» NIR
»
Manager for Manufacturing and Packaging Operations within Wyeth, Ireland. He lectures at the European College of Validation in Brighton and the Dublin Institute of Technology. He has studied Strategy, Leadership and Innovation within the Pharmaceutical Industry at the Harvard Business School, Boston.
Figure 3. Fitted linear models of number of API classifi ed pixels and applied soil concentration (µg 50 mm-2
sodium salt and (b) sulfacetamide sodium salt. CI represents 95% Confi dence Interval and PI is 95% Prediction Interval.
Carl Sullivan, Ph.D., is a lecturer in mathematics at the Dublin Institute of Technology, Ireland. He completed a Ph.D. in statistics in the area of outlier detection using Kalman fi lters and a BA in Mathematics. His current research involves the use mathematical tools for image analysis, in particular the use of marginal and conditional residuals to detect outlying data relative to neighbouring observations. He also has a keen interest in the use of Group Theory with regard to molecular vibration.
) for (a) sulfadimidine Conclusions
The results presented indicate that NIR-CI combined with simple and fast data analysis is a feasible method for the sensitive detection and quantifi cation of API residues on manufacturing equipment. The analysis of the NIR hyperspectral datacubes via a classifi cation function allowed the development of a quantifi cation model based on the number of API classifi ed pixels per soil. The linear models obtained had R2
values of
0.96 and 0.99 for sulfadimidine sodium salt and sulfacetamide sodium salt soils respectively. Limits of detection were also calculated for the quantifi cation models with values for sulfadimidine sodium salt and sulfacetamide sodium salt soils respectively of 27.10 and 13.68 µg 50 mm2
. Diff erent soiling simulation protocols to improve replicate precision
as well as further data analysis of the hypercubes to extract additional quantitative information may result in improved quantifi cation models with possibly lower LODs. This research points to the potential future use of portable NIR-CI systems for continued cleaning verifi cation of pharmaceutical manufacturing equipment.
Acknowledgements
The research leading to these results has received funding from the European Community´s Seventh Framework Program (FP7/2207- 2013) under grant agreement number 285836.
Author Biographies
Patrick J. Cullen, Ph.D., coordinates pharmaceutical technology research at the Dublin Institute of Technology, Ireland. He has a background in both pharmaceutical manufacturing and optical measurement. His current research interests include the development of chemical and 3D imaging systems for pharmaceutical control.
Ian Jones has 10 years’ experience in the pharmaceutical manufacturing sector with expertise in process technology and product robustness in addition to pharmaceutical characterization. He was the Technology
108 | | September/October 2013 - 15TH ANNIVERSARY ISSUE 2. 3. 4. 5. 6. 7. 8. 9.
References 1.
Laura Alvarez-Jubete, Ph.D., is a Post-Doctoral Researcher at the Dublin Institute of Technology, Ireland. She has previous experience in projects dealing with diff erent aspects of analytical chemistry in the food and pharmaceutical sector. Her current research topic includes the application of chemical imaging and chemometrics in pharmaceutical technology and process control.
Jaya Mishra is a Post-Graduate Research scholar at the Dublin Institute of Technology, Ireland. She has a degree in Electrical Engineering and her area of interests includes chemometrics, multivariate statistical analysis, Imaging (color and spectral).
I. Jones, P.J. Cullen, A. Greene, Using PAT to Support the Transition from Cleaning Process Validation to Continued Cleaning Process Verifi cation, Journal of Validation Technology. Winter (2012)
FDA. Guidance for Industry. Process Validation general Principles and Practices. Current Good Manufacturing Practices. 2011.
M. Jamrógiewicz, Application of the near-infrared spectroscopy in the pharmaceutical technology, J. Pharm. Biomed. Anal. 66 (2012) 1-10.
M.P. Lang, N.A. Kocaoglu-Vurma, W.J. Harper, L.E. Rodriguez-Saona, Multicomponent Cleaning Verifi cation of Stainless Steel Surfaces for the Removal of Dairy Residues Using Infrared Microspectroscopy, J. Food Sci. 76 (2011) C303-C308.
A.A. Gowen, C.P. O’Donnell, P.J. Cullen, S.E.J. Bell, Recent applications of Chemical Imaging to pharmaceutical process monitoring and quality control, Eur. J. Pharm. Biopharm. 69 (2008) 10-22.
