# Modeling Path Importance for Effective Alzheimer’s Disease Drug Repurposing

Shunian Xiang1∗ Patrick J. Lawrence1∗ Bo Peng2 ChienWei Chiang1 PhD  Dokyoon
Kim3 PhD  Li Shen3 PhD  and Xia Ning1,2,4† PhD  1Biomedical Informatics
Department, The Ohio State University, Columbus, OH 43210, USA
2Computer Science and Engineering Department, The Ohio State University,
Columbus, OH 43210, USA
3Department of Biostatistics, Epidemiology, and Informatics, University of
Pennsylvania, Philadelphia, PA 19104 USA
4Translational Data Analytics Institute, The Ohio State University, Columbus,
OH 43210, USA
∗Co-first author; authors contributed equally to this work
†E-mail<EMAIL_ADDRESS>

###### Abstract

Recently, drug repurposing has emerged as an effective and resource-efficient
paradigm for $\mathop{\mathtt{AD}}\limits$ drug discovery. Among various
methods for drug repurposing, network-based methods have shown promising
results as they are capable of leveraging complex networks that integrate
multiple interaction types, such as protein-protein interactions, to more
effectively identify candidate drugs. However, existing approaches typically
assume paths of the same length in the network have equal importance in
identifying the therapeutic effect of drugs. Other domains have found that
same length paths do not necessarily have the same importance. Thus, relying
on this assumption may be deleterious to drug repurposing attempts. In this
work, we propose $\mathop{\mathtt{MPI}}\limits$ (Modeling Path Importance), a
novel network-based method for $\mathop{\mathtt{AD}}\limits$ drug repurposing.
$\mathop{\mathtt{MPI}}\limits$ is unique in that it prioritizes important
paths via learned node embeddings, which can effectively capture a network’s
rich structural information. Thus, leveraging learned embeddings allows
$\mathop{\mathtt{MPI}}\limits$ to effectively differentiate the importance
among paths. We evaluate $\mathop{\mathtt{MPI}}\limits$ against a commonly
used baseline method that identifies anti-$\mathop{\mathtt{AD}}\limits$ drug
candidates primarily based on the shortest paths between drugs and
$\mathop{\mathtt{AD}}\limits$ in the network. We observe that among the top-50
ranked drugs, $\mathop{\mathtt{MPI}}\limits$ prioritizes 20.0% more drugs with
anti-$\mathop{\mathtt{AD}}\limits$ evidence compared to the baseline. Finally,
Cox proportional-hazard models produced from insurance claims data aid us in
identifying the use of etodolac, nicotine, and
$\mathop{\mathtt{BBB}}\limits$-crossing
$\mathop{\mathtt{ACE}\texttt{-}\mathtt{INHs}}\limits$ as having a reduced risk
of $\mathop{\mathtt{AD}}\limits$, suggesting such drugs may be viable
candidates for repurposing and should be explored further in future studies.

###### keywords:

Alzheimer’s Disease; Drug Repurposing; Machine Learning.

© 2023 The Authors. Open Access chapter published by World Scientific
Publishing Company and distributed under the terms of the Creative Commons
Attribution Non-Commercial (CC BY-NC) 4.0 License.

##  1 Introduction

Alzheimer’s Disease, denoted $\mathop{\mathtt{AD}}\limits$, is a progressive
neurodegenerative disorder that accounts for 60%-70% of dementia cases and
affects more than 50 million people worldwide today [1, 2]. Given the large
number of affected individuals and $\mathop{\mathtt{AD}}\limits$’s life-
threatening nature [3], extensive resources have been dedicated to developing
$\mathop{\mathtt{AD}}\limits$-modifying drugs. Since 2003, inefficacy or
toxicity has accounted for a 95+% failure rate among candidates evaluated for
$\mathop{\mathtt{AD}}\limits$ treatment [4, 5]. Furthermore, none of the
current US Food and Drug Administration (FDA)-approved
$\mathop{\mathtt{AD}}\limits$ drugs are curative; they only slow disease
progression. Because of the immense resources required to conduct clinical
trials [6], the numerous failed clinical trials have necessitated the
development of a more resource-efficient method for
$\mathop{\mathtt{AD}}\limits$ drug discovery. In the last decade, the
identification of new therapeutic indications for existing FDA-approved drugs,
referred to as drug repurposing [7], has emerged as an effective and resource-
efficient paradigm for drug discovery [8]. This is an attractive option as the
toxicity, pharmacokinetics, and pharmacodynamics of FDA-approved drugs have
already been thoroughly investigated by previous clinical trials [7, 9].

Recently, the curation of comprehensive drug databases has enabled the
development of computational methods for $\mathop{\mathtt{AD}}\limits$ drug
repurposing [10, 11, 12, 13, 14]. Among all the methods, network-based methods
have shown promising results and emerged as a popular approach [13, 15, 16].
Network-based methods utilize comprehensive protein-protein, drug-target, and
$\mathop{\mathtt{AD}}\limits$-protein interactions to effectively reveal
potential therapeutic effects of drugs on $\mathop{\mathtt{AD}}\limits$.
Though promising, existing methods [13] measure the therapeutic effects of
drugs on $\mathop{\mathtt{AD}}\limits$ primarily using count and length of the
paths connecting drug nodes and the $\mathop{\mathtt{AD}}\limits$ node in the
network. Paths of the same length are considered equivalently effective at
identifying the therapeutic effect of drugs by these methods. However, in
other domains, paths of the same length have been shown to exhibit
substantially different levels of importance [17, 18]. As such, assuming equal
length paths have equal importance could be detrimental to effective drug
repurposing for $\mathop{\mathtt{AD}}\limits$.

In this work, we propose a novel method to conduct drug repurposing for
$\mathop{\mathtt{AD}}\limits$, $\mathop{\mathtt{MPI}}\limits$ (Modeling Path
Importance), to address this limitation. Similar to existing methods [13, 14],
$\mathop{\mathtt{MPI}}\limits$ leverages the interactions between drugs and
$\mathop{\mathtt{AD}}\limits$ via proteins as indications of the potential
therapeutic effects of drugs on $\mathop{\mathtt{AD}}\limits$. Based on the
interactions, $\mathop{\mathtt{MPI}}\limits$ introduces a scoring function to
score and rank drugs for their anti-$\mathop{\mathtt{AD}}\limits$
effectiveness. $\mathop{\mathtt{MPI}}\limits$ is unique in that it learns node
embeddings [19] and prioritizes important paths via these learned embeddings.
Recent work [20] has shown that the learned node embeddings can effectively
capture the rich structure information within a network. Thus, scoring paths
using node embeddings allows $\mathop{\mathtt{MPI}}\limits$ to utilize the
network structure information to better prioritize paths for effective
$\mathop{\mathtt{AD}}\limits$ drug repurposing. Specifically, in this study,
$\mathop{\mathtt{MPI}}\limits$ leverages $\mathop{\mathtt{DeepWalk}}\limits$
[21], a widely used network learning approach, to generate node embeddings.
Edges are scored using a normalized dot product between the learned node
embeddings; paths and drugs are scored by multiplying individual edge scores.
Note that because $\mathop{\mathtt{MPI}}\limits$ serves as a general
framework, other network learning approaches, such as
$\mathop{\mathtt{Node2Vec}}\limits$ [20] and graph neural networks [22], could
also be easily incorporated to generate node embeddings.

