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      Exploring Common Mechanisms of Adverse Drug Reactions and Disease Phenotypes

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            Abstract

            Adverse drug reactions (ADRs) are a major concern in clinical healthcare, significantly affecting patient safety and drug development. ADRs are ranked as the second primary reason for drug withdrawals and the fourth leading cause of mortality in the US [1]. The need for a deeper understanding of ADR mechanisms is crucial for improving drug safety profiles in drug design and repurposing, and in making informed healthcare decisions as they can reveal the complexity of in-vivo human phenotypic responses [2]. By understanding the underlying mechanisms of ADRs, we can gain insight into a drug’s mechanism of action, which can assist in identifying new drug targets, predicting new therapeutic indications, and advancing personalized medicine.

            In this study, we introduce a novel network-based method for exploring the mechanisms underlying ADRs at a molecular level. For this purpose, we construct a comprehensive knowledge graph of 17,010 nodes across six node types (drugs, diseases, genes, proteins, disease phenotypes (DPs), and ADRs) and 159,556 edges across seven edge types (drug–ADR, drug–protein, diseases–gene, disease–DP, gene–protein, protein–protein, ADR–DP). We hypothesize that phenotypically similar ADRs and DPs might result from targeting the same biological mechanisms and pathways. By considering such similarities, our method investigates the commonalities regarding their impact on the protein-protein interaction network and robustly identifies protein sets associated with the underlying biological mechanisms. Applying our proposed method on ADRs from the SIDER database (version 4) [3], we identified the mechanism of action for 67 ADRs (e.g., ventricular arrhythmia, hyperchloremia, vasculitis). To evaluate the relevance and novelty of the identified proteins, we performed pathway enrichment analysis and a literature search. While our findings align with prior knowledge, we also unveiled novel associations that have not been previously reported.

            We further demonstrate how our method can be used for drug repurposing, suggesting candidate drugs for eight different phenotypes, including ventricular fibrillation, precocious puberty, and peptic ulcers. These drugs were selected based on multiple criteria. Moreover, among the drugs suggested by our method, some of them are currently being investigated in clinical trials [4] for each phenotype.

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            Author and article information

            Conference
            RExPO24 Conference
            REPO4EU
            29 April 2024
            Affiliations
            [1 ] Institute for Computational Systems Biology, University of Hamburg, Hamburg, Germany ( https://ror.org/00g30e956)
            [2 ] Brigham and Women’s Hospital, Harvard Medical School, Boston, MA, USA ( https://ror.org/04b6nzv94)
            Author notes
            Author information
            https://orcid.org/0000-0002-4737-3133
            https://orcid.org/0000-0002-4310-8527
            https://orcid.org/0000-0003-4920-4357
            https://orcid.org/0000-0002-1153-8047
            https://orcid.org/0000-0002-0282-0462
            https://orcid.org/0000-0002-7592-2080
            Article
            10.58647/REXPO.24000040.v1
            b5d9c3f6-7bbf-416e-91f3-e9c26cd0e848

            This work has been published open access under Creative Commons Attribution License CC BY 4.0 , which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited. Conditions, terms of use and publishing policy can be found at www.scienceopen.com .

            RExPO24
            3
            Munich, Germany
            3-5 July 2024
            History
            : 29 April 2024
            Product

            REPO4EU

            Categories

            The datasets generated during and/or analysed during the current study are available from the corresponding author on reasonable request.
            Computer science
            Adverse drug reaction, disease phenotype, network-based analysis, network diffusion, drug repurposing

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