Abstract
Traditional drug development for Alzheimer’s disease (AD) is costly,
time consuming and burdened by a very low success rate. An alternative
strategy is drug repositioning, redirecting existing drugs for another
disease. The large amount of biological data accumulated to date
warrants a comprehensive investigation to better understand AD
pathogenesis and facilitate the process of anti-AD drug repositioning.
Hence, we generated a list of anti-AD protein targets by analyzing the
most recent publically available ‘omics’ data, including genomics,
epigenomics, proteomics and metabolomics data. The information related
to AD pathogenesis was obtained from the OMIM and PubMed databases.
Drug-target data was extracted from the DrugBank and Therapeutic Target
Database. We generated a list of 524 AD-related proteins, 18 of which
are targets for 75 existing drugs—novel candidates for repurposing as
anti-AD treatments. We developed a ranking algorithm to prioritize the
anti-AD targets, which revealed CD33 and MIF as the strongest
candidates with seven existing drugs. We also found 7 drugs inhibiting
a known anti-AD target (acetylcholinesterase) that may be repurposed
for treating the cognitive symptoms of AD. The CAD protein and 8
proteins implicated by two ‘omics’ approaches (ABCA7, APOE, BIN1,
PICALM, CELF1, INPP5D, SPON1, and SOD3) might also be promising targets
for anti-AD drug development. Our systematic ‘omics’ mining suggested
drugs with novel anti-AD indications, including drugs modulating the
immune system or reducing neuroinflammation that are particularly
promising for AD intervention. Furthermore, the list of 524 AD-related
proteins could be useful not only as potential anti-AD targets but also
considered for AD biomarker development.
Introduction
Alzheimer’s disease (AD) is the most common form of dementia (6% of
people above age 65 [[38]1]), affecting ~48 million people worldwide in
2015 according to the world health organization. AD brain pathology is
characterized by neuronal tau inclusions and amyloid plaques, mainly
consisting of Aβ[40/42] peptides generated by the cleavage of APP
protein. Aβ[42] peptide is occurring in a tenth of the amount of
Aβ[40], but aggregates faster than Aβ[40] and is more toxic in cell
culture assays [[39]2]. The Aβ accumulation is an early event that
could trigger downstream events (e.g., misprocessing of the tau protein
and brain inflammation) [[40]3]. AD is one of the most costly chronic
diseases, with a global cost of $605 billion as estimated by the World
Alzheimer's Association. So far, there are 5 FDA approved drugs on the
market according to the Alzheimer’s Association, but none of them can
cure AD. There is an urgent need to develop novel anti-AD therapies,
however traditional drug development takes a long time (10–17 years),
requires massive financial investments, and yet is burdened by a very
low success rate (~0.4% for AD from year 2001 to 2012 [[41]4, [42]5]).
Drug repositioning (repurposing) is used to redirect approved and
clinical trial drugs for treating another disease [[43]6]. It is an
attractive strategy to pursue for AD [[44]7] that can dramatically
reduce drug development time, cost and safety risk, because drug
toxicity data are often available from former phase I/II clinical
trials.
Previous studies have applied various methods of analyzing ‘omics’ data
to identify promising drugs for repurposing, including comparison
analyses of gene expression patterns (connectivity maps) [[45]8], text
mining [[46]9], network analyses [[47]10], exploration of data from
genome wide association studies (GWASs) [[48]11] and the analysis of
pathogenesis knowledge from the Online Mendelian Inheritance in Man
(OMIM) database [[49]12]. In addition, computational methods have been
used to predict drug-protein interactions [[50]13], drug off-targets
[[51]14], drug side effects [[52]15] and drug-disease associations
[[53]16]. Our group recently developed a comprehensive drug
repositioning strategy based on mining genomic, proteomic and
metabolomic data that revealed 9 drugs with new anti-diabetes
indications [[54]6]. In the current study, we used an improved approach
that added epigenomic data and a ranking strategy for anti-AD drug
repositioning.
Most AD patients have sporadic late-onset disease, and are free from
rare mutations in known causal AD genes (APP, PSEN1 and PSEN2) [[55]3].
