What question did this study set out to answer?

The research explores how 3D printing and AI can enhance the design of personalized solid oral dosage forms for individualized treatment.

January 18, 2026Open Access

3d Printing Technologies and Artificial Intelligence in Personalized Solid Oral Dosage Form Design: Current Progress and Future Perspectives

Puntos clave

The research explores how 3D printing and AI can enhance the design of personalized solid oral dosage forms for individualized treatment.
Examined various 3D printing technologies including fused deposition modeling and binder jetting.
Analyzed the role of AI in determining formulation compositions and optimizing printing parameters.
Investigated challenges in material standardization and regulatory frameworks affecting adoption.
Demonstrated enhanced flexibility in creating complex tablets with tailored release profiles.
Showed AI can predict drug-excipient compatibility and optimize printing conditions.
Identified significant barriers to widespread use, including standardization and regulatory issues.

Resumen

Three-dimensional (3D) printing is transforming oral drug delivery by enabling the precise fabrication of solid dosage forms with patient-specific doses, geometries, and release profiles, thus supporting the paradigm of personalized medicine. Unlike conventional batch manufacturing, additive manufacturing techniques such as fused deposition modeling, binder jetting, and allow rapid, on-demand production of complex tablets and polypills, including multi-layer and compartmentalized structures that can combine several active pharmaceutical ingredients (APIs) with distinct release kinetics in a single unit. This flexibility is particularly valuable for populations requiring fine dose titration or simplified regimens, such as paediatric, geriatric, and polymorbid patients, and for chronic or complex diseases like epilepsy, diabetes, and cardiovascular disorders Artificial intelligence (AI) significantly amplifies this potential by learning quantitative relationships between formulation composition, printing parameters, and in vitro performance, then using these models to automatically propose tablet architectures that meet predefined therapeutic or pharmacokinetic targets.AI-driven tools can predict excipient–drug compatibility, optimize printing conditions, and implement real-time monitoring and adaptive control, thereby reducing trial-and-error, enhancing process robustness, and enabling consistent quality in both centralized and point-of-care manufacturing settings. Despite these advantages, widespread adoption is constrained by challenges in standardizing materials and printers, ensuring long- term stability and reproducibility, and the absence of harmonized regulatory frameworks tailored to decentralized, small-batch production. Continuing integration of Quality by Design principles, digital quality control, and AI-based decision support is essential to translate AI-guided 3D printing of oral dosage forms from experimental platforms into routine, clinically integrated personalized therapy.

Leer artículo completoexternamente

Preguntar a la IA

Me gusta

Guardar

Ver artículo completo