38

it is not surprising to recognize that the

diagnosis and therapeutic treatment of AD

are poorly understood4,5.

We have noted previously that the current

practice of patient care has had limited impact

on the prevention, prediction, accurate diagnosis,

and effective treatment of complex diseases6,7.

This notable lack of progress, in concert with

a growing awareness of the complexity and

variability of individual patients, as well as our

limited understanding of causal mechanisms

of onset, progression and treatment of most

diseases have led to a growing demand for a

paradigm shift8. The clamor for change has

The Silent Epidemic of Alzheimer’s Disease: Can Precision Medicine Provide Effective Drug Therapies? by Stephen C. Waring DVM, Ph.D and Stephen Naylor, Ph.D

A lzheimer’sdiseaseisacomplex,multi-facetedcondition.Ourunderstandingofthecausalonset,

diagnosis,progression,andtreatmentofthediseaseislimitedandbesetwithconfusion.Itis

estimatedthat~35millionadultsworldwideareafflictedwithAlzheimer’sdisease,representing

lessthan1%oftheglobaladultpopulation.However,asignificantandpoorlyunderstood“risk

factor”forAlzheimer’sdiseaseisaging,and> > > > >95%ofpatientsare65yearsofageorolder.Thisrapidly

growingsegmentoftheglobalpopulationismorepronetoonsetofAlzheimer’s,andrepresentsasignificant

but“silent”epidemicthreateningtooverwhelmtheworld.Inthisworkwediscussthecurrentlimitations

ofourunderstandingofcausalonset,diagnosisandprogressionofthedisease,andthepaucityofeffective

therapeutictreatments.WeconsiderthepotentialofsystemsbiologyandPrecisionMedicinetounraveland

provideinsightintothecomplexcausalpathwayandnetworkbiologyofthedisease,aswellasfacilitatesafe

andeffectivetherapeuticdrugtreatments.

Introduction

Alzheimer’s disease (AD) is a progressive and

irreversible neurocognitive disorder1,2. Disease

onset and manifestation results ultimately in

loss of memory and other cognitive abilities,

as well as the inability to carry out simple daily

tasks. AD is one of the many forms of dementia

that also includes vascular dementia, dementia

with Lewy bodies, frontotemporal lobar

degeneration, Creutzfeldt-Jakob disease,

normal pressure hydrocephalus, Parkinson’s

disease dementia, Huntington’s disease, and

mixed dementia2. The pathway and network

pathobiology, causality and progression of AD

are both complex and multi-faceted3. Therefore,

led to the emergent growth of “P-Medicine”.

The P-Medicine list of endeavors includes

Personalized, Precision, Preventive, Predictive,

Pharmacotherapeutic and Patient Participatory

Medicine. In particular, it has been suggested

that Precision Medicine appears to offer some

potential in complex disease analysis and

understanding6,7. A report published by the

National Research Council defined Precision

Medicine as “…. the tailoring of medical treat-

ment to the individual characteristics of each

patient. It does not literally mean the creation

of drugs or medical devices that are unique to

a patient, but rather the ability to classify indi-

viduals into subpopulations that differ in their

39

CO

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susceptibility to a particular disease, in the

biology and/or prognosis of those diseases they

may develop, or in their response to a specific

treatment”9. Such a development leads to the

question, can Precision Medicine provide any

meaningful insight into AD?

P-Medicine in the guise of both Personalized

and Precision Medicine is over a decade old,

and continues to be accompanied by enthusi-

astic expectations6. However, it is unclear as

to the actual impact of Personalized/Precision

Medicine on even a “simple” disease such as

diabetes, which is now aptly described as a

global pandemic7. The International Diabetes

Federation estimated that ~733 million adults

were suffering from this disease either in the

form of impaired glucose tolerance (i.e.

pre-diabetes) or diabetes in 201510. This

represents ~15.5% of the adult global population.

Based on the statistics the initial and tentative

conclusion is that to date, the impact of

Personalized and Precision Medicine on a

“simple” disease such as diabetes, with a

significant and well defined population base

of patients has been minimal7.

In stark contrast, only ~46.8 million adults

worldwide were deemed in 2015 to have some

form of neurocognitive disorder as defined by

dementia criteria11. AD is by far the most

common type of dementia and accounts for

~60-80% of all cases. It is estimated that there

are ~35 million adults worldwide suffering

from AD, which represents less than <1% of the

adult population11,12. We consider here the

expectations of Personalized/Precision

Medicine advocates as they pertain to the

treatment of a complex disease state such as

AD. This analysis is compounded by the fact

that the global AD population appears to

be a relatively small, well defined group from

an age-related perspective, but paradoxically

also heterogeneous.

Americans now diagnosed with the disease12.

Every sixty-seven seconds someone in the USA

is diagnosed with AD, and this is projected to

change to every thirty-three seconds by 2050.

It is startling to note that >96% (~5.1 million,)

of those US adults with AD are over the age of

65, which represents 1-in-9 older Americans

(see Figure 1)12. Recent reports have indicated a

cautiously encouraging decline in the number

of new cases annually15,16. However, while in

effect leading to a lower trajectory of incidence,

the impact on overall numbers with AD at any

given point in time will be small due to the

vast increase in numbers of individuals at risk.

This is due to the fact that a large segment of the

US “Baby Boomer” population (born 1946-1964)

has begun to reach the age of sixty-five and is

now at elevated risk for AD onset as highlighted

in Figure 1. By 2030 the US population that is

65 years of age and older will constitute 20% of

the total population, and it is estimated that by

2050, the number of people aged 65 and older

in the US with AD will triple12.

90.0

76.5

45.0

22.5

0

2015 2020 2030 2040 2050

14

10.5

7

3.5

0

5.1

Nu

mb

er

wit

h A

lzh

eim

er’

s D

ise

ase

Nu

mb

er

of

pe

op

le 6

5+

ye

ars

of

ag

e

5.8

8.4

11.6

Figure 1. Projected prevalence of Alzheimer’s disease in the USA adult population for individuals

65 years of age and older.

The solid blocks represent the total number of adults 65 years of age and older

The Red line represents the estimated number of 65 years of age and older patients with AD.

