CN117813503A

CN117813503A - Monitoring and management of cell therapy induced toxicity

Info

Publication number: CN117813503A
Application number: CN202280051987.2A
Authority: CN
Inventors: Q·宋
Original assignee: Kite Pharma Inc
Current assignee: Kite Pharma Inc
Priority date: 2021-07-30
Filing date: 2022-07-29
Publication date: 2024-04-02

Abstract

The present disclosure relates generally to compositions and methods for identifying a cell therapy patient as likely to experience toxicity or less likely to experience toxicity after cell therapy. These methods are based on the discovery that pre-treatment covariates, such as serum IL-15 and MCP-1 levels in the patient or viability of the administered cells, can be used to predict the likelihood of such toxic onset. Once a patient is identified as likely to experience toxicity or less likely to experience toxicity, compositions and methods for monitoring and managing such toxicity are also provided.

Description

Monitoring and management of cell therapy induced toxicity

Cross Reference to Related Applications

The present application claims priority from U.S. provisional patent application No. 63/227,677 filed on day 30, 7, 2021 and U.S. provisional patent application No. 63/279,615 filed on day 15, 11, 2021, each of which is hereby incorporated by reference in its entirety.

Technical Field

The present disclosure relates to methods for determining whether a patient is likely to experience toxicity following cell therapy treatment.

Background

Chimeric antigen receptor T cells (also referred to as CAR T cells) are T cells that have been genetically engineered to produce artificial T cell receptors for use in immunotherapy. CAR-T therapy has the potential to improve management of lymphomas and possibly solid cancers. Two anti-CD 19 CAR T cell products, alemtujose (axi-cel) and telapraxise, have been approved for the management of relapsed/refractory large B cell lymphomas.

However, CAR-T therapy is associated with two common toxicities, cytokine Release Syndrome (CRS) and immune effector cell associated neurotoxicity syndrome (ICANS), which are commonly observed acutely after therapy. In addition, advanced toxicity includes prolonged cytopenia and tumor-shedding effects at the target.

CRS is a systemic inflammatory response triggered by the release of cytokines by CAR-T cells upon tumor recognition. CAR-T cells may also activate bystander immune cells such as macrophages, which in turn release inflammatory cytokines and contribute to the pathophysiological mechanisms of CRS. CRS usually occurs with symptoms of fever, myalgia, stiffness, fatigue, and loss of appetite. CRS may also lead to multiple organ dysfunction.

ICANS may occur during CRS, or more commonly after CRS has resolved. ICANS usually presents as a toxic encephalopathy with difficulty in finding words, aphasia and confusion, but in more severe cases, it may progress to a low level of consciousness, coma, seizures, motor weakness and cerebral edema. The extent of cytokine, chemokine and CAR-T cell expansion has been linked to the severity of neurotoxicity.

For patients receiving CAR-T infusion, CRS and neurotoxicity need to be monitored. Such monitoring needs to be performed daily in a certified health care facility for 7 days, given the potential severity of toxicity. In addition, the patient is instructed to remain in the vicinity of the authenticated healthcare facility for at least 4 weeks after infusion. But such monitoring is costly.

There is a strong need for methods of predicting such toxic episodes so that only those patients in need of toxic treatment must remain on site, which can help to shorten unnecessary hospitalization times. Moreover, those patients predicted to be likely to experience toxicity may receive appropriate treatment or precautions for toxicity.

Disclosure of Invention

The present disclosure provides compositions and methods for identifying a cell therapy patient as likely to experience toxicity or less likely to experience toxicity after cell therapy. These methods are based on the discovery that pre-treatment covariates, such as serum IL-15 and MCP-1 levels in the patient or viability of the administered cells, can be used to predict the likelihood of such toxic onset. Once a patient is identified as likely to experience toxicity or less likely to experience toxicity, compositions and methods for monitoring and managing such toxicity are also provided.

One embodiment provides a method of identifying a patient as likely to experience toxicity or less likely to experience toxicity following cell therapy comprising measuring the level of IL-15 (interleukin-15) or MCP-1 (monocyte chemotactic protein-1) in a blood sample of the patient, identifying the patient as likely to experience toxicity following cell therapy when the IL-15 or MCP-1 level is above a corresponding reference level, or identifying the patient as less likely to experience toxicity following cell therapy when the IL-15 or MCP-1 level is below a corresponding reference level, wherein the cell therapy comprises administering immune cells.

In some embodiments, the immune cells comprise T cells. In some embodiments, the T cells are engineered to express a Chimeric Antigen Receptor (CAR). In some embodiments, the CAR has binding specificity for CD19 (cluster of differentiation 19) protein. In some embodiments, the cell therapy comprises alemtuquor.

In some embodiments, the blood sample is a serum sample. In some embodiments, the blood sample is obtained from the patient prior to cell therapy. In some embodiments, the blood sample is obtained after a pre-adaptation treatment of the patient. In some embodiments, the pre-adaptation treatment reduces lymphocytes in the patient. In some embodiments, the pre-adaptation treatment comprises intravenous (iv) administration of cyclophosphamide and fludarabine on day 5, day 4, and/or day 3 prior to cell therapy.

In some embodiments, the toxicity is selected from the group consisting of: cytokine Release Syndrome (CRS), neural Events (NEs), and combinations thereof. In some embodiments, the toxicity is early onset toxicity. In some embodiments, early-onset toxicity occurs within four days after cell therapy.

In some embodiments, the reference level of IL-15 or MCP-1 is determined from patients who experience toxicity following cell therapy and from patients who do not experience toxicity following cell therapy.

In some embodiments, the method further comprises measuring the viability of the cells used in the cell therapy, wherein the patient is identified as likely to experience toxicity after the cell therapy when the IL-15 or MCP-1 level is above the corresponding reference level and the cell viability is above the reference cell viability, or wherein the patient is identified as unlikely to experience toxicity after the cell therapy when the IL-15 or MCP-1 level is below the corresponding reference level and the cell viability is below the reference cell viability.

In some embodiments, the patient is identified as likely to experience toxicity after cell therapy when the IL-15 and MCP-1 levels are above the corresponding reference levels and the cell viability is above the reference cell viability, or wherein the patient is identified as unlikely to experience toxicity after cell therapy when the IL-15 and MCP-1 levels are below the corresponding reference levels and the cell viability is below the reference cell viability.

In some embodiments, the method further comprises obtaining one or more of the following levels in the patient: baseline hemoglobin, baseline tumor burden, baseline LDH, baseline creatinine, and baseline calcium.

In some embodiments, the method further comprises monitoring the patient for toxicity in the medical care facility when the patient is identified as likely experiencing toxicity.

In some embodiments, the method further comprises preventing or treating toxicity in the patient when the patient is identified as likely to experience toxicity. In some embodiments, treating or preventing comprises administering an agent selected from the group consisting of: antihistamines, corticosteroids, antihypertensives, IL-6 inhibitors, GM-CSF inhibitors, and non-steroidal anti-inflammatory drugs. In some embodiments, treating or preventing comprises administering an agent selected from the group consisting of: tozucchini, dexamethasone, levetiracetam, rentzia, methylprednisolone, anakinra, stetuximab, lu Suoti, cyclophosphamide, IVIG (intravenous immunoglobulin) and ATG (anti-thymus cytoglobulin).

In some embodiments, the method further comprises releasing the patient from the medical care facility within two days after the patient enters the medical care facility when the patient is identified as unlikely to experience toxicity.

In one embodiment, kits or packages useful for identifying a patient as likely to experience toxicity following cell therapy are also provided that include polynucleotide primers or probes or antibodies for measuring the expression levels of IL-15 and MCP-1 in a biological sample.

In one embodiment, there is also provided a method for preventing or treating toxicity in a patient undergoing cell therapy, comprising administering to the patient an agent that prevents or treats Cytokine Release Syndrome (CRS) or a Neurological Event (NE), wherein the patient has been identified as likely to experience toxicity following cell therapy based on the level of IL-15 (interleukin-15) or MCP-1 (monocyte chemotactic protein-1) in a blood sample of the patient being above a corresponding reference level.

In some embodiments, the agent is selected from the group consisting of: antihistamines, corticosteroids, antihypertensives, IL-6 inhibitors, GM-CSF inhibitors, and non-steroidal anti-inflammatory drugs. In some embodiments, the agent is selected from the group consisting of: tozucchini, dexamethasone, levetiracetam, rentzia, methylprednisolone, anakinra, stetuximab, lu Suoti, cyclophosphamide, IVIG (intravenous immunoglobulin) and ATG (anti-thymus cytoglobulin).

