ReviewPharmaceutics2021
Machine Learning for Process Monitoring and Control of Hot-Melt Extrusion: Current State of the Art and Future Directions.
Review in Pharmaceutics, 2021. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 14 papers.
What it found
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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
14 citing papers in PubMed.
- Active learning in pharmaceutical 3D printing: a multi-dataset comparison.Drug delivery and translational research · 2026Article
- Control Strategy Considerations for the Continuous Manufacturing of Low-dose Oral Solid Dosage Formulations.The AAPS journal · 2026Review
- AI-driven pilot platforms and computational pharmaceutics: accelerating innovation in small molecule drug development under industry 4.0 and 5.0 paradigms.Frontiers in pharmacology · 2026Review
- Advanced QbD-Based Process Optimization of Clopidogrel Tablets with Insights into Industrial Manufacturing Design.Pharmaceutics · 2025Article
- Aspects and Implementation of Pharmaceutical Quality by Design from Conceptual Frameworks to Industrial Applications.Pharmaceutics · 2025Review
- Process Development for the Continuous Manufacturing of Carbamazepine-Nicotinamide Co-Crystals Utilizing Hot-Melt Extrusion Technology.Pharmaceutics · 2025Article
- Physics-Informed Neural Networks in Polymers: A Review.Polymers · 2025Review
- Towards Sustainable and Dynamic Modeling Analysis in Korean Pine Nuts: An Online Learning Approach with NIRS.Foods (Basel, Switzerland) · 2024Article
- Development of a Machine Learning Model to Predict the Color of Extruded Thermoplastic Resins.Polymers · 2024Article
- In-line NIR coupled with machine learning to predict mechanical properties and dissolution profile of PLA-Aspirin.Functional composite materials · 2024Article
- Interpretable Machine Learning Methods for Monitoring Polymer Degradation in Extrusion of Polylactic Acid.Polymers · 2023Article
- Data-Driven Prediction of the Formation of Co-Amorphous Systems.Pharmaceutics · 2023Article
- Novel 3D Printed Modular Tablets Containing Multiple Anti-Viral Drugs: a Case of High Precision Drop-on-Demand Drug Deposition.Pharmaceutical research · 2022Article
- NIR-Based Intelligent Sensing of Product Yield Stress for High-Value Bioresorbable Polymer Processing.Sensors (Basel, Switzerland) · 2022Article
Corrections and comments
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Authors and funding
4 authors.
Funding
Abstract
In the last few decades, hot-melt extrusion (HME) has emerged as a rapidly growing technology in the pharmaceutical industry, due to its various advantages over other fabrication routes for drug delivery systems. After the introduction of the 'quality by design' (QbD) approach by the Food and Drug Administration (FDA), many research studies have focused on implementing process analytical technology (PAT), including near-infrared (NIR), Raman, and UV-Vis, coupled with various machine learning algorithms, to monitor and control the HME process in real time. This review gives a comprehensive overview of the application of machine learning algorithms for HME processes, with a focus on pharmaceutical HME applications. The main current challenges in the application of machine learning algorithms for pharmaceutical processes are discussed, with potential future directions for the industry.
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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.