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Leane M, Pitt K, Reynolds G, Tantuccio A, Moreton C, Crean A, Kleinebudde P, Carlin B, Gamble J, Gamlen M, Stone E, Kuentz M, Gururajan B, Khimyak YZ, Van Snick B, Andersen S, Misic Z, Peter S, Sheehan S. Ten years of the manufacturing classification system: a review of literature applications and an extension of the framework to continuous manufacture. Pharm Dev Technol 2024; 29:395-414. [PMID: 38618690 DOI: 10.1080/10837450.2024.2342953] [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: 02/08/2024] [Accepted: 04/10/2024] [Indexed: 04/16/2024]
Abstract
The MCS initiative was first introduced in 2013. Since then, two MCS papers have been published: the first proposing a structured approach to consider the impact of drug substance physical properties on manufacturability and the second outlining real world examples of MCS principles. By 2023, both publications had been extensively cited by over 240 publications. This article firstly reviews this citing work and considers how the MCS concepts have been received and are being applied. Secondly, we will extend the MCS framework to continuous manufacture. The review structure follows the flow of drug product development focussing first on optimisation of API properties. The exploitation of links between API particle properties and manufacturability using large datasets seems particularly promising. Subsequently, applications of the MCS for formulation design include a detailed look at the impact of percolation threshold, the role of excipients and how other classification systems can be of assistance. The final review section focusses on manufacturing process development, covering the impact of strain rate sensitivity and modelling applications. The second part of the paper focuses on continuous processing proposing a parallel MCS framework alongside the existing batch manufacturing guidance. Specifically, we propose that continuous direct compression can accommodate a wider range of API properties compared to its batch equivalent.
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Affiliation(s)
- Michael Leane
- Drug Product Development, Bristol Myers Squibb, Moreton, UK
| | - Kendal Pitt
- Leicester School of Pharmacy, De Montfort University, Leicester, UK
| | - Gavin Reynolds
- Oral Product Development, Pharmaceutical Technology & Development, AstraZeneca, Macclesfield, UK
| | - Anthony Tantuccio
- Technology Intensification, Hovione LLC, East Windsor, New Jersey, USA
| | | | - Abina Crean
- SSPC, the SFI Centre for Pharmaceutical Research, School of Pharmacy, University College Cork, Cork, Ireland
| | - Peter Kleinebudde
- Faculty of Mathematics and Natural Sciences, Institute of Pharmaceutics and Biopharmaceutics, Heinrich Heine University Düsseldorf, Düsseldorf, Germany
| | - Brian Carlin
- Owner, Carlin Pharma Consulting, Lawrenceville, New Jersey, USA
| | - John Gamble
- Drug Product Development, Bristol Myers Squibb, Moreton, UK
| | - Michael Gamlen
- Chief Scientific Officer, Gamlen Tableting Ltd, Heanor, UK
| | - Elaine Stone
- Consultant, Stonepharma Ltd. ATIC, Loughborough, UK
| | - Martin Kuentz
- Institute for Pharma Technology, University of Applied Sciences and Arts Northwestern Switzerland, School of Life Sciences FHNW, Muttenz, Switzerland
| | - Bindhu Gururajan
- Pharmaceutical Development, Novartis Pharma AG, Basel, Switzerland
| | - Yaroslav Z Khimyak
- School of Pharmacy, University of East Anglia, Norwich Research Park, Norwich, UK
| | - Bernd Van Snick
- Oral Solids Development, Drug Product Development, JnJ Innovative Medicine, Beerse, Belgium
| | - Sune Andersen
- Oral Solids Development, Drug Product Development, JnJ Innovative Medicine, Beerse, Belgium
| | - Zdravka Misic
- Innovation Research and Development, dsm-firmenich, Kaiseraugst, Switzerland
| | - Stefanie Peter
- Research and Development Division, F. Hoffmann-La Roche AG, Basel, Switzerland
| | - Stephen Sheehan
- External Development and Manufacturing, Alkermes Pharma Ireland Limited, Dublin 4, Ireland
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2
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Wu S, Ketcham SA, Corredor C, Both D, Zhao Y, Drennen JK, Anderson CA. Adaptive modeling optimized by the data fusion strategy: Real-time dying cell percentage prediction using capacitance spectroscopy. Biotechnol Prog 2024; 40:e3424. [PMID: 38178645 DOI: 10.1002/btpr.3424] [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: 09/09/2023] [Revised: 11/20/2023] [Accepted: 12/19/2023] [Indexed: 01/06/2024]
