Despite recent advances in life sciences and technology, drug development is still a costly and time-consuming process with a low rate of success [1]. Discovering a new drug usually takes more than 10 years and costs around $2 billion on average [2]. The number of targetable human genes is approximately 3000, and the identification of serious and even deadly drug side effects is ongoing [3,4]. To overcome these difficulties, many researchers have turned to drug repurposing, which is the practice of identifying novel clinical indicators for existing marketed drugs [5-7].

The past few decades have produced a few successful cases of drug repurposing. For example, sildenafil, originally developed to treat cardiovascular disease, was unexpectedly discovered to be effective against erectile dysfunction [8]. Thalidomide, once used for morning sickness, has been repurposed for the treatment of multiple myeloma [9], and metformin, originally a treatment for type 2 diabetes, has been studied for the treatment of depression, aging, obesity, and even cancer [10,11]. Beta blockers, initially indicated for hypertension, and topiramate, originally used as an antiepileptic, are both repurposed for migraineurs [12,13]. Because of its significant advantages over traditional approaches, in terms of development time, cost, and previous clinical studies, drug repurposing has attracted significant attention from pharmaceutical firms, scientists, and governments in recent years [7,14].

Methodologies for drug repurposing and their successful applications have been widely discussed. Chen et al [15] designed a system-based algorithm called the reverse gene expression score based on several large-scale publicly accessible datasets and demonstrated the potency and efficacy of vorinostat, geldanamycin, and gemcitabine for the treatment of liver cancers. Xu et al [16] found that emricasan had an inhibitory effect on the Zika virus by screening more than 6000 compounds. With the rapid development of natural language processing and deep learning techniques, robust solutions have recently been proposed and have demonstrated potential. Researchers have integrated more than 20 different datasets into a knowledge graph to predict potential drug and target pairs [17-19]. Hamilton et al [20] queried drug-gene-drug interactions within a low-dimensional embedding of biomedical knowledge graphs to predict missing or unobserved links for drug repurposing. Chang et al [21] proposed a novel deep learning model called “CDRscan” that can successfully predict the feasibility of drug repurposing and recommend the most effective anticancer agents for an individual patient. Öztürk et al [22] represented drugs and protein sequences using convolutional neural networks to predict the binding affinities of drug-target interactions.

Academic publications are produced at high volume, with around 3000 new articles currently published per day [23]. No researcher nor clinician can read and comprehend all the relevant articles in their domain [24]. The “known” knowledge has turned into “unknown known” knowledge, with hidden information and patterns waiting to be discovered. This growing body of scholarly data opens a new era of exploiting literature and data to enable data-driven discovery [24]. Literature-based discovery, which connects disconnected entities in literature in PubMed, has been successful in identifying several cases of drug repurposing, such as fish oil for Raynaud’s syndrome, magnesium for migraine headaches, and proton pump inhibitors for atrial fibrillation [25-27]. Swanson [26] demonstrated that bibliometrics can be a useful approach to knowledge discovery and recommended that his method could be extended to other disconnected sets of scientific literature to enable cross-disciplinary innovation [28]. With entitymetrics — bibliometric indicators based on entities studied in the medical literature — researchers without domain knowledge can understand the medical function of a drug [29], identify complex undiscovered biological relationships between drugs and targets [30], and detect implicit gene-gene relationships using literature in PubMed [31]. This research demonstrates the potential of applying bibliometrics to medicine to support data-driven discovery. It represents the next generation of bibliometric studies [32] and already shows great promise [33].

In this research, we extended bibliometric indicators for biomedical entities mentioned in the PubMed literature to investigate drug repurposing. We used aspirin (salicylic acid) as the target drug. Aspirin is one of the most well-recognized and well-studied drugs with a history dating back to 1500 BC [34]. It was originally used as an analgesic to treat mild to moderate pain. It has been used clinically for the treatment of at least 10 diseases, including coronary artery disease, cerebrovascular disease, peripheral arterial disease, preeclampsia, diabetes, colorectal cancer, Kawasaki disease, Alzheimer’s disease, and arthritis [34,35]. New indications for aspirin are still being reported [36-38]. Aspirin has a remarkably wide range of effects and therefore provides an ideal case with which to study drug repurposing. The work described in this paper primarily aimed to identify patterns in the different repurposing phases of aspirin by analyzing the diseases, drugs, and genes related to aspirin. We propose 4 measures based on entitymetrics to identify the characteristics and patterns of drug repurposing for aspirin: Popularity Index (P₁), Promising Index (P₂), Prestige Index (P₃), and Collaboration Index (CI).

