ArticleActa pharmaceutica Sinica. B2021
Integrated
Article in Acta pharmaceutica Sinica. B, 2021. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 19 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
19 citing papers in PubMed.
- Overcoming the oral delivery challenges of venetoclax: advancing a clinically improved Bcl-2 inhibitor.Drug delivery and translational research · 2026Review
- A Novel Microfluidic Strategy for the Fabrication of α-Lactalbumin Nanomicelles Paves a New Path for Antiobesity Technology.Advanced healthcare materials · 2026Article
- Machine learning empowered formulation design, optimization and characterization of nanoparticulate drug delivery systems: Current applications, challenges, and future perspectives.Acta pharmaceutica Sinica. B · 2026Review
- Structured engineering of self-emulsifying drug delivery systems (SEDDS)International journal of pharmaceutics: X · 2025Review
- Quality by Design and In Silico Approach in SNEDDS Development: A Comprehensive Formulation Framework.Pharmaceutics · 2025Review
- Computer-driven formulation development of Ginsenoside Rh2 ternary solid dispersion.Drug delivery and translational research · 2025Article
- Article
- Development and characterization of a self-nano emulsifying drug delivery system (SNEDDS) for Ornidazole to improve solubility and oral bioavailability of BCS class II drugs.Scientific reports · 2024Article
- The Dawn of a New Pharmaceutical Epoch: Can AI and Robotics Reshape Drug Formulation?Advanced healthcare materials · 2024Article
- Design and Evaluation of Ginkgolides Gastric Floating Controlled Release Tablets Based on Solid Supersaturated Self-nanoemulsifying.AAPS PharmSciTech · 2023Article
- A dataset of formulation compositions for self-emulsifying drug delivery systems.Scientific data · 2023Article
- FormulationAI: a novel web-based platform for drug formulation design driven by artificial intelligence.Briefings in bioinformatics · 2023Article
- Predicting liposome formulations by the integrated machine learning and molecular modeling approaches.Asian journal of pharmaceutical sciences · 2023Article
- Molecular Design of Interfaces of Model Food Nanoemulsions: A Combined Experimental and Theoretical Approach.Antioxidants (Basel, Switzerland) · 2023Article
- Application of Design of Experiments in the Development of Self-Microemulsifying Drug Delivery Systems.Pharmaceuticals (Basel, Switzerland) · 2023Review
- Highly Accessible Computational Prediction andOxidative medicine and cellular longevity · 2023Article
- Could artificial intelligence revolutionize the development of nanovectors for gene therapy and mRNA vaccines?Nano today · 2022Article
- Prediction of lipid nanoparticles for mRNA vaccines by the machine learning algorithm.Acta pharmaceutica Sinica. B · 2022Article
- Fundamental Aspects of Lipid-Based Excipients in Lipid-Based Product Development.Pharmaceutics · 2022Review
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
6 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
The drug formulation design of self-emulsifying drug delivery systems (SEDDS) often requires numerous experiments, which are time- and money-consuming. This research aimed to rationally design the SEDDS formulation by the integrated computational and experimental approaches. 4495 SEDDS formulation datasets were collected to predict the pseudo-ternary phase diagram by the machine learning methods. Random forest (RF) showed the best prediction performance with 91.3% for accuracy, 92.0% for sensitivity and 90.7% for specificity in 5-fold cross-validation. The pseudo-ternary phase diagrams of meloxicam SEDDS were experimentally developed to validate the RF prediction model and achieved an excellent prediction accuracy (89.51%). The central composite design (CCD) was used to screen the best ratio of oil-surfactant-cosurfactant. Finally, molecular dynamic (MD) simulation was used to investigate the molecular interaction between excipients and drugs, which revealed the diffusion behavior in water and the role of cosurfactants. In conclusion, this research combined machine learning, central composite design, molecular modeling and experimental approaches for rational SEDDS formulation design. The integrated computer methodology can decrease traditional drug formulation design works and bring new ideas for future drug formulation design.
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.