1
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Taseva AR, Persoons T, Healy AM, D'Arcy DM. Application of shadowgraph imaging (SGI) particle characterisation data to interpret the impact of varying test conditions on powder dissolution and to develop an automated agglomeration identification method (AIM) in the USP flow-through apparatus. Int J Pharm 2024; 666:124778. [PMID: 39349225 DOI: 10.1016/j.ijpharm.2024.124778] [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: 06/04/2024] [Revised: 09/27/2024] [Accepted: 09/27/2024] [Indexed: 10/02/2024]
Abstract
The aims of this work were 1) to explore the application of shadowgraph imaging (SGI) as a real time monitoring tool to characterize ibuprofen particle behaviour during dissolution testing under various conditions in the USP 4 flow-through apparatus and 2) to investigate the potential to develop an SGI-based automated agglomeration identification method (AIM) for real time agglomerate detection during dissolution testing. The effect of surfactant addition, changes in the drug mass and flow rate, the use of sieved and un-sieved powder fractions, and the use of different drug crystal habits were investigated. Videos at every sampling time point during dissolution were taken and analysed by SGI. The AIM was developed to characterize agglomerates based on two criteria - size and solidity. All detections were confirmed by manual video observation and a reference agglomerate data set. The method was validated under new dissolution conditions with un-sieved particles. Characterisation of particle dispersion behaviour by SGI enabled interpretation of the impact of dissolution test conditions. Higher numbers of early detections reflected greater dissolution rates with increased surfactant concentration, using sieved fraction or plate-shaped crystals, but was impacted by drug mass tested. An AIM was successfully developed and applied to detect agglomerates during dissolution, suggesting potential, with appropriate method development, for application in quality control.
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Affiliation(s)
- Alexandra R Taseva
- School of Pharmacy and Pharmaceutical Sciences, Trinity College Dublin, Ireland; SSPC, The Science Foundation Ireland Research Centre for Pharmaceuticals, Trinity College Dublin, Ireland.
| | - Tim Persoons
- Department of Mechanical, Manufacturing & Biomedical Engineering, Trinity College Dublin, Ireland; SSPC, The Science Foundation Ireland Research Centre for Pharmaceuticals, Trinity College Dublin, Ireland.
| | - Anne Marie Healy
- School of Pharmacy and Pharmaceutical Sciences, Trinity College Dublin, Ireland; SSPC, The Science Foundation Ireland Research Centre for Pharmaceuticals, Trinity College Dublin, Ireland.
| | - Deirdre M D'Arcy
- School of Pharmacy and Pharmaceutical Sciences, Trinity College Dublin, Ireland; SSPC, The Science Foundation Ireland Research Centre for Pharmaceuticals, Trinity College Dublin, Ireland.
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2
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Neugebauer P, Zettl M, Moser D, Poms J, Kuchler L, Sacher S. Process analytical technology in Downstream-Processing of Drug Substances- A review. Int J Pharm 2024; 661:124412. [PMID: 38960339 DOI: 10.1016/j.ijpharm.2024.124412] [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: 04/30/2024] [Revised: 06/11/2024] [Accepted: 06/29/2024] [Indexed: 07/05/2024]
Abstract
Process Analytical Technology (PAT) has revolutionized pharmaceutical manufacturing by providing real-time monitoring and control capabilities throughout the production process. This review paper comprehensively examines the application of PAT methodologies specifically in the production of solid active pharmaceutical ingredients (APIs). Beginning with an overview of PAT principles and objectives, the paper explores the integration of advanced analytical techniques such as spectroscopy, imaging modalities and others into solid API substance production processes. Novel developments in in-line monitoring at academic level are also discussed. Emphasis is placed on the role of PAT in ensuring product quality, consistency, and compliance with regulatory requirements. Examples from existing literature illustrate the practical implementation of PAT in solid API substance production, including work-up, crystallization, filtration, and drying processes. The review addresses the quality and reliability of the measurement technologies, aspects of process implementation and handling, the integration of data treatment algorithms and current challenges. Overall, this review provides valuable insights into the transformative impact of PAT on enhancing pharmaceutical manufacturing processes for solid API substances.
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Affiliation(s)
- Peter Neugebauer
- Research Center Pharmaceutical Engineering GmbH, 8010 Graz, Austria; Institute of Process and Particle Engineering, Graz University of Technology, 8010 Graz, Austria
| | - Manuel Zettl
- Research Center Pharmaceutical Engineering GmbH, 8010 Graz, Austria
| | - Daniel Moser
- Research Center Pharmaceutical Engineering GmbH, 8010 Graz, Austria
| | - Johannes Poms
- Research Center Pharmaceutical Engineering GmbH, 8010 Graz, Austria
| | - Lisa Kuchler
- Research Center Pharmaceutical Engineering GmbH, 8010 Graz, Austria
| | - Stephan Sacher
- Research Center Pharmaceutical Engineering GmbH, 8010 Graz, Austria.
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3
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Pan Z, Cao X, Ke W, Wang J, Wang Y, Du S, Xue F. Effect of Additives on the Morphology of γ-Aminobutyric Acid Crystals. ACS OMEGA 2024; 9:29928-29938. [PMID: 39005767 PMCID: PMC11238310 DOI: 10.1021/acsomega.4c04625] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Grants] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 05/15/2024] [Revised: 06/16/2024] [Accepted: 06/18/2024] [Indexed: 07/16/2024]
Abstract
The effect of surfactant, polymer, and tailor-made additives on the crystallization of γ-aminobutyric acid (GABA) was studied in this work. Cooling crystallization of GABA in water yielded plate-like crystals. In the presence of sodium stearate, polyhedral block-like crystals of GABA were obtained. Hydroxyethyl cellulose (HEC) led to rod-like crystals, in which the morphology was associated with additive concentrations. Six kinds of amino acids were used as tailor-made additives, and they exhibit different influences on crystal shape and size. The induction time of GABA was determined in the absence and presence of additives. The results showed that sodium stearate promoted nucleation, while HEC, l-Lysine, l-histidine, and l-tyrosine inhibited nucleation. Crystal face indexing, Hirshfeld surface analysis, and molecular dynamics (MD) simulation in aqueous solution-crystal systems were carried out to investigate the affecting factors of different crystal faces. The polymer additive was selected as an example during MD simulation to calculate intermolecular interactions between the crystal face and solvent or additive. The effect of the additive on the mobility of the solute in solution was also evaluated by mean-square displacement. The additive offers an effective approach for changing crystal morphology and particle size and adapting it to different production requirements.
