Future Perspectives-Personalized Nanomedicine and Next-Generation Clinical Trials in Breast Cancer

Authors

Sahana HD
Department of Biochemistry, The oxford Medical College, Hospital, Yadavanahalli, Bengaluru
Mutthuraj Dasegowda
Division of Molecular Biology, JSS Academy of Higher Education & Research, Mysuru, Karnataka, India
Kanthesh M Basalingappa
Division of Molecular Biology, JSS Academy of Higher Education & Research, Mysuru, Karnataka, India
Subhanshi Vishwas
Allied Science (Department of Chemistry), Invertis University, Bareilly, 243001, UP, India

Synopsis

The chapter highlights the role of personalized nanomedicine in overcoming the limitations of conventional breast cancer therapies by integrating nanocarriers (liposomes, dendrimers, polymeric nanoparticles, exosomes) with precision oncology. These systems enable targeted drug delivery, reduced toxicity, and improved therapeutic outcomes using molecular profiling, multi-omics, and tumor microenvironment insights. To translate these advances clinically, next-generation trial designs (adaptive, basket, umbrella trials), supported by AI, machine learning, digital twins, and real-world evidence, are emphasized as more flexible and efficient than traditional methods. The chapter also discusses preclinical models (organoids, xenografts), as well as regulatory and ethical considerations. Future directions involve combining nanomedicine with immunotherapy, gene editing, and wearable devices, requiring multidisciplinary collaboration to ensure safe and effective clinical translation. Personalized nanomedicine, supported by innovative trials, holds strong promise for shaping the future of precision oncology in breast cancer.

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Published

9 September 2025

How to Cite

Sahana HD, Dasegowda, M. ., Basalingappa, K. M. ., & Vishwas, S. . (2025). Future Perspectives-Personalized Nanomedicine and Next-Generation Clinical Trials in Breast Cancer. In R. . Pal, B. . Ghosh, M. . Koli, & P. . Dutta (Eds.), Next-Gen Nanomedicine for Breast Cancer: From Bench to Bedside and Beyond (pp. 243-273). Deep Science Publishing. https://doi.org/10.70593/978-93-7185-537-2_10