Skip to main content
SpringerLink
Log in

Intelligent Classifier: a Tool to Impel Drug Technology Transfer from Academia to Industry

  • Original Article
  • Published:
Journal of Pharmaceutical Innovation Aims and scope Submit manuscript

Abstract

Purpose

Pharmaceutical technology transfer is one of the components of pharmaceutical innovation. Currently, a gap exists in pharmaceutical technology transfer from academia to industry. This study aims to develop an objective model to identify valuable pharmaceutical technologies for transferring in order to drive pharmaceutical innovation.

Methods

We created a support vector machine classifier model using the data of pharmaceutical patents held by universities to predict the licensing outcomes of those patents. We collected data on 369 United States (US) pharmaceutical patents, using 142 licensed patents as the positive samples and 227 unlicensed patents as the negative samples. We also collected the licensing data of the patents, and the distinguished patent features were selected for model training and generation. Upon optimization, the machine learning model was evaluated using different scoring methods.

Results

Our support vector machine-based model achieved a fairly good performance of 82.50% in precision and 88.89% in specificity.

Conclusions

To the best of our knowledge, our study is the first to apply the machine learning approach to predict the licensing outcomes for pharmaceutical patent valuation and technology transfer. Our work is a good alternative to the current patent valuation methods available in the market, and it could be further developed for practical use in real business contexts.

This is a preview of subscription content, log in via an institution to check access.

Access this article

Log in via an institution

Price excludes VAT (USA)
Tax calculation will be finalised during checkout.

Instant access to the full article PDF.

Institutional subscriptions

Fig. 1
Fig. 2

Similar content being viewed by others

References

  1. Ni J, Shao R, Ung COL, Wang Y, Hu Y, Cai Y. Valuation of pharmaceutical patents: a comprehensive analytical framework based on technological, commercial, and legal factors. J Pharm Innov. 2015;10(3):281–5.

    Article  Google Scholar 

  2. Butler D. Translational research: crossing the valley of death. Nature News. 2008;453(7197):840–2.

    Article  CAS  Google Scholar 

  3. Henderson R, Jaffe AB, Trajtenberg M. Universities as a source of commercial technology: a detailed analysis of university patenting, 1965–1988. Rev Econ Stat. 1988;80(1):119–27.

    Article  Google Scholar 

  4. Fernandez JM, Stein RM, Lo AW. Commercializing biomedical research through securitization techniques. Nat Biotechnol. 2012;30:964–75.

    Article  CAS  PubMed  Google Scholar 

  5. Ruckman K, McCarthy I. Why do some patents get licensed while others do not? Ind Corp Change. 2017;26(4):667–88.

    Article  Google Scholar 

  6. Vapnik VN. An overview of statistical learning theory. IEEE Trans Neural Netw. 1999;10(5):988–99.

    Article  CAS  PubMed  Google Scholar 

  7. Zhang Y, Yang Y, Zhang H, Jiang X, Xu B, Xue Y, et al. Prediction of novel pre-microRNAs with high accuracy through boosting and SVM. Bioinformatics. 2011;27(10):1436–7.

    Article  CAS  PubMed  Google Scholar 

  8. Walt SVD, Colbert SC, Varoquaux G. The NumPy array: a structure for efficient numerical computation. Comput Sci Eng. 2011;13(2):22–30.

    Article  Google Scholar 

  9. Oliphant T, SciPy TE. Open source scientific tools for Python. Comput Sci Eng. 2007;9:10–20.

    Article  CAS  Google Scholar 

  10. Racine J. Gnuplot 4.0: a portable interactive plotting utility. J Appl Econ. 2006;21(1):133–41.

    Article  Google Scholar 

  11. Pedregosa F, Varoquaux G, Gramfort A, Michel V, Thirion B, Grisel O, et al. Scikit-learn: machine learning in Python. J Mach Learn Res. 2011;12:2825–30.

    Google Scholar 

  12. Chang CC, Lin CJ. LIBSVM: a library for support vector machines. ACM Trans Intell Syst Technol. 2011;2(3):27.

    Article  Google Scholar 

  13. Cervantes M. Academic patenting: how universities and public research organizations are using their intellectual property to boost research and spur innovative start-ups. WIPO Small And Medium-sized Enterprises Documents 2003. http://www.wipo.int/sme/en/documents/academic_patenting.html. Accessed 1 April 2018.

