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Original Research ARTICLE

Front. Pharmacol., 14 January 2021 | https://doi.org/10.3389/fphar.2020.587526

Open Innovation in Medical and Pharmaceutical Research: A Literature Landscape Analysis

www.frontiersin.orgAndy Wai Kan Yeung1,2*, www.frontiersin.orgAtanas G. Atanasov2,3,4,5*, www.frontiersin.orgHelen Sheridan6, www.frontiersin.orgElisabeth Klager2, www.frontiersin.orgFabian Eibensteiner2,7, www.frontiersin.orgSabine Völkl-Kernsock2, www.frontiersin.orgMaria Kletecka-Pulker2, www.frontiersin.orgHarald Willschke2,8 and www.frontiersin.orgEva Schaden2,8*
  • 1Oral and Maxillofacial Radiology, Applied Oral Sciences and Community Dental Care, Faculty of Dentistry, The University of Hong Kong, Hong Kong, China
  • 2Ludwig Boltzmann Institute for Digital Health and Patient Safety, Medical University of Vienna, Vienna, Austria
  • 3Institute of Genetics and Animal Biotechnology of the Polish Academy of Sciences, Magdalenka, Poland
  • 4Institute of Neurobiology, Bulgarian Academy of Sciences, Sofia, Bulgaria
  • 5Department of Pharmacognosy, University of Vienna, Vienna, Austria
  • 6NatPro Centre. School of Pharmacy and Pharmaceutical Sciences, Trinity College Dublin, Dublin, Ireland
  • 7Division of Pediatric Nephrology and Gastroenterology, Department of Pediatrics and Adolescent Medicine, Comprehensive Center for Pediatrics, Medical University of Vienna, Vienna, Austria
  • 8Department of Anaesthesia, Intensive Care Medicine and Pain Medicine, Medical University Vienna, Vienna, Austria

Open innovation in medical and pharmaceutical research has grown steadily over the last decade. However, the performance of the published literature in terms of the scientific impact and gaining social media attention remains largely unexplored. The scientific literature of open innovation was examined by means of bibliometric analyses to identify the most prolific authors, organizations, countries, journals, research areas, and recurring terms. By accessing the Web of Science Core Collection and Altmetric electronic databases, citation-related and Altmetric data were evaluated. Public-private partnerships and a selection of newly introduced potential novel drugs in the analyzed publications were identified. North America and Europe were the major literature contributors. Research outputs were mainly published in journals focused on business and economics, pharmacology and pharmacy, and engineering. Many pharmaceutical and biotechnological companies contributed to the analyzed publications, with higher mean citation counts and social media attention (Altmetric score) than nonindustry articles. Public-private partnerships fostered financial support, sharing of expertise and intellectual property, and research collaborations. In summary, open innovation might serve as a powerful strategy to both benefit the involved industry entities and accelerate the development of solutions and products for the betterment of human health.

Introduction

Companies rely on innovation in order to develop and stay competitive in the face of dynamically evolving scientific, technological, and societal conditions. Traditionally, the industry's approach has been to generate innovation internally within specialized research and development departments. However, the shortcomings of this approach are that, on the one hand, there is always a lot of potentially valuable innovation generated in parallel outside of companies and, on the other hand, internally generated innovation cannot always be translated into commercial products or services benefiting the company. Therefore, in recent times, there has been a paradigm shift toward open innovation, a model that does not exclude the traditional internal innovation but is open to also incorporate externally generated innovation (Hodson, 2016). Open innovation, as defined by Chesbrough and Bogers (2014), is “a distributed innovation process based on purposively managed knowledge flows across organizational boundaries, using pecuniary and nonpecuniary mechanisms in line with the organization's business model.” The innovation derived from external sources can include ideas, technologies, and research and development. In open innovation, companies have moved from the pursuit of intellectual property protection to engaging in cross industry-academia and industry-public collaboration and engagement. Early adopters of the open innovation concept have been the so-called “high-technology” industries in the area of information technology, computers, and pharmaceuticals. Beyond these early adopters, other industrial areas with medical significance that shortly followed embracing the open innovation concept include industries from the areas of medical devices, chemicals, and bioscience tools and services (Chesbrough and Crowther, 2006). Depending on the direction of movement of new knowledge, three different types of open innovation processes have been distinguished (Gassmann and Enkel, 2004). The outside-in (inbound) process involves the integration of internal knowledge of an organization with knowledge derived from external sources; the inside-out (outbound) process involves generating profits for the organization by transferring internally generated knowledge or intellectual property to external entities; and the third, “coupled,” process involves a combination of both inbound and outbound processes by the formation of alliances with complementary external entities (Gassmann and Enkel, 2004). Analysis of the literature on open innovation in healthcare revealed that open inbound innovation was the most widely applied approach in this sector (Wass and Vimarlund, 2016).

In pharmaceutical companies, open innovation differs according to partnerships, incentives, and goals (Bianchi et al., 2011). Schuhmacher et al. (2013) placed open innovation models into four categories, the knowledge creator, knowledge translator, knowledge integrator, and knowledge leverager, and assigned pharmaceutical companies to these models, depending on their specific approach (Schuhmacher et al., 2013). As a prominent example from the pharma industry, Eli Lilly has a strong history of collaboration that led to launching of their Open Innovation Drug Discovery program in 2009 (Alvim-Gaston et al., 2014).

