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1
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Lou H, Ding L, Wu T, Li W, Khalaf R, Smyth HDC, Reid DL. Emerging Process Modeling Capabilities for Dry Powder Operations for Inhaled Formulations. Mol Pharm 2023; 20:5332-5344. [PMID: 37783568 DOI: 10.1021/acs.molpharmaceut.3c00557] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 10/04/2023]
Abstract
Dry powder inhaler (DPI) products are commonly formulated as a mixture of micronized drug particles and large carrier particles, with or without additional fine particle excipients, followed by final powder filling into dose containment systems such as capsules, blisters, or reservoirs. DPI product manufacturing consists of a series of unit operations, including particle size reduction, blending, and filling. This review provides an overview of the relevant critical process parameters used for jet milling, high-shear blending, and dosator/drum capsule filling operations across commonly utilized instruments. Further, this review describes the recent achievements regarding the application of empirical and mechanistic models, especially discrete element method (DEM) simulation, in DPI process development. Although to date only limited modeling/simulation work has been accomplished, in the authors' perspective, process design and development are destined to be more modeling/simulation driven with the emphasis on evaluating the impact of material attributes/process parameters on process performance. The advancement of computational power is expected to enable modeling/simulation approaches to tackle more complex problems with better accuracy when dealing with real-world DPI process operations.
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Affiliation(s)
- Hao Lou
- Drug Product Technologies, Process Development, Amgen, One Amgen Center Drive, Thousand Oaks, California 91320, United States
| | - Li Ding
- Drug Product Technologies, Process Development, Amgen, One Amgen Center Drive, Thousand Oaks, California 91320, United States
| | - Tian Wu
- Drug Product Technologies, Process Development, Amgen, One Amgen Center Drive, Thousand Oaks, California 91320, United States
| | - Weikun Li
- Drug Product Technologies, Process Development, Amgen, One Amgen Center Drive, Thousand Oaks, California 91320, United States
| | - Ryan Khalaf
- Drug Product Technologies, Process Development, Amgen, One Amgen Center Drive, Thousand Oaks, California 91320, United States
| | - Hugh D C Smyth
- College of Pharmacy, The University of Texas at Austin, 2409 West University Avenue, PHR 4.214, Austin, Texas 78712, United States
| | - Darren L Reid
- Drug Product Technologies, Process Development, Amgen, 360 Binney Street, Cambridge, Massachusetts 02142, United States
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Qu S, Zhang W, You C. Modeling of Combustion Characteristics of Particles in Transient Gas–Solid Reacting Flow via a Machine Learning Approach. Ind Eng Chem Res 2022. [DOI: 10.1021/acs.iecr.2c02697] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/24/2022]
Affiliation(s)
- Sibo Qu
- Key Laboratory for Thermal Science and Power Engineering of the Ministry of Education, Department of Energy and Power Engineering, Tsinghua University, Beijing100084, P. R. China
| | - Wei Zhang
- Key Laboratory for Thermal Science and Power Engineering of the Ministry of Education, Department of Energy and Power Engineering, Tsinghua University, Beijing100084, P. R. China
| | - Changfu You
- Key Laboratory for Thermal Science and Power Engineering of the Ministry of Education, Department of Energy and Power Engineering, Tsinghua University, Beijing100084, P. R. China
- Shanxi Research Institute for Clean Energy, Tsinghua University, Taiyuan030000, P. R. China
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Zhou L, Ma H, Liu Z, Zhao Y. Development and verification of coarse‐grain CFD‐DEM for non‐spherical particles in a gas‐solid fluidized bed. AIChE J 2022. [DOI: 10.1002/aic.17876] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/08/2022]
Affiliation(s)
- Lianyong Zhou
- Institute of Process Equipment, College of Energy Engineering Zhejiang University Hangzhou China
| | - Huaqing Ma
- Institute of Process Equipment, College of Energy Engineering Zhejiang University Hangzhou China
| | - Zihan Liu
- Institute of Process Equipment, College of Energy Engineering Zhejiang University Hangzhou China
| | - Yongzhi Zhao
- Institute of Process Equipment, College of Energy Engineering Zhejiang University Hangzhou China
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Zhu LT, Chen XZ, Ouyang B, Yan WC, Lei H, Chen Z, Luo ZH. Review of Machine Learning for Hydrodynamics, Transport, and Reactions in Multiphase Flows and Reactors. Ind Eng Chem Res 2022. [DOI: 10.1021/acs.iecr.2c01036] [Citation(s) in RCA: 6] [Impact Index Per Article: 3.0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/30/2022]
Affiliation(s)
- Li-Tao Zhu
- Department of Chemical Engineering, School of Chemistry and Chemical Engineering, State Key Laboratory of Metal Matrix Composites, Shanghai Jiao Tong University, Shanghai, 200240, P. R. China
| | - Xi-Zhong Chen
- Department of Chemical and Biological Engineering, University of Sheffield, Sheffield, S1 3JD, U.K
| | - Bo Ouyang
- Department of Chemical Engineering, School of Chemistry and Chemical Engineering, State Key Laboratory of Metal Matrix Composites, Shanghai Jiao Tong University, Shanghai, 200240, P. R. China
| | - Wei-Cheng Yan
- School of Chemistry and Chemical Engineering, Jiangsu University, Zhenjiang, Jiangsu 212013, China
| | - He Lei
- Department of Chemical Engineering, School of Chemistry and Chemical Engineering, State Key Laboratory of Metal Matrix Composites, Shanghai Jiao Tong University, Shanghai, 200240, P. R. China
| | - Zhe Chen
- Department of Chemical Engineering, School of Chemistry and Chemical Engineering, State Key Laboratory of Metal Matrix Composites, Shanghai Jiao Tong University, Shanghai, 200240, P. R. China
| | - Zheng-Hong Luo
- Department of Chemical Engineering, School of Chemistry and Chemical Engineering, State Key Laboratory of Metal Matrix Composites, Shanghai Jiao Tong University, Shanghai, 200240, P. R. China
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Xie Z, Gu X, Shen Y. A Machine Learning Study of Predicting Mixing and Segregation Behaviors in a Bidisperse Solid–Liquid Fluidized Bed. Ind Eng Chem Res 2022. [DOI: 10.1021/acs.iecr.2c00071] [Citation(s) in RCA: 5] [Impact Index Per Article: 2.5] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/30/2022]
Affiliation(s)
- Zhouzun Xie
- School of Chemical Engineering, University of New South Wales, Sydney, New South Wales 2052, Australia
| | - Xinyu Gu
- Institute for Superconducting and Electronic Materials, University of Wollongong, Wollongong, New South Wales 2500, Australia
| | - Yansong Shen
- School of Chemical Engineering, University of New South Wales, Sydney, New South Wales 2052, Australia
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Ouyang B, Zhu LT, Su YH, Luo ZH. A hybrid mesoscale closure combining CFD and deep learning for coarse-grid prediction of gas-particle flow dynamics. Chem Eng Sci 2022. [DOI: 10.1016/j.ces.2021.117268] [Citation(s) in RCA: 4] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/26/2022]
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