clinical onset of AD dementia by
inputting into the fit function the output signal of the diagnostic
biomarker determined in step (b) and determining the time to
clinical onset of AD dementia. FIGS. 5A and 5B show an example of
predicting the time to clinical onset of AD dementia for a
hypothetical MCI subject. In some embodiments, the fit function is
a linear function.
[0028] Alternatively, the method may further comprise predicting
the time to clinical onset of AD dementia, comprising:
(1) plotting the output signals of the diagnostic biomarker for the
AD cells as a function of their AD duration, wherein each AD
duration is the age difference between an AD subject's age at the
time of clinical onset of AD and the AD subject's age at the time
of collecting one or more cells for generating the output signals
of the diagnostic biomarker; (2) plotting the output signals of the
diagnostic biomarker for the AC cells as a function of their age
difference, wherein each age difference is the difference between
an AC subject's age at the time of collecting one or more cells for
generating the output signals of the diagnostic biomarker and the
age of the oldest AC subject at the time of collecting one or more
cells for generating the output signals of the diagnostic
biomarker; (3) fitting a function to the plotted output signals of
steps (1) and (2); and (4) if MCI is indicated in step (c),
predicting the time to clinical onset of AD dementia by inputting
into the fit function the output signal of the diagnostic biomarker
determined in step (b) and determining the time to clinical onset
of AD dementia. In some embodiments, the fit function is a logistic
function (see FIGS. 6A, 6B, and 6C).
[0029] In another aspect, a method of monitoring the progression of
MCI comprises repeating steps (a) through (c) above at one or more
subsequent points in time, wherein the subject has progressed
toward the clinical onset of AD dementia if the output signals
determined in step (b) above have increased over time.
[0030] In a further aspect, a method of screening for a compound
useful for treating MCI or preventing the clinical onset of AD
dementia comprises:
(a) obtaining one or more cells from a subject; (b) determining an
output signal of one or more diagnostic biomarkers using the one or
more cells from the subject, wherein the diagnostic biomarker is
chosen from AD Index Biomarker, Morphometric Imaging Biomarker and
PKC Epsilon Biomarker; (c) comparing the output signal determined
in step (b) to output signals of the diagnostic biomarker for AC
cells and for AD cells, wherein MCI is indicated in the subject if
the output signal determined in step (b) is less than the lowest
output signal for the AD cells but greater than the highest output
signal for the AC cells; (d) if MCI is indicated in step (c),
determining the output signal of the diagnostic biomarker in step
(b) after contacting cells from the subject with a compound for an
initial time period and/or for an ongoing time period; and (e)
comparing the output signal determined in step (d) to the output
signal determined in step (b), wherein the test compound is
indicated as useful for the treatment of MCI or the prevention of
the clinical onset of AD dementia if the output signal determined
in step (d) is less than the output signal determined in step
(b).
[0031] The present disclosure also includes a method of screening
for a compound useful for treating MCI or preventing the clinical
onset of AD dementia, comprising:
(a) obtaining one or more cells from a non-AD, non-demented,
non-MCI subject; (b) contacting the one or more cells with an
A.beta. peptide; (c) determining an output signal of one or more
diagnostic biomarkers using the one or more cells contacted with
the A.beta. peptide, wherein the diagnostic biomarker is chosen
from AD Index Biomarker, Morphometric Imaging Biomarker and PKC
Epsilon Biomarker; (d) comparing the output signal determined in
step (c) to output signals of the diagnostic biomarker for AC cells
and for AD cells, wherein MCI is triggered in the subject by step
(b) if the output signal determined in step (c) is less than the
lowest output signal for the AD cells but greater than the highest
output signal for the AC cells; (e) if MCI is indicated in step
(d), determining the output signal of the diagnostic biomarker in
step (c) after contacting cells from the subject with a compound
for an initial time period and/or for an ongoing time period; and
(f) comparing the output signal determined in step (e) to the
output signal determined in step (b), wherein the test compound is
indicated as useful for the treatment of MCI or the prevention of
the clinical onset of AD dementia if the output signal determined
in step (d) is less than the output signal determined in step
(b).
