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Bhalode P, Razavi SM, Tian H, Roman-Ospino A, Scicolone J, Callegari G, Dubey A, Koolivand A, Krull S, O'Connor T, Muzzio FJ, Ierapetritou MG. Statistical data treatment for residence time distribution studies in pharmaceutical manufacturing. Int J Pharm 2024; 657:124133. [PMID: 38642620 DOI: 10.1016/j.ijpharm.2024.124133] [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] [Received: 01/18/2024] [Revised: 04/13/2024] [Accepted: 04/15/2024] [Indexed: 04/22/2024]
Abstract
Residence time distribution (RTD) method has been widely used in the pharmaceutical manufacturing for understanding powder dynamics within unit operations and continuous integrated manufacturing lines. The dynamics thus captured is then used to develop predictive models for unit operations and important RTD-based applications ensuring product quality assurance. Despite thorough efforts in tracer selection, data acquisition, and calibration model development to obtain tracer concentration profiles for RTD studies, there can exist significant noise in these profiles. This noise can make it challenging to identify the underlying signal and get a representative RTD of the system under study. Such concerns have previously indicated the importance of noise handling for RTD measurements in literature. However, the literature does not provide sufficient information on noise handling or data treatment strategies for RTD studies. To this end, we investigate the impact of varying levels of noise using different tracers on measurement of RTD profile and its applications. We quantify the impact of different denoising methods (time and frequency averaging methods). Through this investigation, we see that Savitsky Golay filtering turns out to a good method for denoising RTD profiles despite varying noise levels. The investigation is performed such that the key features of the RTD profile (which are important for RTD based applications) are preserved. Subsequently, we also investigate the impact of denoising on RTD-based applications such as out-of-specification (OOS) analysis and RTD modeling. The results show that the degree of noise levels considered in this work do not significantly impact the RTD-based applications.
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Affiliation(s)
- Pooja Bhalode
- Center of Plastics Innovation, University of Delaware, DE, USA
| | - Sonia M Razavi
- Department of Chemical and Biochemical Engineering, Rutgers University, NJ, USA
| | - Huayu Tian
- Department of Chemical and Biomolecular Engineering, University of Delaware, DE, USA
| | - Andres Roman-Ospino
- Department of Chemical and Biochemical Engineering, Rutgers University, NJ, USA
| | - James Scicolone
- Department of Chemical and Biochemical Engineering, Rutgers University, NJ, USA
| | - Gerardo Callegari
- Department of Chemical and Biochemical Engineering, Rutgers University, NJ, USA
| | - Atul Dubey
- Pharmaceutical Continuous Manufacturing (PCM), United States Pharmacopeia, 12601 Twinbrook Parkway, Rockville, MD, USA
| | - Abdollah Koolivand
- Office of Pharmaceutical Quality, Center for Drug Evaluation Research, Food and Drug Administration, 10903 New Hampshire Ave, Silver Springs, MD 20993, USA
| | - Scott Krull
- Office of Pharmaceutical Quality, Center for Drug Evaluation Research, Food and Drug Administration, 10903 New Hampshire Ave, Silver Springs, MD 20993, USA
| | - Thomas O'Connor
- Office of Pharmaceutical Quality, Center for Drug Evaluation Research, Food and Drug Administration, 10903 New Hampshire Ave, Silver Springs, MD 20993, USA
| | - Fernando J Muzzio
- Department of Chemical and Biochemical Engineering, Rutgers University, NJ, USA
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Cao J, Shen H, Zhao S, Ma X, Chen L, Dai S, Xu B, Qiao Y. Sample Size Requirements of a Pharmaceutical Material Library: A Case in Predicting Direct Compression Tablet Tensile Strength by Latent Variable Modeling. Pharmaceutics 2024; 16:242. [PMID: 38399296 PMCID: PMC10893091 DOI: 10.3390/pharmaceutics16020242] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/22/2023] [Revised: 01/26/2024] [Accepted: 01/31/2024] [Indexed: 02/25/2024] Open
Abstract
The material library is an emerging, new data-driven approach for developing pharmaceutical process models. How many materials or samples should be involved in a particular application scenario is unclear, and the impact of sample size on process modeling is worth discussing. In this work, the direct compression process was taken as the research object, and the effects of different sample sizes of material libraries on partial least squares (PLS) modeling in the prediction of tablet tensile strength were investigated. A primary material library comprising 45 materials was built. Then, material subsets containing 5 × i (i = 1, 2, 3, …, 8) materials were sampled from the primary material library. Each subset underwent sampling 1000 times to analyze variations in model fitting performance. Both hierarchical sampling and random sampling were employed and compared, with hierarchical sampling implemented with the help of the tabletability classification index d. For each subset, modeling data were organized, incorporating 18 physical properties and tableting pressure as the independent variables and tablet tensile strength as the dependent variable. A series of chemometric indicators was used to assess model performance and find important materials for model training. It was found that the minimum R2 and RMSE values reached their maximum, and the corresponding values were kept almost unchanged when the sample sizes varied from 20 to 45. When the sample size was smaller than 15, the hierarchical sampling method was more reliable in avoiding low-quality few-shot PLS models than the random sampling method. Two important materials were identified as useful for building an initial material library. Overall, this work demonstrated that as the number of materials increased, the model's reliability improved. It also highlighted the potential for effective few-shot modeling on a small material library by controlling its information richness.
