Formulation and Physiological and Biopharmaceutical Issues in the Development of Oral Lipid-Based Drug Delivery Systems
Bibliographic record
Abstract
The rapidly increasing availability of drug receptor structural characteristics has permitted the receptor-guided synthesis of potential new drug molecules. This synthesis strategy frequently results in the creation of polycyclic and highly hydrophobic compounds, with attendant poor oral bioavailability resulting from low solubility and slow dissolution rate in the primarily aqueous contents of the gastrointestinal (GI) tract. In an attempt to improve the solubility-limited bioavailabiliy associated with these compounds, formulators have turned to the use of lipid excipients in which the compounds can be solubilized prior to oral administration. This new class of excipients presents the pharmaceutical scientist with a number of new challenges at all stages of the formulation development process, beginning with the excipient selection and stability assessment of the prototype formulation, up to and including scale-up and mass production of the final market-image product. The interaction of lipid-based formulations with the gastrointestinal system and associated digestive processes presents additional challenges and opportunities that will be understood more fully as we begin to unravel the intricacies of the GI processing of lipid excipients. For example, an increasing body of evidence has shown that certain lipids are capable of inhibiting both presystemic drug metabolism and drug efflux by the gut wall mediated by p-glycoprotein (PGP). And, it is well known that lipids are capable of enhancing lymphatic transport of hydrophobic drugs, thereby reducing drug clearance resulting from hepatic first-pass metabolism. This review addresses the current state of knowledge regarding oral lipid-based formulation development and scale-up issues and the physiological and biopharmaceutical aspects pertinent to the development of an orally bioavailable and efficacious dosage form.
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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 | 0.002 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.002 | 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".