C. De Bleye, P.F. Chavez, J. Mantanus, R. Marini, P. Hubert, E. Rozet, E. Ziemons, Critical review of near-infrared spectroscopic methods validations in pharmaceutical applications, J. Pharm. Biomed. Anal. 69 (2012) 125-132.
G. Reich, Near-infrared spectroscopy and imaging: Basic principles and pharmaceutical applications, Adv. Drug Delivery. Rev. 57 (2005) 1109-1143.
G.J. Edelman, E. Gaston, T.G. van Leeuwen, P.J. Cullen, M.C.G. Aalders, Hyperspectral imaging for non-contact analysis of forensic traces, Forensic Sci. Int.
B. Boldrini, W. Kessler, K. Rebner, R.W. Kessler, Hyperspectral imaging: a review of best practice, performance and pitfalls for in-line and on-line applications, Journal of Near Infrared Spectroscopy. 20 (2012) 483-508.
10. J. Neter, W. Wasserman, G. Whitmore, Applied statistics. 4th ed. 1993: Allyn & Bacon, Inc. 11. International Conference on Harmonisation (ICH) Q2b, 62, 1997, 27463-27467.
12. D.K. Corrigan, N.A. Salton, C. Preston, S. Piletsky, Towards the development of a rapid, portable, surface enhanced Raman spectroscopy based cleaning verifi cation system for the drug nelarabine, J. Pharm. Pharmacol. 62 (2010) 1195-1200.
Page 1 |
Page 2 |
Page 3 |
Page 4 |
Page 5 |
Page 6 |
Page 7 |
Page 8 |
Page 9 |
Page 10 |
Page 11 |
Page 12 |
Page 13 |
Page 14 |
Page 15 |
Page 16 |
Page 17 |
Page 18 |
Page 19 |
Page 20 |
Page 21 |
Page 22 |
Page 23 |
Page 24 |
Page 25 |
Page 26 |
Page 27 |
Page 28 |
Page 29 |
Page 30 |
Page 31 |
Page 32 |
Page 33 |
Page 34 |
Page 35 |
Page 36 |
Page 37 |
Page 38 |
Page 39 |
Page 40 |
Page 41 |
Page 42 |
Page 43 |
Page 44 |
Page 45 |
Page 46 |
Page 47 |
Page 48 |
Page 49 |
Page 50 |
Page 51 |
Page 52 |
Page 53 |
Page 54 |
Page 55 |
Page 56 |
Page 57 |
Page 58 |
Page 59 |
Page 60 |
Page 61 |
Page 62 |
Page 63 |
Page 64 |
Page 65 |
Page 66 |
Page 67 |
Page 68 |
Page 69 |
Page 70 |
Page 71 |
Page 72 |
Page 73 |
Page 74 |
Page 75 |
Page 76 |
Page 77 |
Page 78 |
Page 79 |
Page 80 |
Page 81 |
Page 82 |
Page 83 |
Page 84 |
Page 85 |
Page 86 |
Page 87 |
Page 88 |
Page 89 |
Page 90 |
Page 91 |
Page 92 |
Page 93 |
Page 94 |
Page 95 |
Page 96 |
Page 97 |
Page 98 |
Page 99 |
Page 100 |
Page 101 |
Page 102 |
Page 103 |
Page 104 |
Page 105 |
Page 106 |
Page 107 |
Page 108 |
Page 109 |
Page 110 |
Page 111 |
Page 112 |
Page 113 |
Page 114 |
Page 115 |
Page 116 |
Page 117 |
Page 118 |
Page 119 |
Page 120 |
Page 121 |
Page 122 |
Page 123 |
Page 124 |
Page 125 |
Page 126 |
Page 127 |
Page 128 |
Page 129 |
Page 130 |
Page 131 |
Page 132 |
Page 133 |
Page 134 |
Page 135 |
Page 136 |
Page 137 |
Page 138 |
Page 139 |
Page 140 |
Page 141 |
Page 142 |
Page 143 |
Page 144 |
Page 145 |
Page 146 |
Page 147 |
Page 148 |
Page 149 |
Page 150 |
Page 151 |
Page 152 |
Page 153 |
Page 154 |
Page 155 |
Page 156