ArticleThe AAPS journal2025
Machine Learning Predicts Drug Release Profiles and Kinetic Parameters Based on Tablets' Formulations.
Article in The AAPS journal, 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 12 papers.
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Who cites it
12 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
- Machine-Learning-Driven Molecular Design and Structure-Property-Performance Relationships in Pharmaceutical Chemistry.Molecules (Basel, Switzerland) · 2026Review
- Artificial intelligence-assisted design and optimization of stimuli-responsive nanocarriers for smart drug delivery.Materials today. Bio · 2026Review
- Co-Loaded PEGylated Nanoliposomes of Bendamustine and Rutin: Formulation, Release Kinetics, and a Hybrid Predictive Modeling Framework.Pharmaceutics · 2026Article
- Application of New Approach Methodologies to Improve Oral Biopharmaceutic Assessments.Pharmaceutics · 2026Review
- Predictive Neural Network Modeling of Nanoporous Anodic Alumina for Controlled Drug Release Implants: An Integrated Machine Learning Approach.Materials (Basel, Switzerland) · 2026Article
- Mechanism-aware and explainable artificial intelligence for drug release prediction in polymeric drug delivery systems.Frontiers in medicine · 2026Article
- In Vitro liposome release profile prediction using explainable machine learning approaches.PloS one · 2026Article
- Combined Experimental and Computational Approaches for Ternary Solid Dispersions to Enhance the Oral Bioavailability of Penfluridol.Pharmaceutics · 2025Article
- Multifunctional Nanomaterial-Integrated Hydrogels for Sustained Drug Delivery: From Synthesis and Characterization to Biomedical Application.Gels (Basel, Switzerland) · 2025Review
- Modeling Polymeric Drug Release: The Emerging Role of Machine Learning.Wiley interdisciplinary reviews. Nanomedicine and nanobiotechnologyReview
Corrections and comments
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Authors and funding
5 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
Direct compression (DC) remains a popular manufacturing technology for producing solid dosage forms. However, the formulation optimisation is a laborious process, costly and time-consuming. The aim of this study was to determine whether machine learning (ML) can be used to accelerate developments by predicting the drug release profiles under dynamic conditions given the composition of formulations. A total of 377 formulations were produced in-house and their release profile under dynamic dissolution conditions was measured from 0 to 480 min across 11 time points. A subsequent ML analysis involved predicting the entire release profile. Six different ML techniques were explored, where random forest (RF) and extreme gradient boosting (XGB) were found to achieve a fivefold cross-validation R
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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.