Novel analytical strategies and tools for cell therapy, gene therapy
and gene-edited cell therapy
By Dr Catarina Carrao
Novel analytical strategies and tools for cell therapy, gene therapy and gene-edited cell therapy
The roadmap for non-traditional biologics, such as cell therapies, gene therapies and gene-edited cell therapies, is sometimes vague and can lead to many challenges1.
There is an expectation for orthogonal analytical techniques to be applied to characterization, stability and release testing2; with all of these tasks sometimes done with small lot sizes, patient sample variability, and short timelines for release1.
Viral vector purity is a major consideration in the development of gene/gene-edited cell therapies to evaluate safety and efficiency concerns. Robust analytical techniques are required to assess purity by determining the presence of full versus empty capsid populations in viral vectors3. Empty capsids present a source of unnecessary, potentially antigenic material, possibly triggering anti-capsid-viruses immune responses4.
Cryo-TEM (Transmission Electron Microscopy) is an efficient technique to determine viral vector purity through accurate quantification of full versus empty capsid populations in Adeno-Associated Viruses (AAV)3, which are the most commonly used type of viral vector applied in gene therapies in clinical trials to date5. Cryo-TEM lets specimens be observed in their native environment albeit embedded in vitreous ice (temperature below -160°C using liquid nitrogen), allowing for the observation of structures at nearly atomic level with limited structural degradation3. Image density assessment, at the center of each capsid with Radius Image Analysis Software, evaluates the proportion of packed-DNA and empty capsid populations6. This approach is highly accurate, with only a small sample volume required (<5μL), providing information on morphology (capsid size/structure), and also the presence of aggregation3.
Developing and validating analytical assays to assess vector productivity, purity, biological activity and safety are essential from a regulatory perspective to understand the Critical Quality Attributes (CQAs), which impact product safety and potency. CQAs of naked DNA vectors are integrity and stability, which need Polymerase Chain-Reaction (PCR) for plasmid identity, together with techniques such as Capillary Gel Electrophoresis (CGE) to monitor degradation; and, Cell-Based Potency Assays to confirm stability2.
Due to the complexity of cell and gene therapy products, a combination of multiple methods is needed to adequately define potency during development. Certain assays may be needed to control process changes, whereas others are more suitable for release testing7. A suitable potency assay should be in place when material is produced for the first clinical trial, and it should be validated prior to phase III clinical trials8.
Potency of cell-based immunotherapy products can be measured with in vivo and in vitro test systems. The development of an adequate biological in vivo potency assay may be hampered by the lack of a relevant animal model, due to the inherent immunological differences between man and animals; and, very often suffer from wide inherent biological viability. Nevertheless, the in vivo assay, might be useful as a tool for product characterization. For example, animals which are transgenic for human major histocompatibility antigens can be used to present human antigens to the immune system of these animals. Also, immuno-compromised animals (e.g., athymic mice) might be used to determine the functional response of adoptively transferred human T-cells as the measurement of potency8.
In vitro assays are preferred for a routine basis analysis, i.e. for monitoring product consistency in batch release testing. Measurable biological activities are, for example, in vitro lysis of target cells by tumor-specific (CD8) T-cells; in-vitro cytokine production by specific cells, e.g. lymphocytes in response to the product; and, co-stimulatory capacity of dendritic cells (DCs). Where a direct measure of potency is not possible, surrogates for potency may be developed to quantify biological activity of the test sample, provided that a correlation between the surrogate and the defined biological activity has been demonstrated. Surrogate analysis may include determination of cell surface markers, activation markers, secretion of factors, expression of a single gene product, or protein expression pattern. If the mechanism of action of the medicinal product can be clearly related to specific antigens (i.e., tumor-specific antigens, tumor-associated antigens), the potency assay could be based on quantification of these antigens by suitable methods (e.g. flow cytometry analysis)8.
Flow cytometry to classify the phenotype of cell therapy products
Chimeric antigen receptor T-cells (CAR-T) have emerged as an extremely promising therapy9; and, central memory and stem cell-like memory T-cell phenotypes are associated with a more sustained proliferative response, and long-term CAR-T persistence10. There is an unmet need for standardized methods and reagents to reliably profile the memory phenotype of CAR-Ts, to better evaluate product quality, and support improvements in CAR-T manufacturing. The use of a standardized memory T-cell panel to evaluate how the T-cell phenotype impacts on the efficacy and longevity of response in patients receiving CAR-T therapies, could more accurately classify the phenotype of these cell therapy products. Recently, Scarfò and colleagues9 from Massachusetts General Hospital in Boston, have validated a standardized flow cytometry T-cell panel containing a pre-validated mixture of antibodies for the identification of naïve, stem cell memory, central memory and effector memory CD4+ and CD8+ T-cell subsets. With the addition of lasers, more detectors, better signal processing, and high parameter applications, flow cytometry continues to develop increasing capabilities, which are moving out of specialty labs and into common practice.
