A recent article published by a Senior Scientist in Cell Therapies at Takeda Oncology in American Pharmaceutical Review outlined the critical importance of flow cytometry-based analytical assays in the development and manufacturing of CAR-T therapies. The application of flow cytometry in this field is an FDA requirement – yet flow cytometry has some well-known drawbacks, with QA, gating and highly manual, subjective analysis processes often with poor reproducibility. If manufacturers of CAR-T therapies could more easily and effectively obtain, handle and manage flow cytometry data, they could potentially accelerate approval and optimise manufacture of these important emerging therapies.
The growing interest in cell therapies intensified following the FDA approval of two autologous Chimeric Antigen Receptor (CAR) T therapies in 2017: Yescarta (axicabtagene ciloleucel) for relapsed or refractory large B-cell lymphoma; and Kymriah (tisagenlecleucel) for relapsed or refractory acute lymphoblastic leukemia. Autologous CAR-T therapies use patients’ cells, while allogeneic CAR-T therapy uses healthy donor cells, with both approaches creating live, constantly changing product that requires sophisticated analytical flow cytometry techniques.
Flow cytometry is used in this field for:
1. The release assays that are required by the FDA
2. Exploratory assays to understand the product before and after infusion, and to track it over time.
The release assays required by the FDA are used to confirm; the identity of the drug product, its potency, purity and viabilityi, ii. These assays must not be more than 5-8 parameter flow cytometry panels and may also be used during process development to understand certain aspects of the process. While the release assays are required by regulators, they only provide a snapshot of information. High parameter flow cytometry – which can analyse many more parameters on the cells at the same time – is helpful in providing additional information that can assist in determining the critical quality attributes (CQA).
When planning the flow cytometry panels, the analytical method should be carefully prepared to include specificity, precision, sensitivity, robustness and stability. The data should, of course, be stored in a secure place and the gating strategy for the analysis plan needs to be determinediii, iv, v – this identifies which cells can be excluded, or gated in to achieve the desired parameter.
Managing flow cytometry data
There are several different steps during the flow cytometry data life cycle which include: data acquisition, processing, sub-population selection, results integration, data analytics and insight generation. Flow cytometry data analysis is built upon the principle of gating, which is necessary for the visualisation of correlations in multiparameter data. During the processing step, gating is traditionally completed manually, which is time and resource-intensive and subject to possible inconsistency. Automated gating is a relatively new computational approach for scientists to use but offers a differentiated approach to the manual method.
Automated gating is based on the mathematical modelling of the fluorescence intensity distribution of particle populations. As well as drastically reducing analysis time, automated gating addresses the challenge of subjectivity in manual methods.
Not all automated approaches solve all challenges though – for example, random variation in automated clustering algorithms can lead to inconsistent results such that comparing results from automated gating with each other, as well as with traditional manual gating results, can be difficult.
The Aigenpulse CytoML suite addresses the need for a solution that allows gated and analysed data to be exported from one platform and imported to another, to reproduce analyses from raw files and facilitate demonstrable reproducibility. It does this by leveraging usable machine learning to empower cytometry processing and analytics. As well as automating processing, the platform enables the reuse of processed cytometry data, integrating population counts identified by manual gating to increase the value of the data and enable cross-project analysis.
A promising development
Surveys of over 300 oncologists from the United States between February and April 2021 found that 91% of them referred at least one patient to CAR-T cell therapy over the previous yearvi. As these therapies continue to generate interest and investment, optimising their development and manufacture is key in bringing them to more patients. Automated gating for more efficient flow cytometry analysis is an important development in this quest.
Contact us for more information on automating your flow cytometry analysis.
iChemistry, Manufacturing, and Control (CMC) Information for Human Gene Therapy Investigational New Drug Applications (INDs). January 2020.
iiGuidance for Industry: Potency Tests for Cellular and Gene Therapy Products. January 2011.
iiiSarikonda G, Mathieu M, Natalia M, et al. Best practices for the development, analytical validation and clinical implementation of flow cytometric methods for chimeric antigen receptor T cell analyses. Cytometry B Clin Cytom. 2020.
ivder Strate BV, Longdin R, Geerlings M, et al. Best practices in performing flow cytometry in a regulated environment: feedback from experience within the European Bioanalysis Forum. Bioanalysis. 2017;9(16):1253-1264.
vSelliah N, Eck S, Green C, et al. Flow Cytometry Method Validation Protocols. Curr Protoc Cytom. 2019;87(1):e53.
viKillmurray C, More Oncologists Are Referring Patients to CAR-T Cell Therapy, While Financial Barriers Remain, Jun 4 2021, Targeted Oncology.