Design and Optimization of a Nanoparticulate Pore Former as a Multifunctional Coating Excipient for pH Transition-Independent Controlled Release of Weakly Basic Drugs for Oral Drug Delivery
Bibliographic record
Abstract
Bioavailability of weakly basic drugs may be disrupted by dramatic pH changes or unexpected pH alterations in the gastrointestinal tract. Conventional organic acids or enteric coating polymers cannot address this problem adequately because they leach out or dissolve prematurely, especially during controlled release applications. Thus, a non-leachable, multifunctional terpolymer nanoparticle (TPN) made of cross-linked poly(methacrylic acid) (PMAA)-polysorbate 80-grafted-starch (PMAA-PS 80-g-St) was proposed to provide pH transition-independent release of a weakly basic drug, verapamil HCl (VER), by a rationally designed bilayer-coated controlled release bead formulation. The pH-responsive PMAA and cross-linker content in the TPN was first optimized to achieve the largest possible increase in medium uptake alongside the smallest decrease in drug release rate at pH 6.8, relative to pH 1.2. Such TPNs maintained an acidic microenvironmental pH (pHm) when loaded in ethylcellulose (EC) films, as measured using pH-indicating dyes. Further studies of formulations revealed that with the 1:2 VER:TPN ratio and 19% coating weight gain, bilayer-coated beads maintained a constant release rate over the pH transition and exhibited extended release up to 18 h. These results demonstrated that the multifunctional TPN as a pHm modifier and pH-dependent pore former could overcome the severe pH-dependent solubility of weakly basic drugs.
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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".