To foster academia-industry interactions, a significant number of universities have created formal Academic Drug Discovery Centres (ADDCs), which include industrial collaboration (Harris et al., 2015). One of the earliest centers, the Actar AB center, was created at the Karolinska Institute in Sweden in 2000, followed by the Harvard NeuroDiscovery center in the US in 2001, with dozens of more ADDCs established in the EU and the US in the following decade (Kirkegaard and Valentin, 2014). Building on the ADDC model, another concept that has emerged in the academia-industry interface is that of the Academic Drug Discovery Consortium. One of the first consortia was founded in the EU, and the longest existing academia-industry collaboration was the formation of the Division of Signal Transduction Therapy between the University of Dundee, six pharma companies, and twenty academic research teams (http://www.lifesci.dundee.ac.uk/unit/division-signal-transduction-therapy-dstt). This type of collaborative initiative can extend beyond the sharing of costs and may improve the efficiency of drug development and reduce duplication of effort (Savage, 2016). Regulators and governments are also increasingly engaged in supporting drug discovery, e.g., in Sweden where the government ring-fenced early-stage funding of €6 M per year (2013–2016) for the Science for Life Drug Discovery and Development platform (SciLifeLab DDD) established in 2011 (Arvidsson et al., 2016). Further examples of regulator and government engagement are the Critical Path Initiative in the United States (FDA) (Woodcock and Woosley, 2008) and the Cooksey Review of health research funding in the United Kingdom (Black, 2006). With the advancement of Internet technology, collaborative work from a distance gained new significance and was manifested in a variety of different forms potentially benefiting open innovation, including the formation of virtual organizations, virtual enterprises, and collaborative virtual laboratories (Camarinha-Matos and Afsarmanesh, 2005; Camarinha-Matos et al., 2006).

Crowdsourcing represents an open innovation approach gaining momentum, which can be used to derive solutions that advance the medical field by collective intelligence and resources and actively engaging patients and the general public. Crowdsourcing approaches used in the context of medical research include innovation challenges (e.g., prize contests), hackathons (short events usually bringing participants together for a few days around a common theme with the aim of solving defined challenges, often with a focus on the area of computer programming), and online systems for collaboration (e.g., websites supporting the engagement of visitors to solve a defined problem) (Tucker et al., 2019).

The direct involvement of nonprofessionals in scientific research, referred to as “citizen science,” is a highly potent collaborative approach for gaining new knowledge (Schnoor, 2007; Bonney et al., 2009). Citizen science has been well-established in research fields, such as ecology and astronomy, and in the context of increasing significance of patient-centered medicine. Currently, there are broad ongoing efforts for citizen science implementation in health and biomedical research, whereby progress in these areas encounters some difficulties related to existing ethical issues, including consideration for possible risks for the involved citizen scientists or other humans (Wiggins and Wilbanks, 2019).

Our goal in this work is to gain new insights into open innovation in medical and pharmaceutical research by performing a full-scale bibliometric analysis, an approach that has proven its value in the quantitative characterization of various outputs of the scientific literature published in other biomedical research areas (Yeung et al., 2018; Yeung et al., 2019a; Yeung et al., 2019b; Cen et al., 2020; Chen and Wang, 2020; Fortuna et al., 2020). Such full-scale bibliometric analysis might reveal information for major institutions, top scientists of the area, research impacts (reflected by citation counts), and main research topics and trends. Researchers and nonexperts can use such data for rapid orientation in the respective scientific area and to achieve some more specific goals, for example, identifying potential new collaboration partners. Open innovation, in general, as well as in medical research in particular, is currently a major trend with vibrant activities apart from publishing research, such as the organization of hackathons (Angelidis et al., 2016) and the establishment of innovation hubs (Rifai et al., 2015). In order to better understand participants and publishing trends in this research area, the present work focuses on the bibliometric characterization of its publication landscape.

Materials and Methods

The digital literature database, Web of Science Core Collection, was accessed in early June 2020 in order to identify and analyze relevant publications. We therefore utilized the following search string: TS = (“open innovation*” OR “external innovation*” OR “patient cent* innovation*”) AND TS = (medic* OR illness* OR disease* OR health* OR pharma*). This search strategy recognized publications presenting the indicated phrases or their derivatives in the title, abstract, or keyword fields. The bibliographic information of the yielded publications was analyzed to reveal the most productive entities in terms of authors, organizations, countries, journals, research areas, etc. The complete records were exported to VOSviewer (www.vosviewer.com, version 1.6.15, Leiden University, The Netherlands), a dedicated bibliometric software, for further analyses and visualization of bibliometric networks. As a subanalysis, crowdsourc* was searched within the dataset in order to identify relevant articles.

Nonparametric tests were used as the data were not normally distributed as tested by Shapiro–Wilk tests. Mann–Whitney U tests were conducted to test if publications with contributions from the industry (i.e., authors affiliated with biotechnology/pharmaceutical companies) had higher mean citation counts per publication than their nonindustry counterparts. Similarly, the group difference in Altmetric Attention Score, a score indicating social media attention tracked by Altmetric (www.altmetric.com), was tested. Articles were considered to have industrial contributions when at least one of the authors was affiliated with industry (articles published jointly with industry professionals and academic researchers were considered to have industrial contribution).