In one embodiment, there is also provided a computer program product for use in conjunction with a computer system, the computer program product comprising a computer-readable storage medium and a computer program mechanism embedded therein, the computer program mechanism comprising executable instructions for performing a method of identifying a patient as likely to experience toxicity following cytotherapy, wherein the instructions comprise: (i) Obtaining a level of IL-15 (interleukin-15) or MCP-1 (monocyte chemotactic protein-1) in a blood sample of the patient; and (ii) comparing the level to a corresponding reference level, wherein when the IL-15 or MCP-1 level is above the corresponding reference level, the patient is identified as likely to experience toxicity following a cell therapy, wherein the cell therapy includes administration of immune cells.

Drawings

Fig. 1 shows the patient condition in definition C.

FIG. 2 shows the ROC of BPM using cell viability +IL-15+MCP-1 for outpatient A3. Here, BPM is rfcrous and the optimal cut-off value is 0.538.

FIG. 3 shows a box plot of predictions of training data for outpatient A3, where BPM employs cell viability +IL-15+MCP-1.

FIG. 4 shows a box plot of predictions of test data for outpatient A3, where BPM employs cell viability +IL-15+MCP-1.

FIG. 5 shows a decision tree employing cell viability +IL-15+MCP-1 for training data for outpatient A3; subjects with "N" on the leaf were classified as "hospitalized"; subjects with "Y" on the leaf were classified as "outpatients".

FIG. 6 shows a decision tree employing cell viability +IL-15+MCP-1 for test data for outpatient A3; subjects with "N" on the leaf were classified as "hospitalized"; subjects with "Y" on the leaf were classified as "outpatients".

FIG. 7 shows a partial dependence graph (based on balanced RF) showing that higher cell viability, IL-15 and MCP-1 are associated with higher likelihood of early onset toxicity.

Fig. 8 is a schematic diagram showing computing components that may be used to implement various features of embodiments described in this disclosure.

Detailed Description

Definition of the definition

The following description sets forth exemplary embodiments of the present technology. However, it should be recognized that such description is not intended as a limitation on the scope of the present disclosure, but is instead provided as a description of exemplary embodiments.

Definition of the definition

As used in this specification, the following words, phrases and symbols are generally intended to have the meanings described below, unless the context in which they are used indicates otherwise.

As used herein, certain terms may have the meanings defined below. As used in the specification and in the claims, the singular forms "a," "an," and "the" include both the singular and the plural, unless the context clearly dictates otherwise. For example, the term "cell" includes a single cell as well as a plurality of cells, including mixtures thereof.

All numerical designations, such as pH, temperature, time, concentration and molecular weight, including ranges, are approximations that vary by 0.1 (+) or (-). It should be understood that all numerical designations are preceded by the term "about", although not always explicitly stated. The term "about" also includes the exact value "X" plus a small increment of "X", such as "X+0.1" or "X-0.1". It is also to be understood that the agents described herein are merely exemplary and that equivalents of such agents are known in the art, although not always explicitly stated.

The term "immunotherapy" refers to the treatment of a subject suffering from a disease or at risk of developing a disease or recurrence by a method comprising inducing, enhancing, suppressing, or otherwise altering an immune response. Examples of immunotherapy include, but are not limited to In T cell therapy. T cell therapies may include adoptive T cell therapy, tumor Infiltrating Lymphocyte (TIL) immunotherapy, autologous cell therapy, engineered autologous cell therapy (eACT) ^TM ) And allogeneic T cell transplantation. However, one of skill in the art will recognize that the conditioning methods disclosed herein will enhance the efficacy of any transplanted T cell therapy. Examples of T cell therapies are described in U.S. patent publication nos. 2014/0154228 and 2002/0006409, U.S. patent No. 7,741,465, U.S. patent No. 6,319,494, U.S. patent No. 5,728,388, and international publication No. WO 2008/081035. In some embodiments, the immunotherapy comprises CAR T cell therapy. In some embodiments, the CAR T cell therapy product is administered via infusion.

The T cells for immunotherapy may be from any source known in the art. For example, T cells may be differentiated from a population of hematopoietic stem cells in vitro, or may be obtained from a subject. T cells may be obtained, for example, from Peripheral Blood Mononuclear Cells (PBMCs), bone marrow, lymph node tissue, cord blood, thymus tissue, tissue from an infection site, ascites, pleural effusion, spleen tissue, and tumors. In addition, T cells may be derived from one or more T cell lines available in the art. Various techniques known to the skilled artisan (such as FICOLL ^TM Isolation and/or apheresis) to obtain T cells from a blood unit collected from a subject. Additional methods for isolating T cells for T cell therapy are disclosed in U.S. patent publication No. 2013/0287748, which is incorporated herein by reference in its entirety.

As used herein, "cytokine" refers to a non-antibody protein released by one cell in response to contact with a specific antigen, wherein the cytokine interacts with a second cell to mediate a response in the second cell. As used herein, "cytokine" refers to a protein released by one cell population that acts as an intercellular mediator on another cell. Cytokines may be expressed endogenously by the cells or administered to the subject. Cytokines can be released by immune cells (including macrophages, B cells, T cells, and mast cells) to spread the immune response. Cytokines can induce various responses in the recipient cells. Cytokines may include homeostatic cytokines, chemokines, pro-inflammatory cytokines, effectors, and acute phase proteins. For example, steady state cytokines, including Interleukins (IL) 7 and IL-15, promote immune cell survival and proliferation, and 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, but are not limited to, IL-1a, IL-1b, IL-6, IL-13, IL-17a, tumor Necrosis Factor (TNF) -alpha, TNF-beta, fibroblast Growth Factor (FGF) 2, granulocyte macrophage colony-stimulating factor (GM-CSF), soluble intercellular adhesion molecule 1 (sICAM-1), soluble vascular cell adhesion molecule 1 (sVCAM-1), vascular Endothelial Growth Factor (VEGF), VEGF-C, VEGF-D, and placental growth factor (PLGF). 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).

A "chemokine" is a cytokine that mediates chemotaxis or directed movement of cells. Examples of chemokines include, but are not limited to, IL-8, IL-16, eosinophil-activating chemokine-3, macrophage-derived chemokine (MDC or CCL 22), monocyte-chemotactic protein 1 (MCP-1 or CCL 2), MCP-4, macrophage inflammatory protein 1 alpha (MIP-1 alpha, MIP-1 a), MIP-1 beta (MIP-1 b), gamma-inducible protein 10 (IP-10), and thymus activation-regulating chemokine (TARC or CCL 17).

The term "genetically engineered" or "engineered" refers to a method of modifying the genome of a cell, including but not limited to deleting a coding region or non-coding region or a portion thereof, or inserting a coding region or a portion thereof. In some embodiments, the modified cell is a lymphocyte, such as a T cell, which can be obtained from a patient or donor. The cells can be modified to express an exogenous construct, such as a Chimeric Antigen Receptor (CAR) or T Cell Receptor (TCR), that is incorporated into the cell genome.

As used herein, "patient" includes any person suffering from cancer (e.g., lymphoma or leukemia). The terms "subject" and "patient" are used interchangeably herein.

The terms "decrease" and "decrease" are used interchangeably herein and indicate any change that is less than the original value. "decrease" and "decrease" are relative terms that require a comparison between before and after measurement. "decrease" and "decrease" include complete depletion. Similarly, the term "increase" means any change above the original value. "increasing", "higher" and "lower" are relative terms that require comparison between before and after measurement and/or between reference standards. In some embodiments, the reference value is obtained from a value of a general population, which may be a general patient population. In some embodiments, the reference value is from a quartile analysis of a general patient population.

"treatment" of a subject refers to any type of intervention or procedure performed on the subject, or administration of an active agent to the subject, with the purpose of reversing, alleviating, ameliorating, inhibiting, slowing or preventing the onset, progression, development, severity or recurrence of symptoms, complications or disorders associated with the disease, or biochemical indicators. In some embodiments, "treating" includes partial remission. In another embodiment, "treating" or "treatment" includes complete remission.

The present disclosure also provides diagnostic, prognostic, and therapeutic methods that are based at least in part on determining the expression level of a gene of interest identified herein.

For example, information obtained using the diagnostic assays described herein can be used to determine whether a subject is likely to have a disease (e.g., cytokine release syndrome), is likely to have the disease, or is suitable for treatment. Based on the diagnostic/prognostic information, the physician can recommend a treatment regimen.