Sporadic AD is associated with multiple genetic variations of small
effect (e.g., most GWAS loci) or moderate effect (e.g., APOE-ε4
[[56]17] and TREM2 rs75932628 T-allele [[57]18, [58]19]), and could be
influenced by other risk factors (e.g., head trauma [[59]20], diabetes
[[60]21] and aging [[61]22]). The complex interactions between genetic
and environmental factors lead to alterations in proteins, metabolites
and epigenetic modifications in the brain tissue and/or body fluids of
AD patients.
The large amount of biological data accumulated to date warrants
comprehensive investigation to better understand AD pathogenesis and
facilitate the process of anti-AD drug repositioning. Hence, the
current study aimed to systematically analyze AD-related ‘omics’ data
to discover potential anti-AD drug targets, develop an algorithm to
rank these drug targets, and suggest a priority for repurposing
existing drugs as potential anti-AD therapies.
Materials and Methods
Database search for potential anti-AD targets
We searched the NHGRI-EBI GWAS Catalog ([62]http://www.ebi.ac.uk/gwas)
to extract AD-associated genetic variations; and the Human Metabolome
Database (HMDB) to extract AD-related metabolites. To shortlist
AD-related proteins and epigenetic changes, we searched the PubMed
database up to June 2016 using the keywords: “Alzheimer’s disease and
proteomics”, “Alzheimer’s disease and protein/proteomics”, “Alzheimer’s
disease and DNA methylation”, “Alzheimer’s disease and epigenetics”. We
incorporated this literature in our study according to the following
criteria: 1) all samples (e.g., serum, plasma, urine or tissue) had to
be human; 2) the disease diagnosis had to be “Alzheimer’s disease” or
“Late-onset Alzheimer’s disease”; and 3) for proteins, all samples had
to be CSF.
For the GWASs, we extracted information on 1) genes; 2) SNPs; 3)
initial sample size; 4) replication sample size; 5) p-value; 6) effect
size: odds ratio (OR) or beta-coefficients; 7) PubMed ID. For the
epigenetic studies, we extracted information on 1) protein ID; 2) gene
ID; 3) patient status; 4) sample size; 5) platform; 6) PubMed ID. For
the proteomics studies, we extracted information on 1) protein name; 2)
gene name; 3) Uniprot ID; 4) sample type; 5) patient status; 6) sample
size; 7) platform; 8) PubMed ID. For the metabolomics studies, we
extracted information on 1) metabolite; 2) sample type; 3)
concentration in patients; 4) patient status; 5) age; 6) gender; 7)
PubMed ID.
Mapping AD-related metabolites to proteins and visualizing the
metabolite-protein network
We extracted the names of proteins that linked to AD-related
metabolites based on the HMDB database. To visualize the association
between these metabolites and the proteins affecting them, we
constructed a metabolite-protein network using Cytoscape software
v3.3.0 ([63]www.cytoscape.org) [[64]23].
Mapping AD-related proteins to existing drugs
We selected a panel of AD-related proteins retrieved from GWASs,
epigenetic and proteomics studies, as well as proteins linking to ≥2
AD-related metabolites retrieved from the HMDB. To establish a link
between these AD-related proteins to drugs, we used two public
databases: the Therapeutic Target Database (TTD version 4.3.02)
containing information on the 236 targets of 20,667 approved, clinical
trial and experimental drugs [[65]24], and the DrugBank database
([66]www.drugbank.ca) containing 4,800 drug entries including >1,350
FDA-approved small molecule drugs, 123 FDA-approved biotech
(protein/peptide) drugs, 71 nutraceuticals and >3,243 experimental
drugs [[67]25]. To focus on the most promising drugs that might be
repurposed for treating AD, only target-drug pairs comprising drugs
that were either approved or had been examined in clinical trials were
selected. From these two drug databases, we extracted information on 1)
drug target name; 2) drug name; 3) original drug indication; 4) drug
stage; and 5) drugs’ modes of action.
Information on pathogenesis and the drugs’ modes of action for anti-AD drug
repositioning
We extracted knowledge about pathogenesis of potential anti-AD targets
from the OMIM database ([68]http://www.omim.org) and a PubMed
literature search. We obtained information on the gain of function
(GOF) or loss of function (LOF) roles of the drug targets in humans or
animal models. Target pathogenesis information together with the drugs’
modes of action retrieved from the drug databases were used to
rationally shortlist promising anti-AD drugs.