Abstract
The previous research showcased a partial least squares (PLS) regression model accurately predicting cell death percentages using in-line capacitance spectra. The current study advances the model accuracy through adaptive modeling employing a data fusion approach. This strategy enhances prediction performance by incorporating variables from the Cole-Cole model, conductivity and its derivatives over time, and Mahalanobis distance into the predictor matrix (X-matrix). Firstly, the Cole-Cole model, a mechanistic model with parameters linked to early cell death onset, was integrated to enhance prediction performance. Secondly, the inclusion of conductivity and its derivatives over time in the X-matrix mitigated prediction fluctuations resulting from abrupt conductivity changes during process operations. Thirdly, Mahalanobis distance, depicting spectral changes relative to a reference spectrum from a previous time point, improved model adaptability to independent test sets, thereby enhancing performance. The final data fusion model substantially decreased root-mean squared error of prediction (RMSEP) by around 50%, which is a significant boost in prediction accuracy compared to the prior PLS model. Robustness against reference spectrum selection was confirmed by consistent performance across various time points. In conclusion, this study illustrates that the data fusion strategy substantially enhances the model accuracy compared to the previous model relying solely on capacitance spectra.
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Affiliation(s)
- Suyang Wu
- Duquesne Center for Pharmaceutical Technology, Duquesne University, Pittsburgh, Pennsylvania, USA
- Duquesne University Graduate School for Pharmaceutical Sciences, Pittsburgh, Pennsylvania, USA
| | - Stephanie A Ketcham
- Manufascutring Science and Technology, Bristol-Myers Squibb, Devens, Massachusetts, USA
| | - Claudia Corredor
- Pharmaceutical Development, Bristol-Myers Squibb, New Brunswick, New Jersey, USA
| | - Douglas Both
- Pharmaceutical Development, Bristol-Myers Squibb, New Brunswick, New Jersey, USA
| | - Yuxiang Zhao
- Global Product Development and Supply, Bristol-Myers Squibb, Devens, Massachusetts, USA
| | - James K Drennen
- Duquesne Center for Pharmaceutical Technology, Duquesne University, Pittsburgh, Pennsylvania, USA
- Duquesne University Graduate School for Pharmaceutical Sciences, Pittsburgh, Pennsylvania, USA
| | - Carl A Anderson
- Duquesne Center for Pharmaceutical Technology, Duquesne University, Pittsburgh, Pennsylvania, USA
- Duquesne University Graduate School for Pharmaceutical Sciences, Pittsburgh, Pennsylvania, 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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Todaro F, Colangelo F, De Gisi S, Farina I, Ferone C, Labianca C, Petrella A, Cioffi R, Notarnicola M. Recycling of Contaminated Marine Sediment and Industrial By-Products through Combined Stabilization/Solidification and Granulation Treatment. MATERIALS (BASEL, SWITZERLAND) 2023; 16:ma16062399. [PMID: 36984279 PMCID: PMC10054810 DOI: 10.3390/ma16062399] [Citation(s) in RCA: 1] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Subscribe] [Scholar Register] [Received: 01/02/2023] [Revised: 02/26/2023] [Accepted: 03/14/2023] [Indexed: 06/01/2023]
Abstract
Stabilization/solidification (S/S) is becoming increasingly important, as it allows the remediation of contaminated sediments and their recovery into materials for civil engineering. This research proposes a cement-free cold granulation process for manufactured low-cost aggregates from marine sediments contaminated with organic compounds and metals. After the chemo-physical characterization of the study materials, two mix designs were prepared in a rotary plate granulator by adding two industrial by-products as geopolymer precursors, coal fly ash (CFA) and Blast Furnace Slag (BFS), but also alkaline activation solutions, water, and a fluidizer. The results indicated that sediments treated with mix 1 (i.e., with a higher percentage of water and fluidifier) represent the optimal solution in terms of metal leachability. The metal leachability was strictly influenced by aggregates' porosity, density, and microstructure. The technical performance (such as the aggregate impact value > 30%) suggested the use of granules as lightweight aggregates for pavement construction. The results indicated that cold granulation represents a sustainable solution to recycling contaminated marine sediments, CFA, and BFS into lightweight artificial aggregates.
