Zeolitic imidazolate frameworks for lung cancer diagnosis and therapy: design principles and biomedical applications

Lung cancer remains one of the most prevalent and lethal malignancies worldwide, with its clinical management being hindered by high invasiveness, early metastasis, therapeutic resistance, and the severe adverse effects associated with conventional treatment modalities. Nanoparticle-based formulations have emerged as promising platforms for lung cancer therapy owing to their ability to enhance drug bioavailability and enable targeted, sustained, and controlled drug delivery. Despite these advantages, their broader clinical applications are often limited by insufficient stability, rapid systemic clearance, and suboptimal targeting efficiency in complex physiological environments. In this context, zeolitic imidazolate frameworks (ZIFs), a distinctive subclass of metal-organic frameworks, have attracted considerable attention because of their high surface area, tunable pore structures, excellent loading capacity, biocompatibility, and stimulus-responsive degradation behaviour. These unique features enable the efficient encapsulation of therapeutic and diagnostic agents and facilitate their controlled release within the tumor microenvironment, thereby improving treatment specificity and reducing off-target effects. This review discusses the fundamental design principles, synthesis strategies, structural characteristics, and functionalization approaches of ZIF-based materials, with particular emphasis on their emerging applications in lung cancer diagnosis and therapy. Recent advances in biosensing, bioimaging, drug delivery, chemotherapy, phototherapy, immunotherapy, and multifunctional therapeutic platforms are critically examined, together with the interactions of ZIF-based systems with the tumor microenvironment. The review further highlights the growing role of artificial intelligence in the screening, design, and optimization of ZIF materials and discusses the major challenges associated with their clinical translation. Collectively, these advances underscore the considerable potential of ZIF-based platforms in developing more effective and safer strategies for lung cancer diagnosis and therapy.