Login Register Cart 0
Generic placeholder image

Letters in Drug Design & Discovery

Editor-in-Chief

ISSN (Print): 1570-1808
ISSN (Online): 1875-628X

Back
Journal Home About Journal Editorial Board Journal Insight Current Issue Volumes/Issues
Subscribe
Review Article

Computational Fluid Dynamics: Insights and Applications in the Pharmaceutical Field

Author(s): Vanshita Singh, Kamal Shah, Akash Garg and Hitesh Kumar Dewangan*

Volume 21, Issue 3, 2024

Published on: 21 December, 2022

Page: [440 - 450] Pages: 11

DOI: 10.2174/1570180820666221117142108

Price: $65

conference banner
Abstract

Computational fluid dynamics (CFD) is a feasible tool to examine and troubleshoot different types of equipment utilized in the pharmaceutical industry or healthcare. As a large number of fluids are processed by unit operations, even some increments in performance and efficiency may escalate profits and reduce costs. CFD methods are primarily used in the automobile and aerospace industries, but in the current era, this technology is extensively applied in the pharmaceutical and chemical industries and has become an important tool for process design scaleup and optimization. CFD is a numerical approach utilizing CFD software to solve equations numerically. This review focuses on the diverse utilization of CFD in the pharmaceutical field and current applications in the COVID 19 pandemic, a recent health crisis that is intimidating the world.

