Data driven drug repositioning in idiopathic pulmonary fibrosis
Inkala, Simo (2023)
2023
Bioteknologian ja biolääketieteen tekniikan maisteriohjelma - Master's Programme in Biotechnology and Biomedical Engineering
Lääketieteen ja terveysteknologian tiedekunta - Faculty of Medicine and Health Technology
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Hyväksymispäivämäärä
2023-08-14
Julkaisun pysyvä osoite on
https://urn.fi/URN:NBN:fi:tuni-202306216875
https://urn.fi/URN:NBN:fi:tuni-202306216875
Tiivistelmä
Idiopathic pulmonary fibrosis (IPF) is a progressive and chronic interstitial lung disease (ILD) that currently has few treatment options with limited efficacy and high cost. This study aims to shed light on the underlying mechanisms of IPF, identify potential biomarkers, and explore novel treatments using a data-driven approach. Additionally, the study evaluates the FAIRness of publicly available transcriptomics data repositories and integrates meta-analytical and network-based methods.
Microarray and RNA-seq datasets of both biopsies and different cell types of IPF patients and healthy controls were collected from GEO and ENA databases. The data were then curated and preprocessed using state-of-the-art methods. Gene co-expression networks were generated for each cell type (epithelial, macrophage, fibroblast, BAL). and biopsy. Subsequently, gene expression meta-analysis was conducted. The results indicate that potential treatments for IPF can be classified into five groups: collagenase enzymes, tyrosine kinase inhibitors, matrix metalloproteinase inhibitors, ion channel modulators and inhibitors, and proteins like monoclonal antibodies. Due to the complex pathogenesis of IPF, combination therapies may be more effective than monotherapies, and these five classes of drugs could be potential candidates. However, further research is necessary to determine the optimal dosages, administration routes, side effects and possible effects upon combination of these drugs.
In conclusion, the systems pharmacological approach used in this study is effective for the identification of new drug candidates for complex and poorly understood diseases like IPF. Combining network-based methods and meta-analytical approaches is an effective strategy, as they provide complementary perspectives. However, a challenge in using public repositories is ensuring the FAIRness of the data, which poses significant challenges despite the well-known principles of FAIR data.
Microarray and RNA-seq datasets of both biopsies and different cell types of IPF patients and healthy controls were collected from GEO and ENA databases. The data were then curated and preprocessed using state-of-the-art methods. Gene co-expression networks were generated for each cell type (epithelial, macrophage, fibroblast, BAL). and biopsy. Subsequently, gene expression meta-analysis was conducted. The results indicate that potential treatments for IPF can be classified into five groups: collagenase enzymes, tyrosine kinase inhibitors, matrix metalloproteinase inhibitors, ion channel modulators and inhibitors, and proteins like monoclonal antibodies. Due to the complex pathogenesis of IPF, combination therapies may be more effective than monotherapies, and these five classes of drugs could be potential candidates. However, further research is necessary to determine the optimal dosages, administration routes, side effects and possible effects upon combination of these drugs.
In conclusion, the systems pharmacological approach used in this study is effective for the identification of new drug candidates for complex and poorly understood diseases like IPF. Combining network-based methods and meta-analytical approaches is an effective strategy, as they provide complementary perspectives. However, a challenge in using public repositories is ensuring the FAIRness of the data, which poses significant challenges despite the well-known principles of FAIR data.