ReviewThe AAPS journal2022
Application of Artificial Neural Networks in the Process Analytical Technology of Pharmaceutical Manufacturing-a Review.
Review in The AAPS journal, 2022. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 20 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.
Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.
Who cites it
20 citing papers in PubMed.
- Article
- Data-efficient prediction in tableting using word embeddings and empirically-guided neural networks.International journal of pharmaceutics: X · 2026Article
- Advanced Control of Continuous Pharmaceutical Manufacturing Processes: A Case Study on the Application of Artificial Neural Network for Predictive Control of a CDC Line.Computers & chemical engineering · 2026Article
- Applications of AI/ML in accelerating the development of pulmonary drug delivery system.Acta pharmaceutica Sinica. B · 2026Review
- Implementing QbD for Nano-Pharmaceuticals and Complex Formulations to Achieve Predictable and High-Quality Outcomes.AAPS PharmSciTech · 2026Review
- The Pharmaceutical Industry's Future: How Artificial Intelligence is Transforming Medicine.Advanced pharmaceutical bulletin · 2025Review
- Leveraging Artificial Intelligence for Advancements in Liquid Dosage Formulations in the Pharmaceutical Industry.Therapeutic innovation & regulatory science · 2025Review
- Artificial intelligence-aided endoscopic in-line particle size analysis during the pellet layering process.Journal of pharmaceutical analysis · 2025Article
- Statistical Optimization of Process Parameters and Purification of Uricase Using IsolateIndian journal of microbiology · 2025Article
- Article
- A Review on QbD-Driven Optimization of Lipid Nanoparticles for Oral Drug Delivery: From Framework to Formulation.International journal of nanomedicine · 2025Review
- Artificial Intelligence-Driven Innovations in Pharmaceutical Development and Drug Delivery Systems.Current topics in medicinal chemistry · 2025Review
- Advancing Pharmaceutical Science with Artificial Neural Networks: A Review on Optimizing Drug Delivery Systems Formulation.Current pharmaceutical design · 2025Review
- The Role of Artificial Intelligence and Machine Learning in Accelerating the Discovery and Development of Nanomedicine.Pharmaceutical research · 2024Article
- Iterative Regression of Corrective Baselines (IRCB): A New Model for Quantitative Spectroscopy.Journal of chemical information and modeling · 2024Article
- Exploring the Potential of Artificial Intelligence as a Facilitating Tool for Formulation Development in Fluidized Bed Processor: a Comprehensive Review.AAPS PharmSciTech · 2024Review
- Comparing the Performance of Raman and Near-Infrared Imaging in the Prediction of the In Vitro Dissolution Profile of Extended-Release Tablets Based on Artificial Neural Networks.Pharmaceuticals (Basel, Switzerland) · 2023Article
- Article
- Application of Machine-Learning Algorithms for Better Understanding the Properties of Liquisolid Systems Prepared with Three Mesoporous Silica Based Carriers.Pharmaceutics · 2023Article
- Data-Driven Prediction of the Formation of Co-Amorphous Systems.Pharmaceutics · 2023Article
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
Industry 4.0 has started to transform the manufacturing industries by embracing digitalization, automation, and big data, aiming for interconnected systems, autonomous decisions, and smart factories. Machine learning techniques, such as artificial neural networks (ANN), have emerged as potent tools to address the related computational tasks. These advancements have also reached the pharmaceutical industry, where the Process Analytical Technology (PAT) initiative has already paved the way for the real-time analysis of the processes and the science- and risk-based flexible production. This paper aims to assess the potential of ANNs within the PAT concept to aid the modernization of pharmaceutical manufacturing. The current state of ANNs is systematically reviewed for the most common manufacturing steps of solid pharmaceutical products, and possible research gaps and future directions are identified. In this way, this review could aid the further development of machine learning techniques for pharmaceutical production and eventually contribute to the implementation of intelligent manufacturing lines with automated quality assurance.
Indexed as
Identifiers
35697951What 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.