Nanoparticulate Drug Delivery Strategies to Address Intestinal Cytochrome P450 CYP3A4 Metabolism towards Personalized Medicine
Bibliographic record
Abstract
Drug dosing in clinical practice, which determines optimal efficacy, toxicity or ineffectiveness, is critical to patients’ outcomes. However, many orally administered therapeutic drugs are susceptible to biotransformation by a group of important oxidative enzymes, known as cytochrome P450s (CYPs). In particular, CYP3A4 is a low specificity isoenzyme of the CYPs family, which contributes to the metabolism of approximately 50% of all marketed drugs. Induction or inhibition of CYP3A4 activity results in the varied oral bioavailability and unwanted drug-drug, drug-food, and drug-herb interactions. This review explores the need for addressing intestinal CYP3A4 metabolism and investigates the opportunities to incorporate lipid-based oral drug delivery to enable precise dosing. A variety of lipid- and lipid-polymer hybrid-nanoparticles are highlighted to improve drug bioavailability. These drug carriers are designed to target different intestinal regions, including (1) local saturation or inhibition of CYP3A4 activity at duodenum and proximal jejunum; (2) CYP3A4 bypass via lymphatic absorption; (3) pH-responsive drug release or vitamin-B12 targeted cellular uptake in the distal intestine. Exploitation of lipidic nanosystems not only revives drugs removed from clinical practice due to serious drug-drug interactions, but also provide alternative approaches to reduce pharmacokinetic variability.
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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 | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".