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Ultrasound-controlled therapeutics for bladder cancer: Mechanisms, recent advances, and future perspectives
Yean Leng Loke1, Xin Yun Lim2, Adilet Beishenaliev3
1Department of Biomedical Sciences, Jeffrey Cheah Sunway Medical School, Faculty of Medical and Life Sciences, Sunway University, Petaling Jaya, 47500, Malaysia.
Abstract:
Bladder cancer remains a significant clinical challenge, characterised by a high recurrence rate and substantial healthcare costs. Although advances in surgery, systemic therapy, and intravesical treatment have improved clinical outcomes, treatment-related toxicity, therapeutic resistance, and the persistently poor prognosis of advanced disease highlight the need for more effective therapeutic strategies. Ultrasound-based micro/nanotherapeutics offer a promising approach that combines deep tissue penetration with high focal precision in a non-invasive, externally controllable manner. This review summarises recent advances in ultrasound-mediated therapeutic strategies for bladder cancer, including ultrasound-mediated drug and gene delivery, sonodynamic therapy (SDT), and high-intensity focused ultrasound (HIFU) ablation. It describes how ultrasound, often in combination with microbubbles or nanobubbles, enhances the delivery of chemotherapeutic drugs and therapeutic genes through cavitation-induced permeabilisation of biological barriers. It then examines the mechanisms underlying SDT-induced reactive oxygen species generation and its integration with chemodynamic therapy, chemotherapy, immunotherapy, and imaging, alongside tumour microenvironment (TME) modulation strategies such as hypoxia relief and antioxidant depletion that augment SDT efficacy. Finally, it discusses HIFU ablation, including nanoparticle-assisted strategies that enhance tumour destruction through combined thermal and mechanical effects. The limitations of each modality and future directions for clinical translation, including standardisation of ultrasound devices and exposure parameters, optimisation of nanoplatform design, and integration of artificial intelligence-based predictive models, are also discussed.