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Ohashi R, Koide T, Fukami T. Effects of wet granulation process variables on the quantitative assay model of transmission Raman spectroscopy for pharmaceutical tablets. Eur J Pharm Biopharm 2023; 191:276-289. [PMID: 37714414 DOI: 10.1016/j.ejpb.2023.09.009] [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: 08/03/2023] [Revised: 09/11/2023] [Accepted: 09/12/2023] [Indexed: 09/17/2023]
Abstract
Transmission Raman spectroscopy (TRS) is a process analytical technology tool for nondestructive analysis of drug content in tablets. Although wet granulation is the most used tablet manufacturing method, most TRS studies have focused on tablets manufactured via direct compression. The effects of upstream process parameter variations, such as granulation, on the prediction performance of TRS quantitative models are unknown. We evaluated the effects of process parameter variations during granulation on the prediction performance of the TRS quantitative model. Tablets with a drug concentration of 1%w/w were used. We developed PLS calibration models for the drug concentration range of 70-130% label claims. Subsequently, we predicted the drug content of the tablets with different granulation parameters. The results of our study demonstrate that the variation in the predicted recovery due to the variation in granulation parameters was practically acceptable. The calibration model showed a good prediction performance for tablets manufactured at different granulation scales and thicknesses. Therefore, we conclude that TRS quantitative models are robust to variations in upstream processes, such as granulation and downstream variations in tableting parameters. These results suggest that TRS is a versatile non-destructive quantitative analysis method that can be applied in tablet manufacturing.
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Affiliation(s)
- Ryo Ohashi
- Department of Molecular Pharmaceutics, Meiji Pharmaceutical University, 2-522-1 Noshio, Kiyose, Tokyo 204-8588 Japan; Formulation R&D Laboratory, R&D Division, SHIONOGI & CO., LTD., Hyogo 660-0813, Japan.
| | - Tatsuo Koide
- Division of Drugs, National Institute of Health Sciences, Tonomachi, Kawasaki-ku, Kawasaki 210-9501, Japan
| | - Toshiro Fukami
- Department of Molecular Pharmaceutics, Meiji Pharmaceutical University, 2-522-1 Noshio, Kiyose, Tokyo 204-8588 Japan
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2
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Razvi SZ, Ma S, Zhong Q, Muliadi A, Shi ZP. Phase-appropriate Application of Process Analytical Technology for Early Pharmaceutical Development of Oral Solid Dosage Forms-the Case Study of Uniformity Screening of Dosage Units and Blends. AAPS J 2023; 25:90. [PMID: 37715005 DOI: 10.1208/s12248-023-00854-x] [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: 05/10/2023] [Accepted: 08/23/2023] [Indexed: 09/17/2023] Open
Abstract
Process analytical technology (PAT) in late-stage drug product development is typically used for real-time process monitoring, in-process control, and real-time release testing. In early research and development (R&D), PAT usage is limited as the manufacturing scale is relatively small with frequent changes and only a few batches are produced on an annual basis. However, process understanding is critical at early R&D in order to identify process and formulation boundaries, so PAT applications could be particularly useful in early-stage R&D. For oral solid dosage form, conventional HPLC-based content uniformity (CU) methods with sampling of 3 tablets per stratified sampling location in early R&D are typically not sufficient to identify these manufacturing process boundaries and temporal profile. Here, we report a screening CU method based on a multivariate model using transmission Raman spectroscopy (TRS) data on a phase-appropriate calibration set of only 16 tablets. This initial model was used for multiple pre-GMP development batches to provide critical information about blend uniformity and content uniformity (CU). In this work, the precision of the TRS method was evaluated; multiple spectral preprocessing approaches were compared regarding their effects on measurement precision as well as their ability to mitigate the photo bleaching effects during precision experiments. Overall, the TRS-based CU method was much faster than a traditional HPLC-based method allowing a much larger number of tablets to be screened. This larger number of analyzed tablets enabled the processes boundaries and temporal changes in CU to be identified while providing proper statistical assurance on product quality.
