Mechanics of small intestine motility for oral macromolecular delivery: modelling segmentation versus peristalsis.
Segmentation may enhance the oral delivery of macromolecules more effectively than peristalsis, according to computational models.
Where it sits
this study against the rest of the octreotide corpusSummary and findings
The study used computational fluid dynamics and machine learning to assess how intestinal motility affects the delivery of insulin and sodium caprate. Segmentation was found to enhance epithelial colocalisation more than peristalsis. Smaller pocket sizes and stronger contractility were optimal for delivery.
Abstract
Intestinal motility, including peristalsis and segmentation, drives complex fluid movements critical for the oral delivery of biologics and other macromolecules. Despite advances, oral delivery remains commercially limited by low bioavailability, often attributed to poor epithelial permeability. However, variability in motility patterns may also play a critical role, influencing intraluminal distribution and thus absorption, yet this aspect remains underexplored. Here, we combine computational fluid dynamics and machine learning to evaluate how motility type, intensity, pocket size, contractility, and fluid composition affect the delivery of a model macromolecule (insulin) and a permeation enhancer (sodium caprate, C10). We find that segmentation, especially at light intensity, consistently enhances epithelial colocalisation over peristalsis. Under segmentation, smaller pocket sizes (2 mL versus 10 mL) and stronger contractility (occlusion ratio 0.3) yielded optimal performance. Our extreme gradient boosting regression model identified pocket volume, contractility, and motility type as dominant predictors of colocalisation. In a comparative analysis, segmentation led to 128% and 137% higher maximum normalised concentrations of insulin and C10, respectively, than moderate peristalsis with a nutritional drink. Overall, segmentation achieved 6.7-fold and 8.0-fold higher average maximum normalised concentrations for insulin and C10, respectively. These results emphasise segmentation, characteristic of the fed state, as a superior motility pattern for macromolecular absorption compared to peristalsis during the migrating motor complex (MMC). By elucidating the interplay between motility and transport, our findings may guide the design of more effective oral formulations and support personalised strategies for drug delivery based on individual motility profiles.
Background
The study addresses the challenge of oral delivery of macromolecules, which is limited by low bioavailability due to poor epithelial permeability. While advances have been made, variability in intestinal motility patterns may also significantly impact absorption. Understanding this variability is crucial for improving oral delivery systems for biologics.
Methods
The researchers used computational fluid dynamics and machine learning to model intestinal motility's effect on the delivery of insulin and sodium caprate. The study evaluated different motility types, intensities, pocket sizes, contractility, and fluid compositions. The primary outcome was the colocalisation of the macromolecule and permeation enhancer with the epithelial surface.
Results
Segmentation, especially at light intensity, enhanced epithelial colocalisation more effectively than peristalsis. Smaller pocket sizes of 2 mL and stronger contractility with an occlusion ratio of 0.3 were optimal. Segmentation resulted in 128% and 137% higher maximum normalised concentrations of insulin and C10, respectively, compared to moderate peristalsis. The study identified pocket volume, contractility, and motility type as key predictors of colocalisation.
Interpretation
The findings suggest that segmentation, a motility pattern characteristic of the fed state, is superior to peristalsis for macromolecular absorption. This study provides a mechanistic understanding that could inform the design of more effective oral formulations. However, the reliance on computational models limits the direct applicability of these findings to clinical settings.
Key findings
- Segmentation led to 128% higher maximum normalised concentrations of insulin than moderate peristalsis.
- Segmentation led to 137% higher maximum normalised concentrations of C10 than moderate peristalsis.
- Segmentation achieved 6.7-fold higher average maximum normalised concentrations for insulin.
- Segmentation achieved 8.0-fold higher average maximum normalised concentrations for C10.
- Pocket volume, contractility, and motility type were dominant predictors of colocalisation.
Limitations
- Computational model, not in vivo data
- No human or animal validation
- Potential oversimplification of biological processes