دورية أكاديمية
Automated Tomographic Assessment of Structural Defects of Freeze-Dried Pharmaceuticals.
| العنوان: | Automated Tomographic Assessment of Structural Defects of Freeze-Dried Pharmaceuticals. |
|---|---|
| المؤلفون: | Müller P; Institut für Multiskalensimulation, Friedrich-Alexander-Universität Erlangen-Nürnberg, Erlangen, Germany., Sack A; Institut für Multiskalensimulation, Friedrich-Alexander-Universität Erlangen-Nürnberg, Erlangen, Germany., Dümler J; Institut für Multiskalensimulation, Friedrich-Alexander-Universität Erlangen-Nürnberg, Erlangen, Germany., Heckel M; Institut für Multiskalensimulation, Friedrich-Alexander-Universität Erlangen-Nürnberg, Erlangen, Germany.; IT Unit, University of Technology Nuremberg, Nuremberg, Germany., Wenzel T; Division of Pharmaceutics, Friedrich-Alexander-Universität Erlangen-Nürnberg, Freeze Drying Focus Group, Erlangen, Germany.; GILYOS GmbH, Würzburg, Germany., Siegert T; Division of Pharmaceutics, Friedrich-Alexander-Universität Erlangen-Nürnberg, Freeze Drying Focus Group, Erlangen, Germany., Schuldt-Lieb S; medac GmbH, Wedel, Germany., Gieseler H; GILYOS GmbH, Würzburg, Germany., Pöschel T; Institut für Multiskalensimulation, Friedrich-Alexander-Universität Erlangen-Nürnberg, Erlangen, Germany. thorsten.poeschel@fau.de. |
| المصدر: | AAPS PharmSciTech [AAPS PharmSciTech] 2024 Jun 25; Vol. 25 (6), pp. 143. Date of Electronic Publication: 2024 Jun 25. |
| نوع المنشور: | Journal Article |
| اللغة: | English |
| بيانات الدورية: | Publisher: Springer Country of Publication: United States NLM ID: 100960111 Publication Model: Electronic Cited Medium: Internet ISSN: 1530-9932 (Electronic) Linking ISSN: 15309932 NLM ISO Abbreviation: AAPS PharmSciTech Subsets: MEDLINE |
| أسماء مطبوعة: | Publication: New York : Springer Original Publication: Arlington, VA : American Association of Pharmaceutical Scientists, c2000- |
| مواضيع طبية MeSH: | Freeze Drying*/methods , Machine Learning*, Pharmaceutical Preparations/chemistry ; Quality Control ; Chemistry, Pharmaceutical/methods ; Tomography, X-Ray Computed/methods ; Robotics/methods ; Technology, Pharmaceutical/methods ; Automation/methods |
| مستخلص: | The topology and surface characteristics of lyophilisates significantly impact the stability and reconstitutability of freeze-dried pharmaceuticals. Consequently, visual quality control of the product is imperative. However, this procedure is not only time-consuming and labor-intensive but also expensive and prone to errors. In this paper, we present an approach for fully automated, non-destructive inspection of freeze-dried pharmaceuticals, leveraging robotics, computed tomography, and machine learning. (© 2024. The Author(s).) |
| References: | Pikal MJ. Mechanisms of protein stabilization during freeze-drying storage: the relative importance of thermodynamic stabilization and glassy state relaxation dynamics. In: Freeze-drying/lyophilization of pharmaceutical & biological products. 3rd ed. CRC Press; 2010. p. 161–98. Wang W. Lyophilization and development of solid protein pharmaceuticals. Int J Pharm. 2000;203:1–60. (PMID: 10.1016/S0378-5173(00)00423-310967427) Härdter N, Geidobler R, Presser I, Winter G. Microwave-assisted freeze–drying: Impact of microwave radiation on the quality of high-concentration antibody formulations. Pharmaceutics. 2023;15:2783. (PMID: 10.3390/pharmaceutics151227833814012310747838) Pardeshi SR, Deshmukh NS, Telange DR, Nangare SN, Sonar YY, Lakade SH, et al. Process development and quality attributes for the freeze-drying process in pharmaceuticals, biopharmaceuticals and nanomedicine delivery: a state-of-the-art review. J Pharm Innov. 