Bridging discovery and clinical science: A framework for advancing atherosclerosis research using innovative models

Area of Science:

• Cardiovascular Research
• Translational Medicine
• Biomarker Discovery

Background:

• Atherosclerotic cardiovascular disease (ASCVD) remains a leading cause of death, with significant residual risk despite existing therapies.
• The multifactorial nature of ASCVD is not fully addressed by traditional research methods.
• Current models like 2D cultures and murine models often fail to replicate human-specific disease aspects.

Purpose of the Study:

• To propose an integrated framework for ASCVD research.
• To enhance the translation of data-driven discoveries into clinical applications.
• To accelerate the development of precision therapies for ASCVD.

Main Methods:

• Leveraging large-scale datasets, multi-omics, polygenic risk scores, and artificial intelligence (AI) for discovery.
• Utilizing advanced in vitro and ex vivo platforms (e.g., stem cell-derived systems, organoids, human tissue models) for validation.
• Integrating data-driven discovery with human-relevant experimental models and selective in vivo studies.

Main Results:

• Advanced platforms offer controlled, human-relevant environments for testing and personalized therapy development.
• In vivo models are crucial for systemic physiology and pharmacokinetic/pharmacodynamic assessments.
• An integrated approach aligns multi-omics/AI discovery with advanced preclinical validation.

Conclusions:

• An integrated framework is proposed to link data-driven discovery with validation in human-relevant models.
• This approach aims to improve translational success for ASCVD therapies.
• The synergy of AI, multi-omics, and advanced models promises accelerated development of precision ASCVD treatments.