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Affiliation(s)
- Zhiying Pan
- School of Pharmaceutical
Sciences (Shandong
Analysis and Test Center), Qilu University
of Technology (Shandong Academy of Sciences), Jinan 250014, P. R. China
| | - Xiaoyu Cao
- School of Pharmaceutical
Sciences (Shandong
Analysis and Test Center), Qilu University
of Technology (Shandong Academy of Sciences), Jinan 250014, P. R. China
| | - Wenyu Ke
- School of Pharmaceutical
Sciences (Shandong
Analysis and Test Center), Qilu University
of Technology (Shandong Academy of Sciences), Jinan 250014, P. R. China
| | - Jingyu Wang
- School of Pharmaceutical
Sciences (Shandong
Analysis and Test Center), Qilu University
of Technology (Shandong Academy of Sciences), Jinan 250014, P. R. China
| | - Yan Wang
- School of Pharmaceutical
Sciences (Shandong
Analysis and Test Center), Qilu University
of Technology (Shandong Academy of Sciences), Jinan 250014, P. R. China
| | - Shichao Du
- School of Pharmaceutical
Sciences (Shandong
Analysis and Test Center), Qilu University
of Technology (Shandong Academy of Sciences), Jinan 250014, P. R. China
| | - Fumin Xue
- School of Pharmaceutical
Sciences (Shandong
Analysis and Test Center), Qilu University
of Technology (Shandong Academy of Sciences), Jinan 250014, P. R. China
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4
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Haruta Y, Ye H, Huber P, Sandor N, Pavesic Junior A, Dayneko S, Qiu S, Yeddu V, Saidaminov MI. Reproducible high-quality perovskite single crystals by flux-regulated crystallization with a feedback loop. NATURE SYNTHESIS 2024; 3:1212-1220. [PMID: 39397876 PMCID: PMC11466857 DOI: 10.1038/s44160-024-00576-8] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Figures] [Subscribe] [Scholar Register] [Received: 12/22/2023] [Accepted: 05/22/2024] [Indexed: 10/15/2024]
Abstract
Controlling the linear growth rate, a critical factor that determines crystal quality, has been a challenge in solution-grown single crystals due to complex crystallization kinetics influenced by multiple parameters. Here we introduce a flux-regulated crystallization (FRC) method to directly monitor and feedback-control the linear growth rate, circumventing the need to control individual growth conditions. When applied to metal halide perovskites, the FRC maintains a stable linear growth rate for over 40 h in synthesizing CH3NH3PbBr3 and CsPbBr3 single crystals, achieving outstanding crystallinity (quantified by a full width at half-maximum of 15.3 arcsec in the X-ray rocking curve) in a centimetre-scale single crystal. The FRC is a reliable platform for synthesizing high-quality crystals essential for commercialization and systematically exploring crystallization conditions, maintaining a key parameter-the linear growth rate-constant, which enables a comprehensive understanding of the impact of other influencing factors.
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Affiliation(s)
- Yuki Haruta
- Department of Chemistry, University of Victoria, Victoria, British Columbia Canada
| | - Hanyang Ye
- Department of Chemistry, University of Victoria, Victoria, British Columbia Canada
| | - Paul Huber
- Department of Chemistry, University of Victoria, Victoria, British Columbia Canada
| | - Nicholas Sandor
- Department of Electrical & Computer Engineering, University of Victoria, Victoria, British Columbia Canada
| | | | - Sergey Dayneko
- Department of Chemistry, University of Victoria, Victoria, British Columbia Canada
| | - Shuang Qiu
- Department of Chemistry, University of Victoria, Victoria, British Columbia Canada
| | - Vishal Yeddu
- Department of Chemistry, University of Victoria, Victoria, British Columbia Canada
| | - Makhsud I. Saidaminov
- Department of Chemistry, University of Victoria, Victoria, British Columbia Canada
- Department of Electrical & Computer Engineering, University of Victoria, Victoria, British Columbia Canada
- Centre for Advanced Materials and Related Technologies (CAMTEC), University of Victoria, Victoria, British Columbia Canada
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5
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Taseva AR, Persoons T, D'Arcy DM. Application of an AI image analysis and classification approach to characterise dissolution and precipitation events in the flow through apparatus. Eur J Pharm Biopharm 2023; 189:36-47. [PMID: 37120067 DOI: 10.1016/j.ejpb.2023.04.020] [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: 12/20/2022] [Revised: 04/19/2023] [Accepted: 04/22/2023] [Indexed: 05/01/2023]
Abstract
Imaging and artificial intelligence (AI) approaches have been used with increasing frequency in pharmaceutical industry in recent years. Characterisation of processes such as drug dissolution and precipitation is vital in quality control testing and drug manufacture. To support existing techniques like in vitro dissolution testing, novel process analytical technologies (PATs) can give an insight into these processes. The aim of this study was to create and explore the potential of an automated image classification model based on image analysis to identify events (dissolution and precipitation) occurring in the flow-through apparatus (FTA) test cell, and the ability to characterise a dissolution process over time. Several precipitation conditions were tested in a USP 4 FTA test cell with images recorded during early (plume formation) and late (particulate re-formation) stages of precipitation. An available MATLAB code was used as a base to develop and validate an anomaly classification model able to detect different events occurring during the precipitation process in the dissolution cell. Two variants of the model were tested on images from a dissolution test in the FTA, with a view to application of the image analysis system to quantitative characterization of the dissolution process over time. It was found that the classification model is highly accurate (>90%) in detecting events occurring in the FTA test cell. The model showed potential to be used to characterise the stages of dissolution and precipitation processes, and as a proof of concept demonstrates potential for deep machine learning image analysis to be applied to kinetics of other pharmaceutical processes.
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Affiliation(s)
- Alexandra R Taseva
- School of Pharmacy and Pharmaceutical Sciences, Trinity College Dublin, Ireland; SSPC, The Science Foundation Ireland Research Centre for Pharmaceuticals, Trinity College Dublin, Ireland.
| | - Tim Persoons
- Department of Mechanical, Manufacturing & Biomedical Engineering, Trinity College Dublin, Ireland; SSPC, The Science Foundation Ireland Research Centre for Pharmaceuticals, Trinity College Dublin, Ireland.
| | - Deirdre M D'Arcy
- School of Pharmacy and Pharmaceutical Sciences, Trinity College Dublin, Ireland; SSPC, The Science Foundation Ireland Research Centre for Pharmaceuticals, Trinity College Dublin, Ireland.
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6
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Xiao Li H, Abdul-Reda Hussein U, Waleed I, Hassan Zain Al-Abdeen S, M. A. Altalbawy F, Hussein Adhab Z, Faisal A, Alshahrani MY, Kamil Zaidan H, Suliman M, Ben Hu X. An advanced computational method for studying drug nanonization using green supercritical-based processing for improvement of pharmaceutical bioavailability in aqueous media. J Mol Liq 2023. [DOI: 10.1016/j.molliq.2023.121805] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 04/08/2023]
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7
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Metherall JP, Carroll RC, Coles SJ, Hall MJ, Probert MR. Advanced crystallisation methods for small organic molecules. Chem Soc Rev 2023; 52:1995-2010. [PMID: 36857636 DOI: 10.1039/d2cs00697a] [Citation(s) in RCA: 10] [Impact Index Per Article: 10.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 03/03/2023]
Abstract
Molecular materials based on small organic molecules often require advanced structural analysis, beyond the capability of spectroscopic techniques, to fully characterise them. In such cases, diffraction methods such as single crystal X-ray diffraction (SCXRD), are one of the most powerful tools available to researchers, providing molecular and structural elucidation at atomic level resolution, including absolute stereochemistry. However SCXRD, and related diffraction methods, are heavily dependent on the availability of suitable, high-quality crystals, thus crystallisation often becomes the major bottleneck in preparing samples. Following a summary of classical methods for the crystallisation of small organic molecules, this review will focus on a number of recently developed advanced methods for crystalline material sample preparation for SCXRD. This review will cover two main areas of modern small organic molecule crystallisation, namely the inclusion of molecules within host complexes (e.g., "crystalline sponge" and tetraaryladamantane based inclusion chaperones) and the use of high-throughput crystallisation, employing "under-oil" approaches (e.g., microbatch under-oil and ENaCt). Representative examples have been included for each technique, together with a discussion of their relative advantages and limitations to aid the reader in selecting the most appropriate technique to overcome a specific analytical challenge.