2023. https://doi.org/10.1186/s43094-023-00551-8 . Manning MC, Chou DK, Murphy BM, Payne RW, Katayama DS. Stability of protein pharmaceuticals: an update. Pharm Res. 2010;27:544–75. (PMID: 10.1007/s11095-009-0045-620143256) Rebizzi M. Development of releasable PEG for transient protein PEGylation, Ph.D. thesis. University of Padova; 2023. Carpenter JF, Chang BS. Lyophilization of protein pharmaceuticals. In: Biotechnology and biopharmaceutical manufacturing, processing, and preservation. Taylor & Francis; 2020. Remmele RL, Krishnan S, Ritter NM, Callahan WL, Warne JM. Development of stable lyophilized protein drug products. Curr Pharm Biotechnol. 2012;13:471–96. (PMID: 10.2174/13892011279936199022283723) Pikal MJ. Freeze drying. In: Swarbrick J, editor. Encyclopedia of pharmaceutical technology, vol. 3. 3rd ed. London: Informa Healthcare; 2007. p. 1807–33. Tang X, Pikal M. Design of freeze-drying processes for pharmaceuticals: Practical advice. Pharm Res. 2004;21:191–200. (PMID: 10.1023/B:PHAM.0000016234.73023.7515032301) Liu J, Viverette T, Virgin M, Anderson M, Dalal P. A study of the impact of freezing on the lyophilization of a concentrated formulation with a high fill depth. Pharm Dev Technol. 2005;10:261–72. (PMID: 10.1081/PDT-5445215926675) Butreddy N, Dudhipala N, Janga KY, Gaddam RP. Lyophilization of small-molecule injectables: An industry perspective on formulation development, process optimization, scale-up challenges, and drug product quality attributes. AAPS PharmSciTech. 2020;21:222. (PMID: 10.1208/s12249-020-01787-w) Barresi AA, Ghio S, Fissore D, Pisano R. Freeze drying of pharmaceutical excipients close to collapse temperature: Influence of the process conditions on process time and product quality. Dry Technol. 2009;27:805–16. (PMID: 10.1080/07373930902901646) Inspection Technical Guides: Lyophilization of Parenteral, USA. 2014. Patel SM, Nail SL, Pikal MJ, Geidobler R, Winter G, Hawe A, et al. Lyophilized drug product cake appearance: What is acceptable? J Pharm Sci. 2017;106:1706–21. (PMID: 10.1016/j.xphs.2017.03.01428341598) Lyophilization of parenteral (7/93) - guide to inspections of lyophilization of parenterals. 2014. https://www.fda.gov/inspections-compliance-enforcement-and-criminal-investigations/inspectionguides/lyophilization-parenteral-793 . Rambhatla S, Ramot R, Bhugra C, Pikal MJ. Heat and mass transfer scale-up issues during freeze drying: II. Control and characterization of the degree of supercooling. AAPS PharmSciTech. 2004;5:54–62. (PMID: 10.1208/pt0504582750483) Schersch K, Betz O, Garidel P, Muehlau S, Bassarab S, Winter G. Systematic investigation of the effect of lyophilizate collapse on pharmaceutically relevant proteins I: Stability after freeze drying. J Pharm Sci. 2010;99:2256–78. (PMID: 10.1002/jps.2200020039389) Liu J. Physical characterization of pharmaceutical formulations in frozen and freeze-dried solid states: Techniques and applications in freeze-drying development. Pharm Dev Technol. 