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Ficzere M, Alexandra Mészáros L, Diószegi A, Bánrévi Z, Farkas A, Lenk S, László Galata D, Kristóf Nagy Z. UV imaging for the rapid at-line content determination of different colourless APIs in their tablets with artificial neural networks. Int J Pharm 2024; 657:124174. [PMID: 38701905 DOI: 10.1016/j.ijpharm.2024.124174] [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: 03/14/2024] [Revised: 04/17/2024] [Accepted: 04/26/2024] [Indexed: 05/05/2024]
Abstract
This paper presents a novel high-resolution and rapid (50 ms) UV imaging system, which was used for at-line, non-destructive API content determination of tablets. For the experiments, amlodipine and valsartan were selected as two colourless APIs with different UV induced fluorescent properties according to the measured solid fluorescent spectra. Images were captured with a LED-based UV illumination (385-395 nm) of tablets containing amlodipine or valsartan and common tableting excipients. Blue or green colour components from the RGB colour space were extracted from the images and used as an input dataset to execute API content prediction with artificial neural networks. The traditional destructive, solution-based transmission UV measurement was applied as reference method. After the optimization of the number of hidden layer neurons it was found that the relative error of the content prediction was 4.41 % and 3.98 % in the case of amlodipine and valsartan containing tablets respectively. The results open the possibility to use the proposed UV imaging-based system as a rapid, in-line tool for 100 % API content screening in order to greatly improve pharmaceutical quality control and process understanding.
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Affiliation(s)
- Máté Ficzere
- Department of Organic Chemistry and Technology, Faculty of Chemical Technology and Biotechnology, Budapest University of Technology and Economics, Műegyetem rkp 3., H-1111 Budapest, Hungary
| | - Lilla Alexandra Mészáros
- Department of Organic Chemistry and Technology, Faculty of Chemical Technology and Biotechnology, Budapest University of Technology and Economics, Műegyetem rkp 3., H-1111 Budapest, Hungary
| | - Anna Diószegi
- Department of Organic Chemistry and Technology, Faculty of Chemical Technology and Biotechnology, Budapest University of Technology and Economics, Műegyetem rkp 3., H-1111 Budapest, Hungary
| | - Zoltán Bánrévi
- Department of Organic Chemistry and Technology, Faculty of Chemical Technology and Biotechnology, Budapest University of Technology and Economics, Műegyetem rkp 3., H-1111 Budapest, Hungary
| | - Attila Farkas
- Department of Organic Chemistry and Technology, Faculty of Chemical Technology and Biotechnology, Budapest University of Technology and Economics, Műegyetem rkp 3., H-1111 Budapest, Hungary
| | - Sándor Lenk
- Department of Atomic Physics, Budapest University of Technology and Economics, Műegyetem rkp 3., H-1111 Budapest, Hungary
| | - Dorián László Galata
- Department of Organic Chemistry and Technology, Faculty of Chemical Technology and Biotechnology, Budapest University of Technology and Economics, Műegyetem rkp 3., H-1111 Budapest, Hungary
| | - Zsombor Kristóf Nagy
- Department of Organic Chemistry and Technology, Faculty of Chemical Technology and Biotechnology, Budapest University of Technology and Economics, Műegyetem rkp 3., H-1111 Budapest, Hungary.
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Alexandra Mészáros L, Madarász L, Kádár S, Ficzere M, Farkas A, Kristóf Nagy Z. Machine vision-based non-destructive dissolution prediction of meloxicam-containing tablets. Int J Pharm 2024; 655:124013. [PMID: 38503398 DOI: 10.1016/j.ijpharm.2024.124013] [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: 12/22/2023] [Revised: 03/15/2024] [Accepted: 03/15/2024] [Indexed: 03/21/2024]
Abstract
Machine vision systems have emerged for quality assessment of solid dosage forms in the pharmaceutical industry. These can offer a versatile tool for continuous manufacturing while supporting the framework of process analytical technology, quality-by-design, and real-time release testing. The aim of this work is to develop a digital UV/VIS imaging-based system for predicting the in vitro dissolution of meloxicam-containing tablets. The alteration of the dissolution profiles of the samples required different levels of the critical process parameters, including compression force, particle size and content of the API. These process parameters were predicted non-destructively by multivariate analysis of UV/VIS images taken from the tablets. The dissolution profile prediction was also executed using solely the image data and applying artificial neural networks. The prediction error (RMSE) of the dissolution profile points was less than 5%. The alteration of the API content directly affected the maximum concentrations observed at the end of the dissolution tests. This parameter was predicted with a relative error of less than 10% by PLS models that are based on the color components of UV and VIS images. In conclusion, this paper presents a modern, non-destructive PAT solution for real-time testing of the dissolution of tablets.