As used throughout, the term "likely" means that the probability of occurrence is higher than the probability of non-occurrence, or alternatively, that the probability of occurrence is higher than a predetermined control average. By way of non-limiting example, a patient that may experience toxicity after cell therapy refers to a patient that has a higher probability of experiencing toxicity than does not. Alternatively, a patient who may experience toxicity following cell therapy refers to a patient who experiences a higher statistical chance of toxicity than the average incidence of toxicity in a population of patients treated with cell therapy. Those of ordinary skill in the art will recognize additional definitions beyond those described above.

It will be appreciated that the information obtained using the diagnostic assays described herein may be used alone or in combination with other information such as, but not limited to, behavioral assessment, genotype or expression level of other genes, clinical chemistry parameters, histopathological parameters, or age, sex and weight of the subject.

Prediction and management of early-onset acute toxicity

For cancer patients receiving current CAR-T treatment, it is desirable to monitor the signs and symptoms of CRS and neurotoxicity daily in a certified healthcare facility following CAR-T infusion. Patients with Cytokine Release Syndrome (CRS) and Neurological Events (NE) of grade No. 3 require intensive management of hospitalized patients.

Using machine learning techniques, the present disclosure describes compositions and methods for predicting early-onset acute toxicity in patients receiving CAR-T treatment. Based on such predictions, the present disclosure also provides methods for preventing toxicity in patients at risk of experiencing toxicity and treating such toxicity on demand.

As demonstrated in the examples, multivariate analysis and machine learning of data obtained from evaluable patients in patients participating in CAR-T therapy clinical trials produced several comparable predictive models of early-onset CRS or NE, with ROC (receiver operating characteristics) AUC under training (area under ROC curve) >0.8 for the best performing model, ROC AUC under test >0.7.

Each of these covariates, when used alone, is independently associated with the likelihood of toxicity. In summary, the predictive power is further enhanced. 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 (CCL 2) levels on day 0 prior to infusion. Additional exemplary covariates include hemoglobin levels, albumin levels, red blood cell counts, and ferritin levels (day 0 prior to infusion); blood concentrations (levels) of urates, calcium, phosphate, creatinine, chloride, LDH (lactate dehydrogenase) and IL-17 (at baseline); and red blood cell count, white blood cell count, neutrophil count, and basophil count (at baseline).

According to one embodiment of the present disclosure, a method of identifying a patient as likely to experience toxicity following cell therapy is provided. In some embodiments, the method entails measuring the level of IL-15 (interleukin-15) in a patient sample. It has been found herein that higher IL-15 levels are associated with a higher incidence of toxicity following cell therapy. Thus, the method also requires identifying the patient as likely to experience toxicity following cell therapy when IL-15 levels are above a reference level (or cutoff level).

According to one embodiment of the present disclosure, a method of identifying a patient as likely to experience toxicity following cell therapy is provided. In some embodiments, the method entails measuring the level of MCP-1 (monocyte chemotactic protein-1) in a patient sample. It has been found herein that higher MCP-1 levels are associated with a higher incidence of toxicity following cell therapy. Thus, the method also requires identifying the patient as likely to experience toxicity following cell therapy when IL-15 levels are above a reference level (or cutoff level).

According to one embodiment of the present disclosure, a method of identifying a patient as likely to experience toxicity following cell therapy is provided. In some embodiments, the method entails measuring cell viability. It has been found herein that higher viability of infused cells correlates with a higher incidence of toxicity following cell therapy. Thus, the method also requires identifying the patient as likely to experience toxicity following cell therapy when cell viability is above a reference level (or cutoff level).

In some embodiments, the measurements useful for predicting toxic episodes are measurements for any one or more of the following covariates: blood hemoglobin levels, albumin levels, red blood cell counts, and ferritin levels (day 0 prior to infusion); blood concentrations (levels) of urates, calcium, phosphate, creatinine, chloride, LDH (lactate dehydrogenase) and IL-17 (at baseline); and red blood cell count, white blood cell count, neutrophil count, and basophil count (at baseline).

In some embodiments, a 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.

In some embodiments, blood samples are obtained from a patient according to a specified time point. For example, for baseline covariates, blood samples were drawn prior to initiation of cell therapy. For covariates on day 0, blood samples were drawn on day 0, where day 0 was the day of administration of infusion. In some embodiments, the blood sample is drawn prior to infusion.

In some embodiments, the patient undergoes a pre-adaptation treatment prior to the cell therapy; thus, day 0 follows the pre-adaptation treatment. In some embodiments, the pre-adaptation treatment is the depletion of leukocytes or lymphocytes. Exemplary lymphocyte clearance protocol consists of 500mg/m intravenous cyclophosphamide ² And fludarabine 30mg/m ² Composition, both were given on days 5, 4 and 3 prior to starting CAR-T infusion.

Any of the covariates mentioned above, i.e., IL-15 levels, MCP-1 levels, reference levels of cell viability (cut-off values) can be determined experimentally or from historical data using methods known in the art. The reference level for each corresponding covariate may be determined either before or after measurement. In some embodiments, the reference level is the level that optimally isolates (distinguishes) patients with different toxicity outcomes following the same cell therapy.

In some embodiments, the reference level is a particular value, such as 0.1ng/mL. However, in some embodiments, the reference level is implicit in multiple reference standards. For example, the measured level may be compared to a plurality of reference numbers, each labeled with toxicity or non-toxicity, using a nearest neighbor method. If the measured level is closer to the reference level associated with a patient experiencing toxicity, the measured level predicts that the patient will also experience toxicity. In this example, the specific reference level is not derived from the reference number, but is effectively compared.

In some embodiments, the reference level is implicit in a formula used to calculate the likelihood based on the measured level. For example, a linear or quadratic discriminant analysis formula may be developed based on training data and used to determine the probability number with a measured level as input.

In some embodiments, these covariates may be used in combination. For example, when both IL-15 and MCP-1 levels are above corresponding reference levels, the patient is identified as likely to experience toxicity following cell therapy. In some embodiments, when both IL-15 levels and cell viability are above corresponding reference levels, the patient is identified as likely to experience toxicity following cell therapy. In some embodiments, when both MCP-1 levels and cell viability are above corresponding reference levels, the patient is identified as likely to experience toxicity following cell therapy. In some embodiments, a patient is identified as likely to experience toxicity following cell therapy when IL-15 levels, MCP-1 levels, and cell viability are all above corresponding reference levels. In some embodiments, one or more additional covariates are also included.

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

In some embodiments of the present invention, in some embodiments, the reference level (plasma concentration) of CCL2 is 600pg/mL, 620pg/mL, 640pg/mL, 650pg/mL, 660pg/mL, 680pg/mL, 700pg/mL, 720pg/mL, 740pg/mL, 750pg/mL, 760pg/mL, 780pg/mL, 800pg/mL, 820pg/mL, 840pg/mL, 850pg/mL, 860pg/mL, 880pg/mL, 900pg/mL, 920pg/mL, 940pg/mL, 950pg/mL, 960pg/mL, 980pg/mL, 1000pg/mL, 1020pg/mL 1040pg/mL, 1050pg/mL, 1060pg/mL, 1080pg/mL, 1100pg/mL, 1120pg/mL, 1140pg/mL, 1150pg/mL, 1160pg/mL, 1180pg/mL, 1200pg/mL, 1220pg/mL, 1240pg/mL, 1250pg/mL, 1260pg/mL, 1280pg/mL, 1300pg/mL, 1320pg/mL, 1340pg/mL, 1350pg/mL, 1360pg/mL, 1380pg/mL, 1400pg/mL, 1420pg/mL, 1440pg/mL, or 1450pg/mL.

In some embodiments, the reference level of 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 of product cell viability is 95%.

In some embodiments, the cell therapy is a therapy requiring administration of immune cells. The immune cells may be T cells, natural Killer (NK) cells, monocytes or macrophages, but are not limited thereto.

In some embodiments, the immune cells are engineered to express a Chimeric Antigen Receptor (CAR), resulting in the production of CAR-T cells, CAR-NK cells, but are not limited thereto. In some embodiments, the CAR has binding specificity for a tumor antigen.

A "tumor antigen" is an antigenic substance produced in tumor cells, i.e., triggering an immune response in a host. Tumor antigens can be used to identify tumor cells and are potential candidates for use in cancer therapy. Normal proteins in the body are not antigenic. However, certain proteins are produced or overexpressed during tumorigenesis and thus appear "foreign" to the body. 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 stages of development, or proteins whose structure is modified by mutation.