« Previous Next »
Graphical Abstract

[1]
Hu, H.H. Computational Fluid Dynamics.Fluid Mech; Elsevier, 2012, pp. 421-472.
[http://dx.doi.org/10.1016/B978-0-12-382100-3.10010-1]
[2]
Raman, R.K.; Dewang, Y.; Raghuwanshi, J.J. A review on applications of computational fluid dynamics. Intl. J. LNCT., 2018, 2(6), 137-143.
[3]
Taha, T.R. An introduction to parallel computational fluid dynamics. IEEE Concurr., 1998, 6(4), 78-78.
[http://dx.doi.org/10.1109/MCC.1998.736434]
[4]
Masic, I.; Parojcic, J.; Djuric, Z. Computational fluid dynamics: Applications in pharmaceutical technology. In: Djuris, J., Ed.; Computer-Aided Applications in Pharmaceutical Technology; Elsevier Inc: Amsterdam, 2013; pp. 233-259.
[http://dx.doi.org/10.1533/9781908818324.233]
[5]
Jathar, K.S.; Kulkarni, V.V. An elementary study of computational fluid dynamics for various engineering applications-A review. Int. Res. J. Eng. Technol., 2015, 2(8), 1292-1296.
[6]
Zawawi, M.H. A Review: Fundamentals of Computational Fluid Dynamics (CFD). AIP Conference Proceedings 2018, 2030: 020252.
[http://dx.doi.org/10.1063/1.5066893]
[7]
Singh, V.; Garg, A.; Dewangan, H.K. Recent advances in drug design and delivery across biological barriers using computational models. LDDD, 2022, 19(10), 865-876.
[http://dx.doi.org/10.2174/1570180819999220204110306]
[8]
Pordal, H.; Matice, C.; Fry, T.J. The role of computational fluid dynamics in the pharmaceutical industry. Pharm. Technol., 2002, 26(2), 72-79.
[9]
Sahu, A.C.F.D. Modell. Mixing Stirred Tanks, 1999, 54(13–14), 2285-2293.
[10]
Al-Baali, A.A-G.; Farid, M.M. fundamentals of computational fluid dynamics. In: Sterilization of Food In Retort Pouches. Food Engineering Series; Springer, Boston, 2006; p. 33-44.
[http://dx.doi.org/10.1007/0-387-31129-7_4]
[11]
Pragati, K. Concept of computational fluid dynamics (CFD) and its applications in food processing equipment design. J. Food Process. Technol., 2012, 3(1), 138.
[12]
Peyret, R. Handbook of Computational Fluid Mechanics; Academic Press: Cambridge, 1996.
[13]
Kremer, D.M.; Hancock, B.C. Process simulation in the pharmaceutical industry: A review of some basic physical models. J. Pharm. Sci., 2006, 95(3), 517-529.
[http://dx.doi.org/10.1002/jps.20583] [PMID: 16447175]
[14]
Babnik, S. A review of computational fluid dynamics (CFD). Simul. Mix. Pharm. Industr., 2020, 3(27), 20732-20736.
[15]
Zhong, W.; Yu, A.; Zhou, G.; Xie, J.; Zhang, H. CFD simulation of dense particulate reaction system: Approaches, recent advances and applications. Chem. Eng. Sci., 2016, 140, 16-43.
[http://dx.doi.org/10.1016/j.ces.2015.09.035]
[16]
Bai, G.; Bee, J.S.; Biddlecombe, J.G.; Chen, Q.; Leach, W.T. Computational fluid dynamics (CFD) insights into agitation stress methods in biopharmaceutical development. Int. J. Pharm., 2012, 423(2), 264-280.
[http://dx.doi.org/10.1016/j.ijpharm.2011.11.044] [PMID: 22172288]
[17]
Hemamanjushree, S.; Tippavajhala, V.K. Simulation of unit operations in formulation development of tablets using computational fluid dynamics. AAPS PharmSciTech, 2020, 21(3), 103.
[http://dx.doi.org/10.1208/s12249-020-1635-1] [PMID: 32166477]
[18]
Tong, Z.B.; Yang, R.Y.; Yu, A.B. CFD-DEM study of the aerosolisation mechanism of carrier-based formulations with high drug loadings. Powder Technol., 2017, 314, 620-626.
[http://dx.doi.org/10.1016/j.powtec.2016.10.004]
[19]
Coates, M.S.; Fletcher, D.F.; Chan, H.K.; Raper, J.A. Effect of design on the performance of a dry powder inhaler using computational fluid dynamics. Part 1: Grid structure and mouthpiece length. J. Pharm. Sci., 2004, 93(11), 2863-2876.
[http://dx.doi.org/10.1002/jps.20201] [PMID: 15389665]
[20]
Gallo-Molina, J.P. Multiscale Anal. Water Oeil Emulsions Comp. Fluid Dyn. Approach, 2017, 56(27), 7757-7767.
[21]
Gallo-Molina, J.P.; Ratkovich, N.; Alvarez, O. The application of computational fluid dynamics to the multiscale study of oil-in-water emulsions. Ind. Eng. Chem. Res., 2018, 57(2), 578-589.