In this study, we construct a network to conduct drug repurposing for
$\mathop{\mathtt{AD}}\limits$ by combining protein-protein interactions
(PPIs), drug-target interactions (DTIs), and
$\mathop{\mathtt{AD}}\limits$-protein interactions (APIs) from multiple data
sources. To investigate the effectiveness of $\mathop{\mathtt{MPI}}\limits$,
we compare $\mathop{\mathtt{MPI}}\limits$ against a commonly utilized network-
based drug repurposing method for $\mathop{\mathtt{AD}}\limits$ [13, 23],
denoted as $\mathop{\mathtt{BSL}}\limits$, using our network. Our experimental
results demonstrate that among the top-50 ranked drugs,
$\mathop{\mathtt{MPI}}\limits$ prioritizes 20% more drugs with
anti-$\mathop{\mathtt{AD}}\limits$ evidence compared to
$\mathop{\mathtt{BSL}}\limits$. We examine published literature and analyze
insurance claims meta data to evaluate the evidence of
anti-$\mathop{\mathtt{AD}}\limits$ activity among
$\mathop{\mathtt{MPI}}\limits$’s top prioritized candidates. The results of
our evaluation find consensus between published experimental results and our
own analysis for a few drug candidates. Notably, angiotensin converting enzyme
inhibitors ($\mathop{\mathtt{ACE}\texttt{-}\mathtt{INHs}}\limits$) represent a
class of drugs that should be further explored for their anti-AD properties.
Moreover, other drugs, such as nicotine, that enhance the brains response to
acetylcholine and reduce cholinergic atrophy should be examined as well.
Conversely, we find that, relative to other evaluated drugs, long-term use of
trihexyphenidyl increases the risk of $\mathop{\mathtt{AD}}\limits$. This was
corroborated by previously published in vivo experiments [24]. Finally, we
find etodolac to confer the lowest risk of developing
$\mathop{\mathtt{AD}}\limits$ among all cyclooxygenase inhibitors
($\mathop{\mathtt{COX}\texttt{-}\mathtt{INHs}}\limits$) in our network.
Altogether, these findings suggest that $\mathop{\mathtt{MPI}}\limits$ may be
a viable option with respect to identifying repurposing candidates to treat
$\mathop{\mathtt{AD}}\limits$.

##  2 Materials and Methods

###  2.1 Network construction

PPIs, DTIs and APIs have shown utility for $\mathop{\mathtt{AD}}\limits$ drug
repurposing [13]. As such, we construct our network using these interactions.
Below, we describe our process for compiling the PPIs, DTIs and APIs used to
construct our network from public data sources. In total, our network has
327,924, 2,854, and 230 edges corresponding to PPIs, DTIs, and APIs. These
edges connect one $\mathop{\mathtt{AD}}\limits$ node, 18,527 protein nodes,
and 386 drug nodes.

####  2.1.1 Protein-protein interactions (PPIs)

Following Chen et al. [13], we include a comprehensive list of human PPIs
consisting of 327,924 interactions. This list aggregates a total of 21
bioinformatics and systems biology databases with combinations of five types
of experimental evidence. We refer the audience of interest to Chen et al.
[13] for a detailed description of the databases.

####  2.1.2 Drug-target interactions (DTIs)

We assemble drug-target interactions and bioactivity data from 4 commonly used
databases (each downloaded in November 2022): the ChEMBL database [25] (v31),
the binding database [26], the therapeutic target database [27], and the
IUPHAR/BPS guide to pharmacology database [28]. We retain the drug-target
interactions that satisfy the following inclusion criteria: 1) binding
affinities, including $\text{K}_{i}$, $\text{K}_{d}$, $\text{IC}_{50}$, or
$\text{EC}_{50}$, must be less than or equal to 10 $\mu$M; 2) protein targets
and their respective proteins must have a unique UniProt [29] accession
number; 3) protein targets must be marked as reviewed in the UniProt database;
4) protein targets must be present in homo sapiens.

Additionally, we retain drugs for which we have sufficient sample size to
conduct quantitative analysis using MarketScan [30] insurance claims meta data
(see Section 2.4). Specifically, included drugs have at least 100 patients
with their first dose at least 2 years prior to an
$\mathop{\mathtt{AD}}\limits$ diagnosis (dx). Additionally, these drugs must
have at least 15 patients who eventually received an
$\mathop{\mathtt{AD}}\limits$ dx. Applying these filters yielded 2,854 edges
connecting 386 FDA-approved drugs to 548 protein targets.

####  2.1.3 $\mathop{\mathtt{AD}}\limits$-protein interactions (APIs)

The $\mathop{\mathtt{AD}}\limits$-associated proteins included in the network
were identified from multiple sources. 54 $\beta$-amyloid-related proteins and
27 tauopathy-related proteins were obtained from Cheng et al. [13]. The
authors identified proteins that satisfied at least one of the following
criteria: 1) the proteins are validated in large-scale amyloid or tauopathy
genome-wide association studies; 2) in vivo experimental models exhibit
evidence that knockdown or overexpression of the protein leads to
$\mathop{\mathtt{AD}}\limits$-like amyloid or tau pathology. We also include
93 unique late-onset $\mathop{\mathtt{AD}}\limits$ common risk proteins
identified by 7 large-scale genetic studies [31, 32, 33, 34, 35, 36, 37]. We
further incorporate a set of 118 $\mathop{\mathtt{AD}}\limits$-associated
proteins introduced in at least 2 out of the 6 following databases (each was
downloaded in November 2022): the online Mendelian inheritance in man database
[38], the comparative toxicogenomics database [39], the HuGE navigator
database [40], the DisGeNET database [41] (v7.0), the ClinVar database [42]
and the Open Targets database [43] (v22.09). In total, our network is
comprised of 230 unique, $\mathop{\mathtt{AD}}\limits$-associated proteins.
Each of the $\mathop{\mathtt{AD}}\limits$-associated proteins are connected to
a single $\mathop{\mathtt{AD}}\limits$ node with each edge between a protein
and the $\mathop{\mathtt{AD}}\limits$ node representing an API in our network.

