Long-Term Collaboration Network Based on ClinicalTrials.gov Database in the Pharmaceutical Industry

Lee

Journal of Open Innovation: Technology, Market, and Complexity

et al. 2018

The pharmaceutical industry, where research and development (R&D) efficiency is central to company survival, has recently faced significant challenges. To increase efficiency, companies must implement strategies such as open innovation (OI), wherein they sell their intellectual property, maximize their use of external resources, adjust their structures, and implement new business models. In this study, we divided 701 U.S. pharmaceutical companies according to their OI strategies to measure and compare their R&D efficiencies between 2001 and 2016. We analyzed the deal data of companies by first dividing them into four groups (inside-out, outside-in, coupled, and closed) to calculate R&D efficiency using stochastic and meta-frontier analyses. In the first group analysis, the coupled group shows high technical efficiency, but in an overall comparison, the inside-out group achieves the highest efficiency values. These values increased between 2005 and 2010, when the R&D crisis in the industry was great at its highest. We thus identified the characteristics of each group based on our results, and presented extensive analyses using a time-series comparison and enterprise-level analysis. We claim that pharmaceutical companies can still cope with the current R&D crisis by implementing different OI strategies.

Section: Discussionmentioning

confidence: 99%

Measuring the Efficiency of U.S. Pharmaceutical Companies Based on Open Innovation Types

Lee

Journal of Open Innovation: Technology, Market, and Complexity

et al. 2018

“…First, in our observation, 27 of 32 organizations (84%) in the other agency class were universities, research institutes, or hospitals. As previously investigated, 29 the knowledge and expertise held by these types of organizations on unprecedented rare diseases may be effectively transferred to leverage the pharmaceutical firm's capability of drug discovery and development. Second, drug development with an orphan designation can benefit the pharmaceutical firm through various incentives, such as tax credits, exemption from FDA application filing fees, public grants for clinical development, protocol assistance, and marketing exclusivity, that lead to fast regulatory approval.…”

Section: Discussionmentioning

confidence: 99%

Entry Into New Therapeutic Areas: The Effect of Alliance on Clinical Trials

Okada

Sengoku

2019

Ther Innov Regul Sci

Background: Entry into a new therapeutic area, that is, one in which a pharmaceutical firm lacks experience, is a considerable challenge for firms that need to overcome scientific and technological barriers. To address this issue, the present study aims to explore the potentiality of alliances in an empirical manner. Methods: From the clinical trials sponsored by 20 major pharmaceutical firms during 2008–2016 listed at ClinicalTrials.gov (n = 14,941 clinical trials), cases of entering a new therapeutic area for a pharmaceutical firm were extracted (n = 73), followed by statistical analyses to evaluate the effect of alliances in this regard. Results: We found that the average number of participating organizations in the cases of entering a new therapeutic area was significantly larger than that in the cases of entering an area in which firms had experience ( P < .01), suggesting that alliance has a positive effect on new therapeutic entry for these pharmaceutical firms. Second, we found that the cases of partnering with nonindustrial or nongovernmental organizations (ie, universities, research institutes, hospitals, funding agencies, and others; n = 32 of the 73) were significantly associated with these new entry trials (adjusted odds ratio = 1.1, P < .05). Furthermore, we identified that 10 of the 32 clinical trials were associated with rare diseases, which is an overrepresentation compared to the occurrence in the universe (1015 of the 14,941; P < 10–5). Conclusions: These findings strongly suggest the importance of alliances with diversified partners in new therapeutic entry and also provide a basis for further detailed investigation of key success factors for pharmaceutical firms.

Proceedings of the 10th ACM International Conference on Bioinformatics, Computational Biology and Health Informatics

“…ClinicalTrials.gov currently lists 304,654 studies since May 2019 with locations in all 50 states and in 208 countries [4]. Even though ClinicalTrials.gov is an abundant source of clinical trial studies with longest history and largest complete data [5], it is, unfortunately, an underutilized information source for health industry and life science research [3]. Considering the rapid growth of the field, there has been a rather limited number of studies being made based on ClinicalTrials.gov reports.…”

Section: Clinicaltrialsgov Initiativementioning

confidence: 99%

“…This negatively impacts the research community because new research groups would not know that a particular clinical trial had already been executed and that the results were unsuccessful, thus costing time and money. As such, a mandated singular repository for such trials is extremely beneficial, because it is a useful resource for researchers to learn from a singular pool of documented successes and unsuccessful ventures for clinical trials [4], and it also helps researchers understand the current state of the pharmaceutical sector [5].…”

Section: Introductionmentioning

confidence: 99%

Network Analysis and Recommendation for Infectious Disease Clinical Trial Research

Elkin

Andrews

Zhu

2019

Clinical trials are crucial for the advancement of treatment and knowledge within the medical community. Since 2007, US federal government took the initiative and required organizations sponsoring clinical trials with at least one site in the United States to submit information on these clinical trials to the ClinicalTrials.gov database, resulting in a rich source of information for clinical trial research. Nevertheless, only a handful of analytic studies have been carried out to understand this valuable data source. In this study, we propose to use network analysis to understand infectious disease clinical trial research. Our goal is to answer two important questions: (1) what are the concentrations and characteristics of infectious disease clinical trial research? and (2) how to accurately predict what type of clinical trials a sponsor (or an investigator) are interested in? The answers to the first question provide effective ways to summarize clinical trial research related to particular disease(s), and the answers to the second question help match clinical trial sponsors and investigators for information recommendation. By using 4,228 clinical trials as the test bed, our study involves 4,864 sponsors and 1,879 research areas characterized by Medical Subject Heading (MeSH) keywords. We extract a set of network measures to show patterns of infectious disease clinical trials, and design a new community based link prediction approach to predict sponsors' interests, with significant improvement compared to baselines. This trans-formative study concludes that using network analysis can tremendously help the understanding of clinical trial research for effective summarization, characterization, and prediction.