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Affiliation(s)
- Francesco Todaro
- Department of Civil, Environmental, Land, Building Engineering and Chemistry (DICATECh), Polytechnic University of Bari, Via E. Orabona n. 4, 70125 Bari, Italy
| | - Francesco Colangelo
- Department of Engineering and INSTM Research Unit, University of Naples “Parthenope”, Centro Direzionale, Isola C4, 80143 Naples, Italy
| | - Sabino De Gisi
- Department of Civil, Environmental, Land, Building Engineering and Chemistry (DICATECh), Polytechnic University of Bari, Via E. Orabona n. 4, 70125 Bari, Italy
| | - Ilenia Farina
- Department of Engineering and INSTM Research Unit, University of Naples “Parthenope”, Centro Direzionale, Isola C4, 80143 Naples, Italy
| | - Claudio Ferone
- Department of Engineering and INSTM Research Unit, University of Naples “Parthenope”, Centro Direzionale, Isola C4, 80143 Naples, Italy
| | - Claudia Labianca
- Department of Civil and Environmental Engineering, The Hong Kong Polytechnic University, Hung Hom, Kowloon, Hong Kong, China
| | - Andrea Petrella
- Department of Civil, Environmental, Land, Building Engineering and Chemistry (DICATECh), Polytechnic University of Bari, Via E. Orabona n. 4, 70125 Bari, Italy
| | - Raffaele Cioffi
- Department of Engineering and INSTM Research Unit, University of Naples “Parthenope”, Centro Direzionale, Isola C4, 80143 Naples, Italy
| | - Michele Notarnicola
- Department of Civil, Environmental, Land, Building Engineering and Chemistry (DICATECh), Polytechnic University of Bari, Via E. Orabona n. 4, 70125 Bari, Italy
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Technological advances and challenges for exploring attribute transmission in tablet development by high shear wet granulation. POWDER TECHNOL 2023. [DOI: 10.1016/j.powtec.2023.118402] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 03/08/2023]
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Gui XJ, Li H, Ma R, Tian LY, Hou FG, Li HY, Fan XH, Wang YL, Yao J, Shi JH, Zhang L, Li XL, Liu RX. Authenticity and species identification of Fritillariae cirrhosae: a data fusion method combining electronic nose, electronic tongue, electronic eye and near infrared spectroscopy. Front Chem 2023; 11:1179039. [PMID: 37188096 PMCID: PMC10175593 DOI: 10.3389/fchem.2023.1179039] [Citation(s) in RCA: 3] [Impact Index Per Article: 3.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/03/2023] [Accepted: 04/17/2023] [Indexed: 05/17/2023] Open
Abstract
This paper focuses on determining the authenticity and identifying the species of Fritillariae cirrhosae using electronic nose, electronic tongue, and electronic eye sensors, near infrared and mid-level data fusion. 80 batches of Fritillariae cirrhosae and its counterfeits (including several batches of Fritillaria unibracteata Hsiao et K.C. Hsia, Fritillaria przewalskii Maxim, Fritillaria delavayi Franch and Fritillaria ussuriensis Maxim) were initially identified by Chinese medicine specialists and by criteria in the 2020 edition of Chinese Pharmacopoeia. After obtaining the information from several sensors we constructed single-source PLS-DA models for authenticity identification and single-source PCA-DA models for species identification. We selected variables of interest by VIP value and Wilk's lambda value, and we subsequently constructed the three-source fusion model of intelligent senses and the four-source fusion model of intelligent senses and near-infrared spectroscopy. We then explained and analyzed the four-source fusion models based on the sensitive substances detected by key sensors. The accuracies of single-source authenticity PLS-DA identification models based on electronic nose, electronic eye, electronic tongue sensors and near-infrared were respectively 96.25%, 91.25%, 97.50% and 97.50%. The accuracies of single-source PCA-DA species identification models were respectively 85%, 71.25%, 97.50% and 97.50%. After three-source data fusion, the accuracy of the authenticity identification of the PLS-DA identification model was 97.50% and the accuracy of the species identification of the PCA-DA model was 95%. After four-source data fusion, the accuracy of the authenticity of the PLS-DA identification model was 98.75% and the accuracy of the species identification of the PCA-DA model was 97.50%. In terms of authenticity identification, four-source data fusion can improve the performance of the model, while for the identification of the species the four-source data fusion failed to optimize the performance of the model. We conclude that electronic nose, electronic tongue, electronic eye data and near-infrared spectroscopy combined with data fusion and chemometrics methods can identify the authenticity and determine the species of Fritillariae cirrhosae. Our model explanation and analysis can help other researchers identify key quality factors for sample identification. This study aims to provide a reference method for the quality evaluation of Chinese herbs.
