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Network-Based Analysis of Protein Interactions among Drugs and Adverse Reactions: Identifying Phenotype-Groupings and Key Genes
Uppsala University, Disciplinary Domain of Medicine and Pharmacy, Faculty of Medicine, Department of Medical Sciences, Clinical pharmacogenomics and osteoporosis. Uppsala University, Science for Life Laboratory, SciLifeLab.ORCID iD: 0000-0001-8687-0629
Uppsala University, Disciplinary Domain of Medicine and Pharmacy, Disciplinary Domain of Medicine and Pharmacy, research centers etc., Uppsala Clinical Research Center (UCR). Uppsala University, Disciplinary Domain of Medicine and Pharmacy, Faculty of Medicine, Department of Medical Sciences, Clinical pharmacogenomics and osteoporosis. Uppsala University, Science for Life Laboratory, SciLifeLab. Uppsala University, Disciplinary Domain of Medicine and Pharmacy, Faculty of Medicine, Department of Medical Sciences, Cardiology.ORCID iD: 0000-0002-2152-4343
Uppsala University, Disciplinary Domain of Medicine and Pharmacy, Faculty of Medicine, Department of Medical Sciences, Clinical pharmacogenomics and osteoporosis. Uppsala University, Science for Life Laboratory, SciLifeLab.ORCID iD: 0000-0003-3465-3280
Uppsala University, Disciplinary Domain of Medicine and Pharmacy, Faculty of Medicine, Department of Medical Sciences, Clinical pharmacogenomics and osteoporosis. Uppsala University, Science for Life Laboratory, SciLifeLab.ORCID iD: 0000-0002-6368-2622
(English)Manuscript (preprint) (Other academic)
Abstract [en]

Background:

Adverse drug reactions (ADRs) present a significant healthcare challenge, leading to morbidity, hospitalizations, and even fatalities. Serious ADRs are in general infrequent, since drugs with a high risk-benefit ratio are rarely approved by the authorities.Genetic factors contribute to serious ADRs, driving pharmacogenomic research to investigate drug-ADR-genetic relationships. These relationships are, however, still largely unstudied due to the scarcity of cases. This scarcity, coupled with the multiple hypothesis problem of genetic studies, poses challenges for these studies. One approach is to group similar ADRs or drugs to bolster sample sizes. However, grouping of drugs and ADRs requires caution to avoid including biologically ill-fitting cases. The objective of our study is to cluster drugs and ADRs based on previous genetic associations and shared protein interactions to propose phenotype groups and genetic targets for investigation.

Methods:

We developed a Bayesian probability model to substantiate protein-protein interactions across different drugs or ADRs. Subsequently, these proximity values were utilised for spectral clustering to form phenotype-groups. Once obtained, the model was reformulated to rank shared proteins for each cluster.

Results:Permutation analysis demonstrated high sensitivity in correctly clustering drugs into therapeutic groups (sensitivity 94-97%) - outperforming other proposed methods - and assigning ADRs to clusters (sensitivity 86%). The model's reformulation enabled the ranking of shared proteins within each cluster, revealing enrichment in KEGG pathways relevant to therapeutic classifications. 

Discussion:

This method successfully replicated known therapeutic drug classifications with high sensitivity, using shared protein interactions among KEGG pathways associated with drug functions. Using the proximity score and spectral clustering we propose phenotype groups and genetic targets for investigations. However, further studies are needed to assess the method's utility for the selection of cases and for target identification in less homogeneous drug-ADR scenarios.

Keywords [en]
Adverse drug reactions; target discovery; study design; protein-protein interaction; statistical modelling; enrichment analysis
National Category
Medical Genetics
Identifiers
URN: urn:nbn:se:uu:diva-526952OAI: oai:DiVA.org:uu-526952DiVA, id: diva2:1853065
Available from: 2024-04-20 Created: 2024-04-20 Last updated: 2024-04-29
In thesis
1. Genomic Analysis of Adverse Drug Reactions
Open this publication in new window or tab >>Genomic Analysis of Adverse Drug Reactions
2024 (English)Doctoral thesis, comprehensive summary (Other academic)
Abstract [en]

Adverse drug reactions (ADRs) pose a significant global challenge, leading to substantial costs, suffering, and even loss of life. Genetic factors can play a role in determining a patient's response to the drug treatments and predicting ADRs. While many genetic associations with ADRs have been identified, there are still numerous ADRs suspected to have genetic components.

In Paper I, the collection and curation strategies for ADR cases in the Swedegene biobank are established, presenting a cohort of 2,550 ADR-cases. Paper II presents the association between genetic variations in human leukocyte antigen (HLA) genes and the development of pancreatitis as a response to azathioprine treatment in patients with Crohn's disease. Paper III reports on an international collaboration to investigate the genetic aetiology of atypical femur fractures (AFF) during bisphosphonate treatment. The study found that previously identified genetic variants did not replicate, and --- as the cohort is the largest of its kind --- provides valuable insights into common genetic factors of AFF. Paper IV examines the genetic associations with central nervous system (CNS) toxicity as an ADR to antimicrobial drugs, identifying correlations with three genes linked to suicide and schizophrenia, although the biological connection remains unclear. Finally, Paper V presents a methodology for the experimental design of ADR studies by analysing the known protein interactions of drugs and proteins associated with ADRs. This approach aims to mitigate the impact of competing genetic correlations by identifying common protein interactions to validate the inclusion of drugs and ADRs in the study. These interactions are then ranked based on importance to the selected drugs and ADRs and used to propose genetic targets of interest. 

Overall, the findings of these studies contribute to the understanding of genetic predispositions to ADRs and provide a novel approach for data-driven experimental design for phenotype and genetic target selection.

Place, publisher, year, edition, pages
Uppsala: Acta Universitatis Upsaliensis, 2024. p. 67
Series
Digital Comprehensive Summaries of Uppsala Dissertations from the Faculty of Medicine, ISSN 1651-6206 ; 2055
Keywords
Adverse drug reactions, Genetic association, Network biology
National Category
Medical Genetics
Identifiers
urn:nbn:se:uu:diva-527102 (URN)978-91-513-2139-4 (ISBN)
Public defence
2024-06-13, Rosénsalen, Akademiska sjukhuset, ing. 95/96, Uppsala, 13:00 (English)
Opponent
Supervisors
Available from: 2024-05-21 Created: 2024-04-23 Last updated: 2024-05-21

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Ås, JoelEriksson, NiclasHallberg, PärWadelius, Mia

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