1
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Neri S, Brandsma ET, Mul FPJ, Kuijpers TW, Matlung HL, van Bruggen R. An AI-based imaging flow cytometry approach to study erythrophagocytosis. Cytometry A 2024. [PMID: 39248056 DOI: 10.1002/cyto.a.24894] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/16/2024] [Revised: 07/03/2024] [Accepted: 07/26/2024] [Indexed: 09/10/2024]
Abstract
Erythrophagocytosis is a process consisting of recognition, engulfment and digestion by phagocytes of antibody-coated or damaged erythrocytes. Understanding the dynamics that are behind erythrophagocytosis is fundamental to comprehend this cellular process under specific circumstances. Several techniques have been used to study phagocytosis. Among these, an interesting approach is the use of Imaging Flow Cytometry (IFC) to distinguish internalization and binding of cells or particles. However, this method requires laborious analysis. Here, we introduce a novel approach to analyze the phagocytosis process by combining Artificial Intelligence (AI) with IFC. Our study demonstrates that this approach is highly suitable to study erythrophagocytosis, categorizing internalized, bound and non-bound erythrocytes. Validation experiments showed that our pipeline performs with high accuracy and reproducibility.
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Affiliation(s)
- S Neri
- Sanquin Research and Landsteiner Laboratory, Academic Medical Centre, Amsterdam, The Netherlands
| | - E T Brandsma
- Saxion, Academy Life Science Engineering and Design, University of Applied Science, Enschede, The Netherlands
| | - F P J Mul
- Department Central Cell Analysis Facility, Sanquin Research and Landsteiner Laboratory, Academic Medical Center, University of Amsterdam, Amsterdam, The Netherlands
| | - T W Kuijpers
- Sanquin Research and Landsteiner Laboratory, Academic Medical Centre, Amsterdam, The Netherlands
| | - H L Matlung
- Sanquin Research and Landsteiner Laboratory, Academic Medical Centre, Amsterdam, The Netherlands
| | - R van Bruggen
- Sanquin Research and Landsteiner Laboratory, Academic Medical Centre, Amsterdam, The Netherlands
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2
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Liang M, Goss M, Cao S, Yang C. Non-Destructive Analysis of Subvisible Particles with Mie-Scattering-Based Light Sheet Technology: System Development. J Pharm Sci 2024; 113:2817-2825. [PMID: 39032825 DOI: 10.1016/j.xphs.2024.07.015] [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: 12/26/2023] [Revised: 07/14/2024] [Accepted: 07/16/2024] [Indexed: 07/23/2024]
Abstract
The characteristics of subvisible particles (SbVPs) are critical quality attributes of injectable and ophthalmic solutions in pharmaceutical manufacturing. However, current compendial SbVP testing methods, namely the light obstruction method and the microscopic particle count method, are destructive and wasteful of target samples. In this study, we present the development of a non-destructive SbVP analyzer aiming to analyze SbVPs directly in drug product (DP) containers while keeping the samples intact. Custom sample housings are developed and incorporated into the analyzer to reduce optical aberrations introduced by the curvature of typical pharmaceutical DP sample containers. The analyzer integrates a light-sheet microscope structure and models the side scattering event from a particle with Mie scattering theory with refractive indices as prior information. Equivalent spherical particle size under assigned refractive index values is estimated, and the particle concentration is determined based on the number of scattering events and the volume sampled by the light sheet. The resulting analyzer's capability and performance to non-destructively analyze SbVPs in DP containers were evaluated using a series of polystyrene bead suspensions in ISO 2R and 6R vials. Our results and analysis show the particle analyzer is capable of directly detecting SbVPs from intact DP containers, sorting SbVPs into commonly used size bins (e.g. ≥ 2 µm, ≥ 5 µm, ≥ 10 µm, and ≥ 25 µm), and reliably quantifying SbVPs in the concentration range of 4.6e2 to 5.0e5 particle/mL with a margin of ± 15 % error based on a 90 % confidence interval.
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Affiliation(s)
- Mingshu Liang
- California Institute of Technology, Electrical Engineering, Pasadena, CA 91125, USA
| | - Monica Goss
- Amgen Process Development, Thousand Oaks, CA 91320, USA
| | - Shawn Cao
- Amgen Process Development, Thousand Oaks, CA 91320, USA.
| | - Changhuei Yang
- California Institute of Technology, Electrical Engineering, Pasadena, CA 91125, USA.
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3
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Gamble JF, Al-Obaidi H. Past, current, and future: Application of image analysis in small molecule pharmaceutical development. J Pharm Sci 2024:S0022-3549(24)00306-X. [PMID: 39153662 DOI: 10.1016/j.xphs.2024.08.003] [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: 06/27/2024] [Revised: 08/09/2024] [Accepted: 08/09/2024] [Indexed: 08/19/2024]
Abstract
The often-perceived limitations of image analysis have for many years impeded the widespread application of such systems as first line characterisation tools. Image analysis has, however, undergone a notable resurgence in the pharmaceutical industry fuelled by developments system capabilities and the desire of scientists to characterize the morphological nature of their particles more adequately. The importance of particle shape as well as size is now widely acknowledged. With the increasing use of modelling and simulations, and ongoing developments though the integration of machine learning and artificial intelligence, the utility of image analysis is increasing significantly driven by the richness of the data obtained. Such datasets provide means to circumvent the requirement to rely on less informative descriptors and enable the move towards the use of whole distributions. Combining the improved particle size and shape measurement and description with advances in modelling and simulations is enabling improved means to elucidate the link between particle and bulk powder properties. In addition to improved capabilities to describe input materials, approaches to characterize single components within multicomponent systems are providing scientists means to understand how their material may change during manufacture thus providing a means to link the behaviour of final dosage forms with the particle properties at the point of action. The aim is to provide an overview of image analysis and update readers with innovations and capabilities to other methods in the small molecule arena. We will also describe the use of AI for the improved analysis using image analysis.
