1
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Gholap AD, Uddin MJ, Faiyazuddin M, Omri A, Gowri S, Khalid M. Advances in artificial intelligence for drug delivery and development: A comprehensive review. Comput Biol Med 2024; 178:108702. [PMID: 38878397 DOI: 10.1016/j.compbiomed.2024.108702] [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: 01/03/2024] [Revised: 05/12/2024] [Accepted: 06/01/2024] [Indexed: 07/24/2024]
Abstract
Artificial intelligence (AI) has emerged as a powerful tool to revolutionize the healthcare sector, including drug delivery and development. This review explores the current and future applications of AI in the pharmaceutical industry, focusing on drug delivery and development. It covers various aspects such as smart drug delivery networks, sensors, drug repurposing, statistical modeling, and simulation of biotechnological and biological systems. The integration of AI with nanotechnologies and nanomedicines is also examined. AI offers significant advancements in drug discovery by efficiently identifying compounds, validating drug targets, streamlining drug structures, and prioritizing response templates. Techniques like data mining, multitask learning, and high-throughput screening contribute to better drug discovery and development innovations. The review discusses AI applications in drug formulation and delivery, clinical trials, drug safety, and pharmacovigilance. It addresses regulatory considerations and challenges associated with AI in pharmaceuticals, including privacy, data security, and interpretability of AI models. The review concludes with future perspectives, highlighting emerging trends, addressing limitations and biases in AI models, and emphasizing the importance of collaboration and knowledge sharing. It provides a comprehensive overview of AI's potential to transform the pharmaceutical industry and improve patient care while identifying further research and development areas.
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Affiliation(s)
- Amol D Gholap
- Department of Pharmaceutics, St. John Institute of Pharmacy and Research, Palghar, Maharashtra, 401404, India.
| | - Md Jasim Uddin
- Department of Pharmaceutical Technology, Faculty of Pharmacy, Universiti Malaya, 50603, Kuala Lumpur, Malaysia.
| | - Md Faiyazuddin
- School of Pharmacy, Al-Karim University, Katihar, Bihar, 854106, India; Centre for Global Health Research, Saveetha Institute of Medical and Technical Sciences, Tamil Nadu, India.
| | - Abdelwahab Omri
- Department of Chemistry and Biochemistry, The Novel Drug and Vaccine Delivery Systems Facility, Laurentian University, Sudbury, ON, P3E 2C6, Canada.
| | - S Gowri
- PG & Research, Department of Physics, Cauvery College for Women, Tiruchirapalli, Tamil Nadu, 620018, India
| | - Mohammad Khalid
- James Watt School of Engineering, University of Glasgow, Glasgow G12 8QQ, UK; Sunway Centre for Electrochemical Energy and Sustainable Technology (SCEEST), School of Engineering and Technology, Sunway University, No. 5, Jalan Universiti, Bandar Sunway, 47500 Selangor Darul Ehsan, Malaysia; University Centre for Research and Development, Chandigarh University, Mohali, Punjab, 140413, India.
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2
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Gosavi AA, Nandgude TD, Mishra RK, Puri DB. Exploring the Potential of Artificial Intelligence as a Facilitating Tool for Formulation Development in Fluidized Bed Processor: a Comprehensive Review. AAPS PharmSciTech 2024; 25:111. [PMID: 38740666 DOI: 10.1208/s12249-024-02816-8] [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: 01/23/2024] [Accepted: 04/23/2024] [Indexed: 05/16/2024] Open
Abstract
This in-depth study looks into how artificial intelligence (AI) could be used to make formulation development easier in fluidized bed processes (FBP). FBP is complex and involves numerous variables, making optimization challenging. Various AI techniques have addressed this challenge, including machine learning, neural networks, genetic algorithms, and fuzzy logic. By integrating AI with experimental design, process modeling, and optimization strategies, intelligent systems for FBP can be developed. The advantages of AI in this context include improved process understanding, reduced time and cost, enhanced product quality, and robust formulation optimization. However, data availability, model interpretability, and regulatory compliance challenges must be addressed. Case studies demonstrate successful applications of AI in decision-making, process outcome prediction, and scale-up. AI can improve efficiency, quality, and cost-effectiveness in significant ways. Still, it is important to think carefully about data quality, how easy it is to understand, and how to follow the rules. Future research should focus on fully harnessing the potential of AI to advance formulation development in FBP.