[http://dx.doi.org/10.1021/acs.iecr.7b03846]
[22]
Agterof, W. Prediction Emulsion Part. Sizes Using Comp. Fluid Dyn. Approach, 2003, 31(1–4), 141-148.
[23]
Pukkella, A.K.; Vysyaraju, R.; Tammishetti, V.; Rai, B.; Subramanian, S. Improved mixing of solid suspensions in stirred tanks with interface baffles: CFD simulation and experimental validation. Chem. Eng. J., 2019, 358, 621-633.
[http://dx.doi.org/10.1016/j.cej.2018.10.020]
[24]
Zhu, T.; Moussa, E.M.; Witting, M.; Zhou, D.; Sinha, K.; Hirth, M.; Gastens, M.; Shang, S.; Nere, N.; Somashekar, S.C.; Alexeenko, A.; Jameel, F. Predictive models of lyophilization process for development, scale-up/tech transfer and manufacturing. Eur. J. Pharm. Biopharm., 2018, 128, 363-378.
[http://dx.doi.org/10.1016/j.ejpb.2018.05.005] [PMID: 29733948]
[25]
Jameson, A. The present status, challenges, and future developments in computational fluid dynamics. In Agard Conference Proceedings Agard Cp, Agard, 1996, pp. 1-1.
[26]
Khalil, E.E. CFD history and applications. C.F.D. Lett., 2012, 4, 43-46.
[27]
Pletcher, R.H.; Tannehill, J.C.; Anderson, D. Computational Fluid Mechanics and Heat Transfer; CRC Press: Boca Raton, 2012.
[28]
Shang, J.J.P.A.S. Three decades accomplishments comp. Fluid Dyn., 2004, 40(3), 173-197.
[29]
Xia, B.; Sun, D.W.J.C. Agriculture, applications of computational fluid dynamics (CFD) in the food industry: A review. Comput. Electron. Agric., 2002, 34(1-3), 5-24.
[30]
Kaushal, P.; Sharma, H.K. Concept of computational fluid dynamics (CFD) and its applications in food processing equipment design. J. Food Process. Technol., 2011, 3, 138.
[31]
Tilch, R.; Tabbal, A.; Zhu, M.; Decker, F.; Löhner, R. Combination of body‐fitted and embedded grids for external vehicle aerodynamics. Eng. Comput., 2008, 25(1), 28-41.
[http://dx.doi.org/10.1108/02644400810841404]
[32]
Lee, B.K. Computational fluid dynamics in cardiovascular disease. Korean Circ. J., 2011, 41(8), 423-430.
[http://dx.doi.org/10.4070/kcj.2011.41.8.423] [PMID: 21949524]
[33]
Benek, J.A.; Donegan, T.L. Apd N.E. Sub+ extended chunera gnd embeddm scheme with applications to viscous flows, A.I.A.A. 8th Computational Fluid Dyn. Conference. Honolulu, 1987, 1, p. 126.
[34]
Ruzycki, C.A.; Javaheri, E.; Finlay, W.H. The use of computational fluid dynamics in inhaler design. Expert Opin. Drug Deliv., 2013, 10(3), 307-323.
[http://dx.doi.org/10.1517/17425247.2013.753053] [PMID: 23289401]
[35]
Jameson, A. Artificial diffusion, upwind biFing, limiters and their effect on accuracy and mulugnd convergence in transonic and hypersonic flows. AIAA Paper 93-3359. 11th Comp. Fluid Dyn. Conference, Orlando, FL1993, p. 3359.
[36]
Kopsch, T.; Murnane, D.; Symons, D. A personalized medicine approach to the design of dry powder inhalers: Selecting the optimal amount of bypass. Int. J. Pharm., 2017, 529(1-2), 589-596.
[http://dx.doi.org/10.1016/j.ijpharm.2017.07.002] [PMID: 28743094]
[37]
Wang, B.; Bredael, G.; Armenante, P.M. Computational hydrodynamic comparison of a mini vessel and a USP 2 dissolution testing system to predict the dynamic operating conditions for similarity of dissolution performance. Int. J. Pharm., 2018, 539(1-2), 112-130.
[http://dx.doi.org/10.1016/j.ijpharm.2018.01.002] [PMID: 29341921]
[38]
Kindgen, S.; Wachtel, H.; Abrahamsson, B.; Langguth, P. Computational fluid dynamics simulation of hydrodynamics and stresses in the PhEur/USP disintegration tester under fed and fasted fluid characteristics. J. Pharm. Sci., 2015, 104(9), 2956-2968.
[http://dx.doi.org/10.1002/jps.24511] [PMID: 26017815]
[39]
D’Arcy, D.M.; Corrigan, O.I.; Healy, A.M. Evaluation of hydrodynamics in the basket dissolution apparatus using computational fluid dynamics-dissolution rate implications. Eur. J. Pharm. Sci., 2006, 27(2-3), 259-267.
[http://dx.doi.org/10.1016/j.ejps.2005.10.007] [PMID: 16314078]
[40]
