ArticleAMIA ... Annual Symposium proceedings. AMIA Symposium2024
Machine Learning for Predicting Drug Release Behavior of PLGA Microspheres.
Article in AMIA ... Annual Symposium proceedings. AMIA Symposium, 2024. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 1 paper.
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
1 citing paper in PubMed.
- Predicting early and complete drug release from long-acting injectables using explainable machine learning.International journal of pharmaceutics · 2026Article
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
PLGA microspheres are widely used in long-acting drug formulations due to their ability to provide sustained release, improving patient adherence and reducing dosing frequency. However, drug release behavior is influenced by complex formulation and processing factors, making traditional trial-and-error development inefficient. This study leverages machine learning to predict drug release profiles from PLGA (poly(lactic-co-glycolic acid)) microsphere formulations. A dataset of 113 PLGA formulations containing small-molecule drugs and large-molecule peptides was collected from published literature. Multiple machine learning models were developed and compared. The best-performing model achieved an R
Indexed as
Identifiers
41726439PMC12919544What 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.