Methods of predicting treatment of type 1 diabetes mellitus

Abstract

Type 1 diabetes mellitus (T1D) is caused by autoimmune disruption of insulin-producing beta cells in the Langerhans island, resulting in survival-dependent exogenous insulin injection. There is a need for treatment that can prevent or delay clinical T1D onset in high risk individuals. A promising therapy is an anti-CD3 monoclonal antibody tollizumab, since several studies have shown that short-term treatments permanently reduce loss of beta cell function, the effect also being observed 7 years after diagnosis and treatment. In addition, an improved method for predicting such prevention or delay is also required. In one aspect, provided herein is a method of predicting the reactivity of an anti-CD3 antibody in preventing or delaying T1D onset.

Description

Cross - Reference to Related Applications

[0001] This application claims the benefit and priority of U.S. Provisional Application No. 63 / 246,184, filed on September 20, 2021, the entire disclosure of which is incorporated herein by reference. Government Licensing Rights

[0002] This invention was made with government support under Grants U01 DK127786, R03 DK117253, and R01 DK121929 awarded by the National Institute of Diabetes and Digestive and Kidney Diseases. The government has certain rights in the invention. Sequence Listing

[0003] This specification includes a sequence listing submitted herewith, which includes a file entitled 178833 - 011501_ST26.xlm, of size: 3,396 bytes, created on September 18, 2022, the contents of which are incorporated herein by reference. Technical Field

[0004] The present disclosure generally relates to compositions and methods for preventing or delaying the onset of clinical type 1 diabetes (T1D) in at - risk subjects, and more particularly to the prognosis of using anti - CD3 antibodies in such prevention or delay. Background Art

[0005] Type 1 diabetes (T1D) is caused by the autoimmune destruction of insulin - producing β - cells in the islets of Langerhans, resulting in a survival dependence on exogenous insulin injections. Approximately 1.6 million Americans have type 1 diabetes, and after asthma, it is one of the most common diseases in children. Despite improved care, most affected individuals with T1D are unable to consistently achieve desired blood glucose goals. There are ongoing concerns about the increased risks of morbidity and mortality for individuals with type 1 diabetes. Two recent studies have indicated that children diagnosed before the age of 10 lose 17.7 life - years, and Scottish men and women diagnosed as adults lose 11 and 13 life - years, respectively.

[0006] In genetically susceptible individuals, T1D undergoes an asymptomatic phase before overt hyperglycemia, characterized first by the appearance of autoantibodies (stage 1), followed by glucose dysmetabolism (stage 2). In stage 2, the metabolic response to a glucose load is impaired, but other metabolic indicators (such as glycosylated hemoglobin) are normal and insulin therapy is not required. These immune and metabolic features identify individuals at high risk of developing a clinical disease (stage 3) with overt hyperglycemia and the need for insulin therapy. When studied in recently onset clinical T1D, several immune interventions have been shown to delay the decline in β-cell function. A promising therapy is the FcR non-binding anti-CD3 monoclonal antibody teplizumab, as several studies have shown that short-term treatment persistently reduces the loss of β-cell function, with effects still observable 7 years after diagnosis and treatment. The drug modifies the function of CD8+ T lymphocytes, which are considered important effector cells leading to β-cell killing.

[0007] To date, interventions before clinical diagnosis (i.e., in stage 1 or 2) have not altered the progression to clinical stage 3 T1D. Thus, there is a need for treatments that can prevent or delay the onset of clinical T1D in high-risk individuals. In addition, there is a need for improved methods for predicting such prevention or delay. SUMMARY OF THE INVENTION

[0008] In one aspect, the present disclosure provides a method for predicting responsiveness to a therapeutic or prophylactic agent for treating type 1 diabetes (T1D), the method comprising administering a therapeutic or prophylactic agent to a subject in need thereof; and determining a glucose and C-peptide response curve (GCRC) change vector by plotting the changes in mean glucose values and mean C-peptide values of an oral glucose tolerance test over a period of time on a two-dimensional grid, wherein a directionality of the change vector towards increasing C-peptide and decreasing glucose indicates metabolic improvement.

[0009] In some embodiments, the oral glucose tolerance test comprises a 1-hour oral glucose tolerance test, a 2-hour oral glucose tolerance test, a 4-hour oral glucose tolerance test, or a combination thereof.

[0010] In some embodiments, the method for predicting responsiveness to a therapeutic or prophylactic agent for treating type 1 diabetes (T1D) comprises administering a therapeutic or prophylactic agent to a subject in need thereof; and determining a glucose and C-peptide response curve (GCRC) change vector by plotting the changes in mean glucose values and mean C-peptide values of a 1-hour oral glucose tolerance test over a period of time on a two-dimensional grid, wherein a directionality of the change vector towards increasing C-peptide and decreasing glucose indicates metabolic improvement.

[0011] In some embodiments, the method of predicting responsiveness to a therapeutic or prophylactic agent for treating type 1 diabetes (T1D) includes administering a therapeutic or prophylactic agent to a subject in need thereof; and determining a glucose and C-peptide response curve (GCRC) change vector by plotting the changes over time of the mean glucose value and the mean C-peptide value of a 2-hour oral glucose tolerance test on a two-dimensional grid, wherein the directionality of the change vector towards increasing C-peptide and decreasing glucose indicates metabolic improvement.

[0012] In some embodiments, the method of predicting responsiveness to a therapeutic or prophylactic agent for treating type 1 diabetes (T1D) includes administering a therapeutic or prophylactic agent to a subject in need thereof; and determining a glucose and C-peptide response curve (GCRC) change vector by plotting the changes over time of the mean glucose value and the mean C-peptide value of a 4-hour oral glucose tolerance test on a two-dimensional grid, wherein the directionality of the change vector towards increasing C-peptide and decreasing glucose indicates metabolic improvement.

[0013] In some embodiments, the method further includes calculating a within-quadrant endpoint (WQE) and an ordered direction endpoint (ODE) based on the GCRC. In some embodiments, the within-quadrant endpoint (WQE) and the ordered direction endpoint (ODE) are based on the directional quadrant of the vector between the baseline and the 6-month coordinates. In some embodiments, the within-quadrant endpoint (WQE) and the ordered direction endpoint (ODE) are based on the percent change in glucose and the percent change in C-peptide.

[0014] In some embodiments, the therapeutic or prophylactic agent includes an immunotherapeutic agent. In some embodiments, the immunotherapeutic agent includes an anti-CD3 antibody or an antigen-binding fragment thereof. In some embodiments, the anti-CD3 antibody is teplizumab, otelixizumab, or frutuximab. In one embodiment, the anti-CD3 antibody is teplizumab.

[0015] In some embodiments, the effective amount of the therapeutic or prophylactic agent includes subcutaneous (SC) injection or intravenous (IV) infusion or oral administration of the anti-CD3 antibody at 10 - 1100 micrograms per square meter (μg / m 2 ) per day for a period of 10 to 14 days. In some embodiments, the effective amount of the therapeutic or prophylactic agent includes administering the anti-CD3 antibody at a total dose of about 9000 μg / m 2 to about 14000 μg / m 2 for a period of 10 to 14 days.

[0016] In some embodiments, the method comprises administering an intravenous infusion of the anti-CD3 antibody to the subject in need thereof for a 14-day period: on days 1-4, administering at 51 μg / m 2 , 103 μg / m 2 , 207 μg / m 2 , and 413 μg / m 2 , respectively, and on each day from days 5-14, administering a dose of 826 μg / m 2 .

[0017] In some embodiments, the anti-CD3 antibody is teprotumumab, otelixizumab, or frilimumab. In some embodiments, the anti-CD3 antibody is teprotumumab.

[0018] In some embodiments, the subject is in stage 1, 2, 3, or 4 of T1D. In some embodiments, the subject is in stage 1 or 2 of T1D, and the method can be used for the prognosis of a prophylactic agent in preventing or delaying the onset of T1D.

[0019] On the other hand, there is provided a method for predicting the reactivity of an anti-CD3 antibody in preventing or delaying the onset of clinical type 1 diabetes (T1D), the method comprising administering a prophylactically effective amount of an anti-CD3 antibody to a non-clinical diabetic subject at risk of clinical T1D; and determining a glucose and C-peptide response curve (GCRC) change vector by plotting the changes in the mean glucose value and mean C-peptide value of an oral glucose tolerance test over a period of time on a two-dimensional grid, wherein a change vector that is incrementally increasing in C-peptide and decreasing in glucose indicates metabolic improvement.

[0020] In some embodiments, the method further comprises calculating a within-quadrant endpoint (WQE) and an ordered direction endpoint (ODE) based on the GCRC. In some embodiments, the within-quadrant endpoint (WQE) and the ordered direction endpoint (ODE) are based on the directional quadrant of the vector between the baseline and the 6-month coordinates. In some embodiments, the within-quadrant endpoint (WQE) and the ordered direction endpoint (ODE) are based on the percentage change in glucose and the percentage change in C-peptide.

[0021] In some embodiments, the non-clinical diabetic subject is in stage 1 or 2 of T1D.

[0022] In some embodiments, the oral glucose tolerance test comprises a 1-hour oral glucose tolerance test, a 2-hour oral glucose tolerance test, a 4-hour oral glucose tolerance test, or a combination thereof.

[0023] In some embodiments, a method of predicting the responsiveness of an anti-CD3 antibody in preventing or delaying the onset of clinical type 1 diabetes (T1D) includes administering a prophylactically effective amount of the anti-CD3 antibody to a non-clinical diabetic subject at risk of clinical T1D; and determining a glucose and C-peptide response curve (GCRC) change vector by plotting the changes over time of the mean glucose value and the mean C-peptide value of a 1-hour oral glucose tolerance test on a two-dimensional grid, wherein a directionality of the change vector toward increasing C-peptide and decreasing glucose indicates metabolic improvement.

[0024] In some embodiments, a method of predicting the responsiveness of an anti-CD3 antibody in preventing or delaying the onset of clinical type 1 diabetes (T1D) includes administering a prophylactically effective amount of the anti-CD3 antibody to a non-clinical diabetic subject at risk of clinical T1D; and determining a glucose and C-peptide response curve (GCRC) change vector by plotting the changes over time of the mean glucose value and the mean C-peptide value of a 2-hour oral glucose tolerance test on a two-dimensional grid, wherein a directionality of the change vector toward increasing C-peptide and decreasing glucose indicates metabolic improvement.

[0025] In some embodiments, a method of predicting the responsiveness of an anti-CD3 antibody in preventing or delaying the onset of clinical type 1 diabetes (T1D) includes administering a prophylactically effective amount of the anti-CD3 antibody to a non-clinical diabetic subject at risk of clinical T1D; and determining a glucose and C-peptide response curve (GCRC) change vector by plotting the changes over time of the mean glucose value and the mean C-peptide value of a 4-hour oral glucose tolerance test on a two-dimensional grid, wherein a directionality of the change vector toward increasing C-peptide and decreasing glucose indicates metabolic improvement.

[0026] In some embodiments, the non-clinical diabetic subject at risk of clinical T1D is a relative of a patient with T1D.

[0027] In some embodiments, the non-clinical diabetic subject at risk of clinical T1D is negative for zinc transporter 8 (ZnT8). In some embodiments, the non-clinical diabetic subject at risk of clinical T1D is HLA-DR4+. In some embodiments, the non-clinical diabetic subject at risk of clinical T1D is not HLA-DR3+. In some embodiments, the non-clinical diabetic subject at risk of clinical T1D is HLA-DR4+ and not HLA-DR3+. In some embodiments, the non-diabetic subject (1) is negative for zinc transporter 8 (ZnT8), (2) is HLA-DR4+, and / or (3) is not HLA-DR3+.