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Affiliation(s)
- Lilla Alexandra Mészáros
- Department of Organic Chemistry and Technology, Budapest University of Technology and Economics, H-1111 Budapest, Műegyetem rakpart 3, Hungary
| | - Lajos Madarász
- Department of Organic Chemistry and Technology, Budapest University of Technology and Economics, H-1111 Budapest, Műegyetem rakpart 3, Hungary
| | - Szabina Kádár
- Department of Organic Chemistry and Technology, Budapest University of Technology and Economics, H-1111 Budapest, Műegyetem rakpart 3, Hungary
| | - Máté Ficzere
- Department of Organic Chemistry and Technology, Budapest University of Technology and Economics, H-1111 Budapest, Műegyetem rakpart 3, Hungary
| | - Attila Farkas
- Department of Organic Chemistry and Technology, Budapest University of Technology and Economics, H-1111 Budapest, Műegyetem rakpart 3, Hungary
| | - Zsombor Kristóf Nagy
- Department of Organic Chemistry and Technology, Budapest University of Technology and Economics, H-1111 Budapest, Műegyetem rakpart 3, Hungary.
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3
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Kataoka M, Itaka Y, Masada T, Minami K, Higashino H, Yamashita S. Near-infrared imaging of in vivo performance of orally administered solid forms to rats: Feasibility study with indocyanine green. Int J Pharm 2024; 649:123677. [PMID: 38061499 DOI: 10.1016/j.ijpharm.2023.123677] [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: 09/15/2023] [Revised: 11/16/2023] [Accepted: 12/04/2023] [Indexed: 12/20/2023]
Abstract
This study demonstrates the applicability of near-infrared (NIR) imaging to evaluating in vivo oral formulation performance. As a NIR probe and model drug, indocyanine green (ICG) and acetaminophen (ACE) were selected, respectively. The fluorescence intensity of ICG greatly increased upon dissolution, with the dissolved ICG passing through the gastrointestinal tract over time. Both compounds (0.05 mg of ICG and 0.5 mg of ACE) were encapsulated in gelatin and hydroxypropyl methylcellulose (HPMC) capsules in the solid form. In vitro, the HPMC capsules showed a disintegration lag time, a feature that was not observed for the gelatin capsules. After oral administration of each capsule to rats, blood samples were collected, followed by fluorescent imaging of the abdominal region. At 0.25 h after HPMC capsule administration, the fluorescence area and intensity were significantly small and relatively weak compared to that of the gelatin capsule. These tendencies resulted from the difference in capsule disintegration times, leading to a change in gastric emptying, which corresponded well with the initial time profile of the plasma concentration of ACE. These results indicate that possibility of NIR imaging with ICG to evaluate in vivo performance of orally administered formulations.
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Affiliation(s)
- Makoto Kataoka
- Faculty of Pharmaceutical Sciences, Setsunan University, 45-1 Nagaotoge-cho, Hirakata, Osaka 573-0101, Japan.
| | - Yoshiya Itaka
- Faculty of Pharmaceutical Sciences, Setsunan University, 45-1 Nagaotoge-cho, Hirakata, Osaka 573-0101, Japan
| | - Takato Masada
- Faculty of Pharmaceutical Sciences, Setsunan University, 45-1 Nagaotoge-cho, Hirakata, Osaka 573-0101, Japan
| | - Keiko Minami
- Faculty of Pharmaceutical Sciences, Setsunan University, 45-1 Nagaotoge-cho, Hirakata, Osaka 573-0101, Japan
| | - Haruki Higashino
- Faculty of Pharmaceutical Sciences, Setsunan University, 45-1 Nagaotoge-cho, Hirakata, Osaka 573-0101, Japan
| | - Shinji Yamashita
- Faculty of Pharmaceutical Sciences, Setsunan University, 45-1 Nagaotoge-cho, Hirakata, Osaka 573-0101, Japan
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4
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Movilla-Meza NA, Sierra-Vega NO, Alvarado-Hernández BB, Méndez R, Romañach RJ. The Use of a Closed Feed Frame for the Development of Near-Infrared Spectroscopic Calibration Model to Determine Drug Concentration. Pharm Res 2023; 40:2903-2916. [PMID: 37700106 DOI: 10.1007/s11095-023-03601-1] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/21/2023] [Accepted: 08/31/2023] [Indexed: 09/14/2023]
Abstract
PURPOSE This study evaluates the use of the closed feed frame as a material sparing approach to develop near-infrared (NIR) spectroscopic calibration models for monitoring blend uniformity. The effect of shear induced by recirculation on NIR spectra was also studied. METHODS Calibration models were developed using NIR spectra obtained in the closed feed frame for two cases. For case 2, blends that flowed through the open feed frame were predicted with the model. The shear effect of the feed frame on the blends was assessed through the characterization of powder properties before and after recirculation. RESULTS The physical characterization of the blends confirmed that the powder properties were not altered after recirculation within the closed feed frame. Both calibration models provided highly accurate predictions of the test sets with low bias (0.03% w/w and -0.06% w/w) and relative standard error of prediction (1.9% and 3.7%), respectively. The predictive performance of the calibration models was not affected by the shear effect. CONCLUSION Recirculation within the closed feed frame did not change the physical properties of the blends studied. The prediction of blends flowing through the open feed frame was possible with a calibration model developed in the closed feed frame. The closed feed frame could reduce the materials needed to develop calibration models by more than 90%.
