Abstract
Background
Rheumatoid Arthritis (RA) is a chronic autoimmune disorder that significantly impacts upon quality of life and work capacity. Treatment of RA aims to control inflammation and alleviate pain, however achieving remission with minimal toxicity is frequently not possible with the current suite of drugs. Additionally with escalating de novo drug development costs, bioinformatic discovery pipelines offer the ability to repurpose already licenced compounds and explore synergistic combinations more efficiently.
Method
Public datasets are mined and pre-processed followed by differential expression analysis to obtain a list of differentially expressed genes (DEGs). DEGs from multiple datasets are merged and mapped to Affymetrix probeset IDs to create a treatment response gene signature. Connectivity mapping analysis is used to obtain a list of alternate drugs with high probability of inducing therapeutic response.
Results
CMap analysis identified a total of 6 statistically significant candidate drugs which induced gene expression profiles indicative of theoretical response. The next step involves in silico toxicity screening on identified candidate drugs to focus in vitro tests on list of optimal drugs.
Conclusion
Analysis with this pipeline illustrates the potential of treatment response DEG extraction from expression datasets to predict novel drugs which may offer new options to refractory patients.
Rheumatoid Arthritis (RA) is a chronic autoimmune disorder that significantly impacts upon quality of life and work capacity. Treatment of RA aims to control inflammation and alleviate pain, however achieving remission with minimal toxicity is frequently not possible with the current suite of drugs. Additionally with escalating de novo drug development costs, bioinformatic discovery pipelines offer the ability to repurpose already licenced compounds and explore synergistic combinations more efficiently.
Method
Public datasets are mined and pre-processed followed by differential expression analysis to obtain a list of differentially expressed genes (DEGs). DEGs from multiple datasets are merged and mapped to Affymetrix probeset IDs to create a treatment response gene signature. Connectivity mapping analysis is used to obtain a list of alternate drugs with high probability of inducing therapeutic response.
Results
CMap analysis identified a total of 6 statistically significant candidate drugs which induced gene expression profiles indicative of theoretical response. The next step involves in silico toxicity screening on identified candidate drugs to focus in vitro tests on list of optimal drugs.
Conclusion
Analysis with this pipeline illustrates the potential of treatment response DEG extraction from expression datasets to predict novel drugs which may offer new options to refractory patients.
Original language | English |
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Pages | A-159 |
Publication status | Published online - Jul 2023 |
Event | The 31st Annual Intelligent Systems For Molecular Biology and the 22nd Annual European Conference on Computational Biology - Centre de Congrès de Lyon, Lyon, France Duration: 23 Jul 2023 → 27 Jul 2023 https://www.iscb.org/ismbeccb2023 |
Conference
Conference | The 31st Annual Intelligent Systems For Molecular Biology and the 22nd Annual European Conference on Computational Biology |
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Abbreviated title | ISMB/ECCB 2023 |
Country/Territory | France |
City | Lyon |
Period | 23/07/23 → 27/07/23 |
Internet address |
Keywords
- Rheumatoid Arthritis
- Connectivity Mapping
- Drug Discovery