Particulate impurities in cell-based medicinal products traced by flow imaging microscopy combined with deep learning for image analysis

Label-free ghost cytometry for manufacturing of cell therapy products

Automation and quality control (QC) are critical in manufacturing safe and effective cell and gene therapy products. However, current QC methods, reliant on molecular staining, pose difficulty in in-line testing and can increase manufacturing costs. Here we demonstrate the potential of using label-free ghost cytometry (LF-GC), a machine learning-driven, multidimensional, high-content, and high-throughput flow cytometry approach, in various stages of the cell therapy manufacturing processes. LF-GC accurately quantified cell count and viability of human peripheral blood mononuclear cells (PBMCs) and identified non-apoptotic live cells and early apoptotic/dead cells in PBMCs (ROC-AUC: area under receiver operating characteristic curve = 0.975), T cells and non-T cells in white blood cells (ROC-AUC = 0.969), activated T cells and quiescent T cells in PBMCs (ROC-AUC = 0.990), and particulate impurities in PBMCs (ROC-AUC ≧ 0.998). The results support that LF-GC is a non-destructive label-free cell analytical method that can be used to monitor cell numbers, assess viability, identify specific cell subsets or phenotypic states, and remove impurities during cell therapy manufacturing. Thus, LF-GC holds the potential to enable full automation in the manufacturing of cell therapy products with reduced cost and increased efficiency.

Stain-Free Approach to Determine and Monitor Cell Heath Using Supervised and Unsupervised Image-Based Deep Learning

Cell-based medicinal products (CBMPs) are a growing class of therapeutics that promise new treatments for complex and rare diseases. Given the inherent complexity of the whole human cells comprising CBMPs, there is a need for robust and fast analytical methods for characterization, process monitoring, and quality control (QC) testing during their manufacture. Existing techniques to evaluate and monitor cell quality typically constitute labor-intensive, expensive, and highly specific staining assays. In this work, we combine image-based deep learning with flow imaging microscopy (FIM) to predict cell health metrics using cellular morphology "fingerprints" extracted from images of unstained Jurkat cells (immortalized human T-lymphocyte cells). A supervised (i.e., algorithm trained with human-generated labels for images) fingerprinting algorithm, trained on images of unstained healthy and dead cells, provides a robust stain-free, non-invasive, and non-destructive method for determining cell viability. Results from the stain-free method are in good agreement with traditional stain-based cytometric viability measurements. Additionally, when trained with images of healthy cells, dead cells and cells undergoing chemically induced apoptosis, the supervised fingerprinting algorithm is able to distinguish between the three cell states, and the results are independent of specific treatments or signaling pathways. We then show that an unsupervised variational autoencoder (VAE) algorithm trained on the same images, but without human-generated labels, is able to distinguish between samples of healthy, dead and apoptotic cells along with cellular debris based on learned morphological features and without human input. With this, we demonstrate that VAEs are a powerful exploratory technique that can be used as a process monitoring analytical tool.

Osmotic properties of T cells determined by flow imaging microscopy in comparison to electrical sensing zone analysis

To develop cryopreservation methods for cell-based medicinal products it is important to understand osmotic responses of cells upon immersion into solutions with cryoprotective agents (CPAs) and during freezing. The aim of this study was to assess the osmotic response of T cells by using flow imaging microscopy (FIM) as a novel cell-sizing technique, and to corroborate the findings with electrical impedance measurements conducted on a Coulter counter. Jurkat cells were used as a potential model cell line for primary T cells. Cell volume responses were used to derive important cell parameters for cryopreservation such as the osmotically inactive cell volume Vb and the membrane permeability towards water and various CPAs. Unlike Coulter counter measurement, FIM, combined with Trypan blue staining can differentiate between viable and dead cells, which yields a more accurate estimation of Vb. Membrane permeabilities to water, dimethyl sulfoxide (Me2SO) and glycerol were measured for Jurkat cells at different temperatures. The permeation of Me2SO into the cells was faster in comparison to glycerol. CPA permeation decreased with decreasing temperature following Arrhenius behavior. Moreover, membrane permeability to water decreased in the presence of CPAs. Vb of Jurkat cells was found to be 49% of the isotonic volume and comparable to that of primary T cells. FIM proved to be a valuable tool to determine the membrane permeability parameters of mammalian cells to water and cryoprotective agents, which in turn can be used to rationally design CPA loading procedures for cryopreservation.