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Affiliation(s)
| | - Nobel O Sierra-Vega
- Department of Chemical Engineering, University of Puerto Rico at Mayagüez, Mayagüez, PR, USA
| | | | - Rafael Méndez
- Department of Chemical Engineering, University of Puerto Rico at Mayagüez, Mayagüez, PR, USA
| | - Rodolfo J Romañach
- Department of Chemistry, University of Puerto Rico at Mayagüez, Mayagüez, PR, USA.
- Center for Structured Organic Particulate Systems (C-SOPS), Department of Chemistry, University of Puerto Rico at Mayagüez, PO Box 9000, Mayagüez, PR, 00681, USA.
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Huang Z, Zhou G, Wang X, Wang T, Zhang H, Wang Z, Zhu B, Li W. Rapid and nondestructive identification of adulterate capsules by NIR spectroscopy combined with chemometrics. J Pharm Biomed Anal 2023; 235:115597. [PMID: 37516065 DOI: 10.1016/j.jpba.2023.115597] [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: 05/04/2023] [Revised: 06/03/2023] [Accepted: 07/19/2023] [Indexed: 07/31/2023]
Abstract
This study aims to develop a rapid and non-destructive method to identify counterfeit and substandard drugs, addressing the critical need for better quality control in drug production. According to the reasons for counterfeit products in actual production, the commonly used solid preparation excipients such as HPMC, MCC, Mg-St and Pregelatinized Starch, as well as three chemical drugs with similar efficacy to Guizhi-Fuling (GZFL) Capsule as adulterants, including Aspirin, Ibuprofen and Sinomenine Hydrochloride were selected and designed as adulteration samples with different levels of adulteration. NIR spectra were collected in a non-invasive mode and analyzed by one-class classification methods. The feasibility of using Near-infrared (NIR) spectroscopy as a detection method to qualitatively identify adulterated samples was explored at three packaging levels of powder, intact capsules and capsules in PVC. The differences between the samples were analyzed by NIR spectra comparison, cluster analysis and principal component analysis. The performance of SVM, OCPLS and DD-SIMCA models in dealing with the authentication of genuine and counterfeit products was established and compared. The results show that the spectra contain sample information and the adulterated samples could be discriminated correctly by established models. Moreover, applying appropriate spectral preprocessing methods can further improve the model's performance. In addition, a PLS regression model was developed to predict the adulteration levels of the three packing level samples, which yielded satisfactory results. This study highlights the potential of NIR spectroscopy combined with Chemometrics as a rapid and non-destructive testing analysis method to accurately identify counterfeit and substandard drugs, thereby ensuring drug quality.
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Affiliation(s)
- Zhaobo Huang
- College of Pharmaceutical Engineering of Traditional Chinese Medicine, Tianjin University of Traditional Chinese Medicine, Tianjin 301617, China; State Key Laboratory of Component-based Chinese Medicine, Tianjin University of Traditional Chinese Medicine, Tianjin 301617, China; Haihe Laboratory of Modern Chinese Medicine, Tianjin 301617, China
| | - Guoming Zhou
- College of Pharmaceutical Engineering of Traditional Chinese Medicine, Tianjin University of Traditional Chinese Medicine, Tianjin 301617, China; State Key Laboratory of Component-based Chinese Medicine, Tianjin University of Traditional Chinese Medicine, Tianjin 301617, China
| | - Xi Wang
- College of Pharmaceutical Engineering of Traditional Chinese Medicine, Tianjin University of Traditional Chinese Medicine, Tianjin 301617, China; State Key Laboratory of Component-based Chinese Medicine, Tianjin University of Traditional Chinese Medicine, Tianjin 301617, China; Haihe Laboratory of Modern Chinese Medicine, Tianjin 301617, China
| | - Tuanjie Wang
- Jiangsu Kanion Pharmaceutical CO. LTD, Lianyungang, Jiangsu 222001, China; State Key Laboratory of New-tech for Chinese Medicine Pharmaceutical Process, Lianyungang, Jiangsu 222001, China
| | - Hongda Zhang
- Jiangsu Kanion Pharmaceutical CO. LTD, Lianyungang, Jiangsu 222001, China; State Key Laboratory of New-tech for Chinese Medicine Pharmaceutical Process, Lianyungang, Jiangsu 222001, China
| | - Zhenzhong Wang
- Jiangsu Kanion Pharmaceutical CO. LTD, Lianyungang, Jiangsu 222001, China; State Key Laboratory of New-tech for Chinese Medicine Pharmaceutical Process, Lianyungang, Jiangsu 222001, China
| | - Beibei Zhu
- College of Pharmaceutical Engineering of Traditional Chinese Medicine, Tianjin University of Traditional Chinese Medicine, Tianjin 301617, China; State Key Laboratory of Component-based Chinese Medicine, Tianjin University of Traditional Chinese Medicine, Tianjin 301617, China; Haihe Laboratory of Modern Chinese Medicine, Tianjin 301617, China.
