JMIR MEDICAL INFORMATICS

Khan et al

Original Paper

Toward Preparing a Knowledge Base to Explore Potential Drugs
and Biomedical Entities Related to COVID-19: AutomatedComputational Approach
Junaed Younus Khan1, BSc; Md Tawkat Islam Khondaker1, BSc; Iram Tazim Hoque1, BSc; Hamada R H Al-Absi2,
MSc; Mohammad Saifur Rahman1, PhD; Reto Guler3,4,5, PhD; Tanvir Alam2, PhD; M Sohel Rahman1, PhD
1

Department of Computer Science and Engineering, Bangladesh University of Engineering and Technology, Dhaka, Bangladesh

2College of Science and Engineering, Hamad Bin Khalifa University, Doha, Qatar

3

International Centre for Genetic Engineering and Biotechnology, Cape Town Component, Cape Town, South Africa

4

Division of Immunology and South African Medical Research Council Immunology of Infectious Diseases, Department of Pathology, Institute of
Infectious Diseases and Molecular Medicine, Faculty of Health Sciences, University of Cape Town, Cape Town, South Africa
5Wellcome Centre for Infectious Diseases Research in Africa, Institute of Infectious Disease and Molecular Medicine, Faculty of Health Sciences,
University of Cape Town, Cape Town, South Africa

Corresponding Author:
Tanvir Alam, PhD
College of Science and Engineering
Hamad Bin Khalifa University
PO Box 34110
Education City
Doha
Qatar
Phone: 974 44542277
Email: talam@hbku.edu.qa

Abstract
Background: Novel coronavirus disease 2019 (COVID-19) is taking a huge toll on public health. Along with the non-therapeutic
preventive measurements, scientific efforts are currently focused, mainly, on the development of vaccines and pharmacologicaltreatment with existing drugs. Summarizing evidences from scientific literatures on the discovery of treatment plan of COVID-19
under a platform would help the scientific community to explore the opportunities in a systematic fashion.
Objective: The aim of this study is to explore the potential drugs and biomedical entities related to coronavirus related diseases,
including COVID-19, that are mentioned on scientific literature through an automated computational approach.
Methods: We mined the information from publicly available scientific literature and related public resources. Six topic-specificdictionaries, including human genes, human miRNAs, diseases, Protein Databank, drugs, and drug side effects, were integrated
to mine all scientific evidence related to COVID-19. We employed an automated literature mining and labeling system througha novel approach to measure the effectiveness of drugs against diseases based on natural language processing, sentiment analysis,
and deep learning. We also applied the concept of cosine similarity to confidently infer the associations between diseases andgenes.
Results: Based on the literature mining, we identified 1805 diseases, 2454 drugs, 1910 genes that are related to coronavirus
related diseases including COVID-19. Integrating the extracted information, we developed the first knowledgebase platform
dedicated to COVID-19, which highlights potential list of drugs and related biomedical entities. For COVID-19, we highlighted
multiple case studies on existing drugs along with a confidence score for their applicability in the treatment plan. Based on our
computational method, we found Remdesivir, Statins, Dexamethasone, and Ivermectin could be considered as potential effective
drugs to improve clinical status and lower mortality in patients hospitalized with COVID-19. We also found that
Hydroxychloroquine could not be considered as an effective drug for COVID-19. The resulting knowledgebase is made available
as an open source tool, named COVID-19Base.
Conclusions: Proper investigation of the mined biomedical entities along with the identified interactions among those would
help the research community to discover possible ways for the therapeutic treatment of COVID-19.

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Khan et al(JMIR Med Inform 2020;8(11):e21648) doi: 10.2196/21648
KEYWORDS
COVID-19; 2019-nCoV; coronavirus; SARS-CoV-2; SARS; remdesivir; statin; statins; dexamethasone; ivermectin;hydroxychloroquine

