Monitoring and management of toxicity induced by cell therapy

Abstract

A method for determining whether a patient is likely to experience toxicity after cell therapy treatment. [Solution] This disclosure relates, in general, to compositions and methods for identifying whether a cell therapy patient is likely or unlikely to experience toxicity after cell therapy. The methods are based on the finding that the likelihood of such toxicity developing can be predicted using pre-treatment covariates such as serum IL-15 and MCP-1 levels in the patient, or the viability of the administered cells. Once a patient is identified as likely or unlikely to experience toxicity, compositions and methods for monitoring and managing toxicity are also provided.

Description

[Technical Field]

[0001] (Cross-reference of related applications) This application claims priority to U.S. Provisional Patent Application No. 63 / 227,677, filed on 30 July 2021, and to U.S. Provisional Patent Application No. 63 / 279,615, filed on 15 November 2021, the entire contents of each of these applications being incorporated herein by reference.

[0002] (Field of invention) This disclosure relates to a method for determining whether or not a patient is likely to experience toxicity after cell therapy treatment. [Background technology]

[0003] Chimeric antigen receptor T cells (also known as CAR T cells) are T cells genetically engineered to produce artificial T cell receptors for use in immunotherapy. CAR-T therapy has the potential to improve the management of lymphoma and, in some cases, solid tumors. Two anti-CD19 CAR T cell products are axicabtagene ciloleucel (axi-cel) and tisagenle Cruucel is approved for the management of relapsed / refractory large B-cell lymphoma.

[0004] However, CAR-T therapy has two common toxicities: cytokine release syndrome (CRS) and immunoeffector cell-associated neurotoxicity syndrome. This is associated with immune effector cell-associated neurotoxicity syndrome (ICANS), which is typically observed acutely after therapy. In addition, delayed toxicity includes prolonged cytopenia and on-target off-tumor effects.

[0005] CRS is a systemic inflammatory response triggered by the release of cytokines following activation of CAR-T cells in response to tumor recognition. CAR-T cells also likely contribute to the pathophysiology of CRS by activating bystander immune cells such as macrophages, which then release inflammatory cytokines. CRS typically presents with symptoms including fever, muscle pain, rigidity, fatigue, and loss of appetite. CRS can also lead to multiple organ failure.

[0006] ICANS can occur during CRS or, more commonly, after CRS has subsided. ICANS typically presents as toxic encephalopathy with word-finding difficulties, aphasia, and confusion, but in more severe cases, it can progress to decreased level of consciousness, coma, seizures, paralysis, and cerebral edema. The levels of cytokines, chemokines, and CAR-T cell proliferation are associated with the severity of neurotoxicity.

[0007] Monitoring for cardiac arrest syndrome (CRS) and neurotoxicity is required for patients receiving CAR-T therapy. Given the potential severity of toxicity, such monitoring should be conducted daily for seven days at an accredited medical facility. In addition, patients are instructed to remain near an accredited medical facility for at least four weeks after infusion. Such monitoring incurs considerable costs.

[0008] There is a strong need for methods to predict the onset of toxicity, which could help reduce unnecessary hospitalizations by ensuring that only those requiring toxicity treatment remain within the facility. Furthermore, those predicted to be at high risk of experiencing toxicity should receive appropriate treatment for toxicity. Alternatively, preventative care can be received. [Overview of the Initiative]

[0009] This disclosure provides compositions and methods for identifying whether a cell therapy patient is likely or unlikely to experience toxicity after cell therapy. The methods are based on the finding that the likelihood of such toxicity developing can be predicted using pre-treatment covariates such as serum IL-15 and MCP-1 levels in the patient, or the viability of the administered cells. Once a patient is identified as likely or unlikely to experience toxicity, compositions and methods for monitoring and managing toxicity are also provided.

[0010] One embodiment provides a method for identifying whether a patient is likely or unlikely to experience toxicity after cell therapy, comprising: measuring the level of IL-15 (Interleukin-15) or MCP-1 (monocyte chemoattractant protein-1) in a blood sample of the patient; identifying the patient as likely to experience toxicity after cell therapy if the IL-15 or MCP-1 level is higher than a corresponding reference level; or identifying the patient as unlikely to experience toxicity after cell therapy if the IL-15 or MCP-1 level is lower than a corresponding reference level, wherein the cell therapy comprises the administration of immune cells.

[0011] In some embodiments, the immune cells include T cells. In some embodiments, the T cells are engineered to express chimeric antigen receptors (CARs). In some embodiments, the CAR has binding specificity to the CD19 (differentiation antigen group 19) protein. In some embodiments, the cell therapy comprises axicaptagensilolucel.

[0012] In some embodiments, the blood sample is a serum sample. In some embodiments, the blood sample is obtained from the patient before cell therapy. In some embodiments, the blood sample is obtained after preconditioning treatment of the patient. In some embodiments, the preconditioning treatment reduces lymphocytes in the patient. In some embodiments, preconditioning includes intravenous (IV) administration of cyclophosphamide and fludarabine given 5 days, 4 days, and / or 3 days prior to cell therapy.

[0013] In some embodiments, toxicity is selected from the group consisting of cytokine release syndrome (CRS), neurologic events (NE), and combinations thereof. In some embodiments, toxicity is early-onset toxicity. In some embodiments, early-onset toxicity occurs within 4 days after cell therapy.

[0014] In some embodiments, the reference level for IL-15 or MCP-1 is determined from patients who experience toxicity after cell therapy and those who do not.

[0015] In some embodiments, the method further includes measuring the viability of cells used in cell therapy, and if the IL-15 or MCP-1 level is higher than the corresponding reference level and the cell viability is higher than the reference cell viability, the patient is identified as likely to experience toxicity after cell therapy; or if the IL-15 or MCP-1 level is lower than the corresponding reference level and the cell viability is lower than the reference cell viability, the patient is identified as not likely to experience toxicity after cell therapy.

[0016] In some embodiments, when IL-15 and MCP-1 levels are higher than the corresponding reference levels and cell viability is higher than the reference cell viability, the patient is identified as being more likely to experience toxicity after cell therapy, or when IL-15 and MCP-1 levels are higher than the corresponding reference levels When the illumination level is lower than the reference cell viability rate, the patient is identified as not being likely to experience toxicity after cell therapy.

[0017] In some embodiments, the method further includes obtaining one or more levels of a patient's baseline hemoglobin, baseline tumor mass, baseline LDH, baseline creatinine, and baseline calcium.

[0018] In some embodiments, the method further includes monitoring the patient for toxicity in a healthcare facility when the patient is identified as likely to experience toxicity.

[0019] In some embodiments, the method further includes preventing or treating toxicity in a patient when the patient is identified as being highly likely to experience toxicity. In some embodiments, treatment or prevention includes the administration of a drug selected from the group consisting of antihistamines, corticosteroids, antihypotensive agents, IL-6 inhibitors, GM-CSF inhibitors, and nonsteroidal anti-inflammatory drugs. In some embodiments, treatment or prevention includes the administration of a drug selected from the group consisting of tocilizumab, dexamethasone, levetiracetam, lenzilumab, methylprednisolone, anakinra, siltuximab, ruxolitinib, cyclophosphamide, IVIG (intravenous immunoglobulin), and ATG (antithymocyte globulin).

[0020] In some embodiments, the method further includes releasing the patient from the medical care facility after two days or less if the patient is identified as not being likely to experience toxicity.

[0021] Also, in one embodiment, a kit or package useful for identifying that a patient is likely to experience toxicity after cell therapy is provided, the kit or package comprising polynucleotide primers or probes or antibodies for measuring the expression levels of IL-15 and MCP-1 in a biological sample.

[0022] Also, in one embodiment, a method for preventing or treating toxicity in a patient undergoing cell therapy is provided, the method comprising administering to the patient an agent for preventing or treating cytokine release syndrome (CRS) or neurological event (NE), wherein the patient has been identified as likely to experience toxicity after cell therapy based on the level of IL-15 (interleukin-15) or MCP-1 (monocyte chemoattractant protein-1) in the patient's blood sample being higher than a corresponding reference level.

[0023] In some embodiments, the agent is selected from the group consisting of antihistamines, corticosteroids, antihypotensive agents, IL-6 inhibitors, GM-CSF inhibitors, and non-steroidal anti-inflammatory drugs. In some embodiments, the agent is selected from the group consisting of tocilizumab, dexamethasone, levetiracetam, ranizumab, methylprednisolone, anakinra, siltuximab, ruxolitinib, cyclophosphamide, IVIG (intravenous immunoglobulin), and ATG (antithymocyte globulin).

[0024] Also, in one embodiment, a computer program product for use with a computer system is provided, the computer program product comprising a computer-readable storage medium and a computer program mechanism embedded therein, the computer mechanism comprising executable instructions for performing a method for identifying that a patient is likely to experience toxicity after cell therapy, the instructions comprising: (i) obtaining the level of IL-15 (interleukin-15) or MCP-1 (monocyte chemoattractant protein-1) in the patient's blood sample; and (ii) comparing the level to a corresponding reference level, wherein the IL-15 or MCP-1 level is vs. When it is identified that the patient is likely to experience toxicity after cell therapy if it is higher than the corresponding reference level, a computer program product is provided for cell therapy, including the administration of immune cells.