Results

The search yielded 384 publications that accumulated over 5,000 citations (Figure 1), with an h-index of 32 and 13.2 citations per publication (CPP). There were 240 articles and 55 review publications, equivalent to a ratio of 4.4–1. There were 54 proceedings and 27 editorial materials. The first article was published in 2006, which used Millenium Pharmaceuticals (now known as Takeda Oncology, a subsidiary of Takeda Pharmaceutical) as one of the examples of early adopters of open innovation in the high-technology industries (Chesbrough and Crowther, 2006).

FIGURE 1
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FIGURE 1. Cumulative publication and citation counts of open innovation in medical and pharmaceutical research.

The 10 most prolific authors, organizations (additionally including companies in the industry with two or more articles), countries, journals, and research areas are listed in Table 1. Most of the top 10 most prolific authors came from Italy, in particular from the University of Salerno. Four of the top 10 most prolific organizations were pharmaceutical companies. The top 10 countries were situated in North America and Europe, except China. Nine of the top 10 journals had an impact factor and were mostly concerning business/economics and pharmacology/pharmacy.

TABLE 1
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TABLE 1. The 10 most prolific authors, organizations (additionally including companies in the industry with two or more articles), countries, journals, and research areas.

There were 51 publications with authors affiliated with biotechnology/pharmaceutical companies, with a mean citation count per publication (CPP) of 23.3 (SD = 39.3), which was significantly higher than purely academic publications (11.6, SD = 43.5) (p = 0.001). Similarly, the former group of publications had an Altmetric score of 14.2 (SD = 34.7), significantly higher than the latter group (5.9, SD = 15.7) (p = 0.013). The public-private partnerships or initiatives introduced in these 51 industry articles are listed in Table 2.

TABLE 2
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TABLE 2. Public-private partnerships or initiatives introduced in the industry articles concerning open innovation in medical and pharma research.

The term map of titles and abstracts showed that academia and pharmaceutical industry collaborated for various aspects, such as novel drug discovery (n = 6, CPP = 19.7) and delivery (n = 5, CPP = 23.0) (Figure 2). Highly cited terms revealed from the bibliometric analysis were high technology (n = 4, CPP = 185.3), outsourcing (n = 8, CPP = 94.3), drug administration (n = 5, CPP = 67.8), outbound open innovation (n = 5, CPP = 50.2), and public-private partnership (n = 14, CPP = 34.4) (Table 3). Meanwhile, the top 20 most recurring author keywords are listed in Table 4. Drug discovery, intellectual property, knowledge management, licensing, drug development, and entrepreneurship were among the most highly cited keywords.

FIGURE 2
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FIGURE 2. The recurring words and phrases in the titles and abstracts of open innovation in medical and pharmaceutical research publications. Words and phrases recurring in at least 1% of all publications (n = 4) were illustrated. The color of the circles represented the citations per publication (CPP) of publications that contained the concerning word or phrase. The size of the circles represented the number of publications. The proximity between the circles represented the frequency of cooccurrence of the concerning words or phrases.

TABLE 3
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TABLE 3. Top 20 most recurring and most highly cited terms in the titles and abstracts, identified with VOSviewer. Terms recurring in at least 1% of publications (n = 4) were analyzed.

TABLE 4
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TABLE 4. Top 20 most recurring author keywords.

Discussion

In this work, we perform for the first time a full-scale bibliometric analysis of the literature on open innovation implications in medical and pharmaceutical research.

We found that publications that documented open innovation in medical and pharmaceutical research mainly began to appear in the mid-2000s. Many pharmaceutical and biotechnology companies were active in open innovation research, as the industry could provide consultations, contract research outsourcing, sponsored grants, and training and education and help spinout companies (Chin-Dusting et al., 2005). An interesting finding is that industry-affiliated articles were cited more often than nonindustry publications. This finding echoed the results of Narin and Rozek (1988), in which pharmaceutical industry articles were found to have higher mean citation counts than those of non-National Institutes of Health (NIH) publications but less mean citation counts than those NIH-supported articles. Generally, this could be interpreted as the research outputs of the industry-affiliated publications were of better quality; however, one of the potential reasons for the higher citation counts could be that the biotechnology/pharmaceutical companies have well-established networks and connections to disseminate research outputs to the academic audience and the general public. Moreover, compared to academic institutions, companies might invest more funding in visibility-promotion campaigns. An extensive analysis of marketing, governmental, and academic data has revealed that medical marketing expenditure in the United States increased from 18 to 30 billion from 1997 to 2016 (Schwartz and Woloshin, 2019). Indeed, findings from the current study demonstrate that industry articles also received more attention from online social media and thus had a higher Altmetric score. Readers should be aware that the Altmetric score is based on mentions in multiple social media channels such as Facebook and Twitter, as well as in Wikipedia, online blogs, and news outlets. The score does not reflect the context of the mentions. Along this line, a previous study showed that press releases by pharmaceutical companies tended to focus on qualitative results and harms and adverse events, but not on quantitative results and study limitations, which might create some bias of online attention to this research (Kuriya et al., 2008).

