Amir Saamaan Fattahi, Elahe Haghighi
Bispecific antibodies (bsAbs) utilize dual targeting to direct immune effectors toward tumor antigens, modify the tumor microenvironment (TME), and simultaneously engage checkpoint and co-stimulatory pathways. Clinical success in CD19-, CD20-, and B-cell maturation antigen (BCMA)-targeted hematologic malignancies highlights their potential; however, applying these therapies to solid tumors faces obstacles such as heterogeneous antigen expression, limited tissue penetration, and safety issues. Next-generation dual-targeting platforms, including Fc-engineered constructs, conditionally activated formats, and bsAb-drug conjugates, seek to improve specificity, efficacy, and tolerability. Still, challenges such as immunogenicity, cytokine release syndrome (CRS), antigen escape, and manufacturing complexity persist. This review integrates mechanistic and translational advances from molecular design and biomarker optimization to adaptive clinical frameworks. It emphasizes the emerging role of artificial intelligence (AI) in the rational design, structural prediction, and enhancement of dual-targeting therapeutics. Collectively, these advancements support modular, programmable strategies that could accelerate clinical translation and advance precision oncology toward more adaptable, intelligent immunotherapy approaches.