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Drug Repurposing and Computational Drug Discovery: Strategies and Advances
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Drug Repurposing and Computational Drug Discovery: Strategies and Advances.
Mithun Rudrapal, PhD (Ed)
© 2024 Apple Academic Press, Inc. Co-published with CRC Press (Taylor & Francis)
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Challenges and Regulatory Issues in Drug
Repurposing and Computational Drug
Discovery
ANDRÉ M. OLIVEIRA1 and MITHUN RUDRAPAL
1
2
ABSTRACT
The drug repositioning process consists of investigating other uses for drugs
developed for a given disease, but which have proven useful in the treatment of other diseases. This approach is advantageous once their physicalchemical, pharmacokinetic and toxicity data are available, which represents
a gain in time and a lower cost. This chapter focuses on some important
cases of repurposing, emphasizing different strategies for the search for
new therapeutic targets for established drugs. The main strategies treated
in our discussion as those knowledge-based (target-based drug-repurposing,
pathway-based drug repurposing, target mechanism-based drug repurposing,
and genome strategy), phenotype-based and computational methods (encompassing machine learning, network models, text mining and semantic inference, established disease–drug pair knowledge and systems biology). In the
context of target-based drug-repurposing, a discussion is made about highthroughput screening (HTS) and banks of three-dimensional structures of
2
244
Drug Repurposing and Computational Drug Discovery: Strategies and Advances
possible molecular targets for new diseases against which these compounds
could be active. Another aspect of the study of drug repositioning that is
extensively explored in this chapter is the applicability of machine learning
methods, particularly pattern recognition techniques. Statistical validation
tools for drug repositioning are also discussed. Area under the receiver
operating characteristic (AUROC) metric is one of such popular techniques,
whose purpose is telling the model’s ability to discriminate between cases
(positive examples) and non-cases (negative examples). The chapter ends
with case studies involving orphan or rare diseases (ODs) and the pharmacological treatment of SARS-CoV-2.With regard to ODs, the chapter
mentions several digital resources for searching information about their
nature and genotypic and phenotypic data, what is useful in the development
of new therapeutic applications for known drugs. COVID-19 treatments that
have been benefited by repurposing strategies involve typically viral main
protease (mainly Mpro or 3CLpro) inhibitors that show anti-inflammatory
and non-SARS-CoV-2 viral activities (such as darunavir, a former anti-HIV
drug).The study of new uses for consecrated drugs has shed light on the
various diseases and their seasonal variations, supported by a voluminous
amount of pharmacological data that shortens the trail to the discovery of
new treatments.
CONTEXT
The practice of drug repositioning, which consists of discovering new therapeutic applications for drugs already established against certain diseases, has
been an object of interest for many decades. However, this practice has only
recently acquired the status of systematic research with its own methods
1
and strategies.
The development of a new drug is a long and costly process,
which does not always yield the desired results. It is estimated that out of
1000 molecules discovered, with potential pharmacological activity, only
one will reach the clinical testing stage.
2,3
The main advantage of drug repositioning in relation to the traditional
development process is the availability of physical-chemical, pharmacokinetic, and toxicity data on the studied compounds, which represents
a gain in time and a lower cost.
4–6
In addition to substances already
marketed for other purposes, the list of candidates for drug repositioning
includes:
245 Challenges and Regulatory Issues in Drug Repurposing
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• Compounds in clinical phase with a relevant mechanism of action for
more than one disease.
• Compounds whose development was halted in phase II or III clinical
trials for some reason related to their side effects or toxicology, but
proved to be adequate in phase I for that specific disease.
• Compounds whose commercialization was interrupted by economic
factors.
• Compounds whose patents are close to their expiration date.
Several examples of drugs successfully used in the treatment of different
diseases were initially conceived for different purposes. Table 10.1 summarizes some successful cases.
Some Examples of Successful Drug Repurposing.
Drug name and former indication New indication Ref.
