ArticleMolecular diversity2025
Leveraging machine learning to predict drug permeation: impact of menthol and limonene as enhancers.
Article in Molecular diversity, 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 5 papers.
What it found
Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.
The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.
The trial behind it
Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.
Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.
Who cites it
5 citing papers in PubMed.
- Usefulness of Low-Resistance Electrodes Used in Iontophoresis to Enhance Skin Permeability of Drugs.Molecules (Basel, Switzerland) · 2026Article
- Ligustrazine attenuates high-fat diet-induced neurobehavioral deficits and hepatic dysfunction via modulation of the HO-1/Nrf-2/SIRT-1/PPAR-α axis.Naunyn-Schmiedeberg's archives of pharmacology · 2026Article
- New and Investigational Treatment Options for Dermatomycosis in the Era of Antifungal Resistance.Journal of fungi (Basel, Switzerland) · 2026Review
- Research progress of penetration enhancers in transdermal drug delivery systems: Multidimensional exploration from mechanisms to clinical application.International journal of pharmaceutics: X · 2025Review
- Incorporation of a rigid 1,3-diketone-containing fragment led to significantly improved AXL inhibitory activity: design, synthesis, and SAR of the anilinopyrimidine AXL inhibitors.Molecular diversity · 2025Article
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
7 authors.
Funding
Abstract
The present study aimed to develop robust machine learning (ML) models to predict the skin permeability of poorly water-soluble drugs in the presence of menthol and limonene as penetration enhancers (PEs). The ML models were also applied in virtual screening (VS) to identify hydrophobic drugs that exhibited better skin permeability in the presence of permeation enhancers i.e. menthol and limonene. The drugs identified through ML-based VS underwent experimental validation using in vitro skin penetration studies. The developed model predicted 80% probability of permeability enhancement for Sumatriptan Succinate (SS), Voriconazole (VCZ), and Pantoprazole Sodium (PS) with menthol and limonene. The in vitro release studies revealed that menthol increased penetration by approximately 2.49-fold, 2.25-fold, and 4.96-fold for SS, VCZ, and PS, respectively, while limonene enhanced permeability by approximately 1.32-fold, 2.27-fold, and 3.7-fold for SS, VCZ, and PS. The results from in silico and in vitro studies were positively correlated, indicating that the developed ML models could effectively reduce the need for extensive in vitro and in vivo experimentation.
Indexed as
Identifiers
39680273What OpenQuestion holds
Registered trials
Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.