TY - JOUR AU - Mészáros, Lilla Alexandra AU - Madarász, Lajos AU - Kádár, Szabina AU - Ficzere, Máté AU - Farkas, Attila AU - Nagy, Zsombor Kristóf TI - Machine vision-based non-destructive dissolution prediction of meloxicam-containing tablets JF - INTERNATIONAL JOURNAL OF PHARMACEUTICS J2 - INT J PHARM VL - 655 PY - 2024 PG - 13 SN - 0378-5173 DO - 10.1016/j.ijpharm.2024.124013 UR - https://m2.mtmt.hu/api/publication/34771157 ID - 34771157 N1 - Export Date: 5 April 2024 CODEN: IJPHD Correspondence Address: Kristóf Nagy, Z.; Department of Organic Chemistry and Technology, Műegyetem rakpart 3, Hungary; email: zsknagy@oct.bme.hu AB - 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. © 2024 The Author(s) LA - English DB - MTMT ER - TY - JOUR AU - Péterfi, Orsolya AU - Mészáros, Lilla Alexandra AU - Szabó-Szőcs, Bence AU - Ficzere, Máté AU - Sipos, Emese AU - Farkas, Attila AU - Galata, Dorián László AU - Nagy, Zsombor Kristóf TI - UV–VIS imaging-based investigation of API concentration fluctuation caused by the sticking behaviour of pharmaceutical powder blends JF - INTERNATIONAL JOURNAL OF PHARMACEUTICS J2 - INT J PHARM VL - 655 PY - 2024 PG - 11 SN - 0378-5173 DO - 10.1016/j.ijpharm.2024.124010 UR - https://m2.mtmt.hu/api/publication/34759117 ID - 34759117 N1 - Department of Organic Chemistry and Technology, Faculty of Chemical Technology and Biotechnology, Budapest University of Technology and Economics, Műegyetem rkp. 3., Budapest, H-1111, Hungary Department of Pharmaceutical Industry and Management, Faculty of Pharmacy, George Emil Palade University of Medicine, Pharmacy, Sciences and Technology of Targu Mures, Gheorghe Marinescu Street 38, Targu Mures, 540142, Romania Export Date: 5 April 2024 CODEN: IJPHD Correspondence Address: Galata, D.L.; Department of Organic Chemistry and Technology, Műegyetem rkp. 3., Hungary; email: galata.dorian.laszlo@vbk.bme.hu AB - Surface powder sticking in pharmaceutical mixing vessels poses a risk to the uniformity and quality of drug formulations. This study explores methods for evaluating the amount of pharmaceutical powder mixtures adhering to the metallic surfaces. Binary powder blends consisting of amlodipine and microcrystalline cellulose (MCC) were used to investigate the effect of the mixing order on the adherence to the vessel wall. Elevated API concentrations were measured on the wall and within the dislodged material from the surface, regardless of the mixing order of the components. UV imaging was used to determine the particle size and the distribution of the API on the metallic surface. The results were compared to chemical maps obtained by Raman chemical imaging. The combination of UV and VIS imaging enabled the rapid acquisition of chemical maps, covering a substantially large area representative of the analysed sample. UV imaging was also applied in tablet inspection to detect tablets that fail to meet the content uniformity criteria. The results present powder adherence as a possible source of poor content uniformity, highlighting the need for 100% inspection of pharmaceutical products to ensure product quality and safety. LA - English DB - MTMT ER - TY - JOUR AU - Madarász, Lajos AU - Mészáros, Lilla Alexandra AU - Köte, Á. AU - Farkas, Attila AU - Nagy, Zsombor Kristóf TI - AI-based analysis of in-line process endoscope images for real-time particle size measurement in a continuous pharmaceutical milling process JF - INTERNATIONAL JOURNAL OF PHARMACEUTICS J2 - INT J PHARM VL - 641 PY - 2023 SN - 0378-5173 DO - 10.1016/j.ijpharm.2023.123060 UR - https://m2.mtmt.hu/api/publication/34004071 ID - 34004071 N1 - Export Date: 27 February 2024 CODEN: IJPHD Correspondence Address: Nagy, Z.K.; Department of Organic Chemistry and Technology, H-1111, Budapest, Műegyetem rakpart 3, Hungary; email: zsknagy@oct.bme.hu Chemicals/CAS: sodium chloride, 7647-14-5, 23724-87-0, 49658-21-1; Excipients; Sodium Chloride Funding details: Mesterséges Intelligencia Nemzeti Laboratórium, MILAB Funding details: European Commission, EC, RRF-2.3.1-21-2022-00004 