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Determining Critical Process Parameters and Process Robustness in Preparative Chromatography - A Model-Based Approach

Degerman, Marcus LU ; Westerberg, Karin LU and Nilsson, Bernt LU (2009) In Chemical Engineering & Technology 32(6). p.903-911
Abstract
This paper presents a model-based method to aid in the process validation for the purification of pharmaceutical drugs. The critical process parameters are identified by simulating process disturbances, and this information is then used to determine if the process control space is robust. Simulations are chosen to analyze the entire control space to also find nonlinearities and interaction effects between the process disturbances, which are used to determine where in the control space the critical quality attributes are the lowest, i.e., the worst case scenario. The real process conditions are estimated by running simulations according to plausible probability distributions using Latin hypercube sampling. The probability of batch failure... (More)
This paper presents a model-based method to aid in the process validation for the purification of pharmaceutical drugs. The critical process parameters are identified by simulating process disturbances, and this information is then used to determine if the process control space is robust. Simulations are chosen to analyze the entire control space to also find nonlinearities and interaction effects between the process disturbances, which are used to determine where in the control space the critical quality attributes are the lowest, i.e., the worst case scenario. The real process conditions are estimated by running simulations according to plausible probability distributions using Latin hypercube sampling. The probability of batch failure can be estimated from this and it is shown that the worst case scenario is improbable for most cases. This information can help in planning validation experiments or determine which critical process parameters need a tighter control. Three case studies are used to illustrate the usefulness of the methods. It was found that the main critical process parameters in all three case Studies were variations in the modifier concentrations, for example, salt in ion-exchange chromatography and hydrophobic interaction chromatography, and the organic modifier in reversed-phase chromatography. (Less)
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author
organization
publishing date
type
Contribution to journal
publication status
published
subject
keywords
Robustness analysis, Preparative chromatography, Latin hypercube sampling, Control space, Critical process parameters, Validation
in
Chemical Engineering & Technology
volume
32
issue
6
pages
903 - 911
publisher
John Wiley & Sons
external identifiers
  • wos:000267464600007
  • scopus:69649084328
ISSN
1521-4125
DOI
10.1002/ceat.200900019
language
English
LU publication?
yes
id
b30045f7-8d82-47a6-af13-5c3fa710b52e (old id 1463381)
date added to LUP
2009-08-18 12:16:03
date last changed
2017-09-03 03:56:00
@article{b30045f7-8d82-47a6-af13-5c3fa710b52e,
  abstract     = {This paper presents a model-based method to aid in the process validation for the purification of pharmaceutical drugs. The critical process parameters are identified by simulating process disturbances, and this information is then used to determine if the process control space is robust. Simulations are chosen to analyze the entire control space to also find nonlinearities and interaction effects between the process disturbances, which are used to determine where in the control space the critical quality attributes are the lowest, i.e., the worst case scenario. The real process conditions are estimated by running simulations according to plausible probability distributions using Latin hypercube sampling. The probability of batch failure can be estimated from this and it is shown that the worst case scenario is improbable for most cases. This information can help in planning validation experiments or determine which critical process parameters need a tighter control. Three case studies are used to illustrate the usefulness of the methods. It was found that the main critical process parameters in all three case Studies were variations in the modifier concentrations, for example, salt in ion-exchange chromatography and hydrophobic interaction chromatography, and the organic modifier in reversed-phase chromatography.},
  author       = {Degerman, Marcus and Westerberg, Karin and Nilsson, Bernt},
  issn         = {1521-4125},
  keyword      = {Robustness analysis,Preparative chromatography,Latin hypercube sampling,Control space,Critical process parameters,Validation},
  language     = {eng},
  number       = {6},
  pages        = {903--911},
  publisher    = {John Wiley & Sons},
  series       = {Chemical Engineering & Technology},
  title        = {Determining Critical Process Parameters and Process Robustness in Preparative Chromatography - A Model-Based Approach},
  url          = {http://dx.doi.org/10.1002/ceat.200900019},
  volume       = {32},
  year         = {2009},
}