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Affiliation(s)
- Sayyeda Zeenat Razvi
- Small Molecule Pharmaceutical Sciences, Genentech Inc, 1 DNA Way, South San Francisco, California, 94080, USA.
| | - Shengli Ma
- Small Molecule Pharmaceutical Sciences, Genentech Inc, 1 DNA Way, South San Francisco, California, 94080, USA
| | - Qiqing Zhong
- Small Molecule Pharmaceutical Sciences, Genentech Inc, 1 DNA Way, South San Francisco, California, 94080, USA
| | - Ariel Muliadi
- Small Molecule Pharmaceutical Sciences, Genentech Inc, 1 DNA Way, South San Francisco, California, 94080, USA
| | - Zhenqi Pete Shi
- Small Molecule Pharmaceutical Sciences, Genentech Inc, 1 DNA Way, South San Francisco, California, 94080, USA.
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3
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Jørgensen AK, Ong JJ, Parhizkar M, Goyanes A, Basit AW. Advancing non-destructive analysis of 3D printed medicines. Trends Pharmacol Sci 2023; 44:379-393. [PMID: 37100732 DOI: 10.1016/j.tips.2023.03.006] [Citation(s) in RCA: 9] [Impact Index Per Article: 9.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/11/2023] [Revised: 03/22/2023] [Accepted: 03/22/2023] [Indexed: 04/28/2023]
Abstract
Pharmaceutical 3D printing (3DP) has attracted significant interest over the past decade for its ability to produce personalised medicines on demand. However, current quality control (QC) requirements for traditional large-scale pharmaceutical manufacturing are irreconcilable with the production offered by 3DP. The US Food and Drug Administration (FDA) and the UK Medicines and Healthcare Products Regulatory Agency (MHRA) have recently published documents supporting the implementation of 3DP for point-of-care (PoC) manufacturing along with regulatory hurdles. The importance of process analytical technology (PAT) and non-destructive analytical tools in translating pharmaceutical 3DP has experienced a surge in recognition. This review seeks to highlight the most recent research on non-destructive pharmaceutical 3DP analysis, while also proposing plausible QC systems that complement the pharmaceutical 3DP workflow. In closing, outstanding challenges in integrating these analytical tools into pharmaceutical 3DP workflows are discussed.
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Affiliation(s)
- Anna Kirstine Jørgensen
- Department of Pharmaceutics, UCL School of Pharmacy, University College London, 29-39 Brunswick Square, London WC1N 1AX, UK
| | - Jun Jie Ong
- Department of Pharmaceutics, UCL School of Pharmacy, University College London, 29-39 Brunswick Square, London WC1N 1AX, UK
| | - Maryam Parhizkar
- Department of Pharmaceutics, UCL School of Pharmacy, University College London, 29-39 Brunswick Square, London WC1N 1AX, UK
| | - Alvaro Goyanes
- Department of Pharmaceutics, UCL School of Pharmacy, University College London, 29-39 Brunswick Square, London WC1N 1AX, UK; Departamento de Farmacología, Farmacia y Tecnología Farmacéutica, I+D Farma (GI-1645), Facultad de Farmacia, Instituto de Materiales (iMATUS) and Health Research Institute of Santiago de Compostela (IDIS), Universidade de Santiago de Compostela, 15782 Santiago de Compostela, Spain; FabRx Ltd., Henwood House, Henwood, Ashford TN24 8DH, UK; FabRx Artificial Intelligence, Carretera de Escairón 14, 27543 Currelos (O Saviñao) Lugo, Spain.
| | - Abdul W Basit
- Department of Pharmaceutics, UCL School of Pharmacy, University College London, 29-39 Brunswick Square, London WC1N 1AX, UK; FabRx Ltd., Henwood House, Henwood, Ashford TN24 8DH, UK; FabRx Artificial Intelligence, Carretera de Escairón 14, 27543 Currelos (O Saviñao) Lugo, Spain.
