Comparing a Statistical Model and Bayesian Approach to Establish the Design Space for the Coating of Ciprofloxacin HCl Beads at Different Scales of Production
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The primary objective of this study was to compare two methods for establishing a design space for critical process parameters that affect ethylcellulose film coating of multiparticulate beads and assess this design space validity across manufacturing scales. While there are many factors that can affect film coating, this study will focus on the effects processing conditions have on the quality and extent of film formation, as evaluated by their impact coating yield and drug release. Ciprofloxacin HCl layered beads were utilized as an active substrate core, ethylcellulose aqueous dispersion as a controlled release polymer, and triethyl citrate as a plasticizer. Thirty experiments were conducted using a central composite design to optimize the coating process and map the response surface to build a design space using either statistical least squares or a Bayesian approach. The response surface was fitted using a linear two-factor interaction model with spraying temperature, curing temperature, and curing time as significant model terms. The design spaces established by the two approaches were in close agreement with the statistical least squares approach being more conservative than the Bayesian approach. The design space established for the critical process parameters using small-scale batches was tested using scale-up batches and found to be scale-independent. The robustness of the design space was confirmed across scales and was successfully utilized to establish process signature for the coating process.
KEY WORDSdisk-Jet technology fluid bed response surface methodology design space Bayesian analysis Ethylcellulose Pseudolatex dispersion Pyrobutton®
The authors would like to thank Dr. Salah Ahmed, CEO of Abon Pharmaceuticals for allowing us to conduct scale-up experiments at their facility, Dr. Brian Carlin and Dr. Rina Choksi from FMC Corp. for their participation in the project, and Oystar Huttlin, Germany for providing the fluid bed Mycrolab at the University of Maryland, Bela Janscik from OPULUS for supplying the Pyrobutton package. The content of this paper was part of the graduate thesis dissertation submitted by Bhaveshkumar H. Kothari to the faculty of the School of Pharmacy, University of Maryland Baltimore in partial fulfillment of the requirements for the doctorate degree in Pharmaceutical Sciences—2013.
Funding for the project was from US Food and Drug Administration (FDA) under grant no. HHSF223201110076A.
- 8.Wicks ZW. Free volume and the coatings formulator. J Coatings Technol. 1986;58:23–32.Google Scholar
- 13.Kothari BH, Fahmy R, Claycamp HG, Moore CMV, Chatterjee S, Hoag SW. A systematic approach of employing quality by design principles: risk assessment and design of experiments to demonstrate process understanding and identify the critical process parameters for coating of the ethylcellulose pseudolatex dispersion using non-conventional fluid bed process. AAPS PharmSciTech. 2016;18:1–23. https://doi.org/10.1208/s12249-016-0569-0.CrossRefGoogle Scholar
- 15.Jones DM. Coating process and equipment. In: Hoag SW, Augsburger LL, editors. Pharmaceutical dosage forms: tablets. New York: Informa Healthcare; 2008. p. 379–97.Google Scholar
- 16.Kukec S, Vrecer F, Dreu R. A study of in situ fluid bed melt granulation using response surface methodology / Uporaba metodologije odgovornih povrsin za studij in situ granulacije s talinami v zvrtincenih plasteh. Acta Pharma. 2012;62(4):497–513. https://doi.org/10.2478/v10007-012-0033-y.CrossRefGoogle Scholar
- 19.Carley K, Kamneva N, Reminga J. Response surface methodology. Center for Computational Analysis of Social and Organistional Systems. 2004; CASOS Technical Report.Google Scholar
- 21.Marucci M, Holmgren A, Carlsson H, Jarke A, Johansson M, Corswant C. Non-uniformity of pellets coating, effect on the dose release profile and how to improve the coating process by reducing the electrostatic charging of the pellets. Chem Biochem Eng Q. 2012;26(4):379–84.Google Scholar
- 23.Melegari C, Bertoni S, Genovesi A, Hughes K, Rajabi-Siahboomi AR, Passerini N, et al. Ethylcellulose film coating of guaifenesin-loaded pellets: a comprehensive evaluation of the manufacturing process to prevent drug migration. Eur J Pharm Biopharm. 2016;100:15–26. https://doi.org/10.1016/j.ejpb.2015.12.001.CrossRefPubMedGoogle Scholar
- 25.Zhang Z, Xiaofeng B. Comparison about the three central composite designs with simulation. Proceedings of the 2009 International Conference on Advanced Computer Control: IEEE Computer Society; 2009. pp 163–7.Google Scholar
- 26.Whitcomb PJ. FDS—powerfull tool for response surface design. Stat-Teaser, News from Stat-Ease, Inc. 2008;(1).Google Scholar
- 30.R. A language environment for statistical computing. Vienna: R Foundation for Statistical Computing; 2011.Google Scholar
- 31.SAS OnlineDoc(R) 9.3. Cary, NC: SAS Institute Inc; 2011.Google Scholar
- 32.Rossi P. Bayesm: Bayesian inference for marketing/micro-economics. R package version 2.2–4. Rossi, P. email@example.com; 2011.Google Scholar
- 33.Casella G, George E. Explaining the Gibbs sampler. Am Stat. 1992;46(3):167–74.Google Scholar