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Affiliation(s)
- John F Gamble
- Bristol Myers Squibb, Reeds Lane, Moreton, Wirral, CH46 1QW, UK; Department of Pharmacy, University of Reading, Reading RG6 6AH, UK.
| | - Hisham Al-Obaidi
- Department of Pharmacy, University of Reading, Reading RG6 6AH, UK
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4
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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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5
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Hybel TE, Jensen SH, Rodrigues MA, Hybel TE, Pedersen MN, Qvick SH, Enemark MH, Bill M, Rosenberg CA, Ludvigsen M. Imaging Flow Cytometry and Convolutional Neural Network-Based Classification Enable Discrimination of Hematopoietic and Leukemic Stem Cells in Acute Myeloid Leukemia. Int J Mol Sci 2024; 25:6465. [PMID: 38928171 PMCID: PMC11203419 DOI: 10.3390/ijms25126465] [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: 05/17/2024] [Revised: 06/07/2024] [Accepted: 06/10/2024] [Indexed: 06/28/2024] Open
Abstract
Acute myeloid leukemia (AML) is a heterogenous blood cancer with a dismal prognosis. It emanates from leukemic stem cells (LSCs) arising from the genetic transformation of hematopoietic stem cells (HSCs). LSCs hold prognostic value, but their molecular and immunophenotypic heterogeneity poses challenges: there is no single marker for identifying all LSCs across AML samples. We hypothesized that imaging flow cytometry (IFC) paired with artificial intelligence-driven image analysis could visually distinguish LSCs from HSCs based solely on morphology. Initially, a seven-color IFC panel was employed to immunophenotypically identify LSCs and HSCs in bone marrow samples from five AML patients and ten healthy donors, respectively. Next, we developed convolutional neural network (CNN) models for HSC-LSC discrimination using brightfield (BF), side scatter (SSC), and DNA images. Classification using only BF images achieved 86.96% accuracy, indicating significant morphological differences. Accuracy increased to 93.42% when combining BF with DNA images, highlighting differences in nuclear morphology, although DNA images alone were inadequate for accurate HSC-LSC discrimination. Model development using SSC images revealed minor granularity differences. Performance metrics varied substantially between AML patients, indicating considerable morphologic variations among LSCs. Overall, we demonstrate proof-of-concept results for accurate CNN-based HSC-LSC differentiation, instigating the development of a novel technique within AML monitoring.
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Affiliation(s)
- Trine Engelbrecht Hybel
- Department of Hematology, Aarhus University Hospital, 8200 Aarhus N, Denmark; (T.E.H.); (M.H.E.)
- Department of Clinical Medicine, Aarhus University, 8200 Aarhus N, Denmark
| | - Sofie Hesselberg Jensen
- Department of Hematology, Aarhus University Hospital, 8200 Aarhus N, Denmark; (T.E.H.); (M.H.E.)
- Department of Clinical Medicine, Aarhus University, 8200 Aarhus N, Denmark
| | | | - Thomas Engelbrecht Hybel
- Department of Hematology, Aarhus University Hospital, 8200 Aarhus N, Denmark; (T.E.H.); (M.H.E.)
| | - Maya Nautrup Pedersen
- Department of Hematology, Aarhus University Hospital, 8200 Aarhus N, Denmark; (T.E.H.); (M.H.E.)
- Department of Clinical Medicine, Aarhus University, 8200 Aarhus N, Denmark
| | - Signe Håkansson Qvick
- Department of Hematology, Aarhus University Hospital, 8200 Aarhus N, Denmark; (T.E.H.); (M.H.E.)
| | - Marie Hairing Enemark
- Department of Hematology, Aarhus University Hospital, 8200 Aarhus N, Denmark; (T.E.H.); (M.H.E.)
- Department of Clinical Medicine, Aarhus University, 8200 Aarhus N, Denmark
| | - Marie Bill
- Department of Hematology, Aarhus University Hospital, 8200 Aarhus N, Denmark; (T.E.H.); (M.H.E.)
- Department of Clinical Medicine, Aarhus University, 8200 Aarhus N, Denmark
| | - Carina Agerbo Rosenberg
- Department of Hematology, Aarhus University Hospital, 8200 Aarhus N, Denmark; (T.E.H.); (M.H.E.)
| | - Maja Ludvigsen
- Department of Hematology, Aarhus University Hospital, 8200 Aarhus N, Denmark; (T.E.H.); (M.H.E.)
- Department of Clinical Medicine, Aarhus University, 8200 Aarhus N, Denmark
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6
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Rosenberg CA, Rodrigues MA, Bill M, Ludvigsen M. Comparative analysis of feature-based ML and CNN for binucleated erythroblast quantification in myelodysplastic syndrome patients using imaging flow cytometry data. Sci Rep 2024; 14:9349. [PMID: 38654058 PMCID: PMC11039460 DOI: 10.1038/s41598-024-59875-x] [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: 12/09/2023] [Accepted: 04/16/2024] [Indexed: 04/25/2024] Open
Abstract
Myelodysplastic syndrome is primarily characterized by dysplasia in the bone marrow (BM), presenting a challenge in consistent morphology interpretation. Accurate diagnosis through traditional slide-based analysis is difficult, necessitating a standardized objective technique. Over the past two decades, imaging flow cytometry (IFC) has proven effective in combining image-based morphometric analyses with high-parameter phenotyping. We have previously demonstrated the effectiveness of combining IFC with a feature-based machine learning algorithm to accurately identify and quantify rare binucleated erythroblasts (BNEs) in dyserythropoietic BM cells. However, a feature-based workflow poses challenges requiring software-specific expertise. Here we employ a Convolutional Neural Network (CNN) algorithm for BNE identification and differentiation from doublets and cells with irregular nuclear morphology in IFC data. We demonstrate that this simplified AI workflow, coupled with a powerful CNN algorithm, achieves comparable BNE quantification accuracy to manual and feature-based analysis with substantial time savings, eliminating workflow complexity. This streamlined approach holds significant clinical value, enhancing IFC accessibility for routine diagnostic purposes.