Introduction
SARS-CoV-2 initially spread widely in China, then in Italy,
and has since been reported worldwide [1,2]. SARS-CoV-2 isa novel coronavirus that causes COVID-19 [3]. Although
SARS-CoV-2 has gained attention as a consequence of the
global COVID-19 pandemic, other known human coronaviruses,
including betacoronaviruses (SARS-CoV, MERS, OC43,
HKU1) and alphacoronaviruses (229E, NL63), have resulted
in severe respiratory syndrome in patients and been of publichealth concern [4]. To combat COVID-19, an urgent solution
is needed for the detection and therapeutic treatment of thisdisease, which requires a comprehensive experimental
investigation of relevant biomedical entities (eg, genes,
noncoding ribonucleic acids [ncRNA], viruses, drugs) [5].
However, this is a relatively slow process due to the inherent
nature of experimental validation. As an alternative, faster insilico methods can be applied [6,7], which can act as a filter
prior to wet lab validation. Virtual screening, molecular docking,and other in silico methods have already been investigated to
discover drugs that may work against COVID-19 [8]. Still, thisis a daunting task due to the large number of possiblecombinations of biomedical entities (eg, drug-gene pairs) thatneed to be examined [9]. To enable comprehensive explorationof potential therapeutic treatments, knowledge base solutions
are proposed; these would allow the scientific community tofocus on a relatively smaller number of potential biomedical
entities that may lead to the discovery of a novel treatment for
COVID-19.
Databases that focus on virus-related diseases for multiple hosts
already exist. For example, in ViRBase [10], the authors
highlighted the association between ncRNAs and viruses in 20
hosts. The VISDB database, based on literature mining,
integrated the virus interaction site in humans for five DNA
oncoviruses and four RNA retroviruses [11]. Virus Pathogen
Resources (VIPR) developed a portal that collected acomprehensive set of information related to coronavirus andhepatitis C virus (HCV), as well as other viruses [12,13].
However, none of the abovementioned databases are particularly
useful for COVID-19/SARS-CoV-2, as those databases were
not specific to the novel coronavirus, or they provided very
limited information about the associated genes, or they did notinclude other factors involved in coronavirus-related diseases,
drugs, and drug side effects. Moreover, there is no one
knowledge base that has integrated all biomedical entities
specific to COVID-19/SARS-CoV-2. To address this gap, we
explored the potential of machine intelligence to automaticallymine the scientific literature, with the goal of developing thefirst comprehensive knowledge base that integrates several
biomedical entities associated with COVID-19/SARS-CoV-2.
To achieve this, we leveraged state-of-the-art natural language
processing algorithms, sentiment analysis, and deep
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learning–based techniques and applied them to a large corpus
of coronavirus-related scientific literature.

Methods
Data Sets
For this study, we used the COVID-19 Open Research Dataset(CORD-19) [14], generated by the Allen Institute for AI. The
data set contains over 138,000 scholarly articles related to
COVID-19 and the coronavirus family of viruses. The data set
was collected using the following query to search PubMed,
PubMed Central (PMC), bioRxiv, and medRxiv: “COVID-19”
OR “Coronavirus” OR “Corona virus” OR “2019-nCoV” OR“SARS-CoV” OR “MERS-CoV” OR “Severe Acute Respiratory
Syndrome” OR “Middle East Respiratory Syndrome.” This
query covers most research articles related to COVID-19 andother coronaviruses (eg, MERS, SARS) and we searched up
until June 9, 2020. Unless otherwise specified, we consideredboth the abstract and full body of the manuscripts (whenavailable) for downstream analysis.

Source of Dictionaries
We collected gene names from HUGO Gene Nomenclature
Committee (HGNC) [15], Protein Data Bank (PDB) entries
from PDB [16], micro ribonucleic acids (miRNAs) from
miRBase [17], disease names from Disease Ontology (DO)[18], drug names from DrugBank [19], and drug side effects
from Side Effect Resource (SIDER) [20].

Overview of Methodology
We extracted disease-drug, disease-gene, drug-PDB pairs, andtheir corresponding sentences from the CORD-19 literature ina co-occurrence–based approach. To evaluate the effectivenessof the disease-drug pairs, we used both a pretrained model(TextBlob) and an unsupervised model (developed by theauthors using the Word2Vec model and K-means clustering)to determine the sentiment scores of the sentences extracted foreach pair. We further used these sentiment scores along withthe minimum distance between the disease and drug term in thecorresponding sentences as input features of our neural networkmodel, which we used for the final classification of thedisease-drug pairs (as positive or negative). To determine theconfidence level of the extracted disease-gene associations, wetransformed each disease and gene of a pair into two separate
vectors using the Word2Vec model and calculated their cosine
similarity. We used the known disease-gene associations fromthe DisGeNET database as the gold standard to determine theconfidence level of the new associations on the basis of cosine
similarity measures. Finally, we extracted the side effects ofthe drugs that were found by our mining from SIDER.
Additionally, a feedback mechanism was incorporated into
COVID-19Base to collect feedback from users for future use.