| | - Wenlong Li
- College of Pharmaceutical Engineering of Traditional Chinese Medicine, Tianjin University of Traditional Chinese Medicine, Tianjin 301617, China; State Key Laboratory of Component-based Chinese Medicine, Tianjin University of Traditional Chinese Medicine, Tianjin 301617, China; Haihe Laboratory of Modern Chinese Medicine, Tianjin 301617, China.
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6
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Simão J, Chaudhary SA, Ribeiro AJ. Implementation of Quality by Design (QbD) for development of bilayer tablets. Eur J Pharm Sci 2023; 184:106412. [PMID: 36828037 DOI: 10.1016/j.ejps.2023.106412] [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: 11/03/2022] [Revised: 02/20/2023] [Accepted: 02/20/2023] [Indexed: 02/25/2023]
Abstract
Bilayer tablets offer various drug release profiles for individual drugs incorporated in each layer of a bilayer tablet, which is rarely achievable by conventional tablets. These tablets also help avoid physicochemical incompatibilities between drugs and excipients. Successful manufacturing of such more complex dosage forms depends upon screening of material attributes of API and excipients as well as optimization of processing parameters of individual unit operations of the manufacturing process that must be strictly monitored and controlled to obtain an acceptable drug product quality and performance in order to achieve safety and efficacy per regulatory requirements. Optimizing formulation attributes and manufacturing processes during critical stages, such as blending, granulation, pre-compression, and main compression, can help avoid problems such as weight variation, segregation, and delamination of individual layers, which are frequently faced during the production of bilayer tablets. The main objective of this review is to establish the basis for the implementation of Quality by Design (QbD) system principles for the design and development of bilayer tablets, encompassing the preliminary and systematic risk assessment of critical material attributes (CMAs) and critical process parameters (CPPs) with respect to in-process and finished product critical quality attributes (CQAs). Moreover, the applicability of the QbD methodology based on its purpose is discussed and complemented with examples of bilayer tablet technology.
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Affiliation(s)
- J Simão
- Faculdade de Farmácia, Universidade de Coimbra, Coimbra, Portugal
| | - S A Chaudhary
- National Institute of Pharmaceutical Education and Research, Ahmedabad, India
| | - A J Ribeiro
- Faculdade de Farmácia, Universidade de Coimbra, Coimbra, Portugal; i3S, IBMC, Rua Alfredo Allen, Porto, Portugal.
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7
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Releasing fast and slow: Non-destructive prediction of density and drug release from SLS 3D printed tablets using NIR spectroscopy. Int J Pharm X 2022; 5:100148. [PMID: 36590827 PMCID: PMC9798196 DOI: 10.1016/j.ijpx.2022.100148] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/18/2022] [Revised: 12/14/2022] [Accepted: 12/16/2022] [Indexed: 12/23/2022] Open
Abstract
Selective laser sintering (SLS) 3D printing is a revolutionary 3D printing technology that has been found capable of creating drug products with varied release profiles by changing the laser scanning speed. Here, SLS 3D printed formulations (printlets) loaded with a narrow therapeutic index drug (theophylline) were produced using SLS 3D printing at varying laser scanning speeds (100-180 mm/s). The use of reflectance Fourier Transform - Near Infrared (FT-NIR) spectroscopy was evaluated as a non-destructive approach to predicting 3D printed tablet density and drug release at 2 h and 4 h. The printed drug products formulated with a higher laser speed exhibited an accelerated drug release and reduced density compared with the slower laser scanning speeds. Univariate calibration models were developed based on a baseline shift in the spectra in the third overtone region upon changing physical properties. For density prediction, the developed univariate model had high linearity (R2 value = 0.9335) and accuracy (error < 0.029 mg/mm3). For drug release prediction at 2 h and 4 h, the developed univariate models demonstrated a linear correlation (R2 values of 0.9383 and 0.9167, respectively) and accuracy (error < 4.4%). The predicted vs. actual dissolution profiles were found to be statistically similar (f2 > 50) for all of the test printlets. Overall, this article demonstrates the feasibility of SLS 3D printing to produce drug products containing a narrow therapeutic index drug across a range of drug release profiles, as well as the potential for FT-NIR spectroscopy to predict the physical characteristics of SLS 3D printed drug products (drug release and density) as a non-destructive quality control method at the point-of-care.