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Affiliation(s)
- Junjie Cao
- Department of Chinese Medicine Informatics, School of Chinese Materia Medica, Beijing University of Chinese Medicine, No. 11, North Third Ring East Road, Beijing 100029, China; (J.C.); (H.S.); (S.Z.); (X.M.); (L.C.)
- Beijing Key Laboratory of Chinese Medicine Manufacturing Process Control and Quality Evaluation, Beijing 100029, China
| | - Haoran Shen
- Department of Chinese Medicine Informatics, School of Chinese Materia Medica, Beijing University of Chinese Medicine, No. 11, North Third Ring East Road, Beijing 100029, China; (J.C.); (H.S.); (S.Z.); (X.M.); (L.C.)
| | - Shuying Zhao
- Department of Chinese Medicine Informatics, School of Chinese Materia Medica, Beijing University of Chinese Medicine, No. 11, North Third Ring East Road, Beijing 100029, China; (J.C.); (H.S.); (S.Z.); (X.M.); (L.C.)
- Beijing Key Laboratory of Chinese Medicine Manufacturing Process Control and Quality Evaluation, Beijing 100029, China
| | - Xiao Ma
- Department of Chinese Medicine Informatics, School of Chinese Materia Medica, Beijing University of Chinese Medicine, No. 11, North Third Ring East Road, Beijing 100029, China; (J.C.); (H.S.); (S.Z.); (X.M.); (L.C.)
- Beijing Key Laboratory of Chinese Medicine Manufacturing Process Control and Quality Evaluation, Beijing 100029, China
| | - Liping Chen
- Department of Chinese Medicine Informatics, School of Chinese Materia Medica, Beijing University of Chinese Medicine, No. 11, North Third Ring East Road, Beijing 100029, China; (J.C.); (H.S.); (S.Z.); (X.M.); (L.C.)
- Beijing Key Laboratory of Chinese Medicine Manufacturing Process Control and Quality Evaluation, Beijing 100029, China
| | - Shengyun Dai
- National Institutes for Food and Drug Control, Beijing 100050, China;
| | - Bing Xu
- Department of Chinese Medicine Informatics, School of Chinese Materia Medica, Beijing University of Chinese Medicine, No. 11, North Third Ring East Road, Beijing 100029, China; (J.C.); (H.S.); (S.Z.); (X.M.); (L.C.)
- Beijing Key Laboratory of Chinese Medicine Manufacturing Process Control and Quality Evaluation, Beijing 100029, China
| | - Yanjiang Qiao
- Department of Chinese Medicine Informatics, School of Chinese Materia Medica, Beijing University of Chinese Medicine, No. 11, North Third Ring East Road, Beijing 100029, China; (J.C.); (H.S.); (S.Z.); (X.M.); (L.C.)