Brief Description of the Drawings

[0025] [Figure 1] Indicates the patient's condition in Definition C.

[0026] [Figure 2] Shows the ROC of BPM with cell viability + IL-15 + MCP-1 for out-patient A3. Here, BPM is RFCRUS, and the optimal cut-off is 0.538.

[0027] [Figure 3] Shows the box plot of the prediction for BPM of the training data with cell viability + IL-15 + MCP-1 for out-patient A3.

[0028] [Figure 4] Shows the box plot of the prediction for BPM of the test data with cell viability + IL-15 + MCP-1 for out-patient A3.

[0029] [Figure 5] Shows the decision tree for cell viability + IL-15 + MCP-1 for the training data with out-patient A3. The subjects on the leaf with "N" are classified as "in-patients". The subjects on the leaf with "Y" are classified as "out-patients".

[0030] [ [Figure 6] Shows the decision tree for cell viability + IL-15 + MCP-1 for the test data with out-patient A3. The subjects on the leaf with "N" are classified as "in-patients". The subjects on the leaf with "Y" are classified as "out-patients".

[0031] [Figure 7]A partially dependent plot (based on balanced RF) is shown, indicating that higher cell viability, IL-15, and MCP-1 are associated with a higher likelihood of early-onset toxicity.

[0032] [Figure 8] This is a schematic diagram illustrating computing components that may be used to implement various features of the embodiments described herein. [Modes for carrying out the invention]

[0033] definition

[0034] The following description outlines exemplary embodiments of the technology. However, such descriptions should not be considered as limiting the scope of the disclosure, but rather as being provided solely for illustrative purposes. definition

[0035] When used herein, the following words, phrases, and symbols are generally intended to have the meanings set forth below, unless the context in which they are used indicates otherwise.

[0036] As used herein, certain terms may have the following defined meanings. As used herein and in the claims, the singular forms "a," "an," and "the" include singular and plural references unless the context clearly indicates otherwise. For example, the term "a cell" includes a single cell as well as multiple cells, and mixtures thereof.

[0037] All numerical values, including ranges such as pH, temperature, time, concentration, and molecular weight, are 0. This is an approximation that varies (+) or (-) with an increment of 1. While not always explicitly stated, please understand that all numerical expressions are preceded by the term "approximately." The term "approximately" includes the exact value of "X" as well as small increments of "X," such as "X+0.1" or "X-0.1." Also, while not always explicitly stated, please understand that the reagents described herein are illustrative only, and equivalents of such reagents may be publicly known in the art.

[0038] The term "immunotherapy" refers to the treatment of individuals who have a disease or are at risk of developing or relapsing a disease, by means of methods including inducing, enhancing, suppressing, or otherwise modifying the immune response. Examples of immunotherapy include, but are not limited to, T-cell therapy. Examples of T-cell therapy include adoptive T-cell therapy, tumor-infiltrating lymphocyte (TIL) immunotherapy, autologous cell therapy, and genetic engineering. Examples include engineered autologous cell therapy (eACT®) and allogeneic T cell transplantation. However, those skilled in the art will recognize that the conditioning methods disclosed herein enhance the efficacy of any transplanted T cell therapy. Examples of T cell therapies are described in U.S. Patent Applications Publications 2014 / 0154228 and 2002 / 0006409, U.S. Patents 7,741,465, 6,319,494, 5,728,388, and International Publication 2008 / 081035. In some embodiments, immunotherapy includes CAR T cell therapy. In some embodiments, the CAR T cell therapeutic product is administered via infusion.

[0039] T cells for immunotherapy may be derived from any source known in the art. For example, T cells may be differentiated in vitro from a hematopoietic stem cell population, or T cells may be obtained from a subject. T cells may be derived from, for example, peripheral blood mononuclear cells. T cells can be obtained from PBMCs, bone marrow, lymph node tissue, umbilical cord blood, thymic tissue, tissue from infection sites, ascites, pleural fluid, splenic tissue, and tumors. In addition, T cells may be derived from one or more T cell lines available in the art. T cells may also be obtained from blood units taken from a subject using various techniques known to those skilled in the art, such as FICOLL® isolation and / or apheresis. Additional methods for isolating T cells for T cell therapy are disclosed in U.S. Patent Application Publication 2013 / 0287748, which is incorporated herein by reference in its entirety.

[0040] As used herein, “cytokine” refers to a non-antibody protein released by a cell in response to contact with a specific antigen, where the cytokine interacts with a second cell to mediate a response in the second cell. As used herein, “cytokine” means a protein released by a population of cells that acts on another cell as an intercellular mediator. Cytokines can be endogenously expressed by cells or administered to a subject. Cytokines can be released by immune cells, including macrophages, B cells, T cells, and mast cells, to propagate an immune response. Cytokines can induce a variety of responses in recipient cells. Examples of cytokines include homeostatic cytokines, chemokines, pro-inflammatory cytokines, effectors, and acute-phase proteins. For example, homeostatic cytokines, including interleukin (IL) 7 and IL-15, can promote the survival and proliferation of immune cells, while pro-inflammatory cytokines can promote inflammatory responses. Examples of homeostatic cytokines include, but are not limited to, IL-2, IL-4, IL-5, IL-7, IL-10, IL-12p40, IL-12p70, IL-15, and interferon (IFN) gamma. Examples of pro-inflammatory cytokines include IL-1a, IL-1b, IL-6, IL-13, IL-17a, tumor necrosis factor (TNF)-alpha, and TNF- Beta, fibroblast growth factor (FGF) 2, granulocyte macrophage colony-stimulating factor (GM- CSF), soluble intercellular adhesion molecule 1 (s ICAM-1), soluble vascular adhesion molecule 1 (sVCAM-1), vascular endothelial growth factor (VEGF), VEGF-C, VEGF-D, and placental growth factor (PLGF ), but are not limited thereto. Examples of effectors include, but are not limited to, granzyme A, granzyme B, soluble Fas ligand (sFasL), and perforin. Examples of acute-phase proteins include, but are not limited to, C-reactive protein (CRP) and serum amyloid A (SAA).

[0041] "Chemokine" is a type of cytokine that mediates chemotaxis or directional movement of cells. Examples of chemokines include IL-8, IL-16, eotaxin, eotaxin-3, macrophage-derived chemokine (MDC or CCL22), monocyte chemotactic protein 1 (MCP-1 or CCL2), MCP-4, macrophage inflammatory protein 1α (MIP-1α, MIP-1a), MIP-1β (MIP-1b), gamma-induced protein 10 (IP-10), and thymus and activation-regulated chemokine (thymus and Examples include activation-regulated chemokines (TARC or CCL17), but these Not limited to this.

[0042] The terms “genetically modified” or “modified” refer to methods of altering the genome of a cell, including but not limited to deleting coding regions or non-coding regions or parts thereof, or inserting coding regions or parts thereof. In some embodiments, the modified cells are lymphocytes, such as T cells, which may be obtained from either a patient or a donor. The cells may be modified to express exogenous constructs, such as chimeric antigen receptors (CARs) or T cell receptors (TCRs). Sex constructs are integrated into the cell's genome.

[0043] As used herein, “Patient” includes any human being suffering from cancer (e.g., lymphoma or leukemia). The terms “Subject” and “Patient” are used interchangeably herein.

[0044] The terms “reduce” and “decrease” are used interchangeably herein and refer to any change that becomes less than the original. “Reduce” and “decrease” are relative terms and require a comparison between before and after measurement. “Reduce” and “decrease” include complete depletion. Similarly, the term “increase” refers to any change that becomes higher than the original value. “Increase,” “higher,” and “lower” are relative terms and require a comparison between before and after measurement and / or between reference standards. In some embodiments, the reference value is obtained from a general population, which may be the general population of patients. In some embodiments, the reference value is derived from an quartile analysis of the general patient population.

[0045] "Treatment" or "to treat" a subject means any type of intervention or process performed on the subject, or administration of an active agent to the subject, for the purpose of reducing, reducing, improving, inhibiting, delaying, or preventing the onset, progression, occurrence, severity, or recurrence of symptoms, complications, conditions, or biochemical signs associated with the disease. In some embodiments, "treatment" or "to treat" includes partial remission. In other embodiments, "treatment" or "to treat" includes complete remission.

[0046] This disclosure further provides diagnostic, prognostic, and therapeutic methods that are at least partially based on determining the expression levels of the genes of interest identified herein.

[0047] For example, information obtained using the diagnostic assays described herein is useful in determining whether a subject is likely to have a disease (e.g., cytokine release syndrome), likely to develop a disease, or is suitable for treatment. Based on the diagnostic / prognostic information, a physician can recommend a treatment protocol.

[0048] When used in general terms, the term “likely” means that the probability of an event occurring is higher than the probability of it not occurring, or alternatively, that the probability of an event occurring is higher than that of a given mean control. As a non-restrictive example, patients likely to experience toxicity after cell therapy are those who are more likely to experience toxicity than those who will not. Alternatively, patients likely to experience toxicity after cell therapy are those who have a higher statistical chance of experiencing toxicity compared to the mean incidence of toxicity in the patient population treated with cell therapy. Those skilled in the art will recognize additional definitions beyond those stated above.