Funding details: Nemzeti Kutatási, Fejlesztési és Innovaciós Alap, NKFIA Funding text 1: Supported by the ÚNKP-22-3-II-BME-154 New National Excellence Program of the Ministry for Culture and Innovation from the source of the National Research, Development and Innovation Fund. Supported by the the European Union project RRF-2.3.1-21-2022-00004 within the framework of the Artificial Intelligence National Laboratory LA - English DB - MTMT ER - TY - JOUR AU - Nagy, Brigitta AU - Szabados-Nacsa, Ágnes AU - Fülöp, Gergő AU - Turák Nagyné, Anikó AU - Galata, Dorián László AU - Farkas, Attila AU - Mészáros, Lilla Alexandra AU - Nagy, Zsombor Kristóf AU - Marosi, György TI - Interpretable artificial neural networks for retrospective QbD of pharmaceutical tablet manufacturing based on a pilot-scale developmental dataset JF - INTERNATIONAL JOURNAL OF PHARMACEUTICS J2 - INT J PHARM VL - 633 PY - 2023 SN - 0378-5173 DO - 10.1016/j.ijpharm.2023.122620 UR - https://m2.mtmt.hu/api/publication/33641694 ID - 33641694 N1 - Department of Organic Chemistry and Technology, Faculty of Chemical Technology and Biotechnology, Budapest University of Technology and Economics, Műegyetem rkp. 3., Budapest, H-1111, Hungary Gedeon Richter Plc., Formulation R&D, Gyömrői u. 19-21, Budapest, H-1103, Hungary CODEN: IJPHD Correspondence Address: Nagy, B.; Department of Organic Chemistry and Technology, Műegyetem rkp. 3., Hungary; email: nagy.brigitta@edu.bme.hu LA - English DB - MTMT ER - TY - JOUR AU - Ficzere, Máté AU - Mészáros, Lilla Alexandra AU - Kállai-Szabó, Nikolett AU - Kovács, Andrea AU - Antal, István AU - Nagy, Zsombor Kristóf AU - Galata, Dorián László TI - Real-time coating thickness measurement and defect recognition of film coated tablets with machine vision and deep learning JF - INTERNATIONAL JOURNAL OF PHARMACEUTICS J2 - INT J PHARM VL - 623 PY - 2022 PG - 8 SN - 0378-5173 DO - 10.1016/j.ijpharm.2022.121957 UR - https://m2.mtmt.hu/api/publication/32912550 ID - 32912550 N1 - Funding Agency and Grant Number: OTKA grant [FK-132133]; Ministry for Innovation and Technology Funding text: This work was supported by OTKA grant FK-132133. The research reported in this paper and carried out at BME has been supported by the National Laboratory of Artificial Intelligence funded by the NRDIO under the auspices of the Ministry for Innovation and Technology. AB - This paper presents a system, where images acquired with a digital camera are coupled with image analysis and deep learning to identify and categorize film coating defects and to measure the film coating thickness of tablets. There were 5 different classes of defective tablets, and the YOLOv5 algorithm was utilized to recognize defects, the accuracy of the classification was 98.2%. In order to characterize coating thickness, the diameter of the tablets in pixels was measured, which was used to measure the coating thickness of the tablets. The proposed system can be easily scaled up to match the production capability of continuous film coaters. With the developed technique, the complete screening of the produced tablets can be achieved in real-time resulting in the improvement of quality control. LA - English DB - MTMT ER - TY - JOUR AU - Mészáros, Lilla Alexandra AU - Farkas, Attila AU - Madarász, Lajos AU - Bicsár, Rozália AU - Galata, Dorián László AU - Nagy, Brigitta AU - Nagy, Zsombor Kristóf TI - UV/VIS imaging-based PAT tool for drug particle size inspection in intact tablets supported by pattern recognition neural networks JF - INTERNATIONAL JOURNAL OF PHARMACEUTICS J2 - INT J PHARM VL - 620 PY - 2022 SN - 0378-5173 DO - 10.1016/j.ijpharm.2022.121773 UR - https://m2.mtmt.hu/api/publication/32803411 ID - 32803411 N1 - Export Date: 19 May 2022 CODEN: IJPHD Correspondence Address: Nagy, Z.K.; Department of Organic Chemistry and Technology, Műegyetem rakpart 3, Hungary; email: zsknagy@oct.bme.hu LA - English DB - MTMT ER - TY - JOUR AU - Galata, Dorián László AU - Zsiros, Boldizsár AU - Mészáros, Lilla Alexandra AU - Nagy, Brigitta AU - Szabó, Edina AU - Farkas, Attila