The ranking algorithm of anti-AD drug targets
To prioritize potential anti-AD drug targets, we developed an algorithm
to score the targets. To calculate the target score, a weighted sum
model [[69]26] was used that employed three criteria: 1) the level of
change of the AD-related proteins that were presented by fold changes
of proteins or the OR of minor alleles; 2) the number of citations of
the paper that reported the AD pathogenesis of the target based on
Google scholar; 3) the number of publications that reported the target
in connection to AD based on the PubMed search. To more comprehensively
consider both the confidence of disease-target association (criteria 1
and 3) and the strength of evidence in support for AD pathogenesis
(criteria 2); we gave each criterion equal weighted values. For the
targets retrieved from metabolomics, we estimated the fold change of
the target based on the fold changes of the corresponding metabolites
adjusted to the total number of metabolites connected to that target,
assuming other linked metabolites did not change. We also used internal
controls to adjust the target scores to known AD-related
proteins/genes: 1) the fold changes of proteins were adjusted to the
fold change (2.37) of Aβ[42] in CSF of AD patients [[70]27]; 2) the OR
of risk alleles were adjusted to the OR (3.7) of APOE-ε4 allele vs. ε3
allele (ALZforum); 3) the number of citations was adjusted to the
number of times the first paper reporting the segregation of an APP
mutation with familial AD [[71]28] was cited (4092, up to Feb 2016); 4)
the number of publications was adjusted to the number of articles with
both APP and AD as keywords (11294, up to Feb 2016). We used the
equations (Eqs [72]1, [73]2 and [74]3) to estimate the target scores of
those targets retrieved from metabolomics (TSm), proteomics (TSp) and
genetics (TSg).
[MATH:
TSm=0.33×<
msubsup>∑i=
1n|Fi<
/mtext>|+N−nN×2.37
+0.34×C<
/mrow>4092+0.33
×H11294; :MATH]
(1)
[MATH:
TSp=0.33×<
mrow>|F|2.37+0.34×
C4092+0.33×H11294; :MATH]
(2)
[MATH:
TSg=0.33×<
mtext>OR3.7+0.34×C4092+0.33×H
11294;
:MATH]
(3)
where F = fold change of the proteins or metabolites between AD and
normal controls (positive if the AD group is higher than the control
group, negative if the AD group is lower than the control group); N =
total number of proteins connected to the metabolite, n = number of
proteins connected to the AD-related metabolite; C = the number of
citations of the target pathogenesis paper; H = the number of
publications reporting both AD and the target; OR = the odds ratio of
the risk allele.
Bioinformatics analyses
Protein-protein interactions of AD-related proteins were analyzed using
the String tool ([75]http://string-db.org) by selecting “experiments”
as active prediction method. Cytoscape software v3.3.0 was used to
visualize the protein-protein interaction network. The pathway
enrichment analysis was conducted using the David online tool
([76]https://david.ncifcrf.gov/) by selecting the KEGG database.
Benjamini corrected p-values <0.05 were considered significant.
Computational analysis of candidate drug targets and repurposed drugs
To validate the anti-AD drug targets derived from our ‘omics’ mining
method, we used the Toppgene tool ([77]https://toppgene.cchmc.org),
which ranks candidate genes based on functional similarity to the
training genes, and the Toppnet tool ([78]https://toppgene.cchmc.org),
which ranks candidate genes based on topological features in
protein-protein interaction networks and their similarity to the
training genes [[79]29]. In the current study, we used 5 training genes
selected based on the strongest AD risk-effect (APP, PSEN1, PSEN2,
APOE, and TREM2).
We also used two online resources (Connectivity Map (Cmap),
[80]http://portals.broadinstitute.org/cmap/; and C2Maps,
[81]http://rdc02.uits.iu.edu:7777/pls/apex/f?p=208:1:2695462252197431::
NO) to analyze the small molecule drugs of the repurposed drugs. Using
Cmap, we analyzed whether the change in the pattern of gene expression
is similar between the repurposed drugs and known anti-AD drugs
(memantine and galantamine) [[82]6]. While C2maps assessed the anti-AD
drug and gene association; based on network mining, literature mining,
and drug effect annotation [[83]30].