2006;11:3–28. https://doi.org/10.1080/10837450500463729 . (PMID: 10.1080/1083745050046372916544906) Kim AI, Akers MJ, Nail SL. The physical state of mannitol after freeze drying: Effects of mannitol concentration, freezing rate, and a noncrystallizing cosolute. J Pharm Sci. 1998;87:931–5. (PMID: 10.1021/js980001d9687336) Stärtzel P, Gieseler H, Gieseler M, Abdul-Fattah AM, Adler M, Mahler HC, et al. Freeze drying of l-arginine/sucrose-based protein formulations, part 2: Optimization of formulation design and freeze-drying process conditions for an l-arginine chloride-based protein formulation system. J Pharm Sci. 2015;104:4241–56. (PMID: 10.1002/jps.2465826422647) Geidobler R, Konrad I, Winter G. Can controlled ice nucleation improve freeze-drying of highly-concentrated protein formulations? J Pharm Sci. 2013;102:3915–9. (PMID: 10.1002/jps.2370423963664) Tsay C, Li Z. Automating visual inspection of lyophilized drug products with multi-input deep neural networks. In: 2019 IEEE 15th international conference on automation science and engineering (CASE). IEEE; 2019. p. 1802–7. Haeuser C, Goldbach P, Huwyler J, Friess W, Allmendinger A. Imaging techniques to characterize cake appearance of freeze-dried products. J Pharm Sci. 2018;107:2810–22. (PMID: 10.1016/j.xphs.2018.06.02530005985) Pisano R, Barresi AA, Capozzi L, Novajra G, Oddone I, Vitale-Brovarone C. Characterization of the mass transfer of lyophilized products based on x-ray micro-computed tomography images. Dry Technol. 2017;35:933–8. (PMID: 10.1080/07373937.2016.1222540) Kunz C, Schuldt-Lieb S, Gieseler H. Freeze-drying from organic co-solvent systems, part 2: Process modifications to reduce residual solvent levels and improve product quality attributes. J Pharm Sci. 2019;108:399–415. (PMID: 10.1016/j.xphs.2018.07.00230017885) Schomberg K, Diener A, Wünsch I, Finke JH, Kwade A. The use of X-ray microtomography to investigate the microstructure of pharmaceutical tablets: Potentials and comparison to common physical methods. Int J Pharm: X. 2021;3:100090. (PMID: 34377974) Yost E, Chalus P, Zhang S, Peter S, Narang AS. Quantitative x-ray microcomputed tomography assessment of internal tablet defects. J Pharm Sci. 2019;108:1818–30. (PMID: 10.1016/j.xphs.2018.12.02430639743) Sondej F, Bück A, Koslowsky K, Bachmann P, Jacob M, Tsotsas E. Investigation of coating layer morphology by micro-computed X-ray tomography. Powder Technol. 2015;273:165–75. (PMID: 10.1016/j.powtec.2014.12.050) Zeitler JA, Gladden LF. In-vitro tomography and non-destructive imaging at depth of pharmaceutical solid dosage forms. Eur J Pharm Biopharm. 2009;71:2–22. (PMID: 10.1016/j.ejpb.2008.08.01218778770) Hancock BC, Mullarney MP. X-ray microtomography of solid dosage forms. Pharm Technol. 2005;29:92–100. Gajjar P, Styliari ID, Nguyen TTH, Carr J, Chen X, Elliott JA, et al. 3d characterisation of dry powder inhaler formulations:Developing X-ray micro computed tomography approaches. Eur J Pharm Biopharm. 2020;151:32–44. Wenzel T, Sack A, Müller P, Pöschel T, Schuldt-Lieb S, Gieseler H. Stability of freeze dried products subjected to microcomputed tomography radiation doses. J Pharm Pharmacol. 2021;73:212–20. (PMID: 10.1093/jpp/rgaa00433793810) Stärtzel P, Gieseler H, Gieseler M, Abdul-Fattah AM, Adler M, Mahler HC, et al. Freeze drying of l-arginine/sucrose-based protein formulations, part 1: Influence of formulation and arginine counter ion on the critical formulation temperature, product performance and protein stability. J Pharm Sci. 2015;104:2345–58. Szegedy C, Liu W, Jia Y, Sermanet P, Reed S, Anguelov D, et al. Going deeper with convolutions. In: 2015 IEEE conference on computer vision and pattern recognition (CVPR); 2015. p. 1–9. Sam SM, Kamardin K, Sjarif SM, Mohamed N. Offline signature verification using deep learning convolutional neural network (CNN) architectures GoogLeNet Inception-v1 and