[0049] It should be understood that the information obtained using the diagnostic assays described herein may be used alone or in combination with other information (e.g., behavioral assessments, genotypes or expression levels of other genes, clinical chemical parameters, histopathological parameters, or the subject's age, sex, and weight). Prediction and management of early-onset acute toxicity

[0050] Cancer patients currently receiving CAR-T therapy require daily monitoring for signs and symptoms of cytokine release syndrome (CRS) and neurotoxicity at an accredited medical facility after CAR-T infusion. Patients with Grade 3 or higher cytokine release syndrome (CRS) and neurological events (NE) require intensive hospitalization.

[0051] Using machine learning techniques, this disclosure describes compositions and methods for predicting early acute toxicity in patients undergoing CAR-T therapy. Based on such predictions, this disclosure also provides methods for preventing toxicity in patients at risk of experiencing it and, if necessary, treating it.

[0052] As demonstrated in the examples, multivariate analysis and machine learning from evaluable patient data in patients involved in clinical trials for CAR-T therapy yielded several comparable predictive models for early-onset CRS or NE, with the best-performing model achieving ROC (receiver operating characteristic) AUC (area under the ROC curve) greater than 0.8 in training and greater than 0.7 in trials. To possess.

[0053] When used individually, each of these covariates independently correlated with the likelihood of toxicity. Collectively, the predictive power is further increased. Exemplary covariates include, but are not limited to, product cell viability (or simply cell viability), serum IL-15 levels on day 0 prior to infusion, and serum MCP-1 (CCL2) levels on day 0 prior to infusion. Additional exemplary covariates include hemoglobin levels, albumin levels, red blood cell count, and ferritin levels (day 0 prior to infusion); urate, calcium, phosphate, creatinine, chloride, LDH (lactate dehydrogenase), and IL-17 (bath). Blood concentration (level) of the substance; as well as red blood cell count, white blood cell count, neutrophil count, and basophil count (baseline).

[0054] According to one embodiment of this disclosure, a method is provided for identifying a patient who is likely to experience toxicity after cell therapy. In some embodiments, the method involves measuring the level of IL-15 (interleukin-15) in a patient's sample. It was found that higher IL-15 levels correlated with a higher incidence of toxicity after cell therapy. Therefore, the method further involves identifying patients who are more likely to experience toxicity after cell therapy when their IL-15 levels are higher than the reference level (or cutoff level).

[0055] According to one embodiment of this disclosure, a method is provided for identifying a patient who is likely to experience toxicity after cell therapy. In some embodiments, the method involves measuring the level of MCP-1 (monocyte chemotactic protein-1) in a patient's sample. In this specification, it has been found that higher MCP-1 levels correlate with a higher incidence of toxicity after cell therapy. Thus, the method further involves identifying a patient who is likely to experience toxicity after cell therapy if IL-15 levels are higher than a reference level (or cutoff level).

[0056] According to one embodiment of this disclosure, a method is provided for identifying a patient who is likely to experience toxicity after cell therapy. In some embodiments, the method involves measuring cell viability. In this specification, it has been found that a higher viability of injected cells correlates with a higher incidence of toxicity after cell therapy. Therefore, the method further involves identifying a patient who is likely to experience toxicity after cell therapy if the cell viability is higher than a reference level (or cutoff level).

[0057] In some embodiments, useful measurements for predicting the onset of toxicity include blood hemoglobin levels, albumin levels, red blood cell count, and ferritin levels (day 0 before infusion); blood concentrations (levels) of urate, calcium, phosphate, creatinine, chloride, LDH (lactate dehydrogenase), and IL-17 (baseline); and one or more covariates of red blood cell count, white blood cell count, neutrophil count, and basophil count (baseline).

[0058] In some embodiments, the blood covariate (e.g., IL-15) is measured in a blood sample obtained from a patient. In some embodiments, the blood sample is a serum sample.

[0059] In some embodiments, blood samples are obtained from the patient according to specified time points. For example, for baseline covariates, blood samples are collected before the start of cell therapy. For day 0 covariates, blood samples are collected on day 0, which is the day the infusion is administered. In some embodiments, blood samples are collected before the infusion.

[0060] In some embodiments, the patient receives preconditioning treatment before cell therapy, and therefore day 0 is after preconditioning treatment. In some embodiments, preconditioning is leukocyte depletion or lymphocyte depletion. An exemplary lymphocyte depletion regimen is intravenous cyclophosphamide 500 mg / m². 2 and fludarabine 30 mg / m² 2It consists of both, and both are given on days 5, 4, and 3 prior to the start of CAR-T infusion.

[0061] Reference levels (cutoff values) for any of the above-mentioned covariates, such as IL-15 levels, MCP-1 levels, and cell viability, can be determined experimentally or from historical data using methods known in the art. Reference levels for each corresponding covariate can be determined before or after measurement. In some embodiments, the reference levels best isolate (differentiate) patients with different toxicity outcomes after the same cell therapy.

[0062] In some embodiments, the reference level is a specific number, such as 0.1 ng / mL. However, in some embodiments, the reference level is implicit across multiple reference standards. For example, a measured level can be compared to a number of reference digits, each labeled either toxic or non-toxic, using the nearest neighbor method. If the measured level is closer to a reference level associated with a patient experiencing toxicity, the measured level predicts that the patient is also likely to experience toxicity. In this example, the specific reference level is not derived from the reference digit, but the comparison is still effective.

[0063] In some embodiments, the reference level is implicit in the formula used to calculate the probability based on the measured level. For example, a linear or quadratic discriminant analysis formula can be developed based on training data and used to determine a probability figure that takes the measured level as input.

[0064] In some embodiments, covariates can be used in combination. For example, a patient is identified as likely to experience toxicity after cell therapy if both IL-15 levels and MCP-1 levels are higher than the corresponding reference levels. In some embodiments, a patient is identified as likely to experience toxicity after cell therapy if both IL-15 levels and cell viability are higher than the corresponding reference levels. In some embodiments, a patient is identified as likely to experience toxicity after cell therapy if both MCP-1 levels and cell viability are higher than the corresponding reference levels. In some embodiments, a patient is identified as likely to experience toxicity after cell therapy if IL-15 levels, MCP-1 levels, and cell viability are all higher than the corresponding reference levels. In some embodiments, one or more additional covariates are also included.

[0065] In some embodiments, the reference level (plasma concentration) for IL-15 is 20 pg / mL, 21 pg / mL, 22 pg / mL, 23 pg / mL, 24 pg / mL, 25 pg / mL, 26 pg / mL, 27 pg / mL, 28 pg / mL, 29 pg / mL, 30 pg / mL, 31 pg / mL, 32 pg / mL, 33 pg / mL, 34 pg / mL, 35 pg / mL, 36 pg / mL, 37 pg / mL, 38 pg / mL, 39 pg / mL, 40 pg / mL, 41 pg / mL, 42 pg / mL, 43 pg / mL, 44 pg / mL, 45 pg / mL, 46 pg / mL, 47 pg / mL, 48 pg / mL, 49 pg / mL, or 50 pg / mL. In an exemplary embodiment, the reference level for IL-15 is 28 pg / mL.

[0066] In some embodiments, the reference levels (plasma concentrations) for CCL2 are 600 pg / mL, 620 pg / mL, 640 pg / mL, 650 pg / mL, 660 pg / mL, 680 pg / mL, 700 pg / mL, 720 pg / mL, 740 pg / mL, 750 pg / mL, 760 pg / mL, 780 pg / mL, 800 pg / mL, 820 pg / mL, 840 pg / mL, 850 pg / mL, 860 pg / mL, 880 pg / mL, 900 pg / mL, 920 pg / mL, 940 pg / mL, 950 pg / mL, 960 pg / mL, 980 pg / mL, 1000 pg / mL, 1020 pg / mL mL, 1040pg / mL, 1050pg / mL, 1060pg / mL, 1080pg / mL, 1100pg / mL, 1120pg / mL, 1140 pg / mL, 1150pg / mL, 1160pg / mL, 1180pg / mL, 1200pg / mL, 1220pg / mL, 1240pg / mL, 1 250pg / mL, 1260pg / mL, 1280pg / mL, 1300pg / mL, 1320pg / mL, 1340pg / mL, 1350pg / m L, 1360pg / mL, 1380pg / mL, 1400pg / mL, 1420pg / mL, 1440pg / mL, or 1450pg / mL.

[0067] In some embodiments, the reference level for product cell viability is 93%, 93.5%, 94%, 94.5%, 95%, 95.5%, 96%, 96.5%, or 97%. In an exemplary embodiment, the reference level for product cell viability is 95%.

[0068] In some embodiments, cell therapy is a therapy involving the administration of immune cells. Immune cells may, but are not limited to, T cells, natural killer (NK) cells, monocytes, or macrophages.