13.8

YEAR

(MIL

LIO

N)

(MIL

LIO

N)

Alzheimer’s Disease – Problems

The impact of any disease is mitigated by our

understanding of causality, prevention, diagnosis,

and therapeutic treatment. The importance of

safe and effective treatment is reflected in the

size of the global pharmaceutical market which

is projected to achieve $1.3 trillion in sales by

201613. It is noteworthy that the size of the

USA pharmaceutical market in 2014 was $376.3

billion. This is greater than the other top ten

markets combined as reflected by sales in Japan

($78. billion), China ($75.9 billion), Germany

($45.7 billion), France ($38.1 billion), Italy

($28.5 billion), United Kingdom ($25.2 billion),

Brazil ($23.8 billion), Spain ($21.0 billion), and

Canada ($20.9 billion)14. In addition the US

pharmaceutical industry spent $51.2 billion on

therapeutic research and development in 2014 13.

We would therefore predict that if any one

country was making significant inroads into the

effective treatment of AD it would be the USA.

Impact of Alzheimer’s Disease

Unfortunately that does not appear to be the

case. AD is now the sixth leading cause of

death in the USA, with an estimated 5.3 million

40

80%

60%

40%

20%

0

-20%

-40%

-60%

BreastCancer

-2%-11%

-14%

-23%

71%

-52%

ProstateCancer

StrokeHeart Disease HIV Alzheimer’s

Figure 2. Percentage change in deaths occurring due to specific disease indication. Data shown for the USA, and

the resultant change between the years 2000-2013 for breast cancer, prostate cancer, heart disease, stroke, HIV/AIDS

and AD respectively.

Pe

rce

nta

ge

Ch

an

ge

(2

00

0-2

01

3)

greatest risk factor for AD beyond age, and the

strongest genetic risk factor reported to date.

With the advent of genome-wide association

studies (GWAS), a number of other genetic

risk factors, all with very modest effects but

potentially relevant pathogenic mechanisms,

have been identified as detailed in Table 1a. To

date, we now know of the three familial (early

onset) genes associated with autosomal

dominant inherited AD (APP, PSEN1, and

PSEN2) accounting for less than 5% of all

disease, a late onset gene (APOE) that accounts

for up to 50% of disease, and several validated

loci for other genetic variants that contribute

to small but yet to be determined percentages

of disease25,26. All represent four basic

functional categories: amyloid-ßeta relevant

(production, degradation, or clearance),

cholesterol metabolism, innate immunity,

and cellular signaling26,27.

Non-genetic factors such as cardiovascular

risk factors, hypertension, diabetes, obesity in

midlife, smoking, and high cholesterol have

also been reported. Whilst these risk factors

may be significant for modifying upstream

As noted above AD is now a significant cause

of death in the US, with 1-in-3 seniors dying in

a given year having been diagnosed with AD or

non-Alzheimer’s dementia17. When compared

to major causes of death associated with breast

cancer, prostate cancer, cardiovascular disease,

stroke, and HIV, all of which have shown a

significant decline over the past 15 years, AD is

the only one of these showing a major increase

over the same period as summarized in Figure

2. There is a major limitation that underlies

interpretation of mortality data for AD having

to do with how this information is recorded on

the death certificate. For many individuals with

AD, the death certificate often lists an acute

complication of disease such as pneumonia as

the underlying cause of death. Therefore, the

impact of AD related to mortality is grossly

underreported. Indeed, while the National

Center for Health Statistics of the Centers

for Disease Control and Prevention (CDC)

lists 84,767 deaths from AD in 2013, deaths

attributed to AD among people 75 and older

(65-74 data not available) in 2010 was around

500,00018. The authors concluded that these

deaths would not be expected in that year if the

individuals did not have AD, clearly underscoring

the limitations of death ‘with’ dementia and

death ‘from’ dementia. It is noteworthy that

the population at risk is expected to increase

over the next 25 years due to the baby boomer

effect (Figure 1), and is compounded by the

long duration of illness, of up to 8 years on

average but as much as 20 years19,20. This signif-

icantly underscores the true magnitude of this

disease in terms of an expected ever increasing

number of deaths, poor health and disability.

Hence our description of AD being the “silent

epidemic” appears to be salient and an issue to

be urgently addressed.

Risk Factors

The biggest risk factor for AD is age, with most

people diagnosed at age 65 or older. But it is

important to consider that while age is a major

influence, this is not a normal part of aging.

The search for other risk factors over the past

20 plus years has been rigorous, with particular

interest in identifying modifiable factors that

could either prevent or delay onset of disease

if interventions can be delivered early enough

in the process to considerably change an

untoward trajectory. Numerous studies have

reported factors that either increase risk or

protect against risk for developing AD21,22.

Many of these factors may be relevant to

pathways involving amyloid processing, lipid/

cholesterol transport and metabolism, neuro-

vascular, neuroinflammatory, and neuropsycho-

logical function. Apolipoprotein E is involved

in cholesterol transport mechanisms as well

as injury repair in brain tissue, and has three

different isoforms, ε2,ε3, and ε4 representing

differing abilities to traffic lipids, with ε4

(APOε4 ) being the least efficient. As a result,

carriers of either one or two copies of APOε4

are at greater risk for developing AD compared

to carriers of the other two isoforms23. An

individual positive for only one allele

(heterozygote) has up to a four-fold increase

risk, whereas an individual with both copies

(homozygote) has a more than 10-fold increase

risk for developing AD24. In fact, APOε4 is the

42

Lipid Amyloid Immunity Inflammatory Neuronal/Cellular Tansport Processing

APOE

ABCA7

CLU

CR1

PICALM

TREM2

SORL1

BIN1

MS4A

333

3

3

3

3

3

33

3

3

3

3

3

3

3

Table 1A. Genetic risk factors associated with AD onset.

Risk Factors Relevant Function

Amyloid/tau Inflammatory/ Neuronal/ Cognitive Processing Oxidative Cellular

Vascular

Cardiovascular Disease

Cerebrovascular Disease

Diabetes

LIfestyle

Midlife Hypertension

Obesity

Midlife High Cholesterol

Smoking

Dietary

Saturated Fats

Homocysteine

Traumatic Brain Injury

3

3

333

3

3

3

3

333

33

3

3

Risk Factors Relevant FunctionA critical issue in considering the expected

temporal role of various risk factors is that not

all factors represent risk equally at the individual

level and not all findings can be generalized to

other populations. Owing to the complex and

multifactorial nature of AD, the epidemiologic

concept of a component causal model28 for dis-

ease is important to consider when interpret-

ing the utility of various findings. This is not

only important for prevention, but for treat-

ment options as well. The component causal

model represents a minimal set of factors that

when present, explain why disease occurred at

a particular point in time for a particular group

of individuals. In the case of AD, this explains

why many of these reported factors are neither

sufficient nor necessary for disease to occur,

and why there are many conflicting interpre-

tations regarding epidemiological findings. It

is not surprising that with so many underlying

neurobiological mechanisms over the spectrum

of aging with potential

relevance to neurodegenerative changes that

could lead to AD, a broad range of genetic

and non-genetic factors, some direct, some

as surrogates for yet to be determined processes,

could be explanatory at a given point in time

and have minimal relevance otherwise.

