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Drug Repurposing Using Biological Networks processes Review Drug Repurposing Using Biological Networks Francisco Javier Somolinos 1 , Carlos León 1,2,3,* and Sara Guerrero-Aspizua 1,2,3 1 Department of Bioengineering, Carlos III University, Leganés, 28911 Madrid, Spain; [email protected] (F.J.S.); [email protected] (S.G.-A.) 2 Network Research on Rare Diseases (CIBERER), U714, 28029 Madrid, Spain 3 Regenerative Medicine and Tissue Engineering Group, Health Research Institute-Jimenez Diaz Foundation University Hospital (IIS-FJD), 28040 Madrid, Spain * Correspondence: [email protected] Abstract: Drug repositioning is a strategy to identify new uses for existing, approved, or research drugs that are outside the scope of its original medical indication. Drug repurposing is based on the fact that one drug can act on multiple targets or that two diseases can have molecular similarities, among others. Currently, thanks to the rapid advancement of high-performance technologies, a massive amount of biological and biomedical data is being generated. This allows the use of computational methods and models based on biological networks to develop new possibilities for drug repurposing. Therefore, here, we provide an in-depth review of the main applications of drug repositioning that have been carried out using biological network models. The goal of this review is to show the usefulness of these computational methods to predict associations and to find candidate drugs for repositioning in new indications of certain diseases. Keywords: drug repurposing; network models; metabolic networks; network analysis; drug interac- tions; drug targets Citation: Somolinos, F.J.; León, C.; Guerrero-Aspizua, S. Drug 1. Introduction Repurposing Using Biological Networks. Processes 2021, 9, 1057. Drug repurposing or drug repositioning is the process of finding new uses for already https://doi.org/10.3390/pr9061057 existing drugs. This is a challenging process but with a great potential both to reduce the cost of drug development [1], as well as to improve its security [2,3]. The traditional process Academic Editor: Carla Vitorino of discovering new drugs is based on complex strategies that include five stages: discovery and preclinical studies, security validation, clinical research through phase trials I, II and Received: 29 April 2021 III, review by the regulatory agency (FDA/EMA) and post-marketing safety-monitoring Accepted: 2 June 2021 (pharmacovigilance) [4]. Drug repositioning uses strategies that simplify this process. In Published: 17 June 2021 general, the drug repurposing process consists of four main steps. a. Identification of a candidate molecule. This can be performed using either exper- Publisher’s Note: MDPI stays neutral imental or computational approaches [5]. Experimental approaches use disease related with regard to jurisdictional claims in data and the understanding of drug phenotype modulation, while computational methods published maps and institutional affil- predict drug–protein interactions [6] or pharmacological secondary effects [7]. iations. b. Acquisition of the candidate molecule. c. Mechanistic evaluation of the drug effect in preclinical models followed by evalu- ation of drug efficacy in clinical trials. This step significantly reduces drug development costs, as it takes into account that there are enough data related to drug safety in phase I Copyright: © 2021 by the authors. clinical trials, since they were already performed for the original indication. Licensee MDPI, Basel, Switzerland. d. Post-marketing safety monitoring (pharmacovigilance). This article is an open access article The contrast of traditional drug development and drug repositioning can be seen in distributed under the terms and Figure1. conditions of the Creative Commons Historically, drug repositioning has been a serendipity. One of the best known ex- Attribution (CC BY) license (https:// amples of successful drug repositioning is that of sildenafil, which started as an antihy- creativecommons.org/licenses/by/ pertensive drug, but was repurposed afterwards as a drug to treat pulmonary arterial 4.0/). Processes 2021, 9, 1057. https://doi.org/10.3390/pr9061057 https://www.mdpi.com/journal/processes Processes 2021, 9, 1057 2 of 14 hypertension and erectile dysfunction and was finally marketed as Viagra® [8]. Another classical example is the case of thalidomide, which was withdrawn from the market after its connection to severe fetal defects, but recent research has shown it to be effective in the treatment of leprosy and multiple myeloma [5]. These drug repositioning success stories have further inspired global pharmaceutical industries to explore the potential capacity Processes 2021, 9, x FOR PEER REVIEW 2 of 15 of existing drugs. In fact, in the last ten