TY - JOUR AU - Galata, Dorián László AU - Sinka Lázárné, Melinda AU - Kiss-Kovács, Dorottya AU - Fülöp, Gergő AU - Dávid, Barnabás AU - Bogáti, Botond AU - Ficzere, Máté AU - Péterfi, Orsolya AU - Nagy, Brigitta AU - Marosi, György AU - Nagy, Zsombor Kristóf TI - Effects of omitting titanium dioxide from the film coating of a pharmaceutical tablet – An industrial case study of attempting to comply with EU regulation 2022/63 JF - EUROPEAN JOURNAL OF PHARMACEUTICAL SCIENCES J2 - EUR J PHARM SCI VL - 196 PY - 2024 PG - 8 SN - 0928-0987 DO - 10.1016/j.ejps.2024.106750 UR - https://m2.mtmt.hu/api/publication/34759143 ID - 34759143 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 Export Date: 2 April 2024 CODEN: EPSCE Correspondence Address: Nagy, Z.K.; Department of Organic Chemistry and Technology, Műegyetem rkp. 3, Hungary; email: zsknagy@oct.bme.hu 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 - CHAP AU - Mrad, Mohamed Azouz AU - Csorba, Kristóf AU - Galata, Dorián László AU - Nagy, Zsombor Kristóf AU - Nagy, Brigitta ED - Vajk, István ED - Dunaev, Dmitriy TI - Droplet Based Prediction of Viscosity of Water-PVP Solutions Using Convolutional Neural Networks T2 - Proceedings of the Automation and Applied Computer Science Workshop 2023 (AACS'23) PB - Budapesti Műszaki Egyetem, Automatizálási és Alkalmazott Informatikai Tanszék CY - Budapest SN - 9789634219262 PY - 2023 SP - 1 EP - 15 PG - 15 UR - https://m2.mtmt.hu/api/publication/34476285 ID - 34476285 AB - The viscosity of a liquid is the property that measures the liquid internal resistance to flow. Monitoring viscosity is a vital component of quality control in several industrial fields including chemical, pharmaceutical, food, and energy-related industries. The most commonly used instrument for measuring viscosity is capillary viscometers, but their cost and complexity pose challenges for industries where accurate and real-time viscosity information is vital. In this work, we prepared thirteen solutions with different water and PVP (Polyvinylpyrrolidone) ratios, measured their different viscosity values, and produced videos of their droplets. We extracted the images of the fully developed droplets from the videos and we used the images to train a Convolutional neural network model to estimate the viscosity values of the WaterPVP solutions. The proposed model was able to predict the viscosity values of the samples using images of their droplets with a high accuracy on the test dataset. LA - English DB - MTMT ER - TY - JOUR AU - Rávai, Bettina AU - Orosz, János Máté AU - Péterfi, Orsolya AU - Galata, Dorián László AU - Bálint, Erika TI - Flow chemical laboratory practice for undergraduate students: synthesis of paracetamol JF - JOURNAL OF FLOW CHEMISTRY J2 - J FLOW CHEM PY - 2023 SN - 2062-249X DO - 10.1007/s41981-023-00303-y UR - https://m2.mtmt.hu/api/publication/34453979 ID - 34453979 N1 - Funding details: FK142712 Funding details: 118/2023, ÚNKP-23-3-I-BME-102 Funding details: NTP-NFTÖ-22-B-0143 Funding details: RRF-2.3.1-21-2022-00015 Funding details: Nemzeti Kutatási, Fejlesztési és Innovaciós Alap, NKFIA AB - Generally, chemical engineering students get well acquainted with the batch synthesis of various active pharmaceutical ingredients, however, only tiny focus is provided to undergraduates on the topic of flow chemistry. In this paper, we report that students participating in the chemical engineering BSc course at the Budapest University of Technology and Economics were encouraged to perform the flow synthesis of paracetamol, a common pain painkiller. Two different synthetic routes for the continuous production of paracetamol were investigated and compared the batch and flow methods. Thus, these experiments allowed the students to discover flow chemistry for themselves under supervision: how to set up a flow system, how to carry out a reaction continuously, and to experience the advantages of flow chemistry over batch synthesis. In addition, students also got familiar with in-line Fourier transform infrared spectroscopy, as one of the reactions was monitored in real-time. LA - English DB - MTMT ER - TY - JOUR AU - Ficzere, Máté AU - Péterfi, Orsolya AU - Farkas, Attila AU - Nagy, Zsombor Kristóf AU - Galata, Dorián László TI - Image-based simultaneous particle size distribution and concentration measurement of powder blend components with deep learning and machine vision JF - EUROPEAN JOURNAL OF PHARMACEUTICAL SCIENCES J2 - EUR J PHARM SCI VL - 191 PY - 2023 SN - 0928-0987 DO - 10.1016/j.ejps.2023.106611 UR - https://m2.mtmt.hu/api/publication/34342443 ID - 34342443 N1 - Export Date: 16 November 2023 CODEN: EPSCE Correspondence Address: Nagy, Z.K.; Department of Organic Chemistry and Technology, Műegyetem rkp 3., Hungary; email: zsknagy@oct.bme.hu LA - English DB - MTMT ER - TY - JOUR AU - Galata, Dorián László AU - Gergely, Szilveszter AU - Nagy, Rebeka AU - Slezsák, János AU - Ronkay, Ferenc György