For example, as stated above, APOE ε4 is

the strongest genetic risk factor for AD.

However, not everyone with APOE ε4 will

develop the disease and about half of the

individuals with AD did not inherit an APOE ε4

allele. Additionally, APOE ε4 may be an age-

dependent risk factor since reports suggest that

for individuals over 80 years of age

other factors may be more important29-31.

Biomarkers are also important factors, both in

establishing risk and in supporting a diagnosis,

assuming they have been validated and

approved. Similar to genetic markers, numerous

studies have reported on certain protein markers

in blood (individual or in a panel) that are

significantly associated with AD or with

potential neurobiological pathways32,33.

However, methods, assays, handling, and tissue

analyzed (plasma or serum) among other issues

Table 1B. Non-genetic risk factors associated with AD onset .

effects relevant to risk for developing AD, they

may be less suitable for direct therapeutic

interventions aimed at specific neurobiological

pathways as shown in Table 1b. However, their

role in interactions with said pathways and

in establishing more precise risk profiles is

important to consider, particularly since drug

monotherapy is unlikely to be effective.

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criteria for diagnosis of dementia due to AD44,

MCI45, and the newly recognized need for

preclinical staging criteria to support promising

drug trials aimed at the period when they may

be most effective46.

Two of the most critical concepts that

influenced not only the urgent need to revise

diagnostic criteria, but that are captured in the

various criteria, have to do with intermediate

stages prior to clinical onset and non-Alzheimer’s

dementias. The explosion of literature on MCI

since it was first described in the late 1990s47

has led to a better understanding of interme-

diate stages, both clinical and neuropatholog-

ical, in the continuum from normal aging to

dementia. This has been incorporated into the

criteria to account for the discordance in clin-

ical-pathological states and to provide a better

framework for establishing pre-clinical stages

upstream from even MCI, much less early AD.

Another significant concept influencing the

need for revisions in diagnostic criteria stems

from the growing body of evidence over

the past decade regarding non-Alzheimer’s

dementias, particularly Lewy body dementia

and frontotemporal dementia, and how they

are distinct as well as how they overlap with

clinical features of Alzheimer’s and with

neuropathological expectations.

The preclinical stage criteria is pivotal,

representing a long overdue recognition that

the pathophysiological processes of neurode-

generation begin well before there is clinical

evidence of dementia, possibly 20 years or

more. Even the well understood and accepted

MCI stage, where there is already substantial

neuronal loss, may be too downstream in the

continuum for disease-modifying therapies

which could account for the significantly

high failure rate of drugs tested as well as

the challenges in developing successful

treatments43. The hypothetical staging

framework proposed by the NIA-AA Preclinical

Workgroup categorizes clinically normal

individuals on a risk trajectory for AD based on

correlated with cognitive deficits and with

progression from MCI to AD22. Another

imaging technique, Pittsburgh Compound B

PET (PIB-PET) is also a significant measure of

amyloid deposition with particular utility in

establishing a risk profile as well as following

neurobiological preclinical progression4,22,42.

The recent development of tau PET imaging

holds great promise since this correlates

more closely with neuronal and synaptic loss.

Preliminary evidence with tau PET (F-T807)

indicates great potential in furthering our

understanding of the temporal relationship

between amyloid, tau, and cognitive

impairment and decline43. Coupled with the

CSF markers, neuroimaging represents the most

significant biomarker capability identified

to date. All together, they represent critical

measurements that must be taken into

consideration when planning clinical trials,

and particularly for prevention trials designed

to prevent the pathological accumulation in the

first place.

Diagnosis

In spite of an avalanche of information

regarding clinical and neuropathological

characterization of AD over the past 30 years,

the diagnosis of the disease has been predicated

on the Neurological and Communicative

Disorders and Stroke and the Alzheimer’s

Disease and Related Disorders Association

(NINCDS–ADRDA) criteria established in 1984.

These criteria considered both clinical

evidence and evidence of moderate to severe

AD neuropathology (plaques and tangles)36.

With the mounting knowledge gained from

state-of-the-art technologies such as imaging

and high-density/high-throughput genetic and

biological assays, and new information on the

clinical course of the disease, a broad consensus

to revise these criteria led to the National

Institute on Aging-Alzheimer’s Association

workgroups (NIA-AA AD Workgroup, NIA-AA

MCI Workgroup, and NIA-AA Preclinical

Workgroup) that reported new diagnostic

guidelines in 2011. These include revised

may vary between laboratories and between

multiplex platforms, making replication and

validation a major challenge. Standardization

efforts are underway and expected to shed

more precise light on the subject in the not

so distant future34.

A number of studies have established the

diagnostic utility of amyloid-βeta and tau

measured in in cerebrospinal fluid (CSF)22, 35.

Amyloid--βeta (Aβ1-42) is a measure of amyloid

deposition in the brain and tau (total and

phosphorylated) is a measure of neuronal

injury and neurodegeneration. The temporal

relationship of these markers in CSF is

hypothesized to reflect a significant departure

from normal for amyloid followed by elevated

tau35,36. However, further evaluation in large

longitudinally followed, well characterized

cohorts is required to refine our understanding

of this relationship. An international standard-

ization protocol and quality control program

has been developed through the Alzheimer’s

Association Global Biomarker Standardization

Consortium37, the Alzheimer’s Disease

Neuroimaging Initiative (ADNI)38, and the

Coalition Against Major Diseases Biomarker

Working Group39 and are now being followed

by many laboratories worldwide to help

support this critical area of investigation.

In addition to biomarkers in blood and CSF,

perhaps the most significant markers, not only

for diagnostic specificity but for profiling risk,

are based on neuroimaging. Specifically they

include structural magnetic resonance imaging

(sMRI) and positive emission tomography with

fluorodeoxyglucose (FDG-PET).4,40-42. Medial

temporal atrophy, particularly the regions of

interest (hippocampus and amygdala) relevant

to short term memory processing and

progression of memory impairment, are

evident by sMRI early in the neurodegenerative

process (pre-clinical stages). FDG-PET detects

differences in cerebral metabolism, specifically

cerebral glucose metabolism, in AD and mild

cognitive impairment (MCI) compared to

cognitively normal individuals. This is highly

44

the presence or absence of amyloidosis,

neurodegeneration, and subtle cognitive

decline (see Figure 3). Two important

distinctions are the Stage 0 group with no

evidence of amyloidosis, neurodegeneration

or cognitive impairment and least likely to be

on the AD trajectory and the newly recognized

SNAP (Suspected non-Alzheimer’s pathophys-

iology) group suggested by the Mayo Clinic48.