Besides, the predominant contributions from North America and Europe made this research field distinct from other biomedical fields such as neuropharmacology or ethnopharmacology, in which Asian and South American countries also had considerable inputs (Yeung et al., 2018; Yeung et al., 2020). Furthermore, in respect to the geographical distribution of author affiliations, the research literature on open innovation in medical and pharmaceutical research also differs from the literature on business incubators, in which China ranked 2nd in terms of the number of published articles (Albort-Morant and Ribeiro-Soriano, 2016). Italy not only was prolific in terms of publication numbers but also produced highly cited articles, for example, an exploratory analysis on the top 20 worldwide biopharmaceutical firms that revealed the usage of various methods (licensing agreements, nonequity alliance, and purchase and supply of technical and scientific services) in partnering with other companies and universities for both inbound and outbound technologies and knowledge (Bianchi et al., 2011). Another highly cited article described the collaboration between the United States and Europe to establish a public-private partnership, called Open PHACTS, to facilitate drug discovery (Williams et al., 2012). A similar public-private partnership was reported in the United States that attempted to establish a national program of active drug safety surveillance by pooling observational data (Stang et al., 2010). In Europe, a BIGCHEM project, funded by the European Union (Horizon 2020), was initiated to foster big data analysis in the age of chemoinformatics to drive pharmaceutical research collaboration between the industry and academia (Tetko et al., 2016). With respect to Asia, open innovation research works were also published but gathered fewer than 30 citations each. For instance, a Japanese study evaluated how much scientific knowledge the pharmaceutical industry has assimilated from universities and research institutes and found that such assimilation was associated with patent applications and patent efficiency (Saito and Sumikura, 2010). In China, it was reported that pharmaceutical firms are involved more actively in open innovation and intellectual property protection, catching up fast once they managed to establish research and development departments and started collaborations (Ren and Su, 2015).

The open innovation movement has yielded a number of promising outcomes in drug discovery. One example is the identification of arylpyrroles with highly potent antimalarial properties through a broad industry-academia partnering and an open-source research approach involving real-time sharing of all data and ideas and enabling broad participation, without pursuing patents (Williamson et al., 2016). Moreover, it was found that JQ1, a thienotriazolodiazepine and BET inhibitor reported with its intellectual property released to the scientific community on an open access basis upon discovery, had a greater uptake by more interdisciplinary research communities and a larger number of downstream patents compared to the conventionally developed equivalents (Arshad et al., 2016).

Seventeen articles within the analyzed dataset were identified by searching for crowdsourc* to reveal crowdsourcing-related research applications. Though these articles were mainly about drug discovery and pharmaceuticals, examples from other sectors could also be identified. In the context of health and astronomy, the NASA Human Health and Performance Directorate organized crowdsourcing competitions to identify viable potential collaborations with the private sector in response to steep budget reductions (Davis et al., 2015). In medicine, contest-based crowdsourcing has similarly benefited the genome study community by identifying new approaches to accelerate the analysis speed of a standard genetics software package by 18–45-fold (Hill et al., 2017). In the context of health and food industry, crowdsourcing could be helpful by utilizing the crowd’s data in various safety and quality aspects such as developing systems of foodborne illness surveillance and identification of contaminated products (Soon and Saguy, 2017).

While our analysis reveals that open innovation is very common in the area of pharmaceutical development (Table 2), it can be observed that open innovation was also applied to other areas of the medical sector, such as medical devices. For example, a pair of open-source ptosis crutch glasses was invented to support the drooping eyelids of patients with myasthenia gravis, and the open-source platform approach allows further open innovation to improve the product (Saidi et al., 2018). Another example was a balloon sinuplasty device invented to treat severe chronic sinusitis. Upon observing the traumatic procedures of the conventional surgical approach, a medical device company invented a device to compress and displace structures in the congested area instead of cutting and removing; by collaborating with the academia to test and fine-tune the device, large-scale clinical studies finally proved its efficacy (Wan and Quan, 2014).

Electronic health records could also be optimized by open innovation in a bottom-up approach, by gathering the feedback of participants attending a workshop who worked together to identify shortcomings and propose solutions to streamline the steps in the workflow (Murphy et al., 2020). To promote the building of infrastructure for digital health, some open-source meta-data registry framework and core catalogs could be shared with the community to encourage collaborations and cross-disciplinary deployment (Hussey et al., 2019). Taking into consideration that information technologies were historically one of the pioneer areas adopting open innovation, as well as the recent development boost in sectors such as telehealth, artificial intelligence, virtual reality, and wearable technologies, it is very likely that in the future, there will be an increasing application of open innovation strategies in areas such as digital therapeutics and digital health technologies.

Interestingly, only two publications within the analyzed literature set referred to "citizen science", with one of them representing a review of Virtual Health Networks (Trequattrini et al., 2015) and the other one focusing on policy implications of open science (Stodden, 2010). Another aspect waiting to be further developed in the future is patient-centered innovation. Focus interviews have shown that stakeholders in the healthcare sector were keen on patient-centered care innovations (Reed et al., 2012), and initiatives have been launched in drug development (Hirsch, 2019) and diabetes therapy (O'Donnell et al., 2019). More patient-centered initiatives should be encouraged, and their efficacy evaluated in the future.

Limitations

Our work focused on the bibliometric analysis of publication- and citation-related data and the semantic content of the title, abstract, and keywords. Therefore, systematic review or analysis of the product approval and improvements of health resulting from open innovation was not covered. Along this line, it should be kept in mind that the ultimate goal of innovation is to yield new products and, therefore, a detailed analysis of product approvals resulting from open innovation represents a very important direction for future work.

Since the search string used in our work included the metaword pharma*, it was expected that the research area of “pharmacology and pharmacy” would be highly ranked in the analyzed literature set. Therefore, readers should be aware that generalizations for the relative prevalence of open innovation in different research sectors should not be directly drawn based on the results of our study and, clearly, open innovation plays a very important role in other research areas outside the medical/healthcare/pharma sectors.