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8
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Hanif S, Sarfraz RM, Syed MA, Mahmood A, Hussain Z. Smart mucoadhesive buccal chitosan/ HPMC scaffold for sore throat: In vitro, ex vivo and pharmacokinetic profiling in humans. J Drug Deliv Sci Technol 2022. [DOI: 10.1016/j.jddst.2022.103271] [Citation(s) in RCA: 4] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/17/2022]
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9
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Galata DL, Zsiros B, Mészáros LA, Nagy B, Szabó E, Farkas A, Nagy ZK. Raman mapping-based non-destructive dissolution prediction of sustained-release tablets. J Pharm Biomed Anal 2022; 212:114661. [PMID: 35180565 DOI: 10.1016/j.jpba.2022.114661] [Citation(s) in RCA: 14] [Impact Index Per Article: 7.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/22/2021] [Revised: 01/15/2022] [Accepted: 02/10/2022] [Indexed: 01/06/2023]
Abstract
In this paper, the applicability of Raman chemical imaging for the non-destructive prediction of the in vitro dissolution profile of sustained-release tablets is demonstrated for the first time. Raman chemical maps contain a plethora of information about the spatial distribution and the particle size of the components, compression force and even polymorphism. With proper data analysis techniques, this can be converted into simple numerical information which can be used as input in a machine learning model. In our work, sustained-release tablets using hydroxypropyl methylcellulose (HPMC) as matrix polymer are prepared, the concentration and particle size of this component varied between samples. Chemical maps of HPMC are converted into histograms with two different methods, an approach based on discretizing concentration values and a wavelet analysis technique. These histograms are then subjected to Principal Component Analysis, the score value of the first two principal components was found to represent HPMC content and particle size. These values are used as input in Artificial Neural Networks which are trained to predict the dissolution profile of the tablets. As a result, accurate predictions were obtained for the test tablets (the average f2 similarity value is higher than 59 with both methods). The presented methodology lays the foundations of the analysis of far more extensive datasets acquired with the emerging fast Raman imaging technology.
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Affiliation(s)
- Dorián László Galata
- Department of Organic Chemistry and Technology, Budapest University of Technology and Economics, Műegyetem rakpart 3, H-1111 Budapest, Hungary
| | - Boldizsár Zsiros
- Department of Organic Chemistry and Technology, Budapest University of Technology and Economics, Műegyetem rakpart 3, H-1111 Budapest, Hungary
| | - Lilla Alexandra Mészáros
- Department of Organic Chemistry and Technology, Budapest University of Technology and Economics, Műegyetem rakpart 3, H-1111 Budapest, Hungary
| | - Brigitta Nagy
- Department of Organic Chemistry and Technology, Budapest University of Technology and Economics, Műegyetem rakpart 3, H-1111 Budapest, Hungary
| | - Edina Szabó
- Department of Organic Chemistry and Technology, Budapest University of Technology and Economics, Műegyetem rakpart 3, H-1111 Budapest, Hungary
| | - Attila Farkas
- Department of Organic Chemistry and Technology, Budapest University of Technology and Economics, Műegyetem rakpart 3, H-1111 Budapest, Hungary.
| | - Zsombor Kristóf Nagy
- Department of Organic Chemistry and Technology, Budapest University of Technology and Economics, Műegyetem rakpart 3, H-1111 Budapest, Hungary
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10
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Lim H, Yu D, Hoag SW. Application of near-infrared spectroscopy in detecting residual crystallinity in carbamazepine – Soluplus® solid dispersions prepared with solvent casting and hot-melt extrusion. J Drug Deliv Sci Technol 2021. [DOI: 10.1016/j.jddst.2021.102713] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 10/20/2022]
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11
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Harnessing artificial intelligence for the next generation of 3D printed medicines. Adv Drug Deliv Rev 2021; 175:113805. [PMID: 34019957 DOI: 10.1016/j.addr.2021.05.015] [Citation(s) in RCA: 57] [Impact Index Per Article: 19.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/19/2021] [Revised: 05/02/2021] [Accepted: 05/13/2021] [Indexed: 02/06/2023]
Abstract
Artificial intelligence (AI) is redefining how we exist in the world. In almost every sector of society, AI is performing tasks with super-human speed and intellect; from the prediction of stock market trends to driverless vehicles, diagnosis of disease, and robotic surgery. Despite this growing success, the pharmaceutical field is yet to truly harness AI. Development and manufacture of medicines remains largely in a 'one size fits all' paradigm, in which mass-produced, identical formulations are expected to meet individual patient needs. Recently, 3D printing (3DP) has illuminated a path for on-demand production of fully customisable medicines. Due to its flexibility, pharmaceutical 3DP presents innumerable options during formulation development that generally require expert navigation. Leveraging AI within pharmaceutical 3DP removes the need for human expertise, as optimal process parameters can be accurately predicted by machine learning. AI can also be incorporated into a pharmaceutical 3DP 'Internet of Things', moving the personalised production of medicines into an intelligent, streamlined, and autonomous pipeline. Supportive infrastructure, such as The Cloud and blockchain, will also play a vital role. Crucially, these technologies will expedite the use of pharmaceutical 3DP in clinical settings and drive the global movement towards personalised medicine and Industry 4.0.