###  2.2 Modeling path importance for $\mathop{\mathtt{AD}}\limits$ drug
repurposing

In this work, we denote the constructed network as $G$. Each node in $G$ is
denoted as $v_{i}$. Specifically, drug nodes, protein nodes and the
$\mathop{\mathtt{AD}}\limits$ node are $v^{d}_{i}$, $v^{g}_{i}$, and
$v^{a}_{i}$ , respectively. Note that the index, $i$, does not apply to the
$\mathop{\mathtt{AD}}\limits$ node as there are not multiple in our network.
Each edge that connects node $v_{i}$ to node $v_{j}$ is denoted as $e_{ij}$.
Each path is denoted as $p_{m}$, and the set of edges involved in a path is
denoted $\mathbb{E}_{p_{m}}$. Below, we denote matrices, scalars and row
vectors using uppercase, lowercase, and bold lowercase letters, respectively.

In $\mathop{\mathtt{MPI}}\limits$, we leveraged
$\mathop{\mathtt{DeepWalk}}\limits$ [21], a widely used node embedding
approach, to learn embeddings for each node in $G$. First, for each node
$v_{i}$ in the network, we conduct 256 random walks originating from this
node, and terminating once the path length reaches 128.
$\mathop{\mathtt{DeepWalk}}\limits$ is then trained by sliding a window of
length 10 over the generated paths. Nodes within the same window are forced to
have similar embeddings following the objective function defined in the
original paper [21]. Node embeddings for $\mathop{\mathtt{MPI}}\limits$ are
produced such that they have 128 dimensions.

After generating node embeddings, we score edge, $e_{ij}$, using a normalized
dot product of the embedding of $v_{i}^{x}$ ($x$=$d$, $g$ or $a$) and
$v_{j}^{y}$ ($y$=$d$, $g$ or $a$) as follows:

$w_{ij}=\frac{\exp(\mathbf{v}_{i}^{x}{\mathbf{v}_{j}^{y}}^{\top})}{\sum_{k\in\mathbb{V}}\exp(\mathbf{v}_{i}^{x}{\mathbf{v}_{k}^{y}}^{\top})},$
(1)

where $w_{ij}$ is the score of the edge $e_{ij}$; $\mathbf{v}_{i}^{x}$ and
$\mathbf{v}_{j}^{y}$ is the learned embedding of node $v_{i}^{x}$ and
$v_{j}^{y}$, respectively; $\exp(\cdot)$ is the exponential function; and
$\mathbb{V}$ is the set of all the nodes in the network. Note that, in
Equation 1, only one of $v_{i}^{x}$ and $v_{j}^{y}$ could be the
$\mathop{\mathtt{AD}}\limits$ node. These edge scores are calculated with node
embeddings which implicitly capture the rich structural information within the
network. Thus, compared to existing methods, $\mathop{\mathtt{MPI}}\limits$
can better leverage a network’s structural information for
$\mathop{\mathtt{AD}}\limits$ drug repurposing. We calculate the score for
each path by multiplying the scores of its individual edges as follows:

$s_{p_{m}}=\prod_{e_{ij}\in\mathbb{E}_{p_{m}}}w_{ij},$ (2)

where $s_{p_{m}}$ is the score of the path $p_{m}$; and $\mathbb{E}_{p}$ is
the set of all the edges in the path $p$. The score for each drug (i.e.,
$s_{v_{i}^{d}}$) is then defined as the summation of the scores from all 3-hop
or shorter paths that originate from the $\mathop{\mathtt{AD}}\limits$ node
and terminate at the drug node.

###  2.3 Baseline method

To evaluate the performance of $\mathop{\mathtt{MPI}}\limits$, we compare
$\mathop{\mathtt{MPI}}\limits$ against a network-based method recently
developed by Cheng et al. [13], denoted as $\mathop{\mathtt{BSL}}\limits$.
$\mathop{\mathtt{BSL}}\limits$ scores drugs based on the shortest distance
between the drug targets and the $\mathop{\mathtt{AD}}\limits$-associated
proteins (Section 2.1.3) in the network. Specifically, we denote
$\mathbb{T}(i)$ as the set of protein targets associated with a given drug
$v_{i}^{d}$, and denote $\mathbb{P}$ as the set of
$\mathop{\mathtt{AD}}\limits$-associated proteins. The proximity between these
two sets is calculated as the average shortest distance between elements in
$\mathbb{T}(i)$ and $\mathbb{P}$ as follows:

$r(\mathbb{T}(i),\mathbb{P})=\frac{1}{|\mathbb{T}(i)|+|\mathbb{P}|}\left(\sum_{v_{j}\in\mathbb{T}(i)}\min_{v_{k}\in\mathbb{P}}d(v_{j},v_{k})+\sum_{v_{k}\in\mathbb{P}}\min_{v_{j}\in\mathbb{T}(i)}d(v_{k},v_{j})\right),$
(3)

where $r(\mathbb{T}(i),\mathbb{P})$ is the proximity between these two sets;
$|\mathbb{T}(i)|$ and $|\mathbb{P}|$ is the size of $\mathbb{T}(i)$ and
$\mathbb{P}$, respectively; and $\min_{v_{k}\in\mathbb{P}}d(v_{j},v_{k})$ is
the shortest distance between $v_{j}$ and any elements in $\mathbb{P}$.
Subsequently, we conduct a permutation test to assess the statistical
significance of the calculated proximity. The resulting z-score from this test
is used as the score of drug $v_{i}$ [13]. In $\mathop{\mathtt{BSL}}\limits$,
a lower drug score implies a higher potential for effective
$\mathop{\mathtt{AD}}\limits$ treatment.

###  2.4 Validation using MarketScan database

We use MarketScan medicare supplemental database from 2012–2021 to evaluate
drug impact on $\mathop{\mathtt{AD}}\limits$ onset via Cox proportional-hazard
models [30]. The MarketScan database includes data for over 8 million unique
individuals and is comprised of demographic information, administrative
information, diagnoses, procedures, and pharmacy records. International
Classification of Disease (ICD)-9/ICD-10 codes denote diagnoses and National
Drug Codes (NDCs) record pharmacy claims. We use the ICD-9/ICD-10 codes listed
in Supplementary Table S4♠ to define $\mathop{\mathtt{AD}}\limits$ and
comorbidities, which are included as covariates in Cox proportional-hazard
models. We conduct our analysis over 1,632,218 unique individuals who were at
least 65 years by 2022 and possessed a minimum of five years insurance
enrollment prior of first $\mathop{\mathtt{AD}}\limits$ diagnosis. Drugs from
our constructed network are mapped to NDC codes by partial matching of generic
names from MartketScan redbook. We only include patients who took or started
taking a drug at least two years prior to $\mathop{\mathtt{AD}}\limits$
diagnosis to mitigate the possibility that patients starting a drug already
had $\mathop{\mathtt{AD}}\limits$ given that $\mathop{\mathtt{AD}}\limits$ is
difficult to diagnosis.