Autologous cellular products, produced as patient-specific lots, present additional constraints in their analytical roadmap due to the limited amounts of material (e.g., for retains and testing), and the short time available for analysis. Rapid sterility testing is fundamental in these therapies, and the need for a complete RMM (Rapid Microbiology Methods) validation solution to reduce the time between sampling and patient treatment is essential. Recently, an automated technology has been developed to assess the sterility of these therapies prior to transfusion. The BacT/ALERT Dual-T system has now been recognized as compendial, and can be used in lieu of the traditional 14-day sterility test to assess the sterility of cell-based products as defined in the EP Chapter 2.6.27 "Microbiological Examination Of Cell-Based Preparations"11. This growth-based system can detect within 24-72 hours an extremely large range of organisms, such as aerobic, anaerobic, facultative microorganisms, yeast and fungi12. The dual-temperature feature of the system (microbial detection at both 32.5°C and 22.5°C) guarantees a complete coverage of growth conditions – and is compliant with the different pharmacopoeias. Also, to ensure accurate results’ traceability, a 21-Code of Federal Regulations (CFR) Part 11-compliant data management system is also provided12.
New technologies for cell enumeration & viability
One of the fundamental challenges for manufacturers of cell therapy products is the counting of viable cells for manufacturing and dosing purposes. The National Institute of Standards and Technology (NIST) has developed an approach to evaluate fundamental aspects of the quality of cell counting methods, using a dilution series experimental design to evaluate precision and proportionality of cell counting measurements13. This design allows developers and manufacturers to harmonize methods and facilitate interoperability of counting methods across research, development, and production phases of the industry14.
Many different analytical methods are used to evaluate cell viability, yet a growing body of evidence suggests that there can be a very poor correlation between viability measurements and the biological functions of cell products. Leo Chan (Nexcelom Inc.), Joe Chalfoun and Adele Penske (NIST Information Technology Laboratory) are developing an approach to benchmark image quality in automated trypan-blue dye exclusion-based viability assays, to assure consistency in data analysis and reduce variability in cell viability results15. This approach is based on the incorporation of a small number of beads into a cell counting sample, where the beads serve as a convenient tool for monitoring characteristics of the image acquisition process for quantitative optical microscopy15. This is particularly useful in automated image-based count/viability measurements, where cells are counted in a counting chamber.
As mRNA therapies have been considered as gene therapies in clinical trials by the Federal Drug Administration (FDA) and European Medicines Agency (EMA), guidelines relating to these products can also be applied to mRNA drugs16. GMP validated analytical methods for quality control include measuring the size or molecular weight of the mRNA, as well as spectroscopic profile and chromatographic profiles, for example, by using Reverse Phase Liquid Chromatography with Mass Spectrometry (RPLC-MS) and CGE2.
Specification tests for a mRNA-drug substance can assess batch-to-batch manufacturing process repeatability, and the quality of mRNA produced that will ensure patient safety. An important mRNA specific test is one that measures mRNA integrity, in order to assess the percentage of intact mRNA, as this will have a direct effect on the potency of the drug17. This can be achieved through several different chromatographic approaches; including Size Exclusion Chromatography, Ion Pair Reverse Phase High-Pressure Liquid Chromatography (IPRP-HPLC) or CGE, with mRNA integrity being reported as a percentage area from the chromatogram obtained17,18. This type of analysis also demonstrates the stability of the product. It is also important to confirm the identity of the mRNA and corresponding sequence, to ensure a correct protein expression in vivo. This can be achieved with Next Generation Sequencing (NGS), confirming the sequence of the mRNA and any potential variants that could be present above 1%17,19. Method design can vary, and is dependent on the properties of the specific mRNA, such as the size and sequence. Capping efficiency and properties of the Poly-(A) tail may be assessed using chromatographic approaches coupled to Ultra-Violet (UV) or Mass Spectrometry (MS) detection17. Potency analysis can be performed using an in vitro translation assay or a cell-based assay; and, could also be correlated to other attributes such as the capping efficiency or Poly-(A) tail length variations, as these are likely to affect potency17.
Tests for mRNA integrity and impurities, such as double-stranded RNA (dsRNA), DNA template, nucleoside triphosphates, and RNA polymerase, ensure purity of the product, which are essential to translation efficiency and immunogenicity. Tests for specific impurities, for example dsRNA, can be completed using the Dot-Blot Assay which uses a dsRNA specific antibody to detect these types of impurity17.