A large number of tumor antigens are known in the art and new tumor antigens can be readily identified by screening. Non-limiting 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 protein, GD2, GD3, GM2, VEGF, VEGFR, integrins, αvβ3, α5β1, ERBB2, ERBB3, MET, IGF1R, EPHA, TRAILR1, TRAILR2, RANKL, FAP, and tenascin.

In some embodiments, the CAR is specific for any of the tumor antigens discussed above, or for any one or more of CD19, CD20, CLL-1, TACI, MAGE, HPV related proteins, GPC-3, and BCMA. In some embodiments, the CAR has dual specificity for two or more antigens (e.g., CD19 and CD 20).

In some embodiments, the CAR is specific for CD19 (cluster of differentiation 19). An exemplary cell therapy targeting CD19 is alemtuzite. Under the trade nameThe sold form of alzem is a treatment for large B-cell lymphomas that have failed conventional treatments.

In some embodiments, the toxicity is selected from the group consisting of: cytokine Release Syndrome (CRS), neural Events (NEs), and combinations thereof. In some embodiments, the toxicity is early onset toxicity. In some embodiments, early onset toxicity occurs within 5 days, 4 days, 3 days, or 2 days after cell therapy.

Key manifestations of CRS include fever, hypotension, tachycardia, hypoxia, cold tremors and headache. Serious events that may be associated with CRS include cardiac arrhythmias (including atrial fibrillation and ventricular tachycardia), cardiac arrest, heart failure, renal insufficiency, capillary leak syndrome, hypotension, hypoxia, multiple organ failure, and hemophagocytic lymphocytosis/macrophage activation syndrome (HLH/MAS). The CRSs may be classified into four different levels, i.e., levels 1 to 4.

The most common neurotoxicity includes encephalopathy, headache, tremors, dizziness, delirium, aphasia and insomnia. Serious events include leukoencephalopathy and seizures. Neurotoxicity can be categorized into four different classes, namely classes 1 to 4.

Patients may be identified as likely to experience toxicity, type of toxicity, and grade. Thus, monitoring, prevention and treatment may be provided to the patient.

Currently, all patients receiving CAR-T therapy need to be monitored in the healthcare facility, which incurs high costs. Using the techniques of the present invention, patients identified as unlikely to experience toxicity can be monitored with outpatient capability. Those identified as likely to experience toxicity may be monitored as hospitalized patients.

Preventive and/or therapeutic measures may also be taken for those patients identified as likely to experience toxicity. Appropriate preventive/therapeutic measures can be taken depending on the predicted toxicity. For example, for predicted CRS, tolizumab 8mg/kg may be administered intravenously (no more than 800 mg) over 1 hour. Alternatively, 10mg of dexamethasone may be administered intravenously once daily. Furthermore, methylprednisolone may be used for more severe CRS.

For predicted neurotoxicity, tolizumab, dexamethasone, levetiracetam, corticosteroids, and/or methylprednisolone may be used. Alternative prophylactic/therapeutic options include anakinra, stetuximab, lu Suoti ni, cyclophosphamide, IVIG (intravenous immunoglobulin) and ATG (anti-thymocyte globulin).

It is also known that severe CRS may be prevented by antihistamines or corticosteroids. Treatment of less severe CRS is supportive, addressing symptoms such as fever, muscle pain, or fatigue. Moderate CRS requires oxygen therapy, and administration of liquids and antihypertensives to raise blood pressure. For moderate to severe CRS, it may be useful to use immunosuppressants such as corticosteroids.

IL-6 inhibitors (e.g., anti-IL-6 antibodies such as tolizumab) are known to be useful in the prevention/treatment of CRS. GM-CSF inhibitors (e.g., anti-GM-CSF antibodies such as Lorentruzumab) can also be effective in preventing or managing cytokine release by reducing activation of bone marrow cells and reducing production of IL-1, IL-6, MCP-1, MIP-1, and IP-10.

Tozucchini, dexamethasone, levetiracetam, rentzia, methylprednisolone, anakinra, stetuximab, lu Suoti, cyclophosphamide, IVIG (intravenous immunoglobulin) and ATG (anti-thymus cytoglobulin).

One embodiment of the present disclosure relates to a method of identifying a patient as likely to experience toxicity or less likely to experience toxicity following 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 the patient as likely to experience toxicity following cell therapy when the IL-15 or MCP-1 level is above the corresponding reference level, or identifying the patient as unlikely to experience toxicity following cell therapy when the IL-15 or MCP-1 level is below the corresponding reference level. In such an embodiment, the cell therapy comprises administering immune cells.

One embodiment of the present disclosure relates to the above method, further comprising preventing or treating toxicity in the patient when the patient is identified as likely to experience toxicity.

One embodiment of the present disclosure relates to the above method, wherein treating or preventing comprises administering an agent selected from the group consisting of: antihistamines, corticosteroids, antihypertensives, IL-6 inhibitors, GM-CSF inhibitors, and non-steroidal anti-inflammatory drugs.

One embodiment of the present invention relates to the above method, wherein treating or preventing comprises administering an agent selected from the group consisting of: tozucchini, dexamethasone, levetiracetam, rentzia, methylprednisolone, anakinra, stetuximab, lu Suoti, cyclophosphamide, IVIG (intravenous immunoglobulin) and ATG (anti-thymus cytoglobulin).

One embodiment of the present disclosure relates to the above method, wherein the immune cells comprise T cells engineered to express a Chimeric Antigen Receptor (CAR).

One embodiment of the present disclosure relates to the above method, wherein the CAR has binding specificity for CD19 (cluster of differentiation 19) protein.

One embodiment of the present disclosure relates to the above method, wherein the blood sample is a serum sample obtained from the patient prior to cell therapy.

One embodiment of the present disclosure relates to the above method, wherein the blood sample is obtained after a pre-adapted treatment of the patient.

One embodiment of the present disclosure relates to the above method, wherein the pre-adaptation treatment reduces lymphocytes in the patient.

One embodiment of the present disclosure relates to the above method, wherein the toxicity is selected from the group consisting of: cytokine Release Syndrome (CRS), neural Events (NEs), and combinations thereof.

One embodiment of the present disclosure relates to the above method, wherein the toxicity is early onset toxicity.

One embodiment of the present disclosure relates to the above method, wherein the early-onset toxicity occurs within four days after the cell therapy.

One embodiment of the present disclosure relates to the above method, wherein the reference level of IL-15 or MCP-1 is determined from a patient experiencing toxicity following cell therapy and a patient not experiencing toxicity following cell therapy.

One embodiment of the present disclosure relates to the above method, further comprising measuring the viability of the cells used in the cell therapy, wherein the patient is identified as likely to experience toxicity after the cell therapy when the IL-15 or MCP-1 level is above the corresponding reference level and the cell viability is above the reference cell viability, or wherein the patient is identified as unlikely to experience toxicity after the cell therapy when the IL-15 or MCP-1 level is below the corresponding reference level and the cell viability is below the reference cell viability.

One embodiment of the present disclosure relates to the above method, further comprising obtaining one or more of the following levels in the patient: baseline hemoglobin, baseline tumor burden, baseline LDH, baseline creatinine, and baseline calcium.

One embodiment of the present disclosure relates to a method for preventing or treating toxicity in a patient undergoing cell therapy, comprising identifying the patient as likely to experience toxicity or less likely 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 the patient as likely to experience toxicity following cell therapy when the IL-15 or MCP-1 level is above the corresponding reference level, or identifying the patient as unlikely to experience toxicity following cell therapy when the IL-15 or MCP-1 level is below the corresponding reference level. In such an embodiment, if the patient has been identified as likely to experience toxicity following cell therapy, an agent that prevents or treats Cytokine Release Syndrome (CRS) or a Neurological Event (NE) is administered to the patient.

One embodiment of the present disclosure relates to the above method, wherein the agent is selected from the group consisting of: antihistamines, corticosteroids, antihypertensives, IL-6 inhibitors, GM-CSF inhibitors, and non-steroidal anti-inflammatory drugs.

One embodiment of the present disclosure relates to the above method, wherein the agent is selected from the group consisting of: tozucchini, dexamethasone, levetiracetam, rentzia, methylprednisolone, anakinra, stetuximab, lu Suoti, cyclophosphamide, IVIG (intravenous immunoglobulin) and ATG (anti-thymus cytoglobulin).

Kit and package, software program

The methods described herein can be performed, for example, by utilizing pre-packaged diagnostic kits, such as those described below, that include at least one probe or primer nucleic acid described herein, which can be conveniently used, for example, to determine whether a subject experiences toxicity or is at risk of experiencing toxicity following cell therapy.

