Drug repositioning, also referred to as drug repurposing, is the process of developing new indications for existing drugs [1]. The financial advantage of drug repositioning over traditional drug development is that much of the cost and time spent in the early stage can be bypassed. In addition, the risk of failure caused by adverse reactions can be better controlled, since the toxicity of the repurposed drugs has already been tested [2]. For these reasons, both the pharmaceutical industry and academic communities are paying particular attention to this field. An increasing number of in silico [3,4] and in vitro [5-7] approaches have been developed to efficiently scan the existing pharmacopoeia for new usage. Other than these strategies primarily focused on preclinical information, side effects data are increasingly used for rational drug repositioning [8-10], due to the direct reflection of the clinical reality of actual patients [11]. The collection of side effects information based on clinical trials is conventionally a time-consuming and labor-intensive process. Making things more difficult, most raw data are not freely available to the public. However, the big data concept and Internet-related technologies are making it easier to access and analyze side effects records.

Hypertension, characterized by aberrantly high arterial blood pressure, is a chronic medical condition affecting almost one billion people worldwide [12]. Despite the availability of several blood pressure-decreasing drugs, hypertension is not effectively controlled in more than half of patients receiving antihypertensive treatment [13]. Among the multiple reasons contributing to this unsatisfactory clinical outcome, an undeniable reality is that most pharmaceutical companies, considering the potential costs and profits, have abandoned antihypertensive drug development [14]. As drug pipelines dry up, new drug development is expensive and time consuming, thus diminishing future profitability [15]. Worse still, the high failure rate (estimated to be >90%) due to toxicity and other reasons has made new drug development a highly risky investment [16]. Therefore, alternative strategies are urgently needed to drive the productivity and cost-effectiveness of antihypertensive drug development. Assuming that hypotension, with the symptom of abnormally low blood pressure, can be regarded as the opposite condition of hypertension, then potential antihypertensive agents may be discovered among drugs that induce hypotension as a side effect.

Established by the US Food and Drug Administration (FDA), the FDA Adverse Event Reporting System (FAERS) [17] is one of the most comprehensive sources of pharmacovigilance big data worldwide. With adverse drug events spontaneously submitted by consumers and indirectly reported by health care professionals, FAERS supports not only FDA’s safety surveillance on all approved drugs, but also the research of scientists and clinicians. Each report provides a variety of clinical information, particularly the drug(s) used by the patient and the adverse reaction(s) that the patient experienced. FAERS’ raw data used to be released without sufficient formatting, thus making a high throughput analysis very difficult. Fortunately, the FDA’s Office of Informatics and Technology Innovation launched a new initiative, OpenFDA, in 2013 [18,19], whose primary goal was to facilitate public access to high-value FDA data (including FAERS) by providing user-friendly and open-source application programming interfaces.

By using the OpenFDA platform and relevant statistical methods [20], we aimed to examine the co-occurrence of specific drugs and hypotension as a side effect, that is, the drug and a hypotension reaction co-occurred in the same adverse event report (Figure 1). Instead of serendipitous searching, we examined the correlation between hypotension incidence and antihypertensive property for a total of 683 unique drugs, so as to identify those that frequently co-occurred with hypotension adverse events. As an exploratory effort in systematically analyzing drug adverse events, we hoped that this study would provide a unique insight into Web-based and data-driven drug repositioning.

In recent years, conventional target-based drug development has been facing various challenges, such as increasingly higher costs and lower productivity. In consequence, both the pharmaceutical industry and the academic community are earnestly seeking new means of drug innovation. In this process, many realize that, along with improvements in clinical informatics, the role of patients can be changed from a minimally informed recipient to a potential participant in drug development [41,42]. By providing raw download access to structured datasets, OpenFDA has created a researcher-friendly portal for quick and easy querying. With patient-centric data being a highly cost-effective and valuable resource, we can make this systematic effort and corroborate the evidence linking hypotension as a side effect to an antihypertensive therapeutic effect.

For hypertension and many other medical conditions, rationally finding unknown therapeutic agents has always been a valuable but difficult task. Even though various methods are established to accelerate the process of selecting repositioning candidates, there remains at least one major barrier between theoretical therapeutic effects and real benefits. Due to the complexity of the human body, many repositioning approaches that are focused on omics and preclinical data may not always be consistent with patients’ clinical outcomes [43]. Regarding this issue, the unique advantage of clinical informatics analysis using FAERS should be appreciable. Basically, hypotension adverse events are observations on humans, as opposed to molecular entities, cell cultures, or animal models. The antihypertensive signals suggested by FAERS tend to be more interpretable and straightforward, presenting relatively fewer challenges to bench-to-bed translation.

Pharmacovigilance data are not the only source of side effect information, since package inserts (also known as drug labels) determined by regulatory agencies have also been studied for drug repositioning [10,44,45]. However, in several ways, our study suggests that the patient-based FAERS data play an irreplaceable role in side effect analysis. As the basis of drug labeling, clinical trials are mostly conducted among a relatively small number of people and over a limited period of time. In contrast, postmarketing drug surveillance is a long-term effort that involves the general population. Therefore, the incidence of various side effects can be more comprehensively monitored through the patients’ self-reports. In addition, drug labels usually focus on the existence of certain side effects, while the prevalence is mentioned less often. However, the frequency of hypotension is an essential parameter for estimating the size of the applicable population, which is directly related to the potential profitability of the repositioned drug. In this regard, the FAERS data enabled us to calculate the significance level of the hypotension ROR for all of the study drugs, efficiently screening for the promising candidates.

Despite the richness of the information, some limitations related to the voluntary nature of FAERS merit additional attention. Since the details about the patients’ medical history and the context of adverse events may not be sufficiently addressed in many spontaneously submitted reports, the co-occurrence of a specific drug and a side effect may not be directly interpreted as a cause-and-effect relationship [46]. To address this issue, the patient-expert relationship needs to be enhanced following the concept of participatory medicine [47,48]. For instance, an Internet-based community can be established to connect patients and experts. The first-hand information reported by patients can then be promptly scrutinized and curated by clinical experts, thus improving the reliability of the raw data. Beyond being a source of information, patients can therefore play a more proactive role in clinical informatics research.

Given the lack of a novel candidate molecule, as well as a new mechanism of action, in antihypertensive drug development for a long time now, our work may have both realistic and long-term implications. First, our results provide a collection of candidate agents that may decrease blood pressure. Since most of the top frequently reported drugs for hypotension events are either well-known antihypertensive drugs or were proved later to show antihypertensive activity, there are grounds for believing that novel antihypertensive agents can be found among other study drugs that we identified (see Multimedia Appendix 1). We therefore suggest that the highly significant signals of certain drugs should be followed up by experimental or clinical investigations. Second, and more important, this study has shown a sustainable way of detecting potential antihypertensive agents. As numerous adverse events are consistently submitted to FAERS and periodically released through the OpenFDA platform, not only the marketed drugs addressed in this study, but also new drugs approved in the future may be highlighted for subsequent hypotension adverse events. Third, we believe the rationale of the opposite condition, linking the hypotension side effect and hypertension treatment, can be expanded to other indications with elaborate study designs. As long as FAERS continues to be updated, more opportunities for drug repositioning may be persistently discovered. Eventually, the newfound agents and the underlying mechanism of action would promote the discovery and development of various therapies.