Latent variable modeling to assist the implementation of Quality-by-Design paradigms in pharmaceutical development and manufacturing: A review

International Journal of Pharmaceutics

Volumn 457, Issue 1, 2013, Pages 283-297

  Tomba, Emanuele ^a   Facco, Pierantonio ^a   Bezzo, Fabrizio ^a   Barolo, Massimiliano ^a  

^a UNIVERSITY OF PADOVA   (Italy)

Author keywords

Latent variable models; Multivariate analysis; Pharmaceutical development; Process analytical technology; Product design; Quality by Design

Indexed keywords

ARTICLE; CONTROL STRATEGY; DRUG INDUSTRY; DRUG MANUFACTURE; EXPERIMENT; HEALTH CARE PLANNING; HUMAN; LATENT VARIABLE MODELING; MODEL; PRIORITY JOURNAL; PROCESS DESIGN; PROCESS MONITORING; PRODUCT DEVELOPMENT; RISK ASSESSMENT;

LATENT VARIABLE MODELS; MULTIVARIATE ANALYSIS; PHARMACEUTICAL DEVELOPMENT; PROCESS ANALYTICAL TECHNOLOGY; PRODUCT DESIGN; QUALITY BY DESIGN;

LEGISLATION, DRUG; MODELS, THEORETICAL; QUALITY CONTROL; TECHNOLOGY, PHARMACEUTICAL;

EID: 84888321434     PISSN: 03785173     EISSN: 18733476     Source Type: Journal    
DOI: 10.1016/j.ijpharm.2013.08.074     Document Type: Review

References (129)

