Phenotypic Similarity of Adverse Drug Reactions and Disease Phenotypes Reveals Common Mechanisms

<p xmlns:xsi="http://www.w3.org/2001/XMLSchema-instance" class="first" dir="auto" id="d1203686e162">Adverse drug reactions (ADRs) remain a major barrier to drug development and a significant threat to patient safety, despite considerable efforts to mitigate drug safety issues. They are the second leading cause of drug withdrawals and the fourth leading cause of mortality in the United States [1]. A major contributing factor to this challenge is our limited understanding of the underlying mechanisms of ADRs. Gaining mechanistic insight into ADRs is essential for improving drug safety, guiding drug design and repurposing, and supporting informed clinical decision-making [2]. Furthermore, understanding ADR mechanisms can illuminate a drug’s mode of action, revealing new therapeutic targets. <p xmlns:xsi="http://www.w3.org/2001/XMLSchema-instance" dir="auto" id="d1203686e164">In this study, we explored whether ADRs and disease phenotypes (DPs) that share similar clinical manifestations also share mechanistic similarities. This dual perspective could facilitate the precise identification of biological pathways involved. To achieve this, we constructed a comprehensive knowledge graph and applied a graph representation learning technique to quantify the mechanistic similarities between phenotypically similar ADRs and DPs. For mechanistically similar ADR-DP pairs, we further introduce a novel network-based method for exploring the mechanisms underlying ADRs at a molecular level [3]. <p xmlns:xsi="http://www.w3.org/2001/XMLSchema-instance" dir="auto" id="d1203686e166">Our analysis reveals substantial mechanistic overlap among ADRs and DPs within specific system organ classes, including cardiac, psychiatric, and metabolism disorders. These findings not only enhance our understanding of the molecular mechanisms associated with ADRs by integrating drug-induced and disease-related phenotypes, but also suggest that drugs interacting with proteins associated with specific DPs are more prone to inducing ADRs with similar phenotypic manifestations. This approach supports the prioritization of drugs with lower ADR risk, ultimately contributing to future efforts toward safer and more targeted therapeutic development.

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Published
2025-06-11
DOI
https://doi.org/10.58647/rexpo.25000085.v1
Primary Topic
Computational Drug Discovery Methods
Type
article
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0.00
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article

Phenotypic Similarity of Adverse Drug Reactions and Disease Phenotypes Reveals Common Mechanisms

Nina K. Wenke, Farzaneh Firoozbakht, Joseph Loscalzo, Maria L. Elkjaer et al.
Computational Drug Discovery Methods
article

Phenotypic Similarity of Adverse Drug Reactions and Disease Phenotypes Reveals Common Mechanisms

Nina K. Wenke, Farzaneh Firoozbakht, Joseph Loscalzo, Maria L. Elkjaer, Rui‐Sheng Wang, Diane E. Handy, Jan Baumbach
article en

Abstract

<p xmlns:xsi="http://www.w3.org/2001/XMLSchema-instance" class="first" dir="auto" id="d1203686e162">Adverse drug reactions (ADRs) remain a major barrier to drug development and a significant threat to patient safety, despite considerable efforts to mitigate drug safety issues. They are the second leading cause of drug withdrawals and the fourth leading cause of mortality in the United States [1]. A major contributing factor to this challenge is our limited understanding of the underlying mechanisms of ADRs. Gaining mechanistic insight into ADRs is essential for improving drug safety, guiding drug design and repurposing, and supporting informed clinical decision-making [2]. Furthermore, understanding ADR mechanisms can illuminate a drug’s mode of action, revealing new therapeutic targets. <p xmlns:xsi="http://www.w3.org/2001/XMLSchema-instance" dir="auto" id="d1203686e164">In this study, we explored whether ADRs and disease phenotypes (DPs) that share similar clinical manifestations also share mechanistic similarities. This dual perspective could facilitate the precise identification of biological pathways involved. To achieve this, we constructed a comprehensive knowledge graph and applied a graph representation learning technique to quantify the mechanistic similarities between phenotypically similar ADRs and DPs. For mechanistically similar ADR-DP pairs, we further introduce a novel network-based method for exploring the mechanisms underlying ADRs at a molecular level [3]. <p xmlns:xsi="http://www.w3.org/2001/XMLSchema-instance" dir="auto" id="d1203686e166">Our analysis reveals substantial mechanistic overlap among ADRs and DPs within specific system organ classes, including cardiac, psychiatric, and metabolism disorders. These findings not only enhance our understanding of the molecular mechanisms associated with ADRs by integrating drug-induced and disease-related phenotypes, but also suggest that drugs interacting with proteins associated with specific DPs are more prone to inducing ADRs with similar phenotypic manifestations. This approach supports the prioritization of drugs with lower ADR risk, ultimately contributing to future efforts toward safer and more targeted therapeutic development.

Harvard University (US), Universität Hamburg (DE)
Openalex Percentile: Top 85%
Computational Drug Discovery Methods
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