♠ Supplementary materials and code can be found here:
https://github.com/ninglab/MPI

##  3 Results

###  3.1 $\mathop{\mathtt{MPI}}\limits$ for $\mathop{\mathtt{AD}}\limits$ drug
repurposing

Figure 1:  Figure 1a shows the network construction process in
$\mathop{\mathtt{MPI}}\limits$. Figure 1b shows the
$\mathop{\mathtt{DeepWalk}}\limits$-based node embedding generation in
$\mathop{\mathtt{MPI}}\limits$. Figure 1c shows the edge, path and drug
scoring in $\mathop{\mathtt{MPI}}\limits$.

In this study, we curate a network consisting PPIs, DTIs and AGIs and propose
a novel network-based method, $\mathop{\mathtt{MPI}}\limits$, for
$\mathop{\mathtt{AD}}\limits$ drug repurposing. We propose
$\mathop{\mathtt{MPI}}\limits$ with the following intuitions: 1) proteins that
associated with $\mathop{\mathtt{AD}}\limits$ are localized in the
corresponding disease module within the comprehensive human PPI network; 2)
the drug target(s) for a disease may also be targeted for other diseases
(e.g., $\mathop{\mathtt{AD}}\limits$) owing to common functional targets and
pathways elucidated by PPIs; 3) if a drug node is linked to the
$\mathop{\mathtt{AD}}\limits$ node through the paths of drug targets and
$\mathop{\mathtt{AD}}\limits$-associated proteins in the PPI, the drug may
have a treatment effect on $\mathop{\mathtt{AD}}\limits$.

We implement $\mathop{\mathtt{MPI}}\limits$ using the following steps: 1)
integrate $\mathop{\mathtt{AD}}\limits$-protein interactions, drug-target
interactions and protein-protein interactions to generate a comprehensive
network (Figure 1a), 2) employ $\mathop{\mathtt{DeepWalk}}\limits$ to learn
node embeddings which capture the structural information within the network
(Figure 1b), and 3) score edges, paths and drugs based on the learned
embeddings to leverage the structural information for better
$\mathop{\mathtt{AD}}\limits$ drug repurposing (Figure 1c). Then we identify
plausible treatment candidates from the top-ranked drugs using a literature
search of the published evidence. We collected 327,924 PPIs from 21
bioinformatics and systems biology databases (Section 2.1.1). We also
collected 2,854 DTIs from 4 commonly used databases (Section 2.1.2), and 230
comprehensive APIs from multiple resources (Section 2.1.3). By aggregating all
the interactions, we construct a drug-protein-$\mathop{\mathtt{AD}}\limits$
network comprised of 386 drug nodes, 18,527 protein nodes, 1
$\mathop{\mathtt{AD}}\limits$ node, and 331,008 edges. More details about the
network construction are available in Section 2. To the best of our knowledge,
$\mathop{\mathtt{MPI}}\limits$ is the first method which effectively
repurposes drug candidates for $\mathop{\mathtt{AD}}\limits$ treatment by
prioritizing paths between drug nodes and the $\mathop{\mathtt{AD}}\limits$
node using learned node embeddings.

(a) Distribution of drug rank by evidence type for
$\mathop{\mathtt{BSL}}\limits$ and $\mathop{\mathtt{MPI}}\limits$ methods.

(b) Drug counts for each type of evidence among
$\mathop{\mathtt{MPI}}\limits$’s and $\mathop{\mathtt{BSL}}\limits$’s top-50
drugs.

Figure 2: Evaluation of drug rank distributions:
$\mathop{\mathtt{MPI}}\limits$ and $\mathop{\mathtt{BSL}}\limits$.

###  3.2 Comparing anti-$\mathop{\mathtt{AD}}\limits$ evidence of
$\mathop{\mathtt{MPI}}\limits$’s and $\mathop{\mathtt{BSL}}\limits$’s top-50
drugs

We compare the top-50 drugs prioritized by $\mathop{\mathtt{MPI}}\limits$ and
$\mathop{\mathtt{BSL}}\limits$ to evaluate their capacity for repurposing
drugs to treat $\mathop{\mathtt{AD}}\limits$. Specifically, we score and rank
all 386 drug nodes in our network using $\mathop{\mathtt{MPI}}\limits$ and
$\mathop{\mathtt{BSL}}\limits$. The complete rankings are reported in
Supplementary Table S3. We then perform a literature search to evaluate the
anti-$\mathop{\mathtt{AD}}\limits$ evidence of the top-50 ranked drugs for
both $\mathop{\mathtt{MPI}}\limits$ and $\mathop{\mathtt{BSL}}\limits$. We
define anti-$\mathop{\mathtt{AD}}\limits$ evidence as any published
experimental result(s), which demonstrate a drug either protects against the
development of $\mathop{\mathtt{AD}}\limits$ or ameliorates aberrant cellular
phenotypes caused by $\mathop{\mathtt{AD}}\limits$. We present
$\mathop{\mathtt{MPI}}\limits$’s and $\mathop{\mathtt{BSL}}\limits$’s top-10
drugs and their anti-$\mathop{\mathtt{AD}}\limits$ evidence in Table 1 and
Table 2, respectively. The complete rankings for the top-50 drugs and their
anti-$\mathop{\mathtt{AD}}\limits$ evidence is available in Supplementary
Tables S1 and S2. Based on the significance of the
anti-$\mathop{\mathtt{AD}}\limits$ evidence, we categorized drugs into the
following 6 types in decreasing order of significance: 1) drugs which are FDA-
approved for $\mathop{\mathtt{AD}}\limits$ treatment (approved); 2) drugs that
have demonstrated anti-$\mathop{\mathtt{AD}}\limits$ effects in completed
clinical trials or are under investigation in $\mathop{\mathtt{AD}}\limits$
clinical trials (clinical); 3) drugs which have demonstrated
anti-$\mathop{\mathtt{AD}}\limits$ effects in in vivo experiments (in vivo);
4) drugs which have demonstrated anti-$\mathop{\mathtt{AD}}\limits$ effects in
in vitro experiments (in vitro); 5) drugs which show
anti-$\mathop{\mathtt{AD}}\limits$ effects in observational studies, cohort
studies or analyses in insurance data (other); 6) drugs that either do not
have the above 5 types of evidence or have been demonstrated ineffective or
damaging for $\mathop{\mathtt{AD}}\limits$ (NA). We present the distribution
of the top-50 drugs from $\mathop{\mathtt{MPI}}\limits$ and
$\mathop{\mathtt{BSL}}\limits$ over the different types of evidence in Figure
2(a) and the counts of each evidence type in Figure 2(b). In Figure 2(a), we
observe more drugs with evidence ranked highly by
$\mathop{\mathtt{MPI}}\limits$ compared to $\mathop{\mathtt{BSL}}\limits$.
This is supported by Figure 2(b), which confirms that
$\mathop{\mathtt{MPI}}\limits$ identified more evidential
anti-$\mathop{\mathtt{AD}}\limits$ drugs compared to
$\mathop{\mathtt{BSL}}\limits$ in the top-50 ranked drugs. Specifically, among
the top-50 ranked drugs, $\mathop{\mathtt{MPI}}\limits$ prioritized 24
evidential anti-$\mathop{\mathtt{AD}}\limits$ drugs while
$\mathop{\mathtt{BSL}}\limits$ only prioritized 20 evidential
anti-$\mathop{\mathtt{AD}}\limits$ drugs, demonstrating an improvement of 20%.
Figures 2(a) and 2(b) also show $\mathop{\mathtt{MPI}}\limits$ outperforms
$\mathop{\mathtt{BSL}}\limits$ in prioritizing drugs with significant
evidence. $\mathop{\mathtt{MPI}}\limits$ prioritizes all the 4 FDA-approved
anti-$\mathop{\mathtt{AD}}\limits$ drugs (e.g., galantamine, rivastigmine,
donepezil and memantine) in our network among the top-50. In contrast,
$\mathop{\mathtt{BSL}}\limits$ prioritizes only a single FDA-approved
anti-$\mathop{\mathtt{AD}}\limits$ drug (donepezil) among the top-50.