[0032] In another aspect, a method of evaluating or monitoring the
therapeutic benefit of an agent for treating MCI or preventing the
clinical onset of AD dementia in a subject comprises:
(a) obtaining one or more cells from a subject; (b) determining an
output signal of one or more diagnostic biomarkers using the one or
more cells from the subject, wherein the diagnostic biomarker is
chosen from AD Index Biomarker, Morphometric Imaging Biomarker and
PKC Epsilon Biomarker; (c) comparing the output signal determined
in step (b) to output signals of the diagnostic biomarker for AC
cells and for AD cells, wherein MCI is indicated in the subject if
the output signal determined in step (b) is less than the lowest
output signal for the AD cells but greater than the highest output
signal for the AC cells; (d) if MCI is indicated in step (c),
determining the output signal of the diagnostic biomarker in step
(b) using one or more cells from the subject after initial,
ongoing, and/or cessation of treatment with an agent; and (e)
comparing the output signal determined in step (d) to the output
signal determined in step (b), wherein the agent is indicated as
providing therapeutic benefit for treating MCI or preventing the
clinical onset of AD dementia in the subject if the output signal
determined in step (d) is equal to or less than the output signal
determined in step (b).
[0033] The output signals for AC cells and for AD cells as
described herein may be determined at or around the same time as
determining the output signal for the subject, or the output
signals for AC cells and for AD cells may be determined ahead of
time, for example, and maintained in a database for comparison to
an output signal determined for a given subject.
[0034] The AC cells as described herein should be age-matched
non-AD, non-MCI cells, i.e., should be obtained from an age-matched
non-AD, non-MCI population. In some embodiments, the AC cells are
age-matched non-AD, non-demented, non-MCI cells, i.e., should be
obtained from an age-matched non-AD, non-demented, non-MCI
population.
[0035] In some embodiments, the methods described herein are
performed using a subject who displays no phenotypic symptoms of
AD, such as a subject who displays no phenotypic symptoms of AD,
but has one or more risk factors for developing AD.
[0036] The output signals of the diagnostic biomarker for AC cells
and AD cells that are compared to the output signal of the subject
can be average output signals. For example, output signals, as well
as the age differences and AD durations as described herein, may be
averaged within five-year age intervals resulting in an average
output signal and average age difference for each five-year age
interval. It will be apparent to those of ordinary skill in the art
that other intervals may be applied.
[0037] In some embodiments, the cells used to determine the output
signals of the biomarkers described herein may be peripheral cells
(i.e., cells obtained from non-CNS tissue), including, but not
limited to fibroblast cells or blood cells. In some embodiments,
the cells are skin fibroblast cells. In other embodiments, the
cells are blood lymphocyte cells.
AD Index Biomarker
[0038] The "AD Index Biomarker" refers to an assay that measures
the change in ratio of a phosphorylated first MAP kinase protein
and a phosphorylated second MAP kinase protein when the cells are
treated with an agent that is a protein kinase C (PKC) activator.
As used herein, determining an output signal of the AD Index
Biomarker comprises (i) contacting one or more cells from a subject
with an agent that is a PKC activator; (ii) measuring the ratio of
a phosphorylated first MAP kinase protein to a phosphorylated
second MAP kinase protein, wherein the phosphorylated first and
second MAP kinase proteins are obtained from the cells after the
contacting in step (i); (iii) measuring the ratio of phosphorylated
first MAP kinase protein to phosphorylated second MAP kinase
protein in one or more cells from the subject that have not been
contacted with the agent that is a PKC activator used in step (i);
and (iv) subtracting the ratio obtained in step (iii) from the
ratio obtained in step (ii).
[0039] The phosphorylated MAP kinase proteins may be sequence
variants of each other and belong to the same family of proteins.
In some embodiments, the phosphorylated first MAP kinase protein is
phosphorylated Erk1 and the phosphorylated second MAP kinase
protein is phosphorylated Erk2.
[0040] The AD Index assay is not limited to the use of any
particular PKC activator. In some embodiments, the PKC activator is
chosen from bradykinin, bryostatin, bryologs, neristatin,
8-[2-(2-pentyl-cyclopropylmethyl)cyclopropyl]-octanoic acid
(DCPLA), and esters of DCPLA. For example, the bryostatin may be
chosen from bryostatin-1, bryostatin-2, bryostatin-3, bryostatin-4,
bryostatin-5, bryostatin-6, bryostatin-7, bryostatin-8,
bryostatin-9, bryostatin-10, bryostatin-11, bryostatin-12,
bryostatin-13, bryostatin-14, bryostatin-15, bryostatin-16,
bryostatin-17, or bryostatin-18. Examples of suitable PKC
activators are disclosed in U.S. Patent Publication No.