Thus, one embodiment of the present disclosure relates to a kit or package useful for identifying a patient as likely to experience toxicity following 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.

Diagnostic procedures can be performed in situ with mRNA isolated from cells or directly on tissue sections (fixed and/or frozen) of native tissue, such as biopsy samples obtained from biopsies or resections, so that nucleic acid purification is not required. Nucleic acid reagents can be used as probes and/or primers for such in situ processes.

In one embodiment, kits or packages are provided that can be used to identify a patient as likely to experience toxicity or less likely to experience toxicity following cell therapy, including polynucleotide primers or probes or antibodies for measuring the expression levels of IL-15 and MCP-1 in a biological sample. In some embodiments, the kit or package further comprises reagents for measuring cell viability.

In one embodiment, the kit further comprises instructions for use. In one aspect, the kit includes a manual containing reference gene expression levels.

FIG. 8 is a block diagram showing a computer system 800 upon which the present technology and any implementation of the related technology may be implemented. Computer system 800 includes a bus 802 or other communication mechanism for communicating information, and one or more hardware processors 804 coupled with bus 802 for processing information. The hardware processor 804 may be, for example, one or more general purpose microprocessors.

Computer system 800 also includes a main memory 806, such as Random Access Memory (RAM), cache and/or other dynamic storage device, coupled to bus 802 for storing information and instructions to be executed by processor 804. Main memory 806 also may be used for storing temporary variables or other intermediate information during execution of instructions to be executed by processor 804. Such instructions, when stored in a storage medium accessible to processor 804, present computer system 800 as a special purpose machine that is customized to perform the operations specified in such instructions.

Computer system 800 also includes a Read Only Memory (ROM) 808 or other static storage device coupled to bus 802 for storing static information and instructions for processor 804. A storage device 810, such as a magnetic disk, optical disk, or USB thumb drive (flash drive), is provided and coupled to bus 802 for storing information and instructions.

Computer system 800 may be coupled via bus 802 to a display 812, such as an LED or LCD display (or touch screen), for displaying information to a computer user. An input device 814, including alphanumeric and other keys, is coupled to bus 802 for communicating information and command selections to processor 804. Another type of user input device is cursor control 816, such as a mouse, a trackball, or cursor direction keys for communicating direction information and command selections to processor 804 and for controlling cursor movement on display 812. In some embodiments, the same directional information and command selections as the cursor control may be achieved via receiving a touch on the touch screen without a cursor. Additional data may be retrieved from external data store 818.

Computer system 800 may include a user interface module for implementing a GUI that may be stored in a mass storage device as executable software code executed by a computing device. By way of example, the modules and other modules may 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, circuitry, data, databases, data structures, tables, arrays, and variables.

Generally, the term "module" as used herein refers to a logical component embodied in hardware or firmware, or a set of software instructions written in a programming language such as Java, C, or C++ that may have entry points and exit points. The software modules may be compiled and linked into an executable program, installed in a dynamically linked library, or may be written in an interpreter language such as BASIC, perl, or Python. It should be appreciated that software modules may be invoked from other modules or from themselves, and/or may be activated in response to a detected event or interrupt. Software modules configured for execution on a computing device may be provided on a computer-readable medium (such as an optical disk, digital video disk, flash drive, magnetic disk, or any other tangible medium) or as a digital download (which may initially be stored in a compressed or installable format that requires installation, decompression, or decryption prior to execution). Such software code may be stored, in part or in whole, on a memory device executing a computing device for execution by the computing device. The software instructions may be embedded in firmware, such as EPROM. It will further be appreciated that the hardware modules may be comprised of connected logic units (such as gates and flip-flops) and/or may be comprised of programmable units (such as programmable gate arrays or processors). The modules or computing device functions described herein are preferably implemented as software modules, but may also be represented in hardware or firmware. Generally, a module as described herein refers to a logical module that may be combined with other modules or divided into sub-modules (regardless of their physical organization or storage space). In some embodiments, the code for the desired analysis is at R Core Team (2019); language and environment for statistical calculations (statistical R foundation, vienna, austria).

Computer system 800 may implement the techniques described herein using custom hardwired logic, one or more ASICs or FPGAs, firmware, and/or program logic in combination with a computer system to make or program computer system 800 a special purpose machine. According to one embodiment, the techniques herein are performed by computer system 800 in response to processor 804 executing 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 storage device 810. Execution of the sequences of instructions contained in main memory 806 causes processor 804 to perform the process steps described herein. In alternative embodiments, hard-wired circuitry may be used in place of or in combination with software instructions.

The term "non-transitory medium" and similar terms as used herein refer to any medium that stores data and/or instructions that cause a machine to operate in a specific manner. Such non-transitory media may include non-volatile media and/or volatile media. Non-volatile media includes, for example, optical or magnetic disks, such as storage device 810. Volatile media includes dynamic memory, such as main memory 806. Common forms of non-transitory media include, for example, a floppy disk, a flexible disk, hard disk, solid state drive, magnetic tape, or any other magnetic data storage medium, a CD-ROM, any other optical data storage medium, any physical medium with patterns of holes, a RAM, a PROM, and EPROM, a FLASH-EPROM, an NVRAM, any other memory chip or cartridge, and networked versions of the foregoing.

Non-transitory media are different from, but may be used in conjunction with, transmission media. The transmission medium participates in the transmission of information between the non-transitory media. For example, transmission media includes coaxial cables, copper wire and fiber optics, including the wires that comprise bus 802. Transmission media can also take the form of acoustic or light waves, such as those generated during radio wave and infrared data communications.

Various forms of media may be involved in carrying one or more sequences of one or more instructions to processor 804 for execution. For example, the instructions may initially be carried on a magnetic disk or solid state drive of a remote computer. The remote computer can load the instructions into its dynamic memory and then send the instructions over a telephone line using the control unit. Control components local to computer system 800 can receive the data on the telephone line and use an infrared transmitter to convert the data to an infrared signal. An infra-red detector can receive the data carried in the infra-red signal and appropriate circuitry can place the data on bus 802. Bus 802 carries the data to main memory 806, from which processor 804 retrieves the instructions and executes. The instructions received by main memory 806 may retrieve the instructions and execute the instructions. The instructions received by main memory 806 may optionally be stored on storage device 810 either before or after execution by processor 804.

Computer system 800 also includes a communication interface 818 coupled to bus 802. Communication interface 818 provides a two-way data communication coupling to one or more network links that are connected to one or more local networks. For example, communication interface 818 may be an Integrated Services Digital Network (ISDN) card, a cable control component, a satellite control component, or a control component that provides a data communication connection to a corresponding type of telephone line. As another example, communication interface 818 may be a Local Area Network (LAN) card to provide a data communication connection to a compatible LAN (or WAN component to communicate with a WAN). Wireless links may also be implemented. In any such implementation, communication interface 818 sends and receives electrical, electromagnetic or optical signals that carry digital data streams representing various types of information.

Network links typically provide data communication through one or more networks to other data devices. For example, a network link may provide a connection through a local network to a host computer or to data equipment operated by an Internet Service Provider (ISP). ISPs in turn provide data communication services through the world wide packet data communication network now commonly referred to as the "Internet". Local area networks and the internet both use electrical, electromagnetic or optical signals that carry digital data streams. The signals through the various networks and the signals on the network links and the signals through communication interface 818, which carry the digital data to and from computer system 800, are exemplary forms of transmission media.

Computer system 800 can send messages and receive data, including program code, through the network(s), network link and communication interface 818. In an Internet example, a server might transmit a requested code for an application program through Internet, ISP, local network and communication interface 818.

The received code may be executed by processor 804 as it is received, and/or stored in storage device 810, or other non-volatile storage for later execution. Each of the processes, methods, and algorithms described in the foregoing sections may be embodied in code modules that are executed by one or more computer systems or computer processors, including computer hardware, and are fully or partially automated by those code modules. These processes and algorithms may be partially or fully implemented in dedicated circuitry.

The various features and processes described above can be used independently of each other or can be combined in various ways. All possible combinations and subcombinations are intended to fall within the scope of this disclosure. Moreover, some method blocks or process blocks may be omitted in some implementations. Nor is the method and process described herein limited to any particular order, and blocks or states associated therewith may be performed in other suitable order. For example, the described blocks or states can be performed in an order different than specifically disclosed, or multiple blocks or states can be combined in a single block or state. Example blocks or states may be performed serially, in parallel, or in other ways. Blocks or states may be added to or removed from the disclosed exemplary embodiments of the present invention. The exemplary systems and components described herein may be configured differently than what is described. For example, elements may be added thereto, removed therefrom, or rearranged as compared to the example embodiments disclosed herein.

