Concentration and composition dependent aggregation of Pluronic- and Poly-(2-oxazolin)-Efavirenz formulations in biorelevant media
Bibliographic record
Abstract
Many drugs and drug candidates are poorly water-soluble. Intestinal fluids play an important role in their solubilization. However, the interactions of intestinal fluids with polymer excipients, drugs and their formulations are not fully understood. Here, diffusion ordered spectroscopy (DOSY) and nuclear Overhauser effect spectroscopy (NOESY), complemented by cryo-TEM were employed to address this. Efavirenz (EFV) as model drug, the triblock copolymers Pluronic® F-127 (PF127) and poly(2-oxazoline) based pMeOx-b-pPrOzi-b-pMeOx (pOx/pOzi) and their respective formulations were studied in simulated fed-state intestinal fluid (FeSSIF). For the individual polymers, the bile interfering nature of PF127 was confirmed and pure pOx/pOzi was newly classified as non-interfering. A different and more complex behaviour was however observed if EFV was involved. PF127/EFV formulations in FeSSIF showed concentration dependent aggregation with separate colloids at low formulation concentrations, a merging of individual particles at the solubility limit of EFV in FeSSIF and joint aggregates above this concentration. In the case of pOx/pOzi/EFV formulations, coincident diffusion coefficients for pOx/pOzi, lipids and EFV indicate joint aggregates across the studied concentration range. This demonstrates that separate evaluation of polymers and drugs in biorelevant media is not sufficient and their mixtures need to be studied to learn about concentration and composition dependent behaviour.
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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) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 0.000 |
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".