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Extracting Disease-Drug Interactions
We extracted disease-drug interactions from the CORD-19
literature and classified them into one of two categories (labels):
positive and negative. The positive label means the drug ispotentially effective against COVID-19, and the negative label

Khan et almeans the opposite. We also determined a confidence score,
which indicates our level of confidence in that automatic label.
Figure 1 shows the workflow of extracting disease-druginteractions and predicting the effectiveness of drugs against
diseases with confidence scores.

Figure 1. Flowchart of extracting disease-drug interactions and predicting the effectiveness of drugs against diseases with confidence scores. CORD-19:
COVID-19 Open Research Dataset.

Disease and Drug Name Extraction
To extract relevant disease-drug pairs from the CORD-19
literature, we employed a dictionary-based approach to detect
mentions of diseases and drugs in the literature. We used Disease
Ontology [18] and DrugBank [19] to prepare the disease anddrug dictionaries. We leveraged the Aho-Corasick algorithm[21] to search the drug and disease names, considering the large
size of the drug and disease dictionaries and the corpus itself.
The Aho-Corasick algorithm is a string-searching algorithm
that efficiently locates multiple patterns in a large amount of
text. The time complexity of the algorithm is O(n + m + z),
where n is the length of the text, m is the total length of all thepatterns to be searched, and z is the total number of occurrencesof the patterns in the text.

Disease-Drug Pairs Extraction
After extracting the disease and drug names separately, we
wanted to mine the literature and identify the sentences that
contain the disease and drug pairs to semantically evaluate theirinteractions. For this purpose, we searched for every
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disease-drug pair from our disease and drug list in the CORD-19
literature and collected every sentence where a co-occurrence
was found. We then created a document for every disease-drugpair, combining all extracted sentences. Thus, we built a
disease-drug pair to document mapping. We did not use apattern-based approach here (as was done previously in [22])as this could result in missing some sentences containing
disease-drug pairs.

Anomaly Removal
As we automatically extracted the sentences containing thedisease-drug pairs, there was a possibility of errors in our
extracted data; therefore, we decided to check and remove any
abnormalities from our collected data before moving on to thenext stage of the pipeline. We used unsupervised anomaly
detection [23] for this task. Unsupervised anomaly detection
detects anomalies in an unlabeled data set by looking for
instances that seem to fit the remainder of the data set the least,
under the assumption that the majority of the instances in thedata set are “normal.” We used the K-means clustering algorithm
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JMIR MEDICAL INFORMATICS[24], as it has been used for anomaly detection in several studies[25-29]. We proceeded as follows. First, we used Doc2Vec [30]to create a numeric representation of each document associatedwith each disease-drug pair. We then fitted these representations
into our K-means model and observed two clear clusters ofeasily discriminable sizes, where the smaller one consisted ofonly 189 instances. As we know that anomalies differ from thenormal instances significantly and occur very rarely in the data,
we could assume that the instances of the smaller cluster wereindeed anomalies. We also checked a number of instances
manually to verify our assumption. We discarded these 189
instances from any further consideration.

Khan et al
to. We weighed this value by dividing it by the distance between
the word and the centroid of its cluster to describe the extent of
its potential positiveness or negativeness. We then calculated
the term frequency–inverse document frequency (tf–idf) score[33,34] of each word in the sentence collection to consider thesignificance of the unique words. Next, we built a tf–idf
representation, T, for each disease-drug pair by replacing eachword of the corresponding sentences with its tf–idf score and asentiment value representation, S, by replacing each word with
its sentiment value. Finally, we took their dot product (T▪S) as
the final sentiment rate of our unsupervised model.

Sentiment Analysis

Neural Network Model for Automatic Labeling andConfidence Score

Overview

Overview

We applied sentiment analysis to automatically assess theeffectiveness of a drug to treat a particular disease in the context
of each extracted drug-disease pair. First, we applied the conceptof transfer learning. We used TextBlob [31], which is apretrained sentiment analysis tool provided as a Python library.
However, it showed some inconsistency in some cases as
expected from a pretrained model and we felt it necessary to
perform unsupervised sentiment analysis, which is the second
model in our pipeline. We obtained a polarity score from the
TextBlob model and a sentiment rate from our unsupervised
model for each disease-drug pair, which were subsequently fed
to our neural network model to predict the final label.