Since our study is based on data derived from just one database (Web of Science), there is an implication that some relevant articles might have been missed. However, different databases count citations differently, so it was not feasible to merge data from multiple databases for the present analysis.

Conclusion

By studying 384 published research articles in the existing academic literature, it was found that open innovation in medical and pharmaceutical research began to appear in the literature in mid-2000s and most of the research was done in North America and Europe, with Asia lagging behind. Publications with authors affiliated with biotechnology or pharmaceutical companies had a higher mean citation count than nonindustry-affiliated articles and also received more social media attention. Various public-private partnerships that concerned financial and expertise support, sharing of intellectual property, and open platforms enabling easier communications, were introduced in the analyzed publications. Pharmaceuticals/pharmacology was revealed as an active industrial sector with medical significance promoting open innovation. However, taking into consideration the strong history of open innovation in the area of information technologies, it is feasible to expect that, in the future, there will be an increasing application of open innovation strategies from companies operating on the interface of information technology and medicine (e.g., in the area of digital health technologies). To harness the significant opportunities offered by open innovation, all relevant parties must be open to the changing landscape and embrace new models of collaboration with enthusiasm, without losing sight that the real quest is the development of solutions and products for the betterment of human health.

Data Availability Statement

The original contributions presented in the study are included in the article/Supplementary Material; further inquiries can be directed to the corresponding authors.

Author Contributions

AA and HW designed the study. AY and AA collected and analyzed data. AY and AA wrote the first draft. HS, EK, FE, SV-K, MK-P, HW, and ES critically revised the draft. All authors agreed to the final version.

Conflict of Interest

The authors declare that the research was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest.

References

Albort-Morant, G., and Ribeiro-Soriano, D. (2016). A bibliometric analysis of international impact of business incubators. J. Bus. Res. 69, 1775–1779. doi:10.1016/j.jbusres.2015.10.054

CrossRef Full Text | Google Scholar

Alvim-Gaston, M., Grese, T., Mahoui, A., Palkowitz, A., Pineiro-Nunez, M., and Watson, I. (2014). Open Innovation Drug Discovery (OIDD): a potential path to novel therapeutic chemical space. Curr. Top. Med. Chem. 14, 294–303. doi:10.2174/1568026613666131127125858

PubMed Abstract | CrossRef Full Text | Google Scholar

Angelidis, P., Berman, L., Casas-Perez, M. L., Celi, L. A., Dafoulas, G. E., Dagan, A., et al. (2016). The hackathon model to spur innovation around global mHealth. J. Med. Eng. Technol. 40, 392–399. doi:10.1080/03091902.2016.1213903

PubMed Abstract | CrossRef Full Text | Google Scholar

Arshad, Z., Smith, J., Roberts, M., Lee, W. H., Davies, B., Bure, K., et al. (2016). Open access could transform drug discovery: a case study of JQ1. Expet Opin. Drug Discov. 11, 321–332. doi:10.1517/17460441.2016.1144587

PubMed Abstract | CrossRef Full Text | Google Scholar

Arvidsson, P. I., Sandberg, K., and Forsberg-Nilsson, K. (2016). Open for collaboration: an academic platform for drug discovery and development at SciLifeLab. Drug Discov. Today. 21, 1690–1698. doi:10.1016/j.drudis.2016.06.026

PubMed Abstract | CrossRef Full Text | Google Scholar

Bianchi, M., Cavaliere, A., Chiaroni, D., Frattini, F., and Chiesa, V. (2011). Organisational modes for Open Innovation in the bio-pharmaceutical industry: an exploratory analysis. Technovation. 31, 22–33. doi:10.21275/v5i5.nov163196

CrossRef Full Text | Google Scholar

Bieringer, T., Buchholz, S., and Kockmann, N. (2013). Future production concepts in the chemical industry: modular–small‐scale–continuous. Chem. Eng. Technol. 36, 900–910. doi:10.22215/etd/2004-05761

CrossRef Full Text | Google Scholar

Black, N. (2006). The Cooksey review of UK health research funding. BMJ. 333, 1231. doi:10.1136/bmj.39059.444120.80

PubMed Abstract | CrossRef Full Text | Google Scholar

Bonney, R., Cooper, C. B., Dickinson, J., Kelling, S., Phillips, T., Rosenberg, K. V., et al. (2009). Citizen science: a developing tool for expanding science knowledge and scientific literacy. Bioscience. 59, 977–984. doi:10.7200/esicm.165.0511.2

CrossRef Full Text | Google Scholar

Braune, E., Van Dam, J., and Szalma, S. (2010). How informatics can potentiate precompetitive open-source collaboration to jump-start drug discovery and development. Clin. Pharmacol. Ther. 87, 614–616. doi:10.1038/clpt.2010.21

PubMed Abstract | CrossRef Full Text | Google Scholar

Camarinha-Matos, L. M., and Afsarmanesh, H. (2005). Collaborative networks: a new scientific discipline. J. Intell. Manuf. 16, 439–452. doi:10.1515/9783110640489-001

CrossRef Full Text | Google Scholar

Camarinha-Matos, L. M., Afsarmanesh, H., and Ollus, M. (2006). Virtual Organizations: systems and practices. Boston: Springer Science & Business Media.