[0028] In some embodiments, the method further comprises determining that the non-clinical diabetic subject at risk of clinical T1D is negative for zinc transporter 8 (ZnT8) antibodies. In some embodiments, the method further comprises determining that the non-clinical diabetic subject at risk of clinical T1D is HLA-DR4+. In some embodiments, the method further comprises determining that the non-clinical diabetic subject at risk of clinical T1D is HLA-DR4+ and not HLA-DR3+. In some embodiments, the method further comprises determining that the non-clinical diabetic subject at risk of clinical T1D is HLA-DR4+ and not HLA-DR3+. In some embodiments, the method further comprises determining that the non-clinical diabetic subject at risk of clinical T1D (1) is negative for zinc transporter 8 (ZnT8), (2) is HLA-DR4+, and / or (3) is not HLA-DR3+.

[0029] In some embodiments, the non-clinical diabetic subject at risk of clinical T1D has two or more diabetes-related autoantibodies selected from the group consisting of: islet cell antibodies (ICA), insulin autoantibodies (IAA), and antibodies against glutamate decarboxylase (GAD), tyrosine phosphatase (IA-2 / ICA512), or ZnT8.

[0030] In some embodiments, the non-clinical diabetic subject at risk of clinical T1D has abnormal glucose tolerance in an oral glucose tolerance test (OGTT). In some embodiments, the abnormal glucose tolerance in the OGTT is a fasting glucose level of 110 - 125 mg / dL, or a 2-hour plasma glucose level ≥ 140 and < 200 mg / dL, or an intermediate glucose value > 200 mg / dL at 30, 60, 90 minutes, or 4 hours of the OGTT.

[0031] In some embodiments, the anti-CD3 antibody is teplizumab, otelixizumab, or frutixizumab. In some embodiments, the anti-CD3 antibody is teplizumab. In some embodiments, the prophylactically effective amount of the antibody comprises subcutaneous (SC) injection or intravenous (IV) infusion or oral administration of the anti-CD3 antibody at 10 - 1100 micrograms per square meter (μg / m2) over a course of 10 to 14 days. In some embodiments, the effective amount of the therapeutic or prophylactic agent comprises administering the anti-CD3 antibody at a total dose of about 9000 μg / m 2 to about 14000 μg / m 2 over a course of 10 to 14 days. In some embodiments, the method comprises administering an intravenous infusion over a course of 14 days: at 51 μg / m on days 1 - 4 respectively 2 、103 μg / m2 , 207 μg / m 2 and 413 μg / m 2 administered, and a dose of 826 μg / m administered each day from day 5 - 14 2 .

[0032] In some embodiments, the prophylactically effective amount delays the median time to clinical diagnosis of T1D by about 50% to about 90%. In some embodiments, the prophylactically effective amount delays the median time to clinical diagnosis of T1D by at least 12 months, at least 18 months, at least 24 months, at least 36 months, at least 48 months, or at least 60 months.

[0033] In some embodiments, the determination of TIGIT+KLRG1+CD8+ T cells is performed by flow cytometry.

[0034] In some embodiments, the method further comprises determining a decrease in the percentage of CD8+ T cells expressing the proliferation marker Ki67 and / or CD57. BRIEF DESCRIPTION OF THE DRAWINGS

[0035] Figures 1A - 1F: The GCRC change vectors show opposite directions between the placebo group and the teplizumab treatment group. For the intervals from the baseline study visit (randomization time) to 3 months of treatment (A - D) and from baseline to 6 months of treatment (E - H), the individual participant GCRC change vectors and the mean treatment group vectors are plotted. A - B: The individual GCRC vector changes from baseline to 3 months of treatment are plotted for the placebo group (A) and the teplizumab treatment group (B). High - risk values (increasing glucose, decreasing C - peptide) are shown as dashed lines, while low - risk (decreasing glucose, increasing C - peptide) vectors are shown as thick lines. The frequency of vector quadrant distribution is significantly different between the treatment groups (p = 0.045). C - D: The mean GCRC at randomization (baseline, shown as a solid line) and at the time of OGTT 3 months after the study (shown as a dashed line) is plotted for the placebo - treated group and the teplizumab treatment group. The mean centroid values of the glucose and C - peptide coordinates are depicted as points within each GCRC, and the change vectors are shown. For the placebo group, the change in the GCRC centroid value during this time period shows a directionality towards the upper - left part of the grid, indicating increasing glucose and decreasing C - peptide values. The teplizumab treatment group shows the opposite directionality, i.e., towards the lower - right part of the grid, indicating increasing C - peptide and decreasing glucose. E - F: The individual GCRC vector changes from baseline to 6 months of treatment are plotted for the placebo group (E) and the teplizumab treatment group (F). The frequency of vector quadrant distribution is significantly different between the treatment groups (p = 0.0044). G - H: The mean GCRC at randomization (baseline, shown as a solid line) and at the time of OGTT 6 months after the study (shown as a dashed line) is plotted for the placebo - treated group and the teplizumab treatment group. The mean centroid values of the glucose and C - peptide coordinates are depicted as points within each GCRC, and the change vectors are shown. For the placebo group, the change in the GCRC centroid value during this time period shows a directionality towards the upper - left part of the grid, indicating increasing glucose and decreasing C - peptide values. The teplizumab treatment group shows the opposite directionality, i.e., towards the lower - right part of the grid, indicating increasing C - peptide and decreasing glucose. At 3 months: placebo group, n = 29; teplizumab group, n = 41. At 6 months: placebo group, n = 24; teplizumab group, n = 44. The absolute frequencies of the individual vector direction quadrants for each time period are shown in Table 1.

[0036] Figures 2A - 2B.Statistically evaluate the treatment effect using the vector angle within the usage direction quadrant. For the time periods from baseline to 3 months and from baseline to 6 months, plot the unadjusted individual values of the treatment endpoints of A. within-quadrant endpoints (WQEs) and B. ordered direction endpoints (ODEs) for the treatment group. For the ODE treatment group comparison, at 3 months, p = 0.018 before adjustment for age and BMI, and p = 0.038 after adjustment. At 6 months, p < 0.001 before adjustment, and p = 0.002 after adjustment. For the WQE treatment group comparison, at 3 months, p = 0.026 before adjustment for age and BMI, and p = 0.072 after adjustment. At 6 months, p < 0.001 before and after adjustment. *Unadjusted p-value < 0.05; ***unadjusted p-value < 0.001. At 3 months, n = 29 placebo-treated individuals and 41 teprilizumab-treated individuals. At 6 months, n = 24 placebo-treated individuals and 44 teprilizumab-treated individuals.

[0037] Figures 3A - 3C .The glucose C-peptide response curve (GCRC) allows visualization and quantification of the evolution of the relationship between glucose and C-peptide as type 1 diabetes develops. A. Plot the hypothetical GCRCs of an individual at the time of diagnosis of type 1 diabetes (dashed line) and 6 months before diagnosis, which show typical glucose and C-peptide values at the 30 (open circles), 60, 90, and 120 minute (open squares) time points of an oral glucose tolerance test. The mean centroid values of the glucose and C-peptide coordinates are indicated by dots within each GCRC. A vector showing the change in the mean GCRC centroid value during this time period shows a directionality towards the upper left part of the grid, indicating an increase in glucose and a decrease in C-peptide values. B. Conceptual diagram showing the application of the GCRC vector to create a right triangle, allowing the combined application of the direction quadrant of change and the angle generated by the triangle to quantify the change in metabolic function. The calculated angle is the angle between the vector (hypotenuse) and the horizontal side of the right triangle. C. Hypothetical examples of vectors for each of the 4 direction quadrants, the vectors originating from a baseline centroid with glucose and C-peptide values fixed at 0. Also shown is the calculated angle between the horizontal line and the vector.

[0038] Figure 4. Angle (qangle, shown in bold) for WQE based on the direction quadrant of the GCRC change vector: The metabolic changes of glucose and C-peptide over a 6-month period will fall into one of the 4 shown direction quadrants. Each quadrant includes the calculated vector angles between the vector and the horizontal boundary and between the vector and the vertical boundary. For vectors falling within the upper quadrant, the qangle between the vector and the horizontal boundary (the angle used to optimize the model for use as an endpoint, shown in red) is calculated. For vectors falling within the lower quadrant, the qangle between the vector and the vertical boundary is calculated. In this example, for the RUQ, the vector angle of 62° between the vector and the horizontal axis will be used as the qangle to develop the WQE. However, for the LRQ, the calculated vector angle of 40° (between the vector and the vertical axis) is used for the qangle. Detailed Description

[0039] Aspects of the present disclosure relate to methods for predicting the responsiveness of therapeutic or prophylactic agents for the treatment of type 1 diabetes (T1D). In some embodiments, methods for predicting the responsiveness of therapeutic or prophylactic agents for the treatment of type 1 diabetes (T1D) include: administering a therapeutic or prophylactic agent to a subject in need thereof; constructing glucose and C-peptide response curves (GCRCs) by plotting mean glucose and C-peptide values of a 1-hour, 2-hour, or 4-hour oral glucose tolerance test on a two-dimensional grid; visually observing changes in the GCRC shape and movement to determine metabolic improvement, and optionally calculating within-quadrant endpoints (WQEs) and ordered direction endpoints (ODEs) based on the GCRC.

[0040] In one aspect, provided herein is a method for predicting the responsiveness of a therapeutic or prophylactic agent for the treatment of type 1 diabetes (T1D), the method comprising: administering a therapeutic or prophylactic agent to a subject in need thereof; and determining a glucose and C-peptide response curve (GCRC) change vector by plotting the changes in mean glucose values and mean C-peptide values of an oral glucose tolerance test over a period of time on a two-dimensional grid, wherein the change vector having a directionality of increasing C-peptide and decreasing glucose indicates metabolic improvement.

[0041] In some embodiments, the oral glucose tolerance test includes a 1-hour oral glucose tolerance test, a 2-hour oral glucose tolerance test, a 4-hour oral glucose tolerance test, or a combination thereof.

[0042] In some embodiments, the method of predicting responsiveness to a therapeutic or prophylactic agent for treating type 1 diabetes (T1D) includes administering a therapeutic or prophylactic agent to a subject in need thereof; and determining a glucose and C-peptide response curve (GCRC) change vector by plotting the changes over time of the mean glucose value and the mean C-peptide value of a 1-hour oral glucose tolerance test on a two-dimensional grid, wherein the directionality of the change vector towards increasing C-peptide and decreasing glucose indicates metabolic improvement.

[0043] In some embodiments, the method of predicting responsiveness to a therapeutic or prophylactic agent for treating type 1 diabetes (T1D) includes administering a therapeutic or prophylactic agent to a subject in need thereof; and determining a glucose and C-peptide response curve (GCRC) change vector by plotting the changes over time of the mean glucose value and the mean C-peptide value of a 2-hour oral glucose tolerance test on a two-dimensional grid, wherein the directionality of the change vector towards increasing C-peptide and decreasing glucose indicates metabolic improvement.

[0044] In some embodiments, the method of predicting responsiveness to a therapeutic or prophylactic agent for treating type 1 diabetes (T1D) includes administering a therapeutic or prophylactic agent to a subject in need thereof; and determining a glucose and C-peptide response curve (GCRC) change vector by plotting the changes over time of the mean glucose value and the mean C-peptide value of a 4-hour oral glucose tolerance test on a two-dimensional grid, wherein the directionality of the change vector towards increasing C-peptide and decreasing glucose indicates metabolic improvement.