1. L. Aboud, and S. Hensley New prescription for drug makers: update the plants Wall Street J. 2003 http://online.wsj.com/article/SB10625358403931000.html (September 12)
2. K. Akamatsu, S. Lakshminarayanan, H. Manako, H. Takada, T. Satou, and S.L. Shah Data-based control of an industrial tubular reactor Control Eng. Pract. 8 2000 783 790 (Pubitemid 30480637)
3. D. am Ende, K.S. Bronk, J. Mustakis, G. O'Connor, C.L. Santa Maria, R. Nosal, and T.J.N. Watson API Quality by Design example from the Torcetrapib manufacturing process J. Pharm. Innov. 2 2007 71 86
4. Y. Andemichael, J. Chen, J.S. Clawson, W. Dai, A. Diederich, S.V. Downing, A.J. Freyer, P. Liu, L.M. Oh, D.B. Patience, S. Sharpe, J. Sisko, J. Tsui, F.G. Vogt, J. Wang, L. Wernersbach, E.C. Webb, J. Wertman, and L. Zhou Process development for a novel pleuromutilin-derived antibiotic Org. Proc. Res. Dev. 13 2009 729 738
5. M. Andersson, A. Ringberg, and C. Gustafsson Multivariate methods in tablet formulation suitable for early drug development: predictive models from a screening design of several linked responses Chemom. Intell. Lab. Syst. 87 2007 125 130
6. F. Arteaga, and A. Ferrer Dealing with missing data in MSPC: several methods, different interpretations, some examples J. Chemom. 16 2002 408 418
7. R. Bergman, M.E. Johansson, T. Lundstedt, E. Seifert, and J. Åberg Optimization of a granulation and tabletting process by sequential design and multivariate analysis Chemom. Intell. Lab. Syst. 44 1998 271 286 (Pubitemid 29016103)
8. F. Boukouvala, F.J. Muzzio, and M. Ierapetritou Design space of pharmaceutical processes using data-driven-based methods J. Pharm. Innov. 5 2010 119 137
9. F. Boukouvala, and M. Ierapetritou Feasibility analysis of black-box processes using an adaptive sampling Kriging-based method Comput. Chem. Eng. 36 2012 358 368
10. A. Burggraeve, T. Van Den Kerkhof, M. Hellings, J.P. Remon, C. Vervaet, and T. De Beer Batch statistical process control of a fluid bed granulation process using in-line spatial filter velocimetry and product temperature measurements Euro. J. Pharm. Sci. 42 2011 584 592
11. A. Burnham, R. Viveros, and J.F. MacGregor Frameworks for latent variable multivariate regression J. Chemom. 10 1996 31 45 (Pubitemid 126469517)
12. A.J. Burnham, J.F. MacGregor, and R. Viveros Latent variable multivariate regression modeling Chemom. Intell. Lab. Syst. 48 1999 167 180 (Pubitemid 29333815)
13. J.L. Burt, A.D. Braem, A. Ramirez, B. Mudryk, L. Rossano, and S. Tummala Model-guided design space development for a drug substance manufacturing process J. Pharm. Innov. 6 2011 181 192
14. J. Camacho, J. Picó, and A. Ferrer Multi-phase analysis framework for handling batch process data J. Chemom. 22 2008 632 643
15. Z. Chen, D. Lovett, and J. Morris Process analytical technologies and real time process control a review of some spectroscopic issues and challenges J. Process Control 21 2011 1467 1482
16. W. Chew, and P. Sharratt Trends in process analytical technology Anal. Methods 2 2010 1412 1438
17. M. Dumarey, H. Wikström, M. Fransson, A. Sparén, P. Tajarobi, M. Josefon, and J. Trygg Combining experimental design and orthogonal projections to latent structures to study the influence of microcrystalline cellulose properties on roll compaction Int. J. Pharm. 416 2011 110 119
18. P. Facco, E. Tomba, F. Bezzo, S. García-Muñoz, and M. Barolo Transfer of process monitoring models between different plants using latent variable techniques Ind. Eng. Chem. Res. 51 2012 7327 7339
19. P. Facco, M. Largoni, E. Tomba, F. Bezzo, and M. Barolo Transfer of process monitoring models between plants: batch systems Chem. Eng. Res. Des. 2013 10.1016/j.cherd.2013.07.010
20. FDA Pharmaceutical CGMPs for the 21st century - a risk based approach. Final report 2004 U.S. Department of Health and Human Services. U.S. Food and Drug Administration
