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This review aims to comprehensively discuss the emerging role of artificial intelligence (AI) and three-dimensional (3D) printing in the design and development of personalized polymeric nanocomposite-based drug delivery systems. The focus is on how integrating these technologies enhances the precision, efficacy, and customization of pharmacotherapy compared with conventional formulations.
An extensive literature survey was conducted using databases such as PubMed, Scopus, Web of Science, and ScienceDirect, focusing on publications from the past decade. Peer-reviewed studies, reviews, and reports on polymeric nanocomposites, AI-based formulation design, and 3D-printed drug delivery systems were critically analyzed. The collected data were synthesized to elucidate design principles, fabrication methods, and the synergistic application of AI and 3D printing in personalized medicine.
Polymeric nanocomposites have demonstrated superior performance in targeted and controlled drug release due to their adaptable physicochemical properties and biocompatibility. The application of AI enables predictive modeling, optimization of formulation parameters, and patient stratification through data-driven algorithms. Concurrently, 3D printing enables the fabrication of patient-specific dosage forms and implants with programmable drug-release profiles. Together, these technologies enable the development of individualized therapeutic systems that enhance treatment outcomes and minimize adverse effects.
This synergistic incorporation of AI and additive manufacturing tackles some of the main obstacles in precision medicine by diminishing trial-and-error in formulation, enhancing reproducibility, and promoting better outcomes during treatment. Such multidisciplinary applications are most promising in cancer, diabetes, neurodegenerative, infectious, and cardiovascular diseases.
The integration of AI and 3D printing represents a transformative advancement in personalized nanocomposite drug delivery. These interdisciplinary approaches collectively enable precise control over drug release kinetics, dosage customization, and formulation design. Future developments focusing on regulatory standardization, ethical data use, and large-scale clinical translation will further accelerate the adoption of AI- and 3D-printing-assisted personalized drug delivery systems in clinical practice. While these technologies hold great potential for personalized and precise therapeutics, their clinical translation remains challenged by regulatory validation, manufacturing reproducibility, and data transparency requirements.
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