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Metabolomics for Cell and Gene Therapy

Metabolomics for Cell and Gene Therapy

Metabolomics is recognized for its ability to provide deep mechanistic insight into cellular and molecular processes that regulate disease onset, progression, and response to treatment. This insight, which cannot be gained from genomics or proteomics alone, is crucial to developing and characterizing the performance of cellular and gene therapy products (CGTs). In this blog, we discuss the importance of maximizing and assuring the therapeutic potency of CGTs and examine published studies that demonstrate the payoff that comes from including metabolomics in potency assurance assessments. The information contained herein was submitted to the FDA in response to an open call for comments on draft guidance for Potency Assurance strategies for CGT products.


Metabolites are the reactants, intermediates, and products of metabolism upon which inputs from the genome, proteome, microbiome, and environment converge, making them the closest reflection of an individual’s real-time health status1. Metabolites indicate disease activity through changes in their abundance, which can be measured using liquid chromatography-tandem mass spectrometry (LC-MS). Untargeted LC-MS measures a wide collection of metabolites in a biological sample, giving a broad readout of the biochemical profile, while targeted LC-MS analyzes specific biochemical pathways of interest. Broad and focused analyses of biochemical pathways can serve several aspects of a potency assurance strategy, including characterizing the mechanism of action, monitoring product control, and evaluating potency in response to changes in the potency assurance procedure. Some examples of these uses are described below.

Mechanism of Action 

The potency of a CGT’s therapeutic effect is directly related to its mechanism of action. Characterizing a CGT’s mechanism of action is therefore important for understanding the product’s potency and factors that may compromise it. In turn, this information can inform manufacturing and control practices that protect and/or maximize potency. 

For example, an FDA-approved treatment for specific cancers is chimeric antigen receptor (CAR) T-cell therapy, in which T-cells are collected from a patient’s tumor, genetically reprogrammed to target specific cancer antigens, and then reintroduced to patients as immunotherapy2,3. Even though this therapy’s mechanism of action is known, potency is less than ideal because the tumor microenvironment can suppress the function of these CAR T-cells through hypoxia and production of immunosuppressive metabolites. One study addressed this issue by using metabolomics, gene editing, and systems biology to identify metabolic regulators of CAR T-cells4. Their findings revealed that overexpression of certain genes altered metabolic signatures, which increased proliferation and decreased metabolic exhaustion in CAR T-cells to thereby enhance their tumor-infiltration and clearance properties. By contrast, low expression of these genes, which reversed metabolic signature changes, dampened the therapeutic effect. Here, metabolomics provided key insight into cellular changes that regulate the mechanism of action of CAR T-cells, which can potentially impact production and selection of these cells to increase potency.

The value of using metabolomics to characterize mechanism of action was demonstrated in another study that focused on a pre-clinical CAR T therapy using IL-17-producing human T-cells5. Even though these cells elicit potent anti-tumor effects in mice, they have not been adapted for clinical use because their potency is low in the human tumor microenvironment. This study tested T-cell anti-tumor properties in response to activation of various receptors. Metabolomics studies showed that weakly stimulated, rather than strongly stimulated cells were better able to infiltrate tumors, proliferate and survive due to a shift from glycolysis to gluconeogenesis for energy, as shown by elevated intracellular phosphoenolpyruvate (PEP), isocitrate, and 3-phosphoglycerate. The shift to gluconeogenesis correlated with lower uptake of glucose from the extracellular environment. These findings show that weakly activated T-cells are less reliant on glucose for energy, making them less deterred by the tumor microenvironment and therefore more therapeutically potent than standard, strongly activated T-cells. This study serves as another example of how metabolomics can help characterize a mechanism of action to provide insight into potency-related characteristics.  

Metabolomics has also provided mechanistic insight into AAV-mediated gene therapies for muscular dystrophy-dystroglycanopathies (MDD). These disorders are caused by loss-of-function mutations in various genes, resulting in a dysfunctional laminin receptor, α-dystroglycan. One study aimed to further characterize the role of the glycan-coding gene FKRP in the onset of MDD6. The authors performed metabolomics analyses on skeletal muscle collected from wild type mice and from FKRP knockout mice that were either untreated or treated with AAV-mediated FKRP replacement therapy. Untreated diseased muscle contained diverse metabolic abnormalities in biomarkers associated with extracellular matrix remodeling and aging, pentoses/pentitols, glycolytic intermediates, and lipid metabolism. These metabolic signatures of disease were rescued by FKRP replacement therapy. By revealing the involvement of previously unsuspected pathways in the pathophysiology of MDD and showing that those pathways were acted on and corrected by gene therapy, metabolomics further characterized this therapy’s mechanism of action, which may have implications for potency in future human studies. 

Control Strategy

A control strategy should mitigate unacceptable risks to product potency during the manufacturing process by controlling materials, determining process parameters, and monitoring in-process samples for product attributes that can affect potency. Metabolomics has been used to address challenges associated with manufacturing CAR T-cell therapies at scale, which may help improve the potency yielded from cell-based manufacturing practices at large.

