Integration of stochastic simulation with multivariate analysis: Short-term facility fit prediction

Biotechnology Progress

Volumn 29, Issue 2, 2013, Pages 368-377

  Stonier, Adam ^a,b   Pain, David ^b   Westlake, Ashley ^b   Hutchinson, Nicholas ^c   Thornhill, Nina F ^d   Farid, Suzanne S ^a  

^a UNIVERSITY COLLEGE LONDON   (United Kingdom)

^b LONZA AG   (Switzerland)

^c PARKER HANNIFIN CORPORATION   (United States)

^d IMPERIAL COLLEGE LONDON   (United Kingdom)

Author keywords

Biopharmaceutical manufacturing processes; Discrete event simulation; Multivariate analysis; Parallel co ordinate plots; Principal component analysis; Stochastic decision support tools

Indexed keywords

DECISION SUPPORT TOOLS; DISCRETE-EVENT SIMULATION MODEL; MANUFACTURING PROCESS; MONTE- CARLO SIMULATIONS; MULTI VARIATE ANALYSIS; MULTIVARIATE STATISTICAL ANALYSIS; MULTIVARIATE STATISTICAL TECHNIQUES; PARALLEL CO-ORDINATE PLOTS;

BATCH DATA PROCESSING; CELL CULTURE; CLUSTERING ALGORITHMS; DECISION SUPPORT SYSTEMS; DISCRETE EVENT SIMULATION; MONTE CARLO METHODS; OPTIMIZATION; PRINCIPAL COMPONENT ANALYSIS; STOCHASTIC MODELS; STOCHASTIC SYSTEMS; TECHNOLOGY TRANSFER;

MULTIVARIANT ANALYSIS;

ANTIBODY;

ARTICLE; BIOTECHNOLOGY; CHEMISTRY; CHROMATOGRAPHY; COMPUTER SIMULATION; EQUIPMENT; ISOLATION AND PURIFICATION; METHODOLOGY; MONTE CARLO METHOD; MULTIVARIATE ANALYSIS; PRINCIPAL COMPONENT ANALYSIS; THEORETICAL MODEL;

ANTIBODIES; BIOTECHNOLOGY; CHROMATOGRAPHY; COMPUTER SIMULATION; MODELS, THEORETICAL; MONTE CARLO METHOD; MULTIVARIATE ANALYSIS; PRINCIPAL COMPONENT ANALYSIS; RESEARCH DESIGN;

EID: 84875965514     PISSN: 87567938     EISSN: 15206033     Source Type: Journal    
DOI: 10.1002/btpr.1678     Document Type: Article

References (35)