Results
Systematic mining of ‘omics’ data revealed potential AD-related proteins
We analyzed 4 epigenetic, 7 proteomic and 18 metabolomic studies, as
well as 31 GWASs; and retrieved 14 epigenetic events associated with
AD, as well as 98 proteins and 86 metabolites that were reported to be
significantly altered in AD patients, and 244 genetic variations
associated with AD implicating 220 genes ([84]Fig 1, [85]S1–[86]S4
Tables). Based on the HMDB, 200 proteins were linked to ≥2 AD-related
metabolites (1179 metabolite-protein pairs). The AD-related
metabolite-protein network ([87]Fig 2) shows highly interconnected
metabolic pathways of various metabolites.
Fig 1. Flow-chart of the drug repositioning strategy for AD based on ‘omics’
data mining.
[88]Fig 1
[89]Open in a new tab
We searched the GWAS Catalogue, PubMed, and HMDB database, and
extracted 244 genetic variations, 14 epigenetic modifications, 98
proteins and 86 metabolites associated with AD. We also extracted 1179
protein-metabolite interactions based on the HMDB database and found
200 proteins linked to ≥2 AD associated metabolites. In total, we
shortlisted 524 AD-related proteins, 8 of which were revealed by 2
‘omics’ approaches. By using the TTD and DrugBank database, we
extracted information on drugs, targets and the drugs’ mode of action.
Considering AD pathogenesis together with the drugs’ mode of action, we
found 19 targets of 92 drugs with anti-AD indication that may be
repurposed. We then scored these targets and found CD33 and MIF to be
the two highest ranked targets. A protein-protein interaction analysis
of 524 AD-related proteins detected a novel network of 11 proteins with
CAD as a hub protein (functional enrichment analysis revealed that 5 of
these 11 proteins are involved in the “Alanine, Aspartate and Glutamate
Metabolism” pathway presented in [90]Fig 3).
Fig 2. AD related protein-metabolite network.
[91]Fig 2
[92]Open in a new tab
1179 protein-metabolite interactions were indicated from the HMDB
database. The zoomed-in inset shows that acetylcholinesterase
([93]P22303), a known anti-AD target, interacts with 2 AD-related
metabolites (Choline and Acetylcholine). The nodes with yellow color
represent metabolites that were altered in AD patients, the nodes with
purple color represent proteins that linked to AD associated
metabolites, and the nodes with green color represent proteins that
linked to ≥2 AD associated metabolites.
In total, ‘omics’ data revealed 524 unique AD-related proteins,
including 8 proteins that showed alterations in two platforms ([94]S5
Table). Among them, ABCA7, APOE, BIN1 and PICALM had reports on
AD-related functional studies, while findings related to CELF1, INPP5D,
SPON1 and SOD3 encourage further analysis regarding their roles in AD
pathogenesis.
The protein-protein interaction analysis of 524 AD-related proteins
detected two core hub proteins: APP (encoded by causal AD gene) and CAD
([95]Fig 3A). CAD links to another 10 proteins, all of which are
associated with AD-related metabolites. The pathway enrichment analysis
revealed that these 11 proteins are significantly enriched in the
“Alanine, Aspartate, Glutamate metabolism” pathway (Benjamini corrected
p-value = 0.000002) ([96]Fig 3B), with 5 proteins (GAD1, GAD2, GFPT1,
GFPT2 and CAD) involved in this pathway.
Fig 3.
[97]Fig 3
[98]Open in a new tab
A) Protein-protein interaction analysis of 524 AD-related proteins
revealed two large protein clusters: the APP network (14 yellow nodes)
and the CAD network (11 red nodes). B) The functional enrichment
analysis found that five proteins (labeled with red stars) of the CAD
network are involved in the “Alanine, Aspartate and Glutamate
Metabolism” pathway (CAD, GAD1, GAD2, GFPT1, GFPT2). The figure was
generated based on the results obtained by the David online tool and
the KEGG database.
