# Insights into preclinical models of calcific aortic valve disease and their translational potential

## TL;DR

This review discusses preclinical models for calcific aortic valve disease and how they can better reflect the disease's complexity to aid in finding new treatments.

## Contribution

The paper provides a comprehensive overview of recent preclinical models for CAVD and suggests ways to improve their translational potential.

## Key findings

- Current preclinical models do not fully replicate the complexity of CAVD and its risk factors.
- Incorporating comorbidities and gender-specific mechanisms can enhance model translational potential.
- The review aims to guide researchers in selecting appropriate models for therapeutic candidate identification.

## Abstract

Calcific aortic valve disease (CAVD) is characterized by a fibrocalcific remodeling of the aortic valve. This pathology is the most prevalent valvular heart disease worldwide and is associated with a poor prognosis. Despite extensive research, no pharmacological treatments are available to slow or reverse valvular degeneration, making aortic valve replacement the only current therapeutic option. This lack of clinical success may stem from an incomplete understanding of the disease's mechanisms and the limitations of current preclinical models, which do not fully replicate the complexity of CAVD and its associated risk factors and comorbidities. Indeed, while existing models offer valuable insights, a deeper understanding of CAVD requires incorporating comorbidities, gender-specific mechanisms, and dynamic cellular and tissue-level changes. This review aims to provide the reader with an overview of preclinical models developed in recent years to study CAVD, assessing their strengths and limitations. We review how these models can be used to mimic and/or investigate the cellular and molecular mechanisms involved in CAVD development, and highlight how key risk factors and comorbidities can be incorporated to enhance the translational potential of research. We hope that this approach will help guide researchers in selecting the most appropriate model for their studies, with the goal of advancing the identification of effective therapeutic candidates.