The percentage of individuals in the Stage 0

group progressing to dementia (rather than

to a higher Stage) would be expected to be

less than 10% and most likely would develop

non-Alzheimer’s dementia. The SNAP group,

while possessing markers of neurodegener-

ation characteristic of AD (medial temporal

atrophy, abnormal FDG-PET, and elevated tau),

does not have amyloidosis which is an

important distinction since this group not only

has a greater risk of cognitive decline than

individuals in Stage 0, but possesses neurode-

generative changes consistent with aging and

non-AD disorders. Therefore, this group fills

a critical gap between cognitively normal

individuals and cognitively impaired individuals,

and provides a more refined clinical-pathological

dementia due to Alzheimer’s, but while they

are not necessary and may not even be available

in a clinical setting, they have tremendous

relevance in establishing the diagnosis for

research interests. Ultimately, the goal is

to accelerate knowledge that will lead to

transforming clinical care for earlier and more

precise diagnosis, more effective and timely

treatment, and improved outcomes for

individuals at risk for developing AD during

their lifetime.

While this is perhaps the most exciting and

potentially transformative developments in

Alzheimer’s research to come along in some

time, there is much work needed to standardize

the use of these criteria in large, broadly

representative populations, specifically the

component parts representing biomarker

and clinical features. There also needs to be

a commitment to updating these criteria at

much shorter intervals so the relevance of

new information can be included to ensure an

ever increasing precision and pace that will

truly advance the collective effort towards a

cure. But in spite of the requisite steps that

Stage 0

Amyloid - /Neurodegeneration -

SNAP

Amyloid - /Neurodegeneration +

Stage 1

Amyloid + /Neurodegeneration -

Stage 2

Amyloid + /Neurodegeneration +

Stage 3

Amyloid + /Neurodegeneration + plus subtle cognitive decline

Distribution inPopulation

Risk forDementia

44%

25%

16%

12%

3%

~5%

10%

10%

20%

45%

MC

I A

DM

CI

No

n-A

D*

Figure 3. Stages of AD. A hypothetical continuum from a normal healthy individual

to one suffering from dementia.

SNAP=Suspected non-Alzheimer pathology

Amyloid +/- = above/below cut-off point for ß-amyloid in CSF, PET

ND+/- = above/below cut-off point for neurodegenerative markers in CSF, PET, MRI

* Probability of Non-AD higher

correlation at autopsy to distinguish between

aging, non-AD, and AD. As this is a new con-

cept just now undergoing vast scrutiny across

many large studies, it will be important to

revisit the constructs relevant to this subgroup

in light of any new findings that would impact

its utility in planning for future treatment trials

and prevention trials.

The recommendations of these three working

groups have different relevance to clinical

practice, with the criteria for both AD and

MCI intended to guide clinical diagnosis as

well as research, while those for preclinical

AD being strictly for research purposes. An

important distinction must be realized

between any criteria established for a clinical

diagnosis of dementia and the need for more

rigorous criteria necessary for use in a

research setting. The latter are designed

specifically to standardize processes to accelerate

scientific understanding and inform better

treatment and prevention trials. For instance,

the new criteria for AD incorporate biomarkers

(CSF and imaging) that help to raise the level

of confidence regarding a clinical diagnosis of

46

There have been only a handful of completed

prevention trials, in large part because they

require large patient sets, prolonged follow-up,

are complicated and expensive to conduct, and

until recently were hindered by the lack of

compelling and safe drugs or interventions to test.

Despite the enormous efforts, our limited

understanding of causal onset, diagnosis and

progression of AD is reflected in the paucity

of effective and safe treatments of the disease.

Symptomatic but limited interventions for AD

have been widely available since the 1990’s, as

summarized in Table 25,49,50. Cholinesterase

inhibitors (CI) such as tacrine, donepezil,

rivastigmine and galantamine were developed

to improve cognition and facilitate patient

function and behaviour50. In China, hupera-

zine-A, an alkaloid derived from the firmoss

Huperiza serrata, was also approved as a CI

treatment for AD patients (used as a supplement

in the USA and Europe). However, the additional

subsequent clinical trials as well as clinical

must be completed, this does represent a very

robust foundation, incorporating state-of-the-

art knowledge and technology, and a timely

opportunity to leverage this gain by not only

looking back on what may have been missed,

but in moving forward beyond just Alzheimer’s-

specific investigations to foster cross-fertili-

zation of ideas and methodologies from other

disciplines and diseases. Only then will the

promise of Precision Medicine relevant to AD

be attainable.

Drug Treatment

The history of drug development for the

treatment of AD is littered with missteps,

misleads and mistakes. Schneider and

colleagues recently reviewed this subject in

some detail and highlighted the following

periods of AD drug development focus5;

i. Cholinergic Era (1970’s-80’)- since the

early 1970’s memory had been related to the

cholinergic system, and that early work in AD

causality indicated a loss of central cholinergic

neurons. These findings lead to the develop-

ment of the first four therapeutic agents

shown in Table 2.

ii. Amyloid Cascade (early 1990’s)- the

hypothesis is predicted on amyloid-βeta

deposition facilitates hyper-phosphorylation

of tau, neurofibrillary tangle formation and

neuronal death. Although the model is simple

the validation and development of drug targets

has been complex and the output of successful

therapeutic drugs has to date been unsuccessful.

iii. Variable Clinical Trials Time Periods- the

regulatory authorities struggled to determine

the appropriate time period for late stage

clinical trials, and included a. 1986-1996, early

symptomatic trials; b. 1990-2011, six month

trials; c. 1994-2010, twelve month trials; d.

2011-present, eighteen month and disease

modifying trials. In summary, eighteen-month,

placebo-controlled trials have become a de

facto standard for trials of mild and moderate

Alzheimer’s disease even though no statistically

significant primary outcomes favoring the test

drug have been demonstrated.