21. FDA Guidance for industry. PAT - a framework for innovative pharmaceutical development, manufacturing and quality assurance 2004 Center for Drug Evaluation and Research, U.S. Food and Drug Administration Rockwille, MD, USA
22. FDA Innovation or stagnation. Challenge and opportunity on the critical path to new medical products 2004 U.S. Department of Health and Human Services. U.S. Food and Drug Administration
23. J. Flores-Cerillo, and J.F. MacGregor Within-batch and batch-to-batch inferential-adaptive control of semibatch reactors: a partial least squares approach Ind. Eng. Chem. Res. 42 2003 3334 3345
24. J. Flores-Cerillo, and J.F. MacGregor Control of batch product quality by trajectory manipulation using LV models J. Process Control 14 2004 539 553
25. J. Flores-Cerillo, and J.F. MacGregor Latent variable MPC for trajectory tracking in batch processes J. Process Control 15 2005 651 663
26. C.A. Floudas Nonlinear and mixed-integer optimization: fundamentals and applications 1995 Oxford University Press, Inc New York, USA
27. J. Gabrielsson, N.-O. Lindberg, and T. Lundstedt Multivariate methods in pharmaceutical applications J. Chemom. 16 2002 141 160
28. J. Gabrielsson, N.-O. Lindberg, M. Pålsson, F. Nicklasson, M. Sjöström, and T. Lundstedt Multivariate methods in the development of a new tablet formulation Drug. Dev. Ind. Pharm. 29 2003 1053 1075 (Pubitemid 37499542)
29. J. Gabrielsson, N.-O. Lindberg, M. Pålsson, F. Nicklasson, M. Sjöström, and T. Lundstedt Multivariate methods in the development of a new tablet formulation: optimization and validation Drug. Dev. Ind. Pharm. 30 2004 1037 1049 (Pubitemid 39577996)
30. T. Garcia, V. McCurdy, T.N.J. Watson, M. am Ende, P. Butterell, K. Vukovinsky, A. Clueh, J. Coffman, S. Cooper, and B. Schuemmelfeder Verification of design spaces developed at subscale J. Pharm. Innov. 7 2012 13 18
31. S. García-Muñoz, J.F. MacGregor, and T. Kourti Product transfer between sites using Joint-Y PLS Chemom. Intell. Lab. Syst. 79 2005 101 114 (Pubitemid 41219510)
32. S. García Muñoz, and D. Settell Application of multivariate latent variable modeling to pilot-scale spray drying monitoring and fault detection: Monitoring with fundamental knowledge Comput. Chem. Eng. 33 2009 2106 2110
33. S. García Muñoz, L. Zhang, and M. Cortese Root cause analysis during process development using Joint-Y PLS Chemom. Intell. Lab. Syst. 95 2009 101 105
34. S. García Muñoz Establishing multivariate specifications for incoming materials using data from multiple scales Chemom. Intell. Lab. Syst. 98 2009 51 57
35. S. García-Muñoz, and C.A. Oksanen Process modeling and control in drug development and manufacturing Comput. Chem. Eng. 34 2010 1007 1008
36. S. García-Muñoz, S. Dolph, and I.I.H.W. Ward Handling uncertainty in the establishment of a design space for the manufacture of a pharmaceutical product Comput. Chem. Eng. 34 2010 1098 1107
37. S. García-Muñoz, and M. Polizzi WSPLS - a new approach towards mixture modeling and accelerated product development Chemom. Intell. Lab. Syst. 114 2012 116 121
38. S. García-Muñoz, and J. Mercado Optimal selection of raw materials for pharmaceutical drug product design and manufacture using mixed integer non-linear programming and multivariate latent variable regression models Ind. Eng. Chem. Res. 52 2013 5934 5942
39. P. Geladi, and B. Kowalski Partial least-squares regression: a tutorial Anal. Chim. Acta 185 1986 1 17
40. K.V. Gernaey, A.E. Cervera-Padrell, and J.M. Woodley A perspective on PSE in pharmaceutical process development and innovation Comput. Chem. Eng. 42 2012 15 29
41. M.W. Hermanto, R.D. Braatz, and M.-S. Chiu Integrated batch-to-batch and nonlinear model predictive control for polymorphic transformation in pharmaceutical crystallization AIChE J. 57 2011 1008 1019
42. D.C. Hinz Process analytical technologies in the pharmaceutical industry: the FDA's PAT initiative Anal. Bioanal. Chem. 384 2006 1036 1042 (Pubitemid 43333983)