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Affiliation(s)
- Carina A Rosenberg
- Department of Hematology, Aarhus University Hospital, Palle Juul-Jensens Boulevard 35, C115, 8200, Aarhus C, Denmark.
| | | | - Marie Bill
- Department of Hematology, Aarhus University Hospital, Palle Juul-Jensens Boulevard 35, C115, 8200, Aarhus C, Denmark
- Department of Clinical Medicine, Aarhus University, Aarhus, Denmark
| | - Maja Ludvigsen
- Department of Hematology, Aarhus University Hospital, Palle Juul-Jensens Boulevard 35, C115, 8200, Aarhus C, Denmark
- Department of Clinical Medicine, Aarhus University, Aarhus, Denmark
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7
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Lopez-Del Rio A, Pacios-Michelena A, Picart-Armada S, Garidel P, Nikels F, Kube S. Sub-Visible Particle Classification and Label Consistency Analysis for Flow-Imaging Microscopy Via Machine Learning Methods. J Pharm Sci 2024; 113:880-890. [PMID: 37924976 DOI: 10.1016/j.xphs.2023.10.041] [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: 02/01/2023] [Revised: 10/30/2023] [Accepted: 10/30/2023] [Indexed: 11/06/2023]
Abstract
Sub-visible particles can be a quality concern in pharmaceutical products, especially parenteral preparations. To quantify and characterize these particles, liquid samples may be passed through a flow-imaging microscopy instrument that also generates images of each detected particle. Machine learning techniques have increasingly been applied to this kind of data to detect changes in experimental conditions or classify specific types of particles, primarily focusing on silicone oil. That technique generally requires manual labeling of particle images by subject matter experts, a time-consuming and complex task. In this study, we created artificial datasets of silicone oil, protein particles, and glass particles that mimicked complex datasets of particles found in biopharmaceutical products. We used unsupervised learning techniques to effectively describe particle composition by sample. We then trained independent one-class classifiers to detect specific particle populations: silicone oil and glass particles. We also studied the consistency of the particle labels used to evaluate these models. Our results show that one-class classifiers are a reasonable choice for handling heterogeneous flow-imaging microscopy data and that unsupervised learning can aid in the labeling process. However, we found agreement among experts to be rather low, especially for smaller particles (< 8 µm for our Micro-Flow Imaging data). Given the fact that particle label confidence is not usually reported in the literature, we recommend more careful assessment of this topic in the future.
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Affiliation(s)
- Angela Lopez-Del Rio
- Pharmaceutical Development Biologicals, Boehringer Ingelheim Pharma GmbH & Co. KG, Biberach an der Riss 88397, Federal Republic of Germany.
| | - Anabel Pacios-Michelena
- Analytical Development Biologicals, Boehringer Ingelheim Pharma GmbH & Co. KG, Biberach an der Riss 88397, Federal Republic of Germany
| | - Sergio Picart-Armada
- Global Computational Biology and Digital Sciences, Boehringer Ingelheim Pharma GmbH & Co. KG, Biberach an der Riss 88397, Federal Republic of Germany
| | - Patrick Garidel
- Pharmaceutical Development Biologicals, Boehringer Ingelheim Pharma GmbH & Co. KG, Biberach an der Riss 88397, Federal Republic of Germany
| | - Felix Nikels
- Analytical Development Biologicals, Boehringer Ingelheim Pharma GmbH & Co. KG, Biberach an der Riss 88397, Federal Republic of Germany
| | - Sebastian Kube
- Pharmaceutical Development Biologicals, Boehringer Ingelheim Pharma GmbH & Co. KG, Biberach an der Riss 88397, Federal Republic of Germany.
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8
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Dimitriadis S, Dova L, Kotsianidis I, Hatzimichael E, Kapsali E, Markopoulos GS. Imaging Flow Cytometry: Development, Present Applications, and Future Challenges. Methods Protoc 2024; 7:28. [PMID: 38668136 PMCID: PMC11054958 DOI: 10.3390/mps7020028] [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: 01/29/2024] [Revised: 03/13/2024] [Accepted: 03/21/2024] [Indexed: 04/29/2024] Open
Abstract
Imaging flow cytometry (ImFC) represents a significant technological advancement in the field of cytometry, effectively merging the high-throughput capabilities of flow analysis with the detailed imaging characteristics of microscopy. In our comprehensive review, we adopt a historical perspective to chart the development of ImFC, highlighting its origins and current state of the art and forecasting potential future advancements. The genesis of ImFC stemmed from merging the hydraulic system of a flow cytometer with advanced camera technology. This synergistic coupling facilitates the morphological analysis of cell populations at a high-throughput scale, effectively evolving the landscape of cytometry. Nevertheless, ImFC's implementation has encountered hurdles, particularly in developing software capable of managing its sophisticated data acquisition and analysis needs. The scale and complexity of the data generated by ImFC necessitate the creation of novel analytical tools that can effectively manage and interpret these data, thus allowing us to unlock the full potential of ImFC. Notably, artificial intelligence (AI) algorithms have begun to be applied to ImFC, offering promise for enhancing its analytical capabilities. The adaptability and learning capacity of AI may prove to be essential in knowledge mining from the high-dimensional data produced by ImFC, potentially enabling more accurate analyses. Looking forward, we project that ImFC may become an indispensable tool, not only in research laboratories, but also in clinical settings. Given the unique combination of high-throughput cytometry and detailed imaging offered by ImFC, we foresee a critical role for this technology in the next generation of scientific research and diagnostics. As such, we encourage both current and future scientists to consider the integration of ImFC as an addition to their research toolkit and clinical diagnostic routine.
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Affiliation(s)
- Savvas Dimitriadis
- Hematology Laboratory, Unit of Molecular Biology and Translational Flow Cytometry, University Hospital of Ioannina, 45100 Ioannina, Greece; (S.D.); (L.D.)
| | - Lefkothea Dova
- Hematology Laboratory, Unit of Molecular Biology and Translational Flow Cytometry, University Hospital of Ioannina, 45100 Ioannina, Greece; (S.D.); (L.D.)
| | - Ioannis Kotsianidis
- Department of Hematology, University Hospital of Alexandroupolis, Democritus University of Thrace, 69100 Alexandroupolis, Greece;
| | - Eleftheria Hatzimichael
- Department of Hematology, Faculty of Medicine, University of Ioannina, 45110 Ioannina, Greece; (E.H.); (E.K.)
| | - Eleni Kapsali
- Department of Hematology, Faculty of Medicine, University of Ioannina, 45110 Ioannina, Greece; (E.H.); (E.K.)
| | - Georgios S. Markopoulos
- Hematology Laboratory, Unit of Molecular Biology and Translational Flow Cytometry, University Hospital of Ioannina, 45100 Ioannina, Greece; (S.D.); (L.D.)
