Rational design of anticancer multidrug nanosystems and their adaptation for glioblastoma treatment
摘要
Multidrug nanosystems emerged as an innovation in anticancer therapy, addressing key limitations of conventional mono- and combination therapies, such as poor tumor selectivity, systemic toxicity, low stability and drug resistance. Following the clinical approval of Vyxeos® in 2018, growing therapeutic interest and advances in nanomedicine have paved the way for a new wave of promising next-generation multidrug nanoparticle candidates. These nanosystems offer the unique ability to co-deliver multiple therapeutic agents, aligning pharmacokinetics, improving tumor targeting, and enabling controlled drug release. By incorporating small molecules, genetic material, peptides, and proteins, multidrug nanosystems can achieve potent anticancer effects that significantly enhance therapeutic outcomes. In glioblastoma context these can play a particularly important role, as treatment is limited by tumor cells resistance, as much as low blood–brain barrier penetration. Here, the design principles underlying anticancer multidrug nanosystems are explored, including concurrent and sequential drug delivery strategies, and highlighting recently proposed advances in drug loading, active targeting, and stimuli-responsive mechanisms. A special focus is placed on how these platforms have been designed to improve or bypass blood–brain barrier penetration, and overcome other glioblastoma resistance mechanism challenges. Besides their therapeutic potential, current challenges, including the need for rational therapeutic combination selection, ensuring biosafety, and balancing potency with cost-effectiveness for clinical translation, are discussed. By summarizing recent advances and addressing the remaining hurdles, this review underscores the transformative potential of multidrug nanosystems in cancer therapy, particularly for the hard-to-treat glioblastoma, and outlines the steps needed to accelerate their path to clinical application.
Graphical Abstract