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Affiliation(s)
- Aachal A Gosavi
- Department of Pharmaceutics, Dr. D. Y. Patil Institute of Pharmaceutical Sciences and Research, Pimpri, Pune, India
| | - Tanaji D Nandgude
- Department of Pharmaceutics, JSPM University's School of Pharmaceutical Sciences, Wagholi, Pune, India
| | - Rakesh K Mishra
- Department of Pharmaceutics, Dr. D. Y. Patil Institute of Pharmaceutical Sciences and Research, Pimpri, Pune, India.
| | - Dhiraj B Puri
- Department of Mechanical Engineering, Birla Institute of Technology and Science-Pilani, K K Birla Goa Campus, Zuarinagar, Sancoale, Goa, India
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3
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Iwata H, Hayashi Y, Koyama T, Hasegawa A, Ohgi K, Kobayashi I, Okuno Y. Feature extraction of particle morphologies of pharmaceutical excipients from scanning electron microscope images using convolutional neural networks. Int J Pharm 2024; 653:123873. [PMID: 38336179 DOI: 10.1016/j.ijpharm.2024.123873] [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: 10/23/2023] [Revised: 01/08/2024] [Accepted: 01/30/2024] [Indexed: 02/12/2024]
Abstract
Scanning electron microscopy (SEM) images are the most widely used tool for evaluating particle morphology; however, quantitative evaluation using SEM images is time-consuming and often neglected. In this study, we aimed to extract features related to particle morphology of pharmaceutical excipients from SEM images using a convolutional neural network (CNN). SEM images of 67 excipients were acquired and used as models. A classification CNN model of the excipients was constructed based on the SEM images. Further, features were extracted from the middle layer of this CNN model, and the data was compressed to two dimensions using uniform manifold approximation and projection. Lastly, hierarchical clustering analysis (HCA) was performed to categorize the excipients into several clusters and identify similarities among the samples. The classification CNN model showed high accuracy, allowing each excipient to be identified with a high degree of accuracy. HCA revealed that the 67 excipients were classified into seven clusters. Additionally, the particle morphologies of excipients belonging to the same cluster were found to be very similar. These results suggest that CNN models are useful tools for extracting information and identifying similarities among the particle morphologies of excipients.
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Affiliation(s)
- Hiroaki Iwata
- Graduate School of Medicine, Kyoto University, 53 Shogoin-kawaharacho, Sakyo-ku, Kyoto 606-8507, Japan.
| | - Yoshihiro Hayashi
- Graduate School of Medicine, Kyoto University, 53 Shogoin-kawaharacho, Sakyo-ku, Kyoto 606-8507, Japan; Pharmaceutical Technology Management Department, Production Division, Nichi-Iko Pharmaceutical Co., Ltd., 205-1, Shimoumezawa Namerikawa-shi, Toyama 936-0857, Japan.
| | - Takuto Koyama
- Graduate School of Medicine, Kyoto University, 53 Shogoin-kawaharacho, Sakyo-ku, Kyoto 606-8507, Japan
| | - Aki Hasegawa
- Graduate School of Medicine, Kyoto University, 53 Shogoin-kawaharacho, Sakyo-ku, Kyoto 606-8507, Japan
| | - Kosuke Ohgi
- Formulation Development Department, Development & Planning Division, Nichi-Iko Pharmaceutical Co., Ltd., 205-1, Shimoumezawa Namerikawa-shi, Toyama 936-0857, Japan
| | - Ippei Kobayashi
- Formulation Development Department, Development & Planning Division, Nichi-Iko Pharmaceutical Co., Ltd., 205-1, Shimoumezawa Namerikawa-shi, Toyama 936-0857, Japan
| | - Yasushi Okuno
- Graduate School of Medicine, Kyoto University, 53 Shogoin-kawaharacho, Sakyo-ku, Kyoto 606-8507, Japan; RIKEN Center for Computational Science, Kobe 650-0047, Japan
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4
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Ficzere M, Péterfi O, Farkas A, Nagy ZK, Galata DL. Image-based simultaneous particle size distribution and concentration measurement of powder blend components with deep learning and machine vision. Eur J Pharm Sci 2023; 191:106611. [PMID: 37844806 DOI: 10.1016/j.ejps.2023.106611] [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: 04/11/2023] [Revised: 08/21/2023] [Accepted: 10/14/2023] [Indexed: 10/18/2023]
Abstract
This work presents a system, where deep learning was used on images captured with a digital camera to simultaneously determine the API concentration and the particle size distribution (PSD) of two components of a powder blend. The blend consisted of acetylsalicylic acid (ASA) and calcium hydrogen phosphate (CHP), and the predicted API concentration was found corresponding with the HPLC measurements. The PSDs determined with the method corresponded with those measured with laser diffraction particle size analysis. This novel method provides fast and simple measurements and could be suitable for detecting segregation in the powder. By examining the powders discharged from a batch blender, the API concentrations at the top and bottom of the container could be measured, yielding information about the adequacy of the blending and improving the quality control of the manufacturing process.