- Beijing Key Laboratory of Chinese Medicine Manufacturing Process Control and Quality Evaluation, Beijing 100029, China
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Steady-state modeling of a new continuous API dryer: Reduced-order model and investigation of dryer performance. Int J Pharm 2023; 635:122701. [PMID: 36773730 DOI: 10.1016/j.ijpharm.2023.122701] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/07/2022] [Revised: 02/01/2023] [Accepted: 02/04/2023] [Indexed: 02/11/2023]
Abstract
In the present study, a reduced-order model is proposed to analyze a novel continuous dryer with an application in the pharmaceutical industry. The model was validated using process data from ibuprofen drying test runs, and the results were in good agreement with the experimental data. The test substance was an ibuprofen paste with an initial LOD of up to 30 w%. The simulations showed that the contact heat transfer coefficient can be correlated with the degree of wetness. Furthermore, a set of simulations was performed to analyze the influence of input parameters on the dryer's performance: i) the inlet air flow rate and ii) the inlet air temperature. The simulation results demonstrated that a variation in the inlet air temperature significantly affects the air temperature profile, while the inlet air flow rate has a minor effect. Besides, it was also established that the inlet solid LoD has the most considerable effect on the product quality (e.g., final solid moisture content). The results showed a deviation of less than 10% for the product LoD and the product temperature in most cases.
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Digital twin of a continuous direct compression line for drug product and process design using a hybrid flowsheet modelling approach. Int J Pharm 2022; 628:122336. [DOI: 10.1016/j.ijpharm.2022.122336] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/13/2022] [Revised: 10/19/2022] [Accepted: 10/20/2022] [Indexed: 11/06/2022]
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Tian H, Bhalode P, Razavi SM, Koolivand A, Muzzio FJ, Ierapetritou MG. Characterization and propagation of RTD uncertainty for continuous powder blending processes. Int J Pharm 2022; 628:122326. [DOI: 10.1016/j.ijpharm.2022.122326] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/15/2022] [Revised: 09/18/2022] [Accepted: 10/16/2022] [Indexed: 10/31/2022]
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Gyürkés M, Madarász L, Záhonyi P, Köte Á, Nagy B, Pataki H, Nagy ZK, Domokos A, Farkas A. Soft sensor for content prediction in an integrated continuous pharmaceutical formulation line based on the residence time distribution of unit operations. Int J Pharm 2022; 624:121950. [PMID: 35753540 DOI: 10.1016/j.ijpharm.2022.121950] [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] [Received: 03/31/2022] [Revised: 06/14/2022] [Accepted: 06/20/2022] [Indexed: 12/01/2022]
Abstract
In this study, a concentration predicting soft sensor was achieved based on the Residence Time Distribution (RTD) of an integrated, three-step pharmaceutical formulation line. The RTD was investigated with color-based tracer experiments using image analysis. Twin-screw wet granulation (TSWG) was directly coupled with a horizontal fluid bed dryer and an oscillating mill. Based on integrated measurement, we proved that it is also possible to couple the unit operations in silico. Three surrogate tracers were produced with a coloring agent to investigate the separated unit operations and the solid and liquid inputs of the TSWG. The soft sensor's prediction was compared to validating experiments of a 0.05 mg/g (15% of the nominal) concentration change with High-Performance Liquid Chromatography (HPLC) reference measurements of the active ingredient proving the adequacy of the soft sensor (RMSE < 4%).
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Affiliation(s)
- Martin Gyürkés
- Department of Organic Chemistry and Technology, Budapest University of Technology and Economics, Műegyetem rkp. 3., H-1111 Budapest, Hungary
| | - Lajos Madarász
- Department of Organic Chemistry and Technology, Budapest University of Technology and Economics, Műegyetem rkp. 3., H-1111 Budapest, Hungary
| | - Petra Záhonyi
- Department of Organic Chemistry and Technology, Budapest University of Technology and Economics, Műegyetem rkp. 3., H-1111 Budapest, Hungary
| | - Ákos Köte
- Department of Organic Chemistry and Technology, Budapest University of Technology and Economics, Műegyetem rkp. 3., H-1111 Budapest, Hungary
| | - Brigitta Nagy
- Department of Organic Chemistry and Technology, Budapest University of Technology and Economics, Műegyetem rkp. 3., H-1111 Budapest, Hungary
| | - Hajnalka Pataki
- Department of Organic Chemistry and Technology, Budapest University of Technology and Economics, Műegyetem rkp. 3., H-1111 Budapest, Hungary
| | - Zsombor Kristóf Nagy
- Department of Organic Chemistry and Technology, Budapest University of Technology and Economics, Műegyetem rkp. 3., H-1111 Budapest, Hungary
| | - András Domokos
- Department of Organic Chemistry and Technology, Budapest University of Technology and Economics, Műegyetem rkp. 3., H-1111 Budapest, Hungary
| | - Attila Farkas
- Department of Organic Chemistry and Technology, Budapest University of Technology and Economics, Műegyetem rkp. 3., H-1111 Budapest, Hungary
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