- Department of Surgery, Faculty of Medicine, University of Ioannina, 45110 Ioannina, Greece
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9
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Wong B, Zhao X, Su Y, Ouyang H, Rhodes T, Xu W, Xi H, Fu D. Characterizing Silicone Oil-Induced Protein Aggregation with Stimulated Raman Scattering Imaging. Mol Pharm 2023; 20:4268-4276. [PMID: 37382286 DOI: 10.1021/acs.molpharmaceut.3c00391] [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] [Indexed: 06/30/2023]
Abstract
Particles in biopharmaceutical products present high risks due to their detrimental impacts on product quality and safety. Identification and quantification of particles in drug products are important to understand particle formation mechanisms, which can help develop control strategies for particle formation during the formulation development and manufacturing process. However, existing analytical techniques such as microflow imaging and light obscuration measurement lack the sensitivity and resolution to detect particles with sizes smaller than 2 μm. More importantly, these techniques are not able to provide chemical information to determine particle composition. In this work, we overcome these challenges by applying the stimulated Raman scattering (SRS) microscopy technique to monitor the C-H Raman stretching modes of the proteinaceous particles and silicone oil droplets formed in the prefilled syringe barrel. By comparing the relative signal intensity and spectral features of each component, most particles can be classified as protein-silicone oil aggregates. We further show that morphological features are poor indicators of particle composition. Our method has the capability to quantify aggregation in protein therapeutics with chemical and spatial information in a label-free manner, potentially allowing high throughput screening or investigation of aggregation mechanisms.
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Affiliation(s)
- Brian Wong
- Department of Chemistry, University of Washington, Seattle, Washington 98195, United States
| | - Xi Zhao
- Analytical Enabling Capabilities, Analytical Research and Development, Merck & Co., Inc., Rahway, New Jersey 07065, United States
- Sterile and Specialty Products, Pharmaceutical Sciences & Clinical Supply, Merck & Co., Inc., Rahway, New Jersey 07065, United States
| | - Yongchao Su
- Analytical Enabling Capabilities, Analytical Research and Development, Merck & Co., Inc., Rahway, New Jersey 07065, United States
| | - Hanlin Ouyang
- Analytical Enabling Capabilities, Analytical Research and Development, Merck & Co., Inc., Rahway, New Jersey 07065, United States
| | - Timothy Rhodes
- Analytical Enabling Capabilities, Analytical Research and Development, Merck & Co., Inc., Rahway, New Jersey 07065, United States
| | - Wei Xu
- Analytical Enabling Capabilities, Analytical Research and Development, Merck & Co., Inc., Rahway, New Jersey 07065, United States
| | - Hanmi Xi
- Analytical Enabling Capabilities, Analytical Research and Development, Merck & Co., Inc., Rahway, New Jersey 07065, United States
| | - Dan Fu
- Department of Chemistry, University of Washington, Seattle, Washington 98195, United States
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10
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Salami H, Wang S, Skomski D. Evaluation of a Self-Supervised Machine Learning Method for Screening of Particulate Samples: A Case Study in Liquid Formulations. J Pharm Sci 2023; 112:771-778. [PMID: 36240862 DOI: 10.1016/j.xphs.2022.10.010] [Citation(s) in RCA: 2] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/15/2022] [Revised: 10/06/2022] [Accepted: 10/06/2022] [Indexed: 11/06/2022]
Abstract
Imaging is commonly used as a characterization method in the pharmaceuticals industry, including for quantifying subvisible particles in solid and liquid formulations. Extracting information beyond particle size, such as classifying morphological subpopulations, requires some type of image analysis method. Suggested methods to classify particles have been based on pre-determined morphological features or use supervised training of convolutional neural networks to learn image representations in relation to ground truth labels. Complications arising from highly complex morphologies, unforeseen classes, and time-consuming preparation of ground truth labels, are some of the challenges faced by these methods. In this work, we evaluate the application of a self-supervised contrastive learning method in studying particle images from therapeutic solutions. Unlike with supervised training, this approach does not require ground truth labels and representations are learned by comparing particle images and their augmentations. This method provides a fast and easily implementable tool of coarse screening for morphological attribute assessment. Furthermore, our analysis shows that in cases with relatively balanced datasets, a small subset of an image dataset is sufficient to train a convolutional neural network encoder capable of extracting useful image representations. It is also demonstrated that particle classes typically observed in protein solutions administered by pre-filled syringes emerge as separated clusters in the encoder's embedding space, facilitating performing tasks such as training weakly-supervised classifiers or identifying the presence of new subpopulations.
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Affiliation(s)
- Hossein Salami
- Analytical Research and Development, Merck & Co., Inc., 126 E. Lincoln Ave., Rahway, NJ 07065, USA
| | - Shubing Wang
- Department of Biometrics Research, Merck & Co., Inc., Kenilworth, NJ, USA
| | - Daniel Skomski
- Analytical Research and Development, Merck & Co., Inc., 126 E. Lincoln Ave., Rahway, NJ 07065, USA.
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11
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Poozesh S, Cannavò F, Manikwar P. Sensitivity and Uncertainty Analysis of Micro-Flow Imaging for Sub-Visible Particle Measurements Using Artificial Neural Network. Pharm Res 2023; 40:721-733. [PMID: 36697932 DOI: 10.1007/s11095-023-03474-4] [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: 10/08/2022] [Accepted: 01/15/2023] [Indexed: 01/26/2023]
Abstract
PURPOSE During biopharmaceutical drug manufacturing, storage, and distribution, proteins in both liquid and solid dosage forms go through various processes that could lead to protein aggregation. The extent of aggregation in the sub-micron range can be measured by analyzing a liquid or post-reconstituted powder sample using Micro-Flow Imaging (MFI) technique. MFI is widely used in biopharmaceutical industries due to its high sensitivity in detecting and analyzing particle size distribution. However, the MFI's sensitivity to various factors makes accurate measurement challenging. Therefore, in light of the inherent variability of the method, this work aims to explore the capabilities of an adopted coupled sensitivity analysis and machine learning algorithm to quantify the influencing factors on the formed sub-visible particles and method variability. METHODS The proposed algorithm consists of two interconnected components, namely a surrogate model with a neural network and a sensitivity analyzer. A machine learning tool based on artificial neural networks (ANN) is constructed with MFI data. The best fit with an optimized configuration is found. Sensitivity and uncertainty analysis is performed using this network as the surrogate model to understand the impacts of input parameters on MFI data. RESULTS Results reveal the most impactful reconstitution preparation factors and others that are masked by the instrument variabilities. It is shown that instrument inaccuracy is a function of size category, with higher variabilities associated with larger size ranges. CONCLUSION Utilizing this tool while assessing the sensitivity of outputs to various parameters, measurement variabilities for analytical characterization tests can be quantified.
