The global burden of chronic and genetic kidney diseases poses a significant challenge to healthcare systems. Current therapies, including dialysis, transplantation, and supportive pharmacotherapies, cannot halt disease progression or address root causes, especially in monogenic disorders like Alport syndrome and Fabry disease. Adeno-associated virus (AAV)-based gene therapy is promising, enabling targeted correction of underlying genetic defects. However, renal delivery faces challenges, including cellular heterogeneity, anatomical barriers, and pre-existing immunity. This review evaluates advances in AAV capsid engineering to overcome these obstacles, focusing on strategies to enhance kidney-specific tropism, transduction efficiency, and immune evasion. We outline the evolution from conventional serotype selection to precision engineering via rational design, directed evolution, and in silico approaches. Artificial intelligence (AI) has emerged as a pivotal accelerator, with machine learning models and generative frameworks enabling data-efficient capsid optimization despite limited datasets. Multimodal AI, reinforcement learning, and agentic systems can refine renal targeting by balancing glomerular penetration, cell specificity, and safety. Future progress relies on scaling high-quality datasets through collaborative consortia, lab-in-the-loop validation, and explainable AI. By combining capsid engineering with renal pathophysiology insights, this roadmap paves the way for curative AAV therapies that move beyond current suboptimal treatments to correct underlying pathogenic mechanisms.
Ghaffarzadeh-Esfahani et al. (Thu,) studied this question.