AU - Nagy, Zsombor Kristóf AU - Farkas, Attila TI - Comparing the Performance of Raman and Near-Infrared Imaging in the Prediction of the In Vitro Dissolution Profile of Extended-Release Tablets Based on Artificial Neural Networks JF - PHARMACEUTICALS J2 - PHARMACEUTICALS-BASE VL - 16 PY - 2023 IS - 9 PG - 12 SN - 1424-8247 DO - 10.3390/ph16091243 UR - https://m2.mtmt.hu/api/publication/34131207 ID - 34131207 N1 - Department of Organic Chemistry and Technology, Faculty of Chemical Technology and Biotechnology, Budapest University of Technology and Economics, Budapest, H-1111, Hungary Department of Applied Biotechnology and Food Science, Faculty of Chemical Technology and Biotechnology, Budapest University of Technology and Economics, Budapest, H-1111, Hungary Department of Innovative Vehicles and Materials, GAMF Faculty of Engineering and Computer Science, John von Neumann University, Kecskemét, H-6000, Hungary Export Date: 6 October 2023 Correspondence Address: Galata, D.L.; Department of Organic Chemistry and Technology, Hungary; email: galata.dorian.laszlo@vbk.bme.hu AB - In this work, the performance of two fast chemical imaging techniques, Raman and near-infrared (NIR) imaging is compared by utilizing these methods to predict the rate of drug release from sustained-release tablets. Sustained release is provided by adding hydroxypropyl methylcellulose (HPMC), as its concentration and particle size determine the dissolution rate of the drug. The chemical images were processed using classical least squares; afterwards, a convolutional neural network was applied to extract information regarding the particle size of HPMC. The chemical images were reduced to an average HPMC concentration and a predicted particle size value; these were used as inputs in an artificial neural network with a single hidden layer to predict the dissolution profile of the tablets. Both NIR and Raman imaging yielded accurate predictions. As the instrumentation of NIR imaging allows faster measurements than Raman imaging, this technique is a better candidate for implementing a real-time technique. The introduction of chemical imaging in the routine quality control of pharmaceutical products would profoundly change quality assurance in the pharmaceutical industry. LA - English DB - MTMT ER - TY - JOUR AU - Péterfi, Orsolya AU - Madarász, Lajos AU - Ficzere, Máté AU - Lestyán-Goda, Katalin AU - Záhonyi, Petra AU - Erdei, Gábor AU - Sipos, Emese AU - Nagy, Zsombor Kristóf AU - Galata, Dorián László TI - In-line particle size measurement during granule fluidization using convolutional neural network-aided process imaging JF - EUROPEAN JOURNAL OF PHARMACEUTICAL SCIENCES J2 - EUR J PHARM SCI VL - 189 PY - 2023 SN - 0928-0987 DO - 10.1016/j.ejps.2023.106563 UR - https://m2.mtmt.hu/api/publication/34101349 ID - 34101349 N1 - 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 Department of Atomic Physics, Faculty of Natural Sciences, Budapest University of Technology and Economics, H-1111, Budafoki 8, Budapest, 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: 4 September 2023 CODEN: EPSCE Correspondence Address: Nagy, Z.K.; Department of Organic Chemistry and Technology, Műegyetem rkp. 3., H-1111, Hungary; email: zsknagy@oct.bme.hu AB - This paper presents a machine learning-based image analysis method to monitor the particle size distribution of fluidized granules. The key components of the direct imaging system are a rigid fiber-optic endoscope, a light source and a high-speed camera, which allow for real-time monitoring of the granules. The system was implemented into a custom-made 3D-printed device that could reproduce the particle movement characteristic in a fluidized-bed granulator. The suitability of the method was evaluated by determining the particle size distribution (PSD) of various granule mixtures within the 100-2000 μm size range. The convolutional neural network-based software was able to successfully detect the granules that were in focus despite the dense flow of the particles. The volumetric PSDs were compared with off-line reference measurements obtained by dynamic image analysis and laser diffraction. Similar trends were observed across the PSDs acquired with all three methods. The results of this study demonstrate the feasibility of performing real-time particle size analysis using machine vision as an in-line process analytical technology (PAT) tool. LA - English DB - MTMT ER - TY - JOUR AU - Mrad, Mohamed Azouz AU - Csorba, Kristóf AU - Galata, Dorián László AU - Nagy, Zsombor Kristóf AU - Charaf, Hassan TI - Droplet Based Estimation of Viscosity of Water–PVP Solutions Using Convolutional Neural Networks JF - PROCESSES J2 - PROCESSES VL - 11 PY - 2023 IS - 7 PG - 13 SN - 2227-9717 DO - 10.3390/pr11071917 UR - https://m2.mtmt.hu/api/publication/34045465 ID - 34045465 N1 - Department of Automation and Applied Informatics, Faculty of Electrical Engineering and Informatics, Budapest