Drug Year MOAa Indication CEIb

Cognex (tacrine)* 1993 CId Mild to Moderate AD 3.5

Aricept (donepezil) 1996 CI Mild to Moderate AD 3.5

Exelon (rivastigmine) 1998 CI Mild to Moderate AD 3.6

Rasadyne (galantamine) 2001 CI Mild to Moderate AD 3.7

Namenda (memantine) 2003 NMBAe Moderate to Severe AD 3.0

Huperazine-A 1994 CI AD (China) 3.0

Risperdal (risperidone) 2015 Antipsychotic Aggression in AD (Australia) 3.6

Table 2. Current therapeutic drugs approved for the treatment of AD. a MOA- Mechanism of action b CEI- CureHunter Effective Index score. It shows the top ranked drug monotherapies for the treatment of AD

based upon an analysis of all relevant articles indexed by the National Library of Medicine (PubMed). Article

relevance is determined using an artificial intelligence approach that combines and weights each article based

upon its level of evidence and the numbers of articles containing outcome statements, clinical trial or study

statements and articles with clear context of study statements. The engine provides an effectiveness index on a

scale of 1-10, with 10 being the most effective (see reference 7 for a detailed description of how a drug is scored).c drug withdrawn from the US market in 2013 due to liver toxicityd CI- Cholinesterase inhibitore NMDA- N-Methyl-D-Aspartate antagonist

iv. Mild Cognitive Impairment Trials (2000-

2005)- MCI was characterized as progressive

memory impairment and could be a potential

clinical target for early treatment intervention.

The results of all the trials in MCI were

negative, with no significant benefit on

progression or onset of AD.

v. Prodromal AD Trials (2010-Present)- The

advancement of trials for prodromal AD

followed the intent to diagnose the disease

before dementia onset and to enrich clinical

trial samples in terms of increasing confidence

that patients actually had AD pathology and

presumably would advance to dementia after

a relatively brief interval. Drug trials for such

an approach are currently ongoing.

vi. Prevention Trials (1996-Present) - these trials

are logical extensions of MCI and prodromal

trials and address the fact that the pathology

of Alzheimer’s disease is progressing perhaps

two decades before the onset of the disease.

47

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practice data demonstrated that CI therapeutic

drugs only manifested modest improvements

on the Mini Mental state Examination scores

for AD patients over a 6-12 month period.

The N-methyl-D-aspartate (NMDA) antagonist,

memantine was the first and only approved

AD therapy that acts on the glutamatergic

system. Despite a significant number of clinical

trials and clinical observation, the drug has

no discernible effect on mild AD, and modest

impact on moderate-to-severe AD patients49,50.

In addition numerous antipsychotic drugs are

commonly used to treat agitation, aggression,

and psychosis in patients with AD. Most are

prescribed off-label, with the exception of

risperidone which was recently approved in

Australia for the short-term treatment (up to

12 weeks) of severe aggression in AD50,51.

Once again it has been noted that “benefits

are moderate” and that serious adverse events

including sedation, parkinsonism, chest

infections, ankle oedema, and an increased

risk of stroke and death are also possible

when taking this drug5 0.

The limited efficacy of available therapeutic

drugs is well documented and is highlighted in

Table 2 by the relatively low CureHunter Effec-

tive Index scores. However, there has also been

considerable concern raised about the

safety and toxicological effects. For example,

tacrine, the first CI introduced in 1993 (Table 2),

was ultimately withdrawn from the US market

in 2013 due to marked hepatotoxicity52. Bayer

was forced to halt its trials of the CI drug

metrifonate due to manifestation of respiratory

paralysis in patients5. More recently the use

of risperidone by AD, as well as dementia

patients, led the FDA to require drugmaker

Janssen-Cilag, a subsidiary of Johnson &

Johnson, to market the drug with a black box

warning. Such a warning appears on the label

of a prescription medication to alert patients,

caregivers and health care providers of

important safety concerns associated with

that medication.

The segmented drug discovery and development

efforts of AD drugs continue unabated to

this day, even though our understanding of

causal onset is unclear. For example there is

disagreement about the role of amyloid plaques

and tau tangles. It appears that they are both

important in the pathology of AD, but their

individual roles in causal onset is still mired in

controversy52. The amyloid cascade hypothesis

continues to dominate drug development,

with targets being developed for individual

steps in the cascade: secretase inhibitors and

modulators, passive and active immunization

with antibodies against various epitopes of

amyloid-βeta monomers, oligomers and fibrils,

fibrilization inhibitors, anti-aggregants and

other approaches5,52. However, both anti-

amyloid and tau-based approaches are currently

being advanced in 18-month trials in patients

with mild and prodromal Alzheimer’s disease.

In addition small molecule, neurotransmitter

approaches are also being actively pursued,

primarily in the form of 5-HT6 receptor

antagonists52.

Unfortunately in recent years the development

of AD drugs has become the graveyard of

expensive failures. For example in 2012, Pfizer/

Johnson & Johnson announced the failure

of a second Phase III trial of the anti-amyloid

antibody bapineuzumab. This prompted the

proclamation that the “amyloid hypothesis is

dead”53. More recently Eli Lilly indicated that

its twice-failed late-stage drug solanezumab

would likely miss the clinical endpoint of

significantly improving cognitive function54.

Since this latter candidate is also an anti-

amyloid drug, it suggests that we are missing

the pathological-clinical link, since if

amyloid is the critical toxic component and

trial patients are at an early stage clinically,

an effective anti-amyloid therapy should

slow disease progression.

The continued failure-rate of late stage

AD candidate drugs has begat a sense of

frustration and desperation on the part of

all concerned stakeholders. In some cases this

has led to an unusual circumstance of events.

In mid-summer of 2015 Vivek Ramaswarmy,

a former hedge-fund manager with no

biotech experience, licensed an abandoned

5HT6 antagonist from GlaxoSmithKline (GSK)

for $5 million and created an AD start-up

company Axovant. The drug candidate had

been previously tested in 1,250 Alzheimer’s

patients and healthy subjects in five different

clinical studies that GSK conducted between

2008 and 2012 and then subsequently aban-

doned. Axovant gathered a team together and

without recruiting a single patient for a pivotal

study of a marginal drug designed to treat

symptoms of the disease, raised $315 million in

an IPO that resulted in a market capitalization

of the company worth greater than $1 billion55.

This exasperating situation was further com-

pounded when Pfizer announced last month

that it was terminating an AD Phase II clinical

trial of PF-05212377. They concluded that this

5HT6 drug would fail to meet the key endpoint

and put the spotlight on the drug’s mechanism

of action, raising fresh questions about the

likely efficacy of other drugs that try to

safeguard cognitive abilities by targeting the

5-HT6 receptor56. The stock price of Axovant

stock tumbled by ~50% over the subsequent

week after the Pfizer announcement. The

Pfizer AD drug failure, compounded by the

Axovant situation clearly indicates that

something has to change in terms of how

we do AD drug discovery and development.