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Affiliation(s)
- Xin-Jing Gui
- School of Pharmacy, Henan University of Chinese Medicine, Zhengzhou, China
- Department of Pharmacy, The First Affiliated Hospital of Henan University of Chinese Medicine, Zhengzhou, China
- Henan Province Engineering Research Center for Clinical Application, Evaluation and Transformation of Traditional Chinese Medicine, Zhengzhou, China
- Co-Construction Collaborative Innovation Center for Chinese Medicine and Respiratory Diseases by Henan and Education Ministry of China, Henan University of Chinese Medicine, Zhengzhou, China
- Henan Provincial Key Laboratory for Clinical Pharmacy of Traditional Chinese Medicine, Zhengzhou, China
| | - Han Li
- School of Pharmacy, Henan University of Chinese Medicine, Zhengzhou, China
| | - Rui Ma
- School of Pharmacy, Henan University of Chinese Medicine, Zhengzhou, China
| | - Liang-Yu Tian
- Zhengzhou Traditional Chinese Hospital of Orthopedics, Zhengzhou, China
| | - Fu-Guo Hou
- School of Pharmacy, Henan University of Chinese Medicine, Zhengzhou, China
| | - Hai-Yang Li
- School of Pharmacy, Henan University of Chinese Medicine, Zhengzhou, China
| | - Xue-Hua Fan
- School of Pharmacy, Henan University of Chinese Medicine, Zhengzhou, China
| | - Yan-Li Wang
- Department of Pharmacy, The First Affiliated Hospital of Henan University of Chinese Medicine, Zhengzhou, China
- Henan Province Engineering Research Center for Clinical Application, Evaluation and Transformation of Traditional Chinese Medicine, Zhengzhou, China
- Co-Construction Collaborative Innovation Center for Chinese Medicine and Respiratory Diseases by Henan and Education Ministry of China, Henan University of Chinese Medicine, Zhengzhou, China
- Henan Provincial Key Laboratory for Clinical Pharmacy of Traditional Chinese Medicine, Zhengzhou, China
| | - Jing Yao
- Department of Pharmacy, The First Affiliated Hospital of Henan University of Chinese Medicine, Zhengzhou, China
- Henan Province Engineering Research Center for Clinical Application, Evaluation and Transformation of Traditional Chinese Medicine, Zhengzhou, China
- Co-Construction Collaborative Innovation Center for Chinese Medicine and Respiratory Diseases by Henan and Education Ministry of China, Henan University of Chinese Medicine, Zhengzhou, China
- Henan Provincial Key Laboratory for Clinical Pharmacy of Traditional Chinese Medicine, Zhengzhou, China
| | - Jun-Han Shi
- Department of Pharmacy, The First Affiliated Hospital of Henan University of Chinese Medicine, Zhengzhou, China
- Henan Province Engineering Research Center for Clinical Application, Evaluation and Transformation of Traditional Chinese Medicine, Zhengzhou, China
- Co-Construction Collaborative Innovation Center for Chinese Medicine and Respiratory Diseases by Henan and Education Ministry of China, Henan University of Chinese Medicine, Zhengzhou, China
- Henan Provincial Key Laboratory for Clinical Pharmacy of Traditional Chinese Medicine, Zhengzhou, China
| | - Lu Zhang
- Department of Pharmacy, The First Affiliated Hospital of Henan University of Chinese Medicine, Zhengzhou, China
- Henan Province Engineering Research Center for Clinical Application, Evaluation and Transformation of Traditional Chinese Medicine, Zhengzhou, China
- Co-Construction Collaborative Innovation Center for Chinese Medicine and Respiratory Diseases by Henan and Education Ministry of China, Henan University of Chinese Medicine, Zhengzhou, China
- Henan Provincial Key Laboratory for Clinical Pharmacy of Traditional Chinese Medicine, Zhengzhou, China
| | - Xue-Lin Li
- Department of Pharmacy, The First Affiliated Hospital of Henan University of Chinese Medicine, Zhengzhou, China
- Henan Province Engineering Research Center for Clinical Application, Evaluation and Transformation of Traditional Chinese Medicine, Zhengzhou, China
