Authors
Asly Chua An Wen, Xiancheng Chen, Wenli Zhang
Published in
Pharmaceutical research. Volume 43. Issue 10. Pages 3469-3504. Epub Sep 28, 2026.
Abstract
Oral nanomedicine offers a promising strategy to enhance drug bioavailability, yet engineering strategies are most effectively evaluated not as isolated features but as sequential decision nodes, because a choice at one barrier mechanically constrains the strategy space at the next. This review presents a barrier-sequenced analytical framework that evaluates nanoparticle strategies at each stage by mechanistic causality, oral-specific evidence quality, and downstream design constraint. Within each barrier category, strategies were distinguish and validated by multiple oral in vivo studies from those supported by limited oral data or remaining at the proof-of-concept stage, and identified the physiological boundary conditions (fed versus fasted states, inflammatory disease, and chronic dosing) under which each principle holds. This review further maps how a formulation choice at one barrier mechanically constrains the strategy space at the next, revealing which multi-barrier combinations are compatible and which are antagonistic. Finally, This review assesses physiologically based pharmacokinetic modeling and machine learning against their current data prerequisites, framing them as hypothesis-generating aids with explicit infrastructure gaps rather than decision-grade design engines. By extracting generalizable design principles and their limits, this framework aims to guide mechanism-informed, patient-centered oral nanotherapies.
PMID:
42806247
Bibliographic data and abstract were imported from PubMed on 13 Nov 2026.
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