We used a deep neural network (DNN) model to automatically
predict the label and confidence score for our disease-drug pairs.
We used a relatively simpler neural network with two hidden
layers as such models commonly perform better for smaller
data sets compared to neural networks with many layers andparameters [35,36].

TextBlob Model

We used the polarity or sentiment score given by the TextBlob
and unsupervised models as the input features for our neuralnetwork model, along with the minimum distance between the
disease and drug term in the corresponding document.

TextBlob is a Python library that is widely used in natural
language processing tasks such as part-of-speech (POS) tagging,noun phrase extraction, sentiment analysis, classification, andtranslation. Given the sentences that we mined for eachdisease-drug pair as input, TextBlob gives a polarity score
between –1 and 1. We recorded the polarity scores for eachdisease-drug pair to use it as a feature for our neural networkmodel.

Unsupervised Model
We used the concept of K-means clustering again for
unsupervised sentiment analysis. First, we trained the Word2Vec[32] model with our mined literature and got a vector
representation of every word. We then ran K-means clusteringon the estimated word vectors and found two clusters (positive
and negative). The positive cluster was decided on the basis of
the presence of several positive words (in the context of adisease-drug pair), including “cure,” “preclude,” “inhibit,”“prescribe,” “reduce,” and “modest.” On the other hand, thenegative cluster contained words like “risky,” “kill,” and“danger.” We then assigned each word a sentiment value, either+1 or –1, based on the cluster (positive or negative) they belong

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Training Data
We manually labeled 200 disease-drug pairs to train our neuralnetwork model. Among them, there were 110 positive instances
and the rest were negative.

Input Features

Model Setup and Output
The DNN structure used in this study is similar to that shownin Figure 2. It consists of one input layer with three neurons(each neuron corresponds to one input feature), two hidden
layers with eight and four neurons respectively, and one output
layer containing one neuron for binary classification (positiveor negative). The transfer functions of the first and second
hidden layers were the rectified linear unit (ReLU) [37] andhyperbolic tangent function (tanh) [38], respectively. The
transfer function of the output layer was a sigmoid function[39]. We trained the DNN model using Xavier initialization[40], which tries to make the variance of the outputs of a layer
equal to the variance of its inputs. We used Adam optimizer[41] and the maximum training epoch was set to 500. We split
our labeled data into training and test sets on an 80:20 ratio. We
trained our model on the training data and achieved 75%
accuracy on the test set.

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Figure 2. Schematic diagram of the deep neural network used to predict the effectiveness of drugs against diseases.

Extracting Disease-Gene Associations
Figure 3 shows the workflow of extracting disease-geneassociations. We extracted gene names along with miRNAs
from the CORD-19 literature in a dictionary-based approach
using HGNC [15] and miRBase [17]. We then extracted theirassociations with diseases in a similar process to the one we
had used to extract the disease-drug pairs and collected all theabstracts where a co-occurrence was found. Next, we applied
the concept of cosine similarity [42] to confidently infer the
associations. We transformed each disease into vector V1, eachgene (and miRNA) into vector V2, and then calculated the cosine
similarity of V1 and V2 for each pair. To create the vector
representations, we trained a Word2Vec model with all the

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collected abstracts. We used the DisGeNET [43] database as
the gold standard to evaluate the performance of cosine
similarity in predicting the gene-disease linkage. First, we
calculated the maximum, average, and minimum cosine
similarity of the pairs that were common both in our findings
and in the DisGeNET database. We found that 99.7% of the
newly discovered pairs lie within this range (determined from
DisGeNET) in terms of cosine similarity. We further classified
the associations into three classes (high, medium, and low) interms of confidence as follows: pairs having cosine similarity
closest to the maximum (minimum) of the known ones wereconsidered as high (low) confidence associations, and theremaining ones (those closest to the average) as medium
confidence associations. Moreover, pairs that were also foundin the DisGeNET database were labeled as verified associations.

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Figure 3. Flowchart of extracting disease-gene and disease-miRNA associations and determining their confidence levels. CORD-19: COVID-19 Open
Research Dataset; HGNC: HUGO Gene Nomenclature Committee; miRNA: micro ribonucleic acid.

Extracting Drug-Protein Associations
We also extracted drug-protein associations from the CORD-19
literature, applying the same co-occurrence–based approach as
mentioned above. We used PDB IDs from the Protein Data
Bank [16] for extracting protein names. Unlike the disease-geneassociations, we did not apply the concept of cosine similarity
here as we did not find any suitable data set that could be usedas the gold standard in this case.

