In Vitro Predictive Model for Intestinal Lymphatic Uptake: Exploration of Additional Enhancers and Inhibitors
Bibliographic record
Abstract
Drug absorption via chylomicrons holds significant implications for both pharmacokinetics and pharmacodynamics. However, a mechanistic understanding of predicting in vivo intestinal lymphatic uptake remains largely unexplored. This study aimed to delve into the intestinal lymphatic uptake of drugs, investigating both enhancement and inhibition using various excipients through our previously established in vitro model. It also examined the applicability of the model by assessing the lymphatic uptake enhancement of a lymphotropic formulation with linoleoyl polyoxyl-6 glycerides using the same model. The model successfully differentiated among olive, sesame, and peanut oils in terms of lymphatic uptake. However, it did not distinguish between oils containing long-chain fatty acids and coconut oil. Coconut oil, known for its abundance of medium-chain fatty acids, outperformed other oils. This heightened uptake was attributed to the superior emulsification of this oil in artificial chylomicron media due to its high content of medium-chain fatty acids. Additionally, the enhanced uptake of the tested formulation with linoleoyl polyoxyl-6 glycerides underscored the practical applicability of this model in formulation optimization. Moreover, data suggested that increasing the zeta potential of Intralipid® using sodium lauryl sulfate (SLS) and decreasing it using (+/−) chloroquine led to enhanced and reduced uptake in the in vitro model, respectively. These findings indicate the potential influence of the zeta potential on intestinal lymphatic uptake in this model, though further research is needed to explore the possible translation of this mechanism in vivo.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 0.001 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".