Drugs with possible anti-AD indication based on knowledge of drugs’ modes of
action and AD pathogenesis
Searching the TTD and DrugBank databases using Uniprot IDs for the
aforementioned 524 protein targets revealed that 19 of them (with
information on AD pathogenesis) were linked to 92 approved or clinical
trial drugs (with data on drugs’ modes of action), supporting their
potential anti-AD roles, such as reducing cognitive impairment or
increasing neuron protection and Aβ clearance [[99]Table 1, [100]S6
Table]. Two of these 19 proteins, acetylcholinesterase (ACHE) and APP,
are known anti-AD drugs targets, corresponding to 17 existing drugs
[[101]Table 1, [102]S6 Table], including three approved drugs for AD
treatment (galantamine, rivastigmine and donepezil). This validates the
ability of our strategy to detect known anti-AD drugs and supports its
potential to discover novel anti-AD indications of existing drugs.
Apart from APP, we found 18 potential anti-AD targets with 75 existing
drugs that might have a novel anti-AD indication [[103]S6 Table]. Of
note, 7 drugs targeting acetylcholinesterase were not previously used
for treating AD symptoms and could be repurposed for anti-AD therapy.
Table 1. 'Omics' data mining revealed potential anti-AD drug targets from
existing approved and clinical trial drugs.
Uniprot ID Database Target name Target score Target source Number of
drugs
[104]P20138 TTD Myeloid cell surface antigen CD33 0.715 GWAS 6
[105]P14174 TTD Macrophage migration inhibitory factor 0.438 Proteomics
1
[106]P22303 TTD/DrugBank Acetylcholinesterase[107]^* 0.384 Metabolomics
10
[108]Q96KS0 TTD Hypoxia-inducible factor-prolyl hydroxylase 0.319
Metabolomics 4
[109]O43497 TTD Voltage-dependent T-type calcium channel alpha-1G
subunit 0.291 GWAS 6
[110]P00747 TTD/DrugBank Plasminogen 0.192 Proteomics 7
[111]P21728 TTD/DrugBank Dopamine D1 receptor 0.171 Metabolomics 13
[112]P00325 DrugBank Alcohol dehydrogenase 1B 0.159 Metabolomics 1
[113]P01009 TTD Alpha-1-antitrypsin 0.155 Proteomics 3
[114]P35228 TTD Nitric oxide synthase, inducible 0.146 Metabolomics 3
[115]P05164 TTD Myeloperoxidase 0.144 Metabolomics 2
[116]P15692 TTD Vascular endothelial growth factor A 0.144 Proteomics 2
[117]P15121 TTD/DrugBank Aldose reductase 0.139 Metabolomics 6
[118]O76074 TTD CGMP-specific 3',5'-cyclic phosphodiesterase 0.138
Metabolomics 6
[119]O14939 DrugBank Phospholipase D2 0.137 Metabolomics 1
[120]P21917 DrugBank Dopamine D4 receptor 0.133 Metabolomics 3
[121]P21964 TTD/DrugBank Catechol-O-methyl-transferase 0.13
Metabolomics 3
[122]P10635 TTD Cytochrome P450 2D6 0.128 Metabolomics 1
[123]P05067 TTD Amyloid precursor protein[124]^* 1.000 Proteomics 14
[125]Open in a new tab
* Represents known anti-AD target
The ranking algorithm revealed two promising anti-AD drug targets
We developed a ranking algorithm to prioritize the anti-AD targets (APP
was set as an internal control with a target score of 1); and determine
which of the drugs targeting these proteins are the most promising to
pursue in validation studies. We evaluated our algorithm using three
known anti-AD drug targets, acetylcholinesterase, TREM2 and APOE, which
revealed a medium/high target score of 0.384, 0.459 and 0.887,
respectively [[126]S7 Table]. The mean target score for the 17 novel
anti-AD targets is 0.235, ranging from 0.143 to 0.782 [[127]S7 Table].
There are two targets with scores greater than that of
acetylcholinesterase: CD33 (0.782) and MIF (0.438), both of which are
linked to microglial activation and neuroinflammation [[128]Fig 4].
Antibodies/inhibitors targeting CD33 and MIF were originally tested in
clinical trials for the treatment of acute myelogenous leukemia (AML)
or solid tumors [[129]S6 Table]. Our results suggest that they might
also be good candidates for treating AD-related neuroinflammation
[[130]Fig 4].