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Affiliation(s)
- Máté Ficzere
- Department of Organic Chemistry and Technology, Faculty of Chemical Technology and Biotechnology, Budapest University of Technology and Economics, Műegyetem rkp 3., Budapest H 1111, Hungary
| | - Orsolya Péterfi
- 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
| | - Attila Farkas
- 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
| | - Zsombor Kristóf Nagy
- 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.
| | - Dorián László Galata
- 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
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5
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Péterfi O, Madarász L, Ficzere M, Lestyán-Goda K, Záhonyi P, Erdei G, Sipos E, Nagy ZK, Galata DL. In-line particle size measurement during granule fluidization using convolutional neural network-aided process imaging. Eur J Pharm Sci 2023; 189:106563. [PMID: 37582409 DOI: 10.1016/j.ejps.2023.106563] [Citation(s) in RCA: 2] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/12/2023] [Revised: 07/24/2023] [Accepted: 08/12/2023] [Indexed: 08/17/2023]
Abstract
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.
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Affiliation(s)
- Orsolya Péterfi
- 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
| | - Lajos Madarász
- 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
| | - Máté Ficzere
- Department of Organic Chemistry and Technology, Faculty of Chemical Technology and Biotechnology, Budapest University of Technology and Economics, Műegyetem rkp. 3., H-1111 Budapest, Hungary
| | - Katalin Lestyán-Goda
- 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
| | - Petra Záhonyi
- 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
| | - Gábor Erdei
- Department of Atomic Physics, Faculty of Natural Sciences, Budapest University of Technology and Economics, H-1111, Budapest, Budafoki 8, Hungary
| | - Emese Sipos
- 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, 540142 Targu Mures, Romania
| | - Zsombor Kristóf Nagy
- Department of Organic Chemistry and Technology, Faculty of Chemical Technology and Biotechnology, Budapest University of Technology and Economics, Műegyetem rkp. 3., H-1111 Budapest, Hungary.
| | - Dorián László Galata
- Department of Organic Chemistry and Technology, Faculty of Chemical Technology and Biotechnology, Budapest University of Technology and Economics, Műegyetem rkp. 3., H-1111 Budapest, Hungary
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6
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Madarász L, Alexandra Mészáros L, Köte Á, Farkas A, Kristóf Nagy Z. AI-based Analysis of In-line Process Endoscope images for Real-time Particle Size Measurement in a Continuous Pharmaceutical Milling Process. Int J Pharm 2023; 641:123060. [PMID: 37209791 DOI: 10.1016/j.ijpharm.2023.123060] [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: 03/24/2023] [Revised: 05/12/2023] [Accepted: 05/13/2023] [Indexed: 05/22/2023]
Abstract
This paper presents a case study on the first in-line application of AI-based image analysis for real-time pharmaceutical particle size measurement in a continuous milling process. An AI-based imaging system, which utilises a rigid endoscope, was tested for the real-time particle size measurement of solid NaCl powder used as a model API in the range of 200-1000µm. After creating a dataset containing annotated images of NaCl particles, it was used to train an AI model for detecting particles and measuring their size. The developed system could analyse overlapping particles without dispersing air, thus broadening its applicability. The performance of the system was evaluated by measuring pre-sifted NaCl samples with the imaging tool, after which it was installed into a continuous mill for in-line particle size measurement of a milling process. By analysing ∼100 particles/s, the system was able to accurately measure the particle size of sifted NaCl samples and detect particle size reduction when applied in the milling process. The Dv50 values and PSDs measured real-time with the AI-based system correlated well with the reference laser diffraction measurements (<6% mean absolute difference over the measured samples). The AI-based imaging system shows great potential for in-line particle size analysis, which, in line with the latest pharmaceutical QC trends, can provide valuable information for process development and control.
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Affiliation(s)
- Lajos Madarász
- Department of Organic Chemistry and Technology, Faculty of Chemical Technology and Biotechnology, Budapest University of Technology and Economics, H-1111 Budapest, Műegyetem rakpart 3, Hungary
| | - Lilla Alexandra Mészáros
- Department of Organic Chemistry and Technology, Faculty of Chemical Technology and Biotechnology, Budapest University of Technology and Economics, H-1111 Budapest, Műegyetem rakpart 3, Hungary
| | - Ákos Köte
- Department of Organic Chemistry and Technology, Faculty of Chemical Technology and Biotechnology, Budapest University of Technology and Economics, H-1111 Budapest, Műegyetem rakpart 3, Hungary
| | - Attila Farkas
- Department of Organic Chemistry and Technology, Faculty of Chemical Technology and Biotechnology, Budapest University of Technology and Economics, H-1111 Budapest, Műegyetem rakpart 3, Hungary
| | - Zsombor Kristóf Nagy
- Department of Organic Chemistry and Technology, Faculty of Chemical Technology and Biotechnology, Budapest University of Technology and Economics, H-1111 Budapest, Műegyetem rakpart 3, Hungary.
