Journal Article

Emerging Therapeutic Strategies for Heart Failure: A Comprehensive Review of Novel Pharmacological and Molecular Targets

Singamaneni, Reva, Chandini, Punuri, Sathwika Eliana, Addanki, Aditya Reddy, Palle, Siri Samhita, Papasani, Palaparthi, Elizabeth Caroline, Kantula, Abhigna, Patnaik, Prashanth Kumar, Padala, Tanvi, Manaswini, Rachakatla

2025

Overview

Heart failure (HF) is a complex clinical syndrome characterized by the heart's inability to meet the body's metabolic demands. HF remains a global health challenge with high morbidity and mortality. Outcomes of beta-blockers, angiotensin receptor-neprilysin inhibitors (ARNIs), and mineralocorticoid receptor antagonists (MRAs) in HF remain suboptimal. HF is a heterogeneous syndrome driven by neurohormonal dysregulation, fibrosis, metabolic disturbances, and inflammation, contributing to symptoms like dyspnea, fatigue, and fluid retention. Recent advances in pharmacological therapies, including sodium-glucose cotransporter 2 inhibitors (SGLT2 inhibitors), soluble guanylate cyclase stimulators (sGC stimulators), and cardiac myosin activators, have shown promise in HF with reduced ejection fraction (HFrEF) and HF with preserved ejection fraction (HFpEF), offering mechanism-specific interventions. Moreover, molecular-targeted therapies, such as clustered regularly interspaced short palindromic repeats (CRISPR)-associated protein 9 (Cas9) gene editing, RNA-based therapeutics, and adeno-associated virus serotype 9-sarcoplasmic reticulum calcium ATPase 2a (AAV9-SERCA2a gene) therapy, are emerging as potential disease-modifying treatments aimed at addressing genetic and inflammatory drivers of cardiomyopathies. Artificial intelligence (AI) is transforming HF care by enhancing predictive modelling, risk stratification, and precision medicine, with applications in multi-omics data integration. AI-driven tools, including machine learning (ML) algorithms, improve echocardiographic phenotyping, optimize treatment strategies, and refine patient selection for therapies. Despite these promising developments, challenges such as data quality, standardization, scalability, and regulatory barriers remain. Furthermore, gene therapies' long-term safety and efficacy are still uncertain, with concerns about immune responses, off-target effects, and sustained gene expression. Regenerative medicine strategies, including induced pluripotent stem cells (iPSC)-derived cardiomyocytes, extracellular vesicles (EVs), and 3D-bioprinted cardiac patches, offer potential solutions for myocardial repair. However, immune rejection, graft integration, and long-term viability remain significant obstacles. Additionally, high costs associated with novel biologics and gene-based therapies limit accessibility, particularly in low-resource settings. The future of HF management depends on overcoming these translational challenges. Key steps include validating AI-driven phenotyping tools in clinical trials, advancing scalable biomanufacturing technologies, and refining regulatory frameworks to facilitate clinical integration. By addressing these barriers, precision medicine, AI, and regenerative therapies can transform HF management, providing more personalized, effective, and accessible treatments and ultimately improving patient outcomes globally.

• Publisher: Springer Nature B.V,Cureus
• Subject: Artificial intelligence / Cardiology / Cardiomyocytes / Cardiomyopathy / Clinical trials / CRISPR / Diabetes / Disease / Ejection fraction / Extracellular vesicles / Gene therapy / Genome editing / Heart failure / Hospitalization / Hypertension / Metabolism / Mortality / Mutation / Pathophysiology / Pharmacology / Precision medicine / Regenerative medicine / Therapeutics / Tumor necrosis factor-TNF