Papers on aspirin-related research published between 1951 and 2018 were collected from PubMed. Since aspirin is known by many names, the search terms were chosen from DrugBank, RxNorm, and MeSH terms [33,61]. The final search query is shown in Textbox 1. Non-journal articles, non-English articles, letters, and editorial commentaries were excluded. In total, 63,387 publications from PubMed were downloaded in XML format.

To better understand the drug repurposing process of aspirin, the relevant research was divided into 4 phases based on previous studies [34,35] and expert suggestions: (1) 1951-1960, the original use; (2) 1961-1990, in-depth studies of pharmacological mechanisms and side effects; (3) 1991-2000, repurposing for cardiovascular diseases; and (4) 2001-2018, repurposing for other diseases, such as colorectal cancer and breast cancer. These phases can also be observed from the evolution and trends of the publications, as shown in Figure 1 and Table 1.

Before extracting biomedical entities, all articles were parsed to obtain PMIDs, publication years, titles, abstracts, authors, journals, and institutions using a dom4j XML parser written in Java. Then, we used spaCy for preprocessing (such as removing the punctuation and stop words) of titles and abstracts in the natural language processing pipeline. In addition, a novel and reliable method of author name disambiguation proposed by Lerchenmueller and Sorenson [67] was used to count distinct authors.

The biomedical entity extraction module provided by the biomedical entity search tool (BEST) [68], a dictionary-based biomedical information extraction tool based on sophisticated information retrieval approaches, was deployed to extract entities such as diseases, drugs, and genes. The BEST dictionary is built from 12 different public sources, including NCBI Entrez Gene, DrugBank, T3DB, Animal TFDB, Therapeutic Target DataBase, PubChem, and MeSH [68]. We obtained 1472 unique disease names, 1640 unique drug names, and 3184 unique gene names from the titles and abstracts. Table 2 shows the top 10 biomedical entities of 3 different types and their frequency of appearance in PubMed articles.

In order to quantify and visualize the academic importance of individual biomedical entities, 4 transparent and easy entitymetric indexes (P3C) were developed: Popularity Index (P₁), Promising Index (P₂), Prestige Index (P₃), and Collaboration Index (CI). These indicators can be considered as the extensions of the indicators proposed by Kissin and Edwin [33] and Kissin [69] for measuring the academic interest of a drug or technique at the article level. In this study, we adapted the indicators from the perspective of biomedical entities with the goal of understanding drug repurposing. Different from Kissin’s indicators, our indicators not only focus on the articles on a given drug but also consider the changes in indicators of biomedical entities (eg, diseases, drugs, and genes) and non-biomedical entities (eg, authors) that are related to the given drug. Detailed explanations of these measures are provided in the following sections.

Figure 1 shows an overview of aspirin-related research in PubMed from 1951 to 2018. The red and blue lines represent the percentage and absolute numbers, respectively, of articles in PubMed per year. The details of the publications, authors, and journals are shown in Table 1. During the evolution of aspirin, Phase 1 (1951-1960) produced 507 articles, most of which were published in journals covering pharmacy-related or general medicine–related topics (Table 1 and Multimedia Appendix 2). Research in Phase I focused on the anti-inflammatory and antipyretic uses of aspirin, and this phase marks the original use of aspirin.

In Phase 2 (1961-1990), a turning point can be identified in 1967, after which the number of relevant papers per year grew dramatically until 1986. Several significant pharmacological discoveries related to aspirin occurred during this period, including the antiplatelet effect [70], mechanism of inhibition of prostaglandin synthesis [71], and acetylation of the cyclo-oxygenase enzyme [72]. The percentage of aspirin-related articles in PubMed reached its peak in 1981, at about 0.32%, and then decreased. Kune et al [73] reported that aspirin could effectively reduce the incidence of colorectal cancer, after which the percentage began to rise again. After 1975, articles began to occur frequently in journals covering specialty areas, such as Circulation and Thrombosis Research. We identify this phase as the in-depth investigation of the pharmacological mechanisms and side effects of aspirin.

