Silicone elastomer formulations for improved performance of a multipurpose vaginal ring releasing dapivirine and levonorgestrel
Bibliographic record
Abstract
A dapivirine-releasing silicone elastomer vaginal ring for reducing women's risk of HIV acquisition has recently been approved. A next-generation multipurpose vaginal ring releasing dapivirine and levonorgestrel is currently in development, offering hormonal contraception and HIV prevention from a single device. Previously, we reported challenges with incorporating levonorgestrel into rings manufactured from addition-cure silicone elastomers due to an irreversible chemical reaction between the levonorgestrel molecule and the hydride-functionalised crosslinker component of the silicone elastomer formulation, leading to low drug content assay, cure inhibition, and reduced ring mechanical properties (which may account for the increased incidence of ring expulsion in vivo). Here, we report on the development and testing of various custom silicone elastomer materials specifically formulated to circumvent these issues. After extensive testing of the custom silicones and subsequent manufacture and testing (Shore M hardness, pot life, content assay, oscillatory rheology, mechanical testing) of rings containing both dapivirine and levonorgestrel, a lead candidate formulation was selected that was amenable to practical ring manufacture via injection molding, exhibited no substantial levonorgestrel binding, and offered suitable mechanical properties.
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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) | 0.001 | 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".