AU - Nagy, Zsombor Kristóf TI - Raman mapping-based non-destructive dissolution prediction of sustained-release tablets JF - JOURNAL OF PHARMACEUTICAL AND BIOMEDICAL ANALYSIS J2 - J PHARMACEUT BIOMED ANAL VL - 212 PY - 2022 PG - 9 SN - 0731-7085 DO - 10.1016/j.jpba.2022.114661 UR - https://m2.mtmt.hu/api/publication/32674803 ID - 32674803 N1 - Cited By :1 Export Date: 5 April 2022 CODEN: JPBAD Correspondence Address: Farkas, A.; Department of Organic Chemistry and Technology, Műegyetem rakpart 3, Hungary; email: farkas.attila@vbk.bme.hu AB - 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. LA - English DB - MTMT ER - TY - JOUR AU - Ficzere, Máté AU - Mészáros, Lilla Alexandra AU - Madarász, Lajos AU - Novák, Márk AU - Nagy, Zsombor Kristóf AU - Galata, Dorián László TI - Indirect monitoring of ultralow dose API content in continuous wet granulation and tableting by machine vision JF - INTERNATIONAL JOURNAL OF PHARMACEUTICS J2 - INT J PHARM VL - 607 PY - 2021 PG - 11 SN - 0378-5173 DO - 10.1016/j.ijpharm.2021.121008 UR - https://m2.mtmt.hu/api/publication/32242466 ID - 32242466 N1 - Export Date: 27 September 2021 CODEN: IJPHD Correspondence Address: Nagy, Z.K.; Department of Organic Chemistry and Technology, Műegyetem rakpart 3, Hungary; email: zsknagy@oct.bme.hu AB - This paper presents new machine vision–based methods for indirect real-time quantification of ultralow drug content during continuous twin-screw wet granulation and tableting. Granulation was performed with a solution containing carvedilol (CAR) as API in the ultralow dose range (0.05 w/w% in the granule) and the addition of riboflavin (RI) as a coloured tracer. An in-line calibration in the range of 0.047–0.058 w/w% was prepared for the measurement of CAR concentration using colour analysis (CA) and particle size analysis (PSA), and the validation with HPLC resulted in respective relative errors of 2.62% and 2.30% showing great accuracy. To improve the technique, a second in-line calibration was conducted in a broader CAR concentration range of 0.039–0.063 w/w% utilizing only half the amount of RI (0.045 w/w%), while doubling the output of the granulation line to 2 kg/h, producing a relative error of 4.51% and 4.29%, respectively. Finally, it was shown that the CA technique can also be carried on to monitor the CAR content of tablets in the 42–62 μg dose range with a relative error of 5.20%. Machine vision was proven to be a potent indirect method for the in-line, determination and monitoring of ultralow API content during continuous manufacturing. LA - English DB - MTMT ER - TY - JOUR AU - Galata, Dorián László AU - Könyves, Zsófia AU - Nagy, Brigitta AU - Novák, Márk AU - Mészáros, Lilla Alexandra AU - Szabó, Edina AU - Farkas, Attila AU - Marosi, György AU - Nagy, Zsombor Kristóf TI - 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 JF - INTERNATIONAL JOURNAL OF PHARMACEUTICS J2 - INT J PHARM VL - 597 PY - 2021 SN - 0378-5173 DO - 10.1016/j.ijpharm.2021.120338 UR - https://m2.mtmt.hu/api/publication/31861632 ID - 31861632 N1 - Export Date: 26 April 2021 CODEN: IJPHD Correspondence Address: Nagy, Z.K.; Department of Organic Chemistry and Technology, email: zsknagy@oct.bme.hu Cited By :1 Export Date: 17 June 2021 CODEN: IJPHD Correspondence Address: Nagy, Z.K.; Department of Organic Chemistry and Technology, email: zsknagy@oct.bme.hu LA - English DB - MTMT ER - TY - JOUR AU - Galata, Dorián László AU - Mészáros, Lilla Alexandra AU - Ficzere, Máté AU - Vass, Panna AU - Nagy, Brigitta AU - Szabó, Edina AU - Domokos, András AU - Farkas, Attila AU - Csontos, István AU - Marosi, György AU - Nagy, Zsombor Kristóf TI - Continuous blending monitored and feedback controlled by machine vision-based PAT tool JF - JOURNAL OF PHARMACEUTICAL AND BIOMEDICAL ANALYSIS J2 - J PHARMACEUT BIOMED ANAL VL - 196 PY - 2021 PG - 9 SN - 0731-7085 DO - 10.1016/j.jpba.2021.113902 UR - https://m2.mtmt.hu/api/publication/31831440 ID - 31831440 N1 - CODEN: JPBAD Correspondence Address: Nagy, Z.K.; Department of Organic Chemistryand Technology, Műegyetem