Alzheimer’s Disease – Systems Biology and

Precision Medicine

The fate of AD drug candidates in the drug

development process over the past twenty

years stands at a remarkable 99.8% failure rate.

In addition the cost of those failures to the

pharmaceutical industry has been in excess of

$15 billion for amyloid-βeta trials alone during

that same time period57. Currently there are

twenty-three Phase I, forty-seven Phase II and

eighteen Phase III AD candidate drugs in US

clinical trials12. However, evaluation of the

individual clinical trial drug candidates reveals

that the vast majority are focused on individual

druggable targets in the amyloid-βeta or tau

48

Genetics has been recognized as an important

causal factor in AD onset acting via the

dominant mutations of APP, PSEN1 and PSEN2,

but occurs in less than 5% of patients. In

contrast it is estimated that the risk of AD

onset is 70% attributable to genetic components,

and is potentiated throughout the lifetime

of the individual26. In addition the two core

pathological harbingers of AD are amyloid

plaque and tau neurofibrillary tangles. More

recently there have been proposals that

a concomitant amyloid-tau cascade mechanism

is responsible for AD onset. This hypothesis

proposes that changes in tau and consequent

neurofibrillary tangle formation are triggered

by toxic concentrations of amyloid-βeta.

The pathways linking amyloid-βeta and tau

are not clearly understood, although several

hypotheses have been proposed. However,

a confounding observation is that post-mortem

examination of each of these classic pathological

hallmarks only explains to a limited extent the

expression of AD in the population, and leads

to troubling questions about the exact role of

Overlap denotes contributing pathways that may serve to accelerate neurodegenerative processes

0 20 40 60 80

APP/Presenilin mutations(autosomal dominant)

AD

Spor

adic

(>95

%)

(late

ons

et)

Fam

ilial

(<5%

)(e

arly

ons

et)

MCI AD

Amyloid - tau cascade

Genetic and non-genetic risk factors

Blood, CSF MRI, PETA- , tau plaques, NF tangles

Blood, CSFI, PET: inflammatory,vascular, disrupted lipid

metabolism

Imaging (MRI, PET): cell death,synaptic loss, atrophy in regions

of interest

Neuropsychological/clinicalcognitive decline

Autopsy

Diagnostic evidence

Clinical onset Biological onset Clinical onset

A- , plaques, NF tangles

Figure 4. Complex, multifactorial neuropathology of AD onset. This consists of numerous, interwoven contributory factors

that includes genetic and non-genetic risk factors, as well as cellular and molecular processes involving β-amyloid, tau,

neuroinflammatory and neurovascular events occurring as a function of time.

Neuroinflammatory/Neurovascular

Age (yrs)

β

β

pathways. Such specific approaches have already

proved to be somewhat futile, and have been

compounded by the lack of understanding of

AD causal onset.

Causal Onset-Systems Level

It is clear that AD is a multifaceted, complex

disease in which there are numerous elements

of causal onset. Therefore it is difficult to

understand how one can develop an effective

drug therapy without having a more complete

understanding of the causal process of disease

onset and progression, and incorporate such

an understanding into therapeutic drug design.

Over the past twenty-five years our understanding

of AD has increased in modest bursts of activity

and insight. It is now recognized that at least

five distinct biological processes are involved

that includes genetics, amyloid plaque, tau

tangles, neurovascular events and neuroinflam-

mation. These individual components are not

only interwoven and interrelated, but possibly

synergistic as in the amyloid-tau cascade, and

are time dependent processes. This is all

captured and summarized in Figure 4.

both amyloid-βeta and tau in AD onset and

progression50.

Recently there has been considerable effort in

understanding the role of the neurovascular

system (NVS) and onset of neurogenerative

diseases such as AD58. The NVS consists of

vascular cells such as endothelium and vascular

smooth muscle cells, as well as glial cells

and neurons. The endothelial cells are a

constitutive part of the blood-brain barrier

(BBB), and the latter controls entry of all

cellular and metabolites into the brain. One

purported involvement of the NVS suggests

the amyloid-βeta-rich environment in AD and

increases the receptor for advanced glycation

end products expression at the BBB and in

neurons amplifying amyloid-βeta-mediated

pathogenic responses. Zlokovic has suggested

the role of BBB dysfunction in amyloid-βeta

accumulation, underlies the contribution of

vascular dysfunction to AD58. Finally, it has

also recently been reported that AD pathogenesis

is not restricted just to the neuronal compartments

50

but involves significant, interactions with

immunological processes in the brain. It has

been suggested that mis-folded and aggregated

proteins bind to receptors on microglia and

astroglia cells, and trigger an innate immune

response characterized by release of inflammatory

mediators, which contribute to AD progression

and severity59. In addition glial activation

which leads to neuroinflammation contributes

to neurodegeneration and synaptic abnormalities

leading to neuronal cell death, lending more

support to the developing hypothesis that

neuroinflammation is associated with AD

pathology60. As stated above, AD causal onset

is multifaceted and temporally mediated

(Figure 4), thus how do we deal with such

complexity in attempting to design effective

therapeutic agents?

Systems Biology-Drug Discovery

The emergence of systems biology in concert

with the development of a suite of accompanying

analytical and bioinformatics tools and

technologies has facilitated the evaluation

and unraveling of complex disease mecha-

nisms8. Therefore it is not surprising that

we and others have suggested such an approach

should ultimately find widespread use in

understanding causal onset, progression and

effective treatment of any complex disease

such as AD3,10,61,62. Recently Bennett and

colleagues have suggested an approach to

drug discovery and development for effective

and safe AD drugs using a systems biology

approach61. They argued that any lead drug

candidate must disrupt molecular networks “…

that lead to the accumulation of AD neuropathology,

and trigger the neurodegenerative process that leads to

cognitive decline, and ultimately to the clinical mani-

festations of cognitive impairment and dementia due to

AD.” Note the emphasis on targeting a network

as opposed to a single pathway such as the

amyloid-βeta pathway. Based on Bennett’s

suggestions as well as our experiences we

would propose the following broad-based

systems biology approach to drug discovery:

i. Network Biology Discovery- multi-omics

analysis at the gene, protein and metabolite

level. This should include DNA methylation,

miRNA, and mRNA transcriptomic data from

human brain material derived from brain

region at the hub of neural networks sub-

serving cognition, as well as AD pathologic

and clinical quantitative traits, to nominate

genes, and therefore proteins, from networks

and nodes involved in the molecular pathways

leading to AD. We were the first group

responsible for developing a Systems biology

methods approach for integrating multi-level

‘‘omics’’ data derived from a mammalian sys-

tem62 and such approaches are now relatively

routine.

ii Identification of Potential Targets- the

network biology analysis should provide a

prioritized list of target genes. An assortment

of analytical tools primarily employing mass

spectrometry should be used to generate

protein expression data which can be analyzed

with standard regression techniques, pathway

analyses, and structural equation models to

provide empirical support for biologic

pathways linking proteins to AD endophenotypes.