Fig 4. The top two anti-AD targets, MIF and CD33, affect microglial
activation.
[131]Fig 4
[132]Open in a new tab
Both CD33 and the MIF receptor (CD74) are expressed on the microglial
cell surface. Antibodies/inhibitors of MIF and CD33 may be assessed for
their effects in modulating AD-related neuroinflammation.
Another two targets (HIF and CACNA1G) had medium target scores of 0.345
and 0.319. Ten small molecule drugs targeting these two proteins might
also be repurposed for treating AD [[133]S6 Table], and warrant further
validation.
Computational analysis validated top ranked anti-AD drug targets
Medium/high Toppgene scores (>0.6) and Toppnet scores (>1E-05) were
observed for 8 of the top 10 anti-AD drug targets ([134]S7 Table),
suggesting that our ranking algorithm corresponds well to other ranking
methods. Toppgene scores for the top 3 targets are 0.67 (CD33), 0.71
(MIF) and 0.83 (ACHE). The Toppnet scores for the top 3 targets are
6.7E-05 (CD33), 1.4E-05 (MIF) and 2.8E-05 (ACHE).
The evaluation of our top candidate drugs (antibodies targeting CD33
and MIF) is important but lacks the appropriate computational methods
to rank antibody drugs. Here, we used two online resources (Cmap and
C2maps) to analyze the small molecule drugs of the repurposed drugs.
Using Cmap, we found that only one drug (edrophonium) showed positive
correlation to memantine (enrichment score = 0.62, p = 0.05). Other
drugs had non-significant results or no gene expression information in
Cmap. Using C2maps, we found that only one drug (physostigmine) had a
high protein ranking score of 0.99 and low enriched drug p-value (p =
2E-23). C2maps also validated 3 known anti-AD drugs, including
galantamine (protein ranking score = 0.99, enriched drug p = 4.7E-4),
rivastigmine (protein ranking score = 0.99, enriched drug p = 1.2E-10)
and donepezil (protein ranking score = 0.99, enriched drug p = 5.8E-9).
The scores of other drugs are not available in C2maps.
Discussion
In the current study, we improved our ‘omics’-based drug repositioning
strategy [[135]6] by adding epigenetic data into the search for drugs
to be repositioned for AD. Epigenetic modifications, especially DNA
methylation, have been reported to be associated with aging [[136]31],
[[137]32], AD [[138]33], and Parkinson’s disease [[139]34].
Furthermore, genetic variations that modulate DNA methylation age may
control biological aging [[140]35], which is the strongest risk factor
for AD. Overall, we revealed 524 AD-related proteins, 18 of which are
targets for 75 existing drugs making them novel candidates for
repurposing as anti-AD treatments. Importantly, 8 AD-related proteins
were implicated by two ‘omics’ approaches, suggesting their priority as
anti-AD targets [[141]S5 Table]. Of note, 4 of them (CELF1, INPP5D,
SPON1 and SOD3) do not have information on AD pathogenesis and need to
be further investigated in functional studies.
The list of 524 AD-related proteins could be useful not only as
potential anti-AD targets, but also considered for developing AD
biomarkers. Moreover, the protein-protein interactions among these 524
proteins point to a core hub CAD protein connected to 10 proteins, each
of which is associated with ≥2 AD-related metabolites. A pathway
analysis of the CAD hub suggested an enriched “Alanine, Aspartate,
Glutamate metabolism” pathway. Further investigations may be initiated
to design drugs targeting CAD to modulate the AD-related imbalance in
neurotransmitters (e.g., glutamate [[142]36] or GABA [[143]37]). The
comprehensive analyses of multiple “omics” data provide a unique
opportunity to understand the most-relevant biomarkers/risk factors
related to AD, thereby facilitating the process of identifying protein
targets and drugs for repurposing.