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7
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Blue LE, Guan X, Joubert MK, Kuhns ST, Moore S, Semin DJ, Wikström M, Wypych J, Goudar CT. State-of-the-art and emerging trends in analytical approaches to pharmaceutical-product commercialization. Curr Opin Biotechnol 2022; 78:102800. [PMID: 36182871 DOI: 10.1016/j.copbio.2022.102800] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/01/2022] [Revised: 08/17/2022] [Accepted: 08/25/2022] [Indexed: 12/14/2022]
Abstract
The biopharmaceutical landscape continues to evolve rapidly, and associated modality complexity and the need to improve molecular understanding require concomitant advances in analytical approaches used to characterize and release the product. The Product Quality Attribute Assessment (PQAA) and Quality Target Product Profile (QTPP) frameworks help catalog and translate molecular understanding to process and product-design targets, thereby enabling reliable manufacturing of high-quality product. The analytical target profile forms the basis of identifying best-fit analytical methods for attribute measurement and continues to be successfully used to develop robust analytical methods for detailed product characterization as well as release and stability testing. Despite maturity across multiple testing platforms, advances continue to be made, several with the potential to alter testing paradigms. There is an increasing role for mass spectrometry beyond product characterization and into routine release testing as seen by the progress in multi-attribute methods and technologies, applications to aggregate measurement, the development of capillary zone electrophoresis (CZE) coupled with mass spectrometry (MS) and capillary isoelectric focusing (CIEF) with MS for measurement of glycans and charged species, respectively, and increased application to host cell protein measurement. Multitarget engaging multispecific modalities will drive advances in bioassay platforms and recent advances both in 1- and 2-D NMR approaches could make it the method of choice for characterizing higher-order structures. Additionally, rigorous understanding of raw material and container attributes is necessary to complement product understanding, and these collectively can enable robust supply of high-quality product to patients.
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Affiliation(s)
- Laura E Blue
- Attribute Sciences, Process Development, Amgen Inc., One Amgen Center Drive, Thousand Oaks, CA 91320, USA
| | - Xiaoyan Guan
- Attribute Sciences, Process Development, Amgen Inc., One Amgen Center Drive, Thousand Oaks, CA 91320, USA
| | - Marisa K Joubert
- Attribute Sciences, Process Development, Amgen Inc., One Amgen Center Drive, Thousand Oaks, CA 91320, USA
| | - Scott T Kuhns
- Attribute Sciences, Process Development, Amgen Inc., One Amgen Center Drive, Thousand Oaks, CA 91320, USA
| | - Stephanie Moore
- Attribute Sciences, Process Development, Amgen Inc., One Amgen Center Drive, Thousand Oaks, CA 91320, USA
| | - David J Semin
- Attribute Sciences, Process Development, Amgen Inc., One Amgen Center Drive, Thousand Oaks, CA 91320, USA
| | - Mats Wikström
- Attribute Sciences, Process Development, Amgen Inc., One Amgen Center Drive, Thousand Oaks, CA 91320, USA
| | - Jette Wypych
- Attribute Sciences, Process Development, Amgen Inc., One Amgen Center Drive, Thousand Oaks, CA 91320, USA
| | - Chetan T Goudar
- Attribute Sciences, Process Development, Amgen Inc., One Amgen Center Drive, Thousand Oaks, CA 91320, USA.
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8
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Iwata H, Hayashi Y, Hasegawa A, Terayama K, Okuno Y. Classification of scanning electron microscope images of pharmaceutical excipients using deep convolutional neural networks with transfer learning. Int J Pharm X 2022; 4:100135. [PMID: 36325273 PMCID: PMC9619299 DOI: 10.1016/j.ijpx.2022.100135] [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: 08/25/2022] [Revised: 10/12/2022] [Accepted: 10/13/2022] [Indexed: 11/06/2022] Open
Abstract
Convolutional Neural Networks (CNNs) are image analysis techniques that have been applied to image classification in various fields. In this study, we applied a CNN to classify scanning electron microscopy (SEM) images of pharmaceutical raw material powders to determine if a CNN can evaluate particle morphology. We tested 10 pharmaceutical excipients with widely different particle morphologies. SEM images for each excipient were acquired and divided into training, validation, and test sets. Classification models were constructed by applying transfer learning to pretrained CNN models such as VGG16 and ResNet50. The results of a 5-fold cross-validation showed that the classification accuracy of the CNN model was sufficiently high using either pretrained model and that the type of excipient could be classified with high accuracy. The results suggest that the CNN model can detect differences in particle morphology, such as particle size, shape, and surface condition. By applying Grad-CAM to the constructed CNN model, we succeeded in finding particularly important regions in the particle image of the excipients. CNNs have been found to have the potential to be applied to the identification and characterization of raw material powders for pharmaceutical development.