43. A. Höskuldsson PLS regression methods J. Chemom. 2 1988 211 228
44. J. Huang, G. Kaul, C. Cai, R. Chatlapalli, P. Hernandez-Abad, K. Ghosh, and A. Nagi Quality by design case study: an integrated approach to drug product and process development Int. J. Pharm. 382 2009 23 32
45. ICH ICH harmonised tripartite guide Specifications: test procedures and acceptance criteria for new drug substances and new drug products: chemical substances. Q6A 1999
46. ICH ICH harmonised tripartite guide Quality risk management Q9 2005
47. ICH ICH harmonised tripartite guide Pharmaceutical quality system Q10 2008
48. ICH ICH harmonised tripartite guide Pharmaceutical development Q8(R2) 2009
49. ICH Quality implementation working group on Q8, Q9 and Q10 Questions & Answers (R4) 2010
50. ICH ICH quality implementation working group Points to consider (R2). ICH-endorsed guide for ICH Q8/Q9/Q10 implementation 2011
51. J.E. Jackson A User's Guide to Principal Components 1991 John Wiley & Sons, Inc New York, USA
52. C.M. Jaeckle, and J.F. MacGregor Product design through multivariate statistical analysis of process data AIChE J. 44 1998 1105 1118 (Pubitemid 128768801)
53. C.M. Jaeckle, and J.F. MacGregor Industrial application of product design through the inversion of latent variable models Chemom. Intell. Lab. Syst. 50 2000 199 210 (Pubitemid 30136023)
54. C.M. Jaeckle, and J.F. MacGregor Product transfer between plants using historical process data AIChE J. 46 2000 1989 1997
55. S.G. Kapsi, L.D. Castro, F.X. Muller, and T.J. Wrzosek Development of a design space for a unit operation: illustration using compression-mix blending process for the manufacture of a tablet dosage form J. Pharm. Innov. 7 2012 19 29
56. N. Kettaneh-Wold Use of experimental design in the pharmaceutical industry J. Pharm. Biomed. Anal. 9 1991 605 610
57. A.O. Kirdar, K.D. Green, and A.S. Rathore Application of multivariate data analysis for the identification and successful resolution of a root cause for a bioprocessing application Biotechnol. Prog. 24 2008 720 726 (Pubitemid 351832729)
58. K.U. Klatt, and W. Marquardt Perspectives for process systems engineering: personal views from academia and industry Comput. Chem. Eng. 33 2009 536 550
59. K.A. Kosanovich, K.S. Dahl, and M.J. Piovoso Improved process understanding using multiway principal component analysis Ind. Eng. Chem. Res. 35 1996 138 146 (Pubitemid 126464931)
60. T. Kourti Application of latent variable methods to process control and multivariate statistical process control in industry Int. J. Adapt. Control Signal Process. 19 2005 213 246 (Pubitemid 40680649)
61. T. Kourti Process analytical technology beyond real-time analyzers: the role of multivariate analysis Crit. Rev. Anal. Chem. 36 2006 257 278 (Pubitemid 44889531)
62. S. Lakshminarayanan, H. Fujii, B. Grosman, E. Dassau, and D.R. Lewin New product design via analysis of historical databases Comput. Chem. Eng. 24 2000 671 676 (Pubitemid 30608299)
63. J. Lepore, and J. Spavins PQLI design space J. Pharm. Innov. 3 2008 79 87
64. Z. Liu, M.-J. Bruwer, J.F. MacGregor, S.S.S. Rathore, D.E. Reed, and M.J. Champagne Modeling and optimization of a tablet manufacturing line J. Pharm. Innov. 6 2011 170 180
65. Z. Liu, M.-J. Bruwer, J.F. MacGregor, S.S.S. Rathore, D.E. Reed, and M.J. Champagne Scale-Up of a pharmaceutical roller compaction process using a Joint-Y partial least squares model Ind. Eng. Chem. Res. 50 2011 10696 10706
66. V. Lourenço, T. Herdling, G. Reich, J.C. Menezes, and D. Lochmann Combining microwave resonance technology to multivariate data analysis as a novel PAT tool to improve process understanding in fluid bed granulation Euro. J. Pharm. Biopharm. 78 2011 513 521
67. V. Lourenço, D. Lochmann, G. Reich, J.C. Menezes, T. Herdling, and J. Schewitz A quality by design study applied to an industrial pharmaceutical fluid bed granulation Euro. J. Pharm. Biopharm. 81 2012 438 447