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Affiliation(s)
- J P Metherall
- Newcastle University, Chemistry - School of Natural Environmental Sciences, Newcastle upon Tyne, NE1 7RU, UK.
| | - R C Carroll
- University of Southampton, School of Chemistry, Southampton, SO17 1BJ, UK
| | - S J Coles
- University of Southampton, School of Chemistry, Southampton, SO17 1BJ, UK
| | - M J Hall
- Newcastle University, Chemistry - School of Natural Environmental Sciences, Newcastle upon Tyne, NE1 7RU, UK.
| | - M R Probert
- Newcastle University, Chemistry - School of Natural Environmental Sciences, Newcastle upon Tyne, NE1 7RU, UK.
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8
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Destro F, Barolo M, Nagy ZK. Quality-by-control of intensified continuous filtration-drying of active pharmaceutical ingredients. AIChE J 2023; 69:e17926. [PMID: 38633424 PMCID: PMC11022276 DOI: 10.1002/aic.17926] [Citation(s) in RCA: 1] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/19/2022] [Accepted: 09/20/2022] [Indexed: 04/19/2024]
Abstract
Continuous manufacturing and closed-loop quality control are emerging technologies that are pivotal for next-generation pharmaceutical modernization. We develop a process control framework for a continuous carousel for integrated filtration-drying of crystallization slurries. The proposed control system includes model-based monitoring and control routines, such as state estimation and real-time optimization, implemented in a hierarchical, three-layer quality-by-control (QbC) framework. We implement the control system in ContCarSim, a publicly available carousel simulator. We benchmark the proposed control system against simpler methods, comprising a reduced subset of the elements of the overall control system, and against open-loop operation (the current standard in pharmaceutical manufacturing). The proposed control system demonstrates superior performance in terms of higher consistency in product quality and increased productivity, proving the benefits of closed-loop control and of model-based techniques in pharmaceutical manufacturing. This study represents a step forward toward end-to-end continuous pharmaceutical processing, and in the evolution of quality-by-design toward quality-by-control.
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Affiliation(s)
- Francesco Destro
- CAPE-Lab—Computer-Aided Process Engineering Laboratory, Department of Industrial Engineering, University of Padova, Padova, Italy
- Davidson School of Chemical Engineering, Purdue University, West Lafayette, Indiana, USA
| | - Massimiliano Barolo
- CAPE-Lab—Computer-Aided Process Engineering Laboratory, Department of Industrial Engineering, University of Padova, Padova, Italy
| | - Zoltan K. Nagy
- Davidson School of Chemical Engineering, Purdue University, West Lafayette, Indiana, USA
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9
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Scott D, Briggs NEB, Formosa A, Burnett A, Desai B, Hammersmith G, Rapp K, Capellades G, Myerson AS, Roper TD. Impurity Purging through Systematic Process Development of a Continuous Two-Stage Crystallization. Org Process Res Dev 2023. [DOI: 10.1021/acs.oprd.2c00317] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 01/05/2023]
Affiliation(s)
- Drew Scott
- Department of Chemical and Life Science Engineering, Virginia Commonwealth University, Richmond, Virginia23284, United States
| | - Naomi E. B. Briggs
- On Demand Pharmaceuticals, 1550 E Gude Drive, Rockville, Maryland20850, United States
| | - Anna Formosa
- On Demand Pharmaceuticals, 1550 E Gude Drive, Rockville, Maryland20850, United States
| | - Annessa Burnett
- Department of Chemical and Life Science Engineering, Virginia Commonwealth University, Richmond, Virginia23284, United States
| | - Bimbisar Desai
- TCG GreenChem, Inc., 701 Charles Ewing Boulevard, Ewing, New Jersey08628, United States
| | - Greg Hammersmith
- On Demand Pharmaceuticals, 1550 E Gude Drive, Rockville, Maryland20850, United States
| | - Kersten Rapp
- On Demand Pharmaceuticals, 1550 E Gude Drive, Rockville, Maryland20850, United States
| | - Gerard Capellades
- Henry M. Rowan College of Engineering, Rowan University, Glassboro, New Jersey08028, United States
| | - Allan S. Myerson
- Department of Chemical Engineering, Massachusetts Institute of Technology, Cambridge, Massachusetts02139, United States
| | - Thomas D. Roper
- Department of Chemical and Life Science Engineering, Virginia Commonwealth University, Richmond, Virginia23284, United States
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10
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P S, Kumari A, Kundu S, Sankar VR, Thella PK, Shah K, Bhargava SK. Design and Optimization of Antisolvent Crystallization of L-aspartic acid using Response Surface Model: Focused Beam Reflectance Measurements. Chem Eng Res Des 2023. [DOI: 10.1016/j.cherd.2023.01.020] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 01/19/2023]
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11
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Song B, Liu T, Yang S, Liu J, Chen J. Data-Driven Operation Modeling and Optimal Design for Batch Cooling Crystallization with a Case Study on β-LGA. Ind Eng Chem Res 2022. [DOI: 10.1021/acs.iecr.2c02937] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/23/2022]
Affiliation(s)
- Bo Song
- Key Laboratory of Intelligent Control and Optimization for Industrial Equipment of Ministry of Education, Dalian University of Technology, Dalian 116024, China
- Institute of Advanced Control Technology, Dalian University of Technology, Dalian 116024, China
| | - Tao Liu
- Key Laboratory of Intelligent Control and Optimization for Industrial Equipment of Ministry of Education, Dalian University of Technology, Dalian 116024, China
- Institute of Advanced Control Technology, Dalian University of Technology, Dalian 116024, China
| | - Siwei Yang
- Key Laboratory of Intelligent Control and Optimization for Industrial Equipment of Ministry of Education, Dalian University of Technology, Dalian 116024, China
- Institute of Advanced Control Technology, Dalian University of Technology, Dalian 116024, China
| | - Jingxiang Liu
- Key Laboratory of Intelligent Control and Optimization for Industrial Equipment of Ministry of Education, Dalian University of Technology, Dalian 116024, China
- School of Marine Electrical Engineering, Dalian Maritime University, Dalian 116026, China
| | - Junghui Chen
- Department of Chemical Engineering, Chung-Yuan Christian University, Chung-Li District, Taoyuan 32023, Taiwan
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12
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Simone E, Beveridge G, Sillers P, Webb J, George N, Hone J. Analysis of the Dissolution and Crystallization of Partly Immiscible Ternary Mixtures Using a Composite Sensor Array of In Situ ATR-FTIR, Laser Backscattering, and Imaging. Ind Eng Chem Res 2022. [DOI: 10.1021/acs.iecr.2c03494] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/13/2022]
Affiliation(s)
- Elena Simone
- Department of Applied Science and Technology, Politecnico di Torino, Torino 10129, Italy
- Food Colloids and Bioprocessing Group, School of Food Science and Nutrition, University of Leeds, Leeds LS29JT, United Kingdom
| | - Gillian Beveridge
- Syngenta Grangemouth Manufacturing Centre, Grangemouth FK3 8XG, United Kingdom
| | - Pauline Sillers
- Syngenta Grangemouth Manufacturing Centre, Grangemouth FK3 8XG, United Kingdom
| | - Jennifer Webb
- Syngenta Jealott’s Hill International Research Centre, Warfield, Bracknell RG42 6EY, United Kingdom
| | - Neil George
- Syngenta Jealott’s Hill International Research Centre, Warfield, Bracknell RG42 6EY, United Kingdom
- School of Chemical and Process Engineering, University of Leeds, Leeds LS29JT, United Kingdom
| | - John Hone
- Syngenta Jealott’s Hill International Research Centre, Warfield, Bracknell RG42 6EY, United Kingdom
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13
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Askr H, Elgeldawi E, Aboul Ella H, Elshaier YAMM, Gomaa MM, Hassanien AE. Deep learning in drug discovery: an integrative review and future challenges. Artif Intell Rev 2022; 56:5975-6037. [PMID: 36415536 PMCID: PMC9669545 DOI: 10.1007/s10462-022-10306-1] [Citation(s) in RCA: 30] [Impact Index Per Article: 15.0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Accepted: 10/24/2022] [Indexed: 11/18/2022]
Abstract