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Affiliation(s)
- Hiroaki Iwata
- Graduate School of Medicine, Kyoto University, 53 Shogoin-kawaharacho, Sakyo-ku, Kyoto 606-8507, Japan
| | - Yoshihiro Hayashi
- Graduate School of Medicine, Kyoto University, 53 Shogoin-kawaharacho, Sakyo-ku, Kyoto 606-8507, Japan,Pharmaceutical Technology Division, Nichi-Iko Pharmaceutical Co., Ltd., 205-1, Shimoumezawa Namerikawa-shi, Toyama 936-0857, Japan,Correspondence to: Y. Hayashi, Pharmaceutical Technology Division, Nichi-Iko Pharmaceutical Co., Ltd.; 205-1, Shimoumezawa Namerikawa-shi, Toyama 936-0857, Japan.
| | - Aki Hasegawa
- Graduate School of Medicine, Kyoto University, 53 Shogoin-kawaharacho, Sakyo-ku, Kyoto 606-8507, Japan
| | - Kei Terayama
- Graduate School of Medical Life Science, Yokohama City University, Tsurumi-ku, Yokohama 230-0045, Japan
| | - Yasushi Okuno
- Graduate School of Medicine, Kyoto University, 53 Shogoin-kawaharacho, Sakyo-ku, Kyoto 606-8507, Japan,RIKEN Center for Computational Science, Kobe 650-0047, Japan,Correspondence to: Y. Okuno, Graduate School of Medicine, Kyoto University, 53 Shogoin-kawaharacho, Sakyo-ku, Kyoto 606-8507, Japan.
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9
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Pantelidis P, Spartalis M, Zakynthinos G, Anastasiou A, Goliopoulou A, Oikonomou E, Iliopoulos DC, Siasos G. Artificial Intelligence: The new "fuel" to accelerate pharmaceutical development. Curr Pharm Des 2022; 28:2127-2128. [PMID: 35909280 DOI: 10.2174/1381612828666220729101103] [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: 06/20/2022] [Accepted: 06/27/2022] [Indexed: 12/07/2022]
Affiliation(s)
- Panteleimon Pantelidis
- 3rd Department of Cardiology, Sotiria Thoracic Diseases General Hospital, National and Kapodistrian University of Athens, Athens, Greece
| | - Michael Spartalis
- 3rd Department of Cardiology, Sotiria Thoracic Diseases General Hospital, National and Kapodistrian University of Athens, Athens, Greece
| | - George Zakynthinos
- 3rd Department of Cardiology, Sotiria Thoracic Diseases General Hospital, National and Kapodistrian University of Athens, Athens, Greece
| | - Artemis Anastasiou
- 3rd Department of Cardiology, Sotiria Thoracic Diseases General Hospital, National and Kapodistrian University of Athens, Athens, Greece
| | - Athina Goliopoulou
- 3rd Department of Cardiology, Sotiria Thoracic Diseases General Hospital, National and Kapodistrian University of Athens, Athens, Greece
| | - Evangelos Oikonomou
- 3rd Department of Cardiology, Sotiria Thoracic Diseases General Hospital, National and Kapodistrian University of Athens, Athens, Greece
| | - Dimitrios C Iliopoulos
- Laboratory of Experimental Surgery and Surgical Research 'N. S. Christeas', National and Kapodistrian University of Athens, Medical School, Athens, Greece
| | - Gerasimos Siasos
- 3rd Department of Cardiology, Sotiria Thoracic Diseases General Hospital, National and Kapodistrian University of Athens, Athens, Greece
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10
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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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11
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Real-time coating thickness measurement and defect recognition of film coated tablets with machine vision and deep learning. Int J Pharm 2022; 623:121957. [PMID: 35760260 DOI: 10.1016/j.ijpharm.2022.121957] [Citation(s) in RCA: 14] [Impact Index Per Article: 7.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/06/2022] [Revised: 06/20/2022] [Accepted: 06/21/2022] [Indexed: 11/22/2022]
Abstract
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.
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12
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Wu JX, Balantic E, van den Berg F, Rantanen J, Nissen B, Friderichsen AV. A generalized image analytical algorithm for investigating tablet disintegration. Int J Pharm 2022; 623:121847. [PMID: 35643346 DOI: 10.1016/j.ijpharm.2022.121847] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/31/2022] [Revised: 05/16/2022] [Accepted: 05/18/2022] [Indexed: 11/24/2022]
Abstract
Commonly used methods for analyzing tablet disintegration are based on visual observations and can thus be user-dependent. To address this, a generally applicable image analytical algorithm has been developed for machine vision-based quantification of tablet disintegration. The algorithm has been tested with a conventional immediate release tablet, as well as model compacts disintegrating mainly through erosion, and finally, with a polymeric slow-release system. Despite differences in disintegration mechanisms between these compacts, the developed image analytical algorithm demonstrated its general applicability through quantifying the extent of disintegration without adaptation of image analytical parameters. The reproducibility of the approach was estimated with commercial tablets, and further, it could differentiate a range of different model compacts. The developed image analytical algorithm mimics the human decision-making processes and the current experience-based visual evaluation of disintegration time. In doing so the algorithmic method allows a user-independent approach for development of the optimal tablet formulation as well as gaining an understanding on how the selection of excipients and manufacturing processes ultimately influences tablet disintegration.