Efficiently manufacturing CAR T-cell therapies is constrained by high cost, difficulty in scaling, and lack of methods and tools that can predict potency of the final product based on measurements taken earlier in the manufacturing workflow7,8. These challenges are exacerbated by the fact that current chemistry, manufacturing, and control analytics are designed for conventional biopharmaceutical manufacturing systems rather than for a ‘living’ product. Thus, innovative tools, methods, and standards are needed to ensure appropriate quality controls for cell-based therapies9,10.

To take the first steps towards addressing this need, one research group rigorously characterized process parameters and longitudinal measurements of chemokines, cell-secreted cytokines, and metabolites from CAR T-cell media at an early step in the manufacturing process11. Using these datasets they developed an AI-based computational framework to identify parameters that can predict the end-of-manufacturing product phenotypes. Their model identified specific feature combinations from early in-culture media that were highly predictive of the final CD4/CD8 ratio, and total live CD4+ and CD8+ naïve and central memory T-cells. These combinations included metabolic signatures associated with central carbon metabolism, which showed that different T-cell subtypes require different levels of glycolytic and oxidative energy metabolism to sustain their phenotypes (i.e., potency). Through this discovery, metabolomics identified an attribute that affects the therapeutic potency of CAR T-cells that can be assessed as part of a future control strategy.


The examples discussed in this blog demonstrate the benefits of using metabolomics in potency assurance strategies for CGTs. While other aspects of CGT development, including discovery of therapeutic targets, identifying off-target effects, and assessing response to treatment are outside the scope of potency assurance, it should be noted that metabolomics can reveal critical insight into these areas as well12-15, and including metabolomics analyses at these stages of product development can help produce a highly sensitive and specific treatment.


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  2. Brentjens RJ, Davila ML, Riviere I, et al. CD19-targeted T cells rapidly induce molecular remissions in adults with chemotherapy-refractory acute lymphoblastic leukemia. Sci Transl Med. Mar 20 2013;5(177):177ra38. doi:10.1126/scitranslmed.3005930 
  3. Porter DL, Levine BL, Kalos M, Bagg A, June CH. Chimeric antigen receptor-modified T cells in chronic lymphoid leukemia. N Engl J Med. Aug 25 2011;365(8):725-33. doi:10.1056/NEJMoa1103849
  4. Renauer P, Park JJ, Bai M, et al. Immunogenetic metabolomics revealed key enzymes that modulate CAR-T metabolism and function. bioRxiv. Mar 15 2023;doi:10.1101/2023.03.14.532663 
  5. Wyatt MM, Huff LW, Nelson MH, et al. Augmenting TCR signal strength and ICOS costimulation results in metabolically fit and therapeutically potent human CAR Th17 cells. Mol Ther. Jul 5 2023;31(7):2120-2131. doi:10.1016/j.ymthe.2023.04.010 
  6. Vannoy CH, Leroy V, Broniowska K, Lu QL. Metabolomics Analysis of Skeletal Muscles from FKRP-Deficient Mice Indicates Improvement After Gene Replacement Therapy. Sci Rep. Jul 11 2019;9(1):10070. doi:10.1038/s41598-019-46431-1 
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  9. Campbell A, Brieva T, Raviv L, et al. Concise Review: Process Development Considerations for Cell Therapy. Stem Cells Transl Med. Oct 2015;4(10):1155-63. doi:10.5966/sctm.2014-0294 
  10. Simon CG, Jr., Lin-Gibson S, Elliott JT, Sarkar S, Plant AL. Strategies for Achieving Measurement Assurance for Cell Therapy Products. Stem Cells Transl Med. Jun 2016;5(6):705-8. doi:10.5966/sctm.2015-0269 
  11. Odeh-Couvertier VY, Dwarshuis NJ, Colonna MB, et al. Predicting T-cell quality during manufacturing through an artificial intelligence-based integrative multiomics analytical platform. Bioeng Transl Med. May 2022;7(2):e10282. doi:10.1002/btm2.10282
  12. Dang NH, Singla AK, Mackay EM, Jirik FR, Weljie AM. Targeted cancer therapeutics: biosynthetic and energetic pathways characterized by metabolomics and the interplay with key cancer regulatory factors. Curr Pharm Des. 2014;20(15):2637-47. doi:10.2174/13816128113199990489 
  13. Garcia-Canaveras JC, Lahoz A. Tumor Microenvironment-Derived Metabolites: A Guide to Find New Metabolic Therapeutic Targets and Biomarkers. Cancers (Basel). Jun 28 2021;13(13)doi:10.3390/cancers13133230
  14. Qiu S, Cai Y, Yao H, et al. Small molecule metabolites: discovery of biomarkers and therapeutic targets. Signal Transduct Target Ther. Mar 20 2023;8(1):132. doi:10.1038/s41392-023-01399-3 
  15. Zang Q, Sun C, Chu X, et al. Spatially resolved metabolomics combined with multicellular tumor spheroids to discover cancer tissue relevant metabolic signatures. Anal Chim Acta. Apr 22 2021;1155:338342. doi:10.1016/j.aca.2021.338342
Laura Sommerville, Ph.D.
Laura is a Senior Medical Writer who uses scientific storytelling to convey clear and engaging narratives to both scientists and lay audiences. As a member of Metabolon’s Clinical Metabolomics group, she has produced many works that address metabolomics-based insights into health and disease and demonstrate its utility in advancing basic and translational research, diagnostic testing, and therapeutic product development.


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