[0069] In some embodiments, immune cells are engineered to express chimeric antigen receptors (CARs), resulting in the production of CAR-T cells and CAR-NK cells, but are not limited to these. In some embodiments, the CARs have binding specificity to tumor antigens.

[0070] "Tumor antigens" are antigenic substances produced in tumor cells, meaning they trigger an immune response in the host. Tumor antigens are useful for identifying tumor cells and are potential candidates for use in cancer therapy. Normal proteins in the body are not antigenic. However, certain proteins are considered "exogenous" to the body because they are produced or overexpressed during tumor formation. This may include normal proteins that are well isolated from the immune system, proteins that are usually produced in very small amounts, proteins that are usually produced only at certain developmental stages, or proteins whose structure has been altered by mutation.

[0071] Numerous tumor antigens are known in the art, and new tumor antigens can be easily identified by screening. Non-exclusive examples of tumor antigens include EGFR, Her2, EpCAM, CD19, CD20, CD30, CD33, CD47, CD52, CD133, CD73, CEA, gpA33, mucin, TAG-72, CIX, PSMA, folate-binding proteins, GD2, GD3, GM2, VEGF, VEGFR, integrins, αVβ3, α5β1, ERBB2, ERBB3, MET, IGF1R, EPHA3, TRAILR1, TRAILR2, RANKL, FAP, and tenascin.

[0072] In some embodiments, the CAR has specificity for any of the tumor antigens discussed above, or for one or more of the following: CD19, CD20, CLL-1, TACI, MAGE, HPV-related proteins, GPC-3, and BCMA. In some embodiments, the CAR has bispecificity for two or more antigens (e.g., CD19 and CD20).

[0073] In some embodiments, CARs have specificity for CD19 (differentiation antigen group 19). An exemplary cell therapy targeting CD19 is axicapbutagensilolucel. Marketed under the trade name Yescarta®, axicapbutagensilolucel is a treatment for large B-cell lymphoma that has failed conventional therapies.

[0074] In some embodiments, toxicity is selected from the group consisting of cytokine release syndrome (CRS), neurological events (NE), and combinations thereof. In some embodiments, toxicity is early-onset toxicity. In some embodiments, early-onset toxicity occurs within 5, 4, 3, or 2 days after cell therapy.

[0075] Important signs of CRS include fever, hypotension, tachycardia, hypoxia, chills, and headache. Serious events that may be associated with CRS include cardiac arrhythmias (including atrial fibrillation and ventricular tachycardia), cardiac arrest, heart failure, renal failure, capillary leak syndrome, hypotension, hypoxia, multiple organ failure, and hemophagocytic lymphohistiocytosis (HLH / macrophage activation syndrome, MAS). CRS can be classified into four different grades, grades 1-4.

[0076] The most common neurotoxic effects include encephalopathy, headache, tremor, dizziness, delirium, aphasia, and insomnia. Serious events include leukoencephalopathy and seizures. Neurotoxicity can be classified into four different grades, grades 1 to 4.

[0077] The patient's likelihood of experiencing toxicity, the type of toxicity, and the grade of toxicity can be identified. Therefore, monitoring, prevention, and treatment can be provided to the patient.

[0078] Currently, all patients receiving CAR-T therapy in medical facilities require monitoring, which incurs considerable costs. Using this technology, patients identified as not being at high risk of experiencing toxicity can be monitored in outpatient facilities. Patients identified as being at high risk of experiencing toxicity can be monitored as inpatients.

[0079] Preventive and / or therapeutic measures may also be taken for patients identified as being at high risk of experiencing toxicity. Depending on the predicted toxicity, appropriate preventive / therapeutic measures may be taken. For example, for predicted CRS, tocilizumab 8 mg / kg may be administered intravenously over 1 hour (not exceeding 800 mg). Alternatively, dexamethasone 10 mg may be administered intravenously once daily. Methylprednisolone may also be used for more severe CRS.

[0080] For anticipated neurotoxicity, tocilizumab, dexamethasone, levetiracetam, corticosteroids, and / or methylprednisolone may be used. Alternative prophylactic / therapeutic options include anakinra, siltuximab, ruxolitinib, cyclophosphamide, IVIG (intravenous immunoglobulin), and ATG (anti-thymocyte globulin).

[0081] It is also known that severe CRS can be prevented with antihistamines or corticosteroids. Treatment for mild to severe CRS is symptomatic, addressing symptoms such as fever, muscle pain, or fatigue. Moderate CRS requires oxygen therapy as well as fluids to raise blood pressure and the administration of antihypertensive agents. For moderate to severe CRS, the use of immunosuppressants such as corticosteroids may be useful.

[0082] IL-6 inhibitors (e.g., anti-IL-6 antibodies such as tocilizumab) are known to be useful in the prevention / treatment of CRS. GM-CSF inhibitors (e.g., anti-GM-CSF antibodies such as renzilumab) may also be effective in preventing or controlling cytokine release by reducing myeloid cell activation and decreasing the production of IL-1, IL-6, MCP-1, MIP-1, and IP-10.

[0083] Tocilizumab, dexamethasone, levetiracetam, lenzirumab, methylprednisolone, anakinra, siltuximab, ruxolitinib, cyclophosphamide, IVIG (intravenous immunoglobulin), and ATG (anti-thymocyte globulin).

[0084] One embodiment of the present disclosure is a method for identifying whether a patient is likely or unlikely to experience toxicity after cell therapy, comprising: measuring the level of at least one of IL-15 (interleukin-15) and MCP-1 (monocyte chemotactic protein-1) in a blood sample of the patient; identifying the patient as likely to experience toxicity after cell therapy if the level of IL-15 or MCP-1 is higher than the corresponding reference level; or identifying the patient as unlikely to experience toxicity after cell therapy if the level of IL-15 or MCP-1 is lower than the corresponding reference level. In one such embodiment, the cell therapy comprises the administration of immune cells.

[0085] One embodiment of the present disclosure further comprises preventing or treating toxicity in a patient when the patient is identified as being highly likely to experience toxicity.

[0086] One embodiment of the present disclosure relates to the method of treatment or prevention comprising the administration of an agent selected from the group consisting of antihistamines, corticosteroids, antihypotensive agents, IL-6 inhibitors, GM-CSF inhibitors, and nonsteroidal anti-inflammatory drugs.

[0087] One embodiment of the present disclosure relates to the method of treatment or prevention comprising the administration of an agent selected from the group consisting of tocilizumab, dexamethasone, levetiracetam, lenzirumab, methylprednisolone, anakinra, siltuximab, ruxolitinib, cyclophosphamide, IVIG (intravenous immunoglobulin), and ATG (anti-thymocyte globulin).

[0088] One embodiment of the present disclosure relates to the method, wherein the immune cells include T cells that have been engineered to express a chimeric antigen receptor (CAR).

[0089] One embodiment of this disclosure relates to the method wherein the CAR has binding specificity to the CD19 (differentiation antigen group 19) protein.

[0090] One embodiment of this disclosure relates to the method described above, wherein the blood sample is a serum sample obtained from a patient prior to cell therapy.

[0091] One embodiment of this disclosure relates to the method by which a blood sample is obtained after a patient's preconditioning treatment.

[0092] One embodiment of this disclosure relates to a method by which preconditioning therapy reduces lymphocytes in a patient.

[0093] One embodiment of the present disclosure relates to the method wherein toxicity is selected from the group consisting of cytokine release syndrome (CRS), neurological events (NE), and combinations thereof.

[0094] One embodiment of this disclosure relates to the above method wherein the toxicity is early-acting toxicity.

[0095] One embodiment of this disclosure relates to the method wherein early toxicity occurs within four days after cell therapy.

[0096] One embodiment of the present disclosure relates to the method by which a reference level for IL-15 or MCP-1 is determined from patients who experience toxicity after cell therapy and patients who do not experience toxicity after cell therapy.

[0097] One embodiment of the present disclosure further comprises measuring the viability of cells used in cell therapy, wherein if the IL-15 or MCP-1 level is higher than the corresponding reference level and the cell viability is higher than the reference cell viability, the patient is identified as likely to experience toxicity after cell therapy, or if the IL-15 or MCP-1 level is lower than the corresponding reference level and the cell viability is lower than the reference cell viability, the patient is identified as not likely to experience toxicity after cell therapy.

[0098] One embodiment of the present disclosure relates to the method described above, further comprising obtaining one or more levels of a patient's baseline hemoglobin, baseline tumor mass, baseline LDH, baseline creatinine, and baseline calcium.

[0099] One embodiment of the present disclosure is a method for preventing or treating toxicity in a patient undergoing cell therapy, comprising identifying whether a patient is likely or unlikely to experience toxicity after cell therapy, comprising measuring the level of at least one of IL-15 (interleukin-15) and MCP-1 (monocyte chemotactic protein-1) in a blood sample of the patient, and identifying that the patient is likely to experience toxicity after cell therapy if the level of IL-15 or MCP-1 is higher than a corresponding reference level, or identifying that the patient is not likely to experience toxicity after cell therapy if the level of IL-15 or MCP-1 is lower than a corresponding reference level. In one such embodiment, if the patient is identified as likely to experience toxicity after cell therapy, the patient is administered an agent to prevent or treat cytokine release syndrome (CRS) or neurological events (NE).