68. K. Lundstedt-Enkel, J. Gabrielsson, H. Olsman, E. Seifert, J. Pettersen, P.M. Lek, A. Boman, and T. Lundstedt Different multivariate approaches to material discovery, process development, PAT and environmental process monitoring Chemom. Intell. Lab. Syst. 84 2006 201 207 (Pubitemid 44708408)
69. J.F. MacGregor, C. Jaeckle, C. Kiparissides, and M. Koutoudi Process monitoring and diagnosis by multiblock PLS methods AIChE J. 40 1994 826 838
70. J.F. MacGregor, and M-.J. Bruwer A framework for the development of design and control spaces J. Pharm. Innov. 3 2008 15 22
71. M.J. Maltesen, S. Bjerregaard, L. Hovgaard, S. Havelund, and M. van de Weert Quality by Design - spray drying of insulin intended for inhalation Euro. J. Pharm. Biopharm. 70 2008 828 838
72. K.V. Mardia, J.T. Kent, and J.M. Bibby Multivariate Analysis 1979 Academic Press Limited London, UK
73. S. Matero, F. Van den Berg, S. Poutiainen, J. Rantanen, and J. Pajander Towards better process understanding: chemometrics and multivariate measurements in manufacturing of solid dosage forms J. Pharm. Sci. 102 2013 1385 1400
74. P. McKenzie, S. Kiang, J. Tom, A.E. Rubin, and M. Futran Can pharmaceutical process development become high tech? AIChE J. 52 2006 3990 3994 (Pubitemid 44935191)
75. N. Meneghetti, E. Tomba, P. Facco, F. Lince, D.L. Marchisio, A.A. Barresi, F. Bezzo, and M. Barolo Supporting the transfer of products between different equipment through latent variable model inversion A. Kraslawski, I. Turunen, Computer-Aided Chemical Engineering 32, 23rd European Symposium on Computer-Aided Process Engineering, 2013 Elsevier Amsterdam, The Netherlands 511 516
76. D.C. Montgomery Design and analysis of experiments 6th edition 2005 John Wiley & Sons, Inc New York, USA
77. A. Mullard 2010 FDA drug approvals Nat. Rev. Drug Discov. 10 2011 82 85
78. K. Muteki, K. Yamamoto, G.L. Reid, and M. Krishnan De-risking scale-up of a high-shear wet granulation process using latent variable modeling and near-infrared spectroscopy J. Pharm. Innov. 6 2011 142 156
79. K. Muteki, V. Swaminathan, S.S. Sekulic, and G.L. Reid De-risking pharmaceutical tablet manufacture through process understanding, latent variable modeling, and optimization technologies AAPS PharmSciTech 12 2011 1324 1334
80. th IFAC Symposium on Advanced Control of Chemical Processes, July 10-13, 2012 2012 Furama Riverfront Singapore
81. K. Muteki, J.E. Morgado, G.L. Reid, J. Wang, G. Xue, F.W. Riley, J.W. Harwood, D.T. Fortin, and I.J. Miller Quantitative structure retention relationship models in an analytical Quality by Design framework: simultaneously accounting for compound properties, mobile-phase conditions, and stationary-phase properties Ind. Eng. Chem. Res. 52 2013 12269 12284 10.1021/ie303459a
82. P. Nomikos, and J.F. MacGregor Monitoring batch processes using multiway principal component analysis AIChE J. 40 1994 1361 1375
83. P. Nomikos, and J.F. MacGregor Multi-way partial least squares in monitoring batch processes Chemom. Intell. Lab. Syst. 30 1995 97 108
84. T. Norioka, S. Kikuchi, Y. Onuki, K. Takayama, and K. Imai Optimization of the manufacturing process for oral formulations using multivariate statistical methods J. Pharm. Innov. 6 2011 157 169
85. M.J. Piovoso, and K.A. Kosanovich Applications of multivariate statistical-methods to process monitoring and controller-design Int. J. Control. 59 1994 743 765
86. M. Polizzi, and S. García-Muñoz A framework for in-silico formulation design using multivariate latent variable regression methods Int. J. Pharm. 418 2011 235 242
87. A.L. Pomerantsev, and O.Y. Rodionova Process analytical technology: a critical view of the chemometricians J. Chemom. 26 2012 299 310
88. P.M. Portillo, M. Ierapetritou, S. Tomassone, C. Mc Dade, D. Clancy, P.P. Avontuur, and F.J. Muzzio Quality by design methodology for development and scale-up of batch mixing processes J. Pharm. Innov. 3 2008 258 270
89. S.J. Qin Statistical process monitoring: basics and beyond J. Chemom. 17 2003 480 502