As Bennett notes “the empirical data will be fed back

to the network modeling stage to refine the network

analyses. This component ensures that the candidate

genes generated from the systems biology component

above are translated in the human brain and that the

measured protein variants themselves are related to AD

endophenotypes”61.

iii. Functional validation- utilization of RNAi

screens to either over-express or knock down

each of the selected genes in neurons. This

provides an efficient, high throughput approach

to generating the data required for the

analysis of transcriptional networks. RNA

profiles derived from each experimental

condition will allow the empirical reconstruction

of the molecular networks for the target, and

human cell types to confirm the pathways

identified in the initial integrative analyses. As

Bennett notes, this component of the pipeline

has several purposes: i) it will refine the net-

works and confirm that the genes nominated

in systems biology analyses actually have the

expected effects when they are disrupted on an

individual basis; ii) it will identify other genes

which may be involved in the network because

they have similar functional consequences

when their expression is perturbed; iii) it will

identify transcriptional programs or ‘‘gene

sets’’ that, in vitro, capture aspects of the

function of a given pathway and can be used

as outcome measures in future drug screening;

and iv) it will also identify nodal points in

each network, ‘‘hubs’’ for a given pathway that

may make particularly effective targets for the

disruption of a given cellular pathway61.

iv- Drug Candidate Screen- selected, prioritized

molecular targets that are expressed in the

aging brain and are associated with pathologic

and/or clinical AD quantitative traits, are

interrogated with the appropriate chemical or

biological library. It is important to note that

the experimental pipeline is by design driven

by empiric observations from the first three

stages. This should culminate in the selection

of target nodal points for molecular screening

which, a priori, were not preselected.

Bennett has suggested that any target should

be a nodal point in a network related to AD

pathology and a clinically quantitative pheno-

type clearly identified by integrative omic data

analysis from human brain tissue. The target

must be expressed in brain and also clearly

demonstrated to be related to AD pathology

and clinical phenotype. In addition we would

argue that any consideration and scoring of

the viability of the target need not simply fit

into the amyloid, tau, or amyloid-tau pathway

of AD. We believe that the target selection

criteria should take into account the systems

analysis of the causal onset involving, genetics,

amyloid plaque, tau tangles, neurovascular

and neuroinflammatory events that occur as

a function of time.

Systems Drug Repurposing

Drug repurposing, also called repositioning,

reprofiling or rescue (DRPx), refers to the

process of taking a drug developed for one

disease indication and applying it to another

different disease indication. Similar risk factors

and biological pathways can underlie seemingly

unrelated diseases, opening the door for novel

hypothesis-driven and data-driven repurposing

strategies63. While we need to accelerate the

development of new drugs, the slow progressive

nature of AD, the long duration of trials, the

large number of patients required, the lack of

comprehensive understanding of the neurobi-

ology, and the limitations of the clinical trial

enterprise make it incredibly costly and

challenging and prone to high failure rates.

Existing knowledge of pharmacological

effects and safety profiles of repurposed

drugs can expedite early phases of clinical

development, shorten drug development

timelines, and reduce failure rates due to

pharmacokinetic and safety issues. Repurposed

agents are more than twice as likely across

all disease indications to make it to market

compared to de novo derived drugs. It is

interesting to note one of the commercially

available AD drugs memantine (Table 2) is a

DRPx drug. It was first synthesized by Eli Lilly

in 1968 and patented as an anti-influenza drug,

but subsequently found serendipitously to have

“beneficial” anti-dementia properties63.

The value of a systematic approach adopted in

drug repurposing efforts facilitate one-of-two

predominant outcomes either i) a known

candidate compound/drug interacting with a

new target, or ii) known target mapping to a

new disease indication. Methods and tools

utilized in DRPx discovery to identify such

possibilities include in vitro and in vivo (cell/

organ/tissue/animal) phenotype model screening,

High Throughput Screening, High Content

Screening, Chemoinformatics, Bioinformatics,

as well as Network and Systems Biology. These

approaches are used in conjunction with

an individual’s molecular drivers of disease”64.

As we discussed the utilization of systems

biology tools in this type of endeavor

is clearly synergistic and can and will facilitate

such efforts. However, what specifically can

precision medicine provide for patients in

the form of safe and effective therapeutic

treatments? As we have discussed, one

of the probable reasons for the continued

failure of large scale clinical trials to identify

effective therapies is the erroneous assumption

that AD is a single clinical disease state. Thus

data that provides a risk profile of a patient

with AD may be of some considerable value

if it can be correctly interpreted. Precision

medicine in contrast to the “one-size-fits-

all-approach” of current medical practice,

attempts to optimize the effectiveness of

disease prevention, and treatment as well as

minimize side effects for persons less likely

to respond to a particular therapeutic. This is

done by considering an individual’s specific

makeup of genetic, biomarker, phenotypic

and psychosocial characteristics6. The meas-

urement of molecular, environmental, and

behavioral factors contributing to a specific

disease improves the understanding of disease

onset and progression as well as response

to treatment. In addition, it allows a more

accurate diagnosis and more effective disease

prevention and treatment strategies specifically

personalized to the individual65.

We and others believe that precision medicine

affords the following opportunities in facilitating

more effective therapeutic drug treatments3,6-8,64,65;

i. Determination of Risk Assessment- currently,

a significant goal of risk assessment is to provide

insight into relevant disease mechanisms and

disentangle genetic from other causal events

that lead to AD onset and progression. For

example increased risk factors for AD onset

include cerebrovascular disease, traumatic

brain injury (TBI) or intellectual activity.

Cerebrovascular changes such as hemorrhagic

available information on known targets, drugs,

biomarkers of disease, and pathways/networks

of disease that can ultimately lead to accelerated

timelines in the discovery and development of

DRPx candidate drugs62.