[0100] One embodiment of the present disclosure relates to the method described above, wherein the agent is selected from the group consisting of antihistamines, corticosteroids, antihypotensive agents, IL-6 inhibitors, GM-CSF inhibitors, and nonsteroidal anti-inflammatory drugs.

[0101] One embodiment of the present disclosure relates to the method described above, wherein the drug is selected from the group consisting of tocilizumab, dexamethasone, levetiracetam, lenzirumab, methylprednisolone, anakinra, siltuximab, ruxolitinib, cyclophosphamide, IVIG (intravenous immunoglobulin), and ATG (antithymocyte globulin).

[0102] One embodiment of the present disclosure further comprises measuring the viability of cells used in cell therapy, wherein if the IL-15 or MCP-1 level is higher than the corresponding reference level and the cell viability is higher than the reference cell viability, the patient is identified as likely to experience toxicity after cell therapy, or if the IL-15 or MCP-1 level is lower than the corresponding reference level and the cell viability is lower than the reference cell viability, the patient is identified as not likely to experience toxicity after cell therapy.

[0103] One embodiment of the present disclosure relates to the method described above, further comprising obtaining one or more levels of a patient's baseline hemoglobin, baseline tumor mass, baseline LDH, baseline creatinine, and baseline calcium. Kits and packages, software programs

[0104] The methods described herein may be carried out by utilizing a pre-packaged diagnostic kit, such as those described below, which include, for example, at least one probe or primor nucleic acid described herein, and can be conveniently used to determine, for example, whether a subject is toxic or at risk of experiencing toxicity after cell therapy.

[0105] Accordingly, certain embodiments of the present disclosure relate to a kit or package useful for identifying patients who are likely to experience toxicity after cell therapy, comprising a polynucleotide primer or probe or antibody for measuring the expression levels of IL-15 and MCP-1 in a biological sample.

[0106] Diagnostic procedures can be performed directly in situ using mRNA isolated from cells, or on (fixed and / or frozen) tissue sections of primary tissue, such as biopsies obtained from biopsies or excisions, without the need for nucleic acid purification. Nucleic acid reagents can be used as probes and / or primers for such in-situ procedures.

[0107] In one embodiment, a kit or package useful for identifying whether a patient is likely or unlikely to experience toxicity after cell therapy, comprising IL-15 and in a biological sample. A kit or package is provided that includes a polynucleotide primer, probe, or antibody for measuring the expression level of MCP-1. In some embodiments, the kit or package further includes a drug for measuring cell viability.

[0108] In one embodiment, the kit further includes instructions for use. In one embodiment, the kit includes a manual that includes reference gene expression levels.

[0109] Figure 8 is a block diagram illustrating a computer system 800 in which any embodiment of the present and related technologies may be implemented. The computer system 800 includes a bus 802 or other communication mechanism for communicating information, and one or more hardware processors 804 connected to the bus 802 for processing information. The hardware processors 804 may be, for example, one or more general-purpose microprocessors.

[0110] The computer system 800 also includes main memory 806, such as random access memory (RAM), a cache, and / or other dynamic storage devices, which are connected to the bus 802 for storing information and instructions executed by the processor 804. Main memory 806 may also be used to store temporary variables or other intermediate information during the execution of instructions by the processor 804. Once such instructions are stored in a storage medium accessible to the processor 804, the computer system 800 becomes a dedicated machine customized to perform the operations specified by the instructions.

[0111] The computer system 800 further includes a read-only memory (ROM) 808 or other static storage device coupled to the bus 802 for storing static information and instructions for the processor 804. A storage device 810, such as a magnetic disk, optical disk, or USB thumb drive (flash drive), is provided for storing information and instructions and is coupled to the bus 802.

[0112] The computer system 800 may be connected via bus 802 to a display 812, such as an LED or LCD display (or touchscreen), to display information to the computer user. An input device 814, including alphanumeric and other keys, is connected to bus 802 to communicate information and command selections to the processor 804. Another type of user input device is a cursor control 816, such as a mouse, trackball, or cursor directional keys, to communicate directional information and command selections to the processor 804 and to control cursor movement on the display 812. In some embodiments, the same directional information and command selection as cursor control may be implemented via receiving touches on a touchscreen without using a cursor. Additional data may be retrieved from an external data storage device 818.

[0113] The computer system 800 may include a user interface module for implementing a GUI that can be stored in a mass storage device as executable software code executed by a computing device. Examples of such modules include components such as software components, object-oriented software components, class components, and task components, processes, functions, attributes, procedures, subroutines, segments of program code, drivers, firmware, microcode, circuits, data, databases, data structures, tables, arrays, and variables.

[0114] Generally, as used herein, the term “module” means logic embodied in hardware or firmware, or software instructions, which may have entry and exit points, written in a programming language such as Java, C, or C++. This refers to a collection of such collections. Software modules can be compiled and linked into executable programs installed in a dynamic link library, or they can be written in an interpreted programming language such as BASIC, Perl, or Python. It will be understood that software modules may be callable from other modules or themselves, and / or may be called in response to detected events or interrupts. Software modules configured to run on a computing device may be provided on computer-readable media such as compact disks, digital video disks, flash drives, magnetic disks, or any other tangible media, or as digital downloads (originally stored in a compressed or installable format that requires installation, decompression, or decryption before execution). Such software code may be partially or completely stored on the memory device of the running computing device for execution by the computing device. Software instructions may be embedded in firmware such as EPROM. Furthermore, it will be understood that hardware modules may consist of connected logic units such as gates and flip-flops, and / or programmable units such as programmable gate arrays or processors. The modules or computing device functions described herein may preferably be implemented as software modules, but may also be represented in hardware or firmware. In general, the modules described herein refer to logical modules that can be combined with other modules or divided into submodules, regardless of their physical organization or storage. In some embodiments, coding for the desired analysis is performed in R Core Team (2019); language and environment for statistical computing (R Foundation for Statistical Computing, Vienna, Austria).

[0115] The computer system 800 may implement the techniques described herein using customized hardwired logic, one or more ASICs or FPGAs, firmware, and / or programmable logic, which, in combination with the computer system, make the computer system 800 a dedicated machine or programmable. According to one embodiment, the techniques described herein are executed by the computer system 800 in response to a processor 804 that executes one or more sequences of one or more instructions contained in main memory 806. Such instructions may be read into main memory 806 from another storage medium, such as a storage device 810. The execution of the sequence of instructions contained in main memory 806 causes the processor 804 to execute the process steps described herein. In alternative embodiments, hardwired circuits may be used instead of, or in combination with, software instructions.

[0116] As used herein, the term “non-temporary medium” and similar terms refer to any medium that stores data and / or instructions that cause a machine to operate in a particular manner. Such non-temporary media may include non-volatile media and / or volatile media. Non-volatile media include, for example, optical or magnetic disks such as storage device 810. Volatile media include dynamic memory such as main memory 806. Common forms of non-temporary media include, for example, floppy disks, flexible disks, hard disks, solid-state drives, magnetic tapes, or any other magnetic data storage media, CD-ROMs, any other optical data storage media, any physical media having a pattern of holes, RAM, PROMs, and EPROMs, FLASH-EPROMs, NVRAMs, any other memory chips or cartridges, and their network versions.

[0117] Non-transient media are separate from transmission media, but may be used together with transmission media. Transmission media are involved in transferring information between non-transient media. For example, the transmission media is bus 8 This includes coaxial cables, copper wires, and optical fibers, including wires containing O2. The transmission medium may also take the form of sound waves or light waves, such as those generated during radio and infrared data communications.

[0118] Various forms of media may be involved in transporting one or more sequences of one or more instructions to the processor 804 for execution. For example, the instructions may first be transported on a magnetic disk or solid-state drive of a remote computer. The remote computer may load the instructions into its dynamic memory and transmit the instructions over a telephone line using component control. Component control local to computer system 800 may receive the data over the telephone line and convert the data into an infrared signal using an infrared transmitter. An infrared detector may receive the data transported by the infrared signal, and appropriate circuitry may place the data on bus 802. Bus 802 transports the data to main memory 806, from which the processor 804 retrieves and executes the instructions. Instructions received by main memory 806 may be retrieved and executed. Instructions received by main memory 806 may optionally be stored on storage device 810 either before or after execution by processor 804.

[0119] The computer system 800 also includes a communication interface 818 connected to bus 802. The communication interface 818 provides bidirectional data communication connected to one or more network links connected to one or more local networks. For example, the communication interface 818 can be used for integrated services digital network (ISDN) card, cable component control, satellite component control, etc. , or it may be a component control that provides data communication connectivity to a corresponding type of telephone line. As another example, the communication interface 818 may be a local area network (LAN) card that provides data communication connectivity to a compatible LAN (or a WAN component for communicating with a WAN). A wireless link may also be implemented. In any such implementation, the communication interface 818 sends and receives electrical, electromagnetic, or optical signals that carry digital data streams representing various types of information.