We also improved our ‘omics’-based drug repositioning strategy by
developing a ranking algorithm to prioritize the drug targets. Previous
scoring algorithms, such as calculating the confidence of drug-protein
interactions [[144]38] and disease–disease, drug–drug and target–target
relationships (constructed based on their similarities) [[145]16],
evaluated the strength of the association but not the therapeutic
rationale based on pathogenesis information of the target and the
action mode of the drug. The current study employed a ranking algorithm
that considered both the strength of the target-disease association and
the quality of the study related to AD pathogenesis of a particular
protein (based on number of citations), therefore providing a target
score considering therapeutic rationale. To validate our ranking
method, we used the online tools, Toppgene and Toppnet, to analyze the
targets’ functional and topological similarity to known AD genes. The
results of our ranking algorithm are reliable, because 8 of our top 10
targets had medium/high scores from Toppgene and Toppnet.
Using a computational method to evaluate our top repurposed drugs
(antibodies targeting CD33 and MIF) is difficult, because most
available computational tools are used for small molecule drugs. Cmap
and C2maps revealed two drugs of interest: edrophonium and
physostigmine, both of which are ACHE inhibitors. Other repurposed
small molecule drugs cannot be properly evaluated using Cmap and
C2maps, because these two methods used the only known anti-AD target
(ACHE) to assess similarity, and thereby may have limitations when
evaluating other targets that have quite different pathogenic
mechanisms. In the future, experimental validation is needed to
evaluate the efficacy and toxicity of the repurposed drugs in cell and
animal models.
CD33 is a transmembrane receptor mainly expressed in myeloid lineage
cells, especially in most leukemic blast cells, so it was a drug target
for the treatment of AML [[146]39]. In brain, it is mainly expressed on
the surface of microglia. It may constitutively repress
monocyte-derived pro-inflammatory cytokines [[147]40]. The CD33
rs3865444 risk C-allele was associated with increased CD33 expression,
decreased Aβ[42] uptake and an increased number of activated microglia
that fail to clear the amyloid plaques in AD patients [[148]41]. Hence,
it might be worthwhile to explore if the repurposing of anti-CD33
antibodies/inhibitors developed for treating acute myelogenous leukemia
(Gemtuzumab ozogamicin, Vadastuximab talirine, Lintuzumab, BI-836858,
HuM195/rGel and HuM-195-Ac-225) are also effective for AD. Notably,
Gemtuzumab ozogamicin carries a toxic calicheamicin-g1 derivative that
may cause severe side effects in some patients of AML and was withdrawn
from the US market in 2010 but is still on the market in Japan on the
basis of a marginally favorable risk-benefit assessment [[149]42]. This
example speaks to the need of careful patient selection (e.g., based on
the AD risk allele in CD33). Also, it is anticipated that an
optimization of antibody conjugates would be needed before the benefit
of anti-CD33 antibodies could be explored in AD clinical trials. Of
note, another anti-CD33 antibody (Lintuzumab) was proved to be safe in
human [[150]43].
MIF is the second top ranking anti-AD target that was previously found
to be elevated in the CSF of AD patients [[151]44, [152]45]. As a
pro-inflammatory cytokine, MIF is essential for promoting microglial
activation [[153]46]. An MIF receptor (CD74) was also documented to be
elevated in microglia of AD cases [[154]47]. Importantly, MIF interacts
with Aβ and the inhibition of MIF was shown to reduce Aβ-induced
toxicity in cells [[155]45]. Therefore, existing anti-MIF antibodies
might be repurposed for treating AD. If the anti-MIF antibody
(clinicaltrial.gov identifier: [156]NCT01765790) in phase I clinical
trial turns out to be safe for humans, another clinical trial may be
initiated to test its efficiency in AD patients with elevated MIF
levels in CSF.
In addition to CD33 and CD74 [[157]40, [158]47], other AD genes (ABCA7
and TREM2) are also expressed in microglia [[159]48, [160]49],
suggesting that the modulation of microglial function may be a
promising mechanism-based strategy for AD intervention. An immune
checkpoint protein (PD-1) plays an important role in down regulating
the immune system. Recently, a PD-1 immune checkpoint blockade was
shown to reduce pathology and improve memory in AD mouse models
[[161]50], suggesting that PD-1 blocker drugs (e.g., Nivolumab and
Pembrolizumab) might be repurposed as AD therapies.
Integrated analysis of AD-related ‘omics’ data and electronic health
records would be required for better understanding AD pathogenesis and
facilitating anti-AD drug repositioning. Also other databases of
references, substances and reactions in chemistry (such as SciFinder)