Premium
Application of a Decision‐Support Tool to Assess Pooling Strategies in Perfusion Culture Processes under Uncertainty
Author(s) -
Lim Ai Chye,
Zhou Yuhong,
Washbrook John,
Sinclair Andrew,
Fish Brendan,
Francis Richard,
John TitchenerHooker Nigel,
Farid Suzanne S.
Publication year - 2008
Publication title -
biotechnology progress
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 0.572
H-Index - 129
eISSN - 1520-6033
pISSN - 8756-7938
DOI - 10.1021/bp049578t
Subject(s) - biopharmaceutical , pooling , computer science , monte carlo method , risk analysis (engineering) , process (computing) , probabilistic logic , resource (disambiguation) , sample (material) , throughput , risk assessment , biochemical engineering , artificial intelligence , engineering , mathematics , microbiology and biotechnology , business , statistics , chemistry , computer network , telecommunications , chromatography , wireless , biology , operating system , computer security
Abstract Biopharmaceutical manufacture is subject to numerous risk factors that may affect operational costs and throughput. This paper discusses the need for incorporating such uncertainties in decision‐making tools in order to reflect the inherent variability of process parameters during the operation of a biopharmaceutical plant. The functionalities of a risk‐based prototype tool to model cost summation, perform mass balance calculations, simulate resource handling, and incorporate uncertainties in order to evaluate the potential risk associated with different manufacturing strategies are demonstrated via a case study. The case study is based upon the assessment of pooling strategies in the perfusion culture of mammalian cells to deliver a therapeutic protein for commercial use. Monte Carlo simulations, which generate random sample behaviors for probabilistic factors so as to imitate the uncertainties inherent in any process, have been applied. This provides an indication of the range of possible output values and hence enables trends or anomalies in the expected performance of a process to be determined.