Inception v3. Procedia Comput Sci. 2019;163:84–91. Xia X, Xu C, Nan B. Inception-v3 for flower classification. In: 2017 2nd international conference on image, vision and computing (ICIVC). IIEEE; 2017. Wang C, Chen D, Hao L, Liu X, Zeng Y, Chen J. Pulmonary image classification based on Inception-v3 transfer learning model. In: 2019 IEEE 4th international conference on signal and image processing (ICSIP). IEEE; 2019. Yang Q, Yu Z, Dai W, Pan SJ. Transfer learning. Cambridge University Press; 2020. (PMID: 10.1017/9781139061773) Lu J, Behbood V, Hao P, Zuo H, Xue S, Zhang G. Transfer learning using computational intelligence: a survey. Knowl-Based Syst. 2015;80:14–23. (PMID: 10.1016/j.knosys.2015.01.010) Zhuang F, Qi Z, Duan K, Xi D, Zhu Y, Zhu H, et al. A comprehensive survey on transfer learning. Proc IEEE. 2020;109:43–76. (PMID: 10.1109/JPROC.2020.3004555) Gao Y, Mosalam KM. Deep transfer learning for image-based structural damage recognition. Comput-Aided Civ Inf Eng. 2018;33:748–68. (PMID: 10.1111/mice.12363) Ghafoorian M, Mehrtash A, Kapur T, Karssemeijer N, Marchiori E, Pesteie Mn, et al. Transfer learning for domain adaptation in MRI: application in brain lesion segmentation. In: Descoteaux M, Maier-Hein L, Franz A, Jannin P, Collins DL, Duchesne S, editors. Medical image computing and computer assisted intervention – MICCAI 2017. Cham: Springer International Publishing; 2017. p. 516–24. Minaee S, Kafieh R, Sonka M, Yazdani S, Jamalipour GHR. Deep-COVID: Predicting COVID-19 from chest X-ray images using deep transfer learning. Med Image Anal. 2020;65:101794. (PMID: 10.1016/j.media.2020.101794327813777372265) Nair P, Mühlbauer S, Roy S, Pöschel T. Can Minkowski tensors of a simply connected porous microstructure characterize its permeability? Phys Fluids 2021;33. https://doi.org/10.1063/5.0045701 . Deng J, Dong W, Socher R, Li L-J, Li K, Fei-Fei L. Imagenet: A large-scale hierarchical image database. In: 2009 IEEE conference on computer vision and pattern recognition. 2009. p. 248–255. Russakovsky O, Deng J, Su H, Krause J, Satheesh S, Ma S, et al. ImageNet large scale visual recognition challenge. Int J Comput Vis. 2015;115:211–52. (PMID: 10.1007/s11263-015-0816-y) Kornblith S, Shlens J, Le QV. Do better imageNet models transfer better? In: Proceedings of the IEEE/CVF conference on computer vision and pattern recognition (CVPR); 2019. Krizhevsky A, Sutskever I, Hinton GE, editors. ImageNet classification with deep convolutional neural networks. Communications of the ACM. 2017;60:84–90. Murphy K. Machine learning: a probabilistic perspective (adaptive computation and machine learning series). MIT Press; 2012. Li H, Lu W. Mixed cross entropy loss for neural machine translation. In: International conference on machine learning, vol. 139; 2021. p. 6242–51. Ho Y, Wookey S. The real-world-weight cross-entropy loss function: modeling the costs of mislabeling. IEEE Access. 2019;7:176073–82. ChatGPT (Large language model). https://chat.openai.com . |
| فهرسة مساهمة: | Keywords: X-ray tomography; freeze-drying; lyopohilisate; non-destructive inspection |
| المشرفين على المادة: | 0 (Pharmaceutical Preparations) |
| تواريخ الأحداث: | Date Created: 20240625 Date Completed: 20240625 Latest Revision: 20240625 |
| رمز التحديث: | 20240626 |
| DOI: | 10.1208/s12249-024-02833-7 |
| PMID: | 38918304 |
| قاعدة البيانات: | MEDLINE |
Find this article in full text from Springer Verlag. 
Full Text Finder | تدمد: | 1530-9932 |
|---|---|
| DOI: | 10.1208/s12249-024-02833-7 |