Exploring the Potential of Artificial Intelligence as a Facilitating Tool for Formulation Development in Fluidized Bed Processor: a Comprehensive Review

This in-depth study looks into how artificial intelligence (AI) could be used to make formulation development easier in fluidized bed processes (FBP). FBP is complex and involves numerous variables, making optimization challenging. Various AI techniques have addressed this challenge, including machi...

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Bibliographic Details
Published in:AAPS PharmSciTech 2024-05, Vol.25 (5), p.111, Article 111
Main Authors: Gosavi, Aachal A., Nandgude, Tanaji D., Mishra, Rakesh K., Puri, Dhiraj B.
Format: Article
Language:English
Subjects:
Algorithms
Applications of Machine Learning and A.I. in Pharmaceutical Development and Technology
Artificial Intelligence
Biochemistry
Biomedical and Life Sciences
Biomedicine
Biotechnology
Chemistry, Pharmaceutical - methods
Drug Compounding - methods
Fuzzy Logic
Machine Learning
Neural Networks, Computer
Pharmacology/Toxicology
Pharmacy
Review Article
Technology, Pharmaceutical - methods
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Summary:This in-depth study looks into how artificial intelligence (AI) could be used to make formulation development easier in fluidized bed processes (FBP). FBP is complex and involves numerous variables, making optimization challenging. Various AI techniques have addressed this challenge, including machine learning, neural networks, genetic algorithms, and fuzzy logic. By integrating AI with experimental design, process modeling, and optimization strategies, intelligent systems for FBP can be developed. The advantages of AI in this context include improved process understanding, reduced time and cost, enhanced product quality, and robust formulation optimization. However, data availability, model interpretability, and regulatory compliance challenges must be addressed. Case studies demonstrate successful applications of AI in decision-making, process outcome prediction, and scale-up. AI can improve efficiency, quality, and cost-effectiveness in significant ways. Still, it is important to think carefully about data quality, how easy it is to understand, and how to follow the rules. Future research should focus on fully harnessing the potential of AI to advance formulation development in FBP.
ISSN:1530-9932
1530-9932
DOI:10.1208/s12249-024-02816-8