[0045] In some embodiments, the step of determining the GCRC change vector includes measuring the mean glucose value and the mean C-peptide value after administering the therapeutic or prophylactic agent, and measuring the mean baseline glucose value and the mean baseline C-peptide value before administering the therapeutic or prophylactic agent.

[0046] In some embodiments, the method includes administering a therapeutic or prophylactic agent to a plurality of subjects in need thereof and determining a plurality of GCRC change vectors. In some embodiments, the frequency of the directionality of the plurality of change vectors towards increasing C-peptide and decreasing glucose indicates metabolic improvement.

[0047] In some embodiments, the method further comprises plotting the average baseline GCRC of a plurality of subjects and the average treated GCRC of a plurality of subjects, determining the average baseline centroid value of the glucose and C-peptide coordinates of the baseline GCRC and the average treated centroid value of the glucose and C-peptide coordinates of the treated GCRC, and determining the change vector between the average baseline centroid value and the average treated centroid value. In some embodiments, a change in the GCRC centroid value over a period of time and / or a change in the directionality of the change vector towards increasing C-peptide and decreasing glucose indicates metabolic improvement. In some embodiments, the period of time includes 3 months, 4 months, 5 months, 6 months after administration of a therapeutic or prophylactic agent.

[0048] In some embodiments, the method comprises (a) administering a placebo to a first plurality of subjects in need, determining a first plurality of GCRC change vectors, determining a first frequency of the directionality of the plurality of change vectors towards increasing C-peptide and decreasing glucose, (b) administering a therapeutic or prophylactic agent to a second plurality of subjects in need, determining a second plurality of GCRC change vectors, determining a second frequency of the directionality of the plurality of change vectors towards increasing C-peptide and decreasing glucose, wherein a second frequency significantly higher than the first frequency indicates metabolic improvement.

[0049] In some embodiments, the method further comprises calculating a within-quadrant endpoint (WQE) and an ordered direction endpoint (ODE) based on the GCRC. In some embodiments, the within-quadrant endpoint (WQE) and the ordered direction endpoint (ODE) are based on the directional quadrant of the vector between the baseline and the 6-month coordinates. In some embodiments, the within-quadrant endpoint (WQE) and the ordered direction endpoint (ODE) are based on the percent change in glucose and the percent change in C-peptide.

[0050] Note that the method can be used for the prognosis of any therapeutic or prophylactic agent in any stage of the treatment or prevention of T1D. T1D is characterized by the destruction of most insulin-producing β-cells by an autoimmune response. Patients with T1D have residual β-cells, but due to the autoimmune disease, these cells are destroyed after proliferation and thus cannot thrive. T1D has 4 stages: Stage 1 - multiple (at least 2) islet antibodies, normal blood glucose, asymptomatic; Stage 2 - multiple islet antibodies, elevated blood glucose, asymptomatic; Stage 3 - islet autoimmunity, elevated blood glucose, symptomatic; Stage 4 - long-term type 1 diabetes. In some aspects of the present disclosure, the method results in the regeneration of β-cells.

[0051] In some embodiments, provided herein is a method for predicting the responsiveness of an anti-CD3 antibody in preventing or delaying the onset of clinical type 1 diabetes (T1D), the method comprising administering a prophylactically effective amount of an anti-CD3 antibody to a non-clinical diabetic subject at risk of clinical T1D; and determining a glucose and C-peptide response curve (GCRC) change vector by plotting the changes in the mean glucose value and the mean C-peptide value of an oral glucose tolerance test over a period of time on a two-dimensional grid, wherein a directionality of the change vector towards increasing C-peptide and decreasing glucose indicates metabolic improvement.

[0052] In some embodiments, the oral glucose tolerance test comprises a 1-hour oral glucose tolerance test, a 2-hour oral glucose tolerance test, a 4-hour oral glucose tolerance test, or a combination thereof.

[0053] In some embodiments, the method for predicting the responsiveness of an anti-CD3 antibody in preventing or delaying the onset of clinical type 1 diabetes (T1D) comprises administering a prophylactically effective amount of an anti-CD3 antibody to a non-clinical diabetic subject at risk of clinical T1D; and determining a glucose and C-peptide response curve (GCRC) change vector by plotting the changes in the mean glucose value and the mean C-peptide value of a 1-hour oral glucose tolerance test over a period of time on a two-dimensional grid, wherein a directionality of the change vector towards increasing C-peptide and decreasing glucose indicates metabolic improvement.

[0054] In some embodiments, the method for predicting the responsiveness of an anti-CD3 antibody in preventing or delaying the onset of clinical type 1 diabetes (T1D) comprises administering a prophylactically effective amount of an anti-CD3 antibody to a non-clinical diabetic subject at risk of clinical T1D; and determining a glucose and C-peptide response curve (GCRC) change vector by plotting the changes in the mean glucose value and the mean C-peptide value of a 2-hour oral glucose tolerance test over a period of time on a two-dimensional grid, wherein a directionality of the change vector towards increasing C-peptide and decreasing glucose indicates metabolic improvement.

[0055] In some embodiments, the method for predicting the responsiveness of an anti-CD3 antibody in preventing or delaying the onset of clinical type 1 diabetes (T1D) comprises administering a prophylactically effective amount of an anti-CD3 antibody to a non-clinical diabetic subject at risk of clinical T1D; and determining a glucose and C-peptide response curve (GCRC) change vector by plotting the changes in the mean glucose value and the mean C-peptide value of a 4-hour oral glucose tolerance test over a period of time on a two-dimensional grid, wherein a directionality of the change vector towards increasing C-peptide and decreasing glucose indicates metabolic improvement.

[0056] In some embodiments, the method further comprises calculating a within quadrant endpoint (WQE) and an ordered direction endpoint (ODE) according to a glucose and C-peptide response curve (GCRC). In some embodiments, the within quadrant endpoint (WQE) and the ordered direction endpoint (ODE) are based on the directional quadrant of the vector between the baseline and the 6-month coordinates. In some embodiments, the within quadrant endpoint (WQE) and the ordered direction endpoint (ODE) are based on the percent change in glucose and the percent change in C-peptide.

[0057] In some embodiments, the method further comprises determining that a non-clinical diabetic subject at risk of clinical T1D has greater than about 5% to greater than about 10% TIGIT+KLRG1+CD8+ T cells among all CD3+ T cells, before or after the administration step, which indicates successful prevention or delay of the onset of clinical T1D. In some embodiments, the determination of the percentage of TIGIT+KLRG1+CD8+ T cells is performed by flow cytometry. In some embodiments, the method further comprises determining a decrease in the percentage of CD8+ T cells expressing the proliferation marker Ki67 and / or CD57.

[0058] In some embodiments, the non-clinical diabetic subject is in stage 1 or stage 2 of T1D.

[0059] In some embodiments, a method of predicting the responsiveness of an anti-CD3 antibody in preventing or delaying the onset of type 1 diabetes (T1D) comprises: providing a non-clinical diabetic subject at risk of T1D; administering a prophylactically effective amount of an anti-CD3 antibody to the non-clinical diabetic subject; constructing a glucose and C-peptide response curve (GCRC) by plotting the mean glucose and C-peptide values of a 1-hour, 2-hour, or 4-hour oral glucose tolerance test on a two-dimensional grid; visually observing changes in the GCRC shape and movement to determine metabolic improvement, and optionally calculating a within quadrant endpoint (WQE) and an ordered direction endpoint (ODE) according to the GCRC. Definitions

[0060] Certain terms are defined below. Additional definitions are provided throughout the application.

[0061] As used herein, the articles "a" and "an" refer to one or more than one (e.g., at least one) of the grammatical object of the article. When used in this application in conjunction with the term "comprising", the use of the word "a" or "an" can mean "one", but it is also consistent with the meanings of "one or more", "at least one", and "more than one".

[0062] As used herein, "about" and "approximately" generally refer to the acceptable degree of error of a quantity being measured given the nature or accuracy of the measurement. Exemplary degrees of error are within 20% (%), typically within 10%, and more typically within 5% of a given value or range of values. The term "significantly" means greater than 50%, preferably greater than 80%, and most preferably greater than 90% or 95%.

[0063] As used herein, the term "comprising" or "comprise" is used with respect to a composition, method, and their corresponding one or more components present in a given embodiment, but open-endedly includes unspecified elements.

[0064] As used herein, the term "consisting essentially of" refers to the components required for a given embodiment. The term allows for the presence of additional elements that do not substantially affect one or more of the essential and novel or functional characteristics of the embodiment of the present disclosure.

[0065] The term "consisting of" refers to a composition, method, and their corresponding one or more components as described herein, which do not include any element not enumerated in the description of the embodiment.

[0066] The term "antibody" as used herein is used in the broadest sense and includes a variety of antibody structures, including but not limited to monoclonal antibodies, polyclonal antibodies, multispecific antibodies (e.g., bispecific antibodies), and antibody fragments as long as they exhibit the desired antigen-binding activity.

[0067] "Antibody fragment" refers to a molecule different from a full antibody that contains a portion of the full antibody that binds the antigen to which the full antibody binds. Examples of antibody fragments include but are not limited to Fv, Fab, Fab', Fab'-SH, F(ab')2; diabodies; linear antibodies; single-chain antibody molecules (e.g., scFv); and multispecific antibodies formed from antibody fragments.

[0068] As used herein, the term "glucose and C-peptide response curve" refers to a plot of the mean glucose value and mean C-peptide value of a 2-hour oral glucose tolerance test over time on a two-dimensional grid.

[0069] In some embodiments, a GCRC can be generated by plotting the mean glucose value (y-axis) and C-peptide value (x-axis) of an OGTT (30, 60, 90, 120 minutes, and 4 hours) on a 2D grid.

[0070] As used herein, the term "preventive agent" refers to a CD3-binding molecule, such as teprotumumab, which can be used to prevent, treat, manage, or improve one or more symptoms of T1D.

[0071] As used herein, the terms "treat", "treatment" and "treating" refer to any indication of success in treating or ameliorating an injury, pathology, disorder or symptom (e.g., cognitive impairment), including any objective or subjective parameter such as alleviation; remission; diminution of symptoms or making symptoms, injury, pathology or disorder more tolerable to the patient; decreasing the rate of progression of a symptom; decreasing the frequency or duration of a symptom or disorder; or in some cases preventing the onset of a symptom. Treatment or amelioration of a symptom can be based on any objective or subjective parameter; including for example the results of a physical examination.

[0072] As used herein, the term "onset" of disease with respect to type 1 diabetes refers to a patient who meets the criteria established by the American Diabetes Association for the diagnosis of type 1 diabetes (see, Mayfield et al., 2006, Am. Fam. Physician 58:1355 - 1362).

[0073] As used herein, the terms "prevent", "preventing" and "prevention" refer to the prevention of the onset of one or more symptoms of T1D in a subject resulting from the administration of a prophylactic or therapeutic agent.

[0074] As used herein, a "regimen" includes a dosing schedule and a dosing protocol. The regimens herein are methods of use and include prophylactic and therapeutic regimens. A "dosing protocol" or "course of treatment" can include the administration of several doses of a therapeutic or prophylactic agent over 1 to 20 days.

[0075] As used herein, the terms "subject" and "patient" are used interchangeably. As used herein, the terms "subject" and "subjects" refer to an animal, preferably a mammal, including non - primate animals (e.g., cows, pigs, horses, cats, dogs, rats and mice) and primate animals (e.g., monkeys or humans), more preferably humans.

[0076] As used herein, the term "preventive effective amount" refers to an amount of teprotumumab sufficient to cause a delay or prevention of the development, recurrence or onset of one or more symptoms of T1D. In some embodiments, the preventive effective amount is preferably an amount of teprotumumab that delays the onset of T1D in a subject by at least 20%, at least 25%, at least 30%, at least 35%, at least 40%, at least 45%, at least 50%, at least 55%, at least 60%, at least 65%, at least 70%, at least 75%, at least 80%, at least 85%, at least 90%, at least 95%.