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Affiliation(s)
- Sadegh Poozesh
- Dosage Form Design and Development, BioPharmaceuticals Development, R&D, AstraZeneca , Gaithersburg, MD, USA.
| | - Flavio Cannavò
- Istituto Nazionale Di Geofisica E Vulcanologia, Sezione Di Catania-Osservatorio Etneo, Piazza Roma, 2-95125, Catania, Italy
| | - Prakash Manikwar
- Dosage Form Design and Development, BioPharmaceuticals Development, R&D, AstraZeneca , Gaithersburg, MD, USA
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12
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Rees P, Summers HD, Filby A, Carpenter AE, Doan M. Imaging flow cytometry: a primer. NATURE REVIEWS. METHODS PRIMERS 2022; 2:86. [PMID: 37655209 PMCID: PMC10468826 DOI: 10.1038/s43586-022-00167-x] [Citation(s) in RCA: 36] [Impact Index Per Article: 18.0] [Reference Citation Analysis] [Abstract] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Accepted: 09/08/2022] [Indexed: 09/02/2023]
Abstract
Imaging flow cytometry combines the high throughput nature of flow cytometry with the advantages of single cell image acquisition associated with microscopy. The measurement of large numbers of features from the resulting images provides rich datasets which have resulted in a wide range of novel biomedical applications. In this primer we discuss the typical imaging flow instrumentation, the form of data acquired and the typical analysis tools that can be applied to this data. Using examples from the literature we discuss the progression of the analysis methods that have been applied to imaging flow cytometry data. These methods start from the use of simple single image features and multiple channel gating strategies, followed by the design and use of custom features for phenotype classification, through to powerful machine and deep learning methods. For each of these methods, we outline the processes involved in analyzing typical datasets and provide details of example applications. Finally we discuss the current limitations of imaging flow cytometry and the innovations which are addressing these challenges.
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Affiliation(s)
- Paul Rees
- Department of Biomedical Engineering, Swansea University, Bay Campus, Swansea SA1 8EN, United Kingdom
- Imaging Platform, Broad Institute of Harvard and MIT, Cambridge, Massachusetts MA 02142, United States of America
| | - Huw D Summers
- Department of Biomedical Engineering, Swansea University, Bay Campus, Swansea SA1 8EN, United Kingdom
| | - Andrew Filby
- Flow Cytometry Core Facility and Innovation, Methodology and Application Research Theme, Biosciences Institute, Faculty of Medical Sciences, Newcastle University, Newcastle upon Tyne, United Kingdom
| | - Anne E Carpenter
- Imaging Platform, Broad Institute of Harvard and MIT, Cambridge, Massachusetts MA 02142, United States of America
| | - Minh Doan
- Bioimaging Analytics, GlaxoSmithKline, Collegeville, PA, United States of America
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13
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Nishiumi H, Deiringer N, Krause N, Yoneda S, Torisu T, Menzen T, Friess W, Uchiyama S. Utility of Three Flow Imaging Microscopy Instruments for Image Analysis in Evaluating four Types of Subvisible Particle in Biopharmaceuticals. J Pharm Sci 2022; 111:3017-3028. [PMID: 35948157 DOI: 10.1016/j.xphs.2022.08.006] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/19/2022] [Revised: 08/04/2022] [Accepted: 08/04/2022] [Indexed: 12/14/2022]
Abstract
Subvisible particles (SVPs) are a critical quality attribute of parenteral and ophthalmic products. United States Pharmacopeia recommends the characterizations of SVPs which are classified into intrinsic, extrinsic, and inherent particles. Flow imaging microscopy (FIM) is useful as an orthogonal method in both the quantification and classification of SVPs because FIM instruments provide particle images. In addition to the conventionally used FlowCam (Yokogawa Fluid Imaging Technologies) and Micro-Flow Imaging (Bio-Techne) instruments, the iSpect DIA-10 (Shimadzu) instrument has recently been released. The three instruments have similar detection principles but different optical settings and image processing, which may lead to different results of the quantification and classification of SVPs based on the information from particle images. The present study compares four types of SVP (protein aggregates, silicone oil droplets, and surrogates for solid free-fatty-acid particles, milled-lipid particles, and sprayed-lipid particles) to compare the results of size distributions and classification abilities obtained using morphological features and a deep-learning approach. Although the three FIM instruments were effective in classifying the four types of SVP through convolutional neural network analysis, there was no agreement on the size distribution for the same protein aggregate solution, suggesting that using the classifiers of the FIM instruments could result in different evaluations of SVPs in the field of biopharmaceuticals.
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Affiliation(s)
- Haruka Nishiumi
- Department of Biotechnology, Graduate School of Engineering, Osaka University, 2-1 Yamadaoka, Suita, Osaka 565-0871, Japan
| | - Natalie Deiringer
- Department of Pharmacy; Pharmaceutical Technology and Biopharmaceutics, Ludwig-Maximilians-Universitaet Muenchen, Munich, Germany
| | - Nils Krause
- Coriolis Pharma Research GmbH, Fraunhoferstr. 18 b, 82152, Martinsried, Germany
| | - Saki Yoneda
- Department of Biotechnology, Graduate School of Engineering, Osaka University, 2-1 Yamadaoka, Suita, Osaka 565-0871, Japan
| | - Tetsuo Torisu
- Department of Biotechnology, Graduate School of Engineering, Osaka University, 2-1 Yamadaoka, Suita, Osaka 565-0871, Japan
| | - Tim Menzen
- Coriolis Pharma Research GmbH, Fraunhoferstr. 18 b, 82152, Martinsried, Germany
| | - Wolfgang Friess
- Department of Pharmacy; Pharmaceutical Technology and Biopharmaceutics, Ludwig-Maximilians-Universitaet Muenchen, Munich, Germany
| | - Susumu Uchiyama
- Department of Biotechnology, Graduate School of Engineering, Osaka University, 2-1 Yamadaoka, Suita, Osaka 565-0871, Japan; U-medico Inc., 2-1 Yamadaoka, Suita, Osaka, 565-0871, Japan.