  14. Salazar A, Hackney R, Howells J. The strategic impact of internet technology in biotechnology and pharmaceutical firms: insights from a knowledge management perspective. Info Technol Manag. 2003;4(2):289–301.

    Article  Google Scholar 

  15. Laursen K, Leone MI, Torrisi S. Technological exploration through licensing: new insights from the licensee’s point of view. Ind Corp Change. 2010;19(3):871–97.

    Article  Google Scholar 

  16. Leone MI, Reichstein T. Licensing-in fosters rapid invention! The effect of the grant-back clause and technological unfamiliarity. Strat Mgmt J. 2012;33(8):965–85.

    Article  Google Scholar 

  17. Neuhäusler P, Frietsch R. Patent families as macro level patent value indicators: applying weights to account for market differences. Scientometrics. 2013;96(1):27–49.

    Article  Google Scholar 

  18. Smith DKW. A new methodology for citation dependent patent evaluations. Carleton University. 2014. (Electronic, M.Sc. thesis) http://curve.carleton.ca/system/files/theses/31557.pdf. Accessed 1 Oct 2017.

  19. Sakakibara M. An empirical analysis of pricing in patent licensing contracts. Ind Corp Chang. 2010;19(3):927–45.

    Article  Google Scholar 

  20. Pai PF, Hong WC, Change PT, Chen CT. The application of support vector machines to forecast tourist arrivals in Barbados: an empirical study. Int J Manag. 2006;23(2):375.

    Google Scholar 

  21. Tseng CY, Chen MS. Incremental SVM model for spam detection on dynamic email social networks. IEEE CSE Int Conf. 2009;4:128–35.

    Google Scholar 

  22. Hua S, Sun Z. Support vector machine approach for protein subcellular localization prediction. Bioinformatics. 2001;17(8):721–8.

    Article  CAS  PubMed  Google Scholar 

  23. Yu CS, Lin CJ, Hwang JK. Predicting subcellular localization of proteins for Gram-negative bacteria by support vector machines based on n-peptide compositions. Protein Sci. 2004;13(5):1402–6.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  24. Powers DM. Evaluation: from precision, recall and F-measure to ROC, 2011 informedness, markedness and correlation. J Mach Learn Tech. 2011;2(1):37–63.

    Google Scholar 

  25. Campos TC. The idea of patents vs. the idea of university. New Bioethnol. 2015;21(2):164–76.

    Article  Google Scholar 

Download references

Funding

This study was funded by the grant MYRG2015-00145-ICMS-QRCM from the University of Macau.

Author information

Authors and Affiliations

  1. State Key Laboratory of Quality Research in Chinese Medicine, Institute of Chinese Medical Sciences, University of Macau, Macau, China

    Hui-Heng Lin, Defang Ouyang & Yuanjia Hu

  2. The Research Center of National Drug Policy and Ecosystem, Nanjing, China

    Yuanjia Hu

Authors
  1. Hui-Heng Lin
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. Defang Ouyang
    View author publications

    You can also search for this author in PubMed Google Scholar

  3. Yuanjia Hu
    View author publications

    You can also search for this author in PubMed Google Scholar

Corresponding author

Correspondence to Yuanjia Hu.

Ethics declarations

Conflict of Interest

The authors declare that they have no conflict of interest.

Research Involving Human Participants or Animals

This article does not contain any studies with human participants or animals performed by any of the authors.

Informed Consent

Not applicable.

Rights and permissions

Reprints and permissions

About this article

Check for updates. Verify currency and authenticity via CrossMark

Cite this article

Lin, HH., Ouyang, D. & Hu, Y. Intelligent Classifier: a Tool to Impel Drug Technology Transfer from Academia to Industry. J Pharm Innov 14, 28–34 (2019). https://doi.org/10.1007/s12247-018-9332-2

Download citation

  • Published:

  • Issue Date:

  • DOI: https://doi.org/10.1007/s12247-018-9332-2

Keywords

Search

Navigation