Google Scholar

Carroll, G. P., Srivastava, S., Volini, A. S., Piñeiro-Núñez, M. M., and Vetman, T. (2017). Measuring the effectiveness and impact of an open innovation platform. Drug Discov. Today. 22, 776–785. doi:10.1016/j.drudis.2017.01.009

PubMed Abstract | CrossRef Full Text | Google Scholar

Cen, Y., Li, Y., Huang, C., and Wang, W. (2020). Bibliometric and visualized analysis of global research on fungal keratitis from 1959 to 2019. Medicine (Baltimore). 99, e20420. doi:10.1097/MD.0000000000020420

PubMed Abstract | CrossRef Full Text | Google Scholar

Chen, Y. M., and Wang, X. Q. (2020). Bibliometric analysis of exercise and neuropathic pain research. J. Pain Res. 13, 1533–1545. doi:10.2147/JPR.S258696

PubMed Abstract | CrossRef Full Text | Google Scholar

Chesbrough, H., and Bogers, M. (2014). “Explicating open innovation: clarifying an emerging paradigm for understanding innovation,” in New Frontiers in open innovation. (Oxford: Oxford University Press), 3–28.

CrossRef Full Text | Google Scholar

Chesbrough, H., and Crowther, A. K. (2006). Beyond high tech: early adopters of open innovation in other industries. R&D Manage. 36, 229–236. doi:10.1016/j.ibusrev.2017.02.007

CrossRef Full Text | Google Scholar

Chin-Dusting, J., Mizrahi, J., Jennings, G., and Fitzgerald, D. (2005). Outlook: finding improved medicines: the role of academic-industrial collaboration. Nat. Rev. Drug Discov. 4, 891–897. doi:10.1038/nrd1879

PubMed Abstract | CrossRef Full Text | Google Scholar

Davis, J. R., Richard, E. E., and Keeton, K. E. (2015). Open innovation at NASA: a new business model for advancing human health and performance innovations. Res. Technol. Manag. 58, 52–58. doi:10.5539/ibr.v13n12p27

CrossRef Full Text | Google Scholar

Fortuna, G., Aria, M., Piscitelli, A., Mignogna, M. D., and Klasser, G. D. (2020). Global research trends in complex oral sensitivity disorder: a systematic bibliometric analysis of the structures of knowledge. J. Oral Pathol. Med. 11, 22–29. doi:10.1111/jop.13077

CrossRef Full Text | Google Scholar

Gassmann, O., and Enkel, E. (2004). “Towards a theory of open innovation: three core process archetypes,” in: R&D Management conference (RADMA) 2004. (Lisbon, Portugal).

Google Scholar

Hall, M. D., Dufton, A. M., Katso, R. M., Gatsi, S. A., Williams, P. M., and Strange, M. E. (2015). Strategic investments in non-communicable diseases (NCD) research in Africa: the GSK Africa NCD open Lab. Cardiovasc. J. Afr. 26, S15–S17. doi:10.5830/CVJA-2015-042

PubMed Abstract | CrossRef Full Text | Google Scholar

Harris, T., Papadopoulos, S., and Goldstein, D. B. (2015). Academic-industrial partnerships in drug discovery in the age of genomics. Trends Biotechnol. 33, 320-2. doi:10.1016/j.tibtech.2015.02.009

PubMed Abstract | CrossRef Full Text | Google Scholar

Hill, A., Loh, P. R., Bharadwaj, R. B., Pons, P., Shang, J., Guinan, E., et al. (2017). Stepwise distributed open innovation contests for software development: acceleration of genome-wide association analysis. GigaScience. 6, 1. doi:10.1093/gigascience/gix009

CrossRef Full Text | Google Scholar

Hirsch, G. (2019). Leaping together toward sustainable, patient-centered innovation: the value of a multistakeholder safe haven for accelerating system change. Clin. Pharmacol. Ther. 105, 798–801. doi:10.1002/cpt.1237

PubMed Abstract | CrossRef Full Text | Google Scholar

Hodson, R. (2016). Open innovation. Nature. 533, S53. doi:10.1038/533S53a

PubMed Abstract | CrossRef Full Text | Google Scholar

Hussey, P., Tully, M., Farrell, S., Coyne, E., Connolly, P., Glynn, C., et al. (2019). Building infrastructure for digital health using Open innovation 2.0 in co design of a meta data registry framework to support integrated care. Int. J. Integrated Care. 19, A18. doi:10.1515/9783110640489-005

CrossRef Full Text | Google Scholar

Kirkegaard, H. S., and Valentin, F. (2014). Academic drug discovery centres: the economic and organisational sustainability of an emerging model. Drug Discov. Today. 19, 1699–1710. doi:10.1504/ijeim.2007.014596

PubMed Abstract | CrossRef Full Text | Google Scholar

Kuriya, B., Schneid, E. C., and Bell, C. M. (2008). Quality of pharmaceutical industry press releases based on original research. PLoS One. 3, e2828. doi:10.1371/journal.pone.0002828

PubMed Abstract | CrossRef Full Text | Google Scholar

Lee, J. A., Chu, S., Willard, F. S., Cox, K. L., Sells Galvin, R. J., Peery, R. B., et al. (2011). Open innovation for phenotypic drug discovery: the PD2 assay panel. J. Biomol. Screen. 16, 588–602. doi:10.1177/1087057111405379

PubMed Abstract | CrossRef Full Text | Google Scholar

Lee, J. A., Shinn, P., Jaken, S., Oliver, S., Willard, F. S., Heidler, S., et al. (2015). Novel phenotypic outcomes identified for a public collection of approved drugs from a publicly accessible panel of assays. PLoS One. 10, e0130796. doi:10.1371/journal.pone.0130796