Infliximab (Crohn’s disease and rheumatoid arthritis) Psoriasis and [7,8]
psoriatic arthritis
Beta-blockers (high blood pressure treatment) Malaria [9]
Cimetidine (peptic ulcer) Human papilloma [10]
virus (HPV)
Glucocorticoids (immunosuppressant agents) Sepsis [11]
Metronidazole (antibacterial and antiprotozoal agent) Dermatitis [12]
Sirolimus (fungicide) Antitumor agent [13]
Thalidomide (antiemetic/insomnia) HIV [14]
Source: Adapted from Ref. [15].
Several drug repositioning methods and strategies have been described
in the literature.
16
oriented methods, disease-oriented methods, and treatment-oriented methods.
According to Figure 10.1 that summarizes the various methods divided into
their respective categories, the choice of method depends on the level of
knowledge about the particularities of the pharmacological system studied.
The different strategies cooperate and reinforce each other, depending on the
quantity and quality of information obtained over time.
Computational chemistry and bioinformatics have gained relevance in
this context, namely, in drug-oriented methods, with the advance of molecular
modeling strategies and biological targets databases. The emerging diseases,
with their own challenges, require new therapeutic treatments and drugs, and
246
Drug Repurposing and Computational Drug Discovery: Strategies and Advances
that represents a rationale for the pursuit of new chemical entities that can be
adequately provided by theoretical and computational means (in silico drug
repurposing1).
The rst cases of drug repositioning were the result of “blind method”
or serendipity (such as sildenal), but with the increase in the quantity and
complexity of new diseases, it has required systematic approaches. In this
scenario, computational repositioning (in silico) acquires great relevance.
Some authors choose to classify in silico drug repositioning methods into:
knowledge-based methods, signature-based methods, and disease-based
methods. This approach was chosen to this chapter.
Categorization of existing drug-repositioning methods.
Source: Reprinted with permission from Ref. [16]. © 2022 Elsevier.
10.2 GENERAL STRATEGIES FOR DRUG REPOSITIONING
The more drugs that are developed and patented, the larger the amount
of information about their properties, modes of action, biological targets,
and adverse effects. This information is stored in databases, many of them
available for academic purposes, and it serves as scaffold for the knowledgebased drug-repositioning strategies, such as target-based drug repurposing,
pathway-based drug repurposing, target mechanism-based drug repurposing,
and genome strategy.
10.2.1 KNOWLEDGE-BASED REPURPOSING
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• Target-based drug repurposing: This strategy is based on the search
for new therapeutic applications of drugs known from their affinity
with several biological targets associated with other diseases. Several
biological target databases are available, such as those exemplified in
• TABLE. If one wishes to know whether a given drug may have a
biological activity diverse from that for which it was previously
designed, this drug is submitted to a virtual scanning (high-throughput
screening, HTS), through which the drug is docked with the set of
targets, looking for those with whom there are the best interactions.
Some Biological Targets Databases.
Database Type of targets Ref.
Brookhaven protein database Proteins, enzymes, nuclei acids [17–19]
Nucleic acid database Nucleic acids [20]
GPCRdb G protein-coupled receptors (GPCRs) [21]
Therapeutic target database Target-regulating microRNAs and [22]
transcription factors, target-interacting
proteins, and patented agents and their targets
TDR targets Tropical diseases targets [23]
Kyoto Encyclopedia of Genes Genome, metabolic pathways, and [24,25]
and Genomes (KEGG) biological substances
247 Challenges and Regulatory Issues in Drug Repurposing
Pathway-based drug repurposing: This strategy utilizes metabolic
pathways, signaling pathways, and protein-interaction networks information
as an attempt to stablish connections among diseases and drugs.26 In this
context, the use of bioinformatics resources, such as proteomics, genomics,
and metabolomics is essential to establish the bases for the study of the
relationship between the drug and the biological environment.