Recently, using artificial intelligence (AI) in drug discovery has received much attention since it significantly shortens the time and cost of developing new drugs. Deep learning (DL)-based approaches are increasingly being used in all stages of drug development as DL technology advances, and drug-related data grows. Therefore, this paper presents a systematic Literature review (SLR) that integrates the recent DL technologies and applications in drug discovery Including, drug-target interactions (DTIs), drug-drug similarity interactions (DDIs), drug sensitivity and responsiveness, and drug-side effect predictions. We present a review of more than 300 articles between 2000 and 2022. The benchmark data sets, the databases, and the evaluation measures are also presented. In addition, this paper provides an overview of how explainable AI (XAI) supports drug discovery problems. The drug dosing optimization and success stories are discussed as well. Finally, digital twining (DT) and open issues are suggested as future research challenges for drug discovery problems. Challenges to be addressed, future research directions are identified, and an extensive bibliography is also included.
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Affiliation(s)
- Heba Askr
- Faculty of Computers and Artificial Intelligence, University of Sadat City, Sadat City, Egypt
| | - Enas Elgeldawi
- Computer Science Department, Faculty of Science, Minia University, Minia, Egypt
| | - Heba Aboul Ella
- Faculty of Pharmacy and Drug Technology, Chinese University in Egypt (CUE), Cairo, Egypt
| | | | - Mamdouh M. Gomaa
- Computer Science Department, Faculty of Science, Minia University, Minia, Egypt
| | - Aboul Ella Hassanien
- Faculty of Computers and Artificial Intelligence, Cairo University, Cairo, Egypt
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14
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Kshirsagar S, Lakshmi Ramana Susarla N, Ramakrishnan S, Nagy ZK. Process intensification of atorvastatin calcium crystallization for target polymorph development via continuous combined cooling and antisolvent crystallization using an oscillatory baffled crystallizer. Int J Pharm 2022; 627:122172. [PMID: 36084877 PMCID: PMC10759184 DOI: 10.1016/j.ijpharm.2022.122172] [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: 06/24/2022] [Revised: 08/31/2022] [Accepted: 09/01/2022] [Indexed: 11/22/2022]
Abstract
In this paper, continuous crystallization of Atorvastatin calcium (ASC) using a continuous oscillatory baffled crystallizer (COBC) has been investigated. Like most API manufacturing, ASC is manufactured batchwise and the pure API is recovered via batch combined cooling and antisolvent crystallization (CCAC) process, which has the challenges of low productivity, wide crystal size distribution (CSD) and sometimes polymorphic form contamination. To overcome the limitations of the batch crystallization, continuous crystallization of ASC was studied in a NiTech (United Kingdom) DN15 COBC, manufactured by Alconbury Weston Ltd. (AWL, United Kingdom), with the aim to improve productivity and CSD of the desired polymorph. The COBC has the advantage of high heat transfer rates and improved mixing that significantly reduces the crystallization time. It also has the advantage of spatial temperature distribution and multiple addition ports to control supersaturation and hence the crystallization process. This work uses an array of process analytical technology (PAT) tools to assess key process parameters that affect the polymorphic outcome and CSD. Two parameters were found to have significant impact on the polymorph, they are ratio of solvent to antisolvent at the point of mixing of the two streams and presence of seeds. The splitting of antisolvent into two addition ports in the COBC was found to give the desired form. The CCAC of ASC in COBC was found to be -30-fold more productive than the batch CCAC process. The cycle time for generating 100 g of desired polymorphic form of ASC also significantly reduced from 22 h in batch process to 12 min in the COBC. The crystals obtained using a CCAC process in a COBC had a narrower CSD compared to that from a batch crystallization process.
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Affiliation(s)
- Shivani Kshirsagar
- Davidson School of Chemical Engineering, Purdue University, West Lafayette, IN 47907, USA; Dr. Reddy's Laboratories Ltd., IPDO, Bachupally, Hyderabad 500090, India
| | | | | | - Zoltan K Nagy
- Davidson School of Chemical Engineering, Purdue University, West Lafayette, IN 47907, USA.
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15
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Kim S, Yeol Lee S, Woong Chang J, Ryook Yang D. Evaluation of the kinetics of unseeded batch cooling crystallization using population balance modeling: sucrose and KNO3 case studies. J IND ENG CHEM 2022. [DOI: 10.1016/j.jiec.2022.11.038] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/21/2022]
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16
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Szilágyi B, Eren A, Quon JL, Papageorgiou CD, Nagy ZK. Monitoring and digital design of the cooling crystallization of a high-aspect ratio anticancer drug using a two-dimensional population balance model. Chem Eng Sci 2022. [DOI: 10.1016/j.ces.2022.117700] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/25/2022]
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17
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Darmali C, Liu YC, Mansouri S, Yazdanpanah N, Nagy ZK, Woo MW. Strategy for Non-Seeded Crystallization of Slow-to-Crystallize Compounds with an Oscillatory Dynamic Baffled Crystallizer: A Case Study with α-Lactose Monohydrate. Org Process Res Dev 2022. [DOI: 10.1021/acs.oprd.2c00043] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/29/2022]
Affiliation(s)
- Christine Darmali
- Department of Chemical and Biological Engineering, Monash University, Clayton, Victoria 3800, Australia
| | - Yiqing Claire Liu
- Davidson School of Chemical Engineering, Purdue University, West Lafayette, Indiana 47907, United States
| | - Shahnaz Mansouri
- School of Chemistry, Monash University, Clayton, Victoria 3800, Australia
| | | | - Zoltan K. Nagy
- Davidson School of Chemical Engineering, Purdue University, West Lafayette, Indiana 47907, United States
| | - Meng W. Woo
- Department of Chemical and Biological Engineering, Monash University, Clayton, Victoria 3800, Australia
- Department of Chemical & Materials Engineering, The University of Auckland, Grafton, Auckland 1023, New Zealand
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18
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Liu LS, Kim JM, Kim WS. In situ discrimination of polymorphs and phase transformation of sulfamerazine using quartz crystal microbalance. Anal Chim Acta 2022; 1221:340137. [DOI: 10.1016/j.aca.2022.340137] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/13/2022] [Revised: 05/21/2022] [Accepted: 06/28/2022] [Indexed: 11/01/2022]
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19
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Destro F, Nagy ZK, Barolo M. A benchmark simulator for quality-by-design and quality-by-control studies in continuous pharmaceutical manufacturing - Intensified filtration-drying of crystallization slurries. Comput Chem Eng 2022; 163:107809. [PMID: 38178942 PMCID: PMC10765423 DOI: 10.1016/j.compchemeng.2022.107809] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/03/2022]
Abstract
This article introduces ContCarSim, a benchmark simulator for the development and testing of quality-by-design and quality-by-control strategies in the continuous intensified filtration-drying of paracetamol/ethanol slurries on a novel carousel technology, developed by Alconbury Weston Ltd (United Kingdom). The simulator is based on a detailed mechanistic mathematical modeling framework, and has been validated with filtration and drying experiments on a prototype equipment. A set of design- and control-relevant challenges to be addressed through ContCarSim are proposed. A case study is developed, to demonstrate the features of the simulator and its suitability to design, test and optimize the unit operation. ContCarSim is expected to promote the transition to end-to-end continuous pharmaceutical manufacturing and the adoption of closed-loop quality control by the pharmaceutical industry. The simulator can also be employed as a benchmark for data analytics and process monitoring studies.