90. T. Rajalahti, and O.M. Kvalheim Multivariate data analysis in pharmaceutics: a tutorial review Int. J. Pharm. 417 2011 280 290
91. R. Ramachandran, J. Ariunan, A. Chaudhury, and M. Ierapetritou Model-based control-loop performance of a continuous direct compaction process J. Pharm. Innov. 6 2011 249 263
92. B. Rambali, L. Baert, and D.L. Massart Using experimental design to optimize the process parameters in fluidized bed granulation on a semi-full scale Int. J. Pharm. 220 2001 149 160 (Pubitemid 32476851)
93. L. Saerens, L. Dierickx, T. Quinten, P. Adriaensens, R. Carleer, C. Vervaet, J.P. Remon, and T. De Beer In-line NIR spectroscopy for the understanding of polymer-drug interaction during pharmaceutical hot-melt extrusion Euro. J. Pharm. Biopharm. 81 2012 230 237
94. Z. Shi, N. Zaborenko, and D.E. Reed Latent variables-based process modeling of a continuous hydrogenation reaction in API synthesis of small molecules J. Pharm. Innov. 8 2013 1 10
95. R. Singh, M. Ierapetritou, and R. Ramachandran An engineering study on the enhanced control and operation of continuous manufacturing of pharmaceutical tablets via roller compaction Int. J. Pharm. 438 2012 307 326
96. R. Singh, M. Ierapetritou, and R. Ramachandran System-wide hybrid MPC-PID control of a continuous pharmaceutical tablet manufacturing process via direct compaction Europ. J. Pharm. Biopharm. 2013 10.1016/j.ejpb.2013.02.019
97. E.T.S. Skibsted, J.A. Westerhuis, A.K. Smilde, and D.T. Witte Examples of NIR based real time release in tablet manufacturing J. Pharm. Biomed. Anal. 43 2007 1297 1305 (Pubitemid 46282375)
98. J.L.P. Soh, F. Wang, N. Boersen, R. Pinal, G.E. Peck, M.T. Carvajal, J. Ceney, H. Valthorsson, and J. Pazdan Utility of multivariate analysis in modeling the effects of raw material properties and operating parameters on granule and ribbon properties prepared in roller compaction Drug Dev. Ind. Pharm. 34 2008 1022 1035
99. N. Souihi, M. Josefson, P. Tajarobi, B. Gururajan, and J. Trygg Design space estimation of the roller compaction process Ind. Eng. Chem. Res. 52 2013 12408 12419 10.1021/ie303580y
100. N. Souihi, M. Dumarey, H. Wikström, P. Tajarobi, M. Fransson, O. Svensson, M. Josefson, and J. Trygg A quality by design approach to investigate the effect of mannitol and dicalcium phosphate qualities on roll compaction Int. J. Pharm. 447 2013 47 61
101. M. Streefland, P.F.G. Van Herpen, B. Van de Waterbeemd, L.A. Van der Pol, E.C. Beuvery, J. Tramper, D.E. Martens, and M. Toft A practical approach for exploration and modeling of the design space of a bacterial vaccine cultivation process Biotechnol. Bioeng. 104 2009 492 504
102. R.E. Swaney, and I.E. Grossmann An index for operational flexibility in chemical process design. Part I: formulation and theory AIChE J. 31 1985 621 630 (Pubitemid 15475399)
103. S. Thirunahari, P. Shan Chow, and R.B.H. Tan Quality by Design (QbD)-based crystallization process development for the polymorphic drug tolbutamide Cryst. Growth Des. 11 2011 3027 3038
104. Y.E. Thomassen, E.N.M. van Sprang, L.A. van der Pol, and W.A.M. Bakker Multivariate data analysis on historical IPV production data for better process understanding and future improvements Biotechnol. Bioeng. 107 2010 96 104
105. E. Tomba Latent variable modeling approaches to assist the implementation of quality-by-design paradigms in pharmaceutical development and manufacturing 2013 Department of Industrial Engineering, University of Padova (Italy) (Ph.D. Dissertation)
106. E. Tomba, M. Barolo, and S. García-Muñoz General framework for latent variable model inversion for the design and manufacturing of new products Ind. Eng. Chem. Res. 51 2012 12886 12900
107. E. Tomba, P. Facco, F. Bezzo, M. Barolo, and S. García-Muñoz Combining fundamental knowledge and latent variable techniques to transfer process monitoring models between plants Chemom. Intell. Lab. Syst. 116 2012 67 77