2014/0315990, which is incorporated herein by reference.
[0041] U.S. Pat. No. 7,595,167 and U.S. Patent Application
Publication Number 2014/0031245 disclose techniques for carrying
out the AD Index assay and are incorporated herein by reference.
Thus, the AD Index assay may be performed as described in those
publications. For example, in certain embodiments, the PKC
activator is bradykinin and the first and second MAP kinase
proteins are Erk1 and Erk2, respectively.
Morphometric Imaging Biomarker
[0042] The "Morphometric Imaging Biomarker" refers to an assay for
measuring cellular aggregation. As used herein, determining an
output signal of the Morphometric Imaging Biomarker comprises (i)
culturing one or more cells from a subject for a time period
sufficient to achieve cell aggregation; (ii) determining the
average area of cell aggregates (A) and dividing the average area
by the number of aggregates (N) to obtain the average area per
number of aggregates (A/N); and (iii) calculating the natural
logarithm of (A/N).
[0043] The one or more cells may be cultured in a cell media for
growth, such as, for example, a protein mixture. In some
embodiments, the protein mixture is a gelatinous protein mixture. A
non-limiting exemplary gelatinous protein mixture is Matrigel.TM..
Matrigel.TM. is the trade name for a gelatinous protein mixture
secreted by the Engelbreth-Holm-Swarm (EHS) mouse sarcoma cells and
marketed by BD Biosciences. This mixture resembles the complex
extracellular environment found in many tissues and is used by cell
biologists as a substrate for cell culture.
[0044] These and other techniques for culturing cells and
determining the area and number of aggregates are described in U.S.
Pat. No. 8,658,134 and International Patent Publication No.
WO2015/103495, which are incorporated herein by reference.
PKC Epsilon Biomarker
[0045] The "PKC Epsilon Biomarker" refers to an assay that measures
the change in PKC.epsilon. when the cells are treated with an
A.beta. peptide. As used herein, determining an output signal of
the PKC Epsilon Biomarker comprises (i) determining the
PKC.epsilon. level in one or more cells from a subject; (ii)
contacting the one or more cells with an A.beta. peptide; (iii)
determining the PKC epsilon level in the one or more cells in step
(ii) after the contacting step; and (iv) calculating the output
signal as the ratio of the slope (S) and intercept (I), (S/I), of
the change in PKC.epsilon. level as a function of A.beta. peptide
concentration. U.S. Patent Publication No. 2014/0038186 discloses
A.beta. peptides, contacting cells with an A.beta. peptide, and
determining PKC.epsilon. levels and is incorporated herein by
reference.
[0046] The following examples are provided by way of illustration
to further describe certain preferred embodiments of the invention,
and are not intended to be limiting of the present disclosure.
EXAMPLES
[0047] The predictive value of the Biomarker Severity Score was
investigated using the AD Index Biomarker, Morphometric Imaging
Biomarker, and PKC Epsilon Biomarker. Output signals for the three
biomarkers, Ln (A/N), S/I, and
(pERK.sub.1/pERK.sub.2).sup.BK+-(pERK.sub.1/pERK.sub.2).sup.BK- for
the Morphometric Imaging Biomarker, PKC Epsilon Biomarker, and AD
Index Biomarker, respectively, were normalized for two patient
populations, AD and AC. The normalized output signals of the
biomarkers were shown as a function of the age difference. For the
AD group, the age difference was between the age of harvesting for
the skin biopsy and the clinical onset of the disease, which was a
measure of the disease duration. For the AC group, the current age
was subtracted from the oldest age in the AC group and was plotted
to the left of the AD onset (dotted vertical line in FIGS. 4A and
4B). The output signals for the PKC.epsilon. and Morphometric
Imaging Biomarkers, as well as the age differences as described
herein, were averaged within five-year age intervals resulting in
an average output signal and average age difference for each
five-year age interval. For the AD Index Biomarker, this average
was not necessary because of the abundance of patient data. The
output signals for the AC cells were plotted on the left of the
disease onset (dotted vertical line in FIGS. 4A and 4B).