[0077] Aspects of the present disclosure are further described in detail below. Additional definitions are set forth throughout this specification. Anti-CD3 Antibodies and Pharmaceutical Compositions

[0078] The terms "anti-CD3 antibody" and "antibody that binds to CD3" refer to an antibody or antibody fragment that can bind to cluster of differentiation 3 (CD3) with sufficient affinity such that the antibody can be used as a prophylactic, diagnostic, and / or therapeutic agent targeting CD3. In some embodiments, the anti-CD3 antibody binds to an unrelated non-CD3 protein to an extent less than about 10% of the binding of the antibody to CD3, as measured, for example, by radioimmunoassay (RIA). In some embodiments, the dissociation constant (Kd) of the antibody that binds to CD3 is <1 μM, <100 nM, <10 nM, <1 nM, <0.1 nM, <0.01 nM, or <0.001 nM (e.g., 10 -8 M or less, e.g., from 10 -8 M to 10 -13 M, e.g., from 10 -9 M to 10 -13 M). In some embodiments, the anti-CD3 antibody binds to an epitope of CD3 that is conserved among CD3 from different species.

[0079] In some embodiments, the anti-CD3 antibody can be ChAglyCD3 (oteseptamab). Oteseptamab is a humanized Fc non-binding anti-CD3 that was initially evaluated by the Belgian Diabetes Registry (BDR) in a Phase 2 study and then developed by Tolerx, which subsequently partnered with GSK for the Phase 3 DEFEND new-onset T1D trials (NCT00678886, NCT01123083, NCT00763451). Oteseptamab is administered intravenously by an 8-day infusion. See, e.g., Wiczling et al., J. Clin. Pharmacol. 50(5) (May 2010) 494–506; Keymeulen et al., N Engl J Med. 2005; 352:2598-608; Keymeulen et al., Diabetologia. 2010; 53:614-23; Hagopian et al., Diabetes. 2013; 62:3901-8; Aronson et al., Diabetes Care. 2014; 37:2746-54; Ambery et al., Diabet Med. 2014; 31:399-402; Bolt et al., Eur. J. Immunol. lYY3. 23:403-411; Vlasakakis et al., Br J Clin Pharmacol (2019) 85 704–714; Guglielmi et al., Expert Opinion on Biological Therapy, 16:6, 841-846; Keymeulen et al., N Engl J Med2005; 352:2598-608; Keymeulen et al., BLOOD 2010, Vol. 115, No. 6; Sprangers et al., Immunotherapy (2011) 3(11), 1303–1316; Daifotis et al., ClinicalImmunology (2013) 149, 268–278; all of these references are incorporated herein by reference.

[0080] In some embodiments, the anti-CD3 antibody can be visilizumab (also known as HuM291; Nuvion). Visilizumab is a humanized anti-CD3 monoclonal antibody characterized by a mutated IgG2 isotype, lack of binding to Fcγ receptors, and the ability to selectively induce apoptosis in activated T cells. It has been evaluated in patients with graft-versus-host disease (NCT00720629; NCT00032279), as well as in patients with ulcerative colitis (NCT00267306) and Crohn's disease (NCT00267709). See, e.g., Sandborn et al., Gut 59(11) (November 2010) 1485-1492, which is incorporated herein by reference.

[0081] In some embodiments, the anti-CD3 antibody can be frilimumab, a fully human anti-CD3 monoclonal antibody (NCT03291249) developed by Tiziana Life Sciences, PLC in NASH and T2D. See, e.g., Ogura et al., Clin Immunol. 2017; 183:240-246; Ishikawa et al., Diabetes. 2007; 56(8):2103-9; Wu et al., J Immunol. 2010; 185(6):3401-7; all of these references are incorporated herein by reference. Frilimumab is a fully human monoclonal antibody that binds to CD3ε. (See U.S. Patent No. 10,688,186, which is incorporated herein by reference in its entirety). Teplizumab

[0082] In some embodiments, the anti-CD3 antibody can be teplizumab. Teplizumab, also known as hOKT3yl(Ala-Ala) (containing alanine at positions 234 and 235), is an anti-CD3 antibody that has been engineered to alter the function of T lymphocytes that mediate the destruction of insulin-producing β cells in the pancreatic islets. Teplizumab binds to an epitope of the CD3ε chain expressed on mature T cells and, by doing so, alters its function. The sequence and composition of teplizumab are disclosed in U.S. Patent Nos. 6,491,916, 8,663,634, and 9,056,906, each of which is incorporated herein by reference in its entirety. The complete sequences of the light and heavy chains are shown below. The bolded portions are the complementarity-determining regions. Teplizumab light chain (SEQ ID NO:1): DIQMTQSPSSLSASVGDRVTITCSASSSVSYMNWYQQTPGKAPKRWIYDTSKLASGVP SRFSGSGSGTDYTFTISSLQPEDIATYYCQQWSSNPFTFGQGTKLQITRTVAAPSVFIFP PSDEQLKSGTASVVCLLNNFYPREAKVQWKVDNALQSGNSQESVTEQDSKDSTYSLS STLTLSKADYEKHKVYACEVTHQGLSSPVTKSFNRGEC Teplizumab heavy chain (SEQ ID NO:2): QVQLVQSGGGVVQPGRSLRLSCKASGYTFTRYTMHWVRQAPGKGLEWIGYINPSRG YTNYNQKVKDRFTISRDNSKNTAFLQMDSLRPEDTGVYFCARYYDDHYCLDYWGQ GTPVTVSSASTKGPSVFPLAPSSKSTSGGTAALGCLVKDYFPEPVTVSWNSGALTSGV HTFPAVLQSSGLYSLSSVVTVPSSSLGTQTYICNVNHKPSNTKVDKKVEPKSCDKTHTC PPCPAPEAAGGPSVFLFPPKPKDTLMISRTPEVTCVVVDVSHEDPEVKFNWYVDGVEV HNAKTKPREEQYNSTYRVVSVLTVLHQDWLNGKEYKCKVSNKALPAPIEKTISKAKG QPREPQVYTLPPSRDELTKNQVSLTCLVKGFYPSDIAVEWESNGQPENNYKTTPPVLD SDGSFFLYSKLTVDKSRWQQGNVFSCSVMHEALHNHYTQKSLSLSPGK

[0083] In some embodiments, provided herein is a pharmaceutical composition. Such a composition comprises a prophylactically effective amount of an anti-CD3 antibody and a pharmaceutically acceptable carrier. In some embodiments, the term “pharmaceutically acceptable” means approved by a regulatory agency of the Federal or a State government or listed in the U.S. Pharmacopeia or other generally recognized pharmacopeia for use in animals and more particularly in humans. The term “carrier” refers to a diluent, adjuvant (e.g., Freund's adjuvant (complete and incomplete)), excipient, or vehicle with which the therapeutic agent is administered. Such pharmaceutical carriers can be sterile liquids, such as water and oils, including those of petroleum, animal, vegetable, or synthetic origin, such as peanut oil, soybean oil, mineral oil, sesame oil, etc. Water is a preferred carrier when the pharmaceutical composition is administered intravenously. Saline solutions and aqueous dextrose and glycerol solutions can also be used as liquid carriers, particularly for injectable solutions. Suitable pharmaceutical excipients include starch, glucose, lactose, sucrose, gelatin, malt, rice, flour, chalk, silica gel, sodium stearate, glycerol monostearate, talc, sodium chloride, skim milk powder, glycerol, propylene, ethylene glycol, water, ethanol, etc. (see, e.g., Handbook of Pharmaceutical Excipients, Arthur H. Kibbe (ed., 2000, incorporated herein by reference in its entirety), Am. Pharmaceutical Association, Washington, D.C.).

[0084] If desired, the composition may also contain minor amounts of wetting or emulsifying agents, or pH buffering agents. These compositions can take the form of solutions, suspensions, emulsions, tablets, pills, capsules, powders, sustained-release formulations, etc. Oral formulations may contain standard carriers such as pharmaceutical grade mannitol, lactose, starch, magnesium stearate, saccharin sodium, cellulose, magnesium carbonate, etc. Examples of suitable pharmaceutical carriers are described in “Remington’s Pharmaceutical Sciences” by E.W. Martin. Such compositions will contain a prophylactically or therapeutically effective amount of a prophylactic or therapeutic agent, preferably in purified form, together with a suitable amount of carrier so as to provide a form for proper administration to the patient. The formulation should suit the mode of administration. In some embodiments, the pharmaceutical composition is sterile and in a form suitable for administration to a subject, preferably an animal subject, more preferably a mammalian subject, and most preferably a human subject.

[0085] In some embodiments, it may be desirable to administer the pharmaceutical composition locally to the area in need of treatment; this can be achieved, for example but not limited to, by local infusion, injection, or by means of an implant, which is a porous, non-porous, or gelatinous material, including membranes (such as sialastic membranes) or fibers. Preferably, when administering an anti-CD3 antibody, care must be taken to use a material that the anti-CD3 antibody cannot absorb.

[0086] In some embodiments, the composition can be delivered in vesicles, particularly liposomes (see Langer, Science 249:1527-1533 (1990); Treat et al., Liposomes in the Therapy of Infectious Disease and Cancer, Lopez-Berestein and Fidler (eds.), Liss, New York, pp. 353-365 (1989); Lopez-Berestein, ibid., pp. 317-327; see generally ibid.).

[0087] In some embodiments, the composition can be delivered in a controlled release or sustained release system. In some embodiments, a pump can be used to effect controlled release or sustained release (see Langer, supra; Sefton, 1987, CRC Crit. Ref. Biomed. Eng. 14:20; Buchwald et al., 1980, Surgery 88:507; Saudek et al., 1989, N. Engl. J. Med. 321:574). In some embodiments, polymeric materials can be used to effect controlled release or sustained release of the antibodies or fragments thereof of the present invention (see, e.g., Medical Applications of Controlled Release, Langer and Wise (eds.), CRC Pres., Boca Raton, Fla. (1974); Controlled Drug Bioavailability, Drug Product Design and Performance, Smolen and Ball (eds.), Wiley, New York (1984); Ranger and Peppas, 1983, J., Macromol. Sci. Rev. Macromol. Chem. 23:61; also see Levy et al., 1985, Science 228:190; During et al., 1989, Ann. Neurol. 25:351; Howard et al., 1989, J. Neurosurg. 71:105); U.S. Patent No. 5,679,377; U.S. Patent No. 5,916,597; U.S. Patent No. 5,912,015; U.S. Patent No. 5,989,463; U.S. Patent No. 5,128,326; PCT Publication No. WO 99 / 15154; and PCT Publication No. WO 99 / 20253. Examples of polymers used in sustained release formulations include, but are not limited to, poly(2-hydroxyethyl methacrylate), poly(methyl methacrylate), poly(acrylic acid), poly(ethylene-co-vinyl acetate), poly(methacrylic acid), poly(lactide-co-glycolide) (PLG), polyanhydrides, poly(N-vinylpyrrolidone), poly(vinyl alcohol), polyacrylamide, poly(ethylene glycol), poly(lactide) (PLA), poly(lactide-co-glycolide) (PLGA), and polyorthoesters. In some embodiments, the polymers used in sustained release formulations are inert, free of leachable impurities, storage stable, sterile, and biodegradable.In some embodiments, a controlled release or sustained release system can be placed near the therapeutic target (i.e., the lung), so that only a fraction of the systemic dose is required (see, e.g., Goodson, Medical Applications of Controlled Release, supra, Volume 2, pp. 115-138 (1984)).

