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Updated: Oct 7, 2025

Drug Repurposing Hypothesis Generation Using the "RE:fine Drugs" System
Published on: December 11, 2016
A case study of a patient-centered reverse translational systems-based approach to understand adverse event profiles
Sarah Kim1, Gezim Lahu2, Majid Vakilynejad3
1Center for Pharmacometrics and Systems Pharmacology, Department of Pharmaceutics, College of Pharmacy, University of Florida, Orlando, Florida, USA.
Abstract:
Adverse drug reactions (ADRs) of targeted therapy drugs (TTDs) are frequently unexpected and long-term toxicities detract from exceptional efficacy of new TTDs. In this proof-of-concept study, we explored how molecular causation involved in trastuzumab-induced cardiotoxicity changes when trastuzumab was given in combination with doxorubicin, tamoxifen, paroxetine, or lapatinib. The data analytical platform Molecular Health Effect was utilized to map population ADR data from the US Food and Drug Administration (FDA) Adverse Event Reporting System to chemical and biological databases (such as UniProt and Reactome), for hypothesis generation regarding the underlying molecular mechanisms causing cardiotoxicity. Disproportionality analysis was used to assess the statistical relevance between adverse events of interest and molecular causation. Literature search was performed to compare the established hypotheses to published experimental findings. We found that the combination therapy of trastuzumab and doxorubicin may affect mitochondrial dysfunction in cardiomyocytes through different molecular pathways such as BCL-X and PGC-1α proteins, leading to a synergistic effect of cardiotoxicity. We found, on the other hand, that trastuzumab-induced cardiotoxicity would be diminished by concomitant use of tamoxifen, paroxetine, and/or lapatinib. Tamoxifen and paroxetine may cause less cardiotoxicity through an increase in antioxidant activities, such as glutathione conjugation. Lapatinib may decrease the apoptotic effects in cardiomyocytes by altering the effects of trastuzumab on BCL-X proteins. This patient-centered systems-based approach provides, based on the trastuzumab-induced ADR cardiotoxicity, an example of how to apply reverse translation to investigate ADRs at the molecular pathway and target level to understand the causality and prevalence during drug development of novel therapeutics.
Insights
Investigating trastuzumab-induced cardiotoxicity reveals that combining it with doxorubicin may worsen heart damage. However, using tamoxifen, paroxetine, or lapatinib concurrently may reduce these adverse drug reactions.
Area of Science:
- Pharmacology and Toxicology
- Cardiovascular Research
- Computational Biology
Background:
- Adverse drug reactions (ADRs) from targeted therapy drugs (TTDs) can be unexpected and limit treatment efficacy.
- Understanding the molecular basis of TTD-induced cardiotoxicity is crucial for patient safety.
Purpose of the Study:
- To explore how molecular mechanisms of trastuzumab-induced cardiotoxicity are altered by combination therapies.
- To investigate the impact of doxorubicin, tamoxifen, paroxetine, and lapatinib on trastuzumab-related cardiotoxicity.
Main Methods:
- Utilized the Molecular Health Effect platform to link FDA ADR data with biological databases (UniProt, Reactome).
- Employed disproportionality analysis to assess statistical relevance between adverse events and molecular pathways.
- Conducted literature searches to validate generated hypotheses.
Main Results:
- Trastuzumab and doxorubicin combination may synergistically induce cardiotoxicity via mitochondrial dysfunction pathways (e.g., BCL-X, PGC-1α).
- Tamoxifen, paroxetine, and lapatinib may reduce trastuzumab-induced cardiotoxicity.
- Tamoxifen/paroxetine may increase antioxidant activity (glutathione conjugation); lapatinib may reduce cardiomyocyte apoptosis by modulating BCL-X.
Conclusions:
- A systems-based approach using reverse translation can elucidate molecular mechanisms of ADRs.
- Findings provide insights into managing cardiotoxicity risks associated with trastuzumab combination therapies.
- This approach aids in understanding ADR causality and prevalence during novel therapeutic development.
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