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Affiliation(s)
- Jian X Wu
- Oral Delivery Technologies, Research & Early Development, Novo Nordisk A/S, Denmark.
| | - Emma Balantic
- Oral Formulation Research, Research & Early Development, Novo Nordisk A/S, Denmark
| | - Frans van den Berg
- Department of Food Science, Faculty of Science, University of Copenhagen, Denmark
| | - Jukka Rantanen
- Department of Pharmacy, Faculty of Health and Medical Sciences, University of Copenhagen, Denmark
| | - Birgitte Nissen
- Oral Formulation Research, Research & Early Development, Novo Nordisk A/S, Denmark
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13
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Xu X, Seijo-Rabina A, Awad A, Rial C, Gaisford S, Basit AW, Goyanes A. Smartphone-enabled 3D printing of medicines. Int J Pharm 2021; 609:121199. [PMID: 34673166 DOI: 10.1016/j.ijpharm.2021.121199] [Citation(s) in RCA: 14] [Impact Index Per Article: 4.7] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/16/2021] [Revised: 10/07/2021] [Accepted: 10/08/2021] [Indexed: 12/12/2022]
Abstract
3D printing is a manufacturing technique that is transforming numerous industrial sectors, particularly where it is key tool in the development and fabrication of medicinees that are personalised to the individual needs of patients. Most 3D printers are relatively large, require trained operators and must be located in a pharmaceutical setting to manufacture dosage forms. In order to realise fully the potential of point-of-care manufacturing of medicines, portable printers that are easy to operate are required. Here, we report the development of a 3D printer that operates using a mobile smartphone. The printer, operating on stereolithographic principles, uses the light from the smartphone's screen to photopolymerise liquid resins and create solid structures. The shape of the printed dosage form is determined using a custom app on the smartphone. Warfarin-loaded Printlets (3D printed tablets) of various sizes and patient-centred shapes (caplet, triangle, diamond, square, pentagon, torus, and gyroid lattices) were successfully printed to a high resolution and with excellent dimensional precision using different photosensitive resins. The drug was present in an amorphous form, and the Printlets displayed sustained release characterises. The promising proof-of-concept results support the future potential of this compact, user-friendly and interconnected smartphone-based system for point-of-care manufacturing of personalised medications.
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Affiliation(s)
- Xiaoyan Xu
- Department of Pharmaceutics, UCL School of Pharmacy, University College London, 29-39 Brunswick Square, London WC1N 1AX, UK
| | - Alejandro Seijo-Rabina
- Departamento de Farmacología, Farmacia y Tecnología Farmacéutica, I+D Farma (GI-1645), Facultad de Farmacia, and Health Research Institute of Santiago de Compostela (IDIS), Universidade de Santiago de Compostela, 15782 Santiago de Compostela, Spain
| | - Atheer Awad
- Department of Pharmaceutics, UCL School of Pharmacy, University College London, 29-39 Brunswick Square, London WC1N 1AX, UK
| | - Carlos Rial
- FabRx Ltd., 7B North Lane, Canterbury CT2 7EB, UK
| | - Simon Gaisford
- Department of Pharmaceutics, UCL School of Pharmacy, University College London, 29-39 Brunswick Square, London WC1N 1AX, UK; FabRx Ltd., 7B North Lane, Canterbury CT2 7EB, UK
| | - Abdul W Basit
- Department of Pharmaceutics, UCL School of Pharmacy, University College London, 29-39 Brunswick Square, London WC1N 1AX, UK; FabRx Ltd., 7B North Lane, Canterbury CT2 7EB, UK.
| | - Alvaro Goyanes
- Department of Pharmaceutics, UCL School of Pharmacy, University College London, 29-39 Brunswick Square, London WC1N 1AX, UK; Departamento de Farmacología, Farmacia y Tecnología Farmacéutica, I+D Farma (GI-1645), Facultad de Farmacia, and Health Research Institute of Santiago de Compostela (IDIS), Universidade de Santiago de Compostela, 15782 Santiago de Compostela, Spain; FabRx Ltd., 7B North Lane, Canterbury CT2 7EB, UK.