108. E. Tomba, M. De Martin, P. Facco, J. Robertson, S. Zomer, F. Bezzo, and M. Barolo General procedure to aid the development of continuous pharmaceutical processes using multivariate statistical modeling - an industrial case study Int. J. Pharm. 444 2013 25 39
109. E. Tomba, P. Facco, F. Bezzo, and S. García-Muñoz Exploiting historical databases to design the target quality profile for a new product Ind. Eng. Chem. Res. 52 2013 8260 8271
110. G.M. Troup, and C. Georgakis Process systems engineering tools in the pharmaceutical industry Comput. Chem. Eng. 51 2013 157 171
111. J. Trygg, and S. Wold Orthogonal projections to latent strucutres (O-PLS) J. Chemom. 16 2002 119 128
112. E.N.M. Van sprang, H.-J. Ramaker, A. Westerhuis, A.K. Smilde, and D. Wienke Statistical batch process monitoring using gray models AIChE J. 51 2005 931 945 (Pubitemid 40440762)
113. S. Verma, Y. Lan, R. Gokhale, and D.J. Burgess Quality by design approach to understand the process of nanosupsension preparation Int. J. Pharm. 377 2009 185 198
114. J. Wan, O. Marjanovic, and B. Lennox Disturbance rejection for the control of batch end-product quality using latent variable models J. Process Control 22 2012 643 652
115. J.A. Westerhuis, and P.M.J. Coenegracht Multivariate modelling of the pharmaceutical two-step process of wet granulation and tabletting with multiblock partial least squares J. Chemom. 11 1997 379 392 (Pubitemid 127478569)
116. J.A. Westerhuis, P.M.J. Coenegracht, and C.F. Lerk Multivariate modelling of the tablet manufacturing process with wet granulation for tablet optimization and in-process control Int. J. Pharm. 156 1997 109 117 (Pubitemid 27400085)
117. H.N. Winkle Implementing quality by design Proc. of PDA/FDA joint regulatory conference, September 24-28, 2007 Washington D.C., USA 2007
118. B.M. Wise, and N.B. Gallagher The process chemometrics approach to process monitoring and fault detection J. Process Control 6 1996 329 348 (Pubitemid 126375552)
119. S. Wold, H. Martens, and H. Wold The multivariate calibration problem in chemistry solved by the PLS method Lecture Notes Math. 973 1983 286 293
120. S. Wold, M. Sjöström, R. Carlson, T. Lundstedt, S. Hellberg, B. Skagerberg, C. Wikström, and J. Öhman Multivariate design Anal. Chim. Acta 191 1986 17 32
121. S. Wold, M. Sjöström, and L. Eriksson PLS-regression: a basic tool of chemometrics Chemom. Intell. Lab. Syst. 58 2001 109 130 (Pubitemid 33033283)
122. F. Yacoub, and J.F. MacGregor Product optimization and control in the latent variable space of nonlinear PLS models Chemom. Intell. Lab. Syst. 70 2004 63 74 (Pubitemid 38102219)
123. F. Yacoub, J. Lautens, L. Lucisano, and W. Banh Application of quality by design principles to legacy drug products J. Pharm. Innov. 6 2011 61 68
124. L.X. Yu Pharmaceutical quality-by-design: product and process development, understanding and control Pharm. Res. 25 2008 781 791
125. B.M. Zacour, J.K. Drennen III, and C.A. Anderson Development of a statistical tolerance-based fluid bed drying design space J. Pharm. Innov. 7 2012 151 162
126. B.M. Zacour, J.K. Drennen III, and C.A. Anderson Development of a fluid bed granulation design space using critical quality attribute weighted tolerance intervals J. Pharm. Sci. 101 2012 2917 2929
127. B.M. Zacour, J.K. Drennen III, and C.A. Anderson Hybrid controls combining first-principle calculations with empirical modeling for fully automated fluid bed processing J. Pharm. Innov. 7 2012 140 150
128. L. Zhang, and S. García-Muñoz A comparison of different methods to estimate prediction uncertainty using partial least squares (PLS): a practitioner's perspective Chemom. Intell. Lab. Syst. 97 2009 152 158
129. S. Zomer, M. Gupta, and A. Scott Application of multivariate tools in pharmaceutical product development to bridge risk assessment to continuous verification in a quality by design environment J. Pharm. Innov. 5 2010 109 118