In Phase 3 (1991-2000), there was a steady and stable growth in the number and percentage of aspirin-related articles per year in PubMed (Figure 1). Compared to the first 10 years (1951-1960), there was a >22-fold increase in the number of articles as well as a >36-fold increase in the number of distinct authors. As shown in Multimedia Appendix 2, in both 1991-1995 and 1996-2000, 4 of the top 5 journals were cardiovascular-related journals. We thus identify this phase as repurposing for cardiovascular diseases.

In Phase 4 (2001-2018), the number of articles per year grew continuously and reached its peak (2164) in 2015, but the percentage significantly reduced (Figure 1). From the information presented in Table 1, we note that the numbers of articles, distinct authors, and journals in Phase 4 were all higher than those in the previous 3 periods. The average number of authors in this period had exceeded the total average (4.39). Journals covering other topics, for example Cancer Management and Research, Drugs & Aging, and World Neurosurgery, were increasingly represented (Multimedia Appendix 2), demonstrating that aspirin had been experimentally applied to many other diseases. We thus mark this phase as repurposing for other diseases.

To analyze drug repurposing through all 4 phases from the biomedical entity perspective, we first computed the P3C indicators of the top 10 diseases, drugs, and genes in the cohort of aspirin articles during the period 1951-2018. The results show that there are distinct patterns of these indicators in different repurposing phases. To describe these patterns in detail, we reorganized the 30 biomedical entities (the top 10 diseases, top 10 drugs, and top 10 genes) into the 4 phases of aspirin research, according to when each achieved its maximum P₁, which indicates the focus of research in the field of aspirin. In each phase, we further analyzed the change patterns of the P3C indicators for the most popular biomedical entities, to investigate the features of different phases of drug repurposing, association between entities and P3C indicators, and possible factors driving drug repurposing at the biomedical entity level.

Only “rheumatoid arthritis” (RA) reached its maximum P₁ in Phase 1, at 9.36%, as shown in Figure 2A and then exhibited a downhill trend for the rest of the 3 phases, reaching a low of 0.63% in 2016-2018. As shown in Figure 2B, for the P₂ of RA, there is only one significant increase of more than 0 in all 4 phases: 0.06 in 1951-1955 (Phase 1). This observation indicates that the popularity of RA in 1951-1955 increased by 6% compared to that in 1945-1950. It can also be observed from Figure 2C that the P₃ of RA was more than 5% during 1951-1980 and reached its maximum in Phase 1 (25%, 1960-1965), indicating that one quarter of the papers studying RA were published in the top 20 journals in the aspirin domain in Phase 1. In the next 3 phases, the P₃ peaked twice, in Phase 2 (1971-1975) and Phase 3 (2001-2005), possibly relating to the discovery of the mechanism of anti-inflammatory and RA-induced cardiovascular diseases. Similar to P₁, as shown in Figure 2D, the CI of RA peaked in 1956-1960 (40.44%), then declined to 1.02% in 2016-2018, indicating that around 40% of authors in Phase 1 were studying RA, but only about 1.02% authors still worked on the same disease in Phase 4.

In summary, in Phase 1, the P₁, P₂, P₃, and CI of RA reached their maxima, or showed a significant increase, indicating that RA was the disease upon which most research was focused in the aspirin domain at this time. However, the value of these indicators showed profound declines in the next 3 phases, which means that aspirin was studied in relation to other diseases and is thus an ideal example of drug repurposing.