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14
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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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15
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Demagny J, Roussel C, Le Guyader M, Guiheneuf E, Harrivel V, Boyer T, Diouf M, Dussiot M, Demont Y, Garçon L. Combining imaging flow cytometry and machine learning for high-throughput schistocyte quantification: A SVM classifier development and external validation cohort. EBioMedicine 2022; 83:104209. [PMID: 35986949 PMCID: PMC9404284 DOI: 10.1016/j.ebiom.2022.104209] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/29/2022] [Revised: 07/25/2022] [Accepted: 07/25/2022] [Indexed: 11/29/2022] Open
Abstract
Background Schistocyte counts are a cornerstone of the diagnosis of thrombotic microangiopathy syndrome (TMA). Their manual quantification is complex and alternative automated methods suffer from pitfalls that limit their use. We report a method combining imaging flow cytometry (IFC) and artificial intelligence for the direct label-free and operator-independent quantification of schistocytes in whole blood. Methods We used 135,045 IFC images from blood acquisition among 14 patients to extract 188 features with IDEAS® software and 128 features from a convolutional neural network (CNN) with Keras framework in order to train a support vector machine (SVM) blood elements’ classifier used for schistocytes quantification. Finding Keras features showed better accuracy (94.03%, CI: 93.75-94.31%) than ideas features (91.54%, CI: 91.21-91.87%) in recognising whole-blood elements, and together they showed the best accuracy (95.64%, CI: 95.39-95.88%). We obtained an excellent correlation (0.93, CI: 0.90-0.96) between three haematologists and our method on a cohort of 102 patient samples. All patients with schistocytosis (>1% schistocytes) were detected with excellent specificity (91.3%, CI: 82.0-96.7%) and sensitivity (100%, CI: 89.4-100.0%). We confirmed these results with a similar specificity (91.1%, CI: 78.8-97.5%) and sensitivity (100%, CI: 88.1-100.0%) on a validation cohort (n=74) analysed in an independent healthcare centre. Simultaneous analysis of 16 samples in both study centres showed a very good correlation between the 2 imaging flow cytometers (Y=1.001x). Interpretation We demonstrate that IFC can represent a reliable tool for operator-independent schistocyte quantification with no pre-analytical processing which is of most importance in emergency situations such as TMA. Funding None.
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16
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Malavolta M, Giacconi R, Piacenza F, Strizzi S, Cardelli M, Bigossi G, Marcozzi S, Tiano L, Marcheggiani F, Matacchione G, Giuliani A, Olivieri F, Crivellari I, Beltrami AP, Serra A, Demaria M, Provinciali M. Simple Detection of Unstained Live Senescent Cells with Imaging Flow Cytometry. Cells 2022; 11:cells11162506. [PMID: 36010584 PMCID: PMC9406876 DOI: 10.3390/cells11162506] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/20/2022] [Revised: 08/08/2022] [Accepted: 08/10/2022] [Indexed: 01/10/2023] Open
Abstract
Cellular senescence is a hallmark of aging and a promising target for therapeutic approaches. The identification of senescent cells requires multiple biomarkers and complex experimental procedures, resulting in increased variability and reduced sensitivity. Here, we propose a simple and broadly applicable imaging flow cytometry (IFC) method. This method is based on measuring autofluorescence and morphological parameters and on applying recent artificial intelligence (AI) and machine learning (ML) tools. We show that the results of this method are superior to those obtained measuring the classical senescence marker, senescence-associated beta-galactosidase (SA-β-Gal). We provide evidence that this method has the potential for diagnostic or prognostic applications as it was able to detect senescence in cardiac pericytes isolated from the hearts of patients affected by end-stage heart failure. We additionally demonstrate that it can be used to quantify senescence “in vivo” and can be used to evaluate the effects of senolytic compounds. We conclude that this method can be used as a simple and fast senescence assay independently of the origin of the cells and the procedure to induce senescence.
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Affiliation(s)
- Marco Malavolta
- Advanced Technology Center for Aging Research, IRCCS INRCA, 60121 Ancona, Italy
- Correspondence: ; Tel.: +39-0718004116
| | - Robertina Giacconi
- Advanced Technology Center for Aging Research, IRCCS INRCA, 60121 Ancona, Italy
| | - Francesco Piacenza
- Advanced Technology Center for Aging Research, IRCCS INRCA, 60121 Ancona, Italy
| | - Sergio Strizzi
- Advanced Technology Center for Aging Research, IRCCS INRCA, 60121 Ancona, Italy
| | - Maurizio Cardelli
- Advanced Technology Center for Aging Research, IRCCS INRCA, 60121 Ancona, Italy
| | - Giorgia Bigossi
- Advanced Technology Center for Aging Research, IRCCS INRCA, 60121 Ancona, Italy
| | - Serena Marcozzi
- Advanced Technology Center for Aging Research, IRCCS INRCA, 60121 Ancona, Italy
| | - Luca Tiano
- Department of Life and Environmental Sciences, Polytechnical University of Marche, 60121 Ancona, Italy
| | - Fabio Marcheggiani
- Department of Life and Environmental Sciences, Polytechnical University of Marche, 60121 Ancona, Italy
| | - Giulia Matacchione
- Department of Clinical and Molecular Sciences, DISCLIMO, Polytechnical University of Marche, 60121 Ancona, Italy
| | - Angelica Giuliani
- Department of Clinical and Molecular Sciences, DISCLIMO, Polytechnical University of Marche, 60121 Ancona, Italy
| | - Fabiola Olivieri
- Department of Clinical and Molecular Sciences, DISCLIMO, Polytechnical University of Marche, 60121 Ancona, Italy
- Center of Clinical Pathology and Innovative Therapy, IRCCS INRCA, 60121 Ancona, Italy
| | - Ilaria Crivellari
- Department of Medicine (DAME), University of Udine, 33100 Udine, Italy
| | | | - Alessandro Serra
- Luminex B.V., Het Zuiderkruis 1, 5215 MV ‘s-Hertogenbosch, The Netherlands
| | - Marco Demaria
- European Research Institute for the Biology of Ageing (ERIBA), University Medical Center Groningen (UMCG), 9713 AV Groningen, The Netherlands
| | - Mauro Provinciali
- Advanced Technology Center for Aging Research, IRCCS INRCA, 60121 Ancona, Italy
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17
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Testing Precision Limits of Neural Network-Based Quality Control Metrics in High-Throughput Digital Microscopy. Pharm Res 2022; 39:263-279. [DOI: 10.1007/s11095-021-03130-9] [Citation(s) in RCA: 2] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/27/2021] [Accepted: 10/18/2021] [Indexed: 10/19/2022]
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18
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Particles in Biopharmaceutical Formulations, Part 2: An Update on Analytical Techniques and Applications for Therapeutic Proteins, Viruses, Vaccines and Cells. J Pharm Sci 2021; 111:933-950. [PMID: 34919969 DOI: 10.1016/j.xphs.2021.12.011] [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: 12/07/2021] [Revised: 12/09/2021] [Accepted: 12/09/2021] [Indexed: 11/21/2022]
Abstract
Particles in biopharmaceutical formulations remain a hot topic in drug product development. With new product classes emerging it is crucial to discriminate particulate active pharmaceutical ingredients from particulate impurities. Technical improvements, new analytical developments and emerging tools (e.g., machine learning tools) increase the amount of information generated for particles. For a proper interpretation and judgment of the generated data a thorough understanding of the measurement principle, suitable application fields and potential limitations and pitfalls is required. Our review provides a comprehensive overview of novel particle analysis techniques emerging in the last decade for particulate impurities in therapeutic protein formulations (protein-related, excipient-related and primary packaging material-related), as well as particulate biopharmaceutical formulations (virus particles, virus-like particles, lipid nanoparticles and cell-based medicinal products). In addition, we review the literature on applications, describe specific analytical approaches and illustrate advantages and drawbacks of currently available techniques for particulate biopharmaceutical formulations.
