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Tracking R&D behavior: bibliometric analysis of drug patents in the Orange Book

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  • Ming-Chao Huang

    (National Cheng Kung University)

  • Shih-Chieh Fang

    (National Cheng Kung University)

  • Shao-Chi Chang

    (National Cheng Kung University)

Abstract

The Publication Approved Drug Products with Therapeutic Equivalence Evaluations (commonly known as the Orange Book) identifies drug products approved by the United States Food and Drug Administration (USFDA) for safety and effectiveness, and provides substantial information on new drug applications (NDAs) with patent data. To explore the patterns among drug patents in the Orange Book, this study used patent bibliometric analysis. The productivity and impact are presented at the assignee level and applicant level, respectively, and the applicant’s patent portfolio is further discussed. 2,033 drug patents are identified in this current study. Our findings indicate that the applicant’s patent portfolio in the Orange Book is helpful in revealing the technological capability and patent strategy of the pharmaceutical incumbents. By linking drug data and patent information, this current study sheds light on patent research in the pharmaceutical industry.

Suggested Citation

  • Ming-Chao Huang & Shih-Chieh Fang & Shao-Chi Chang, 2011. "Tracking R&D behavior: bibliometric analysis of drug patents in the Orange Book," Scientometrics, Springer;Akadémiai Kiadó, vol. 88(3), pages 805-818, September.
    • Handle: RePEc:spr:scient:v:88:y:2011:i:3:d:10.1007_s11192-011-0400-3
    DOI: 10.1007/s11192-011-0400-3
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    Cited by:

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    2. Subramanian, Annapoornima M. & Bo, Wang & Kah-Hin, Chai, 2018. "The role of knowledge base homogeneity in learning from strategic alliances," Research Policy, Elsevier, vol. 47(1), pages 158-168.
    3. Ricardo Arencibia-Jorge & Elena Corera-Alvarez & Zaida Chinchilla-Rodríguez & Félix Moya-Anegón, 2016. "Scientific output of the emerging Cuban biopharmaceutical industry: a scientometric approach," Scientometrics, Springer;Akadémiai Kiadó, vol. 108(3), pages 1621-1636, September.
    4. Shu, Fei & Julien, Charles-Antoine & Zhang, Lin & Qiu, Junping & Zhang, Jing & Larivière, Vincent, 2019. "Comparing journal and paper level classifications of science," Journal of Informetrics, Elsevier, vol. 13(1), pages 202-225.
    5. Du, Jian & Li, Peixin & Guo, Qianying & Tang, Xiaoli, 2019. "Measuring the knowledge translation and convergence in pharmaceutical innovation by funding-science-technology-innovation linkages analysis," Journal of Informetrics, Elsevier, vol. 13(1), pages 132-148.

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