Target mechanism-based drug repurposing: This is a strategy that matches
the two previous ones in a unique semantic body, establishing connections
between the biological targets with the role they play in metabolism and how
the drug studied affects those relationships.
27
Genome strategy: When using the genomic information of a disease,
available in databases such as NCBI Gene Expression Omnibus (GEO
it is possible to map which potential targets can interact with a given drug,
leading to the discovery of other diseases related to the same genes.
28,29
),
248
Drug Repurposing and Computational Drug Discovery: Strategies and Advances
10.2.2 SIGNATURE-BASED REPURPOSING (PHENOTYPE-BASED
REPURPOSING)
This strategy is based on statistical comparison between electronic health
records (EHRs), available from various sources, using data mining and
30
pattern recognition methods.
agent was discovered through this way.
Metformin’s application as an anticancer
31
DRUG REPOSITIONING
10.3.1 MACHINE LEARNING
Machine learning (ML) can be defined as the process through which an
artificial intelligence (AI) is able to make decisions based on information
learned during its use, that is, without having been previously programmed
for this purpose. This way is especially interesting when dealing with highly
complex problems such as the discovery of new drugs (or new therapeutic
32
actions for known drugs
). Among the ML methods applicable to drug
repositioning, we can mention logistic regression, support vector machine
(SVM), neural network (NN), and deep learning (DL).
Logistic regression: Logistic regression is a type of mathematical model
that makes it possible to obtain the probability of a given result from a
discrete input variable, which can admit two values (such as yes or no) or
several values.
33
An example of its application to drug repositioning is the
PREDICT method, which correlates drug–drug similarities with disease–
disease similarities.
34
Support vector machine (SVM): This mathematical approach uses clas-
sication and regression analysis within a supervised learning framework.
For this purpose, the SVM takes the input data and classies them into two
groups, based on a set of pre-established data used as a test. The algorithm
creates a model that assigns points to each of the two categories in order to
obtain the best possible separation. An example of this approach is the application of data, such as chemical structure, biological activity, and adverse
effects to create an SVM-based classication function that has proven to be
very efcient.
35
Neural network (NN): Neural networks are a subset of machine learning
and are inspired by the human brain, mimicking the way that biological
249 Challenges and Regulatory Issues in Drug Repurposing
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neurons signal to one another. NN are composed of node layers, containing
input and output layers. Each node (or articial neuron) connects to another
and has an associated weight and threshold. If the output of any individual
node is above the specied threshold value, that node is activated, sending
data to the next layer of the network. Otherwise, no data are passed along
to the next layer of the network.
36
An example of the application of neural
networks to drug repositioning is the work of Menden and collaborators
with antitumor agents,37 using as input data genomic and chemical structure
information of the compounds.
Deep learning (DL): The main application of deep learning is in pattern
recognition, which is useful in classifying a large volume of data. This makes
it possible to identify complex structures in large amounts of raw data,
through algorithms that allow the connections between the points to adjust to
the information that is assembled alongside the process. A study in 678 drugs
38
across A549, MCF-7, and PC-3 cell lines from the LINCS Program
linked to 12 therapeutic using DL is described by Aliper and co-workers.
and
39
10.3.2 NETWORK MODELS
An interesting strategy in drug repositioning is to use algorithms that network
nodes, in which each node is occupied by a drug, disease or biological target,
and the connections between them are analyzed. From this computational
effort, relationships emerge that are not obvious and that may be useful in
discovering new applications for existing drugs.
40,41
10.3.3 TEXT MINING AND SEMANTIC INFERENCE
Taking into account the enormity of data available in databases about the
most diverse diseases, the use of intelligent data mining serves very well
the purposes of the search for new applications for the drugs currently used.
This task presents some challenges that are typical of the search for qualified
information in scientific texts, such as:
• The disambiguation of terms used with different meanings depending
on the context.
• The treatment required to fragmented data or lacking self-consistent
information.
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