[0048] The inventors found that the Severity Scores remained
constant for AC cells, representing the baseline for the
biomarkers. See, e.g., FIGS. 4A and 4B. The Severity Scores also
significantly separated the outputs for AC and AD cells, leaving a
gap (greater than 40%) in which Biomarker Severity Scores of MCI
patients would fall, indicating that each of the biomarkers can
detect the signature of AD several years before dementia onset,
providing a predictive risk of progression to AD dementia. See,
e.g., FIGS. 4A and 4B. In particular, the results provided strong
evidence that patients measured with Severity Scores within the
separation "gap" will have synaptic loss and MCI that will progress
to the stage of AD dementia and its associated pathological
hallmarks. It was also found that the Biomarker Severity Scores
progressively increased to the time of AD dementia onset (see,
e.g., FIGS. 2B, 4A, 4B, 5A, 5B, 6A-6C, 7B, and 8A-8D) and that the
Severity Scores remained significantly above baseline at the time
of dementia onset (see, e.g., FIGS. 2A and 2B).
[0049] Two out of three biomarkers showed a linear increase of the
output signal with the age difference (disease duration) for the AD
group (linear fit lines in FIGS. 3A and 3B). The lowest Severity
Score for AD was .about.12% for the Morphometric Imaging Biomarker
and for the PKC Epsilon Biomarker (intersection of linear fit lines
with the y axis in FIGS. 3A and 3B). This non-zero value of
.about.12% for the Severity Score suggests that both biomarkers can
detect patients before the onset of dementia. To assess this
possibility, the Severity Scores for AC cells were included to
access the lowest values of the output signals for the biomarkers
(FIGS. 4A, 4B, 5A, and 5B). Due to the normalization procedure in
FIGS. 3A and 3B, the maximum signal for the age difference explored
was 100%. However, the dependence of the Severity Score on the AD
group age difference (disease duration) was expected to saturate
for large age differences. Therefore, the linear dependence in the
AD group was considered only as a first approximation, and a
potentially improved approximation was made as a sigmoidal/logistic
function which saturated for large age differences (FIGS.
6A-6C).
[0050] The output signals for the AD Index Biomarker were
determined as the change in ratio of phosphorylated ERK.sub.1 and
ERK.sub.2 when skin fibroblast cells were treated with bradykinin
(BK+), and were quantified by the difference,
(pERK.sub.1/pERK.sub.2).sup.BK+-(pERK.sub.1/pERK.sub.2).sup.BK- as
described in Khan et al., "An internally controlled peripheral
biomarker for Alzheimer's disease: Erk1 and Erk2 responses to the
inflammatory signal bradykinin,"Proc Natl Acad Sci 29;103(35),
13203-7 (2006), and Khan et al., "Early diagnostic accuracy and
pathophysiologic relevance of an autopsy-confirmed Alzheimer's
disease peripheral biomarker," Neurobiol Aging, 31(6), 889-900
(2010), the methods of which are incorporated herein by reference.
See also FIGS. 2A and 2B.
[0051] The output signals for the Morphometric Imaging Biomarker
were determined by culturing the cells on a thick (1.8 mm)
substrate of Matrigel for 48 hours and using image analysis
software to determine (A/N), as described in Chirila et al.,
"Spatiotemporal Complexity of Fibroblast Networks Screens for
Alzheimer's Disease," J Alzheimer's Disease 33, 165-176 (2013) and
Chirila et al., "Fibroblast aggregation rate converges with
validated peripheral biomarkers for Alzheimer's disease," J
Alzheimer's Disease 42, 1279-94 (2014), the methods of which are
incorporated herein by reference. See also FIG. 3A.
[0052] The output signals for the PKC Epsilon Biomarker were
determined by measuring the change in PKC.epsilon. when the cells
were treated with spherical aggregates of .beta.-amyloid
Amylospheroids (ASPD), and were quantified by the ratio of the
slope (S) and Intercept (I), S/I, as described in Khan et al.,
"PKC.epsilon. Deficits in Alzheimer's Disease Brains and Skin
Fibroblasts," Journal of Alzheimer's Disease, 2015;43(2):491-509,
the methods of which are incorporated herein by reference. See also
FIG. 3B.
[0053] As shown in FIGS. 4A and 4B, both the Morphometric Imaging
Biomarker (FIG. 4A) and the PKC Epsilon Biomarker (FIG. 4B) showed
slight changes with the age difference for the AC group as well as
a significant gap of >40% between the AC and AD outputs. The
Morphometric Imaging Biomarker showed a slight increase with the
age difference, while the PKC Epsilon a slight decrease for the AC
group (AC fit lines in FIGS. 4A and 4B). The Severity Scores for
both biomarkers for the AC group was below 15% and was almost flat
for the age differences studied. A saturation toward the lower
limit as the age difference became more negative was also expected
for the AC group. Therefore, the linear approximation was only a
first approximation and a potentially improved approximation was a
sigmoidal/logistic function which saturated for the lower
limit.
