Artificial intelligence-driven intelligent nanocarriers for cancer theranostics: A paradigm shift with focus on brain tumors.
Authors
Affiliations (5)
Affiliations (5)
- Protein Research Center, Shahid Beheshti University, Tehran, Iran.
- Department of Polymer, School of Chemical Engineering, College of Engineering, University of Tehran, Tehran, Iran.
- Department of Physics, Faculty of Sciences, University of Zabol, Zabol, Iran. Electronic address: [email protected].
- Natural Products and Medicinal Plants Research Center, North Khorasan University of Medical Sciences, Bojnurd, Iran; Department of Medical Nanotechnology, School of Medicine, North Khorasan University of Medical Science, Bojnurd, Iran; Food and Drug Research Center, Food and Drug Administration, Ministry of Health and Medical Education, Tehran, Iran.
- Faculty of Applied Sciences and Biotechnology, Shoolini University, Solan, Himachal Pradesh, India; Department of Medical Research, China Medical University Hospital, China Medical University, Taichung, Taiwan. Electronic address: [email protected].
Abstract
Artificial intelligence (AI) and nanotechnology are revolutionizing brain cancer theranostics by enhancing drug delivery and diagnostic accuracy. This review examines AI-enhanced engineering strategies for developing intelligent nanocarriers that target glioblastoma and other metastatic central nervous system malignancies. AI encompasses several computational methods, including machine learning (ML) and its subset deep learning (DL). Here, ML algorithms learn design rules for nanocarriers, and DL networks intricate pattern recognition for tumor segmentation and adaptive release. These approaches enable stimuli-responsive nanocarriers to react to tumor microenvironmental signals (eg, pH, enzyme activity) and external stimuli (eg, ultrasound), optimizing targeted medication release while minimizing off-target effects. Magnetic resonance imaging (MRI) and positron emission tomography (PET), in conjunction with AI, enhance tumor detection and segmentation, while the integration of multiomics data facilitates tailored treatment planning. Advanced technologies encompass transferrin-functionalized nanoparticles for traversing the blood-brain barrier (BBB) and dual-stimuli-responsive drug delivery systems. Notwithstanding general progress, apprehensions surrounding batch variability and industrial scalability persist. This review also addresses ethical concerns and cost disparities associated with AI-based therapeutics. The primary development target areas are federated learning for data privacy, explainable artificial intelligence (XAI) for regulatory transparency, and quantum ML for molecular-scale optimization. This paper charts the course to patient-specific, scalable neuro-oncology nanomedicine through the convergence of computational modeling, intelligent materials, and advanced imaging modalities. These themes are explored in greater detail in the introduction, where we lay the groundwork for intelligent nanocarriers, their design with the help of AI, and the clinical need for diagnostics-therapeutics convergence in brain cancer.