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Affiliation(s)
- Francesco Destro
- CAPE-Lab – Computer-Aided Process Engineering Laboratory, Department of Industrial Engineering, University of Padova, via Marzolo 9, 35131 Padova PD (Italy)
| | - Zoltan K. Nagy
- Davidson School of Chemical Engineering, Purdue University, West Lafayette, IN 47906, USA
| | - Massimiliano Barolo
- CAPE-Lab – Computer-Aided Process Engineering Laboratory, Department of Industrial Engineering, University of Padova, via Marzolo 9, 35131 Padova PD (Italy)
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20
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Xiouras C, Cameli F, Quilló GL, Kavousanakis ME, Vlachos DG, Stefanidis GD. Applications of Artificial Intelligence and Machine Learning Algorithms to Crystallization. Chem Rev 2022; 122:13006-13042. [PMID: 35759465 DOI: 10.1021/acs.chemrev.2c00141] [Citation(s) in RCA: 2] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/28/2022]
Abstract
Artificial intelligence and specifically machine learning applications are nowadays used in a variety of scientific applications and cutting-edge technologies, where they have a transformative impact. Such an assembly of statistical and linear algebra methods making use of large data sets is becoming more and more integrated into chemistry and crystallization research workflows. This review aims to present, for the first time, a holistic overview of machine learning and cheminformatics applications as a novel, powerful means to accelerate the discovery of new crystal structures, predict key properties of organic crystalline materials, simulate, understand, and control the dynamics of complex crystallization process systems, as well as contribute to high throughput automation of chemical process development involving crystalline materials. We critically review the advances in these new, rapidly emerging research areas, raising awareness in issues such as the bridging of machine learning models with first-principles mechanistic models, data set size, structure, and quality, as well as the selection of appropriate descriptors. At the same time, we propose future research at the interface of applied mathematics, chemistry, and crystallography. Overall, this review aims to increase the adoption of such methods and tools by chemists and scientists across industry and academia.
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Affiliation(s)
- Christos Xiouras
- Chemical Process R&D, Crystallization Technology Unit, Janssen R&D, Turnhoutseweg 30, 2340 Beerse, Belgium
| | - Fabio Cameli
- Department of Chemical and Biomolecular Engineering, University of Delaware, 150 Academy Street, Newark, Delaware 19716, United States
| | - Gustavo Lunardon Quilló
- Chemical Process R&D, Crystallization Technology Unit, Janssen R&D, Turnhoutseweg 30, 2340 Beerse, Belgium.,Chemical and BioProcess Technology and Control, Department of Chemical Engineering, Faculty of Engineering Technology, KU Leuven, Gebroeders de Smetstraat 1, 9000 Ghent, Belgium
| | - Mihail E Kavousanakis
- School of Chemical Engineering, National Technical University of Athens, Heroon Polytechniou 9, 15780 Zografou, Greece
| | - Dionisios G Vlachos
- Department of Chemical and Biomolecular Engineering, University of Delaware, 150 Academy Street, Newark, Delaware 19716, United States
| | - Georgios D Stefanidis
- School of Chemical Engineering, National Technical University of Athens, Heroon Polytechniou 9, 15780 Zografou, Greece.,Laboratory for Chemical Technology, Ghent University; Tech Lane Ghent Science Park 125, B-9052 Ghent, Belgium
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21
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Wang L, Zhu Y, Gan C. Predictive Control of Particle Size Distribution of Crystallization Process Using Deep Learning based Image Analysis. AIChE J 2022. [DOI: 10.1002/aic.17817] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/08/2022]
Affiliation(s)
- Liangyong Wang
- State Key Laboratory of Synthetical Automation for Process Industries Northeastern University Shenyang China
| | - Yaolong Zhu
- State Key Laboratory of Synthetical Automation for Process Industries Northeastern University Shenyang China
| | - Chenyang Gan
- State Key Laboratory of Synthetical Automation for Process Industries Northeastern University Shenyang China
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22
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Sun F, Liu T, Nagy ZK, Ni X. Extended sectional quadrature method of moments for crystal growth and nucleation with application to seeded cooling crystallization. Chem Eng Sci 2022. [DOI: 10.1016/j.ces.2022.117625] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/25/2022]
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23
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Effect of oscillatory flow conditions on crystalliser fouling investigated through non-invasive imaging. Chem Eng Sci 2022. [DOI: 10.1016/j.ces.2021.117188] [Citation(s) in RCA: 2] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/17/2022]
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24
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Destro F, Barolo M. A review on the modernization of pharmaceutical development and manufacturing - Trends, perspectives, and the role of mathematical modeling. Int J Pharm 2022; 620:121715. [PMID: 35367580 DOI: 10.1016/j.ijpharm.2022.121715] [Citation(s) in RCA: 15] [Impact Index Per Article: 7.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/19/2022] [Revised: 03/23/2022] [Accepted: 03/29/2022] [Indexed: 01/20/2023]
Abstract
Recently, the pharmaceutical industry has been facing several challenges associated to the use of outdated development and manufacturing technologies. The return on investment on research and development has been shrinking, and, at the same time, an alarming number of shortages and recalls for quality concerns has been registered. The pharmaceutical industry has been responding to these issues through a technological modernization of development and manufacturing, under the support of initiatives and activities such as quality-by-design (QbD), process analytical technology, and pharmaceutical emerging technology. In this review, we analyze this modernization trend, with emphasis on the role that mathematical modeling plays within it. We begin by outlining the main socio-economic trends of the pharmaceutical industry, and by highlighting the life-cycle stages of a pharmaceutical product in which technological modernization can help both achieve consistently high product quality and increase return on investment. Then, we review the historical evolution of the pharmaceutical regulatory framework, and we discuss the current state of implementation and future trends of QbD. The pharmaceutical emerging technology is reviewed afterwards, and a discussion on the evolution of QbD into the more effective quality-by-control (QbC) paradigm is presented. Further, we illustrate how mathematical modeling can support the implementation of QbD and QbC across all stages of the pharmaceutical life-cycle. In this respect, we review academic and industrial applications demonstrating the impact of mathematical modeling on three key activities within pharmaceutical development and manufacturing, namely design space description, process monitoring, and active process control. Finally, we discuss some future research opportunities on the use of mathematical modeling in industrial pharmaceutical environments.
