Compounded Topical Gabapentin for Neuropathic Pain: Does Choice of Base Affect Efficacy?
Bibliographic record
Abstract
The objective of this study was to investigate the effect of Lipoderm Cream, VersaBase Gel, and Emollient Cream on the release and permeation of gabapentin formulated for neuropathic pain. Gabapentin of different strengths (1%, 5%, and 10%) was compounded with the bases, diffusion of the drug from thebases, and permeation through artificial skin model studied with Franz diffusionsystem. Steady-state flux, cumulative permeation, and lag times were calculated,and release mechanisms modelled with first order, second-order, Higuchi, Korsmeyer-Peppas, and Hixon-Crowell kinetic models. Gabapentin recovery from VersaBase Gel, Lipoderm Cream, and Emollient Cream was 100.8 ± 2.7%, 101.3 ± 1.2%, and 104.9 ± 3.3%, respectively. Gabapentin completely diffused out of the three bases within 6 hours of application according to the Higuchi model. Flux of the drug appeared to be concentration-dependent with no permeation occurring at 1% strength. Whereas, 5% and 10% strengths in Lipoderm Cream permeated the skin rapidly, the same concentrations in Emollient Cream and VersaBase Gel required 60-minutes and 120-minutes lag times, respectively. For the three bases, a strong correlation was observed between lag times and flux. The overall permeation in VersaBase Gel and Lipoderm Base was not significantly different (P>0.05). However, Emollient Cream resulted in a significantly lower total permeation compared to other bases (P<0.05). As the formulations are for pain management, products with no lag times and higher flux are preferable. Although VersaBase Gel and Emollient Cream displayed some gabapentin permeability, it is important to consider gabapentin stability in these bases prior to use.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.002 |
| 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 teacher head, 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".