- Co-Construction Collaborative Innovation Center for Chinese Medicine and Respiratory Diseases by Henan and Education Ministry of China, Henan University of Chinese Medicine, Zhengzhou, China
- Henan Provincial Key Laboratory for Clinical Pharmacy of Traditional Chinese Medicine, Zhengzhou, China
- *Correspondence: Rui-Xin Liu, ; Xue-Lin Li,
| | - Rui-Xin Liu
- Department of Pharmacy, The First Affiliated Hospital of Henan University of Chinese Medicine, Zhengzhou, China
- Henan Province Engineering Research Center for Clinical Application, Evaluation and Transformation of Traditional Chinese Medicine, Zhengzhou, China
- Co-Construction Collaborative Innovation Center for Chinese Medicine and Respiratory Diseases by Henan and Education Ministry of China, Henan University of Chinese Medicine, Zhengzhou, China
- Henan Provincial Key Laboratory for Clinical Pharmacy of Traditional Chinese Medicine, Zhengzhou, China
- Engineering Research Center for Pharmaceutics of Chinese Materia Medica and New Drug Development, Ministry of Education, Beijing, China
- *Correspondence: Rui-Xin Liu, ; Xue-Lin Li,
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Casian T, Nagy B, Kovács B, Galata DL, Hirsch E, Farkas A. Challenges and Opportunities of Implementing Data Fusion in Process Analytical Technology-A Review. Molecules 2022; 27:4846. [PMID: 35956791 PMCID: PMC9369811 DOI: 10.3390/molecules27154846] [Citation(s) in RCA: 10] [Impact Index Per Article: 5.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/30/2022] [Revised: 07/20/2022] [Accepted: 07/22/2022] [Indexed: 12/03/2022] Open
Abstract
The release of the FDA's guidance on Process Analytical Technology has motivated and supported the pharmaceutical industry to deliver consistent quality medicine by acquiring a deeper understanding of the product performance and process interplay. The technical opportunities to reach this high-level control have considerably evolved since 2004 due to the development of advanced analytical sensors and chemometric tools. However, their transfer to the highly regulated pharmaceutical sector has been limited. To this respect, data fusion strategies have been extensively applied in different sectors, such as food or chemical, to provide a more robust performance of the analytical platforms. This survey evaluates the challenges and opportunities of implementing data fusion within the PAT concept by identifying transfer opportunities from other sectors. Special attention is given to the data types available from pharmaceutical manufacturing and their compatibility with data fusion strategies. Furthermore, the integration into Pharma 4.0 is discussed.
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Affiliation(s)
- Tibor Casian
- Department of Pharmaceutical Technology and Biopharmacy, “Iuliu Hatieganu” University of Medicine and Pharmacy, 400012 Cluj-Napoca, Romania;
| | - Brigitta Nagy
- Department of Organic Chemistry and Technology, Budapest University of Technology and Economics, H-1111 Budapest, Hungary; (D.L.G.); (E.H.); (A.F.)
| | - Béla Kovács
- Department of Biochemistry and Environmental Chemistry, George Emil Palade University of Medicine, Pharmacy, Science, and Technology of Târgu Mureș, 540139 Târgu Mureș, Romania;
| | - Dorián László Galata
- Department of Organic Chemistry and Technology, Budapest University of Technology and Economics, H-1111 Budapest, Hungary; (D.L.G.); (E.H.); (A.F.)
| | - Edit Hirsch
- Department of Organic Chemistry and Technology, Budapest University of Technology and Economics, H-1111 Budapest, Hungary; (D.L.G.); (E.H.); (A.F.)
| | - Attila Farkas
- Department of Organic Chemistry and Technology, Budapest University of Technology and Economics, H-1111 Budapest, Hungary; (D.L.G.); (E.H.); (A.F.)
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