[0088] Controlled release systems are discussed in the review by Langer (1990, Science 249:1527-1533). Any technique known to those of skill in the art can be used to produce a sustained release formulation containing one or more antibodies or fragments thereof of the invention. See, e.g., U.S. Patent No. 4,526,938; PCT Publication No. WO 91 / 05548; PCT Publication No. WO 96 / 20698; Ning et al., 1996, Radiotherapy & Oncology 39:179-189; Song et al., 1995, PDA Journal of Pharmaceutical Science & Technology 50:372-397; Cleek et al., 1997, Pro. Int'l. Symp. Control. Rel. Bioact. Mater. 24:853-854; and Lam et al., 1997, Proc. Int'l. Symp. Control Rel. Bioact. Mater. 24:759-760, each of which is incorporated herein by reference in its entirety.

[0089] The pharmaceutical compositions can be formulated to be compatible with its intended route of administration. Examples of routes of administration include but are not limited to parenteral, such as intravenous, intradermal, subcutaneous, oral, intranasal (e.g., inhalation), transdermal (topical), transmucosal, and rectal administration. In some embodiments, the compositions are formulated, according to conventional procedures, as pharmaceutical compositions suitable for intravenous, subcutaneous, intramuscular, oral, intranasal, or topical administration to humans. In some embodiments, the pharmaceutical compositions are formulated according to conventional procedures for subcutaneous administration to a human body. Typically, compositions for intravenous administration are solutions in sterile isotonic aqueous buffers. When necessary, the compositions may also contain solubilizing agents and local anesthetics (such as lidocaine) to relieve pain at the injection site.

[0090] The composition can be formulated for parenteral administration by injection, such as by bolus injection or continuous infusion. Formulations for injection can be presented in unit dosage forms (e.g., in ampoules or multi-dose containers) and contain a preservative. The composition can take the form of a suspension, solution or emulsion in an oily or aqueous vehicle and can contain formulating agents such as suspending, stabilizing and / or dispersing agents. Alternatively, the active ingredient can be in powder form for reconstitution before use with a suitable vehicle (e.g., sterile pyrogen-free water).

[0091] In some embodiments, the present disclosure provides dosage forms that allow for continuous administration of an anti-CD3 antibody over a period of hours or days (e.g., in association with a pump or other device for such delivery), e.g., over a period of 1 hour, 2 hours, 3 hours, 4 hours, 6 hours, 8 hours, 10 hours, 12 hours, 16 hours, 20 hours, 24 hours, 30 hours, 36 hours, 4 days, 5 days, 7 days, 10 days or 14 days. In some embodiments, the present invention provides dosage forms that allow for administration of continuously increasing doses, e.g., from 51 μg / m 2 / day to 826 μg / m 2 / day over a period of 24 hours, 30 hours, 36 hours, 4 days, 5 days, 7 days, 10 days or 14 days.

[0092] The composition can be formulated in neutral form or in the form of a salt. Pharmaceutically acceptable salts include those formed with anions such as those derived from hydrochloric, phosphoric, acetic, oxalic, tartaric acids, etc.; and those formed with cations such as those derived from sodium hydroxide, potassium hydroxide, ammonium hydroxide, calcium hydroxide, ferric hydroxide, isopropylamine, triethylamine, 2-ethylaminoethanol, histidine, procaine, etc.

[0093] Generally, the components of the compositions disclosed herein are provided separately or mixed together in unit dosage forms, e.g., as a dry lyophilized powder or an anhydrous concentrate in a hermetically sealed container, such as an ampoule or sachet that specifies the amount of the active agent. When the composition is administered by infusion, it can be dispensed with an infusion bottle containing sterile pharmaceutical grade water or saline. When the composition is administered by injection, an ampoule of sterile water for injection or saline can be provided so that the components can be mixed before administration.

[0094] In particular, the present disclosure provides that the anti-CD3 antibody or its pharmaceutical composition can be packaged in a sealed container (such as an ampoule or sachette) indicating the pharmaceutical dose. In some embodiments, the anti-CD3 antibody or its pharmaceutical composition is provided in the sealed container in the form of a dry-sterilized lyophilized powder or an anhydrous concentrate, and can be reconstituted to an appropriate concentration, for example, with water or saline for administration to a subject. Preferably, the anti-CD3 antibody or its pharmaceutical composition is provided in the sealed container in the form of a dry-sterile lyophilized powder in a unit dose of at least 5 mg, more preferably at least 10 mg, at least 15 mg, at least 25 mg, at least 35 mg, at least 45 mg, at least 50 mg, at least 75 mg or at least 100 mg. The lyophilized prophylactic agent or pharmaceutical composition herein should be stored in its original container at a temperature between 2 °C and 8 °C, and the prophylactic or therapeutic agent or pharmaceutical composition of the present invention should be administered within 1 week after reconstitution, preferably within 5 days, 72 hours, 48 hours, 24 hours, 12 hours, 6 hours, 5 hours, 3 hours or 1 hour after reconstitution. In some embodiments, the pharmaceutical composition is provided in the sealed container in a liquid form indicating the amount and concentration of the pharmaceutical agent. Preferably, the liquid form of the administered composition is provided in the sealed container in an amount of at least 0.25 mg / ml, more preferably at least 0.5 mg / ml, at least 1 mg / ml, at least 2.5 mg / ml, at least 5 mg / ml, at least 8 mg / ml, at least 10 mg / ml, at least 15 mg / ml, at least 25 mg / ml, at least 50 mg / ml, at least 75 mg / ml or at least 100 mg / ml. The liquid form should be stored in its original container at a temperature between 2 °C and 8 °C.

[0095] In some embodiments, the present disclosure provides that the composition of the present invention is packaged in a sealed container (such as an ampoule or sachette) indicating the amount of the anti-CD3 antibody.

[0096] If desired, the composition can be provided in a packaging or dispenser device, which can contain one or more unit dosage forms containing the active ingredient. The packaging can, for example, comprise a metal or plastic foil, such as a blister pack.

[0097] The amount of the composition of the present invention effective to prevent or ameliorate one or more symptoms associated with T1D can be determined by standard clinical techniques. The exact dose to be used in the formulation will also depend on the route of administration and the severity of the condition, and should be decided according to the judgment of the practitioner and the circumstances of each patient. The effective dose can be extrapolated from dose-response curves derived from in vitro or animal model test systems. Methods and Uses

[0098] The methods disclosed herein can be used for the prognosis of any therapeutic or prophylactic agent in the treatment or prevention of T1D at any stage, including stages 1, 2, 3, or 4. In some embodiments, the present disclosure encompasses administering an anti-human CD3 antibody such as teplizumab to an individual predisposed to type 1 diabetes or having preclinical type 1 diabetes but not meeting the diagnostic criteria established by the American Diabetes Association or the Immunology of Diabetes Society to prevent or delay the onset of type 1 diabetes and / or prevent or delay the need for exogenous insulin administration to such patients.

[0099] In some embodiments, new metabolic endpoints can be used to detect the effect of an agent on the treatment or prevention of T1D after administration. Glucose and C-peptide response curves (GCRC) can be constructed, for example, by plotting the mean glucose and C-peptide values of a 2-hour oral glucose tolerance test on a two-dimensional grid. Changes in the shape and movement of the GCRC can be visually compared between a placebo group and a treatment group. Changes in the GCRC in the placebo group can reflect significant metabolic deterioration, while if the changes in the GCRC in the treatment group indicate metabolic improvement, the agent is effective in the treatment or prevention of T1D. Quantitative comparisons can also be used, including two new metabolic endpoints indicating changes in the GCRC: within-quadrant endpoint (WQE) and ordered direction endpoint (ODE).

[0100] In some embodiments, the within-quadrant endpoint (WQE) can be used. Specifically, the prediction of T1D risk or prognosis can be enhanced by dividing the 360° continuum into 4 directional quadrants from 0° to 90°. Each directional quadrant can be considered to have its own characteristic risk, which can predict the overall risk when included in a model. The directional quadrants can be named: Upper right quadrant (RUQ) Upper left quadrant (LUQ) Lower right quadrant (RLQ) Lower left quadrant (LLQ)

[0101] The angle can be calculated from a right triangle formed by the directional quadrants according to the individual change vector. The calculated negative angle can be converted to a positive one. The percentage change in centroid glucose from baseline to 6 months on the y-axis and the percentage change in centroid C-peptide from baseline to 6 months on the x-axis can be used for the calculation of the angle. The hypotenuse of the triangle represents the distance from the baseline to the centroid of the GCRC at 6 months from the baseline (i.e., the change vector). The formulas for calculating the angle and the hypotenuse are shown below: Radians = arctan (glucose change % / C-peptide change %) Angle = radians * 57.296 Hypotenuse = sqrt((% Glucose change * % Glucose change) + (% C - peptide change * % C - peptide change)) (The formula uses the percentage changes of glucose and C - peptide instead of the actual values to standardize the units of the triangle sides.)

[0102] A Cox regression model can be developed that includes the angular changes calculated over 6 months within each quadrant as independent variables for predicting type 1 diabetes. Since the change vector of an individual can only fall into one of the quadrants, the values of the other quadrants are assigned as 0. Based on this paradigm, the model has been shown to significantly predict type 1 diabetes. However, for some quadrants, other models can also predict if the calculated angle is subtracted from 90°.

[0103] The following equation shows the coefficients for the direction quadrant angles (qangle) of an exemplary model for detecting the maximum difference (p < 0.001) between the oral insulin - detectable group and the placebo group. WQE = 0.02455 * qangle1+0.01464 * qangle2 + 0.00831 * qangle3−0.00465 * qangle4 where qangle1 = RUQ, qangle2 = LUQ, qangle3 = LLQ, and qangle4 = RLQ (Since 3 of the 4 direction quadrants are always negative for an individual, indicator variables coded as "0" are used.)

[0104] The following summary shows the steps for calculating WQE: -- Calculate the glucose and C - peptide coordinates of the centroid of the baseline and 6 - month GCRC. -- Convert the changes in the glucose and C - peptide centroid coordinates to percentage changes. -- Determine the directional quadrant of the vector between the baseline and 6 - month coordinates. -- Calculate the angle within the quadrant between the horizontal line and the vector using the standard formula based on a right - angled triangle. -- Convert the negative angles in direction quadrants 2 and 4 (qangle2 and qangle4) to positive angles. ---- Use the calculated angle for qangle1 and qangle2. Use (90° - calculated angle) for qangle3 and (90 - qangle4) in the model.

[0105] The process of converting angles from negative to positive and subtracting the angle from 90° is shown below: If the vector of the centroid change is in the RUQ, then qangle1 = calculated angle If the vector of the centroid change is in the LUQ, then qangle2 = calculated angle * (- 1) If the vector of the centroid change is in the LLQ, then qangle3 = 90 - the calculated angle If the vector of the centroid change is in the LLQ, then qangle4 = 90 - the calculated angle * (-1)

[0106] In some embodiments, an ordered direction endpoint (ODE) can be used. Using a 360° scale, based on previous evidence of sequential directionality during the progression of type 1 diabetes longitudinally, the values of the 4 quadrants can be assigned as follows. Lower right quadrant (RLQ): 0° Lower left quadrant (LLQ): 90° Upper right quadrant (RUQ): 180° Upper left quadrant (LUQ): 270°

[0107] The values can be added to the qangle value obtained from the WQE model. The sum is then divided by 360 to produce a scale with a maximum value of 1.00.