[0120] A network link typically provides data communication to other data devices over one or more networks. For example, a network link provides data to an Internet Service Provider (ISP) over a local network. Therefore, it can provide a connection to the host computer or data equipment being operated. Next, the ISP provides data communication services via a global packet data communication network now commonly referred to as the “Internet.” Both local networks and the Internet use electrical, electromagnetic, or optical signals to carry digital data streams. Signals carried to and from the computer system 800 via various networks, as well as signals over network links and via the communication interface 818, are exemplary forms of transmission media.

[0121] The computer system 800 can send messages and receive data, including program code, via a network, network links, and a communication interface 818. In the case of the internet, a server can transmit requested code for an application program via the internet, an ISP, a local network, and the communication interface 818.

[0122] The received code may be executed by processor 804 upon receipt and / or stored in storage device 810 or other non-volatile storage device for later execution. Each of the processes, methods, and algorithms described in the previous section may be executed by one or more computer systems or computer processors, including computer hardware. These are embodied in code modules and can be fully or partially automated by the code modules. The processes and algorithms can be partially or fully implemented in application-specific circuits.

[0123] The various features and processes described above may be used independently of each other or combined in various ways. All possible combinations and subcombinations are intended to fall within the scope of this disclosure. In addition, certain methods or process blocks may be omitted in some implementations. The methods and processes described herein are also not limited to any particular order, and the associated blocks or states may be executed in any other appropriate order. For example, the described blocks or states may be executed in an order other than the specifically disclosed order, or multiple blocks or states may be combined into a single block or state. Exemplary blocks or states may be executed in series, in parallel, or in any other way. Blocks or states may be added to or removed from the disclosed exemplary embodiments. Exemplary systems and components described herein may be configured differently from those described. For example, elements may be added, removed, or rearranged compared to the disclosed exemplary embodiments.

[0124] Any process description, element, or block in the flowcharts described herein and / or in the accompanying drawings should be understood as potentially representing a module, segment, or portion of code containing one or more executable instructions for implementing a particular logical function or step in the process. As will be understood by those skilled in the art, alternative implementations in which elements or functions are removed and executed in an order different from the shown or considered order, including substantially simultaneous or reversed order depending on the functions involved, are included within the scope of the embodiments described herein.

[0125] Many variations and modifications may be made to the embodiments described above, and it should be emphasized that such elements should be understood as being among other acceptable examples. All such modifications and variations are intended to be included in the scope of this disclosure as herein. The foregoing description details certain embodiments of the invention. However, it will be understood that the invention can be carried out in many ways, regardless of how much the foregoing is detailed in the text. Also, as stated above, the use of certain terms when describing certain features or aspects of the invention should not be construed as implying that the terms are redefined herein so as to be limited to including any particular characteristics of the features or aspects of the invention to which they relate. Accordingly, the scope of embodiments should be interpreted in accordance with the appended claims and any equivalents thereof.

[0126] Various operations of the exemplary methods described herein may be performed at least partially by one or more processors that are configured temporarily (e.g., by software) or permanently to perform the relevant operations. Similarly, the methods described herein may be at least partially processor-implemented, with a specific processor or a set of processors being hardware examples. For example, at least some of the operations of the methods may be performed by one or more processors. Furthermore, one or more processors may also be configured to support the performance of the relevant operations in a “cloud computing” environment or as “software as a service” (SaaS). It is possible. For example, at least some of the operations may be performed by a group of computers (as an example of machines including processors), and these operations may be performed via a network (e.g., the Internet) and via one or more suitable interfaces (e.g., an Application Program Interface (API)). It is accessible. [Examples]

[0127] The following embodiments are included to demonstrate specific embodiments of the Disclosure. Those skilled in the art will understand that the techniques disclosed in the following embodiments represent techniques that function well in the implementation of the Disclosure and can therefore be considered to constitute a particular form for such implementation. However, those skilled in the art will understand that many modifications can be made in the specific embodiments disclosed, without departing from the spirit and scope of the Disclosure, and still achieving similar or comparable results. Example 1: Prediction of early cytokine release syndrome and neurological events after axicaptagensilol in large B-cell lymphoma based on a machine learning algorithm.

[0128] In the ZUMA-1 clinical trial, refractory large B-cell lymphoma In key studies of axicaptagen silolucel (axi-cel) in patients with LBCL, grade 3 or higher cytokine release syndrome (CRS) and neurological events (NE) occurred in 13% and 28% of patients, respectively, requiring intensive hospitalization. With increasing safety experience, the management of CRS and NE has been evaluated in several exploratory safety management cohorts of ZUMA-1. Cohort 4 evaluated the use of levetiracetam prophylaxis and early corticosteroids and / or tocilizumab on the incidence and severity of CRS and NE. The impact of adding prophylactic corticosteroids to the toxicity management regimen of Cohort 4 was evaluated in Cohort 6. Notably, some treated patients had early-onset versus late-onset CRS or NE, which naturally require separate management. To facilitate toxicity management, this embodiment developed a predictive algorithm for early-onset acute toxicity (within 3-4 days after axi-cel) based on machine learning from ZUMA-1 data.

[0129] Methods: This post-hoc analysis included patients from phase 1 and phase 2 cohorts 1, 2, 4, and 6 of ZUMA-1. Covariates (over 1500; 227 measured before axi-cel infusion) included baseline product, patient and tumor characteristics, and inflammatory soluble blood biomarker levels. Data from patients in cohorts 1, 2, and 4 were randomly divided into a training set (70%) and a trial set (30%). Univariate and multivariate analyses, as well as clinical feasibility considerations, were applied to select covariate subsets for further analysis. Machine learning (e.g., logistic regression, random forest, XGBoost, and AdaBoost classifiers) was applied to three categories of covariates (1. clinical; 2. mechanistic [e.g., product attributes, inflammatory blood biomarkers]; 3. hybrids of 1 and 2) to construct best-performing models (predictive performance assessed by area under the curve [AUC] for trial data). The optimal cutoff for predictive scores was selected using receiver operating characteristics (ROC) or classification tree analysis. Data from patients in Cohort 6 were included to validate the best-performing model generated using the training data.

[0130] Results: Multivariate analysis and machine learning from data obtained from 149 evaluable patients in ZUMA-1 cohorts 1, 2, and 4 yielded several comparable predictive models for early-onset CRS or NE (best-performing models with ROC AUC >0.8 in training and >0.7 in trials). Covariates in the best-performing models included product cell viability, centrally measured IL-15 and CCL2 (MCP-1) serum levels and locally measured blood cell counts, blood chemistry analyzers, tumor volume, and serum lactate dehydrogenase levels on day 0 (before axi-cel treatment). Best-performing models with fewer than 5 covariates contained only mechanism covariates or a hybrid mix of covariates. The three covariate mechanism models (product cell viability and serum levels of IL-15 and CCL2 (MCP-1) on day 0 (all positively associated with early toxicity)) performed comparably to the larger best-performing models (ROC AUC >0.7 in the study). IL-15 and product cell viability on day 0 A classification tree segmented based on rates showed potential to classify patients based on the early versus late onset of toxicity (specificity > 0.85).

[0131] Machine learning applied to covariates measured before axi-cel injection yielded predictive models for early-onset CRS or NE that can be used for toxicity prediction, monitoring, and management. High-performance hybrid or mechanistic models supported the importance of T cell viability (product cytocompatibility) and conditioning-related elevations of factors (IL-15 and CCL2) that influence toxicity. Example 2: Prediction of early cytokine release syndrome and neurological events

[0132] This embodiment describes the data used to construct the algorithm in Embodiment 1, and the procedure for developing a predictive algorithm, which includes feature screening and selection, multivariate modeling, model evaluation, and classification of the test population by a predictive algorithm. data

[0133] All analyses were performed on a safety analysis set of ZUMA1 patients (i.e., those who received any amount of axicapbutagensilol eucel) on the cutoff date of November 6, 2019.

[0134] The population included (a) Phase 1 and Phase 2 cohorts 1 and 2 at the 36-month cutoff (Phase 1 had 7 subjects with DLBCL, PMBCL, or TFL; Phase 2 cohort 1 had 77 subjects with refractory DLBCL; and Phase 2 cohort 2 had 24 subjects with refractory PMBCL and TFL), (b) Phase 2 cohort 3 (38 subjects with relapsed or refractory transplant-ineligible DLBCL, PMBCL, or TFL), and (c) Phase 2 cohort 4 (41 subjects with relapsed or refractory DLBCL, PMBCL, TFL, or HGBCL after two or more systemic therapies).

[0135] The following timeframes were considered: Day 1, Day 0, Day 1, Day 2; Day 2, Day 0, Day 1, Day 2, Day 3; and Day 0, Day 1, Day 2, Day 3, Day 4. For each of the above timeframes, the following three outpatient definitions were defined (see Figure 1 and Table 1). Definition A: A patient who, within a given time frame, meets both of the following criteria: (a) worst grade 1 or no CRS (i.e., CRS worst grade 1 or less), and (b) no neurological events (NE). Definition B: A patient who does not develop either CRS or NE within a given time frame. Definition C (proposed by Medical Affair and Clinical Research).