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12
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Liu L, Zhang K, Sun Z, Dong Q, Li L, Zang H. A new perspective in understanding the dissolution behavior of nifedipine controlled release tablets by NIR spectroscopy with aquaphotomics. J Mol Struct 2021. [DOI: 10.1016/j.molstruc.2021.129872] [Citation(s) in RCA: 3] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/29/2022]
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13
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Real-time release testing of dissolution based on surrogate models developed by machine learning algorithms using NIR spectra, compression force and particle size distribution as input data. Int J Pharm 2021; 597:120338. [DOI: 10.1016/j.ijpharm.2021.120338] [Citation(s) in RCA: 17] [Impact Index Per Article: 5.7] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/28/2020] [Revised: 01/26/2021] [Accepted: 01/30/2021] [Indexed: 12/28/2022]
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14
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Shi G, Lin L, Liu Y, Chen G, Luo Y, Wu Y, Li H. Pharmaceutical application of multivariate modelling techniques: a review on the manufacturing of tablets. RSC Adv 2021; 11:8323-8345. [PMID: 35423324 PMCID: PMC8695199 DOI: 10.1039/d0ra08030f] [Citation(s) in RCA: 5] [Impact Index Per Article: 1.7] [Reference Citation Analysis] [Abstract] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/20/2020] [Accepted: 01/26/2021] [Indexed: 11/21/2022] Open
Abstract
The tablet manufacturing process is a complex system, especially in continuous manufacturing (CM). It includes multiple unit operations, such as mixing, granulation, and tableting. In tablet manufacturing, critical quality attributes are influenced by multiple factorial relationships between material properties, process variables, and interactions. Moreover, the variation in raw material attributes and manufacturing processes is an inherent characteristic and seriously affects the quality of pharmaceutical products. To deepen our understanding of the tablet manufacturing process, multivariable modeling techniques can replace univariate analysis to investigate tablet manufacturing. In this review, the roles of the most prominent multivariate modeling techniques in the tablet manufacturing process are discussed. The review mainly focuses on applying multivariate modeling techniques to process understanding, optimization, process monitoring, and process control within multiple unit operations. To minimize the errors in the process of modeling, good modeling practice (GMoP) was introduced into the pharmaceutical process. Furthermore, current progress in the continuous manufacturing of tablets and the role of multivariate modeling techniques in continuous manufacturing are introduced. In this review, information is provided to both researchers and manufacturers to improve tablet quality.
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Affiliation(s)
- Guolin Shi
- Institute of Chinese Materia Medica, China Academy of Chinese Medical Sciences Beijing 100700 China
| | - Longfei Lin
- Institute of Chinese Materia Medica, China Academy of Chinese Medical Sciences Beijing 100700 China
| | - Yuling Liu
- Institute of Chinese Materia Medica, China Academy of Chinese Medical Sciences Beijing 100700 China
| | - Gongsen Chen
- Institute of Chinese Materia Medica, China Academy of Chinese Medical Sciences Beijing 100700 China
| | - Yuting Luo
- Institute of Chinese Materia Medica, China Academy of Chinese Medical Sciences Beijing 100700 China
| | - Yanqiu Wu
- Institute of Chinese Materia Medica, China Academy of Chinese Medical Sciences Beijing 100700 China
| | - Hui Li
- Institute of Chinese Materia Medica, China Academy of Chinese Medical Sciences Beijing 100700 China
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15
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Tackling quantitative polymorphic analysis through fixed-dose combination tablets production. Pyrazinamide polymorphic assessment. J Pharm Biomed Anal 2020; 194:113786. [PMID: 33281002 DOI: 10.1016/j.jpba.2020.113786] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/01/2020] [Revised: 11/17/2020] [Accepted: 11/18/2020] [Indexed: 11/23/2022]
Abstract