Any process descriptions, elements, or blocks in the flowcharts described herein and/or depicted in the figures should be understood as potentially representing modules, segments, or portions of code which include one or more executable instructions for implementing specific logical functions or steps in the process. As will be appreciated by one of skill in the art, alternative implementations are included within the scope of the embodiments described herein in which elements or functions may be deleted from the order shown or discussed, including substantially concurrently or in reverse order, depending on the functionality involved.

It should be emphasized that many variations and modifications can be made to the above-described embodiments, the elements of which should be understood to be within other acceptable examples. All such modifications and variations are intended to be included herein within the scope of this disclosure. The foregoing description details certain embodiments of the invention. However, it should be appreciated that the invention can be implemented in numerous ways, no matter how detailed the foregoing appears in text. Also as described above, it should be noted that when describing certain features or aspects of the present invention, the use of specific terminology should not be taken to imply that the terminology is being re-defined herein to be restricted to including any specific characteristics of the features or aspects of the invention with which that terminology is associated. Accordingly, the scope of these embodiments should be construed in accordance with the appended claims and any equivalents thereof.

Various operations of the exemplary methods described herein may be performed, at least in part, by one or more processors that are temporarily configured (e.g., by software) or permanently configured to perform the relevant operations. Similarly, the methods described herein may be implemented, at least in part, by processors, where one or more particular processors are examples of hardware. For example, at least some operations of the method may be performed by one or more processors. In addition, one or more processors may also be operative to support performing related operations in a "cloud computing" environment or as a "software as a service" (SaaS). For example, at least some of the operations may be performed by a set of computers (as examples of machines including processors), where the operations are accessible via a network (e.g., the internet) and via one or more suitable interfaces (e.g., application Program Interfaces (APIs)).

Examples

The following examples are included to demonstrate specific embodiments of the present disclosure. It should be appreciated by those of skill in the art that the techniques disclosed in the examples which follow represent techniques sufficiently functioning in the practice of the present disclosure and thus may be considered to constitute particular modes for its practice. However, those of skill in the art should, in light of the present disclosure, appreciate that many changes can be made in the specific embodiments which are disclosed and still obtain a like or similar result without departing from the spirit and scope of the disclosure.

Example 1: based on machine learning calculationPredicting early-onset cytokins after treatment of large B cell lymphomas by using method Sub-release syndrome and neurological event

In clinical trial ZUMA-1, a critical study of Alcalix (axi-cel) in patients with refractory large B-cell lymphoma (LBCL) demonstrated that Cytokine Release Syndrome (CRS) and Neurological Events (NE) of grade > 3 occur in 13% and 28% of patients, respectively, requiring intensive management in hospitalized patients. As security experience increases, management of CRS and NEs has been evaluated in several exploratory security management queues of ZUMA-1. Cohort 4 assessed the effect of levetiracetam prophylaxis and early use of corticosteroids and/or tolizumab on the incidence and severity of CRS and NE. The effect of adding a prophylactic corticosteroid to the toxicity management regimen of cohort 4 was evaluated in cohort 6. Notably, some patients receiving treatment have differences in CRS or NE between early and late onset, necessitating different management. To facilitate toxicity management, this example developed predictive algorithms for early-onset acute toxicity (within 3 to 4 days after administration of axi-cel) from ZUMA-1 data based on machine learning.

The method comprises the following steps: the post hoc analysis included patients from ZUMA-1 phase 1 and phase 2 cohorts 1, 2, 4 and 6. Covariates (> 1500; 227 of which were measured prior to the infusion of axi-cel) included product baseline, patient and tumor characteristics, and proinflammatory soluble blood biomarker levels. Data from patients in cohorts 1, 2, and 4 were randomly split into training (70%) and test (30%). Univariate analysis and multivariate analysis were applied and clinical feasibility considerations were applied to select covariate subsets for further analysis. Machine learning (e.g., logistic regression, random forest, XGBoost, and AdaBoost classifier) was applied to 3 classes of covariates (1, clinical; 2, mechanisms [ e.g., product attributes, inflammatory blood biomarkers ]; hybrids of 3,1, and 2) to construct the best performance model (predictive performance assessed by the area under the curve [ AUC ] of the test data). The optimal cut-off value for the predictive score is selected by Receiver Operating Characteristics (ROC) or classification tree analysis. Data from patients in queue 6 is included to verify the best performance model generated using the training data.

Results: multivariate analysis and machine learning of data obtained from 149 evaluable patients in ZUMA-1 cohorts 1, 2 and 4 produced several comparable predictive models of early-onset CRS or NE (ROC AUC in training >0.8 for best-performing models, ROC AUC in testing > 0.7). Covariates in the best performance model included product cell viability, centrally measured day 0 (pre-axi-cel treatment) IL-15 and CCL2 (MCP-1) serum levels, as well as locally measured blood cell counts, blood chemistry analytes, tumor burden, and serum lactate dehydrogenase levels. The best performance model with <5 covariates contained only the mechanism covariates or heterozygous mixtures of covariates. The mechanism model with 3 covariates (product cell viability and serum levels of IL-15 and CCL2 (MCP-1) on day 0, both positively correlated with early-onset toxicity) performed in a manner comparable to the larger best-performing model (ROC AUC >0.7 in the test). The split classification tree based on day 0 IL-15 and product cell viability showed the potential to classify patients by premature versus late virulence (specificity > 0.85).

Machine learning applied to covariates measured prior to infusion of axi-cel generates predictive models for early-onset CRS or NEs, which can be used for toxicity prediction, monitoring, and management. The importance of T cell viability (product cell fitness) and adaptation-related elevation factors affecting toxicity (IL-15 and CCL 2) was demonstrated by high performance heterozygous models or mechanistic models.

Example 2: predicting early cytokine release syndrome and neural events

This embodiment describes data for constructing the algorithm in embodiment 1, and a process of developing a predictive algorithm, including: feature screening and selection, multivariate modeling, model evaluation, and classification of the test population by a predictive algorithm.

Data

All analyses were performed in the safety analysis set of ZUMA1 patients (i.e. receiving any amount of aliskiren) with a deadline of 2019, 11, 6.

These populations included (a) stage 1, and cohorts 1 and 2, with a cutoff day of 36 months (7 subjects with DLBCL, PMBCL, or TFL in stage 1; 77 subjects with refractory DLBCL in stage 2 cohort 1; 24 subjects with refractory PMBCL and TFL in stage 2; b) stage 3 (38 subjects with recurrent or refractory DLBCL, PMBCL, or TFL and no transplant qualification), and (c) stage 4 (41 subjects with recurrent or refractory DLBCL, PMBCL, TFL or HGBCL after 2 or more systemic normals).

Consider the following time window: 1, day 0, day 1, day 2; 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 time windows, three outpatient definitions are defined (see fig. 1 and table 1):

definition a: patients meeting both: (a) Worst class 1 or no CRS (i.e., CRS worst class < =1), and (b) no Neural Event (NE) during a given time window;

definition B: patients with no onset of CRS nor NE during a given time window;

definition C (proposed by medical affairs and clinical research team).

For each definition, patients that do not meet the "outpatient" criteria described above are designated as "inpatients", respectively.

TABLE 1 outpatient definition

Note that: in definition C, the time window is a condition defining "outpatient" or "inpatient". For example, if the prescribed time is from day 0 to day 2, all criteria will be checked within day 0, day 1, day 2 after infusion.

Covariate and feature selection

Covariates (or more than 1500; 227 of which were measured prior to infusion of axi-cel) included product baseline, patient and tumor characteristics, and proinflammatory soluble blood biomarker levels. Major classes of covariates or analytes include:

Baseline characteristics such as ECOG performance, disease type, disease stage, international Prognostic Index (IPI) category, tumor burden, etc.;

laboratory analytes in both chemical and hematology aspects;

serum cytokines and inflammatory markers;

product characteristics, including product cell viability, number and percentage of CD4 and CD8, CD4/CD8 ratio, phenotype/resetting phenotype on CD4 and CD8, IFN- γ in co-culture, etc.; and

cell growth information, including cell doubling time (in days) and expansion rate.

The data was randomly divided into training sets (e.g., 70% of the samples) to fit the model and test sets (e.g., the remaining 30% of the samples) were used to provide an unbiased assessment of the model's performance.

