A hybrid approach unveils drug repurposing candidates targeting an Alzheimer pathophysiology mechanism

Lage-Rupprecht, Vanessa; Schultz, Bruce; Dick, Justus; Namysł, Marcin; Zaliani, Andrea; Gebel, Stephan; Pleß, Ole; Reinshagen, Jeanette; Ellinger, Bernhard; Ebeling, Christian; Esser, Andrea; Jacobs, Marc; Claussen, Carsten; Hofmann‐Apitius, Martin

doi:10.1016/j.patter.2021.100433

Cited by 14 publications

(9 citation statements)

References 87 publications

(102 reference statements)

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“…OrphaNet (accessed on 2021-11-01) is an open-source network that focuses on rare diseases and includes information on biological entities such as orphan gene targets and drugs, as well as clinical 2/18 entities such as clinical trials and biobanks [12]. In contrast, the Human Brain Pharmacome (HBP) is a publicly available biomedical knowledge graph representing neurodegenerative diseases with a particular focus on Alzheimer's disease [13]. The data in the graph is built on the Biological Expression Language (BEL) framework and includes information on proteins, biological processes, and other relevant features [14].…”

Section: Knowledge Graph Selectionmentioning

confidence: 99%

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

Gadiya

Gribbon

Hofmann-Apitius

et al. 2023

Preprint

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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 providing 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 Alzheimers disease (AD). This is followed by a systematic review of the underlying patent landscape to decipher trends and applications in patent 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.

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Section: Knowledge Graph Selectionmentioning

confidence: 99%

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

Gadiya

Gribbon

Hofmann-Apitius

et al. 2023

Preprint

Self Cite

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“…For table interpretation, a common, publicly available benchmark can hardly be found, neither for general data nor for our use case [18], which motivated us to prepare the data for experiments ourselves. To this end, we annotated 13 documents with tables from our internal biomedical data collection.…”

Section: Evaluation Data Setmentioning

confidence: 99%

“…Second, in Namysl et al [22], the basic formulation of the table recognition task is complemented by a table interpretation module implemented as a rule-based method that leverages regular expressions (RegEx) and an approximate string matching algorithm. It is worth noting that this method was also previously employed to extract and structure quantitative information from a vast number of biomedical articles, as presented by Adams et al [1] and Lage-Rupprecht et al [18].…”

Section: Introductionmentioning

confidence: 99%

Flexible Hybrid Table Recognition and Semantic Interpretation System

et al. 2023

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Extracting information from documents containing quantitative data in tabular format is an important but still unsolved task due to the heterogeneity of document layouts. This work aims to take a step toward developing a solution to this problem. This paper proposes a flexible, hybrid table extraction system consisting of a deep learning-based table detection module, a heuristic-based structure recognition method, and a graph-based semantic interpretation component. The proposed system is modular and supports the most frequent table layouts. Moreover, it handles both the documents in image format and PDF files with embedded text. The proposed system outperforms the baseline method and achieves results on par with state-of-the-art approaches on the challenging benchmarks from ICDAR 2013 and ICDAR 2019 table interpretation competitions. Moreover, we correct an issue with the evaluation script used in the latter competition and report extended results of the proposed method in comparison with a leading commercial product. Finally, our table extraction system achieves a high F$$_1$$ 1 score in the scenario where raw documents are given as input and the targeted information is contained in a subset of table columns. The presented system achieves results competitive with leading methods in the field. It has already been evaluated on general-purpose data and biomedical benchmarks. We intend to continuously improve our approach and process data from other domains, e.g., financial documents. To support future research on information extraction from documents, we make the evaluation scripts and results from our experiments publicly available at https://github.com/mnamysl/tabrec-sncs.

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“… 2 In our project, a collaboration between two computer science and one life science institute, we aimed to establish new ways to repurpose drugs to overcome these shortcomings. In our holistic approach that was recently published in Patterns , 3 we combined knowledge mined from a variety of sources with focused experimental screening data of selected drug candidates into our drug-target-mechanism-oriented data model, the Human Brain PHARMACOME (HBP). Mutual analysis led to the identification of interesting repurposing candidates in the Alzheimer disease context.…”

Section: Main Textmentioning

confidence: 99%

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confidence: 99%

A hybrid approach for identifying drug repurposing candidates and their mechanisms: An interview with Vanessa Lage-Rupprecht and two co-authors

2022

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A hybrid approach unveils drug repurposing candidates targeting an Alzheimer pathophysiology mechanism

Cited by 14 publications

References 87 publications

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

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

Flexible Hybrid Table Recognition and Semantic Interpretation System

A hybrid approach for identifying drug repurposing candidates and their mechanisms: An interview with Vanessa Lage-Rupprecht and two co-authors

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