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Affiliation(s)
- Francesco Destro
- CAPE-Lab - Computer-Aided Process Engineering Laboratory, Department of Industrial Engineering, University of Padova, via Marzolo 9, 35131 Padova PD, Italy
| | - Massimiliano Barolo
- CAPE-Lab - Computer-Aided Process Engineering Laboratory, Department of Industrial Engineering, University of Padova, via Marzolo 9, 35131 Padova PD, Italy.
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25
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Liu T, Cui Y, Wang Y, Nagy ZK. Seeded Cooling Crystallization Process Optimization of β Form l-Glutamic Acid Based on Variable Moving Horizon State Estimation. Ind Eng Chem Res 2022. [DOI: 10.1021/acs.iecr.1c03973] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/29/2022]
Affiliation(s)
- Tao Liu
- Institute of Advanced Control Technology, Dalian University of Technology, Dalian 116024, China
| | - Yan Cui
- Institute of Advanced Control Technology, Dalian University of Technology, Dalian 116024, China
| | - Yao Wang
- Institute of Advanced Control Technology, Dalian University of Technology, Dalian 116024, China
| | - Zoltan Kalman Nagy
- Davidson School of Chemical Engineering, Purdue University, West Lafayette, Indiana 47907, United States
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26
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Kang YS, Ward JD, Nagy ZK. A new framework and a hybrid method for one-dimensional population balance modeling of batch thermocycling crystallization. Comput Chem Eng 2022. [DOI: 10.1016/j.compchemeng.2021.107588] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/26/2022]
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27
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Process Intensification and Control Strategies in Cooling Crystallization: Crystal Size and Morphology Optimization of α-PABA. Chem Eng Res Des 2022. [DOI: 10.1016/j.cherd.2022.01.029] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/17/2022]
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28
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Domokos A, Madarász L, Stoffán G, Tacsi K, Galata D, Csorba K, Vass P, Nagy ZK, Pataki H. Real-Time Monitoring of Continuous Pharmaceutical Mixed Suspension Mixed Product Removal Crystallization Using Image Analysis. Org Process Res Dev 2021. [DOI: 10.1021/acs.oprd.1c00372] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/29/2022]
Affiliation(s)
- András Domokos
- Budapest University of Technology and Economics, Department of Organic Chemistry and Technology, H-1111 Budapest, Hungary
| | - Lajos Madarász
- Budapest University of Technology and Economics, Department of Organic Chemistry and Technology, H-1111 Budapest, Hungary
| | - György Stoffán
- Budapest University of Technology and Economics, Department of Organic Chemistry and Technology, H-1111 Budapest, Hungary
| | - Kornélia Tacsi
- Budapest University of Technology and Economics, Department of Organic Chemistry and Technology, H-1111 Budapest, Hungary
| | - Dorián Galata
- Budapest University of Technology and Economics, Department of Organic Chemistry and Technology, H-1111 Budapest, Hungary
| | - Kristóf Csorba
- Budapest University of Technology and Economics, Department of Automation and Applied Informatics, H-1111 Budapest, Hungary
| | - Panna Vass
- Budapest University of Technology and Economics, Department of Organic Chemistry and Technology, H-1111 Budapest, Hungary
| | - Zsombor K. Nagy
- Budapest University of Technology and Economics, Department of Organic Chemistry and Technology, H-1111 Budapest, Hungary
| | - Hajnalka Pataki
- Budapest University of Technology and Economics, Department of Organic Chemistry and Technology, H-1111 Budapest, Hungary
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29
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Zhang F, Du K, Guo L, Xu Q, Shan B. Comparative Study of Preprocessing on an ATR‐FTIR Calibration Model for In Situ Monitoring of Solution Concentration in Cooling Crystallization. Chem Eng Technol 2021. [DOI: 10.1002/ceat.202100104] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/06/2022]
Affiliation(s)
- Fangkun Zhang
- Qingdao University of Science & Technology College of Automation and Electronic Engineering 266061 Qingdao China
| | - Kang Du
- Qingdao University of Science & Technology College of Automation and Electronic Engineering 266061 Qingdao China
| | - Luyu Guo
- Qingdao University of Science & Technology College of Automation and Electronic Engineering 266061 Qingdao China
| | - Qilei Xu
- Qingdao University of Science & Technology College of Automation and Electronic Engineering 266061 Qingdao China
| | - Baoming Shan
- Qingdao University of Science & Technology College of Automation and Electronic Engineering 266061 Qingdao China
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30
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Destro F, Hur I, Wang V, Abdi M, Feng X, Wood E, Coleman S, Firth P, Barton A, Barolo M, Nagy ZK. Mathematical modeling and digital design of an intensified filtration-washing-drying unit for pharmaceutical continuous manufacturing. Chem Eng Sci 2021; 244:116803. [PMID: 38229929 PMCID: PMC10790184 DOI: 10.1016/j.ces.2021.116803] [Citation(s) in RCA: 9] [Impact Index Per Article: 3.0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/18/2022]
Abstract
This paper introduces a comprehensive mathematical model of a novel integrated filter-dryer carousel system, designed for continuously filtering, washing and drying a slurry stream into a crystals cake. The digital twin includes models for dead-end filtration, cake washing and convective cake drying, based on dynamic multi-component mass, energy and momentum balances. For set of feed conditions and control inputs, the model allows tracking the solvents and impurities content in the cake (critical quality attributes, CQAs) throughout the whole process. The model parameters were identified for the isolation of paracetamol from a multi-component slurry, containing a non-volatile impurity. The calibrated model was used for identifying the probabilistic design space and maximum throughput for the process, expressing the combinations of the carousel feed conditions and control inputs for which the probability of meeting the target CQAs is acceptable.