rakpart 3, Hungary; email: zsknagy@oct.bme.hu Export Date: 26 April 2021 CODEN: JPBAD Correspondence Address: Nagy, Z.K.; Department of Organic Chemistryand Technology, Műegyetem rakpart 3, Hungary; email: zsknagy@oct.bme.hu CODEN: JPBAD Correspondence Address: Nagy, Z.K.; Department of Organic Chemistryand Technology, Műegyetem rakpart 3, Hungary; email: zsknagy@oct.bme.hu Chemicals/CAS: calcium phosphate dibasic, 14567-84-1, 14567-92-1, 21063-37-6, 7757-93-9, 7789-77-7; microcrystalline cellulose, 39394-43-9, 51395-75-6; riboflavin, 83-88-5 Tradenames: Agilent 8453, Hewlett Packard, United States; Mathlab 9.8.0.1380330, Mathworks, United States; MEGA TS1214-MP, Basler, Germany; QUICK BIN Manufacturers: Merck, HungaryBasler, Germany; brabender technologie, Germany; apokromat, Hungary; mechacad, Hungary; Hewlett Packard, United States; Mathworks, United States Funding details: Hungarian Scientific Research Fund, OTKA, FK-132133, KH-129584 Funding details: Richter Gedeon Talentum Alapítvány Funding details: National Research, Development and Innovation Office Funding text 1: This work was supported by OTKA grants: FK-132133 and KH-129584 . The research presented in this paper was supported by the Ministry of Innovation and the National Research , Development and Innovation Office within the framework of the Artificial Intelligence National Laboratory Programme. This work was created with the financial contribution of Gedeon Richter’s Talentum Foundation (1103 Budapest, Gyömrői út 19-21.). Funding text 2: This work was supported by OTKA grants: FK-132133 and KH-129584. The research presented in this paper was supported by the Ministry of Innovation and the National Research, Development and Innovation Office within the framework of the Artificial Intelligence National Laboratory Programme. This work was created with the financial contribution of Gedeon Richter's Talentum Foundation (1103 Budapest, Gy?mr?i ?t 19-21.). Funding Agency and Grant Number: OTKAOrszagos Tudomanyos Kutatasi Alapprogramok (OTKA) [FK-132133, KH-129584]; Ministry of Innovation; National Research, Development and Innovation Office; Gedeon Richter's Talentum Foundation (Budapest, Gyomroi ut) Funding text: This work was supported by OTKA grants: FK-132133 and KH-129584. The research presented in this paper was supported by the Ministry of Innovation and the National Research, Development and Innovation Office within the framework of the Artificial Intelligence National Laboratory Programme. This work was created with the financial contribution of Gedeon Richter's Talentum Foundation (1103 Budapest, Gyomroi ut 19-21.). AB - In a continuous powder blending process machine vision is utilized as a Process Analytical Technology (PAT) tool. While near-infrared (NIR) and Raman spectroscopy are reliable methods in this field, measurements become challenging when concentrations below 2 w/w% are quantified. However, an active pharmaceutical ingredient (API) with an intense color might be quantified in even lower quantities by images recorded with a digital camera. Riboflavin (RI) was used as a model API with orange color, its Limit of Detection was found to be 0.015 w/w% and the Limit of Quantification was 0.046 w/w% using a calibration based on the pixel value of images. A calibration for in-line measurement of RI concentration was prepared in the range of 0.2-0.45 w/w%, validation with UV/VIS spectrometry showed great accuracy with a relative error of 2.53 %. The developed method was then utilized for a residence time distribution (RTD) measurement in order to characterize the dynamics of the blending process. Lastly, the technique was applied in real-time feedback control of a continuous powder blending process. Machine vision based direct or indirect API concentration determination is a promising and fast method with a great potential for monitoring and control of continuous pharmaceutical processes. (C) 2021 The Authors. Published by Elsevier B.V. This is an open access article under the CC BY license LA - English DB - MTMT ER -