Encapsulation of mRNA inside Lipid Nanoparticles (LNPs) provides stability, delivery and safety advantages17. The FDA liposome drug product guidance for industry, which includes CMC guidance, describes the CQAs specific to liposomal technology products including physiochemical properties such as particle size, size distribution, encapsulation efficiency and release of the drug from the liposome20. State-of-the-art technologies such as Ultra-Performance Liquid Chromatography (UPLC) coupled to a charged aerosol detector, Dynamic Light Scattering (DLS) or microscopy using CryoTEM to support characterization studies for LNPs, are all methods that can be used to ensure quality control17. Encapsulation efficiency can be determined using a RiboGreen® Assay, one of the most sensitive detection dyes with linear fluorescence detection in the range of 1-200 ng21.
One factor that remains a fundamental concern with gene-edited cell therapies, especially when multiplex approaches are adopted, is the potential for off-target effects as a result of nuclease activity at unintended homologous sites; and, any downstream consequences arising from such off-target activity22.
Currently, a growing range of strategies to address off-target activity by assessing secondary target sites exist, including SELEX (Systematic Evolution of Ligand by Exponential Enrichment)23, Digenome-seq (in vitro Cas-9-digested whole-genome sequencing)24, GUIDE-seq (Genome-wide Unbiased Identification of DSBs Enabled by Sequencing)25, CIRCLE-seq (Circularization for In vitro Reporting of Cleavage Effects by Sequencing)26, and DISCOVER-seq (Discovery of In Situ Cas Off-targets and Verification by Sequencing)27.
Mutagenesis levels within cells at the identified sites are preferentially examined by these deep sequencing and targeted PCR approaches, as opposed to Whole Genome Sequencing (WGS), which lacks adequate sequencing depth to detect low frequency mutations in bulk populations of cells22.
Machine Learning & Artificial Intelligence (AI)
Improved in silico approaches to predict genome wide off-target activity are set to continue, alongside the development of machine-learning methods which benefit from the increasing availability of large-scale genome-editing activity datasets22. Also, intelligent machines could dissect the whole genome and isolate the immune particularities of individual patient’s disease in a matter of minutes, and create a treatment that is customized to the patient’s genetic specificity and immune system capability28.
Researchers at the National Eye Institute (NEI) and NIST, recently used AI successfully to evaluate stem cell-derived “patches” of retinal pigment epithelium tissue for implanting into the eyes of patients with age-related macular degeneration (AMD) - a leading cause of blindness29,30. Schaub and colleagues developed a robust characterization methodology composed of Quantitative Bright-field Absorbance Microscopy (QBAM) and Deep Neural Networks (DNNs) to non-invasively predict tissue function and cellular donor identity30. The methodology was validated using clinical-grade induced Pluripotent Stem Cell-derived Retinal Pigment Epithelial cells (iPSC-RPE). QBAM images of iPSC-RPE were used to train DNNs, that were able to predict monolayer cell resistance, polarized Vascular Endothelial Growth Factor (VEGF) secretion; and, also matched stem cell monolayers to the stem cell donors30.
AI-based methods of validating stem cell-derived tissues are a significant improvement over conventional assays, which are low-yield, expensive, and require a trained user29. Once segmented, hundreds of features can be calculated per cell; and, using these features, cell function can be predicted, outlier samples can be identified, and donor identity can be confirmed. All of this information can be obtained on the tissue that is being implanted into the patient in just minutes30.
The potential application in a biomanufacturing setting of AI, where thousands of manufactured cell units could be non-invasively tested and qualified for clinical use by a technician, is tremendous; and, will revolutionize analytics in non-traditional biologics.
- Maribel Rios, A. R. M. Analytical Testing Strategies for CAR T-Cell Products. BioProcess International (2019).
- Intertek. Gene Therapy Characterisation and Release Testing. https://www.intertek.com/pharmaceutical/biopharmaceuticals/gene-therapy-characterisation/ (2020).
- Ponzi, J. Determining the proportion of DNA-packed and empty capsid populations by Cryo-TEM (Transmission Electron Microscopy) in Adeno-associated viruses. Intertek (2020).
- FDA. in 2008-D-0205 (ed Center for Biologics Evaluation and Research) (Federal Drug Administration, https://www.fda.gov/regulatory-information/search-fda-guidance-documents/chemistry-manufacturing-and-control-cmc-information-human-gene-therapy-investigational-new-drug, 2020).
- Ginn, S. L., Amaya, A. K., Alexander, I. E., Edelstein, M. & Abedi, M. R. Gene therapy clinical trials worldwide to 2017: An update. The Journal of Gene Medicine 20, e3015, doi:10.1002/jgm.3015 (2018).
- Emsis. RADIUS - The EM Imaging Software. (2020).
- Reavie, L. What you should know about potency assays: Development Strategy, Potency Assay, 2020).
