Key result
Sex-specific computational modeling achieves ~77% accuracy and predicts higher pro-fibrotic drug susceptibility in premenopausal females.
Why the study?
Most computational signaling network models for cardiac fibrosis lack sex specificity or are skewed toward male data, despite biological sex being crucial in cardiovascular health.
Does integrating estrogen signaling into a cardiac fibroblast computational model predict sex-specific differences in anti-fibrotic drug efficacy?
Does integrating estrogen signaling into a cardiac fibroblast computational model predict sex-specific differences in anti-fibrotic drug efficacy?
Absolute Event Rate: 77% vs 81%
Sex-specific computational modeling demonstrates that estrogen levels fundamentally alter cardiac fibroblast signaling networks, providing a mechanistic hypothesis for why heart failure drugs might exhibit differential efficacy in women.
Hypothesis-generating for sex-specific fibrosis responses; leaves open clinical translation without human validation.
Heart disease is the leading cause of death in both men and women. Cardiac fibrosis is the uncontrolled accumulation of extracellular matrix proteins which can exacerbate the progression of heart failure, and there are currently no drugs approved specifically to target matrix accumulation in the heart. Computational signaling network models (SNMs) can be used to facilitate discovery of novel drug targets. However, the vast majority of SNMs are not sex-specific and/or are developed and validated using data skewed towards male in vitro and in vivo samples. Biological sex is an important consideration in cardiovascular health and drug development. In this study, we integrate a previously constructed cardiac fibroblast SNM with estrogen signaling pathways to create sex-specific SNMs. The sex-specific SNMs maintained previously high validation when compared to in vitro experimental studies in the literature. A sex-specific perturbation analysis and drug screen uncovered several potential pathways that warrant further study in the pursuit of sex-specific treatment recommendations for cardiac fibrosis. Author Summary Heart failure is a leading cause of death for both men and women, but we still do not have adequate therapies to prevent or reverse this disease. One factor that contributes to heart failure is scarring of cardiac tissue, also known as fibrosis. Computer models can help find new heart failure drugs by simulating hundreds of biological reactions that regulate fibrosis at the molecular level. Unfortunately, the differences in male and female patients are not usually considered for these drug discovery simulations, which can result in drugs that work well for some individuals but not for other individuals. In our study, we added sex-specific biological reactions to a computer model in order to identify drugs that could treat fibrosis differently in male and female patients. Our simulations also predicted why premenopausal women may generally develop less fibrosis than men, while post-menopausal women may develop similar levels of fibrosis as men.
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Watts et al. (2023) studied Cardiac fibrosis. Integration of estrogen (E2) signaling into a computational signaling network model vs. Male-specific model and previous non-sex-specific model was evaluated on Model prediction accuracy when compared to independent in vitro experimental studies. A sex-specific cardiac fibroblast computational model maintained 77% predictive accuracy and predicted that premenopausal female conditions exhibit more pro-fibrotic drug effects than male conditions.
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