ReviewPharmaceutical research2026
Engineering Oral Nanoparticles: Navigating Biological Barriers in the Gastrointestinal Tract.
Review in Pharmaceutical research, 2026. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Not yet cited in PubMed.
What it found
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The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.
The trial behind it
Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.
Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.
Who cites it
0 citing papers in PubMed.
No citing paper in PubMed yet.
Corrections and comments
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Authors and funding
3 authors.
Funding
No grant is acknowledged in the PubMed record.
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.
Indexed as
Identifiers
42806247What OpenQuestion holds
Registered trials
Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.