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Affiliation(s)
- Francesco Destro
- CAPE-Lab – Computer-Aided Process Engineering Laboratory, Department of Industrial Engineering, University of Padova, 35131 Padova PD, Italy
| | - Inyoung Hur
- Davidson School of Chemical Engineering, Purdue University, West Lafayette, IN 47906, USA
| | - Vivian Wang
- Office of Pharmaceutical Quality, Center for Drug Evaluation and Research, Food & Drug Administration, Silver Spring, MD, USA
| | - Mesfin Abdi
- Office of Pharmaceutical Quality, Center for Drug Evaluation and Research, Food & Drug Administration, Silver Spring, MD, USA
| | - Xin Feng
- Office of Pharmaceutical Quality, Center for Drug Evaluation and Research, Food & Drug Administration, Silver Spring, MD, USA
| | - Erin Wood
- Office of Pharmaceutical Quality, Center for Drug Evaluation and Research, Food & Drug Administration, Silver Spring, MD, USA
| | | | - Paul Firth
- Alconbury Weston Ltd, Stoke-on-Trent, UK
| | | | - Massimiliano Barolo
- CAPE-Lab – Computer-Aided Process Engineering Laboratory, Department of Industrial Engineering, University of Padova, 35131 Padova PD, Italy
| | - Zoltan K. Nagy
- Davidson School of Chemical Engineering, Purdue University, West Lafayette, IN 47906, USA
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31
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Radcliffe AJ, Reklaitis GV. Bayesian hierarchical modeling for online process monitoring and quality control, with application to real time image data. Comput Chem Eng 2021. [DOI: 10.1016/j.compchemeng.2021.107446] [Citation(s) in RCA: 3] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/26/2022]
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32
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Moguel-Castañeda JG, Romero-Bustamante JA, Velazquez-Camilo O, Puebla H, Hernandez-Martinez E. Diagnosis of the Cane Sugar Crystallization Process by Multifractal Analysis of Temperature Time Series. Chem Eng Technol 2021. [DOI: 10.1002/ceat.202100231] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/05/2022]
Affiliation(s)
- Jazael G. Moguel-Castañeda
- Universidad Autónoma Metropolitana-Azcapotzalco Departamento de Energía Av. San Pablo Xalpa 02200 México City México
| | - Jorge A. Romero-Bustamante
- Universidad Autónoma Metropolitana-Azcapotzalco Departamento de Energía Av. San Pablo Xalpa 02200 México City México
| | - Oscar Velazquez-Camilo
- Universidad Veracruzana Facultad de Ciencias Químicas, Región Veracruz Bv. Adolfo Ruíz Cortines 94294 Veracruz México
| | - Hector Puebla
- Universidad Autónoma Metropolitana-Azcapotzalco Departamento de Energía Av. San Pablo Xalpa 02200 México City México
| | - Eliseo Hernandez-Martinez
- Universidad Veracruzana Facultad de Ciencias Químicas, Región Xalapa Circuito Universitario Gonzálo Aguirre Beltrán 91000 Veracruz México
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33
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Anti-solvent sonocrystallization to enhance the dissolution rate of clopidogrel using Box-Behnken design. CHEMICAL PAPERS 2021. [DOI: 10.1007/s11696-021-01605-y] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 10/21/2022]
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34
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35
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Zhang F, Shan B, Wang Y, Zhu Z, Yu ZQ, Ma CY. Progress and Opportunities for Utilizing Seeding Techniques in Crystallization Processes. Org Process Res Dev 2021. [DOI: 10.1021/acs.oprd.1c00103] [Citation(s) in RCA: 15] [Impact Index Per Article: 5.0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/29/2022]
Affiliation(s)
- Fangkun Zhang
- College of Automation and Electronic Engineering, Qingdao University of Science & Technology, Qingdao, 266061, P. R. China
| | - Baoming Shan
- College of Automation and Electronic Engineering, Qingdao University of Science & Technology, Qingdao, 266061, P. R. China
| | - Yinglong Wang
- College of Chemical Engineering, Qingdao University of Science and Technology, Qingdao, 266061, P. R. China
| | - Zhaoyou Zhu
- College of Chemical Engineering, Qingdao University of Science and Technology, Qingdao, 266061, P. R. China
| | - Zai-Qun Yu
- Institute of Chemical & Engineering Sciences, Agency for Science, Technology and Research (A*STAR), 1 Pesek Road, Jurong Island, Singapore 627833
| | - Cai Y. Ma
- Centre for the Digital Design of Drug Products, School of Chemical and Process Engineering, University of Leeds, Leeds, LS2 9JT, United Kingdom
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36
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Orehek J, Češnovar M, Teslić D, Likozar B. Mechanistic crystal size distribution (CSD)-based modelling of continuous antisolvent crystallization of benzoic acid. Chem Eng Res Des 2021. [DOI: 10.1016/j.cherd.2021.04.007] [Citation(s) in RCA: 6] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/16/2022]
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37
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Wu Y, Gao Z, Rohani S. Deep learning-based oriented object detection for in situ image monitoring and analysis: A process analytical technology (PAT) application for taurine crystallization. Chem Eng Res Des 2021. [DOI: 10.1016/j.cherd.2021.04.013] [Citation(s) in RCA: 8] [Impact Index Per Article: 2.7] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/25/2022]
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38
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Savvopoulos SV, Voutetakis SS, Kuhn S, Ipsakis D. Theoretical Feedback Control Scheme for the Ultrasound-Assisted Continuous Antisolvent Crystallization of Aspirin in a Tubular Crystallizer. Ind Eng Chem Res 2021. [DOI: 10.1021/acs.iecr.1c00227] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/28/2022]
Affiliation(s)
- Symeon V. Savvopoulos
- Department of Chemical Engineering, KU Leuven, Celestijnenlaan 200F, 3001 Leuven, Belgium
| | - Spyros S. Voutetakis
- Chemical Process and Energy Resources Institute, Centre for Research and Technology, Hellas, 57001 Thermi, Thessaloniki, Greece
| | - Simon Kuhn
- Department of Chemical Engineering, KU Leuven, Celestijnenlaan 200F, 3001 Leuven, Belgium
| | - Dimitris Ipsakis
- Industrial, Energy and Environmental Systems Laboratory, School of Production Engineering and Management, Technical University of Crete, 73100 Chania, Greece
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39
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Independent Validation of an In Silico Tool for a Pilot-Scale Pharmaceutical Crystallization Process Development. Processes (Basel) 2021. [DOI: 10.3390/pr9040640] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/17/2022] Open
Abstract
There are many published models for predicting crystal size distribution (CSD) in the literature. However, none of them have been independently and comprehensively tested, which is important for industrial acceptance and confidence of these models. Therefore, in this study, using solubility and kinetic data from the literature, an in silico tool for predicting the crystallization process performance of a model compound system (paracetamol in ethanol) was developed and challenged by independent experiments at the 50 L pilot scale. The solute concentration was tracked, and the final CSD was quantified using three measurement techniques including a novel image analysis tool. The reported parameter uncertainties were also addressed using Monte Carlo simulations. The results showed that, when the models were used within their validity range (e.g., suspended solids), they were able to describe the observed process trends/dynamics (CSD and solute concentration) under varying experimental conditions (cooling time and seed mass) with R2 ranging from 0.72 and 0.90. Overall, the results indicate that, using Monte Carlo simulations to account for known parametric uncertainties, the models can support model-based approaches for crystallization process development from scale-down to scale-up studies as well as control evaluation.
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40
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Applications of machine vision in pharmaceutical technology: A review. Eur J Pharm Sci 2021; 159:105717. [DOI: 10.1016/j.ejps.2021.105717] [Citation(s) in RCA: 21] [Impact Index Per Article: 7.0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/20/2020] [Revised: 01/08/2021] [Accepted: 01/11/2021] [Indexed: 02/07/2023]
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41
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Application of PAT-Based Feedback Control Approaches in Pharmaceutical Crystallization. CRYSTALS 2021. [DOI: 10.3390/cryst11030221] [Citation(s) in RCA: 10] [Impact Index Per Article: 3.3] [Reference Citation Analysis] [Abstract] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 11/17/2022]
Abstract
Crystallization is one of the important unit operations for the separation and purification of solid products in the chemical, pharmaceutical, and pesticide industries, especially for realizing high-end, high-value solid products. The precise control of the solution crystallization process determines the polymorph, crystal shape, size, and size distribution of the crystal product, which is of great significance to improve product quality and production efficiency. In order to develop the crystallization process in a scientific method that is based on process parameters and data, process analysis technology (PAT) has become an important enabling platform. In this paper, we review the development of PAT in the field of crystallization in recent years. Based on the current research status of drug crystallization process control, the monitoring methods and control strategies of feedback control in the crystallization process were systematically summarized. The focus is on the application of model-free feedback control strategies based on the solution and solid information collected by various online monitoring equipment in product engineering, including improving particle size distribution, achieving polymorphic control, and improving purity. In this paper, the challenges of feedback control strategy in the crystallization process are also discussed, and the development trend of the feedback control strategy has been prospected.