[0108] The summary of the steps of the ODE calculation can be the same as the summary of the steps of the WQE calculation, except that instead of inserting the qangle value into the model, the qangle is added to the specified values indicated above for the direction quadrants.

[0109] In some embodiments, risk factors for identifying susceptible subjects include having a first- or second-degree relative diagnosed with type 1 diabetes, impaired fasting glucose level (e.g., a glucose level of 100 - 125 mg / dl measured at least once after fasting (8 hours without food intake)), impaired glucose tolerance in response to a 75 g OGTT (e.g., a 2-hour glucose level of 140 - 199 mg / dl in response to a 75 g OGTT measured at least once), HbA1c between 5.7% - 6.4% or an HbA1c increase greater than or equal to 10% when compared to HbA1c values within the previous 12-month period, HLA types DR3, DR4, or DR7 in Caucasians, HLA types DR3 or DR4 in individuals of African descent, HLA types DR3, DR4, or DR9 in individuals of Japanese descent, exposure to viruses (e.g., Coxsackievirus B, enterovirus, adenovirus, rubella virus, cytomegalovirus, Epstein - Barr virus), a positive diagnosis of at least one other autoimmune disorder according to standards recognized in the prior art (e.g., thyroid disease, celiac disease), and / or detection of autoantibodies in serum or other tissues, particularly ICA and type 1 diabetes - related autoantibodies. In some embodiments, subjects identified as being predisposed to type 1 diabetes have at least one risk factor as described herein and / or as known in the art. The present disclosure also encompasses the identification of subjects predisposed to type 1 diabetes, where the subjects present a combination of two or more, three or more, four or more, or more than five risk factors disclosed herein or known in the art.

[0110] Serum autoantibodies associated with type 1 diabetes or with susceptibility to type 1 diabetes are islet cell autoantibodies (e.g., anti - ICA512 autoantibodies), glutamic acid decarboxylase autoantibodies (e.g., anti - GAD65 autoantibodies), IA2 antibodies, ZnT8 antibodies, and / or anti - insulin autoantibodies. Thus, in a specific example according to this embodiment, the present invention encompasses treating an individual in whom the following autoantibodies are detectable, which are associated with susceptibility to type 1 diabetes or with early - stage type 1 diabetes (e.g., anti - IA2, anti - ICA512, anti - GAD, or anti - insulin autoantibodies), where the individual has not been diagnosed with type 1 diabetes and / or is a first - or second - degree relative of a type 1 diabetes patient. In some embodiments, the presence of autoantibodies is detected by: ELISA, electrochemiluminescence (ECL), radioassay (see, e.g., Yu et al., 1996, J. Clin. Endocrinol. Metab. 81:4264 - 4267), agglutination PCR (Tsai et al., ACS Central Science 2016 2(3), 139 - 147), or by any other method for immunospecific detection of antibodies described herein or known to those of ordinary skill in the art.

[0111] Beta cell function can be evaluated before, during, and after therapy by the methods described herein or by any methods known to those of ordinary skill in the art. For example, the Diabetes Control and Complications Trial (DCCT) Research Group has established the monitoring of the percentage of glycosylated hemoglobin (HA1 and HA1c) as a standard for evaluating glycemic control (DCCT, 1993, N. Engl. J. Med. 329:977-986). Alternatively, the characterization of daily insulin requirements, C-peptide levels / responses, hypoglycemic episodes, and / or FPIR can be used as markers of beta cell function or for establishing a treatment index (see Keymeulen et al., 2005, N. Engl. J. Med. 352:2598-2608; Herold et al., 2005, Diabetes 54:1763-1769; U.S. Patent Application Publication No. 2004 / 0038867A1; and Greenbaum et al., 2001, Diabetes 50:470-476, respectively). For example, FPIR is calculated as the sum of insulin values at 1 minute and 3 minutes after an IGTT, which is performed according to the study protocol of the Islet Cell Antibody Registry users (see, e.g., Bingley et al., 1996, Diabetes 45:1720-1728 and McCulloch et al., 1993, Diabetes Care 16:911-915).

[0112] In some embodiments, an individual predisposed to T1D can be a non-clinical diabetic subject who is a relative of a patient with T1D. In some embodiments, the non-clinical diabetic subject has two or more diabetes-related autoantibodies selected from the group consisting of: islet cell antibodies (ICA), insulin autoantibodies (IAA), and antibodies against glutamic acid decarboxylase (GAD), tyrosine phosphatase (IA-2 / ICA512), or ZnT8.

[0113] In some embodiments, the non-clinical diabetic subject has abnormal glucose tolerance in an oral glucose tolerance test (OGTT). Abnormal glucose tolerance in an OGTT is defined as a fasting glucose level of 110-125 mg / dL, or a 2-hour plasma glucose level ≥140 and <200 mg / dL, or an intermediate glucose value >200 mg / dL at 30, 60, or 90 minutes of the OGTT.

[0114] In some embodiments, non-clinical diabetic subjects responsive to an anti-CD3 antibody such as teprotumumab are negative for ZnT8 antibody. In some embodiments, such non-clinical diabetic subjects are HLA-DR4+. In some embodiments, such non-clinical diabetic subjects are not HLA-DR3+. In some embodiments, such non-clinical diabetic subjects are HLA-DR4+ and not HLA-DR3+. In some embodiments, such non-diabetic subjects are negative for anti-ZnT8 antibody, are HLA-DR4+, and are not HLA-DR3+. In some embodiments, such non-clinical diabetic subjects responsive to an anti-CD3 antibody such as teprotumumab exhibit an increase in the frequency (or relative amount) of TIGIT+KLRG1+CD8+ T cells in peripheral blood mononuclear cells (e.g., as determined by flow cytometry) after administration (e.g., after 1 month, 2 months, 3 months, or longer or shorter time).

[0115] In some embodiments, a prophylactically effective amount comprises subcutaneous (SC) injection or intravenous (IV) infusion of an anti-CD3 antibody such as teprotumumab at 10 - 1100 micrograms per square meter (μg / m 2 ) over a period of 10 to 14 days. In one example, a prophylactically effective amount comprises an intravenous infusion of an anti-CD3 antibody such as teprotumumab over a period of 14 days: administered at 51 μg / m 2 , 103 μg / m 2 , 207 μg / m 2 and 413 μg / m 2 on days 1 - 4 respectively, and a dose of 826 μg / m 2 is administered each day from days 5 - 14. In some embodiments, a prophylactically effective amount delays the median time to clinical diagnosis of T1D by at least 50%, at least 80%, or at least 90%, from about 50% to about 90%. In some embodiments, a prophylactically effective amount delays the median time to clinical diagnosis of T1D by at least 12 months, at least 18 months, at least 24 months, at most 36 months, at least 48 months, or at least 60 months or longer, or delays by about 12 months to about 28 months, about 12 months to about 24 months, about 12 months to about 36 months, about 12 months to about 48 months, about 12 months to about 60 months or longer.

[0116] In some embodiments, the administration of an anti-CD3 antibody, such as teprotumumab, can be repeated at intervals of 2 months, 4 months, 6 months, 8 months, 9 months, 10 months, 12 months, 15 months, 18 months, 24 months, 30 months, or 36 months. In some embodiments, as described herein, or as known in the art, the efficacy of treatment with an anti-CD3 antibody, such as teprotumumab, is determined at 2 months, 4 months, 6 months, 9 months, 12 months, 15 months, 18 months, 24 months, 30 months, or 36 months after a previous treatment.

[0117] In some embodiments, one or more of the following unit doses of an anti-CD3 antibody, such as teprotumumab, are administered to a subject to prevent, treat, or ameliorate one or more symptoms of T1D: about 0.5 - 50 μg / kg, about 0.5 - 40 μg / kg, about 0.5 - 30 μg / kg, about 0.5 - 20 μg / kg, about 0.5 - 15 μg / kg, about 0.5 - 10 μg / kg, about 0.5 - 5 μg / kg, about 1 - 5 μg / kg, about 1 - 10 μg / kg, about 20 - 40 μg / kg, about 20 - 30 μg / kg, about 22 - 28 μg / kg, or about 25 - 26 μg / kg. In some embodiments, one or more of the following unit doses of an anti-CD3 antibody, such as teprotumumab, are administered to a subject to prevent, treat, or ameliorate one or more symptoms of T1D: about 200 μg / kg, 178 μg / kg, 180 μg / kg, 128 μg / kg, 100 μg / kg, 95 μg / kg, 90 μg / kg, 85 μg / kg, 80 μg / kg, 75 μg / kg, 70 μg / kg, 65 μg / kg, 60 μg / kg, 55 μg / kg, 50 μg / kg, 45 μg / kg, 40 μg / kg, 35 μg / kg, 30 μg / kg, 26 μg / kg, 25 μg / kg, 20 μg / kg, 15 μg / kg, 13 μg / kg, 10 μg / kg, 6.5 μg / kg, 5 μg / kg, 3.2 μg / kg, 3 μg / kg, 2.5 μg / kg, 2 μg / kg, 1.6 μg / kg, 1.5 μg / kg, 1 μg / kg, 0.5 μg / kg, 0.25 μg / kg, 0.1 μg / kg, or 0.05 μg / kg.

[0118] In some embodiments, one or more of the following doses of an anti-CD3 antibody, such as teprotumumab, are administered to a subject: 5 - 1200 μg / m 2 , such as 51 - 826 μg / m 2。In some embodiments, one or more of the following unit doses of an anti-CD3 antibody such as teprotumumab are administered to a subject to prevent, treat T1D, slow the progression of T1D, delay the onset of one or more symptoms of T1D, or improve one or more symptoms of T1D: 1200 ug / m 2 、1150 ug / m 2 、1100 ug / m 2 、1050 ug / m 2 、1000 ug / m 2 、950 ug / m 2 、900 ug / m 2 、850 ug / m 2 、800 ug / m 2 、750 ug / m 2 、700 ug / m 2 、650 ug / m 2 、600 ug / m 2 、550 ug / m 2 、500 ug / m 2 、450 ug / m 2 、400 ug / m 2 、350 ug / m 2 、300 ug / m 2 、250 ug / m 2 、200 ug / m 2 、150 ug / m 2 、100 ug / m 2 、50 ug / m 2 、40 ug / m 2 、30 ug / m 2 、20 ug / m 2 、15 ug / m 2 、10 ug / m 2 or 5 ug / m 2 。

[0119] In some embodiments, a treatment regimen is administered to a subject that includes one or more doses of a prophylactically effective amount of an anti-CD3 antibody such as teprotumumab, wherein the treatment course is administered over 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, or 14 days. In some embodiments, the treatment regimen includes administering a prophylactically effective amount of the dose daily, every 2 days, every 3 days, or every 4 days. In some embodiments, the treatment regimen includes administering a prophylactically effective amount of the dose on Monday, Tuesday, Wednesday, and Thursday of a given week and not administering a prophylactically effective dose on Friday, Saturday, and Sunday of the same week until 14, 13, 12, 11, 10, 9, or 8 doses have been administered. In some embodiments, the dose administered each day of the regimen is the same.