Univariate screening

Univariate analysis was performed one at a time for each covariate, wherein the association of covariates with outpatient/inpatient status was evaluated, and those variables that passed the screening criteria were selected for multivariate modeling.

Feature selection by analytical methods

After K-nearest neighbor (KNN) interpolation of the missing data, the following statistical and model-based method is applied to the features screened by univariate. Features are ranked by each of these methods, and top ranked features are selected. Features selected by three, four, or all five methods described below may be considered "analytically important" features.

Evidence weight and information value: evidence rightsHeavy (WOE) + Informative Value (IV) is a simple method for estimating the predictive power of a feature on a result of interest. WOE splits the data of each feature into several bins, e.g., j=10 bins, and calculates the predictive capability (i.e., "evidence") of the feature for the results within each bin. For each feature, IV then combines the WOEs of all bins into a single score calculated as: iv= Σ _j (non-event) _j Ratio-event of (2) _j Ratio) WOE _j . Selecting features with higher IV values as candidate features for a machine learning model (e.g., IV values>=0.3 or IV value>=0.5 is considered "moderately good" or "good", respectively.

SelectkBest using analysis of variance: selectkBest is a univariate feature selection method used to identify features that best explain the results. Specifically, for each feature, an analysis of variance (ANOVA) is performed and corresponding F statistics are calculated that represent the ratio of interpretable to unexplained variation between the feature and the result. The SelectKBest function then selects the feature with the k highest scores (e.g., lowest p-values) as the "best" feature.

Additional tree classifier: additional tree classifiers (also known as extremely randomized trees) are a class of integrated learning techniques that aggregate the results of many decorrelated decision trees into a "forest" to output classification results. Gini importance may be used to select the features of highest importance (e.g., 30 features) when predicting the outcome.

Recursive Feature Elimination (RFE): recursive Feature Elimination (RFE) is applied to a fitted model that has importance weights (e.g., model coefficients, importance attributes) assigned to the features and eliminates the worst performing features of the model until a desired number of features are achieved. Top ranked features (e.g., 30 features) may be selected for model construction.

RFE-based logistic regression: RFE is applied to logistic regression models, with variable importance defined by model coefficients.

RFE-based random forest: RFE is applied to a model estimated using random forests, where segmentation is determined using specific criteria (e.g., gini index is used as a default value), and feature importance scores are used to evaluate variable importance.

Feature selection by SME (subject matter expert)

SME (subject matter expert) reviews a list of analytically important features from univariate and multivariate methods, considers clinical feasibility and provides 3 classes of covariates for further analysis:

clinical covariates. For example, tumor-associated (LDH, burden), disease stage, blood cell count (WBC, RBC), cell-associated analyte (Hgb), metabolic state-associated analyte;

Mechanism covariates. For example, product cell viability, IL-15 on day 0, MCP-1 on day 0, cytokines, chemokines, and other product attributes; and

heterozygous (clinical + mechanism) covariates.

A list of covariates is generated as input candidate covariates for the classification model construction.

Multivariable modeling using machine learning algorithms

Five machine learning algorithms were applied to covariates (clinical covariates, mechanistic covariates, and heterozygous covariates) in each of these lists. All classification algorithms rely on a set of superparameters that are "tuned" to find the combination that yields the best performance. The model with the best predicted performance among these five machine learning algorithms is considered the Best Performance Model (BPM). These machine learning algorithms are briefly described as follows:

logistic regression: logistic regression is a parametric method of modeling the logarithmic probability of binary events occurring as a linear combination of features. In our method, a random undersampled dataset fed into a logistic regression algorithm we call log egrus (logistic regression with random undersampling) is used.

Random forests: random forests are integrated learning methods designed to reduce the variance that can be generated from a single model (i.e., decision tree). Random forest classification utilizes bootstrap aggregation (bagging), which first bootstrap training data, make predictions, and then aggregate results from various models to make more accurate predictions overall. The present embodiment uses a random undersampled dataset fed into a random forest algorithm called RFCRUS (random forest classifier with random undersampling).

Extreme gradient boost (XGBoost): boosting is an integrated machine learning technique in which many weak learners (e.g., decision trees) are iteratively combined to form a final strong learner. The models were added successively until no further improvement was possible. Gradient boosting refers to using any differentiable loss function and gradient descent optimization algorithm to achieve boosting. Extreme gradient boosting refers to a rapid and efficient implementation of the gradient boosting algorithm. The present embodiment uses a random undersampled dataset fed into XGBoost called XGBCRUS (XGBoost classifier with random undersampling).

Balanced Random Forest Classifier (BRFC): a Balanced Random Forest Classifier (BRFC) differs from a random forest classifier in that it uses balanced bootstrap samples of training data. BRFC differs from the random undersampled dataset fed into the random forest algorithm in that it does not pre-process training data prior to learning the random forest classifier.

Random undersampled lifting classifier (RUSBoost): adaptive boosting (AdaBoost) is an integrated boosting machine learning method that seeks to combine multiple weak classifiers (i.e., decision stumps) into a single strong classifier. The classifier adaptively re-weights training samples based on classification from previous learners, with greater weights given to misclassified samples. The final prediction is a weighted average of all weak learners, giving the strong learners more weight. Random undersampling lifting (RUSBoost) adapts AdaBoost to situations with unbalanced data by random undersampling at each iteration of the lifting algorithm.

Model evaluation

Receiver Operating Characteristics (ROC) and AUC: receiver Operating Characteristics (ROC) curves are a method for evaluating and comparing the performance of classification models. The false positive rate and true positive rate of the classifier are evaluated on a grid defining whether the observation is a possible (predictive probability) cut point classified as an event or a non-event, and then these values are plotted. The area under the ROC curve (AUC) can also be calculated.

Tables 2 through 6 show covariates and AUCs selected from BPM, where BPM was selected as the one of the five machine learning algorithms with the highest AUC from the test data.

TABLE 2 covariates selected from hybrid die types

Covariates that define positive and negative correlations with all 9 outpatients are denoted ∈and ∈respectively. Covariates with different associated directions in 9 outpatient definitionsAnd (3) representing. The model that makes 100% correct predictions has an AUC value equal to 1.

TABLE 3 AUC of BPM of selected covariates based on heterozygous model

Outpatient definition	Training AUC	Test AUC
			A2(RFCRUS)	0.93	0.716
B2(XGBCRUS)	0.948	0.779
			C2(LOGREGRUS)	0.757	0.715
A3(RUSBoost)	0.945	0.712
			B3(BRFC)	0.988	0.684
C3(RFCRUS)	0.879	0.647
			A4(LOGREGRUS)	0.831	0.777
B4(RFCRUS)	1	0.748
			C4(RUSBoost)	0.897	0.668

TABLE 4 covariates selected from reduced hybrid patterns

Covariates that define positive and negative correlations with all 9 outpatients are denoted ∈and ∈respectively. The model that makes 100% correct predictions has an AUC value equal to 1.

TABLE 5 AUC of BPM based on selected covariates of reduced hybrid modes

Outpatient definition	Training AUC	Test AUC
			A2(RFCRUS)	0.867	0.737
B2(RFCRUS)	0.914	0.669
			C2(RUSBoost)	0.839	0.633
A3(RUSBoost)	0.854	0.736
			B3(XGBCRUS)	0.873	0.688
C3(XGBCRUS)	0.785	0.77
			A4(RFCRUS)	0.899	0.741
B4(RFCRUS)	1	0.878
			C4(RFCRUS)	0.903	0.638

TABLE 6 covariates and AUC selected from reduced mechanism models, and selected clinical parameters/laboratory added Parameters (parameters)

Classifying test populations by predictive algorithms

Once the best covariates were identified, the present example uses two methods to classify the test population. The performance of the classification with respect to the test population is measured by the confusion matrix.

Confusion matrix: the confusion matrix of the classifier summarizes the number of correct predictions and incorrect predictions by category in the form of a list. The confusion matrix is useful for understanding the accuracy of the classifier's predictions and the type of errors that the classifier is more likely to produce. Accuracy (accuracy represents the proportion of observations correctly classified into true categories (positive or negative), sensitivity (true positive rate) and specificity (true negative rate) are calculated from the numbers in the confusion matrix.

Model-based method

The present embodiment applies BPM to training data and obtains a predictive probability, and then makes a ROC curve based on the predictive probability of a subject from the training data, selecting the best cut point as the cut-off value with the maximum about log index (about log index=sensitivity+specificity-1). Subjects with a predicted probability above the cutoff value are classified as "outpatients"; other subjects were classified as "hospitalized patients".