PubMed Abstract | CrossRef Full Text | Google Scholar

Murphy, B. P., O’raghallaigh, P., and Carr, M. (2020). Nurturing the digital baby: open innovation for development and optimization. Health Inf. J. 7, 12–39. doi:10.1177/1460458220906067

CrossRef Full Text | Google Scholar

Narin, F., and Rozek, R. P. (1988). Bibliometric analysis of US pharmaceutical industry research performance. Res. Pol. 17, 139–154. doi:10.1016/0048-7333(88)90039-x

CrossRef Full Text | Google Scholar

O'donnell, S., Lewis, D., Marchante Fernández, M., Wäldchen, M., Cleal, B., Skinner, T., et al. (2019). Evidence on user-led innovation in diabetes technology (the OPEN project): protocol for a mixed methods study. JMIR Res. Protoc. 8, e15368. doi:10.2196/15368

PubMed Abstract | CrossRef Full Text | Google Scholar

Reed, P., Conrad, D. A., Hernandez, S. E., Watts, C., and Marcus-Smith, M. (2012). Innovation in patient-centered care: lessons from a qualitative study of innovative health care organizations in Washington State. BMC Fam. Pract. 13, 120. doi:10.1186/1471-2296-13-120

PubMed Abstract | CrossRef Full Text | Google Scholar

Ren, S., and Su, P. (2015). Open innovation and intellectual property strategy: the catch-up processes of two Chinese pharmaceutical firms. Technol. Anal. Strat. Manag. 27, 1159–1175. doi:10.1016/0048-7333(83)90024-0

CrossRef Full Text | Google Scholar

Rifai, N., Topol, E., Chan, E., Lo, Y. M., and Wittwer, C. T. (2015). Disruptive innovation in laboratory medicine. Clin. Chem. 61, 1129–1132. doi:10.1373/clinchem.2015.243667

PubMed Abstract | CrossRef Full Text | Google Scholar

Saidi, T., Sivarasu, S., and Douglas, T. S. (2018). Open source modular ptosis crutch for the treatment of myasthenia gravis. Expet Rev. Med. Dev. 15, 137–143. doi:10.1080/17434440.2018.1421455

CrossRef Full Text | Google Scholar

Saito, H., and Sumikura, K. (2010). An empirical analysis on absorptive capacity based on linkage with academia. Int. J. Innovat. Manag. 14, 491–509. doi:10.1016/b978-0-12-811065-2.00006-0

CrossRef Full Text | Google Scholar

Savage, N. (2016). Competition: unlikely partnerships. Nature. 533, S56-8. doi:10.1038/533S56a

PubMed Abstract | CrossRef Full Text | Google Scholar

Schnoor, J. L. (2007). Citizen science. Environ. Sci. Technol. 41, 5923. doi:10.1017/cbo9780511522031.003

PubMed Abstract | CrossRef Full Text | Google Scholar

Schuhmacher, A., Germann, P. G., Trill, H., and Gassmann, O. (2013). Models for open innovation in the pharmaceutical industry. Drug Discov. Today. 18, 1133–1137. doi:10.1016/j.drudis.2013.07.013

PubMed Abstract | CrossRef Full Text | Google Scholar

Schwartz, L. M., and Woloshin, S. (2019). Medical marketing in the United States, 1997–2016. JAMA. 321, 80–96. doi:10.4324/9781315591186

PubMed Abstract | CrossRef Full Text | Google Scholar

Soon, J. M., and Saguy, I. S. (2017). Crowdsourcing: a new conceptual view for food safety and quality. Trends Food Sci. Technol. 66, 63–72. doi:10.1017/cbo9780511522031.005

CrossRef Full Text | Google Scholar

Stang, P. E., Ryan, P. B., Racoosin, J. A., Overhage, J. M., Hartzema, A. G., Reich, C., et al. (2010). Advancing the science for active surveillance: rationale and design for the Observational Medical Outcomes Partnership. Ann. Intern. Med. 153, 600-6. doi:10.7326/0003-4819-153-9-201011020-00010

PubMed Abstract | CrossRef Full Text | Google Scholar

Stewart, S., Barone, P., Bellisario, A., Cooney, C., Sharp, P., Sinskey, A., et al. (2016). Leveraging industry-academia collaborations in Adaptive biomedical innovation. Clin. Pharmacol. Ther. 100, 647–653. doi:10.1002/cpt.504

PubMed Abstract | CrossRef Full Text | Google Scholar

Stodden, V. (2010). Open science: policy implications for the evolving phenomenon of user-led scientific innovation. J. Sci. Commun. 9, A05. doi:10.1017/cbo9780511522031.007

CrossRef Full Text | Google Scholar

Tetko, I. V., Engkvist, O., Koch, U., Reymond, J. L., and Chen, H. (2016). BIGCHEM: challenges and opportunities for big data analysis in chemistry. Mol Inform. 35, 615–621. doi:10.1002/minf.201600073

PubMed Abstract | CrossRef Full Text | Google Scholar

Lessl, M., Schoepe, S., Sommer, A., Schneider, M., and Asadullah, K. (2011). Grants4Targets—an innovative approach to translate ideas from basic research into novel drugs. Drug Discov. Today. 16, 288–292. doi:10.1016/j.drudis.2010.11.013

PubMed Abstract | CrossRef Full Text | Google Scholar

Woodcock, J., and Woosley, R. (2008). The FDA critical path initiative and its influence on new drug development. Annu. Rev. Med. 59, 1–12. doi:10.1146/annurev.med.59.090506.155819

PubMed Abstract | CrossRef Full Text | Google Scholar

Trequattrini, R., Lombardi, R., Lardo, A., Della Rosa, S., and Bolici, F. (2015). A Review of Virtual Health Networks: online communities that manage health information reducing environmental uncertainty,” in: 10th International forum on knowledge assets dynamics, IFKAD: culture, innovation and entrepreneurship: connecting the knowledge dots, 1504–1514.