Comparison of cell viability methods for human mesenchymal/stromal stem cells and human A549 lung carcinoma cells after freeze-thaw stress

For the safety and efficacy of frozen cell therapy products, determination of cellular viability is key. However, results of cell viability measurements do not only depend on the cell line or on the inflicted stress, but also on the assay used, making inter-experimental comparisons difficult. The aim of this study was thus to assess commonly used viability assays in clinically relevant human mesenchymal/stromal stem cells and human A549 lung carcinoma cells. Post freeze-thaw stress viability and proliferation were evaluated under different conditions using trypan blue, acridine orange/DAPI stain, alamarBlue, ATP, and neutral red assays. Significant differences in cell viability between metabolic assays were observed, likely due to their distinct intrinsic detection mechanisms. Membrane-integrity based assays generally overestimated cell viabilities in this study. Furthermore, noticeable differences in inter-assay sensitivities were observed. These differences highlight that cell viability methods should be meticulously selected and their associated results carefully interpreted in a relevant context to ensure reliable conclusions. Indeed, although cell membrane integrity based assays are a popular choice to determine cellular quality attributes after freezing and thawing, we demonstrate that metabolic assays may be more suitable in this context.

Screening techniques for monitoring the sub-visible particle formation of free fatty acids in biopharmaceuticals

Free fatty acid (FFA) particles that originate from the enzymatic hydrolysis of polysorbate (PS) via co-purified host cell proteins generally appear abruptly in drug products during real-time (long-term) storage. Efforts were taken to understand the kinetics of FFA particle formation, aiming for a mitigation strategy. However, it is rather challenging particularly in the sub-visible particle (SVP) range, due to either the insufficient sensitivity of the analytical techniques used or the interference of the formulation matrices of proteinaceous drug products. In this study, we examined the feasibility of Raman microscopy, backgrounded membrane imaging (BMI) and total holographic characterization (THC) on the detection of FFA sub-visible particles (SVPs). The results indicate that THC is the most sensitive technique to track their occurrence during the course of PS hydrolysis. Moreover, with this technique we are able to distinguish different stages of FFA particle formation in the medium. In addition, a real time stability study of a biopharmaceutical was analyzed, demonstrating the viability of THC to monitor SVPs in a real sample and correlate it to the visible particles (VPs) occurrence.

Novel machine learning models for flow imaging microscopy sub-visible particle classification in protein formulations

Understanding the particulate content of formulated drug products is essential for ensuring patient safety. In particular, it is critical to assess the presence of aggregated proteins or extraneous particles (e.g. fibres) that pose potential dangers. Additionally, it is useful to be able to distinguish non-proteinaceous particles, such as silicone oil droplets that commonly occur in formulations stored in pre-filled syringes. Standard particle counting methods (e.g. light obscuration) provide only total numbers of particles of a given size, but provide no mechanism for particle classification. Significant recent work has focused on the use of flow imaging microscopy to enable simultaneous classification and counting of particles using machine learning (ML) models including convolutional neural networks (CNN). In this paper we expand upon this theme by exploring techniques for achieving high prediction accuracy when the size of the labeled dataset used for model training is limited. We demonstrate that maximum performance can be achieved by combining multiple techniques such as data augmentation, transfer learning, and novel (to this field) models combining imaging and tabular data.

Cryoprotection in Human Mesenchymal Stromal/Stem Cells: Synergistic Impact of Urea and Glucose

Standard freezing protocols of clinically relevant cell lines commonly employ agents such as fetal bovine serum and dimethyl sulfoxide, which are a potential concern from both a regulatory and a patient safety perspective. The aim of this work was to develop formulations with safe and well tolerated excipients for the (cryo-) preservation of cell therapy products. We evaluated the cryoprotective capabilities of urea and glucose through measurements of cell metabolic activity. Freezing of clinically relevant human mesenchymal stromal/stem cells and human dermal fibroblasts at ≤ - 65°C at equimolar ratios of urea and glucose resulted in comparable viabilities to established dimethyl sulfoxide. Pre-incubation of human mesenchymal stromal/stem cells in trehalose and addition of mannitol and sucrose to the formulation further enhanced cell viability after freeze-thaw stress. Other cell types assessed (A549 and SK-N-AS) could not satisfactorily be preserved with urea and glucose, highlighting the need for tailored formulations to sustain acceptable cryopreservation.

