Alzheimer’s
disease
(AD)
is a neurodegenerativ
e disorder
characterised
by
a slow
progres-
sive
deterioration
of cognitive
capacity.
Drugs
are
urgently
needed
for
the
treatment
of AD
and
unfortunately
almost
all
clinical
trials
of AD
drug
candidates
have
failed
or been
discon-
tinued
to date.
Mathematical,
computational
and
statistical
tools
can
be
employed
in the
construction
of clinical
trial
simulators
to assist
in the
improvement
of trial
design
and
enhance
the
chances
of success
of potential
new
therapies.
Based
on
the
analysis
of a set
of clinical
data
provided
by
the
Alzheimer’s
Disease
Neuroimaging
Initiative
(ADNI)
we
developed
a simple
stochastic
mathematical
model
to simulate
the
development
and
pro-
gression
of Alzheimer’s
in a longitudinal
cohort
study.
We
show
how
this
modelling
frame-
work
could
be
used
to assess
the
effect
and
the
chances
of success
of hypothetical
treatments
that
are
administered
at different
stages
and
delay
disease
development.
We
demonstrate
that
the
detection
of the
true
efficacy
of an
AD
treatment
can
be
very
challeng-
ing,
even
if the
treatment
is highly
effective.
An
important
reason
behind
the
inability
to
detect
signals
of efficacy
in a clinical
trial
in this
therapy
area
could
be
the
high
between-
and
within-individual
variability
in the
measuremen
t of diagnostic
markers
and
endpoints,
which
consequently
results
in the
misdiagno
sis
of an
individual’s
disease
state.