Logic-based machine learning predicts how escitalopram attenuates cardiomyocyte hypertrophy.
Summary
The authors introduce LogiRx, a mechanistic AI approach that predicts drug-induced signaling pathways. They demonstrate that escitalopram attenuates cardiomyocyte hypertrophy via an off-target serotonin receptor/PI3Kγ pathway, validating predictions in neonatal cardiomyocytes, adult mice, and human databases.
Key Findings
- Developed LogiRx, a logic-based mechanistic ML framework predicting drug-induced pathways.
- Predicted and validated that escitalopram inhibits cardiomyocyte hypertrophy via a serotonin receptor/PI3Kγ pathway.
- Escitalopram reduced hypertrophy in cultured cardiomyocytes and in a mouse hypertrophy/fibrosis model.
- Database analyses showed lower incidence of cardiac hypertrophy in patients on escitalopram compared with SSRIs not targeting the serotonin receptor.
Clinical Implications
Escitalopram may reduce cardiac hypertrophy risk in certain populations, suggesting a potential adjunctive strategy to limit remodeling. Mechanistic targets (serotonin receptor/PI3Kγ) offer opportunities for precision therapy development.
Why It Matters
This work couples explainable AI with experimental validation to uncover an actionable off-target mechanism, enabling drug repurposing to limit cardiac remodeling. It bridges computational predictions with translational evidence across species.
Limitations
- Not a randomized clinical trial; human evidence is observational.
- Off-target mechanism and dose-response for clinical translation require prospective trials.
Future Directions
Prospective, randomized studies to test escitalopram’s antihypertrophic effect and target engagement; exploration of PI3Kγ modulation and serotonin receptor selectivity for precision therapeutics.
Study Information
- Study Type
- Cohort
- Research Domain
- Pathophysiology
- Evidence Level
- III - Mechanistic experimental work with supportive observational human data
- Study Design
- OTHER