University of Technology and Economics, Műegyetem rkp.3, Budapest, H-1111, Hungary Department of Organic Chemistry and Technology, Faculty of Chemical Technology and Biotechnology, Budapest University of Technology and Economics, Budafoki út 8., F. II, Budapest, H-1111, Hungary Export Date: 14 August 2023 Correspondence Address: Mrad, M.A.; Department of Automation and Applied Informatics, Műegyetem rkp.3, Hungary; email: mmrad@edu.bme.hu AB - The viscosity of a liquid is the property that measures the liquid’s internal resistance to flow. Monitoring viscosity is a vital component of quality control in several industrial fields, including chemical, pharmaceutical, food, and energy-related industries. In many industries, the most commonly used instrument for measuring viscosity is capillary viscometers, but their cost and complexity pose challenges for these industries where accurate and real-time viscosity information is vital. In this work, we prepared fourteen solutions with different water and PVP (Polyvinylpyrrolidone) ratios, measured their different viscosity values, and produced videos of their droplets. We extracted the images of the fully developed droplets from the videos and we used the images to train a convolutional neural network model to estimate the viscosity values of the water–PVP solutions. The proposed model was able to accurately estimate the viscosity values of samples of unseen chemical formulations with the same composition with a low MSE score of 0.0243 and R2 score of 0.9576. The proposed method has potential applications in scenarios where real-time monitoring of liquid viscosity is required. LA - English DB - MTMT ER - TY - JOUR AU - Galata, Dorián László AU - Zsiros, Boldizsár AU - Knyihár, Gábor AU - Péterfi, Orsolya AU - Mészáros, Lilla Alexandra AU - Ronkay, Ferenc György AU - Nagy, Brigitta AU - Szabó, Edina AU - Nagy, Zsombor Kristóf AU - Farkas, Attila TI - Convolutional neural network-based evaluation of chemical maps obtained by fast Raman imaging for prediction of tablet dissolution profiles JF - INTERNATIONAL JOURNAL OF PHARMACEUTICS J2 - INT J PHARM VL - 640 PY - 2023 SN - 0378-5173 DO - 10.1016/j.ijpharm.2023.123001 UR - https://m2.mtmt.hu/api/publication/33804039 ID - 33804039 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 Automation and Applied Informatics, Faculty of Electrical Engineering and Informatics, Budapest University of Technology and Economics, H-1117, Budapest Magyar Tudósok körútja 2 QB-207, Hungary Department of Drugs Industry and Pharmaceutical Management, University of Medicine, Pharmacy, Sciences and Technology of Târgu Mureș, Gheorghe Marinescu 38, Târgu Mureș, 540139, Romania Department of Innovative Vehicles and Materials, GAMF Faculty of Engineering and Computer Science, John von Neumann University, Kecskemét, 6000, Hungary Export Date: 1 June 2023 CODEN: IJPHD Correspondence Address: Nagy, Z.K.; Department of Organic Chemistry and Technology, Műegyetem rkp. 3., Hungary; email: zsknagy@oct.bme.hu LA - English DB - MTMT ER - TY - JOUR AU - Tacsi, Kornélia AU - Stoffán, György Nimród AU - Galata, Dorián László AU - Pusztai, Éva AU - Gyürkés, Martin AU - Nagy, Brigitta AU - Szilágyi, Botond AU - Nagy, Zsombor Kristóf AU - Marosi, György AU - Pataki, Hajnalka TI - Improvement of drug processability in a connected continuous crystallizer system using formulation additive JF - INTERNATIONAL JOURNAL OF PHARMACEUTICS J2 - INT J PHARM VL - 635 PY - 2023 SN - 0378-5173 DO - 10.1016/j.ijpharm.2023.122725 UR - https://m2.mtmt.hu/api/publication/33672106 ID - 33672106 N1 - Funding Agency and Grant Number: National Research Development and Innovation Office of Hungary [K-143039, FK-132133, FK-143019, PD-142970, FK-138475]; Hungarian Academy of Sciences - National Research, Development and Innovation Fund of Hungary [2019-1.3.1 -KK -2019-00004, UNKP-22-3-II-BME-171, UNKP-22-4-II-BME-137, UNKP-22-5-BME-300]; New National Excellence Program of the Ministry for Culture and Innovation from the source of the National Research, Development and Innovation Fund; Egis Pharma- ceuticals PLC Funding text: This work was financially supported by the National Research Development and Innovation Office of Hungary (K-143039, FK-132133, FK-143019, PD-142970, FK-138475) . Hajnalka Pataki and Brigitta Nagy is thankful for the Janos Bolyai Research Scholarship of the Hungarian Academy of Sciences. The research was funded by the National Research, Development and Innovation Fund of Hungary in the frame of the 2019-1.3.1 -KK -2019-00004 project. The research was also supported by the UNKP-22-3-II-BME-171, UNKP-22-4-II-BME-137 and UNKP-22-5-BME-300 New National Excellence Program of the Ministry for Culture and Innovation from the source of the National Research, Development and Innovation Fund. The authors thank Egis Pharma- ceuticals PLC for supporting the research with the overflow crystallizer device. LA - English DB - MTMT ER -