1. Stonier A, Smith M, Hutchinson N, Farid SS. Dynamic simulation framework for design of lean biopharmaceutical manufacturing operations. Comp Aided Chem Eng. 2009;26:1069-1074.
2. Stonier A, Simaria AS, Smith M, Farid SS. Decisional tool to assess current and future process robustness in an antibody purification facility. Biotechnol Prog. 2012;28:1019-1028.
3. Kelley B. Industrialization of mAb production technology The bioprocessing industry at a crossroads. Mabs 2009;1:443-452.
4. Chang J. Process validation challenges for tech transfer. Pharm Outsourcing 2001;12:12-16.
5. Thomassen YE, van Sprang ENM, van der Pol LA, Bakker WAM. Multivariate data analysis on historical IPV production data for better process understanding and future improvements. Biotechnol Bioeng. 2010;107:96-104.
6. Kirdar AO, Conner JS, Baclaski J, Rathore AS. Application of multivariate analysis toward biotech processes: case study of a cell-culture unit operation. Biotechnol Prog. 2007;23:61-67.
7. Lee HW, Christie A, Liu JJ, Yoon S. Estimation of raw material performance in mammalian cell culture using near infrared spectra combined with chemometrics approaches. Biotechnol Prog. 2012;28:824-832.
8. Kirdar AO, Chen G, Weidner J, Rathore AS. Application of near infrared (NIR) spectroscopy for screening of raw materials used in the cell culture medium for the production of a recombinant therapeutic protein. Biotechnol Prog. 2010;26:527-531.
9. Pate ME, Turner MK, Thornhill NF, Titchener-Hooker NJ. The use of principal component analysis for the modelling of high performance liquid chromatography. Bioprocess Eng. 1999;21(3):261-272.
10. Pate ME, Turner MK, Thornhill NF, Titchener-Hooker NJ. Principal component analysis of nonlinear chromatography. Biotechnol Prog. 2004;20:215-222.
11. Malmquist G, Danielsson R. Alignment of chromatographic profiles for principal component analysis-A prerequisite for fingerprinting methods. J Chromatogr A. 1994;687:71-88.
12. Larson TM, Davis J, Lam H, Cacia J. Use of process data to assess chromatographic performance in production-scale protein purification columns. Biotechnol Prog. 2003;19:485-492.
13. Edwards-Parton S, Thornhill NF, Bracewell DG, Liddell JM, Titchener-Hooker NJ. Principal component score modeling for the rapid description of chromatographic separations. Biotechnol Prog. 2008;24:202-208.
14. Hou Y, Jiang C, Shukla AA, Cramer SM. Improved process analytical technology for protein A chromatography using predictive principal component analysis tools. Biotechnol Bioeng. 2011;108:59-68.
15. Farid SS, Washbrook J, Titchener-Hooker NJ. Decision-support tool for assessing biomanufacturing strategies under uncertainty: stainless steel versus disposable equipment for clinical trial material preparation. Biotechnol Prog. 2005;21:486-497.
16. Farid SS, Washbrook J, Titchener-Hooker NJ. Combining multiple quantitative and qualitative goals when assessing biomanufacturing strategies under uncertainty. Biotechnol Prog. 2005;21:1183-1191.
17. Lim AC, Zhou Y, Washbrook J, Sinclair A, Fish B, Francis R, Titchener-Hooker NJ, Farid SS. Application of a decision-support tool to assess pooling strategies in perfusion culture processes under uncertainty. Biotechnol. Prog. 2005;21:1231-1242.
18. Lim AC, Washbrook J, Titchener-Hooker NJ, Farid SS. A computer-aided approach to compare the production economics of fed-batch and perfusion culture under uncertainty, Biotechnol Bioeng. 2006;93:687-697.
19. Pollock J, Ho SV, Farid SS. Fed-batch and perfusion culture processes: operational, economic and environmental feasibility under uncertainty. Biotechnol Bioeng. 2013;110:206-219.
20. Sin G, Gernaey KV, Eliasson Lantz A. Good modelling practice (GMoP) for PAT applications: Propagation of input uncertainty and sensitivity analysis. Biotechnol Prog. 2009;25:1043-1053.
21. George ED, Farid SS. Strategic biopharmaceutical portfolio development: an analysis of constraint-induced implications. Biotechnol Prog. 2008;24:698-713.
22. Rajapakse A, Titchener-Hooker NJ, Farid SS. Modelling of the biopharmaceutical drug development pathway and portfolio management. Comput Chem Eng. 2005;29:1357-1368.
23. Thornhill NF, Melbø H, Wiik J. Multi-dimensional visualization and clustering of historical process data. Ind Eng Chem Res. 2006;45:5971-5985.
24. Wang XZ, Medasani S, Marhoon F, Albazzaz H. Multidimensional visualization of principal component scores for process historical data analysis. Ind Eng Chem Res. 2004;43:7036-7048.
25. Legmann R, Schreyer HB, Combs RG, McCormick EL, Russo AP, Rodgers ST. A predictive high-throughput scale-down model of monoclonal antibody production in CHO cells. Biotechnol Bioeng. 2009;104:1107-1120.
26. Amanullah A, Otero JM, Mikola M, Hsu A, Zhang J, Aunins J, Schreyer HB, Hope JA, Russo AP. Novel micro-bioreactor high throughput technology for cell culture process development: Reproducibility and scalability assessment of fed-batch CHO cultures. Biotechnol Bioeng. 2010;106:57-67.
27. Abu-Absi SF, Yang L, Thompson P, Jiang C, Kandula S, Schilling B, Shukla AA. Defining process design space for monoclonal antibody cell culture. Biotechnol Bioeng. 2010;106:894-905.
28. Hutchinson N, Chhatre S, Baldascini H, Davies JL, Bracewell DG, Hoare M. Ultra scale-down approach to correct dispersive and retentive effects in small-scale columns when predicting larger scale elution profiles. Biotechnol Prog. 2009;25:1103-1110.
29. Helton JC, Davis FJ. Latin hypercube sampling and the propagation of uncertainty in analyses of complex systems. Reliab Eng Syst Saf. 2003;81:23-69.
30. Rummel RJ. Applied Factor Analysis. Evanston: Northwestern University Press; 1970.
31. Jackson J E. A User's Guide to Principal Components. NewYork: Wiley-Interscience; 1991.
32. Valle S, Li W, Qin SJ. Selection of the number of principal components: the variance of the reconstruction error criterion with a comparison to other methods. Ind Eng Chem Res. 1999;38:4389-4401.
33. Zwick WR, Velicer WF. Comparison of five rules for determining the number of components to retain. Psychol Bull. 1986;99:432-442.
34. Cattell RB. The scree test for the number of factors. Multivariate Behav Res. 1966;April:245-276.
35. Rozett RW, Petersen EM. Methods of factor analysis of mass spectra. Anal Chem. 1975;47:1301-1308.