As shown in Figure 3, there are 9 top biomedical entities in the aspirin domain that reached their maximum P₁ in Phase 2, including 3 diseases (“asthma”; “hypersensitivities, drug”; and “ulcer, gastric”) and 6 drugs (indomethacin, acetaminophen, dipyridamole, vitamin F, adenosine, and prostacyclin). The 3 diseases can all be side effects of aspirin, while the 6 drugs can be divided into 3 categories: (1) competitors of aspirin, that is, indomethacin and acetaminophen, which are analgesic and antipyretic drugs, respectively, with fewer side effects; (2) the antiplatelet drug dipyridamole; and (3) precursor substances in the pathway of the mechanism of action of aspirin (vitamin F, adenosine, and prostacyclin). In contrast with RA, the P₁ of these biomedical entities increased from Phase 1, peaked in Phase 2, and then decreased, indicating that the side effects and mechanisms of aspirin were studied in detail in Phase 2. The P₁ of indomethacin in 1976-1980 (16.75%) was the highest among these 9 entities in Phase 2, and vitamin F in 1981-1985 (11.19%) ranked second.

Figure 4 shows the P₂ of these 9 biomedical entities in the aspirin domain over time. The P₂ of the 3 side effects had a significant increase of more than zero in Phase 2, indicating that interest in the side effects of aspirin increased sharply: 1961-1965 and 1976-1980, for “asthma”; 1961-1965 for “hypersensitivities, drug”; and 1961-1965 for “ulcer, gastric.” The time periods in which the P₂ of the 6 drugs showed significant increases are generally later than those for the side effects, such as 1971-1975 for indomethacin and 1981-1985 for prostacyclin. This observation indicates that the discovery and in-depth study of side effects may have positive effects on the discovery of the mechanism of action of aspirin as well as the development of alternatives with fewer side effects.

Figure 5 shows the P₃ of these 9 biomedical entities in the aspirin domain, demonstrating a feature common to all 9 entities: a gradual decline with a fluctuation in P₃ after reaching a maximum in Phase 1 or Phase 2. The highest initial P₃ values of “hypersensitivities, drug” and “ulcer, gastric” occurred in Phase 1, revealing that both side effects had been taken seriously by researchers in Phase 1. The P₃ of “hypersensitivities, drug” in 1956-1960 (33.33%) was higher than that of RA in 1956-1960 (25.00%). In 2011-2015, the P₃ of only 2 entities are over the 5% threshold: 5.82% for adenosine and 10.00% for prostacyclin. In the aspirin domain, papers studying these 2 entities published in the top 20 journals comprised more than 5% of papers published in all of the journals indexed by the PubMed in 2011-2015. This observation indicates that the 2 entities were still important foci of research in the aspirin domain.

It can be observed from Figure 3, Figure 5, and Table 3 that P₃, on average, achieved its maxima 10.7 years earlier than P₁. In particular, for “hypersensitivities, drug” and “ulcer, gastric,” the intervals can be as long as 20 years. This observation indicates that P₃ can reflect an early sign of academic interest into biomedical entities, a phenomenon that could be potentially valuable for tracking the research frontiers of a drug.

The results of the CI of these 9 biomedical entities in the aspirin domain are presented in Figure 6, which shows that the CIs for these biomedical entities have similar trends to those of P₁ over time. Among all 9 biomedical entities during 1951-2018, indomethacin achieved the highest maximum CI in 1976-1980 (19.79%), indicating that it became a strong competitor to aspirin as an analgesic agent in Phase 2. This result also demonstrates that during the last 5-year period (2011-2015), the CIs of only 2 of the 9 entities were >5%, indicating that the 2 entities were still the subject of research by a considerable number of scientists (>2230) in the aspirin research community in 2011-2015. The 2 biomedical entities include “asthma” (6.21%) and adenosine (5.50%).

Based on the observation of P3C in Phase 2 and previous studies on aspirin [34,35], we can conclude that, on one hand, the in-depth investigation of the side effects and mechanism of action of aspirin provided the knowledge basis and research direction for drug repurposing. On the other hand, due to the market competition from other drugs, as well as the serious side effects, pharmaceutical companies attempted to discover new indicators for aspirin, in order to maintain the sales volume of aspirin.