We also observe in Table 1 and Table 2 that $\mathop{\mathtt{MPI}}\limits$ is
more effective than $\mathop{\mathtt{BSL}}\limits$ at prioritizing
anti-$\mathop{\mathtt{AD}}\limits$ drugs among the very top (top-10) of the
ranking list. That is, among the top-10 drugs, 6 drugs from
$\mathop{\mathtt{MPI}}\limits$ have anti-$\mathop{\mathtt{AD}}\limits$
evidence including the FDA-approved $\mathop{\mathtt{AD}}\limits$ drug
galantamine, while only 4 drugs from $\mathop{\mathtt{BSL}}\limits$ are
evidential. As presented in Section 2, compared to
$\mathop{\mathtt{BSL}}\limits$, $\mathop{\mathtt{MPI}}\limits$ learns node
embeddings to capture the rich structural information within the network, and
leverage the structural information to better identify
anti-$\mathop{\mathtt{AD}}\limits$ drugs. The superior performance of
$\mathop{\mathtt{MPI}}\limits$ over $\mathop{\mathtt{BSL}}\limits$
demonstrates the effectiveness of leveraging the network structural
information to conduct repurposing to identify candidates for
$\mathop{\mathtt{AD}}\limits$ treatment. We also notice that both
$\mathop{\mathtt{MPI}}\limits$ and $\mathop{\mathtt{BSL}}\limits$ prioritize
17 drugs in concordance within their top-50 drug lists. Among the 17 drugs, 5
drugs demonstrate anti-$\mathop{\mathtt{AD}}\limits$ evidence: donepezil is an
FDA-approved anti-$\mathop{\mathtt{AD}}\limits$ drug; nicotine and rasagiline
have clinical anti-$\mathop{\mathtt{AD}}\limits$ evidence; and fluvoxamine and
fluoxetine have in vivo anti-$\mathop{\mathtt{AD}}\limits$ evidence. The drugs
nicotine, rasagiline, fluvoxamine, and fluoxetine could be promising
repurposing candidates. We leave the investigation of these drugs to future
research.

Table 1: Top-10 Drugs from $\mathop{\mathtt{MPI}}\limits$

Drug | MOA | Indication |  Anti-$\mathop{\mathtt{AD}}\limits$ | Evidence
---|---|---|---|---
varenicline |  $\mathop{\mathtt{AChR}\texttt{-}\mathtt{Ag}}\limits$ | smoking cessation | N | -
fosinopril |  $\mathop{\mathtt{ACE}\texttt{-}\mathtt{INH}}\limits$ | hypertension | Y |  in vivo [44]
nicotine |  $\mathop{\mathtt{AChR}\texttt{-}\mathtt{Ag}}\limits$ | smoking cessation | Y |  clinical [45]
nizatidine | histamine receptor antagonist | duodenal ulcer disease | N | -
piroxicam |  $\mathop{\mathtt{COX}\texttt{-}\mathtt{INH}}\limits$ | osteoarthritis | Y |  other [46, 47]
meloxicam |  $\mathop{\mathtt{COX}\texttt{-}\mathtt{INH}}\limits$ | osteoarthritis | Y |  in vivo [48, 49, 50]
galantamine |  $\mathop{\mathtt{AChE}\texttt{-}\mathtt{INH}}\limits$ | Alzheimer’s disease | Y | approved
bromfenac |  $\mathop{\mathtt{COX}\texttt{-}\mathtt{INH}}\limits$ | inflammation | N | -
etodolac |  $\mathop{\mathtt{COX}\texttt{-}\mathtt{INH}}\limits$ | osteoarthritis | Y |  in vivo [51]
pyridostigmine |  $\mathop{\mathtt{AChE}\texttt{-}\mathtt{INH}}\limits$ | myasthenia gravis | N | -

  * •

In this table, the column “Drug” shows the identified top-10 ranked drugs; the
column “MOA” shows the mechanism of action of each drug; the column
“Indication” presents the indication of each drug; the column
“Anti-$\mathop{\mathtt{AD}}\limits$ ” indicates if the drug has evidenced
anti-$\mathop{\mathtt{AD}}\limits$ effects; and the column “Evidence” presents
the type of the evidence. In this table,
$\mathop{\mathtt{ACE}\texttt{-}\mathtt{INH}}\limits$ represents the
angiotensin converting enzyme inhibitor;
$\mathop{\mathtt{COX}\texttt{-}\mathtt{INH}}\limits$ represents the
cyclooxygenase inhibitor;
$\mathop{\mathtt{AChE}\texttt{-}\mathtt{INH}}\limits$ represents the
acetylcholinesterase inhibitor; and
$\mathop{\mathtt{AChR}\texttt{-}\mathtt{Ag}}\limits$ represents the
acetylcholine receptor agonist.