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4
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Zhao X, Wang N, Zhu M, Qiu X, Sun S, Liu Y, Zhao T, Yao J, Shan G. Application of Transmission Raman Spectroscopy in Combination with Partial Least-Squares (PLS) for the Fast Quantification of Paracetamol. Molecules 2022; 27:molecules27051707. [PMID: 35268808 PMCID: PMC8911717 DOI: 10.3390/molecules27051707] [Citation(s) in RCA: 4] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/10/2022] [Revised: 03/02/2022] [Accepted: 03/03/2022] [Indexed: 11/16/2022] Open
Abstract
In recent years, transmission Raman spectroscopy (TRS) has emerged as a potent new tool for rapid, nondestructive quantitation in pharmaceutical manufacturing. In order to expand the applicability of TRS and enhance its use in product quality monitoring during drug production, we aimed, in the present study, to apply partial least-squares (PLS) approaches to build a model consisting of 150 handmade tablets and covering 15 levels through the use of a multifactor orthogonal design of experiment (DOE), which was used to predict concentrations of validation tablets made by hand. The difference between results according to HPLC and TRS were negligible. The model was used to predict the active pharmaceutical ingredient (API) content in four random commercial paracetamol tablets, and corrected with the spectra of the commercial tablets to obtain four corresponding models. The results show that the content relative error in the model’s predictions after correction with commercially available tablets was significantly lower than that before correction. The corrected model was used to make predictions for 20 tablets from the brand Panadol. Compared with the HPLC results, the prediction relative error was basically less than 4.00%, and the relative standard deviation (RSD) of the content was 0.86%.
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Affiliation(s)
- Xuejia Zhao
- Institute of Medicinal Biotechnology, Chinese Academy of Medical Sciences & Peking Union Medical College, No. 1, Tian Tan Xi Li, Beijing 100050, China; (X.Z.); (M.Z.); (X.Q.); (S.S.); (Y.L.); (T.Z.)
| | - Ning Wang
- College of Life Science and Technology, Beijing University of Chemical Technology, North Third Ring Road 15, Beijing 100029, China;
| | - Minghui Zhu
- Institute of Medicinal Biotechnology, Chinese Academy of Medical Sciences & Peking Union Medical College, No. 1, Tian Tan Xi Li, Beijing 100050, China; (X.Z.); (M.Z.); (X.Q.); (S.S.); (Y.L.); (T.Z.)
| | - Xiaodan Qiu
- Institute of Medicinal Biotechnology, Chinese Academy of Medical Sciences & Peking Union Medical College, No. 1, Tian Tan Xi Li, Beijing 100050, China; (X.Z.); (M.Z.); (X.Q.); (S.S.); (Y.L.); (T.Z.)
| | - Shengnan Sun
- Institute of Medicinal Biotechnology, Chinese Academy of Medical Sciences & Peking Union Medical College, No. 1, Tian Tan Xi Li, Beijing 100050, China; (X.Z.); (M.Z.); (X.Q.); (S.S.); (Y.L.); (T.Z.)
| | - Yitong Liu
- Institute of Medicinal Biotechnology, Chinese Academy of Medical Sciences & Peking Union Medical College, No. 1, Tian Tan Xi Li, Beijing 100050, China; (X.Z.); (M.Z.); (X.Q.); (S.S.); (Y.L.); (T.Z.)
| | - Ting Zhao
- Institute of Medicinal Biotechnology, Chinese Academy of Medical Sciences & Peking Union Medical College, No. 1, Tian Tan Xi Li, Beijing 100050, China; (X.Z.); (M.Z.); (X.Q.); (S.S.); (Y.L.); (T.Z.)
| | - Jing Yao
- China National Institutes for Food and Drug Control, No. 2, Tian Tan Xi Li, Beijing 100050, China
- Correspondence: (J.Y.); (G.S.)
| | - Guangzhi Shan
- Institute of Medicinal Biotechnology, Chinese Academy of Medical Sciences & Peking Union Medical College, No. 1, Tian Tan Xi Li, Beijing 100050, China; (X.Z.); (M.Z.); (X.Q.); (S.S.); (Y.L.); (T.Z.)
- Correspondence: (J.Y.); (G.S.)
