ArticleScientific reports2025
RSM and AI based machine learning for quality by design development of rivaroxaban push-pull osmotic tablets and its PBPK modeling.
Article in Scientific reports, 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.
- Advances in Machine Learning-Enhanced PBPK Models for Brain-Targeted Drug Delivery via Nanocarriers: A Comprehensive Review.Journal of functional biomaterials · 2026Review
- A multi-stage coupling analysis and design optimization method for quality characteristics.Scientific reports · 2026Article
- Integration of Machine Learning With PBPK and QSAR Modeling Approaches to Facilitate Drug Discovery and Development.CPT: pharmacometrics & systems pharmacology · 2026Review
- Opportunities for machine learning and artificial intelligence in physiologically-based pharmacokinetic (PBPK) modeling.Advanced drug delivery reviews · 2025Review
- A hybrid innovation method based on quality by design and agile scrum paradigms for the development of medicinal products.Scientific reports · 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
4 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
The study is based on applying Artificial Neural Network (ANN) based machine learning and Response Surface Methodology (RSM) as simultaneous bivariate approaches in developing controlled-release rivaroxaban (RVX) osmotic tablets. The influence of different types of polyethylene oxide, osmotic agents, coating membrane thickness, and orifice diameter on RVX release profiles was investigated. After obtaining the trial formulation data sets from Central Composite Design (CCD), an ANN-based model was trained to get the optimized formulations. The Physiological-based Pharmacokinetic (PBPK) modeling of the predicted formulation was performed by GastroPlus™ to simulate in vivo plasma profiles under fasting and fed conditions. In vitro release tests showed zero-order RVX release for up to 12 h. Using graphical and numerical methods, the predicted formulation generated by the prediction profiler was cross-validated by the CCD-based optimized formulation. Analysis of Variance (ANOVA) findings revealed no significant difference between the predicted and optimized formulations and these formulations have a shelf life of 22.47 and 17.87 months, respectively. The PBPK modeling of RVX push-pull osmotic pump (PPOP) tablets suggested enhanced bioavailability in the fasted (up to 82%) and fed (up to 98.5%) state compared to immediate-release tablets. The results indicated that ANN can be effectively used for osmotic systems due to their complex nature and nonlinear interactions between dependent and independent variables.
Indexed as
Identifiers
What 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.