Table 2: Top-10 Drugs from $\mathop{\mathtt{BSL}}\limits$

Drug | MOA | Indication |  Anti-$\mathop{\mathtt{AD}}\limits$ | Evidence
---|---|---|---|---
tetracycline | bacterial 30S | respiratory tract |  Y | in vitro [52]
ribosomal subunit inhibitor | infections
selegiline | monoamine oxidase inhibitor | Parkinson’s Disease | N | -
ceftriaxone | bacterial cell wall |  gonorrhea |  Y | in vivo [53]
synthesis inhibitor
ibuprofen |  $\mathop{\mathtt{COX}\texttt{-}\mathtt{INH}}\limits$ | headache | N | -
levobunolol | adrenergic receptor antagonist | glaucoma | N | -
ketoprofen |  $\mathop{\mathtt{COX}\texttt{-}\mathtt{INH}}\limits$ | rheumatoid arthritis | N | -
carbidopa | aromatic L-amino acid |  Parkinson’s Disease |  N | -
decarboxylase inhibitor
sulindac |  $\mathop{\mathtt{COX}\texttt{-}\mathtt{INH}}\limits$ | osteoarthritis | Y |  in vivo [54]
biotin | vitamin B | supplement | Y |  in vivo [55]
lansoprazole | ATPase inhibitor | heartburn | N | -

  * •

In this table, the column “Drug” shows the identified top-10 ranked drugs; the
column “MOA” shows the mechanism of action of each drug; the column
“Indication” presents the indication of each drug; the column
“Anti-$\mathop{\mathtt{AD}}\limits$ ” indicates if the drug has evidenced
anti-$\mathop{\mathtt{AD}}\limits$ effects; and the column “Evidence” presents
the type of the evidence. In this table,
$\mathop{\mathtt{ACE}\texttt{-}\mathtt{INH}}\limits$ represents the
angiotensin converting enzyme inhibitor;
$\mathop{\mathtt{COX}\texttt{-}\mathtt{INH}}\limits$ represents the
cyclooxygenase inhibitor;
$\mathop{\mathtt{AChE}\texttt{-}\mathtt{INH}}\limits$ represents the
acetylcholinesterase inhibitor; and
$\mathop{\mathtt{AChR}\texttt{-}\mathtt{Ag}}\limits$ represents the
acetylcholine receptor agonist.

###  3.3 Identifying repurposing candidates with
anti-$\mathop{\mathtt{AD}}\limits$ activity

In order to identify plausible candidates for repurposing, we produce Cox
proportional-hazard models (see Section 2.4) to ascertain whether there is
consensus between the MarketScan insurance data and the
$\mathop{\mathtt{AD}}\limits$-related evidence we found for top ranked
candidates prioritized by $\mathop{\mathtt{MPI}}\limits$. Specifically, we use
hazard ratios ($\mathop{\mathtt{HR}}\limits$) to identify whether any
evidential drug elicited reduced the risk of $\mathop{\mathtt{AD}}\limits$
diagnosis among patients who took the drug compared against those that did
not. We present each drug’s $\mathop{\mathtt{HR}}\limits$ with their
significance levels in Supplementary Table S5b; the
$\mathop{\mathtt{HR}}\limits$ for each drug’s covariates (sex, age, and
additional common comorbidities) are reported in Supplementary Table S5c-t. A
$\mathop{\mathtt{HR}}\limits$ below 1 indicates that a drug has a protective
effect, while a $\mathop{\mathtt{HR}}\limits$ above 1 indicates that a drug
has a damaging effect. Figure 3(a) plots Kaplan-Meier
($\mathop{\mathtt{KM}}\limits$) survival curves. These plots depict a
patient’s likelihood of being diagnosed with $\mathop{\mathtt{AD}}\limits$
following long-term use of either an individual prescribed drug or a drug with
a given mechanism of action (MOA). For MOAs, we group highly-prioritized drugs
with published evidence of anti-$\mathop{\mathtt{AD}}\limits$ activity (see
Table 1). Bupropion ($\mbox{$\mathop{\mathtt{HR}}\limits$}=1.04$; non-
significant) was included as a negative control as clinical trials found the
drug had no significant effect on cognition in AD patients [56].
Trihexyphenydil ($\mbox{$\mathop{\mathtt{HR}}\limits$}=1.71;\ \alpha<0.001$)
was included as a positive control for damaging effects due to the evidence
documented in Supplementary Table S1. The
$\mathop{\mathtt{COX}\texttt{-}\mathtt{INHs}}\limits$ group includes the
following drugs: piroxicam, meloxicam, etodolac, and flurbiprofen. The
$\mathop{\mathtt{ACE}\texttt{-}\mathtt{INHs}}\limits$ group includes the
following drugs: fosinopril, trandolapril, and lisinopril. Note that we only
include blood brain barrier ($\mathop{\mathtt{BBB}}\limits$) crossing
$\mathop{\mathtt{ACE}\texttt{-}\mathtt{INHs}}\limits$ in this group as
non-$\mathop{\mathtt{BBB}}\limits$-crossing
$\mathop{\mathtt{ACE}\texttt{-}\mathtt{INHs}}\limits$ have exhibited very
limited effects on $\mathop{\mathtt{AD}}\limits$ [57]. We also include time-
to-event analysis for 4 of $\mathop{\mathtt{BSL}}\limits$’s top prioritized
drugs (See Supplementary Figure S1). Unlike $\mathop{\mathtt{MPI}}\limits$, we
observe only one of $\mathop{\mathtt{BSL}}\limits$’s drugs (sulindac) with
reduced time-to-event compared to bupropion; however, this difference is not
significant.

(a) Drugs and MOAs with
published anti-$\mathop{\mathtt{AD}}\limits$ evidence

(b) $\mathop{\mathtt{COX}\texttt{-}\mathtt{INHs}}\limits$. Shaded regions
represent 95% confidence
intervals.

(c) $\mathop{\mathtt{ACE}\texttt{-}\mathtt{INHs}}\limits$. Shaded regions
represent 95% confidence
intervals.

