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

Yojana Gadiya , Philip Gribbon , Martin Hofmann-Apitius , Andrea Zaliani
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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.

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药物专利景观:一种从药物发现角度理解专利的新方法
专利通过为发现提供法律保护和激励研发投资,在药物发现过程中发挥着至关重要的作用。通过识别专利数据资源中的模式,研究人员可以深入了解制药和生物技术行业的市场趋势和优先事项,并对潜在新药靶点的出现等更基本的方面提供更多的视角。在本文中,我们使用专利富集工具PEMT来提取、整合和分析罕见病(RD)和阿尔茨海默病(AD)的专利文献。接下来是对潜在专利前景的系统审查,以解读这些疾病专利的趋势和应用。为此,我们讨论了参与AD和RD药物发现研究的知名组织。这使我们能够从特定的组织(制药或大学)角度了解AD和RD的重要性。接下来,我们分析了专利与个体治疗靶点相关的历史焦点,并将其与市场情景相关联,从而确定疾病的突出靶点。最后,我们在专利的帮助下确定了这两种疾病中的药物再利用活动。这导致确定了适用于适应症领域的现有再利用药物和新的潜在治疗方法。该研究表明,专利文件的适用性从法律扩展到药物发现、设计和研究,从而为未来的药物发现工作提供了宝贵的资源。此外,这项研究试图理解专利文件中数据的重要性,并提出为基于机器学习的应用准备数据的必要性。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
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来源期刊
Artificial intelligence in the life sciences
Artificial intelligence in the life sciences Pharmacology, Biochemistry, Genetics and Molecular Biology (General), Computer Science Applications, Health Informatics, Drug Discovery, Veterinary Science and Veterinary Medicine (General)
CiteScore
5.00
自引率
0.00%
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0
审稿时长
15 days
期刊最新文献
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