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  4. Pharmaceutical patent landscaping: A novel approach to understand patents from the drug discovery perspective
 
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March 31, 2023
Journal Article
Title

Pharmaceutical patent landscaping: A novel approach to understand patents from the drug discovery perspective

Abstract
Patents play a crucial role in the drug discovery process by providing legal protection for discoveries and incentivising investments in research and development. By identifying patterns within patent data resources, researchers can gain insight into the market trends and priorities of the pharmaceutical and biotechnology industries, as well as provide additional perspectives on more fundamental aspects such as the emergence of potential new drug targets. In this paper, we used the patent enrichment tool, PEMT, to extract, integrate, and analyse patent literature for rare diseases (RD) and Alzheimer's disease (AD). This is followed by a systematic review of the underlying patent landscape to decipher trends and applications in patents for these diseases. To do so, we discuss prominent organisations involved in drug discovery research in AD and RD. This allows us to gain an understanding of the importance of AD and RD from specific organisational (pharmaceutical or university) perspectives. Next, we analyse the historical focus of patents in relation to individual therapeutic targets and correlate them with market scenarios allowing the identification of prominent targets for a disease. Lastly, we identified drug repurposing activities within the two diseases with the help of patents. This resulted in identifying existing repurposed drugs and novel potential therapeutic approaches applicable to the indication areas. The study demonstrates the expanded applicability of patent documents from legal to drug discovery, design, and research, thus, providing a valuable resource for future drug discovery efforts. Moreover, this study is an attempt towards understanding the importance of data underlying patent documents and raising the need for preparing the data for machine learning-based applications.
Author(s)
Gadiya, Yojana  
Fraunhofer-Institut für Translationale Medizin und Pharmakologie ITMP  
Gribbon, Philip  
Fraunhofer-Institut für Translationale Medizin und Pharmakologie ITMP  
Hofmann-Apitius, Martin  
Fraunhofer-Institut für Algorithmen und Wissenschaftliches Rechnen SCAI  
Zaliani, Andrea  
Fraunhofer-Institut für Translationale Medizin und Pharmakologie ITMP  
Journal
Artificial Intelligence in the Life Sciences  
Open Access
DOI
10.1016/j.ailsci.2023.100069
10.24406/publica-1184
File(s)
1-s2.0-S2667318523000132-main.pdf (1.28 MB)
Rights
CC BY-NC-ND 4.0: Creative Commons Attribution-NonCommercial-NoDerivatives
Language
English
Fraunhofer-Institut für Translationale Medizin und Pharmakologie ITMP  
Fraunhofer-Institut für Algorithmen und Wissenschaftliches Rechnen SCAI  
Keyword(s)
  • Patent analysis

  • Drug Discovery

  • Patent landscape

  • Target importance

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