Figure 3: Unadjusted Kaplan$\\--$Meier plots for cox proportional-hazard
models

###  3.4 Analyzing the MOAs of $\mathop{\mathtt{MPI}}\limits$’s top-50 drugs

To identify groups of drugs whose anti-$\mathop{\mathtt{AD}}\limits$
properties should be further examined and explored, we examine the top-50
drugs prioritized by $\mathop{\mathtt{MPI}}\limits$ for any common MOAs. We
find that $\mathop{\mathtt{COX}\texttt{-}\mathtt{INHs}}\limits$ and
$\mathop{\mathtt{ACE}\texttt{-}\mathtt{INHs}}\limits$ are the most common MOAs
prioritized by $\mathop{\mathtt{MPI}}\limits$. Both
$\mathop{\mathtt{COX}\texttt{-}\mathtt{INHs}}\limits$ and
$\mathop{\mathtt{ACE}\texttt{-}\mathtt{INHs}}\limits$ have published evidence
of anti-$\mathop{\mathtt{AD}}\limits$ activity. That said, experimental
results suggest that long-term administration of
$\mathop{\mathtt{COX}\texttt{-}\mathtt{INHs}}\limits$ may only have protective
properties, reducing the risk of $\mathop{\mathtt{AD}}\limits$ onset [46].
Moreover, meloxicam ($\mbox{$\mathop{\mathtt{HR}}\limits$}=0.86;\
\alpha<0.05$), has even shown therapeutic potential, reversing cognitive
decline via inhibition of neuronal apoptosis [49, 48]. However, in Figure
3(a), we observe that $\mathop{\mathtt{COX}\texttt{-}\mathtt{INHs}}\limits$ as
a class do not yield reduced risk of $\mathop{\mathtt{AD}}\limits$ compared to
the negative control. That said, we find etodolac significantly reduces the
risk of $\mathop{\mathtt{AD}}\limits$
($\mbox{$\mathop{\mathtt{HR}}\limits$}=0.78;\ \alpha<0.001$) compared to other
$\mathop{\mathtt{COX}\texttt{-}\mathtt{INHs}}\limits$, including flurbiprofen
($\mbox{$\mathop{\mathtt{HR}}\limits$}=0.95$; non-significant) (Figure 3(b)).
This suggests that only certain
$\mathop{\mathtt{COX}\texttt{-}\mathtt{INHs}}\limits$, such as etodolac, may
elicit protective effects against $\mathop{\mathtt{AD}}\limits$ onset.
Importantly, this may be a result of differences in target as etodolac targets
COX2, while flurbiprofen targets COX1. On the other hand,
$\mathop{\mathtt{ACE}\texttt{-}\mathtt{INHs}}\limits$ were found to also
protect against $\mathop{\mathtt{AD}}\limits$ onset in Figure 3(a).
Specifically, we evaluate only
$\mathop{\mathtt{ACE}\texttt{-}\mathtt{INHs}}\limits$ that cross the blood
brain barrier ($\mathop{\mathtt{BBB}}\limits$) as previous insurance claims
metadata analyses have indicated those that do not cross the
$\mathop{\mathtt{BBB}}\limits$ have no effect on $\mathop{\mathtt{AD}}\limits$
[57]. To see if any of the $\mathop{\mathtt{BBB}}\limits$ crossing
$\mathop{\mathtt{ACE}\texttt{-}\mathtt{INHs}}\limits$ have a greater
protective effect that others, we produce a $\mathop{\mathtt{KM}}\limits$ plot
for fosinopril, lisinopril, and trandolapril (Figure 3(c)). Unlike for
$\mathop{\mathtt{COX}\texttt{-}\mathtt{INHs}}\limits$,
$\mathop{\mathtt{ACE}\texttt{-}\mathtt{INHs}}\limits$ do not elicit any
significant by-drug difference in $\mathop{\mathtt{AD}}\limits$ onset as
illustrated in Figure 3(c). While $\mathop{\mathtt{MPI}}\limits$ prioritized
four $\mathop{\mathtt{BBB}}\limits$ crossing ($\mathop{\mathtt{BBBx}}\limits$)
and four non-$\mathop{\mathtt{BBBx}}\limits$
$\mathop{\mathtt{ACE}\texttt{-}\mathtt{INHs}}\limits$ in the top-50, the
$\mathop{\mathtt{BBBx}}\limits$
$\mathop{\mathtt{ACE}\texttt{-}\mathtt{INHs}}\limits$ had a lower average rank
compared to the non-$\mathop{\mathtt{BBBx}}\limits$
$\mathop{\mathtt{ACE}\texttt{-}\mathtt{INHs}}\limits$ (15 and 19,
respectively).

Another important distinction between
$\mathop{\mathtt{COX}\texttt{-}\mathtt{INHs}}\limits$ and
$\mathop{\mathtt{ACE}\texttt{-}\mathtt{INHs}}\limits$ is that
$\mathop{\mathtt{ACE}\texttt{-}\mathtt{INHs}}\limits$ have been shown to have
some ameliorative potential; whereas,
$\mathop{\mathtt{COX}\texttt{-}\mathtt{INHs}}\limits$ have only shown
protective effects. In fact, fosninpril and lisinopril (ranked $2^{nd}$ and
$24^{th}$ by $\mathop{\mathtt{MPI}}\limits$, respectively) was found to reduce
cognitive decline in animal models of $\mathop{\mathtt{AD}}\limits$ [44, 58].
In Figure 3(a), we find that $\mathop{\mathtt{BBBx}}\limits$
$\mathop{\mathtt{ACE}\texttt{-}\mathtt{INHs}}\limits$ consistently exhibit
decreased risk of $\mathop{\mathtt{AD}}\limits$ relative to our negative
control drug, bupropion. Additionally, there does not appear to be a
significant difference between any of the $\mathop{\mathtt{BBBx}}\limits$
$\mathop{\mathtt{ACE}\texttt{-}\mathtt{INHs}}\limits$ with respect to their
protection against $\mathop{\mathtt{AD}}\limits$, indicating that they are
possibly all viable candidates for repurposing. This is in agreement with
other published evidence that has identified $\mathop{\mathtt{BBBx}}\limits$
$\mathop{\mathtt{ACE}\texttt{-}\mathtt{INHs}}\limits$ as having protective
effect on $\mathop{\mathtt{AD}}\limits$ development. Interestingly,
$\mathop{\mathtt{MPI}}\limits$ prioritized 133.6% more
$\mathop{\mathtt{COX}\texttt{-}\mathtt{INHs}}\limits$ and 700.9% more
$\mathop{\mathtt{ACE}\texttt{-}\mathtt{INHs}}\limits$ than
$\mathop{\mathtt{BSL}}\limits$ in the top-50 from all such drugs in our
network. $\mathop{\mathtt{MPI}}\limits$’s ability to prioritize more drugs
from MOAs with known anti-AD activity suggests that it may be a more viable
option when identifying candidates for drug repurposing.