Google Scholar

Tucker, J. D., Day, S., Tang, W., and Bayus, B. (2019). Crowdsourcing in medical research: concepts and applications. PeerJ. 7, e6762. doi:10.7717/peerj.6762

PubMed Abstract | CrossRef Full Text | Google Scholar

Wan, H.-H., and Quan, X. I. (2014). Toward a framework of the process of open innovation—Case of acclarent in the medical device Industry. Int. J. Innovat. Technol. Manag. 11, 1450032. doi:10.7324/japs.2019.90515

CrossRef Full Text | Google Scholar

Wass, S., and Vimarlund, V. (2016). Healthcare in the age of open innovation - a literature review. HIM J. 45, 121–133. doi:10.1177/1833358316639458

CrossRef Full Text | Google Scholar

Wiggins, A., and Wilbanks, J. (2019). The rise of citizen science in health and biomedical research. Am. J. Bioeth. 19, 3–14. doi:10.2139/ssrn.3620176

CrossRef Full Text | Google Scholar

Williams, A. J., Harland, L., Groth, P., Pettifer, S., Chichester, C., Willighagen, E. L., et al. (2012). Open PHACTS: semantic interoperability for drug discovery. Drug Discov. Today. 17, 1188–1198. doi:10.1016/j.drudis.2012.05.016

PubMed Abstract | CrossRef Full Text | Google Scholar

Williamson, A. E., Ylioja, P. M., Robertson, M. N., Antonova-Koch, Y., Avery, V., Baell, J. B., et al. (2016). Open source drug discovery: highly potent antimalarial compounds derived from the tres cantos arylpyrroles. ACS Cent. Sci. 2, 687–701. doi:10.1021/acscentsci.6b00086

PubMed Abstract | CrossRef Full Text | Google Scholar

Yeung, A. W. K., Heinrich, M., Kijjoa, A., Tzvetkov, N. T., and Atanasov, A. G. (2020). The ethnopharmacological literature: an analysis of the scientific landscape. J. Ethnopharmacol. 250, 112414. doi:10.1016/j.jep.2019.112414

PubMed Abstract | CrossRef Full Text | Google Scholar

Yeung, A. W. K., Tzvetkov, N. T., and Atanasov, A. G. (2018). When neuroscience meets pharmacology: a neuropharmacology literature analysis. Front. Neurosci. 12, 852. doi:10.3389/fnins.2018.00852

PubMed Abstract | CrossRef Full Text | Google Scholar

Yeung, A. W. K., Tzvetkov, N. T., El-Tawil, O. S., Bungǎu, S. G., Abdel-Daim, M. M., and Atanasov, A. G. (2019a). Antioxidants: scientific literature landscape analysis. Oxid Med. Cell Longev. 2019, 8278454. doi:10.1155/2019/8278454

PubMed Abstract | CrossRef Full Text | Google Scholar

Yeung, A. W. K., Tzvetkov, N. T., Gupta, V. K., Gupta, S. C., Orive, G., Bonn, G. K., et al. (2019b). Current research in biotechnology: exploring the biotech forefront. Curr. Res. Biotechnol. 1, 34–40. doi:10.1002/biot.201000035

CrossRef Full Text | Google Scholar

Keywords: public-private partnership, pharmaceutical industry, drug discovery, patient-centered innovation, intellectual property, crowdsourcing, knowledge management, open innovation

Citation: Yeung AWK, Atanasov AG, Sheridan H, Klager E, Eibensteiner F, Völkl-Kernsock S, Kletecka-Pulker M, Willschke H and Schaden E (2021) Open Innovation in Medical and Pharmaceutical Research: A Literature Landscape Analysis. Front. Pharmacol. 11:587526. doi: 10.3389/fphar.2020.587526

Received: 28 July 2020; Accepted: 16 November 2020;
Published: 14 January 2021.

Edited by:

Jean-Pierre Valentin, UCB Biopharma SPRL, Belgium

Reviewed by:

Chieko Kurihara, National Institutes for Quantum and Radiological Science and Technology, Japan
Tanja Mueller, University of Strathclyde, United Kingdom

Copyright © 2021 Yeung, Atanasov, Sheridan, Klager, Eibensteiner, Völkl-Kernsock, Kletecka-Pulker, Willschke and Schaden. This is an open-access article distributed under the terms of the Creative Commons Attribution License (CC BY). The use, distribution or reproduction in other forums is permitted, provided the original author(s) and the copyright owner(s) are credited and that the original publication in this journal is cited, in accordance with accepted academic practice. No use, distribution or reproduction is permitted which does not comply with these terms.

*Correspondence: Andy Wai Kan Yeung, ndyeung@hku.hk; Atanas G. Atanasov, atanas.atanasov@univie.ac.at; Eva Schaden, eva.schaden@meduniwien.ac.at