Living Sample Viability Measurement Methods from Traditional Assays to Nanomotion

Living sample viability measurement is an extremely common process in medical, pharmaceutical, and biological fields, especially drug pharmacology and toxicology detection. Nowadays, there are a number of chemical, optical, and mechanical methods that have been developed in response to the growing demand for simple, rapid, accurate, and reliable real-time living sample viability assessment. In parallel, the development trend of viability measurement methods (VMMs) has increasingly shifted from traditional assays towards the innovative atomic force microscope (AFM) oscillating sensor method (referred to as nanomotion), which takes advantage of the adhesion of living samples to an oscillating surface. Herein, we provide a comprehensive review of the common VMMs, laying emphasis on their benefits and drawbacks, as well as evaluating the potential utility of VMMs. In addition, we discuss the nanomotion technique, focusing on its applications, sample attachment protocols, and result display methods. Furthermore, the challenges and future perspectives on nanomotion are commented on, mainly emphasizing scientific restrictions and development orientations.

Combining Machine Learning and Backgrounded Membrane Imaging: A Case Study in Comparing and Classifying Different Types of Biopharmaceutically Relevant Particles

This study investigates how backgrounded membrane imaging (BMI) can be used in combination with convolutional neural networks (CNNs) in order to quantitatively and qualitatively study subvisible particles in both protein biopharmaceuticals and samples containing synthetic model particles. BMI requires low sample volumes and avoids many technical complications associated with imaging particles in solution, e.g., air bubble interference, low refractive index contrast between solution and particles of interest, etc. Hence, BMI is an attractive technique for characterizing particles at various stages of drug product development. However, to date, the morphological information encoded in brightfield BMI images has scarcely been utilized. Here we show that CNN based methods can be useful in extracting morphological information from (label-free) brightfield BMI particle images. Images of particles from biopharmaceutical products and from laboratory prepared samples were analyzed with two types of CNN based approaches: traditional supervised classifiers and a recently proposed fingerprinting analysis method. We demonstrate that the CNN based methods are able to efficiently leverage BMI data to distinguish between particles comprised of different proteins, various fatty acids (representing polysorbate degradation related particles), and protein surrogates (NIST ETFE reference material) only based on BMI images. The utility of using the fingerprinting method for comparing morphological differences and similarities of particles formed in distinct drug products and/or laboratory prepared samples is further demonstrated and discussed through three case studies.

Report on Webinar Series Cell and Gene Therapy: From Concept to Clinical Use

With the launch of the UK Academy of Pharmaceutical Sciences Advanced Therapy Medicinal Products Focus Group in late 2020, a webinar series reviewing the current and emerging trends in cell and gene therapy was held virtually in May 2021. This webinar series was timely given the recent withdrawal of the United Kingdom from the European Union and the global COVID-19 pandemic impacting all sectors of the pharmaceutical sciences research landscape globally and in the UK. Delegates from the academic, industry, regulatory and NHS sectors attended the session where challenges and opportunities in the development and clinical implementation of cell and gene therapies were discussed. Globally, the cell and gene therapy market has reached a value of 4.3 billion dollars in 2020, having increased at a compound annual growth rate of 25.5% since 2015. This webinar series captured all the major developments in this rapidly evolving area and highlighted emerging concepts warranting cross-sector efforts from across the community in the future.

Particles in Biopharmaceutical Formulations, Part 2: An Update on Analytical Techniques and Applications for Therapeutic Proteins, Viruses, Vaccines and Cells

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.

Formulation Considerations for Autologous T Cell Drug Products

Genetically modified autologous T cells have become an established immunotherapy in the fight against cancer. The manufacture of chimeric antigen receptor (CAR) and αβ-T cell receptor (TCR) transduced T cells poses unique challenges, including the formulation, cryopreservation and fill-finish steps, which are the focus of this review. With an increasing number of marketing approvals for CAR-T cell therapies, comparison of their formulation design and presentation for administration can be made. These differences will be discussed alongside the emergence of automated formulation and fill-finish processes, the formulation design space, Monte Carlo simulation applied to risk analysis, primary container selection, freezing profiles and thaw and the use of dimethyl sulfoxide and alternative solvents/excipients as cryopreservation agents. The review will conclude with a discussion of the pharmaceutical solutions required to meet the simplification of manufacture and flexibility in dosage form for clinical treatment.