Uncertainty‐conscious methodology for process performance assessment in biopharmaceutical drug product manufacturing

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

[1]  Marianthi G. Ierapetritou,et al.  An integrated approach for dynamic flowsheet modeling and sensitivity analysis of a continuous tablet manufacturing process , 2012, Comput. Chem. Eng..

[2]  Sunil Chhatre,et al.  Global sensitivity analysis for the determination of parameter importance in the chromatographic purification of polyclonal antibodies , 2008 .

[3]  Venkat Venkatasubramanian,et al.  Leveraging Bayesian Approach to Predict Drug Manufacturing Performance , 2016, Journal of Pharmaceutical Innovation.

[4]  Dimitrios I. Gerogiorgis,et al.  Process modelling and simulation for continuous pharmaceutical manufacturing of ibuprofen , 2015 .

[5]  Dimitrios I. Gerogiorgis,et al.  Plantwide design and economic evaluation of two Continuous Pharmaceutical Manufacturing (CPM) cases: Ibuprofen and artemisinin , 2015, Comput. Chem. Eng..

[6]  Eyal Dassau,et al.  Combining Six-Sigma with Integrated Design and Control for Yield Enhancement in Bioprocessing , 2006 .

[7]  Mića Jovanović,et al.  Continuous improvement concepts as a link between quality assurance and implementation of cleaner production: Case study in the generic pharmaceutical industry , 2016 .

[8]  Fernando J. Muzzio,et al.  Pharmaceutical engineering science—New approaches to pharmaceutical development and manufacturing , 2010 .

[9]  E. Nadaraya On Estimating Regression , 1964 .

[10]  Erick C. Jones,et al.  A framework for effective Six Sigma implementation , 2010 .

[11]  Salvador García-Muñoz,et al.  Optimal Selection of Raw Materials for Pharmaceutical Drug Product Design and Manufacture using Mixed Integer Nonlinear Programming and Multivariate Latent Variable Regression Models , 2013 .

[12]  Francesco Cadini,et al.  A Bayesian Monte Carlo-based algorithm for the estimation of small failure probabilities of systems affected by uncertainties , 2016, Reliab. Eng. Syst. Saf..

[13]  Ignacio E. Grossmann,et al.  Recent advances in mathematical programming techniques for the optimization of process systems under uncertainty , 2015, Comput. Chem. Eng..

[14]  Vandana Gupta,et al.  Dermal Drug Delivery for Cutaneous Malignancies: Literature at a Glance , 2016, Journal of Pharmaceutical Innovation.

[15]  Lazaros G. Papageorgiou,et al.  An iterative mixed integer optimisation approach for medium term planning of biopharmaceutical manufacture under uncertainty , 2008 .

[16]  Carlo Meloni,et al.  Scheduling dispensing and counting in secondary pharmaceutical manufacturing , 2009 .

[17]  Marianthi Ierapetritou,et al.  System-wide hybrid MPC-PID control of a continuous pharmaceutical tablet manufacturing process via direct compaction. , 2013, European journal of pharmaceutics and biopharmaceutics : official journal of Arbeitsgemeinschaft fur Pharmazeutische Verfahrenstechnik e.V.

[18]  Richard D. Braatz,et al.  Model‐based design of a plant‐wide control strategy for a continuous pharmaceutical plant , 2013 .

[19]  Lazaros G. Papageorgiou,et al.  Optimal planning and campaign scheduling of biopharmaceutical processes using a continuous-time formulation , 2016, Comput. Chem. Eng..

[20]  Hirokazu Sugiyama,et al.  Improving lead time of pharmaceutical production processes using Monte Carlo simulation , 2014, Comput. Chem. Eng..

[21]  A. Costa,et al.  Hybrid genetic optimization for solving the batch-scheduling problem in a pharmaceutical industry , 2015, Comput. Ind. Eng..

[22]  M. Wing Goodale,et al.  Cumulative adverse effects of offshore wind energy development on wildlife , 2016 .

[23]  Jouni Savolainen Global sensitivity analysis of a feedback-controlled stochastic process model , 2013, Simul. Model. Pract. Theory.

[24]  Shabnam Rasoulian,et al.  Optimal design of large‐scale chemical processes under uncertainty: A ranking‐based approach , 2014 .

[25]  Imad Alsyouf,et al.  A framework for assessing the cost effectiveness of lean tools , 2011 .

[26]  Arghavan Louhghalam,et al.  Variance decomposition and global sensitivity for structural systems , 2010 .

[27]  Hanfried Seyfarth Die neue FDA Aseptic Guidance Teil 3: Qualifizierung / Validierung , 2005 .

[28]  Masahiko Hirao,et al.  Systematic retrofitting methodology for pharmaceutical drug purification processes , 2015, Comput. Chem. Eng..

[29]  Ignacio E. Grossmann,et al.  Enterprise‐wide optimization: A new frontier in process systems engineering , 2005 .