[0136] Patients who did not meet the above criteria for "outpatients" were assigned as "inpatients" according to each definition. [Table 1] Note: In Definition C, the time frame is a condition for defining "outpatient" or "inpatient." For example, if days 0-2 are given, all criteria must be confirmed within days 0, 1, and 2 after infusion. Covariate and feature selection

[0137] Covariates (or >1500, measured before axi-cel injection 227) included baseline products, patient and tumor characteristics, and inflammatory soluble blood biomarker levels. The main categories of covariates or analytes included: Baseline characteristics, such as ECOG performance, disease type, disease stage, International Prognostic Index (IPI) category, tumor burden, etc. Laboratory analytes in both chemistry and hematology; Serum cytokines and inflammatory markers; Product characteristics, including product cell viability, number and percentage of CD4 and CD8 cells, as well as CD4 / CD8 ratio, phenotype / re-gated phenotype related to CD4 and CD8, IFN-gamma in co-culture, and more; Cell proliferation information, including cell doubling time (in days) and proliferation rate.

[0138] The data was randomly split into a training set (e.g., 70% of the sample) to fit the model, and the test set (e.g., the remaining 30% of the sample) was used to provide an unbiased evaluation of the model's performance. Univariate screening

[0139] Univariate analysis is performed on each covariate one at a time, where the association between the covariates and the status of outpatients / inpatients is evaluated, and these variables that meet the screening criteria are selected for multivariate analysis. Used in modeling. Feature selection using an analytical approach

[0140] After performing K-Nearest Neighbor (KNN) imputation on the missing data, the following statistical and model-based approaches were applied to features that passed univariate screening. Features were ranked, and the top-ranked features were selected by each of these approaches. Features selected by three, four, or all five of the methods described below may be considered "analytically important" features.

[0141] Weight of evidence and informational value: The weight of evidence (WOE) + informational value (IV) is used to estimate the predictive power of a feature for the desired outcome. This is a simple method. WOE divides the data for each feature into several bins (e.g., j=10 bins) and calculates the predictive power (i.e., "evidence") of the feature for the outcome within each bin. Then, for each feature, IV combines the WOEs of all bins into a single score, which is IV = Σ j (Percentage of non-events) j - Proportion of events j ) * WOE jIt is calculated as follows. Features with higher IV values ​​are selected as candidates for the machine learning model (for example, an IV value of 0.3 or higher or 0.5 or higher is considered "moderately good" or "good," respectively).

[0142] SelectkBest using ANOVA: SelectkBest is a univariate feature selection method used to identify features that best explain outcomes. Specifically, for each feature, an analysis of variance (ANOVA) was performed to calculate the corresponding F-statistic, which represents the ratio of explained variance to unexplained variance between the feature and the outcome. The SelectKBest function then selected the feature with the highest k-score, e.g., the lowest p-value, as the "best" feature.

[0143] Extra Tree Classifier: An extra tree classifier (also known as a highly randomized tree) is a type of ensemble learning technique that aggregates the results of many uncorrelated decision trees into a "forest" to output classification results. Using Gini importance, it is possible to select the features with the highest importance (e.g., 30 features) when predicting outcomes.

[0144] Recursive Feature Elimination (RFE): Recursive Feature Elimination (RF) E) is applied to a fitted model with importance weights assigned to features (e.g., model coefficients, importance attributes), and the worst-performing features for the model are eliminated until the desired number of features are achieved. The top-ranked features, e.g., 30 features, may be selected for model construction.

[0145] RFE-based logistic regression: RFE was applied to a logistic regression model using variable importance defined by model coefficients.

[0146] RFE-based random forest: RFE is applied to a model estimated using a random forest, splits are determined using specific criteria (e.g., the Gini exponent is used as the default), and variable importance is evaluated using feature importance scores. Feature selection by SME (Subject Matter Specialist)

[0147] The Subject Expert (SME) examines a list of analytically significant features from univariate and multivariate approaches, considers clinical feasibility, and provides the following three categories of covariates for further analysis. Clinical covariates. For example, tumor-related factors (LDH, dose), disease stage, blood cell counts (WBC, R). BC), cell-related analytes (Hgb), metabolic state-related analytes; Mechanical covariates; for example, product cell viability, IL-15 on day 0, MCP-1 on day 0, cytokines, chemokines, and other product attributes; and Hybrid (clinical + mechanical) covariates.

[0148] A list of covariates was generated as importable candidates for building the classification model. Multivariate modeling using machine learning algorithms

[0149] Five machine learning algorithms were applied to the covariates in each of these lists (clinical covariates, mechanical covariates, and hybrid). All classification algorithms rely on a set of hyperparameters that are "tuned" to find the combination that yields optimal performance. The model with the best predictive performance among the five machine learning algorithms was considered the Best Performance Model (BPM). A brief description of these machine learning algorithms is provided below.

[0150] Logistic Regression: Logistic regression is a parametric method that models the log-odds of the probabilities of binary events occurring as a linear combination of features. Our approach uses a randomly undersampled dataset supplied to the logistic regression algorithm, which we call LOGREGRUS (Logistic Regression with Random Under Sampling).

[0151] Random Forest: Random forests are ensemble learning methods designed to reduce the variance that can arise from a single model (i.e., a decision tree). Random forest classification utilizes bootstrap aggregation (bagging), a technique in which the training data is first bootstrapped, predictions are made, and then the results from the individual models are aggregated to make a more accurate overall prediction. This example is RFCRUS (Random Forest Classifier with Random Under Sampling). We used a random undersampling dataset supplied to a random forest algorithm, which is referred to as a Stroke classifier.

[0152] Extreme Gradient Boosting (XGBoost): Booster Gradient boosting is an ensemble machine learning technique in which many weak learners (e.g., decision trees) are iteratively combined to form a final strong learner. Models are added sequentially until no further improvements can be made. Gradient boosting refers to an implementation of boosting that uses an arbitrary differentiable loss function and a gradient descent optimization algorithm. Extreme gradient boosting refers to a rapid and efficient implementation of the gradient boosting algorithm. This embodiment is XGBCRUS (XGBoost Classifier with Random Under Sampling). We used a randomly undersampling dataset supplied to XGBoost, which is referred to as an XGBoost classifier with undersampling.

[0153] Balanced Random Forest Classifier (BRFC) The Balanced Random Forest Classifier (BRFC) differs from the Random Forest Classifier in that it uses a balanced bootstrap sample of the training data. This is because it does not preprocess the training data before training the Random Forest Classifier, and therefore differs from the random undersampling datasets fed to the Random Forest algorithm.

[0154] Random undersampling boost classifier (RUSBoost): Adaptive boosting (AdaBoost) uses multiple weak classifiers (i.e., decision strains). This is an ensemble boosting machine learning method that attempts to combine ) into a single strong classifier. This adaptively reweights the training samples based on the classification from previous learners, Larger weights are assigned to misclassified samples. The final prediction is a weighted average of all weak learners, with more weight given to strong learners. Random Under-Sampling Boost (RUSBoost) is a boosting algorithm. By randomly undersampling in each iteration of the rhythm, AdaBoost can be adapted to cases with imbalanced data. Model Evaluation

[0155] Receiver Operating Characteristic (ROC) and Area of ​​Convergence (AUC): The Receiver Operating Characteristic (ROC) curve is a method for evaluating and comparing the performance of classification models. The false positive rate and true positive rate for a classifier are evaluated across a grid of possible (predicted probability) cut points that define whether an observation is classified as an event or a non-event, and these values ​​are plotted. The Area Under the ROC curve (AUC) can also be calculated.

[0156] Tables 2-6 show the selected covariates and AUC from BPM, where BPM was selected as having the highest AUC among the five machine learning algorithms from the test data. [Table 2] Covariates that are positively and negatively associated with all nine outpatient definitions are indicated by ↑ and ↓, respectively. Covariates that had different directions of association across the nine outpatient definitions are:

number

[0157] Once the best covariates were identified, this embodiment classified the test population using two approaches. The performance of the classification for the test population was measured by a confusion matrix.

[0158] Confusion Matrix: A confusion matrix for a classifier summarizes the number of accurate and inaccurate predictions for each class in the form of a contingency table. The confusion matrix is ​​useful for understanding the predictive accuracy of the classifier and the types of errors it is likely to make. Accuracy (accuracy represents the proportion of observations that are accurately classified into either the positive or negative true class), sensitivity (true positive rate), and specificity (true negative rate) are calculated from the numbers in the confusion matrix. Model-based approach

[0159] In this example, BPM was applied to the training data to obtain prediction probabilities, and then an ROC curve was created based on the prediction probabilities of the subjects from the training data. The optimal cut point was selected as the cutoff value where the Yoden exponent was maximized (Yorden exponent = sensitivity + specificity - 1). Subjects with prediction probabilities above this cutoff value were classified as "outpatients." Other patients were classified as "inpatients."

[0160] BPM for A3: For the minimal mechanism model for outpatient-defined A3 (using cell viability + IL-15 on day 0 + MCP-1 on day 0 as covariates), this example selected Random Forest (RF) as the best-performing algorithm. BP The ROC and box plot (RFCRUS, optimal cutoff: 0.538) of M are shown in Figures 2 and 3, along with cell viability + IL-15 + MCP-1 for outpatient A3. The confusion matrix is ​​shown in Table 7. [Table 7] Sensitivity: 0.7115, Specificity: 0.7500, Accuracy: 0.7308 Subjects with a predicted probability greater than 0.538 are classified as "outpatients".

