ArticleInternational journal of pharmaceutics2026
Predicting early and complete drug release from long-acting injectables using explainable machine learning.
Article in International journal of pharmaceutics, 2026. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Not yet cited in PubMed.
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
0 citing papers in PubMed.
No citing paper in PubMed yet.
Corrections and comments
- Update of
Authors and funding
2 authors.
Funding
Abstract
Polymer-based long-acting injectables (LAIs) have transformed the treatment of chronic diseases by enabling controlled drug delivery, thus reducing dosing frequency and extending therapeutic duration. Achieving controlled drug release from LAIs requires extensive optimization of the complex underlying physicochemical properties. Machine learning (ML) can accelerate LAI development by modeling the complex relationships between LAI properties and drug release. However, recent ML studies have provided limited information on key properties that modulate drug release, as existing approaches rely on time as a primary input feature, obscuring the independent contributions of material characteristics to release dynamics. This paper presents a novel data transformation and explainable ML approach to synthesize actionable information from 321 LAI formulations by predicting early drug release at 24, 48, and 72 h, classifying release profile types, and predicting complete release profiles. These three experiments investigate the influence of LAI material characteristics in early and complete drug release profiles. A moderate correlation (0.37) is observed between the true and predicted drug release at 72 h, while an F1-score of 0.72 is obtained in classifying the types of drug release profiles. For the first time, we demonstrate that time-independent ML frameworks achieve equivalent performance to time-dependent approaches in predicting complete drug release profiles, including complex delayed biphasic and triphasic curves. Shapley additive explanations reveal the relative influence of material characteristics during early time points, between drug release profile classes and for complete release, which fill several gaps in previous in-vitro and ML-based studies. The novel approach and findings can provide a quantitative strategy and recommendations for scientists to optimize the drug-release dynamics of LAI. The source code for the model implementation is publicly available in
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.