D’Arcy, D.M.; Liu, B.; Bradley, G.; Healy, A.M.; Corrigan, O.I. Hydrodynamic and species transfer simulations in the USP 4 dissolution apparatus: Considerations for dissolution in a low velocity pulsing flow. Pharm. Res., 2010, 27(2), 246-258.
[http://dx.doi.org/10.1007/s11095-009-0010-4] [PMID: 20012167]
[41]
McCarthy, L.G.; Bradley, G.; Sexton, J.C.; Corrigan, O.I.; Healy, A.M. Computational fluid dynamics modeling of the paddle dissolution apparatus: Agitation rate, mixing patterns, and fluid velocities. AAPS PharmSciTech, 2004, 5(2), 50-59.
[http://dx.doi.org/10.1208/pt050231] [PMID: 15760089]
[42]
Todaro, V.; Tim, P.; Geoffrey, G.; Anne, M.H.; Deirdre, M.D. Characterization and simulation of hydrodynamics in the paddle, basket and flow-through dissolution testing apparatuses - A review. Dissolut. Technol., 2017, 24, 24-36.
[43]
Ghorbani, M. Using computational fluid dynamics modelling to study continuous manufacturing process of drug substance ingredient. Worldwide J. Multidiscipl. Res. Develop., 2016, 2, 59-61.
[44]
Jamaleddine, T.J.; Ray, M.B. Application of computational fluid dynamics for simulation of drying processes: A review. Dry. Technol., 2010, 28(2), 120-154.
[http://dx.doi.org/10.1080/07373930903517458]
[45]
Huang, L.; Kumar, K.; Mujumdar, A.S. A parametric study of the gas flow patterns and drying performance of co-current spray dryer: Results of a computational fluid dynamics study. Dry. Technol., 2003, 21(6), 957-978.
[http://dx.doi.org/10.1081/DRT-120021850]
[46]
Charton, S.; Randriamanantena, T.; Verdin, N.; Nemri, M. Relevance of CFD in the development of separation processes. Procedia Chem., 2016, 21, 441-445.
[http://dx.doi.org/10.1016/j.proche.2016.10.061]
[47]
Dewangan, H.K.; Tomar, S. Nanovaccine for transdermal delivery system. J. Drug Deliv. Sci. Technol., 2022, 67, 102988.
[http://dx.doi.org/10.1016/j.jddst.2021.102988]
[48]
Dewangan, H.K. Rational application of nanoadjuvant for mucosal vaccine delivery system. J. Immunol. Methods, 2020, 481-482, 112791.
[http://dx.doi.org/10.1016/j.jim.2020.112791] [PMID: 32387695]
[49]
Garg, A.; Dewangan, H.K. Nanoparticles as adjuvants in vaccine delivery. Rev. Ther. Drug Carrer System, 1615, 37(2), 183-204.
[http://dx.doi.org/10.1615/CritRevTherDrugCarrierSyst.2020033273]
[50]
Dewangan, H.K.; Pandey, T.; Maurya, L.; Singh, S. Rational design and evaluation of HBsAg polymeric nanoparticles as antigen delivery carriers. Int. J. Biol. Macromol., 2018, 111, 804-812.
[http://dx.doi.org/10.1016/j.ijbiomac.2018.01.073] [PMID: 29343454]
[51]
Dewangan, H.K.; Singh, S.; Maurya, L.; Srivastava, A.; Hepatitis, B. Hepatitis B antigen loaded biodegradable polymeric nanoparticles: Formulation optimization and in-vivo immunization in BALB/c mice. Curr. Drug Deliv., 2018, 15(8), 1204-1215.
[http://dx.doi.org/10.2174/1567201815666180604110457] [PMID: 29866006]
[52]
Dewangan, H.K.; Pandey, T.; Singh, S. Nanovaccine for immunotherapy and reduced hepatitis-B virus in humanized model. Artif. Cells Nanomed. Biotechnol., 2018, 46(8), 2033-2042.
[PMID: 29179600]
[53]
Dewangan, H.K. The emerging role of nanosuspensions for drug delivery and stability. Curr. Nanomed., 2021, 11(4), 213-223.
[http://dx.doi.org/10.2174/2468187312666211222123307]
[54]
Raghuvanshi, A.; Shah, K.; Dewangan, H.K. Ethosome as antigen delivery carrier: Optimisation, evaluation and induction of immunological response via nasal route against hepatitis B. J. Microencapsul., 2022, 39(4), 352-363.
[http://dx.doi.org/10.1080/02652048.2022.2084169] [PMID: 35635238]
[55]
Feng, Y.; Marchal, T.; Sperry, T.; Yi, H. Influence of wind and relative humidity on the social distancing effectiveness to prevent COVID-19 airborne transmission: A numerical study. J. Aerosol Sci., 2020, 147, 105585.
[http://dx.doi.org/10.1016/j.jaerosci.2020.105585] [PMID: 32427227]

Rights & Permissions Print Cite
© 2024 Bentham Science Publishers | Privacy Policy