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

  • Review Article
  • Applications of Machine Learning and A.I. in Pharmaceutical Development and Technology
  • Published:
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Abstract

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.

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Data Availability

The data supporting this study's findings are available from the corresponding author upon reasonable request.

Abbreviations

FBP:

Fluid bed processing

PATs:

Process analytical technologies

AI:

Artificial intelligence

FBG:

Fluid bed granulation

ANNs:

Artificial neural networks

QbD:

Quality by design

NLP:

Natural language processing

RPA:

Robotic process automation

ML:

Machine learning

DL:

Deep learning

APIs:

Active pharmaceutical ingredients

SVMs:

Support vector machines

GAs:

Genetic Algorithms

MLP:

Multilayer Perceptron

GRNN:

Generalized Regression Neural Network

MNN:

Modular Neural Network

RBFNN:

Radial Basis Function Neural Network

SOM:

Self-organizing maps

OCT:

Optical coherence tomography

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Acknowledgements

The authors thank Dr. D. Y. Patil Institute of Pharmaceutical Sciences and Research, Pimpri, Pune, India, for their ongoing assistance.

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Authors and Affiliations

  1. Department of Pharmaceutics, Dr. D. Y. Patil Institute of Pharmaceutical Sciences and Research, Pimpri, Pune, India

    Aachal A. Gosavi & Rakesh K. Mishra

  2. Department of Pharmaceutics, JSPM University’s School of Pharmaceutical Sciences, Wagholi, Pune, India

    Tanaji D. Nandgude

  3. Department of Mechanical Engineering, Birla Institute of Technology and Science-Pilani, K K Birla Goa Campus, Zuarinagar, Sancoale, Goa, India

    Dhiraj B. Puri

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  1. Aachal A. Gosavi
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Gosavi, A.A., Nandgude, T.D., Mishra, R.K. et al. Exploring the Potential of Artificial Intelligence as a Facilitating Tool for Formulation Development in Fluidized Bed Processor: a Comprehensive Review. AAPS PharmSciTech 25, 111 (2024). https://doi.org/10.1208/s12249-024-02816-8

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