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Belay NF, Busche S, Manici V, Shaukat M, Arndt SO, Schmidt C. Evaluation of Transmission Raman spectroscopy and NIR Hyperspectral Imaging for the assessment of content uniformity in solid oral dosage forms ✰. Eur J Pharm Sci 2021; 166:105963. [PMID: 34352284 DOI: 10.1016/j.ejps.2021.105963] [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: 11/23/2020] [Revised: 07/14/2021] [Accepted: 07/31/2021] [Indexed: 11/24/2022]
Abstract
PURPOSE The objective of the present study was to explore and compare fast and non-destructive Transmission Raman Spectroscopy (TRS) and Near Infrared Hyperspectral imaging (NIR HSI) for the development of predictive quantitative methods to determine content uniformity (CU) of tablets. METHODS A set of single Active Pharmaceutical Ingredients (API) tablets with nine concentration levels of caffeine ranging from 12.75%w/w to 17.75%w/w and another set of double API tablets with five concentration levels of model API A* (5.25%w/w - 9.25%w/w) and caffeine (7%w/w - 13%w/w) were prepared. Chemometric prediction models were developed using partial least square (PLS 1) and later tested using a test set for both single and double API tablets. RESULTS Calibration PLS1 models were developed for both single and double APIs using a combination of S-G 1st derivative and SNV data pre-processing steps that offer an optimal model performance with the lowest cross-validation error and bias. The root mean square error of prediction (RMSEP) for the PLS1 model for single API caffeine tablets using TRS and NIR HSI was 0.27% and 0.36% respectively. The RMSEP for the PLS1 models built using TRS for the double API tablets was 0.29% for API A and 0.34% for caffeine. Similarly, for the NIR HIS prediction models the RMSEP was 0.43% for API A and 0.56% for caffeine. CONCLUSION Overall TRS presented a 25-30% more accurate prediction capability compared to NIR HSI in this specific sample sets. Nevertheless, both TRS ad NIR HSI possess the potential to be employed as rapid, nondestructive techniques to replace classical wet- chemistry methods for at- or off-line determination of tablet CU.
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Affiliation(s)
| | - Stefan Busche
- Merck KGaA, Frankfurter Straße 250, 64293 Darmstadt, Germany.
| | | | - Manuela Shaukat
- Merck KGaA, Frankfurter Straße 250, 64293 Darmstadt, Germany
| | | | - Carsten Schmidt
- Merck KGaA, Frankfurter Straße 250, 64293 Darmstadt, Germany
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Igne B, Liu Y, Shi Z, Alam MA, Garrett A, Daughtry S, Liesum L, Nielsen S. Multivariate Spectroscopic Method Lifecycle Management as Part of the Quality Management System. J Pharm Sci 2021; 110:2925-2933. [PMID: 33785351 DOI: 10.1016/j.xphs.2021.03.013] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/18/2020] [Revised: 03/16/2021] [Accepted: 03/18/2021] [Indexed: 10/21/2022]
Abstract
Multivariate model based spectroscopic methods require model maintenance through their lifecycle. A survey conducted by the International Consortium for Innovation and Quality in Pharmaceutical Development (IQ) in 2019 showed that regulatory reporting categories for the model related changes can be a hurdle for the routine use of these types of methods. This article introduces industry best practices on multivariate method and model lifecycle management within the Pharmaceutical Quality System. Case studies are provided to demonstrate how the Established Conditions and Post-Approval Change Management Protocol concepts may be leveraged to allow regulatory flexibility for change management and to encourage the use of these techniques for the development and commercialization of pharmaceutical products.
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Affiliation(s)
- Benoît Igne
- Analytical Development, Vertex Pharmaceuticals Inc., Boston, MA, USA.
| | - Yang Liu
- Pfizer, Worldwide Research and Development, Analytical R&D, Groton, CT, USA
| | - Zhenqi Shi
- Lilly Research Laboratory, Eli Lilly and Company, Indianapolis, IN, USA
| | - Md Anik Alam
- Pfizer, Worldwide Research and Development, Analytical R&D, Groton, CT, USA
| | - Aaron Garrett
- Global Quality Laboratory, Eli Lilly and Company, Indianapolis, IN, USA
| | - Sean Daughtry
- Analytical Development, Vertex Pharmaceuticals Inc., Boston, MA, USA
| | - Lorenz Liesum
- Roche, Pharma Technical Innovation, F. Hoffmann- La Roche Ltd, 4070 Basel, Switzerland
| | - Sarah Nielsen
- Janssen Supply Chain, Johnson & Johnson, New Brunswick, NJ, USA
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