Pyrazinamide (PZA), Rifampicin (RIF), Isoniazid (ISH) and Ethambutol (ETB) form the core for the treatment of Tuberculosis, today a devastating disease in low-income populations around the world. These drugs are usually administrated by fixed-dose combination (FDC) products, to favour the patient compliance and prevent bacterial resistance. PZA exists in four enantiotropically-related polymorphs (Forms α, δ, β and γ), but only Form α is considered suitable for pharmaceutical products due to its stability and bioavailability properties. The classical approaches to address solid-state (microscopy, X-ray diffraction and calorimetry) shows limitations for quantification of polymorphs in the presence of excipients and other active components, as in the case of FDC tablets. In this work, an overall strategy was developed using near infrared spectroscopy (NIR) coupled to partial least squares regression (PLS) to quantify Form α of PZA in drug substance (raw material) and PZA/RIF/ISH-FDC tablets. For this purpose, two PLS models were constructed, one for drug substance preparing training (n = 30) and validation (n = 18) samples with a ternary composition (Form α/Form δ/Form γ), and other for FDC drug products, also including the appropriate amount of RIF, ISH and the matrix of excipients in order to simulate the environment of PZA/RIF/ISH association. The NIR-PLS models were optimized using a novel smart approach based on radial optimization (full range, 3 L V and MSC-D' and SNV-D' as pre-treatment, for raw material and FDC tablets, respectively). During the validation step, both methods showed no bias or systematic errors and yielded satisfactory recoveries (102.5 ± 3.1 % for drug substance and 98.7 ± 1.5 % for FDC tablets). When commercial drug substance was tested, NIR-PLS was able to predict the content of Form α (0.98 ± 0.01 w/w). The model for FDC tablets allowed estimating polymorphic purity in intact (0.984 ± 0.003 w/w), sectioned (0.986 ± 0.002 w/w), and powered (0.985 ± 0.004 w/w) tablets, showing the methodology could be applied to a different stage of the process (i.e premixed-powders or granulates). The suitability of the method was also verified when Form α was satisfactorily analysed in FDC fortified with Form δ and Form γ to reach 0.78, 0.88 and 0.98 w/w, Form α. This strategy results in an excellent alternative to ensure the polymorphic purity of PZA throughout the overall pharmaceutical manufacturing process.
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3D printing tablets: Predicting printability and drug dissolution from rheological data. Int J Pharm 2020; 590:119868. [DOI: 10.1016/j.ijpharm.2020.119868] [Citation(s) in RCA: 38] [Impact Index Per Article: 9.5] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/16/2020] [Revised: 09/03/2020] [Accepted: 09/05/2020] [Indexed: 02/03/2023]
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Digital Twins in Pharmaceutical and Biopharmaceutical Manufacturing: A Literature Review. Processes (Basel) 2020. [DOI: 10.3390/pr8091088] [Citation(s) in RCA: 60] [Impact Index Per Article: 15.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/17/2022] Open
Abstract
The development and application of emerging technologies of Industry 4.0 enable the realization of digital twins (DT), which facilitates the transformation of the manufacturing sector to a more agile and intelligent one. DTs are virtual constructs of physical systems that mirror the behavior and dynamics of such physical systems. A fully developed DT consists of physical components, virtual components, and information communications between the two. Integrated DTs are being applied in various processes and product industries. Although the pharmaceutical industry has evolved recently to adopt Quality-by-Design (QbD) initiatives and is undergoing a paradigm shift of digitalization to embrace Industry 4.0, there has not been a full DT application in pharmaceutical manufacturing. Therefore, there is a critical need to examine the progress of the pharmaceutical industry towards implementing DT solutions. The aim of this narrative literature review is to give an overview of the current status of DT development and its application in pharmaceutical and biopharmaceutical manufacturing. State-of-the-art Process Analytical Technology (PAT) developments, process modeling approaches, and data integration studies are reviewed. Challenges and opportunities for future research in this field are also discussed.
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Wang X, Du S, Zhang R, Jia X, Yang T, Zhang X. Drug-drug cocrystals: Opportunities and challenges. Asian J Pharm Sci 2020; 16:307-317. [PMID: 34276820 PMCID: PMC8261079 DOI: 10.1016/j.ajps.2020.06.004] [Citation(s) in RCA: 32] [Impact Index Per Article: 8.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/08/2020] [Revised: 06/11/2020] [Accepted: 06/16/2020] [Indexed: 02/06/2023] Open
Abstract
Recently, drug-drug cocrystal attracts more and more attention. It offers a low risk, low-cost but high reward route to new and better medicines and could improve the physiochemical and biopharmaceutical properties of a medicine by addition of a suitable therapeutically effective component without any chemical modification. Having so many advantages, to date, the reported drug-drug cocrystals are rare. Here we review the drug-drug cocrystals that reported in last decade and shed light on the opportunities and challenges for the development of drug-drug cocrystals.
