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Review

Research and development of anti-Parkinson’s drugs: an analysis from the perspective of technology flows measured by patent citations

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Pages 127-135 | Received 02 Nov 2018, Accepted 04 Jan 2019, Published online: 19 Jan 2019
 

ABSTRACT

Introduction: By 2020, nearly one million people will live with Parkinson’s disease (PD) in the U.S. This disorder has a significant impact on patients’ quality of life and is a burden on families and society. Protracted efforts have been made to treat the disease. Cumulative technological innovations are encapsulated by patents, and patent citations have been used to analyze technology diffusion processes in R&D, which is essential to identifying technology evolution trends and providing a review of PD treatment from the perspective of technology flows.

Areas covered: A patent citation network was utilized to analyze technology flows. Patents related to anti-PD drugs were collected from the U.S. Patent and Trademark Office (U.S. PTO) database. A total of 1,231 patents and 2,995 internal citations granted between 1988 and 2017 were included and analyzed.

Expert opinion: To launch drugs with greater efficiency and safety, approaches such as long-acting sustained release, controlled osmotic release, and other novel drug delivery systems should be emphasized. Multi-target agents could effectively reduce side effects in mono-drug therapy and are worth further exploration. Investors should keep an eye on alpha-synuclein–related therapy, gene therapy, and other experimental therapies that might trigger a historic revolution in the treatment domain.

Article highlights

  • Technology diffusion in the field of anti-PD drugs over the past three decades can be divided into four periods according to patent temporal changes: the pre-bloom period (1988–1997), which focused on a few neurotransmitter targets; the first bloom period (1998–2004), which featured the large enrichment of technology and action varieties; the decline period (2005–2008), which involved the exit and recession of neurotrophic immunophilin ligands; and the second bloom period (2009–2017), which saw the entrance of experimental technologies that have the potential to modify PD.

  • Anti-PD pharmacological technologies and targets vary considerably. In the patent citation network, technologies frequently flow among various technology clusters. From this perspective, most of the technologies in this study are considerably concerned about external technological development.

  • Patents for drugs acting as glutamatergic receptor modulators account for the largest proportion of the network, followed by dopamine agonists and cholinergic receptor modulators, which have continued to develop over the past 30 years.

  • Pharmaceutical giants Pfizer and Teva are the most active patentees in the network, with Pfizer possessing patents covering actions of dopamine agonists, NMDA antagonists, and 5-HT receptor modulators by acquiring several companies, while Teva has concentrated on rasagiline.

  • Topological features and patent information on the main actions in the network are compared in this study. Patents related to levodopa prodrugs and MAO inhibitors possess the most frequent citations.Patents claiming extended-release technologies are frequently cited, which demonstrates the importance of drug delivery strategies in the treatment of PD.

This box summarizes key points contained in the article.

Declaration of interest

The authors have no relevant affiliations or financial involvement with any organization or entity with a financial interest or financial conflict with the subject matter or materials discussed in the manuscript. This includes employment, consultancies, honoraria, stock ownership or options, expert testimony, grants or patents received or pending, or royalties.

Reviewer disclosures

Peer reviewers on this manuscript have no relevant financial or other relationships to disclose.

Additional information

Funding

This paper received financial support from The Macau Science and Technology Development Fund; The University of Macau for the research by projects 013/2015/A1 and MYRG2016-00144-ICMS-QRCM.

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