Extracting Side Effects of Drugs
The drugs we are suggesting through this literature mining may
come with different side effects. Therefore, we also exploredthe possible side effects of the drugs. We collected the drugs
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with their corresponding side effects from SIDER [20] andmapped them with the drugs mentioned in the CORD-19
literature to extract the possible side effects.

Feedback Mechanism
We implemented a feedback mechanism in COVID-19Base for
future improvement. This mechanism enables expert users fromthe scientific community to share their valuable feedback on
the label (positive or negative) for a particular interaction
determined by the automatic natural language processing–based
approach. The users can voluntarily label each sentence that ismined from the literature as a source of an interaction. This
feedback will be recorded and further processed to enrich thelabeled data set, which can be leveraged in the next version of
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COVID-19Base to further improve the prediction quality for
determining effective disease-drug interactions. The
accompanying tutorial (user manual) on COVID-19Basehighlighted an example of how a user can use the feedback
mechanism.

Results
Terms and Interactions Highlighted in CORD-19 Data
Set

entries from the CORD-19 literature (Table 1). Among thedisease-drug pairs, 21,581 were positive and 1318 were negative.
Among the disease-gene associations, 2088 were verified, and82 associations were found with high-confidence, 12,231 withmedium-confidence, and 1488 with low-confidence. Moreresults are shown in Table 1. Notably, a small proportion (1.5%)of the findings were manually labeled. Interestingly, we found
194 drug-PDB pairs for coronavirus-related diseases, whichindicates the rapid growth of experimental work to understandthe interaction mechanisms of drugs and target proteins.

Based on our computational workflow, we identified 1805
diseases, 2454 drugs, 1910 genes, 11 miRNAs, and 70 PDB
Table 1. Pairs of terms as identified in the analyzed set of documentsa.

aInteraction or association

Number of extracted pairs of terms

Disease-drug

22,899 (21,581 positive, 1318 negative)

Disease-gene

15,889 (2088 verified, 82 high, 12,231 medium, 1488 low)

Disease-miRNA

56 (48 medium, 8 low)

Drug-Protein Data Bank

194

Positive (negative) indicates an (in)effective association. High, medium, and low refer to confidence associations.

COVID-19–Related Terms and Interactions
Our computational workflow identified 514 drugs and 417 genesthat are directly associated with COVID-19 (Table 2). Amongthe 514 drugs, 492 were found to have a positive associationand 22 had a negative association. Among the 417 genes, 347
were medium-confidence associations and 70 were
low-confidence associations.

Table 2. Biomedical terms that are related to COVID-19a.

aInteraction or association

Number of extracted pairs of terms

COVID-19–drug

514 (492 positive, 22 negative)

COVID-19–gene417 (347 medium, 70 low)

COVID-19–miRNA

3 (2 medium, 1 low)

Positive (negative) indicates an (in)effective association. High, medium, and low refer to confidence associations.

Genes Related to COVID-19
Our automated workflow identified C-reactive protein (CRP)
as one of the COVID-19–associated genes with “medium”
confidence. CRP is a known clinical biomarker for SARS [44]and the level of CRP increases significantly in patients with
SARS. The level of CRP was also higher for patients with
COVID-19 in some clinical cases [45,46]. More than 25 papers(from the CORD-19 data set) related to the association between
CRP and COVID-19 were identified through our computational
workflow. Furthermore, the genes ELANE, AZU1, MPO,PRTN3, CTSG, and TCN1 were shown to be significantly alteredin patients with COVID-19 [47], and our automatically preparedknowledge base highlights all of them as associated with
COVID-19 with “medium” or “low” confidence. The ACE2
and TMPRSS2 genes are known to be involved in SARS-CoV-2
infection [48]; in fact, SARS-CoV-2 uses angiotensin-converting
enzyme 2 (ACE2) as a receptor for entry into host cells [49,50].
The spike protein of SARS-CoV-2 binds with the ACE2 receptor
and the protease TMPRSS2 mediates the infection process [51].
It is important to note that ACE2 and TMPRSS2 were not directlylisted in DisGeNET as genes associated with COVID-19. In
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spite of that, our data-driven approach based on a gold-standard
data set from DisGeNET was able to infer the association of
ACE2 and TMPRSS2 with COVID-19 with “medium”
confidence, which suggests that our approach is efficacious.
Analyzing the complete ACE2 interaction network, Wicik et al[52] listed several element genes (ACE2, ANPEP, DPP4, CCL2,
MEPIA, TFRC, ADAM17, NPC1, FABP2, TMPRSS2, CLEC4M)
and all of these genes were identified as COVID-19–associatedin our automatically prepared knowledge base. In addition, we
mined three miRNAs (hsa-miR-4661-3p, hsa-miR-429, andhsa-miR-183) that were mentioned in the abstracts of
COVID-19–related literature.