years, governments, researchers, academics and pharmaceutical companies have encouraged activities to support studies related to drug repositioning [9]. Figure 1. Differences between traditional (A) and reposition (B) drug development. From [4], under open access (CC BY-NC). Figure 1. Differences between traditional (A) and reposition (B) drug development. From [4], under open accessDrug (CC repositioningBY-NC). is based on the fact that any drug can act on multiple targets, that two different diseases may have cellular and molecular similarities and that a target can exhibitHistorically, pleiotropic drug effects.repositioning With the has help been of a current serendipity. existing One high-throughput of the best known technologies, exam- ples theof successful amount of drug data repositioning generated is rapidly is that of increasing. sildenafil, These which technologies started as an foster antihyper- the use of tensivecomputational drug, but was methodologies repurposed toafterwards find associations as a drug between to treatdrugs, pulmonary diseases arterial and targetshyper- and tensionprovide and erectile evidence dysfunction to boost the and drug was repurposing finally marketed process as Viagra [10]. ® [8]. Another classical example Theis the rapid case developmentof thalidomide, of emergingwhich was information withdrawn technologies, from the market including after its cloud con- com- nectionputing, to severe social fetal media defects, and the but Internet recent rese of Things,arch has provide shown a it large to be amount effective of in data the generatedtreat- mentthat of leprosy is in continuous and multiple growth myeloma in numerous [5]. Th fieldsese drug of research. repositioning However, success there stories is an have inherent furthercomplexity inspired inglobal theanalysis pharmaceutical of these industri data thates ariseto explore from theirthe potential huge variety, capacity the of speed ex- at istingwhich drugs. they In arefact, obtained in the last and ten their years, volume governments, [11]. Recent researchers, advances academics in technologies, and phar- such as maceuticalnext-generation companies sequencing have encouraged and high-performance activities to support biomedical studies data related capture to drug technologies, repo- sitioningas well [9]. as the reduction of costs, allow researchers to generate large amounts of experimen- talDrug data. repositioning These include is databased generated on the fact by that powerful any drug analytical can act technologies, on multiple targets, such as DNAthat or two RNAdifferent sequencing, diseases and may mass have spectrometry cellular and for molecular different similarities applications, and such that as a transcriptomics target can exhibit(gene pleiotropic expression effects. and With co-expression the help of data), current proteomics existing high-throughput (protein profiles technologies, and interaction the amountdata of proteins),of data generated metabolomics is rapidly (metabolic increasing. profiles) These and technologies epigenomics foster (methylation the use data of of computationalDNA), among methodologies others. Large to amounts find associat of clinicalions databetween available drugs, in diseases electronic and health targets records and (EHR),provide clinical evidence trials to boost and biobanks the drug are repurposing added to theseprocess already [10]. complex omic data. These dataThe aresrapid also development stored in heterogeneous, of emerging information normally unstructured technologies, formats, including which cloud makes com- data puting,integration social media extremely and the complex Internet and of difficult.Things, provide Even though a large several amount databases of data providegenerated direct that accessis in continuous to structured growth data, in such numerous as gene fields expression of research. (e.g., EBIHowever, Expression there atlas),is an inherent there is still a large part of the genomic data that is only available in raw unstructured format (e.g., complexity in the analysis of these data that arise from their huge variety, the speed at Sequence Read Archive). For these reasons, there is an urgent need for computational which they are obtained and their volume [11]. Recent advances in technologies, such as approaches that can integrate, analyze and interpret this type of datasets. next-generation sequencing and high-performance biomedical data capture technologies, as well as the reduction of costs, allow researchers to generate large amounts of experi- mental data. These include data generated by powerful analytical technologies, such as DNA or RNA sequencing, and mass spectrometry for different applications, such as tran- scriptomics (gene expression and co-expression data), proteomics
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