ArticleInternational journal of pharmaceutics: X2025
Making
Article in International journal of pharmaceutics: X, 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 9 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
9 citing papers in PubMed.
- Artificial Intelligence in Photodynamic Therapy: From Molecular Design of Photosensitizers to Intelligent Delivery, Adaptive Dosimetry, and Personalized Prognostication of Results.International journal of molecular sciences · 2026Review
- Engineering Oral Nanoparticles: Navigating Biological Barriers in the Gastrointestinal Tract.Pharmaceutical research · 2026Review
- Delivering Degradation: Nanomedicine and Programmable Proximity Platforms for Targeted Protein Degradation.Pharmaceutics · 2026Review
- Nanoparticulate and Hydrogel Vehicles for Stimuli-Responsive and Sustained Controlled Release of Active Pharmaceutical Ingredients.Pharmaceuticals (Basel, Switzerland) · 2026Review
- Machine-Learning-Driven Optimization of Functional Excipients and Their Biointeractions in Drug Formulations.ACS pharmacology & translational science · 2026Review
- Artificial intelligence empowered biomaterials for cancer therapy: From rational design to clinical translation.Chinese journal of cancer research = Chung-kuo yen cheng yen chiu · 2026Article
- Advances in Drug Delivery Systems for Breast Cancer: From Microenvironment Barriers and Smart Carriers to Clinical Translation Strategies.Drug design, development and therapy · 2026Review
- In Vitro liposome release profile prediction using explainable machine learning approaches.PloS one · 2026Article
- MakingInternational journal of pharmaceutics: X · 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
9 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
Machine learning and artificial intelligence (AI) is transforming the way pharmaceutical products are developed across drug discovery, process engineering, and pharmaceutics functions. AI for nanomedicine development is enabling faster and more accurate prediction of critical quality attributes (CQAs). However, the full potential of AI is limited by the quality and accessibility of data. Unlike adjacent fields such as the chemical sciences, the pharmaceutics domain lacks curated, open-access databases, particularly for nanomedicines. To address this, here we curate an open-access local database focused on liposomal formulations. The database includes formulation parameters,
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.