[0161] Figure 4 shows a box plot of the predicted BPM for outpatient A3, based on experimental data including cell viability, IL-15, and MCP-1. The confusion matrix is ​​shown in Table 7. [Table 8] Sensitivity: 0.7000, Specificity: 0.7143, Accuracy: 0.7073 Subjects with a predicted probability greater than 0.538 are classified as "outpatients". Tree-based approach

[0162] Next, this embodiment selects the training data that constitutes the root node of the tree. Decision trees were constructed by splitting the best covariates into subsets that constitute successors. The splits were based on a set of splitting rules based on classification features. The decision trees can be described as a combination of splits for the selected best covariates to classify objects with high accuracy. The resulting decision trees are illustrated in Figure 5 (training data) and Figure 6 (test data). The corresponding confusion matrices are shown in Tables 9 and 10. [Table 9] Sensitivity: 0.6275, Specificity: 0.7500, Accuracy: 0.6916 [Table 10] Sensitivity: 0.6000, Specificity: 0.9048, Accuracy: 0.7561 Direction

[0163] Next, this embodiment demonstrated the relationship between disease toxicity and covariates by using a partial dependency plot to leverage the effects of other covariates in the machine learning model. The plot is presented in Figure 7. The plot suggests that the cutoff value for cell viability is approximately 95%, the cutoff value for IL-15 is approximately 28 pg / mL, and the cutoff value for CCL2 is approximately 1300 pg / mL.

[0164] The directionality of covariates associated with the onset of toxicity can also be presented by the estimated coefficients in the logistic regression of outpatients (yes / no) ~ cell survival rate + IL-15 + MCP-1. Negative coefficients indicate that all three covariate mechanisms were positively associated with early-onset toxicity (Table 11).

Table 11

[0165] Unless otherwise defined, all technical and scientific terms used herein have the same meaning as commonly understood by one of ordinary skill in the art to which this invention belongs.

[0166] The invention illustrated herein can be suitably implemented in the absence of any element or elements, limitation or limitations not specifically disclosed herein. For example, terms such as "comprising," "including," "containing" are to be read expansively and not restrictively. Further, the terms and expressions used herein are used as terms of description and not of limitation, and there is no intention to exclude any equivalents of the features shown and described or portions thereof, but it is recognized that various modifications are possible within the scope of the claimed invention.

[0167] Therefore, although the invention has been specifically disclosed by way of preferred embodiments, it is to be understood that any optional features, modifications, improvements, and variations of the invention embodied herein disclosed in this specification may be used by those skilled in the art, and such modifications, improvements, and variations are considered to be within the scope of the invention. The materials, methods, and examples provided herein are representative of preferred embodiments, are exemplary, and are not intended as limitations on the scope of the invention.

[0168] The present invention is described extensively and comprehensively herein. Each of the narrower species and subgenera belonging within the comprehensive disclosure also forms part of the present invention. This includes a comprehensive description of the present invention with the conditional or negative limitation of removing any subject from a genus, regardless of whether the removed material is specifically stated herein.

[0169] In addition, if any feature or aspect of the present invention is described in terms of the Markush group, a person skilled in the art will recognize that the present invention is also described in terms of any individual member or subgroup of a member of the Markush group.

[0170] All publications, patent applications, patents, and other references referred to herein are incorporated in whole by reference to the same extent that each is incorporated by reference individually. In case of any conflict, this specification, including definitions, shall prevail.

[0171] While this disclosure has been described in conjunction with the embodiments described above, it should be understood that the foregoing description and examples are illustrative and not intended to limit the scope of this disclosure. Other aspects, advantages, and modifications within the scope of this disclosure will be apparent to those skilled in the art to whom this disclosure relates.

Claims

1. A method for identifying whether a patient is likely to experience toxicity after cell therapy, The procedure involves measuring the level of at least one of IL-15 (interleukin-15) and MCP-1 (monocyte chemotactic protein-1) in the patient's blood sample, This includes identifying that the patient is likely to experience toxicity after the cell therapy if the level of IL-15 or MCP-1 is higher than the corresponding reference level, or identifying that the patient is not likely to experience toxicity after the cell therapy if the level of IL-15 or MCP-1 is lower than the corresponding reference level. The cell therapy is a method comprising the administration of immune cells.

2. The method according to claim 1, further comprising preventing or treating the toxicity in the patient when the patient is identified as being highly likely to experience the toxicity.

3. The method according to claim 2, wherein treatment or prevention comprises the administration of a drug selected from the group consisting of antihistamines, corticosteroids, antihypotensive agents, IL-6 inhibitors, GM-CSF inhibitors, and nonsteroidal anti-inflammatory drugs.

4. The method according to claim 3, wherein the treatment or prevention comprises the administration of an agent selected from the group consisting of tocilizumab, dexamethasone, levetiracetam, lenzirumab, methylprednisolone, anakinra, siltuximab, ruxolitinib, cyclophosphamide, IVIG (intravenous immunoglobulin), and ATG (anti-thymocyte globulin).

5. The method according to any one of claims 1 to 4, wherein the immune cells include T cells that have been modified to express a chimeric antigen receptor (CAR).

6. The method according to claim 5, wherein the CAR has binding specificity to the CD19 (differentiation antigen group 19) protein.

7. The method according to any one of claims 1 to 6, wherein the blood sample is a serum sample obtained from the patient before the cell therapy.

8. The method according to claim 7, wherein the blood sample is obtained after preconditioning treatment of the patient.

9. The method according to claim 8, wherein the preconditioning treatment reduces the number of lymphocytes in the patient.

10. The method according to any one of claims 1 to 9, wherein the toxicity is selected from the group consisting of cytokine release syndrome (CRS), neurological events (NE), and combinations thereof.

11. The method according to claim 10, wherein the toxicity is an early-onset toxicity.

12. The method according to claim 11, wherein the early onset toxicity occurs within four days after the cell therapy.

13. The method according to any one of claims 1 to 12, wherein the reference level for IL-15 or MCP-1 is determined from patients who experience the toxicity after the cell therapy and patients who do not experience the toxicity after the cell therapy.

14. The method according to any one of claims 1 to 13, further comprising measuring the viability of cells used in the cell therapy, wherein if the IL-15 or MCP-1 level is higher than the corresponding reference level and the cell viability is higher than the reference cell viability, the patient is identified as likely to experience toxicity after the cell therapy, or if the IL-15 or MCP-1 level is lower than the corresponding reference level and the cell viability is lower than the reference cell viability, the patient is identified as not likely to experience toxicity after the cell therapy.

15. The method according to any one of claims 1 to 14, further comprising obtaining one or more levels of baseline hemoglobin, baseline tumor mass, baseline LDH, baseline creatinine, and baseline calcium in the patient.

16. A method for preventing or treating toxicity in patients receiving cell therapy, Identifying whether the patient is likely or unlikely to experience toxicity after cell therapy, The procedure involves measuring the level of at least one of IL-15 (interleukin-15) and MCP-1 (monocyte chemotactic protein-1) in the patient's blood sample, Identifying that, if the level of IL-15 or MCP-1 is higher than the corresponding reference level, the patient is likely to experience toxicity after the cell therapy, or if the level of IL-15 or MCP-1 is lower than the corresponding reference level, the patient is not likely to experience toxicity after the cell therapy. A method comprising administering to a patient an agent that prevents or treats cytokine release syndrome (CRS) or neurological events (NE) if the patient is identified as being highly likely to experience toxicity after the cell therapy.

17. The method according to claim 16, wherein the drug is selected from the group consisting of antihistamines, corticosteroids, antihypotensive agents, IL-6 inhibitors, GM-CSF inhibitors, and nonsteroidal anti-inflammatory drugs.

18. The method according to claim 16, wherein the drug is selected from the group consisting of tocilizumab, dexamethasone, levetiracetam, lenzirumab, methylprednisolone, anakinra, siltuximab, ruxolitinib, cyclophosphamide, IVIG (intravenous immunoglobulin), and ATG (antithymocyte globulin).

19. The method according to claim 16, further comprising measuring the viability of cells used in the cell therapy, wherein if the IL-15 or MCP-1 level is higher than the corresponding reference level and the cell viability is higher than the reference cell viability, the patient is identified as likely to experience toxicity after the cell therapy, or if the IL-15 or MCP-1 level is lower than the corresponding reference level and the cell viability is lower than the reference cell viability, the patient is identified as not likely to experience toxicity after the cell therapy.

20. The method according to any one of claims 16 to 19, further comprising obtaining one or more levels of baseline hemoglobin, baseline tumor mass, baseline LDH, baseline creatinine, and baseline calcium in the patient.

21. A kit or package useful for identifying patients who are likely to experience toxicity after cell therapy, comprising a polynucleotide primer or probe or antibody for measuring the expression levels of IL-15 and MCP-1 in a biological sample.

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