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Uncertainty‐conscious methodology for process performance assessment in biopharmaceutical drug product manufacturing
Author(s) -
Casola Gioele,
Sugiyama Hirokazu,
Siegmund Christian,
Mattern Markus
Publication year - 2018
Publication title -
aiche journal
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 0.958
H-Index - 167
eISSN - 1547-5905
pISSN - 0001-1541
DOI - 10.1002/aic.16020
Subject(s) - idef0 , biopharmaceutical , process (computing) , computer science , sensitivity (control systems) , reliability engineering , pharmaceutical manufacturing , monte carlo method , process design , product (mathematics) , process validation , work in process , process engineering , industrial engineering , manufacturing engineering , engineering , computer integrated manufacturing , operations management , mathematics , bioinformatics , statistics , genetics , geometry , electronic engineering , verification and validation , biology , operating system
This work presents an uncertainty‐conscious methodology for the assessment of process performance—for example, run time—in the manufacturing of biopharmaceutical drug products. The methodology is presented as an activity model using the type 0 integrated definition (IDEF0) functional modeling method, which systematically interconnects information, tools, and activities. In executing the methodology, a hybrid stochastic–deterministic model that can reflect operational uncertainty in the assessment result is developed using Monte Carlo simulation. This model is used in a stochastic global sensitivity analysis to identify tasks that had large impacts on process performance under the existing operational uncertainty. Other factors are considered, such as the feasibility of process modification based on Good Manufacturing Practice, and tasks to be improved is identified as the overall output. In a case study on cleaning and sterilization processes, suggestions were produced that could reduce the mean total run time of the processes by up to 40%. © 2017 American Institute of Chemical Engineers AIChE J , 64: 1272–1284, 2018