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19
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Umar M, Krause N, Hawe A, Simmel F, Menzen T. Towards quantification and differentiation of protein aggregates and silicone oil droplets in the low micrometer and submicrometer size range by using oil-immersion flow imaging microscopy and convolutional neural networks. Eur J Pharm Biopharm 2021; 169:97-102. [PMID: 34597817 DOI: 10.1016/j.ejpb.2021.09.010] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.7] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/17/2021] [Revised: 08/09/2021] [Accepted: 09/23/2021] [Indexed: 11/18/2022]
Abstract
Biopharmaceutical product characterization benefits from the quantification and differentiation of unwanted protein aggregates and silicone oil droplets to support risk assessment and control strategies as part of the development. Flow imaging microscopy is successfully applied to differentiate the two impurities in the size range larger than about 5 µm based on their morphological appearance. In our study we applied the combination of oil-immersion flow imaging microscopy and convolutional neural networks to extend the size range below 5 µm. It allowed to differentiate and quantify heat stressed therapeutic monoclonal antibody aggregates from artificially generated silicone oil droplets with misclassification rates of about 10% in the size range between 0.3 and 5 µm. By comparing the misclassifications across the tested size range, particles in the low submicron size range were particularly difficult to differentiate as their morphological appearance becomes very similar.
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Affiliation(s)
- Muhammad Umar
- Coriolis Pharma Research GmbH, Fraunhoferstraße 18 b, 82152 Martinsried, Germany
| | - Nils Krause
- Coriolis Pharma Research GmbH, Fraunhoferstraße 18 b, 82152 Martinsried, Germany
| | - Andrea Hawe
- Coriolis Pharma Research GmbH, Fraunhoferstraße 18 b, 82152 Martinsried, Germany
| | - Friedrich Simmel
- Technical University of Munich, Physics Department, Am Coulombwall 4 a, 85748 Garching, Germany
| | - Tim Menzen
- Coriolis Pharma Research GmbH, Fraunhoferstraße 18 b, 82152 Martinsried, Germany.
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20
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Kleiber A, Kraus D, Henkel T, Fritzsche W. Review: tomographic imaging flow cytometry. LAB ON A CHIP 2021; 21:3655-3666. [PMID: 34514484 DOI: 10.1039/d1lc00533b] [Citation(s) in RCA: 19] [Impact Index Per Article: 6.3] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 06/13/2023]
Abstract
Within the last decades, conventional flow cytometry (FC) has evolved as a powerful measurement method in clinical diagnostics, biology, life sciences and healthcare. Imaging flow cytometry (IFC) extends the power of traditional FC by adding high resolution optical and spectroscopic information. However, the conventional IFC only provides a 2D projection of a 3D object. To overcome this limitation, tomographic imaging flow cytometry (tIFC) was developed to access 3D information about the target particles. The goal of tIFC is to visualize surfaces and internal structures in a holistic way. This review article gives an overview of the past and current developments in tIFC.
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Affiliation(s)
- Andreas Kleiber
- Leibniz Institute of Photonic Technology, Albert-Einstein-Straße 9, D-07745 Jena, Germany
| | - Daniel Kraus
- Leibniz Institute of Photonic Technology, Albert-Einstein-Straße 9, D-07745 Jena, Germany
| | - Thomas Henkel
- Leibniz Institute of Photonic Technology, Albert-Einstein-Straße 9, D-07745 Jena, Germany
| | - Wolfgang Fritzsche
- Leibniz Institute of Photonic Technology, Albert-Einstein-Straße 9, D-07745 Jena, Germany
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21
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Stanley J, Hui H, Erber W, Clynick B, Fuller K. Analysis of human chromosomes by imaging flow cytometry. CYTOMETRY PART B-CLINICAL CYTOMETRY 2021; 100:541-553. [PMID: 34033226 DOI: 10.1002/cyto.b.22023] [Citation(s) in RCA: 5] [Impact Index Per Article: 1.7] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Subscribe] [Scholar Register] [Received: 12/29/2020] [Revised: 04/18/2021] [Accepted: 05/14/2021] [Indexed: 12/29/2022]
Abstract
Chromosomal analysis is traditionally performed by karyotyping on metaphase spreads, or by fluorescent in situ hybridization (FISH) on interphase cells or metaphase spreads. Flow cytometry was introduced as a new method to analyze chromosomes number (ploidy) and structure (telomere length) in the 1970s with data interpretation largely based on fluorescence intensity. This technology has had little uptake for human cytogenetic applications primarily due to analytical challenges. The introduction of imaging flow cytometry, with the addition of digital images to standard multi-parametric flow cytometry quantitative tools, has added a new dimension. The ability to visualize the chromosomes and FISH signals overcomes the inherent difficulties when the data is restricted to fluorescence intensity. This field is now moving forward with methods being developed to assess chromosome number and structure in whole cells (normal and malignant) in suspension. A recent advance has been the inclusion of immunophenotyping such that antigen expression can be used to identify specific cells of interest for specific chromosomes and their abnormalities. This capability has been illustrated in blood cancers, such as chronic lymphocytic leukemia and plasma cell myeloma. The high sensitivity and specificity achievable highlights the potential imaging flow cytometry has for cytogenomic applications (i.e., diagnosis and disease monitoring). This review introduces and describes the development, current status, and applications of imaging flow cytometry for chromosomal analysis of human chromosomes.