Numerous challenges still exist to execute on

cost-effective DRPx efforts since to evaluate a

large number of drugs for a specific disease; it

is difficult to unify the needed computational

approaches because the available information

for different diseases or drugs varies. For example,

to use target-based methods to reposition

drugs for 100 different targets, one would have

to know the biomarkers or available pathways

for each of those specific targets. The knowledge

needed for this type of drug repositioning

might be unavailable or difficult to derive from

the literature or available databases. However,

a number of computational systems biology

DRPx companies have taken such an approach.

For example CureHunter Inc (Portland,

Oregon) utilizes an Integrated Systems Biology

platform which effectively synthesizes all the

data and knowledge acquired from “reading”

the US National Library of Medicine to produce

a clinical outcome database. This database is

structured for autonomous prediction of new

disease indications, such as AD, for any given

drug, biomarker, or active biological agent.

The value of such a computational systems

biology approach to AD treatment is the

effective utilization of pathway/network

biology, large clinical datasets, electronic

medical records, bioinformatic and artificial

intelligence tools. This facilitates the decon-

struction of complex disease processes such

as those demonstrated to occur in AD (Figure

4), and then make useful predictions based

on pathway/network analysis as to a druggable

and viable target(s)10.

Precision Medicine

It was recently suggested that the “..goal of

precision medicine is to deliver optimally

targeted and timed interventions tailored to

51

CO

GN

OS

CIE

NT

I

52

infarcts, small and large ischemic cortical

infarcts, vasculopathies, and white matter

changes, increase the risk of dementia. Possible

mechanisms through which stroke could lead

to cognitive impairment and AD include direct

damage of brain regions that are important in

memory function (such as the thalamus), an

increase in amyloid-βeta deposition, and an

induction of inflammatory responses impairing

cognitive function65.

ii. Detection of Latent Pathophysiological

Processes- treatment is routinely initiated after

disease symptomology is clearly manifested,

and is rarely initiated based on risk assessment

alone. The availability of diagnostic tools to

determine latent pathophysiologic processes

provides opportunity for effective early-stage

intervention. For example neuroimaging

efforts show unusual promise in AD because

of their growing ability to measure brain

function at increasingly higher levels of

organization,and is being considered for

more widespread application64.

iii. Personalized Intervention to an Individual’s

Molecular Risk and Disease Pathology Profile-

as discussed in detail above there are currently

no effective preventive or therapeutic measures

for AD. We have noted that almost all failed

clinical trials performed to date have neglected

the underlying clinical and molecular hetero-

geneity of the disease. In an attempt to address

this methodological shortcoming, “a first round

of ongoing trials including the Dominantly

Inherited Alzheimer Network (DIAN) , the

Alzheimer’s Prevention Initiative and A4

trial are now incorporating information of

underlying pathological mechanisms, selecting

patients considered most likely to respond to

the anticipated action of the therapeutic

tested”65. It is hoped that these trials prove

to be more successful in terms of outcome of

providing an effective safe treatment for AD.

Indeed overall it is hoped that the impact of

precision medicine via the three initiatives

discussed here will provide a more insightful

strategy for optimal therapeutic intervention

to “prevent, stop or slow progression of AD”64.

Conclusions

The “silent epidemic” gathers momentum.

The essence of the crisis is due to a lack of

mechanistic understanding of disease causali-

ty,onset, progression and effective prevention

and treatment. We have argued in this paper

that the need to evaluate AD via a systems

biology perspective is imperative. The underlying

heterogeneity of AD in clinical trials has until

recently been misunderstood and poorly con-

sidered. This has led to a paucity of effective

therapeutic treatments, and a trail of failed

expensive clinical trials. In the case of

precision medicine it is still a fledgling process.

The ultimate facilitation of precision medicine

should be to enable clinicians to prescribe

effective and safe therapeutic drugs for

individual patients. Whilst that is an admirable

goal, it is nonetheless lofty and is often

wafted around as something that is attainable

“tomorrow”. The reality is somewhat different,

as attested to here and described above.

There is hope, but alas it continues to be

accompanied by hype, which should have no

part in this silent epidemic crisis. Ken Kaitin

from the Tufts Center for the Study of Drug

Development recently stated that “….finding

newer and better medications to treat

Alzheimer’s disease is one of the great scientific

challenges of the 21st century. Success will

ultimately lie in all stakeholders participating

in the process, sharing in both the risks— and

the rewards—of the effort.”. Now that’s a

rallying cry to get behind and continue to

exploit the use of systems biology and precision

medicine tools to develop safe and efficacious

therapies for AD. We believe that the

combined use of such an approach will help

unlock the hindrances of AD complexity and

facilitate the development of safe, efficacious

drugs or drug combination cocktails.

Stephen Waring, DVM Ph.D, is an Epidemiologist

and Senior Research Scientist with the Essentia

Institute of Rural Health in Duluth, Minnesota, and

Adjunct Professor of Pharmacology at the University

of Minnesota, College of Pharmacy, at Duluth. His

research into genetic and non-genetic risk factors

associated with Alzheimer’s disease and other

cognitive disorders has spanned 25 years, and

includes work at Mayo Clinic (Rochester, MN USA)

during the 1990s as part of the team that produced

novel characterizations of mild cognitive impairment

(MCI) and MRI volumetric studies of brain regions

relevant to Alzheimer’s disease. He serves as on the

Leadership Council of the state of Minnesota ACT

on Alzheimer’s initiative as well as the Medical and

Scientific Advisory Council for the Minnesota-North

Dakota Alzheimer’s Association.

Stephen Naylor, Ph.D, is the current Founder,

Chairman and CEO of MaiHealth Inc, a systems/

network biology level diagnostics company in the

health/wellness and precision medicine sector. He

was also the Founder, CEO and Chairman of Predictive

Physiology & Medicine (PPM) Inc, one of the world’s

first personalized medicine companies. He serves as

an Advisory Board Member of CureHunter Inc. In the

past he has held professorial chairs in Biochemistry

& Molecular Biology; Pharmacology; Clinical

Pharmacology and Biomedical Engineering, all at

Mayo Clinic (Rochester, MN USA) from 1990-2001.

Correspondence should be addressed to him at

snaylor@midmarkcap.com

Acknowledgements

We would like to thank Mr. Andrew Jackson

(flaircreativedesign.com) for his considerable

help and artistic capabilities in producing the

figures contained in the article. In addition

we would like to thank Mr. Judge Schonfeld,

Founder and CEO of CureHunter Inc. for his

help and advice in determining the CureHunter

Effectiveness Index values for commercially

available AD therapeutic drugs.

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