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14
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Rodrigues CP, Duchesne C, Poulin É, Lapointe-Garant PP. In-line cosmetic end-point detection of batch coating processes for colored tablets using multivariate image analysis. Int J Pharm 2021; 606:120953. [PMID: 34329698 DOI: 10.1016/j.ijpharm.2021.120953] [Citation(s) in RCA: 3] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/25/2021] [Revised: 07/23/2021] [Accepted: 07/25/2021] [Indexed: 11/16/2022]
Abstract
In this study, an in-line Process Analytical Technology (PAT) for cosmetic (non-functional) coating unit operations is developed using images of the tablet bed acquired in real-time by an inexpensive industrial camera and lighting system. The cosmetic end-point of multiple batches, run under different operating conditions, is automatically computed from these images using a Multivariate Image Analysis (MIA) methodology in conjunction with a stability determination strategy. The end-points detected by the algorithm differed, on average, by 3% in terms of total batch time from those identified visually by a trained operator. Since traditional practice typically relies on a coating overage to ensure full batch aspect homogeneity in the face of disturbances, the current in-line method can be used to reduce coating material and processing time (over 40% for the operating policy adopted in this work). Additionally, monitoring of the color features calculated by the algorithm allowed the identification of abnormal process conditions affecting visible coating uniformity. This work also addresses practical challenges related to image acquisition in the harsh environment of a pan coater, bringing this tool closer to a state of maturity for implementation in production units and opening the path for their optimization, monitoring, and automatic control.
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Affiliation(s)
- Cecilia Pereira Rodrigues
- Laboratoire d'observation et d'optimisation des procédés (LOOP), Université Laval, Pavillon Adrien-Pouliot Québec (Québec), G1V 0A6, Canada
| | - Carl Duchesne
- Laboratoire d'observation et d'optimisation des procédés (LOOP), Université Laval, Pavillon Adrien-Pouliot Québec (Québec), G1V 0A6, Canada.
| | - Éric Poulin
- Laboratoire d'observation et d'optimisation des procédés (LOOP), Université Laval, Pavillon Adrien-Pouliot Québec (Québec), G1V 0A6, Canada
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15
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Farkas D, Madarász L, Nagy ZK, Antal I, Kállai-Szabó N. Image Analysis: A Versatile Tool in the Manufacturing and Quality Control of Pharmaceutical Dosage Forms. Pharmaceutics 2021; 13:pharmaceutics13050685. [PMID: 34068724 PMCID: PMC8151645 DOI: 10.3390/pharmaceutics13050685] [Citation(s) in RCA: 14] [Impact Index Per Article: 4.7] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/16/2021] [Revised: 04/28/2021] [Accepted: 05/04/2021] [Indexed: 11/16/2022] Open
Abstract
In pharmaceutical sciences, visual inspection is one of the oldest methods used for description in pharmacopeias and is still an important part of the characterization and qualification of active ingredients, excipients, and dosage forms. With the development of technology, it is now also possible to take images of various pharmaceutical dosage forms with different imaging methods in a size range that is hardly visible or completely invisible to the human eye. By analyzing high-quality designs, physicochemical processes can be understood, and the results can be used even in the optimization of the composition of the dosage form and in the development of its production. The present study aims to show some of the countless ways image analysis can be used in the manufacturing and quality assessment of different dosage forms. This summary also includes measurements and an evaluation of, amongst others, a less studied dosage form, medicated foams.
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Affiliation(s)
- Dóra Farkas
- Department of Pharmaceutics, Semmelweis University, Hőgyes Str. 7, H-1092 Budapest, Hungary; (D.F.); (I.A.)
| | - Lajos Madarász
- Department of Organic Chemistry and Technology, Budapest University of Technology and Economics, Műegyetem rakpart 3, H-1111 Budapest, Hungary; (L.M.); (Z.K.N.)
| | - Zsombor K. Nagy
- Department of Organic Chemistry and Technology, Budapest University of Technology and Economics, Műegyetem rakpart 3, H-1111 Budapest, Hungary; (L.M.); (Z.K.N.)
| | - István Antal
- Department of Pharmaceutics, Semmelweis University, Hőgyes Str. 7, H-1092 Budapest, Hungary; (D.F.); (I.A.)
| | - Nikolett Kállai-Szabó
- Department of Pharmaceutics, Semmelweis University, Hőgyes Str. 7, H-1092 Budapest, Hungary; (D.F.); (I.A.)
- Correspondence:
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16
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Lou H, Lian B, Hageman MJ. Applications of Machine Learning in Solid Oral Dosage Form Development. J Pharm Sci 2021; 110:3150-3165. [PMID: 33951418 DOI: 10.1016/j.xphs.2021.04.013] [Citation(s) in RCA: 12] [Impact Index Per Article: 4.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/13/2021] [Revised: 04/18/2021] [Accepted: 04/19/2021] [Indexed: 02/07/2023]
Abstract
This review comprehensively summarizes the application of machine learning in solid oral dosage form development over the past three decades. In both academia and industry, machine learning is increasingly applied for multiple preformulation/formulation and process development studies. Further, this review provides the authors' perspectives on how pharmaceutical scientists can use machine learning for right projects and in right ways; some key ingredients include (1) the determination of inputs, outputs, and objectives; (2) the generation of a database containing high-quality data; (3) the development of machine learning models based on dataset training and model optimization; (4) the application of trained models in making predictions for new samples. It is expected by the authors and others that machine learning will promisingly play a more important role in tomorrow's projects for solid oral dosage form development.