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Affiliation(s)
- Jason Stanley
- Translational Cancer Pathology Laboratory, School of Biomedical Sciences, The University of Western Australia, Crawley, Western Australia, Australia
| | - Henry Hui
- Translational Cancer Pathology Laboratory, School of Biomedical Sciences, The University of Western Australia, Crawley, Western Australia, Australia
| | - Wendy Erber
- Translational Cancer Pathology Laboratory, School of Biomedical Sciences, The University of Western Australia, Crawley, Western Australia, Australia.,PathWest Laboratory Medicine, Nedlands, Western Australia, Australia
| | - Britt Clynick
- Institute for Respiratory Health, Harry Perkins Institute of Medical Research, Nedlands, Western Australia, Australia
| | - Kathy Fuller
- Translational Cancer Pathology Laboratory, School of Biomedical Sciences, The University of Western Australia, Crawley, Western Australia, Australia
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22
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Rodrigues MA, Probst CE, Zayats A, Davidson B, Riedel M, Li Y, Venkatachalam V. The in vitro micronucleus assay using imaging flow cytometry and deep learning. NPJ Syst Biol Appl 2021; 7:20. [PMID: 34006858 PMCID: PMC8131758 DOI: 10.1038/s41540-021-00179-5] [Citation(s) in RCA: 20] [Impact Index Per Article: 6.7] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/02/2020] [Accepted: 02/16/2021] [Indexed: 02/07/2023] Open
Abstract
The in vitro micronucleus (MN) assay is a well-established assay for quantification of DNA damage, and is required by regulatory bodies worldwide to screen chemicals for genetic toxicity. The MN assay is performed in two variations: scoring MN in cytokinesis-blocked binucleated cells or directly in unblocked mononucleated cells. Several methods have been developed to score the MN assay, including manual and automated microscopy, and conventional flow cytometry, each with advantages and limitations. Previously, we applied imaging flow cytometry (IFC) using the ImageStream® to develop a rapid and automated MN assay based on high throughput image capture and feature-based image analysis in the IDEAS® software. However, the analysis strategy required rigorous optimization across chemicals and cell lines. To overcome the complexity and rigidity of feature-based image analysis, in this study we used the Amnis® AI software to develop a deep-learning method based on convolutional neural networks to score IFC data in both the cytokinesis-blocked and unblocked versions of the MN assay. We show that the use of the Amnis AI software to score imagery acquired using the ImageStream® compares well to manual microscopy and outperforms IDEAS® feature-based analysis, facilitating full automation of the MN assay.
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Affiliation(s)
| | | | - Artiom Zayats
- Amnis Flow Cytometry, Luminex Corporation, Seattle, WA, USA
| | - Bryan Davidson
- Amnis Flow Cytometry, Luminex Corporation, Seattle, WA, USA
| | - Michael Riedel
- Amnis Flow Cytometry, Luminex Corporation, Seattle, WA, USA
| | - Yang Li
- Amnis Flow Cytometry, Luminex Corporation, Seattle, WA, USA
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23
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Kiyoshi M, Tada M, Shibata H, Aoyama M, Ishii-Watabe A. Characterization of Aggregated Antibody-Silicone Oil Complexes: From Perspectives of Morphology, 3D Image, and Fcγ Receptor Activation. J Pharm Sci 2020; 110:1189-1196. [PMID: 33069712 DOI: 10.1016/j.xphs.2020.10.022] [Citation(s) in RCA: 4] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/07/2020] [Revised: 10/06/2020] [Accepted: 10/07/2020] [Indexed: 11/30/2022]
Abstract
Pre-filled syringes (PFS) have been in widespread use as an administration device for therapeutic antibodies in recent decades. Generally, the inner barrel and syringe of PFS are coated with silicone oil (SO) for lubrication. Multiple studies have focused on the fact that the SO adsorbs denatured antibody molecules, and induces antibody aggregation. Aggregated antibodies are recognized as a potential risk for evoking immunogenic responses in patients. The characteristics of the aggregated antibody-SO complexes, including their concentration, population, shape, three-dimensional (3D) image, and Fcγ Receptors (FcγRs) activation have been obscurely acknowledged so far. In the present work, we prepared aggregated antibody-SO complexes by agitation and analyzed using multifaceted techniques such as flow imaging, confocal fluorescence microscopy, and cell-based assays for FcγRs activation. The results emphasized that the SO accelerates the increase in sub-visible particles and antibody aggregation. The confocal fluorescence microscopy analysis revealed the high-resolution 3D images of aggregated antibody-SO complexes. The FcγRs reporter cell assay clarified that the pre-mixed and agitated Ab + SO have higher FcγRs activation capability compared to the agitated Ab. Overall, this study advances the view that SO has an effect to increase the risk of agitation-induced aggregated antibody particles.
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Affiliation(s)
- Masato Kiyoshi
- Division of Biological Chemistry and Biologicals, National Institute of Health Sciences, Kawasaki, Kanagawa 210-9501, Japan.
| | - Minoru Tada
- Division of Biological Chemistry and Biologicals, National Institute of Health Sciences, Kawasaki, Kanagawa 210-9501, Japan
| | - Hiroko Shibata
- Division of Biological Chemistry and Biologicals, National Institute of Health Sciences, Kawasaki, Kanagawa 210-9501, Japan
| | - Michihiko Aoyama
- Division of Biological Chemistry and Biologicals, National Institute of Health Sciences, Kawasaki, Kanagawa 210-9501, Japan
| | - Akiko Ishii-Watabe
- Division of Biological Chemistry and Biologicals, National Institute of Health Sciences, Kawasaki, Kanagawa 210-9501, Japan
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