[0120] In some embodiments, a treatment regimen is administered to a subject that includes one or more doses of a prophylactically effective amount of an anti-CD3 antibody such as teprotumumab, wherein the prophylactically effective amount is 200 μg / kg / day, 175 μg / kg / day, 150 μg / kg / day, 125 μg / kg / day, 100 μg / kg / day, 95 μg / kg / day, 90 μg / kg / day, 85 μg / kg / day, 80 μg / kg / day, 75 μg / kg / day, 70 μg / kg / day, 65 μg / kg / day, 60 μg / kg / day, 55 μg / kg / day, 50 μg / kg / day, 45 μg / kg / day, 40 μg / kg / day, 35 μg / kg / day, 30 μg / kg / day, 26 μg / kg / day, 25 μg / kg / day, 20 μg / kg / day, 15 μg / kg / day, 13 μg / kg / day, 10 μg / kg / day, 6.5 μg / kg / day, 5 μg / kg / day, 3.2 μg / kg / day, 3 μg / kg / day, 2.5 μg / kg / day, 2 μg / kg / day, 1.6 μg / kg / day, 1.5 μg / kg / day, 1 μg / kg / day, 0.5 μg / kg / day, 0.25 μg / kg / day, 0.1 μg / kg / day, or 0.05 μg / kg / day; and / or wherein the prophylactically effective amount is 1200 μg / m 2 / day, 1150 μg / m 2 / day, 1100 μg / m 2 / day, 1050 μg / m 2 / day, 1000 μg / m 2 / day, 950 μg / m 2 / day, 900 μg / m 2 / day, 850 μg / m 2 / day, 800 μg / m 2 / day, 750 μg / m2 / day, 700 ug / m 2 / day, 650 ug / m 2 / day, 600 ug / m 2 / day, 550 ug / m 2 / day, 500 ug / m 2 / day, 450 ug / m 2 / day, 400 ug / m 2 / day, 350 ug / m 2 / day, 300 ug / m 2 / day, 250 ug / m 2 day, 200 ug / m 2 / day, 150 ug / m 2 / day, 100 ug / m 2 / day, 50 ug / m 2 / day, 40 ug / m 2 day, 30 ug / m 2 / day, 20 ug / m 2 / day, 15 ug / m 2 / day, 10 ug / m 2 / day or 5 ug / m 2 / day.

[0121] In some embodiments, 1200 ug / m 2 or less, 1150 ug / m 2 or less, 1100 ug / m 2 or less, 1050 ug / m 2 or less, 1000 ug / m 2 or less, 950 ug / m 2 or less, 900 ug / m 2 or less, 850 ug / m 2 or less, 800 ug / m 2 or less, 750 ug / m 2 or less, 700 ug / m 2 or less, 650 ug / m 2 or less, 600 ug / m 2 or less, 550 ug / m 2 or less, 500 ug / m 2 or less, 450 ug / m 2 or less, 400 ug / m 2 or less, 350 ug / m 2 or less, 300 ug / m 2 or less, 250 ug / m 2 or less, 200 ug / m2 or less, 150 ug / m 2 or less, 100 ug / m 2 or less, 50 ug / m 2 or less, 40 ug / m 2 or less, 30 ug / m 2 or less, 20 ug / m 2 or less, 15 ug / m 2 or less, 10 ug / m 2 or less or 5 ug / m 2 An intravenous dose of an anti-CD3 antibody such as teplyzumab or less is administered over the following times to prevent, treat, or improve one or more symptoms of type 1 diabetes: about 24 hours, about 22 hours, about 20 hours, about 18 hours, about 16 hours, about 14 hours, about 12 hours, about 10 hours, about 8 hours, about 6 hours, about 4 hours, about 2 hours, about 1.5 hours, about 1 hour, about 50 minutes, about 40 minutes, about 30 minutes, about 20 minutes, about 10 minutes, about 5 minutes, about 2 minutes, about 1 minute, about 30 seconds, or about 10 seconds. In some embodiments, the total dose over the duration of the regimen is less than 9000 ug / m 2 , 8000 ug / m 2 , 7000 ug / m 2 , 6000 ug / m 2 , and can be less than 5000 ug / m 2 , 4000 ug / m 2 , 3000 ug / m 2 , 2000 ug / m 2 or 1000 ug / m 2 . In some embodiments, the total dose over the duration of the regimen is more than 9000 ug / m 2 , for example from about 9000 ug / m 2 to about 14000 ug / m 2 . In some embodiments, the daily dose administered in the regimen is 100 ug / m 2 to 200 ug / m 2 , 100 ug / m 2 to 500 ug / m 2 , 100 ug / m 2 to 1000 ug / m 2 or 500 ug / m 2 to 1100 ug / m 2 .

[0122] In some embodiments, the dose is escalated in the first quarter, first half, or first two-thirds of the dose of the treatment regimen (e.g., within the first 2, 3, 4, 5, or 6 days of a 10, 12, 14, 16, 18, or 20-day regimen with one dose per day) until the daily prophylactic effective amount of an anti-CD3 antibody such as teprotumumab is reached. In some embodiments, a treatment regimen comprising one or more doses of an anti-CD3 antibody such as teprotumumab at a prophylactic effective amount is administered to a subject, wherein the prophylactic effective amount is increased by 0.01 μg / kg, 0.02 μg / kg, 0.04 μg / kg, 0.05 μg / kg, 0.06 μg / kg, 0.08 μg / kg, 0.1 μg / kg, 0.2 μg / kg, 0.25 μg / kg, 0.5 μg / kg, 0.75 μg / kg, 1 μg / kg, 1.5 μg / kg, 2 μg / kg, 4 μg / kg, 5 μg / kg, 10 μg / kg, 15 μg / kg, 20 μg / kg, 25 μg / kg, 30 μg / kg, 35 μg / kg, 40 μg / kg, 45 μg / kg, 50 μg / kg, 55 μg / kg, 60 μg / kg, 65 μg / kg, 70 μg / kg, 75 μg / kg, 80 μg / kg, 85 μg / kg, 90 μg / kg, 95 μg / kg, 100 μg / kg, or 125 μg / kg per day as the treatment progresses; or increased by 1 μg / m 2 、5 μg / m 2 、10 μg / m 2 、15 μg / m 2 、20 μg / m 2 、30 μg / m 2 、40 μg / m 2 、50 μg / m 2 、60 μg / m 2 、70 μg / m 2 、80 μg / m 2 、90 μg / m 2 、100 μg / m 2 、150 μg / m 2 、200 μg / m 2 、250 μg / m 2 、300 μg / m 2 、350 μg / m 2 、400 μg / m 2 、450 μg / m 2 、500 μg / m 2 、550 μg / m 2 、600 μg / m 2 or 650 μg / m 2. In some embodiments, a treatment regimen comprising one or more doses of a prophylactically effective amount of an anti-CD3 antibody, such as teprotumumab, is administered to a subject, wherein the prophylactically effective amount is increased 1.25-fold, 1.5-fold, 2-fold, 2.25-fold, 2.5-fold, or 5-fold until the daily prophylactically effective amount of the anti-CD3 antibody, such as teprotumumab, is reached.

[0123] In some embodiments, one or more of the following doses of an anti-CD3 antibody (such as teprotumumab, otelixizumab, or frutremumab) are administered intramuscularly to a subject to prevent, treat, or improve one or more symptoms of T1D: 200 μg / kg or less, preferably 175 μg / kg or less, 150 μg / kg or less, 125 μg / kg or less, 100 μg / kg or less, 95 μg / kg or less, 90 μg / kg or less, 85 μg / kg or less, 80 μg / kg or less, 75 μg / kg or less, 70 μg / kg or less, 65 μg / kg or less, 60 μg / kg or less, 55 μg / kg or less, 50 μg / kg or less, 45 μg / kg or less, 40 μg / kg or less, 35 μg / kg or less, 30 μg / kg or less, 25 μg / kg or less, 20 μg / kg or less, 15 μg / kg or less, 10 μg / kg or less, 5 μg / kg or less, 2.5 μg / kg or less, 2 μg / kg or less, 1.5 μg / kg or less, 1 μg / kg or less, 0.5 μg / kg or less, or 0.2 μg / kg or less.

[0124] In some embodiments, one or more of the following doses of an anti-CD3 antibody (such as teprotumumab, otelixizumab, or frutremumab) are administered subcutaneously to a subject to prevent, treat, or improve one or more symptoms of T1D: 200 μg / kg or less, preferably 175 μg / kg or less, 150 μg / kg or less, 125 μg / kg or less, 100 μg / kg or less, 95 μg / kg or less, 90 μg / kg or less, 85 μg / kg or less, 80 μg / kg or less, 75 μg / kg or less, 70 μg / kg or less, 65 μg / kg or less, 60 μg / kg or less, 55 μg / kg or less, 50 μg / kg or less, 45 μg / kg or less, 40 μg / kg or less, 35 μg / kg or less, 30 μg / kg or less, 25 μg / kg or less, 20 μg / kg or less, 15 μg / kg or less, 10 μg / kg or less, 5 μg / kg or less, 2.5 μg / kg or less, 2 μg / kg or less, 1.5 μg / kg or less, 1 μg / kg or less, 0.5 μg / kg or less, or 0.2 μg / kg or less.

[0125] In some embodiments, one or more of the following doses of an anti-CD3 antibody (such as teprotumumab, otelixizumab, or frutremumab) are administered intravenously to a subject to prevent, treat, or ameliorate one or more symptoms of T1D: 100 μg / kg or less, preferably 95 μg / kg or less, 90 μg / kg or less, 85 μg / kg or less, 80 μg / kg or less, 75 μg / kg or less, 70 μg / kg or less, 65 μg / kg or less, 60 μg / kg or less, 55 μg / kg or less, 50 μg / kg or less, 45 μg / kg or less, 40 μg / kg or less, 35 μg / kg or less, 30 μg / kg or less, 25 μg / kg or less, 20 μg / kg or less, 15 μg / kg or less, 10 μg / kg or less, 5 μg / kg or less, 2.5 μg / kg or less, 2 μg / kg or less, 1.5 μg / kg or less, 1 μg / kg or less, 0.5 μg / kg or less, or 0.2 μg / kg or less. In some embodiments, an intravenous dose of an anti-CD3 antibody (such as teprotumumab, otelixizumab, or frutremumab) of 100 μg / kg or less, 95 μg / kg or less, 90 μg / kg or less, 85 μg / kg or less, 80 μg / kg or less, 75 μg / kg or less, 70 μg / kg or less, 65 μg / kg or less, 60 μg / kg or less, 55 μg / kg or less, 50 μg / kg or less, 45 μg / kg or less, 40 μg / kg or less, 35 μg / kg or less, 30 μg / kg or less, 25 μg / kg or less, 20 μg / kg or less, 15 μg / kg or less, 10 μg / kg or less, 5 μg / kg or less, 2.5 μg / kg or less, 2 μg / kg or less, 1.5 μg / kg or less, 1 μg / kg or less, 0.5 μg / kg or less, or 0.2 μg / kg or less is administered over the following times to prevent, treat, or ameliorate one or more symptoms of T1D: about 6 hours, about 4 hours, about 2 hours, about 1.5 hours, about 1 hour, about 50 minutes, about 40 minutes, about 30 minutes, about 20 minutes, about 10 minutes, about 5 minutes, about 2 minutes, about 1 minute, about 30 seconds, or about 10 seconds.