In Phase 3, 5 top biomedical entities comprising 4 diseases and 1 drug reached their maximum P₁, as shown in Figure 7A. The 4 diseases were all cardiovascular-related, including “coronary disease” (P₁ of 18.88% in 1996-2000), “cerebral ischemia” (P₁ of 2.57% in 1996-2000), “intracranial vascular disorder” (P₁ of 5.73% in 1991-1995), and “ischemic heart disease” (P₁ of 3.01% in 1996-2000). Compared with Figures 2 and 3, the P₁ of the previous 10 biomedical entities that peaked in the Phase 1 or Phase 2 were considerably lower than that of coronary disease, indicating that cardiovascular-related disease was the focus of the aspirin domain in that time. Coronary disease is often referred as ischemic heart disease and is the most common cardiovascular-related disease worldwide; similarly, cerebral ischemia and intracranial vascular disorder represent the same condition, commonly known as stroke. These conditions were reportedly the first and second most common causes of death worldwide in the early 21st century [74]. The demand for the prevention and treatment of such fatal diseases could be one of the factors driving the repurposing of aspirin for cardiovascular-related diseases.

The only drug that reached its maximum P₁ in Phase 3 is heparin (11.92% in 1996-2000). As one of the most common anticoagulant drugs, heparin has always been the reference drug for repurposing aspirin to treat cardiovascular-related diseases, which could be the reason for the increase in the academic interest in heparin in the aspirin domain. There was another peak of heparin in Phase 2 (5.03%, 1971-1975), which could be related to an increase in research into the mechanisms of the antiplatelet effect of aspirin in Phase 2.

Figure 7B shows the changes in P₂ of these 5 biomedical entities over time. All 5 biomedical entities demonstrated a significant increase in Phase 3. “Coronary disease” and “cerebral ischemia” increased in 1991-1995, and “intracranial vascular disorder”, “ischemic heart disease,” and heparin increased in 1991-1995. The P₂ of the 2 entities also showed significant increases in Phase 2, consistent with the fact that aspirin was clinically used for coronary disease before the discovery of its antiplatelet effect: 0.02 in 1976-1980 for “coronary disease” and 0.10 in 1971-1975 for heparin.

The pattern of P₃ for these 5 entities over time is displayed in Figure 7C. All 5 biomedical entities reached their maxima in Phase 2, earlier than the maximum of P₁. “Coronary disease” reached a maximum in 1971-1975, and heparin reached a maximum in 1961-1965. The difference from the previous phases is that the P₃ of these 5 biomedical entities peaked again in Phase 3. For instance, “coronary disease” peaked in 1991-1995, and heparin peaked in 1991-1995, indicating that these biomedical entities were important topics of research in both Phase 1 and Phase 3.

Figure 7D shows the CI of the 5 biomedical entities during 1951-2018, in which the CI demonstrated a dynamic trajectory very similar to that of P₁. The maximum of “coronary disease” in Phase 3 is highest at 22.91% in 1996-2000, indicating that “coronary disease” attracted the greatest share of the authors in the aspirin domain. “Coronary disease” and “cerebral ischemia” in Phase 4 and heparin in Phases 2 and 4 surpassed the threshold value of 5%. The CI of “cerebral ischemia” steadily grew after Phase 3, showing a different trend from the other 4 biomedical entities, which increased in Phase 1 and Phase 2, peaked in Phase 3, and then dramatically decreased. This observation may illustrate that “cerebral ischemia,” unlike the other biomedical entities, is still increasing in popularity and collaboration, so additional increases are still expected.

In Figure 8, there are 15 biomedical entities that reached their maximum P₁ in Phase 4. Unlike the previous phases, most of the biomedical entities were genes and can be divided into 3 categories according to the diseases to which they are related: (1) inflammatory-related genes (eg, COX-2, LPLA2, and TNF-α), (2) cardiovascular-related genes (eg, COX-1, CD143, plasminogen, LDLCQ3, GPIIb, P2Y₁₂, and tPA), and (3) cancer-related genes (eg, TNFa, COX-2, COX-1, and LPLA2). These observations indicate that aspirin was actively studied for these 3 aspects of diseases from the perspective of genes in Phase 4. In particular, the maximum P₁ of COX-2 was the highest among these 15 biomedical entities at 21.97% in 2001-2005, revealing that COX-2 was considered to be very important in the aspirin domain at that time.