$\mathop{\mathtt{MPI}}\limits$ also highly prioritizes drugs that increase the
brain’s response to acetylcholine, either by reducing its degradation
(acetylcholinesterase inhibitors,
$\mathop{\mathtt{AChE}\texttt{-}\mathtt{INHs}}\limits$) or by stimulating its
receptors (acetylcholine receptor agonists,
$\mathop{\mathtt{AChR}\texttt{-}\mathtt{Ags}}\limits$). This is important as
acetylcholine’s ($\mathop{\mathtt{ACh}}\limits$) synaptic bioavailability is
an important contributor to $\mathop{\mathtt{AD}}\limits$ progression. That
is, there is evidence that cholenergic atrophy and
$\mathop{\mathtt{ACh}}\limits$ deficiency is linked with cognitive decline in
$\mathop{\mathtt{AD}}\limits$ patients [59]. Moveover, many of the current
FDA-approved drugs indicated to slow $\mathop{\mathtt{AD}}\limits$ progression
target this mechanism of disease progression via
$\mathop{\mathtt{AChE}\texttt{-}\mathtt{INHs}}\limits$ (e.g., donepezil,
rivastigmine, and galantamine).
$\mathop{\mathtt{AChR}\texttt{-}\mathtt{Ags}}\limits$, also enhances
$\mathop{\mathtt{ACh}}\limits$ signaling. Such drugs, such as nicotine,
accomplish this by increasing the response of $\mathop{\mathtt{ACh}}\limits$
receptors located on the post-synaptic neuron. Interestingly, nicotine, was
found to significantly improve cognition in patients with mild cognitive
impairment, which is a precursor to $\mathop{\mathtt{AD}}\limits$ [45]. We
also find long-term nicotine use to have a protective effect
($\mbox{$\mathop{\mathtt{HR}}\limits$}=0.532;\ \alpha<0.001$), with respect to
$\mathop{\mathtt{AD}}\limits$ onset. In Figure 3(a), we observe similar risk
of developing $\mathop{\mathtt{AD}}\limits$ to
$\mathop{\mathtt{ACE}\texttt{-}\mathtt{INHs}}\limits$ after six to seven
years. Conversely, we find evidence that long-term use of trihexyphenidyl,
which reduces the activity of $\mathop{\mathtt{ACh}}\limits$ receptors, is
associated with $\mathop{\mathtt{AD}}\limits$-like neurodegeneration in rats
[24]. This is corroborated by Figure 3(a), where we observe the highest risk
of $\mathop{\mathtt{AD}}\limits$ elicited by trihexyphenidyl. More than eight
years on trihexyphenidyl was associated with a substantial increase in the
risk of $\mathop{\mathtt{AD}}\limits$ relative to the other drugs evaluated in
Figure 3(a). These findings confirm that $\mathop{\mathtt{ACh}}\limits$
signaling is closely linked with $\mathop{\mathtt{AD}}\limits$ progression. As
such, exploring other drugs and drug classes which either increase
$\mathop{\mathtt{ACh}}\limits$ synaptic bioavailability or enhance neuronal
response to $\mathop{\mathtt{ACh}}\limits$ should be further examined for
anti-$\mathop{\mathtt{AD}}\limits$ activity.

##  4 Discussion

In this work, we propose a novel network-based,
$\mathop{\mathtt{AD}}\limits$-specific drug repurposing approach called
$\mathop{\mathtt{MPI}}\limits$. $\mathop{\mathtt{MPI}}\limits$ improves upon
prior network-based methods by leveraging node embeddings learned via
$\mathop{\mathtt{DeepWalk}}\limits$ to prioritize
$\mathop{\mathtt{AD}}\limits$-associated paths. Moreover, the use of learned
embeddings allows $\mathop{\mathtt{MPI}}\limits$ to more effectively capture a
network’s rich topology than previous approaches, such as
$\mathop{\mathtt{BSL}}\limits$. In a direct comparison, we find that 20% more
of $\mathop{\mathtt{MPI}}\limits$’s highly prioritized drug candidates
(top-50) have published anti-AD evidence compared to
$\mathop{\mathtt{BSL}}\limits$’s highly prioritized drug candidates. In
addition to evidence in literature, we leverage insurance claims data to
produce Cox proportional-hazard models. Among all the drugs we evaluate, these
models identified $\mathop{\mathtt{BBBx}}\limits$
$\mathop{\mathtt{ACE}\texttt{-}\mathtt{INHs}}\limits$ as having the lowest
risk of $\mathop{\mathtt{AD}}\limits$. Similarly, etodolac was found to have
the lowest risk of $\mathop{\mathtt{AD}}\limits$ among the four
$\mathop{\mathtt{COX}\texttt{-}\mathtt{INHs}}\limits$ we evaluated (Figure
3(b)), indicating that this drug in particular may have protective effect
despite the class as a whole not exhibiting a significantly reduced risk of
$\mathop{\mathtt{AD}}\limits$ compared to our negative control (Figure 3(a)).
Additionally, $\mathop{\mathtt{MPI}}\limits$ highly prioritizes drugs that
target the cholinergic system. Each of the approved
$\mathop{\mathtt{AD}}\limits$ drugs in our dataset that are also
$\mathop{\mathtt{AChE}\texttt{-}\mathtt{INHs}}\limits$ are prioritized in the
top-50 by $\mathop{\mathtt{MPI}}\limits$. $\mathop{\mathtt{MPI}}\limits$ also
highly prioritizes nicotine, an
$\mathop{\mathtt{AChR}\texttt{-}\mathtt{Ags}}\limits$. This prioritization is
supported by both literature and our Cox models, which suggest nicotine is
associated with reduced risk of $\mathop{\mathtt{AD}}\limits$. Altogether, the
results presented in this work highlight etodolac, nicotine, and
$\mathop{\mathtt{ACE}\texttt{-}\mathtt{INHs}}\limits$ as viable candidates for
repurposing to treat $\mathop{\mathtt{AD}}\limits$ and, as such, deserve
further examination in future studies.

Despite its promising results, $\mathop{\mathtt{MPI}}\limits$ exhibits a few
limitations. The PPI network we construct is a simplification of molecular
pathways. Like many other network-based approaches,
$\mathop{\mathtt{MPI}}\limits$ does not consider loops nor the directionality
of PPI as these can be difficult for models to learn. In our context, this
means that highly ranked candidates are only likely to be in close proximity
to $\mathop{\mathtt{AD}}\limits$-related genes. To improve drug
prioritization, models must be capable of identifying drugs that are both
upstream of and in close proximity to these
$\mathop{\mathtt{AD}}\limits$-related genes. In future studies, we will
leverage directed interactions either by hard coding them or learning them.
One way directionality might be learned is through the use of multi-omics
data. Examining how changes to genomic and epigenomic profiles affect gene
expression could facilitate learning where genes are in pathways. Furthermore,
by leveraging multi-omics data, we may be able to provide more personalized
drug recommendations.

##  5 Acknowledgments

This project was made possible, in part, by support from the National
Institute of Aging grant no. 5R01AG071470. Any opinions, findings and
conclusions or recommendations expressed in this paper are those of the
authors and do not necessarily reflect the views of the funding agency.

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