- EMA. in EMA/CHMP/BWP/271475/2006 rev.1 (ed European Medicines Agency) (https://www.ema.europa.eu/en/documents/scientific-guideline/guideline-potency-testing-cell-based-immunotherapy-medicinal-products-treatment-cancer-revision-1_en.pdf, 2016).
- Scarfò, I. et al. Application of a Standardized Flow Cytometry Panel for Defining and Monitoring the Immunophenotype of CAR-T Cells. Blood 134, 5626-5626, doi:10.1182/blood-2019-128745 (2019).
- Fraietta, J. A. et al. Determinants of response and resistance to CD19 chimeric antigen receptor (CAR) T cell therapy of chronic lymphocytic leukemia. Nature Medicine 24, 563-571, doi:10.1038/s41591-018-0010-1 (2018).
- Paris, A. A modern compendial automated and rapid sterility testing technology for cell and gene therapy products. Cytotherapy 22, S167, doi:10.1016/j.jcyt.2020.03.351 (2020).
- bioMérieux. BACT/ALERT® 3D Dual-T, 2020).
- Sarkar, S. et al. Evaluating the quality of a cell counting measurement process via a dilution series experimental design. Cytotherapy 19, 1509-1521, doi:10.1016/j.jcyt.2017.08.014 (2017).
- Sumona Sarkar, L. P., Sheng Lin-Gibson & Steven P Lund. Standards Landscape in Cell Counting: Implications for Cell & Gene Therapy. Cell & Gene Therapy Insights 5(1), doi:10.18609/cgti.2019.016 (2019).
- NIST. Cell Counting for Cell Therapies, 2020).
- CBER. (ed FDA) (Center for Biologics Evaluation and Research, Federal Drug Administration, https://www.fda.gov/vaccines-blood-biologics/cellular-gene-therapy-products, 2020).
- Riley, J.-A. CMC considerations for mRNA-based therapeutics analytical approaches to characterize critical quality attributes. Intertek (2019).
- Godler, D. E. et al. Improved Methodology for Assessment of mRNA Levels in Blood of Patients with FMR1 Related Disorders. BMC Clinical Pathology 9, 5, doi:10.1186/1472-6890-9-5 (2009).
- Qin, D. Next-generation sequencing and its clinical application. Cancer Biol Med 16, 4-10, doi:10.20892/j.issn.2095-3941.2018.0055 (2019).
- CDER. (ed FDA-2016-D-2817) (Center for Drug Evaluation and Research, FDA, https://www.fda.gov/regulatory-information/search-fda-guidance-documents/liposome-drug-products-chemistry-manufacturing-and-controls-human-pharmacokinetics-and, 2018).
- ThermoFisherScientific. Quant-iT™ RiboGreen™ RNA Assay Kit, 2020).
- Ashmore-Harris, C. & Fruhwirth, G. O. The clinical potential of gene editing as a tool to engineer cell-based therapeutics. Clinical and Translational Medicine 9, 15, doi:10.1186/s40169-020-0268-z (2020).
- Martell, R. E., Nevins, J. R. & Sullenger, B. A. Optimizing Aptamer Activity for Gene Therapy Applications Using Expression Cassette SELEX. Molecular Therapy 6, 30-34, doi:10.1006/mthe.2002.0624 (2002).
- Kim, D. et al. Digenome-seq: genome-wide profiling of CRISPR-Cas9 off-target effects in human cells. Nature Methods 12, 237-243, doi:10.1038/nmeth.3284 (2015).
- Cheng, Y. & Tsai, S. Q. Illuminating the genome-wide activity of genome editors for safe and effective therapeutics. Genome Biol 19, 226-226, doi:10.1186/s13059-018-1610-2 (2018).
- Tsai, S. Q. et al. CIRCLE-seq: a highly sensitive in vitro screen for genome-wide CRISPR–Cas9 nuclease off-targets. Nature Methods 14, 607-614, doi:10.1038/nmeth.4278 (2017).
- Wienert, B., Wyman, S. K., Yeh, C. D., Conklin, B. R. & Corn, J. E. CRISPR off-target detection with DISCOVER-seq. Nature Protocols 15, 1775-1799, doi:10.1038/s41596-020-0309-5 (2020).
- Sniecinski, I. & Seghatchian, J. Artificial intelligence: A joint narrative on potential use in pediatric stem and immune cell therapies and regenerative medicine. Transfus Apher Sci 57, 422-424, doi:10.1016/j.transci.2018.05.004 (2018).
- DeMott, K. (National Eye Institute, https://www.nih.gov/news-events/news-releases/nih-nist-researchers-use-artificial-intelligence-quality-control-stem-cell-derived-tissues, 2019).
- Schaub, N. J. et al. Deep learning predicts function of live retinal pigment epithelium from quantitative microscopy. J Clin Invest 130, 1010-1023, doi:10.1172/jci131187 (2020).