Figure 8 also shows that the P₁ of 2 diseases peaked in Phase 4. One is “diabetes,” whose P₁ in 2006-2010 was 6.83%. In fact, as early as 1875, Ebstein and Müller [75] discovered that aspirin had the effect of lowering blood glucose levels. Inspired by this observation, scientists have since been trying to use aspirin for the treatment of diabetes [75]. There are several peaks in the P₁ of “diabetes” in previous phases. In the 21st century, it has been recommended that patients with diabetes who have an increased risk of cardiovascular disease take aspirin as a primary preventative [5,76]; this could be the reason why the academic interest in “diabetes” in the aspirin domain increased again. The other disease is “carcinomas, colorectal.” Its P₁ peaked in 2001-2005 and then increased significantly after a small decline in 2006-2010, a pattern which is very different from other diseases in the aspirin domain. Repurposing aspirin for the treatment of colorectal carcinomas appears to be a focus of research in the aspirin domain today. The P₁ of 3 drugs also peaked in Phase 4, including the antiplatelet drugs clopidogrel and ticlopidine, which are competitors of aspirin as antiplatelet drugs [35], and warfarin, which is an anticoagulation drug that is similar to heparin and has been found to be superior to aspirin for secondary prevention of ischemic stroke with nonvalvular atrial fibrillation [77,78].

Figure 9 presents the changes in P₂ of these 15 biomedical entities over time. All of the genes demonstrate an increase of more than 0 in Phase 4. Unlike these genes, the diseases and drugs showed several significant increases of more than 0 in different phases, which reflects a longer history of research in the aspirin domain. For example, the increases occurred in 1956-1960, 1996-2000, and 2001-2005 for “diabetes”; 1996-2000, 2001-2005, and 2006-2010 for clopidogrel; and 1971-1975 and 1991-1995 for warfarin.

The changes in P₃ of these 15 biomedical entities over time are shown in Figure 10, from which we can make two observations. First, the P₃ of these biomedical entities demonstrated that the time period of the maximum of P₃ was much earlier than that of the maximum of P₁. Second, unlike the biomedical entities noted in previous sections, the diseases and drugs had ≥2 significant peaks in different phases. For instance, “diabetes” had peaks of 42.86% in 1956-1960, 25.00% in 1971-1975, and 14.22% in 1996-2000, and “carcinomas, colorectal” had peaks of 33.33% in 1981-1985, 15.91% in 1991-1995, and 14.15% in 2006-2010. These numbers indicate that these entities attracted considerable interest in the field of aspirin research and high-impact papers on these conditions were published. However, the genes usually had only one peak in P₃ in Phase 3 or 4, illustrating that these genes are relatively new topics in the aspirin domain.

The CI data for these 15 biomedical entities are presented in Figure 11, which shows that the maximum CI for COX-2 is the highest, at 34.37%, in 2001-2015, denoting that COX-2 was the focus of aspirin research in Phase 4; the research and development of Vioxx, a selective COX-2 inhibitor with fewer side effects, may be one of the reasons [79]. The CI of 2 drugs, clopidogrel (25.54% in 2001-2015) and ticlopidine (20.74% in 2006-2010), reveals fierce competition between aspirin and these alternative antiplatelet drugs. This competition could have driven the repurposing of aspirin for other diseases, especially cancers, that have an urgent demand for effective treatment.

This study examines drug repurposing from the perspective of the evolution of biomedical entities, using aspirin as the study subject. It is of paramount importance for drug discovery to identify the factors that drive repurposing as well as potential patterns among biomedical entities in various phases of the drug research timeline. The main contribution of this paper is twofold. First, we proposed 4 entitymetric indices (P3C) to quantify changes in academic interest in biomedical entities and to reveal the granular process of drug repurposing. Second, we divided aspirin research into 4 phases, including original use (1951-1960), in-depth studies of pharmacological mechanisms and side effects (1961-1990), repurposing for cardiovascular-related diseases (1991-2000), and repurposing for other diseases (2001-2018), taking into consideration 3 granular perspectives—disease, drug, and gene—that contribute to a comprehensive understanding of the features of the repurposing process.

