ArticleScientific data2025
A formulation dataset of poly(lactide-co-glycolide) nanoparticles for small molecule delivery.
Article in Scientific data, 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 6 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.
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Who cites it
6 citing papers in PubMed.
- A Machine Learning Platform for Imputing Missing Critical Quality Attributes in Pharmaceutical Development.The AAPS journal · 2026Article
- MC-NODE: A Mechanism-Decomposed Neural Differential Model for PLGA Microsphere Drug Release Prediction and Attribution.Pharmaceuticals (Basel, Switzerland) · 2026Article
- Tumor-Targeted Delivery Therapy Based on PLGA Nanoparticles.Journal of functional biomaterials · 2026Review
- Mechanism-aware and explainable artificial intelligence for drug release prediction in polymeric drug delivery systems.Frontiers in medicine · 2026Article
- A formulation dataset of poly(lactide-co-glycolide) nanoparticles for small molecule delivery.Scientific data · 2025Article
- Modeling Polymeric Drug Release: The Emerging Role of Machine Learning.Wiley interdisciplinary reviews. Nanomedicine and nanobiotechnologyReview
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
Abstract
Poly(lactide-co-glycolide) (PLGA) nanoparticles are promising drug delivery systems, widely recognized for their ability to overcome various limitations associated with conventional formulations. However, designing and optimizing such formulations is a complex and non-trivial process that heavily relies on a lengthy, iterative approach, often involving trial and error. To address the limitations of traditional approaches, formulation scientists are increasingly incorporating artificial intelligence, particularly machine learning, to rationalize and accelerate the process. Despite decades of intensive research into PLGA nanoparticles, a notable shortage remains in the availability of comprehensive open-source datasets essential for driving this accelerated development process forward. Here, we present a literature-curated dataset of 433 PLGA nanoparticle formulations encompassing 65 small molecules. The dataset aims to bridge existing data gaps and provide a comprehensive resource for research on nanoparticle formulations.
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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.