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Affiliation(s)
- Hao Lou
- Department of Pharmaceutical Chemistry, University of Kansas, Lawrence, KS 66047, United States; Biopharmaceutical Innovation and Optimization Center, University of Kansas, Lawrence, KS 66047, United States.
| | - Bo Lian
- College of Pharmacy, University of Arizona, Tucson, AZ 85721, United States
| | - Michael J Hageman
- Department of Pharmaceutical Chemistry, University of Kansas, Lawrence, KS 66047, United States; Biopharmaceutical Innovation and Optimization Center, University of Kansas, Lawrence, KS 66047, United States
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17
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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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18
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Gao M, Liu S, Chen J, Gordon KC, Tian F, McGoverin CM. Potential of Raman spectroscopy in facilitating pharmaceutical formulations development - An AI perspective. Int J Pharm 2021; 597:120334. [PMID: 33540015 DOI: 10.1016/j.ijpharm.2021.120334] [Citation(s) in RCA: 9] [Impact Index Per Article: 3.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/17/2020] [Revised: 01/26/2021] [Accepted: 01/27/2021] [Indexed: 01/17/2023]
Abstract
Drug development is time-consuming and inherently possesses a high failure rate. Pharmaceutical formulation development is the bridge that links a new chemical entity (NCE) to pre-clinical and clinical trials, and has a high impact on the efficacy and safety of the final drug product. Further, the time required for this process is escalating as formulation techniques are becoming more complicated due to the rising demands for drug products with better efficacy and patient compliance, as well as the inherent difficulties of addressing the unfavorable properties of NCEs such as low water solubility. The advent of artificial intelligence (AI) provides possibilities to accelerate the drug development process. In this review, we first examine applications of AI methods in different types of pharmaceutical formulations and formulation techniques. Moreover, as availability of data is the engine for the advancement of AI, we then suggest a potential way (i.e. applying Raman spectroscopy) for faster high-quality data gathering from formulations. Raman techniques have the capability of analyzing the composition and distribution of components and the physicochemical properties thereof within formulations, which are prominent factors governing drug dissolution profiles and subsequently bioavailability. Thus, useful information can be obtained bridging formulation development to the final product quality.
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Affiliation(s)
- Ming Gao
- Nycrist Pharmtech Limited, 2/2D, A3, Science and Technology Park, 3009 Guanguang Rd, Guangming, Shenzhen, Guangdong 518107, China
| | - Sibo Liu
- Nycrist Pharmtech Limited, 2/2D, A3, Science and Technology Park, 3009 Guanguang Rd, Guangming, Shenzhen, Guangdong 518107, China
| | - Jianan Chen
- Department of Medical Biophysics, University of Toronto, Princess Margaret Cancer Research Tower, MaRS Centre, 101 College Street, Toronto, Ontario M5G 1L7, Canada
| | - Keith C Gordon
- Dodd-Walls Centre, Department of Chemistry, University of Otago, PO Box 56, Dunedin 9054, New Zealand
| | - Fang Tian
- Nycrist Pharmtech Limited, 2/2D, A3, Science and Technology Park, 3009 Guanguang Rd, Guangming, Shenzhen, Guangdong 518107, China
| | - Cushla M McGoverin
- Nycrist Pharmtech Limited, 2/2D, A3, Science and Technology Park, 3009 Guanguang Rd, Guangming, Shenzhen, Guangdong 518107, China.
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19
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Đuriš J, Kurćubić I, Ibrić S. Review of machine learning algorithms' application in pharmaceutical technology. ARHIV ZA FARMACIJU 2021. [DOI: 10.5937/arhfarm71-32499] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/18/2022] Open
Abstract
Machine learning algorithms, and artificial intelligence in general, have a wide range of applications in the field of pharmaceutical technology. Starting from the formulation development, through a great potential for integration within the Quality by design framework, these data science tools provide a better understanding of the pharmaceutical formulations and respective processing. Machine learning algorithms can be especially helpful with the analysis of the large volume of data generated by the Process analytical technologies. This paper provides a brief explanation of the artificial neural networks, as one of the most frequently used machine learning algorithms. The process of the network training and testing is described and accompanied with illustrative examples of machine learning tools applied in the context of pharmaceutical formulation development and related technologies, as well as an overview of the future trends. Recently published studies on more sophisticated methods, such as deep neural networks and light gradient boosting machine algorithm, have been described. The interested reader is also referred to several official documents (guidelines) that pave the way for a more structured representation of the machine learning models in their prospective submissions to the regulatory bodies.
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