Our entitymetric results indicate that aspirin is representative of the process of drug repurposing. The research findings can be summarized as follows. In Phase 1, aspirin was routinely used to ease pain, fever, and inflammation and was often used in the treatment of RA [34], with a P3C that peaked in 1951-1960. Despite the widespread use of aspirin, at this stage, its mechanism of action was not well understood [34]. In Phase 2, the side effects and mechanisms of actions of aspirin were studied extensively, as shown by the maxima of P₁ and CI, as well as a significant increase in P₂ for the relevant biomedical entities in 1961-1990. The anti-platelet effect [70], inhibition of prostaglandin synthesis [71], and acetylation effect on the enzyme cyclo-oxygenase [72] were uncovered. These discoveries provided a solid knowledge foundation for the successful repurposing of aspirin. The highest P₁ in 1961-1990 was for indomethacin (16.75%), denoting fierce competition with aspirin for its original use. This could be one of the factors contributing to the repurposing of aspirin.

In Phase 3, aspirin was successfully used for several cardiovascular-related diseases because of its antiplatelet effects [80]. The related diseases and drugs achieved their highest values of P₁ and CI as well as significant increases in P₂ in 1991-2000. As these diseases are the most common diseases worldwide, according to data from the World Health Organization [74], the demand for the prevention and treatment of fatal diseases is also another potential factor driving drug repurposing. In the last phase, there was a large number of studies suggesting the use of aspirin for other diseases, especially colorectal cancer [36]. The greatest difference from previous phases is that aspirin was studied at the genetic level. Ten genes reached their maxima of P₁ and CI as well as an apparent increase in P₂ in 2001-2018. This observation could indicate that the development of modern science and technology, such as gene sequencing, molecular simulation, and deep learning, accelerates the process of drug repurposing of aspirin. Meanwhile, 2 fatal diseases — diabetes and colorectal carcinoma — as well as 3 competitive drugs of aspirin as an antiplatelet agent — clopidogrel, ticlopidine, and warfarin (an anticoagulant and competitor with aspirin for stroke prevention) — also had peak P₁ and CI values and a great increase in P₂.

Methodologically, in this study, we developed 4 entitymetrics and demonstrated how to use them to investigate the process of drug repurposing. The results demonstrate that the maxima of P₁, P₃, and CI are closely associated with the different phases of research of aspirin repurposing. The P₁ and CI metrics can indicate dynamic trends in academic interest in a given biomedical entity over a long time period. For instance, long-lasting increases in P₁ and CI signal interest in repurposing, while P₂ is more sensitive to immediate changes in academic interest in a specific biomedical entity, since it takes into consideration data from the two most recent periods. Moreover, P₃ can reflect a research focus far earlier than the other 3 indices, which means that a continuously high P₃ may be valuable as an early signal of the emergence and transfer of research topics in drug research. If P₃ does indeed have predictive power, it could be due to the involvement of top domain experts in the peer review of manuscripts in top journals with high impact factors [81,82]. Additionally, due to their easy implementation and interpretability, these indices can be applied in multiple domains, such as drug assessment, drug discovery, and pharmacovigilance.

There are several limitations in the current paper. First, the data included in our analysis are limited to articles indexed in PubMed. Some real-world data, such as electronic health records, clinical trials, and social media, in which aspirin and its related biomedical entities were mentioned, should be included. In our future work, we will use different types of data sources for studying drug repurposing and take into account other entities related to drugs, including other biomedical entities, such as pathways, proteins, and cells, and non-biomedical entities, such as authors, institutions, and countries. The landscape of collaborations between academic institutions and pharmaceutical companies could affect the drug repurposing process. Second, there are several ways of measuring the impact of a journal, such as the impact factor and relative citation ratio. Third, this study mainly focused on investigating the repurposing journey of aspirin, but we did not test whether it can be used to predict future drug repurposing. In future studies, we will evaluate the different impact measures of a journal and choose a proper measure better fitted to the chosen drug. Furthermore, we will also aim to test the proposed metrics on other drugs to understand their repurposing journeys (eg, metformin) to see whether generalized patterns exist in different repurposing processes.