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Affiliation(s)
- Xiaojuan Wang
- Department of Phamacy, The First Affiliated Hospital of Zhengzhou University, Zhengzhou 450052, China
| | - Shuzhang Du
- Department of Phamacy, The First Affiliated Hospital of Zhengzhou University, Zhengzhou 450052, China
| | - Rui Zhang
- Department of Phamacy, The First Affiliated Hospital of Zhengzhou University, Zhengzhou 450052, China
| | - Xuedong Jia
- Department of Phamacy, The First Affiliated Hospital of Zhengzhou University, Zhengzhou 450052, China
| | - Ting Yang
- Department of Phamacy, The First Affiliated Hospital of Zhengzhou University, Zhengzhou 450052, China
| | - Xiaojian Zhang
- Department of Phamacy, The First Affiliated Hospital of Zhengzhou University, Zhengzhou 450052, China
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Li Q, Huang Y, Tian K. Optimal modeling pattern of variables selection on analog complex using UVE-PLS regression. IOP SCINOTES 2020. [DOI: 10.1088/2633-1357/ab8d46] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/12/2022] Open
Abstract
Abstract
This study aimed to determine the composition of chemical complex by partial least square (PLS) regression models combined with uninformative variable elimination (UVE). The near-infrared (NIR) spectra of the forty samples were determined and then UVE was used to compress full NIR spectra from 12011 redundant variables to dozens of variables. Finally, 54, 16, 27, 31 and 42 variables were selected by UVE for 2,2,4-Trimethylpentane, Heptane, Cyclohexane, Ethyl formate and Butyl acetate respectively. Selected variables were used as the inputs of PLS model for quantitative analysis which made the prediction of the model more robust and accurate compared with the conventional PLS.
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Ojala K, Myrskyranta M, Liimatainen A, Kortejärvi H, Juppo A. Prediction of drug dissolution from Toremifene 80 mg tablets by NIR spectroscopy. Int J Pharm 2020; 577:119028. [PMID: 31954865 DOI: 10.1016/j.ijpharm.2020.119028] [Citation(s) in RCA: 7] [Impact Index Per Article: 1.8] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/09/2019] [Revised: 01/08/2020] [Accepted: 01/09/2020] [Indexed: 10/25/2022]
Abstract
The aim of our study was to justify substitution of dissolution analysis for NIR measurement of Toremifene 80 mg tablets. We studied implementation of a NIRS method by integrating the method development to discrimination power of the dissolution method. Hence, we analyzed 20 DoE tablet batches and studied which of the critical formulation factors affecting dissolution were statistically significant. To study if these factors can be detected by NIRS, PLS calibration models were developed. Finally, PLS model was built to correlate NIR data with the actual dissolution results to predict the released amount of toremifene in 30 min. To obtain the data the tablet batches were measured by NIR using diffuse reflectance technique and multivariate analysis tool was used to calibrate the NIRS models. Correlations between the critical formulation factors and the NIR spectra of Toremifene 80 mg tablet were shown and it was thus justified to develop a NIRS prediction model for dissolution. Variance (R2), standard error of estimate (SEE) and standard error of prediction (SEP) of the model were 90.0%, 4.3% and 5.9%, respectively. It was thus shown that multi-phased and time consuming dissolution procedure could be substituted for fast non-invasive NIRS method.
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Affiliation(s)
- Krista Ojala
- Orion Pharma, P.O. Box 425, 20101 Turku, Finland.
| | | | | | | | - Anne Juppo
- Division of Pharmaceutical Technology and Industrial Pharmacy, University of Helsinki, P.O. Box 56, 00014 University of Helsinki, Finland
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Razuc M, Grafia A, Gallo L, Ramírez-Rigo MV, Romañach RJ. Near-infrared spectroscopic applications in pharmaceutical particle technology. Drug Dev Ind Pharm 2019; 45:1565-1589. [DOI: 10.1080/03639045.2019.1641510] [Citation(s) in RCA: 13] [Impact Index Per Article: 2.6] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/24/2022]
Affiliation(s)
- M. Razuc
- Instituto de Química del Sur (INQUISUR), Universidad Nacional del Sur (UNS)-CONICET, Bahía Blanca, Argentina
- Departamento de Biología, Bioquímica y Farmacia, Universidad Nacional del Sur (UNS), Bahía Blanca, Argentina
| | - A. Grafia
- Planta Piloto de Ingeniería Química (PLAPIQUI), Universidad Nacional del Sur (UNS)- CONICET, Bahía Blanca, Argentina
| | - L. Gallo
- Departamento de Biología, Bioquímica y Farmacia, Universidad Nacional del Sur (UNS), Bahía Blanca, Argentina
- Planta Piloto de Ingeniería Química (PLAPIQUI), Universidad Nacional del Sur (UNS)- CONICET, Bahía Blanca, Argentina
| | - M. V. Ramírez-Rigo
- Departamento de Biología, Bioquímica y Farmacia, Universidad Nacional del Sur (UNS), Bahía Blanca, Argentina
- Planta Piloto de Ingeniería Química (PLAPIQUI), Universidad Nacional del Sur (UNS)- CONICET, Bahía Blanca, Argentina
| | - R. J. Romañach
- Department of Chemistry, Center for Structured Organic Particulate Systems, University of Puerto Rico – Mayagüez, Mayagüez, Puerto Rico
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