[0054] There was a significant signal gap between the AC and AD
groups, which were in the normalized form of the Severity Score of
.about.50% for the PKC Epsilon Biomarker (FIG. 4B) and .about.40%
for the Morphometric Imaging Biomarker (FIG. 4A)). This gap
indicates a population presence identified in clinical studies as
MCI patients (see FIGS. 1A and 1B) and shows that these two
biomarkers have predictive value. In particular, MCI patients
should fill the gap between the AC and AD groups where the signal
is greater than 15% and lower than 55% (FIGS. 4A, 4B, 5A, 5B, and
8A-8D).
[0055] In a first approximation, it was assumed that MCI patients
would follow the same linear trend with age difference as the AD
patients (extended AD linear fit line in FIGS. 5A and 5B). The MCI
patients would then fill the gap between the AD and AC populations
on the left of AD dementia onset (vertical line) (see FIGS. 5A and
5B). The intersection of the extended AD linear fit line with the
.about.15% Severity Score gave a prediction of the time in advance
of clinical onset for which these two biomarkers can detect an MCI
patient (lower horizontal arrow in FIGS. 5A and 5B). Under this
approach, the Morphometric Imaging Biomarker detected MCI patients
.about.10 years before clinical onset, while the PKC.epsilon.
biomarker detected patients >27 years before clinical onset. A
hypothetical MCI patient (triangle) and predicted time to AD onset
(upper horizontal arrow) for the Morphometric Imaging and
PKC.epsilon. Biomarkers are shown in FIG. 5A and 5B,
respectively.
[0056] A potentially improved approximation for the location of the
MCI patients group was assumed to follow the logistic function
(FIGS. 6A-6C). The intersection of the logistic functions with the
.about.15% Severity Score gave an estimate of the time in advance
of the clinical onset for which the biomarkers can detect an MCI
patient (horizontal arrows in FIGS. 6A-6C). In this approximation,
the Morphometric Imaging Biomarker detected MCI patients .about.4
years before the clinical onset (FIG. 6A), while the PKC Epsilon
Biomarker detected MCI patients .about.5 years before clinical
onset (FIG. 6B). Furthermore, the Cut-Off line, which was
determined as the intersection of the logistic fit curve with the
AD onset vertical line was the same for both of those biomarkers,
i.e. .about.45%.
[0057] The location of the MCI patients were in the gap between AC
and AD patients and likely followed a logistic type curve. However,
the AC group should be at some distance to the left of the AD onset
vertical line. Additionally, the AD group should show an upper
limit and therefore a saturation of the signal. These
considerations indicate that the predictive value of these two
biomarkers should be in between the two applied approximations.
Therefore, it was found that the Morphometric Imaging Biomarker
should be able to detect MCI patients in between 4 and 10 years
while the PKC Epsilon Biomarker should be able to detect MCI
patients in between 5 and 25 years.
[0058] The remarkable overlap of the biomarkers in their normalized
form of the Severity Score is represented by the logistic fit
functions in FIG. 7A. The inflection points determined as the
intersection between the AD onset line and the logistic curves were
practically the same for the Morphometric Imaging and PKC Epsilon
Biomarkers. FIG. 7B also shows the significant overlap for the
average for the total number of synapses in the outer molecular
layer of the hippocampal dentate gyms and the average MMSE score
for the three populations, AC, MCI, and AD. FIGS. 7A and 7B
indicate that the three biomarkers track synaptic loss.
[0059] The output signals of the three biomarkers were not noise
free. Noise can arise from the measurement methods, instruments, or
human manipulation. Ranking the output signals of the biomarkers
for the AC and AD groups alleviated noise and produced similar
results as the "age difference" approach, as shown in FIGS. 8A-8D.
The ranking of the output signal for the three biomarkers showed
the same gap between the AD and AC groups for the MCI patients and
the logistic dependence was more evident in this representation
(FIGS. 8A-8D).
[0060] All of the references, patents and printed publications
mentioned in the instant disclosure are hereby incorporated by
reference in their entirety into this application. It should be
understood that the foregoing embodiments are examples of the
present disclosure and that modifications or alterations may be
made therein without departing from the spirit and the scope of the
invention as set forth in the appended claims.
* * * * *