Data-driven approaches, ML/AI and model uncertainty to better process understanding | AIChE

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Data-driven approaches, ML/AI and model uncertainty to better process understanding

Chair(s)

Co-chair(s)

Ferguson, S., University College Dublin

This session features works related to data-driven approaches to pharmaceutical and biopharmaceutical processes characterization and optimization. Computational approaches include DoE, AI/ML and Bayesian optimization. Uncertainty quantification in computational modeling in pharmaceutical industry is considered.

Presentations

3:30 PM
(628a) Application of Experimental Design Via Bayesian Optimization (EDBO) to Pharmaceutical Process Characterization and Control Strategy Design
3:55 PM
(628b) Leveraging Small Data for Enhanced Process Development
4:20 PM
(628c) Development of Experimentally Validated Machine Learning (ML) Based Model to Predict the Thawing Time of Biologics during Large Scale Freeze-Thawing Cycles
4:45 PM
(628d) Predictive Modeling for Glycan Method Analysis and Species Prediction
5:10 PM
(628e) Optimal Reaction Pathway Design Using Computer-Aided Process Chemistry
5:35 PM
(628g) Verification, Validation, and Uncertainty Quantification for Computational Modeling and Simulation in the Pharmaceutical Industry

Topics 

Pharmaceutical Development

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Individuals

AIChE Pro Members $150.00
AIChE Emeritus Members $105.00
AIChE Graduate Student Members Free
AIChE Undergraduate Student Members Free
AIChE Explorer Members $225.00
Non-Members $225.00

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