Case Studies
In this section, we discuss interesting and useful findings from
our automatically prepared knowledge base in the context ofpotential drugs that can be investigated for the potential
therapeutic treatment of COVID-19.

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Case Study 1: Dexamethasone Can be Considered an
Effective Drug for COVID-19
Dexamethasone, an inexpensive and commonly used steroid,
is a major breakthrough in the fight against COVID-19. We
found dexamethasone to be a positive (ie, effective) drug for
COVID-19, automatically labeled as such through our
computational workflow with a confidence score of 77.61%.
Our computational workflow also discovered the effectivenessof this drug against pneumonia, respiratory failure, and diarrhea,
which are strongly correlated to COVID-19 [53,54]. Thus,
further exploration of this drug to fight COVID-19 is likely tobe fruitful. Recent studies suggest that dexamethasone reduces
the risk of death from COVID-19 from 40% to 28% for patientson ventilators and from 25% to 20% for patients needing oxygen[55].

Case Study 2: Ivermectin Might be Considered an
Effective Drug for COVID-19
Ivermectin is an effective drug against pneumonia and diarrhea,
and has recently been claimed to successfully treat patients with
COVID-19 as well [56]. It is a US Food and Drug
Administration (FDA)–approved drug used for parasitic
infections, which has the potential to be repurposed. Ivermectin
inhibits the replication of SARS-CoV-2 in vitro [57]. Recently,
a team of medical doctors in Bangladesh reported quick
recoveries of patients with COVID-19 using this drug [58]. We
found ivermectin to be a positive (ie, effective) drug for
COVID-19, automatically labeled with a confidence score of
77.91%. It was also labeled a positive drug for pneumonia anddiarrhea in our knowledge base.

Case Study 3: Remdesivir Seems Effective Against
COVID-19
Remdesivir has been identified as a positive (ie, effective) drug
for COVID-19, automatically labeled as such through ourpipeline, with a confidence score of 68.18%. Thus, it seems to
be a promising drug for further investigation for treating
COVID-19. Interestingly, it was recently being considered as
an effective drug for treating COVID-19 [59]. Notably,
remdesivir is an antiviral drug originally developed for Ebola
treatment [60,61]. A recent clinical trial conducted by the
National Institute of Allergy and Infectious Diseases (NIAID)
showed that remdesivir helped patients with COVID-19 recoverfaster and improved their survival rates. Adult patients treatedwith remdesivir were found to recover 4 days faster, an
improvement of 31% compared to other patients; in addition,
the overall death rate dropped from 11.6% to 8% [62].
Remdesivir is now under consideration for use against
COVID-19 in more than ten clinical trials [63]. We found 6LU7
was one of the PDB entries for remdesivir. After exploring thecorresponding literature [64], we found that remdesivir was
shown to be an effective inhibitor of the main SARS-CoV-2
protease using molecular docking [65,66].

Case Study 4: Hydroxychloroquine Is Not an Effective
Treatment for COVID-19
Antimalaria drug hydroxychloroquine, which is one of the mosttalked-about drugs for treating COVID-19, was also found in
our mining, albeit with a negative interaction. Our model found
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it is a negative (ie, ineffective) drug with 64.67% confidence.
Additionally, it also revealed that this drug has 111 side effects
including anemia, hemorrhage, liver disorder, hepatitis
fulminant, cardiomyopathy, and cardiac failure, which makes
it a risky option, especially for patients with heart and livercomplications. Although the FDA had previously granted
authorization to use this drug for COVID-19, it has recently
cautioned against its use outside of a hospital setting or a clinical
trial due to its side effects and risk factors [67].