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42
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Pan HJ, Ward JD. Dimensionless Framework for Seed Recipe Design and Optimal Control of Batch Crystallization. Ind Eng Chem Res 2021. [DOI: 10.1021/acs.iecr.0c06132] [Citation(s) in RCA: 5] [Impact Index Per Article: 1.7] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/28/2022]
Affiliation(s)
- Hao-Jen Pan
- Dept. of Chemical Engineering, National Taiwan University, Taipei 106-07, Taiwan
| | - Jeffrey D. Ward
- Dept. of Chemical Engineering, National Taiwan University, Taipei 106-07, Taiwan
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43
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Rao G, Sattar MA, Wajman R, Jackowska-Strumiłło L. Quantitative Evaluations with 2d Electrical Resistance Tomography in the Low-Conductivity Solutions Using 3d-Printed Phantoms and Sucrose Crystal Agglomerate Assessments. SENSORS 2021; 21:s21020564. [PMID: 33466874 PMCID: PMC7830363 DOI: 10.3390/s21020564] [Citation(s) in RCA: 5] [Impact Index Per Article: 1.7] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 11/17/2020] [Revised: 12/29/2020] [Accepted: 01/10/2021] [Indexed: 11/30/2022]
Abstract
Crystallization is a significant procedure in the manufacturing of many pharmaceutical and solid food products. In-situ electrical resistance tomography (ERT) is a novel process analytical tool (PAT) to provide a cheap and quick way to test, visualize, and evaluate the progress of crystallization processes. In this work, the spatial accuracy of the nonconductive phantoms in low-conductivity solutions was evaluated. Gauss–Newton, linear back projection, and iterative total variation reconstruction algorithms were used to compare the phantom reconstructions for tap water, industrial-grade saturated sucrose solution, and demineralized water. A cylindrical phantom measuring 10 mm in diameter and a cross-section area of 1.5% of the total beaker area was detected at the center of the beaker. Two phantoms with a 10-mm diameter were visualized separately in noncentral locations. The quantitative evaluations were done for the phantoms with radii ranging from 10 mm to 50 mm in demineralized water. Multiple factors, such as ERT device and sensor development, Finite Element Model (FEM) mesh density and simulations, image reconstruction algorithms, number of iterations, segmentation methods, and morphological image processing methods, were discussed and analyzed to achieve spatial accuracy. The development of ERT imaging modality for the purpose of monitoring crystallization in low-conductivity solutions was performed satisfactorily.
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44
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Acevedo D, Wu WL, Yang X, Pavurala N, Mohammad A, O'Connor TF. Evaluation of focused beam reflectance measurement (FBRM) for monitoring and predicting the crystal size of carbamazepine in crystallization processes. CrystEngComm 2021. [DOI: 10.1039/d0ce01388a] [Citation(s) in RCA: 5] [Impact Index Per Article: 1.7] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/16/2022]
Abstract
Pharmaceutical crystallization affects the properties of APIs as it determines the purity and crystal size distribution, among other attributes. This work presents two CLD–CSD models, theoretical and empirical, for a model compound.
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Affiliation(s)
- David Acevedo
- Office of Pharmaceutical Quality
- CDER
- FDA
- Silver Spring
- USA
| | - Wei-Lee Wu
- Office of Pharmaceutical Quality
- CDER
- FDA
- Silver Spring
- USA
| | | | | | - Adil Mohammad
- Office of Pharmaceutical Quality
- CDER
- FDA
- Silver Spring
- USA
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Orehek J, Teslić D, Likozar B. Continuous Crystallization Processes in Pharmaceutical Manufacturing: A Review. Org Process Res Dev 2020. [DOI: 10.1021/acs.oprd.0c00398] [Citation(s) in RCA: 36] [Impact Index Per Article: 9.0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 01/04/2023]
Affiliation(s)
- Jaka Orehek
- National Institute of Chemistry, Hajdrihova 19, 1001 Ljubljana, Slovenia
- Lek d. d., Sandoz, a Novartis division, Verovškova 57, 1526 Ljubljana, Slovenia
| | - Dušan Teslić
- Lek d. d., Sandoz, a Novartis division, Verovškova 57, 1526 Ljubljana, Slovenia
| | - Blaž Likozar
- National Institute of Chemistry, Hajdrihova 19, 1001 Ljubljana, Slovenia
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46
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MS A, Hazra D, Steele G, Pal S. Crystallization process modifications to address polymorphic and particle size challenges in early stage development of an API salt. Chem Eng Res Des 2020. [DOI: 10.1016/j.cherd.2020.09.021] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/27/2022]
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Curitiba Marcellos CF, Senna Figueiredo CM, Tavares FW, Souza MB, Cunha Lage PL, Silva JFC, Secchi AR, Barreto AG. Inferring kinetic dissolution of
NaCl
in aqueous glycol solution using a low‐cost apparatus and population balance model. CAN J CHEM ENG 2020. [DOI: 10.1002/cjce.23774] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/10/2022]
Affiliation(s)
| | | | - Frederico W. Tavares
- Department of Chemical Engineering Federal University of Rio de Janeiro (COPPE) Rio de Janeiro Brazil
| | - Maurício Bezerra Souza
- Department of Chemical Engineering Federal University of Rio de Janeiro Rio de Janeiro Brazil
| | | | | | - Argimiro R. Secchi
- Department of Chemical Engineering Federal University of Rio de Janeiro (COPPE) Rio de Janeiro Brazil
| | - Amaro G. Barreto
- Department of Chemical Engineering Federal University of Rio de Janeiro Rio de Janeiro Brazil
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48
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Simulation and experimental investigation of a novel supersaturation feedback control strategy for cooling crystallization in semi-batch implementation. Chem Eng Sci 2020. [DOI: 10.1016/j.ces.2020.115807] [Citation(s) in RCA: 6] [Impact Index Per Article: 1.5] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/21/2022]
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49
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Öner M, Montes FC, Ståhlberg T, Stocks SM, Bajtner JE, Sin G. Comprehensive evaluation of a data driven control strategy: Experimental application to a pharmaceutical crystallization process. Chem Eng Res Des 2020. [DOI: 10.1016/j.cherd.2020.08.032] [Citation(s) in RCA: 13] [Impact Index Per Article: 3.3] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/26/2022]
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50
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Ferreira C, Cardona J, Agimelen O, Tachtatzis C, Andonovic I, Sefcik J, Chen YC. Quantification of particle size and concentration using in-line techniques and multivariate analysis. POWDER TECHNOL 2020. [DOI: 10.1016/j.powtec.2020.08.015] [Citation(s) in RCA: 3] [Impact Index Per Article: 0.8] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 10/23/2022]
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