BPM of A3: for the conclusive mechanism model of A3 (using covariate cell viability + day 0 IL-15+ day 0 MCP-1) for the patient with the door clinic, the present example selects Random Forest (RF) as the best performing algorithm. ROC and box plots for the BPM (RFCRUS; optimal cut-off value: 0.538) with cell viability + IL-15+ mcp-1 for outpatient A3 are shown in fig. 2 and 3. The confusion matrix is shown in table 7.

Table 7 confusion matrix for training data with cutoff = 0.538

Sensitivity: 0.7115, specificity: 0.7500, accuracy: 0.7308

Subjects with a predictive probability >0.538 were classified as "outpatients"

A box plot of predictions for test data for outpatient A3 is shown in fig. 4, where BPM employs cell viability + IL-15+ mcp-1. The confusion matrix is shown in table 7.

Table 8 confusion matrix for test data for cutoff = 0.538

Sensitivity: 0.7000, specificity: 0.7143, accuracy: 0.7073

Subjects with a predictive probability >0.538 were classified as "outpatients"

Tree-based method

The present embodiment then builds a decision tree by partitioning the selected best covariates of the root nodes in the training data that make up the tree into subsets that make up the subsequent child nodes. The segmentation is based on a set of segmentation rules based on classification features. Decision trees can be described as segmenting the best covariates selected to classify subjects to obtain high accuracy combinations. The resulting decision tree is shown in fig. 5 (training data) and fig. 6 (test data). The corresponding confusion matrices are shown in tables 9 and 10.

TABLE 9 confusion matrix for training data on the decision tree in FIG. 5

Sensitivity: 0.6275, specificity: 0.7500, accuracy: 0.6916

TABLE 10 confusion matrix for test data on the decision tree in FIG. 6

Sensitivity: 0.6000, specificity: 0.9048, accuracy: 0.7561

Directionality of

The present embodiment then uses the partial dependency graph to show the relationship between early onset toxicity and covariates by exploiting the effects of other covariates in the machine learning model. The graph is presented in fig. 7. The graph suggests a cutoff for cell viability of about 95%, a cutoff for IL-15 of about 28pg/mL, and a cutoff for CCL2 of about 1300pg/mL.

The directionality of covariates accompanying the onset of toxicity can also be presented by logistic regression (yes/no) of outpatients to estimated coefficients in cell viability + IL-15+ mcp-1. Negative coefficients indicate that all three mechanism covariates are positively correlated with early-onset toxicity (Table 11).

TABLE 11 logistic regression (yes/no) from outpatient-cell viability+IL-15+MCP-1 estimation coefficients and associated p-value。

***

Unless defined otherwise, 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.

The inventive content illustratively described herein suitably may be practiced in the absence of any element or elements, limitation or limitations, not specifically disclosed herein. Thus, for example, the terms "comprising," "including," "containing," and the like are to be construed broadly and are not limited. In addition, the terms and expressions which have been employed herein have been used as terms of description and not of limitation, and there is no intention in the use of such terms and expressions of excluding 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 invention claimed.

Thus, it should be understood that although the present invention has been specifically disclosed by preferred embodiments and optional features, modification, variation and variation of the inventive concepts herein disclosed may be resorted to by those skilled in the art, and that such modifications, improvements and variations are considered to be within the scope of this invention. The materials, methods, and examples provided herein represent preferred embodiments, are illustrative, and are not intended to limit the scope of the present invention.

The present invention has been described broadly and generically herein. Each narrower species and subgeneric grouping that fall within the generic disclosure also form part of the invention. This includes the generic description of the invention with a proviso or negative limitation removing any subject matter from the genus, regardless of whether or not the excised material is specifically recited herein.

Furthermore, where features or aspects of the invention are described in terms of Markush groups, those skilled in the art will recognize that the invention is also thereby described in terms of any individual member or subgroup of members of the Markush group.

All publications, patent applications, patents, and other references mentioned herein are expressly incorporated by reference in their entirety to the same extent as if each was individually incorporated by reference. In case of conflict, the present specification, including definitions, will control.

It should be understood that while the disclosure has been described in conjunction with the above-described embodiments, the foregoing description and examples are intended to illustrate and not limit the scope of the disclosure. Other aspects, advantages, and modifications within the scope of the disclosure will be apparent to those skilled in the art to which the disclosure pertains.

Claims

1. A method of identifying a patient as likely to experience toxicity or less likely to experience toxicity following 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 the patient as likely to experience toxicity following the cell therapy when the IL-15 or MCP-1 level is above a corresponding reference level, or identifying the patient as unlikely to experience toxicity following the cell therapy when the IL-15 or MCP-1 level is below a corresponding reference level,

Wherein the cell therapy comprises administration of immune cells.

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

3. The method of claim 2, wherein the treatment or prevention comprises administration of an agent selected from the group consisting of: antihistamines, corticosteroids, antihypertensives, IL-6 inhibitors, GM-CSF inhibitors, and non-steroidal anti-inflammatory drugs.

4. The method of claim 3, wherein the treatment or prevention comprises administration of an agent selected from the group consisting of: tozucchini, dexamethasone, levetiracetam, rentzia, methylprednisolone, anakinra, stetuximab, lu Suoti, cyclophosphamide, IVIG (intravenous immunoglobulin) and ATG (anti-thymus cytoglobulin).

5. The method of any one of claims 1-4, wherein the immune cells comprise T cells engineered to express a Chimeric Antigen Receptor (CAR).

6. The method of claim 5, wherein the CAR has binding specificity for CD19 (cluster of differentiation 19) protein.

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

8. The method of claim 7, wherein the blood sample is obtained after pre-adaptation treatment of the patient.

9. The method of claim 8, wherein the pre-adaptation treatment reduces lymphocytes in the patient.

10. The method of any one of claims 1 to 9, wherein the toxicity is selected from the group consisting of: cytokine Release Syndrome (CRS), neural Events (NEs), and combinations thereof.

11. The method of claim 10, wherein the toxicity is early onset toxicity.

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

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

14. The method of any one of claims 1 to 13, further comprising measuring the viability of cells used in the cell therapy, wherein the patient is identified as likely to experience toxicity after the cell therapy when the IL-15 or MCP-1 level is above the corresponding reference level and cell viability is above a reference cell viability, or wherein the patient is identified as unlikely to experience toxicity after the cell therapy when the IL-15 or MCP-1 level is below the corresponding reference level and cell viability is below the reference cell viability.

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

16. A method for preventing or treating toxicity in a patient undergoing cell therapy, comprising:

identifying the patient as likely to experience toxicity or unlikely to experience toxicity following cell therapy, including:

identifying the patient as likely to experience toxicity following the cell therapy when the IL-15 or MCP-1 level is above a corresponding reference level, or identifying the patient as unlikely to experience toxicity following the cell therapy when the IL-15 or MCP-1 level is below a corresponding reference level, an

If the patient has been identified as likely to experience toxicity following the cell therapy, an agent that prevents or treats Cytokine Release Syndrome (CRS) or a Neurological Event (NE) is administered to the patient.

17. The method of claim 16, wherein the agent is selected from the group consisting of: antihistamines, corticosteroids, antihypertensives, IL-6 inhibitors, GM-CSF inhibitors, and non-steroidal anti-inflammatory drugs.

18. The method of claim 16, wherein the agent is selected from the group consisting of: tozucchini, dexamethasone, levetiracetam, rentzia, methylprednisolone, anakinra, stetuximab, lu Suoti, cyclophosphamide, IVIG (intravenous immunoglobulin) and ATG (anti-thymus cytoglobulin).

19. The method of claim 16, further comprising measuring the viability of cells used in the cell therapy, wherein the patient is identified as likely to experience toxicity after the cell therapy when the IL-15 or MCP-1 level is above the corresponding reference level and the cell viability is above a reference cell viability, or wherein the patient is identified as unlikely to experience toxicity after the cell therapy when the IL-15 or MCP-1 level is below the corresponding reference level and the cell viability is below the reference cell viability.

20. The method of any one of claims 16 to 19, further comprising obtaining one or more of the following levels for the patient: baseline hemoglobin, baseline tumor burden, baseline LDH, baseline creatinine, and baseline calcium.

21. A kit or package useful for identifying a patient as likely to experience toxicity following cell therapy, comprising polynucleotide primers or probes or antibodies for measuring the expression levels of IL-15 and MCP-1 in a biological sample.