Case Study 5: Statins Drugs Could be Effective Against
COVID-19
Statins are effective as lipid-lowering drugs and mainly used
for the treatment of cardiovascular diseases [68]. Statins arealso well known for their anti‐inflammatory effects [69] andsome studies have supported the use of these drugs as part of a
COVID‐19 treatment protocol [70]. Multiple clinical trials(eg, NCT04343001, NCT04380402) have been launched todetermine the efficacy of statins against COVID-19 [71,72]. In
our knowledge base, the majority of statin classes were shown
to be effective against COVID-19. For example, ulinastatin,
rosuvastatin, fluvastatin, and lovastatin were labeled as positive(ie, effective) drugs against COVID-19 with 94.04%, 79.38%,
78.88%, and 70.75% confidence scores, respectively. Throughour automated computational workflow, we found only one
mention of atorvastatin in the literature [73]. In that single
article, Deliwala et al [73] mentioned atorvastatin as part of aprevention plan against cortical stroke for a 31-year-old female
patient with COVID-19, without referring to the effectivenessof atorvastatin against COVID-19. Consequently, our knowledge
base labeled atorvastatin with a negative sentiment and a rather
low confidence score (61.22%) for COVID-19. We anticipate
that as the number of articles related to atorvastatin use in
COVID-19 treatment protocols increases, our model will beable to effectively infer the sentiment (effective versus
ineffective) of this drug. Based on our finding, it is safe to statethat statins, as low-cost and well-tolerated drugs, should be
investigated in more detail in clinical trials; such drugs mayhelp low- and middle-income countries in particular, where
expensive drugs might not be affordable.

Discussion
Principal Findings
In our knowledge base, through a computational workflow, we
not only extracted the drugs and other biomedical terms thatare mentioned in the literature, but also identified “term pairs”
based on their co-occurrence, which will allow the scientificcommunity to investigate in depth the associations between
term pairs like disease-gene and disease-drug. Many drugs wereassociated with COVID-19, representing the cumulative effortof the scientific community to repurpose existing drugs rather
than pursue novel drug discoveries, which is a rational approachin a pandemic situation [74]. We leveraged an automatedapproach to highlight the effectiveness of drugs against thedisease based on sentiment analysis of the text in the literature.
Through this literature mining, we found dexamethasone,
ivermectin, remdesivir, and others in the list of potential drugs
for COVID-19 treatment. We highlighted hydroxychloroquine
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JMIR MEDICAL INFORMATICS
as an ineffective drug against COVID-19. We extracted
disease-gene associations from the literature and, based on
cosine similarity against the gold-standard DisGeNET data set,
provided a confidence level for the associations between
diseases and genes. We found 194 drug-PDB associations, whichhighlighted the large amount of work performed by the scientificcommunity to understand the mechanism behind drug-target
interactions and virus-host protein interaction mechanisms forcoronavirus-related diseases. Surprisingly, we found few
miRNAs related to COVID-19, indicating the primary focus of
the scientific community is toward protein-based drugs rather
than RNA-based drugs, though there have been successful
RNA-based antiviral drugs. One such drug is Miravirsen, whichbinds miR-122 to prevent it from hybridizing with the RNA
genome of HCV, depriving HCV of its essential cellular cofactor
and blocking HCV replication [75]. We expect more researchalong these lines in the coming months.

Research Implications
Currently, we are facing the largest public health emergency
since the 1918 influenza outbreak [76]. From the beginning of
this outbreak, the scientific community has invested large
amounts of effort to create vaccines and identify therapeutic
solutions. Vaccines for SARS-CoV-2 might come too late tohave any effect on the first wave of the COVID-19 pandemic[77]. However, vaccines might be useful in subsequent waves
of COVID-19 or in a postpandemic scenario in which
COVID-19 becomes a seasonal virus [77]. In this scenario, theidentification of drugs with good efficacy and minimal side
effects is a rational goal that can be achieved in the near future
to combat SARS-CoV-2 [48]. Although promising
pharmacological results with repurposed drugs are emerging
every day, unfortunately, no drug has been approved thus far
for the treatment of COVID-19. Repurposed drugs are underinvestigation worldwide, many in preclinical and clinical stages[78]. With increasing information about SARS-CoV-2, alongwith publications about similar respiratory diseases (eg,pneumonia, SARS), it will be essential to investigate existing
drugs that are already known to be effective against otherrespiratory diseases. As a prime example, dexamethasone, an
FDA-approved drug, was known to be effective against
pneumonia [79], respiratory failure [80], and other diseases.
However, there was no evidence of its effectiveness against
COVID-19 until its recent breakthrough in a clinical trial [55].
Although final approval of the drug is still pending, had it been
investigated earlier, more lives could have been saved.
The research in this study is expected to support the scientificcommunity and decision makers in identifying candidate drugs

Khan et alwith proper evidence from the scientific literature. This will
also help stakeholders explore existing drugs that are alreadyknown to be effective against other respiratory diseases.
Although careful manual curation of the identified associationsof biomedical entities is the ultimate goal, our novel approach
estimates the effectiveness of drugs for coronavirus-relateddiseases based on natural language processing, sentiment
analysis, and deep learning to help the scientific community
shorten the potential list of drugs, ultimately saving time andresources.

Tool and Availability
We made our computational workflow and the resulting databasean open source tool named COVID-19Base for use by thescientific community [81,82]. It not only identifies the terms
and associations, but also highlights the relevant literature
through its digital object identifier (DOI) so that any researcher
using this tool can easily check the original source for moredetailed information. As the number of scientific publications
related to COVID-19 is constantly increasing, we will update
the knowledge base on a monthly basis and integrate all recent
updates in the knowledge base. COVID-19Base has alreadygone through its first transformation (from COVID-19Base 1.0
to COVID-19Base 2.0), as the CORD-19 data set was updated
during the manuscript preparation phase. The earlier version of
the CORD-19 data set contained about 44,000 papers, whereas
the current version includes more than 138,000. The knowledge
base materials and the source code of our computational
approach are available on GitHub [83].

Limitations
Understandably, our findings as presented in the knowledge
base may have some errors due to the inherent limitations of
the methods and approaches adopted. This is why the identified
inferences and associations are made available to users forreview and a feedback mechanism is included in
COVID-19Base.

Conclusions
We proposed a dictionary-based automated computational
workflow to find the associations of six different thematic areas
relatedto
COVID-19/SARS-CoV-2
and
other
coronavirus-related diseases in humans. We prepared aknowledge base and made it available as a tool for the scientificcommunity. We believe this knowledge base will help theresearch community explore the existing drugs and biomedical
entities for coronavirus-related diseases, and the lessons learned
before this outbreak will allow us to find an effective treatment
for COVID-19.

Acknowledgments
The open access publication of this article was funded by the College of Science and Engineering, Hamad Bin Khalifa University.

Conflicts of Interest
None declared.

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Abbreviations
ACE2: angiotensin-converting enzyme 2
CORD-19: COVID-19 Open Research Dataset
CRP: C-reactive protein
DNN: deep neural network
DO: Disease Ontology
FDA: Food and Drug Administration
HCV: hepatitis C virus
HGNC: HUGO Gene Nomenclature Committee
miRNA: micro ribonucleic acidncRNA: noncoding ribonucleic acid
NIAID: National Institute of Allergy and Infectious Diseases
PDB: Protein Data Bank
POS: part-of-speech
ReLU: rectified linear unit
tf–idf: term frequency–inverse document frequency

Edited by G Eysenbach, C Lovis; submitted 26.06.20; peer-reviewed by A Civit, A Sarafi Nejad; comments to author 21.08.20; revised
version received 23.08.20; accepted 06.09.20; published 10.11.20
Please cite as:
Khan JY, Khondaker MTI, Hoque IT, Al-Absi HRH, Rahman MS, Guler R, Alam T, Rahman MS
Toward Preparing a Knowledge Base to Explore Potential Drugs and Biomedical Entities Related to COVID-19: AutomatedComputational Approach
JMIR Med Inform 2020;8(11):e21648
URL: http://medinform.jmir.org/2020/11/e21648/
doi: 10.2196/21648
PMID: 33055059©Junaed Younus Khan, Md Tawkat Islam Khondaker, Iram Tazim Hoque, Hamada R H Al-Absi, Mohammad Saifur Rahman,
Reto Guler, Tanvir Alam, M Sohel Rahman. Originally published in JMIR Medical Informatics (http://medinform.jmir.org),
10.11.2020. This is an open-access article distributed under the terms of the Creative Commons Attribution License(https://creativecommons.org/licenses/by/4.0/), which permits unrestricted use, distribution, and reproduction in any medium,provided the original work, first published in JMIR Medical Informatics, is properly cited. The complete bibliographic information,a link to the original publication on http://medinform.jmir.org/, as well as this copyright and license information must be included.

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