[0126] In some embodiments, one or more of the following doses of an anti-CD3 antibody (such as teprotumumab, otelixizumab, or frutalimumab) are orally administered to a subject to prevent, treat, or improve one or more symptoms of T1D: 100 μg / kg or less, preferably 95 μg / kg or less, 90 μg / kg or less, 85 μg / kg or less, 80 μg / kg or less, 75 μg / kg or less, 70 μg / kg or less, 65 μg / kg or less, 60 μg / kg or less, 55 μg / kg or less, 50 μg / kg or less, 45 μg / kg or less, 40 μg / kg or less, 35 μg / kg or less, 30 μg / kg or less, 25 μg / kg or less, 20 μg / kg or less, 15 μg / kg or less, 10 μg / kg or less, 5 μg / kg or less, 2.5 μg / kg or less, 2 μg / kg or less, 1.5 μg / kg or less, 1 μg / kg or less, 0.5 μg / kg or less, or 0.2 μg / kg or less. In some embodiments, an oral dose of an anti-CD3 antibody (such as teprotumumab, otelixizumab, or frutalimumab) of 100 μg / kg or less, 95 μg / kg or less, 90 μg / kg or less, 85 μg / kg or less, 80 μg / kg or less, 75 μg / kg or less, 70 μg / kg or less, 65 μg / kg or less, 60 μg / kg or less, 55 μg / kg or less, 50 μg / kg or less, 45 μg / kg or less, 40 μg / kg or less, 35 μg / kg or less, 30 μg / kg or less, 25 μg / kg or less, 20 μg / kg or less, 15 μg / kg or less, 10 μg / kg or less, 5 μg / kg or less, 2.5 μg / kg or less, 2 μg / kg or less, 1.5 μg / kg or less, 1 μg / kg or less, 0.5 μg / kg or less, or 0.2 μg / kg or less is administered over the following times to prevent, treat, or improve one or more symptoms of T1D: about 6 hours, about 4 hours, about 2 hours, about 1.5 hours, about 1 hour, about 50 minutes, about 40 minutes, about 30 minutes, about 20 minutes, about 10 minutes, about 5 minutes, about 2 minutes, about 1 minute, about 30 seconds, or about 10 seconds.

[0127] In some embodiments (wherein escalating doses are administered in the first few days of the dosing regimen), the dose on day 1 of the regimen is 5 - 100 μg / m2 / day, such as 51 μg / m2 / day, and escalates to the daily doses as described immediately above by day 3, 4, 5, 6, or 7. For example, on day 1, a dose of approximately 51 μg / m 2 / day is administered to the subject, and on day 2, a dose of approximately 103 μg / m 2 / day, about 207 ug / m is administered on day 3 2 / day, about 413 ug / m is administered on day 4 2 / day, and 826 ug / m is administered on subsequent days of the regimen (e.g., days 5 - 14) 2 / day. In some embodiments, about 227 ug / m 2 / day of the dose is administered to the subject on day 1, about 459 ug / m 2 / day on day 2, and about 919 ug / m 2 / day on day 3 and subsequent days. In some embodiments, about 284 ug / m 2 / day of the dose is administered to the subject on day 1, about 574 ug / m 2 / day on day 2, and about 1148 ug / m 2 / day on day 3 and subsequent days.

[0128] In some embodiments, the initial dose is 1 / 4, to 1 / 2, to equal to the daily dose at the end of the regimen, but administered in batches at intervals of 6, 8, 10, or 12 hours. For example, a 13 ug / kg / day dose is administered in four doses of 3 - 4 ug / kg at 6 - hour intervals to reduce the level of cytokine release caused by the administration of the antibody. In some embodiments, to reduce the likelihood of cytokine release and other adverse reactions, the first 1, 2, 3, or 4 doses or all doses in the regimen are administered more slowly by intravenous administration. For example, 51 ug / m 2 / day of the dose can be administered over about 5 minutes, about 15 minutes, about 30 minutes, about 45 minutes, about 1 hour, about 2 hours, about 4 hours, about 6 hours, about 8 hours, about 10 hours, about 12 hours, about 14 hours, about 16 hours, about 18 hours, about 20 hours, and about 22 hours. In some embodiments, the dose is administered over a period of, for example, 20 to 24 hours by slow infusion. In some embodiments, the dose is infused in a pump, preferably increasing the concentration of the administered antibody as the infusion progresses.

[0129] In some embodiments, a set fraction of the dose of the above 51 ug / m 2 / day to 826 ug / m 2 / day regimen is administered in escalating doses. In some embodiments, the fraction is 1 / 10, 1 / 4, 1 / 3, 1 / 2, 2 / 3, or 3 / 4 of the daily dose of the above regimen. Thus, when the fraction is 1 / 10, the daily dose will be 5.1 ug / m 2 on day 1, 10.3 ug / m 2 on day 2, 20.7 ug / m 2 on day 3, 41.3 ug / m on day 42 , and 82.6 ug / m from day 5 to day 14 2 . When the fraction is 1 / 4, the dose will be 12.75 ug / m on day 1 2 , 25.5 ug / m on day 2 2 , 51 ug / m on day 3 2 , 103 ug / m on day 4 2 , and 207 ug / m from day 5 to day 14 2 . When the fraction is 1 / 3, the dose will be 17 ug / m on day 1 2 , 34.3 ug / m on day 2 2 , 69 ug / m on day 3 2 , 137.6 ug / m on day 4 2 , and 275.3 ug / m from day 5 to day 14 2 . When the fraction is 1 / 2, the dose will be 25.5 ug / m on day 1 2 , 51 ug / m on day 2 2 , 103 ug / m on day 3 2 , 207 ug / m on day 4 2 , and 413 ug / m from day 5 to day 14 2 . When the fraction is 2 / 3, the dose will be 34 ug / m on day 1 2 , 69 ug / m on day 2 2 , 137.6 ug / m on day 3 2 , 275.3 ug / m on day 4 2 , and 550.1 ug / m from day 5 to day 14 2 . When the fraction is 3 / 4, the dose will be 38.3 ug / m on day 1 2 , 77.3 ug / m on day 2 2 , 155.3 ug / m on day 3 2 , 309.8 ug / m on day 4 2 , and 620 ug / m from day 5 to day 14 2 . In some embodiments, the regimen is the same as one of the above regimens, except that it is administered over days 1 to 4, days 1 to 5, or days 1 to 6. For example, in some embodiments, the dose will be 17 ug / m on day 1 2 , 34.3 ug / m on day 2 2 , 69 ug / m on day 3 2 , 137.6 ug / m on day 4 2 , and 275.3 ug / m on days 5 and 6 2 .

[0130] In some embodiments, instead of being administered via daily doses over several days, anti-CD3 antibodies (such as teprotumumab, otelixizumab, or frilimumab) are administered via infusion in a continuous manner over 4 hours, 6 hours, 8 hours, 10 hours, 12 hours, 15 hours, 18 hours, 20 hours, 24 hours, 30 hours, or 36 hours. The infusion can be constant or can start at a lower dose, for example, during the first 1, 2, 3, 5, 6, or 8 hours of the infusion, and then be increased to a higher dose thereafter. During the infusion, the patient receives a dose equal to that administered in the 5- to 20-day regimens described above. For example, approximately 150 μg / m 2 ², 200 μg / m 2 ², 250 μg / m 2 ², 500 μg / m 2 ², 750 μg / m 2 ², 1000 μg / m 2 ², 1500 μg / m 2 ², 2000 μg / m 2 ², 3000 μg / m 2 ², 4000 μg / m 2 ², 5000 μg / m 2 ², 6000 μg / m 2 ², 7000 μg / m 2 ², 8000 μg / m 2 ², 9000 μg / m 2 ², 10000 μg / m 2 ², 11000 μg / m 2 ², 12000 μg / m 2 ², 13000 μg / m 2 or 14000 μg / m 2 ². In particular, the rate and duration of the infusion are designed to minimize the level of free anti-CD3 antibody (such as teprotumumab, otelixizumab, or frilimumab) in the subject after administration. In some embodiments, the level of free anti-CD3 antibody such as teprotumumab should not exceed 200 ng / ml of free antibody. Additionally, the infusion is designed to achieve at least 50%, 60%, 70%, 80%, 90%, 95%, or 100% of combined T cell receptor coating and modulation.

[0131] In some embodiments, anti-CD3 antibodies (such as teprotumumab, otelixizumab, or frilimumab) are administered chronically to treat, prevent type 1 diabetes, or slow the progression or delay the onset of one or more symptoms of type 1 diabetes, or improve one or more symptoms of type 1 diabetes. For example, in some embodiments, a low dose of an anti-CD3 antibody such as teprotumumab is administered once a month, twice a month, three times a month, once a week, or even more frequently as an alternative to the above 6- to 14-day dosing regimen or after administering such a regimen to enhance or maintain its effect. Such a low dose can be from 1 ug / m 2 to 100 ug / m 2 of any dose, such as approximately 5 ug / m 2 、10 ug / m 2 、15 ug / m 2 、20 ug / m 2 、25 ug / m 2 、30 ug / m 2 、35 ug / m 2 、40 ug / m 2 、45 ug / m 2 or 50 ug / m 2 .

[0132] In some embodiments, a subject can be redosed at some time after an anti-CD3 antibody (such as teprotumumab, otelixizumab, or frilimumab) dosing regimen, for example, based on one or more physiological parameters, or can be redosed as a matter of course. Such redosing and / or evaluation of the need for redosing can be administered 2 months, 4 months, 6 months, 8 months, 9 months, 1 year, 15 months, 18 months, 2 years, 30 months, or 3 years after the administration of the dosing regimen, and such redosing can include a treatment course administered every 6 months, every 9 months, every 1 year, every 15 months, every 18 months, every 2 years, every 30 months, or every 3 years indefinitely. Examples Example 1: Prevention of rapid metabolic decline within 3 months after teprotumumab treatment in individuals at high risk of type 1 diabetes Summary

[0133] Endpoints that can provide early identification of treatment efficacy are needed to more effectively conduct type 1 diabetes prevention trials. To this end, we evaluated whether metabolic endpoints could be used to detect the effect of teplizumab on rapid β-cell decline within 3 months after treatment in high-risk individuals in the TrialNet teplizumab trial. Glucose and C-peptide response curves (GCRC) were constructed by plotting the mean glucose and C-peptide values of a 2-hour oral glucose tolerance test on a two-dimensional grid. Visual comparison was made of changes in the shape and movement of the GCRC for each group. Within 3 months after randomization, changes in the GCRC in the placebo group reflected marked metabolic deterioration. By 6 months, the GCRC resembled that typical at diagnosis. In contrast, changes in the GCRC in the teplizumab group indicated metabolic improvement. Quantitative comparisons, including two new metabolic endpoints indicating GCRC change, namely the within-quadrant endpoint (WQE) and the ordered direction endpoint (ODE), were consistent with the visual impression of the perceived treatment efficacy at the 3- and 6-month time points. In summary, an analytical approach combining visual evidence with the new endpoints demonstrated that teplizumab delays rapid metabolic decline and improves metabolic status within 3 months after treatment; this effect persists for at least 6 months. Introduction

[0134] Type 1 diabetes is an autoimmune disease that results in insulin deficiency due to the destruction of pancreatic β-cells (1). In the Type 1 Diabetes TrialNet TN10 anti-CD3 prevention study, a single 14-day course of the Fc receptor non-binding anti-CD3ε monoclonal antibody teplizumab was able to delay diabetes onset by 32.5 months in a group of autoantibody-positive high-risk individuals (2; 3). In a subsequent longitudinal analysis of this study (3), after a decline in C-peptide response before entry into the trial, teplizumab treatment improved the mean area under the C-peptide curve (AUC) over a 6-month period. In addition, treatment reversed the decline in insulin secretion observed before enrollment.

[0135] These findings suggest that metabolic endpoints may be valuable for obtaining more precise information about the action of teplizumab. The utility of using metabolic endpoints to examine the action of teplizumab may potentially be extended subsequently to evaluate preventive treatments in other clinical trials. The C-peptide response to an oral glucose or mixed meal tolerance test (OGTT; MMTT) is mainly used to evaluate β-cell function before and after diabetes diagnosis (4). However, several studies have shown that combined stimulated glucose and C-peptide markers improve the prediction of type 1 diabetes, the identification of heterogeneity in autoantibody-positive populations, and the detection of subtle changes in β-cell function (5 - 11).

[0136] Accordingly, we infer that these combined measures can be used to investigate two important questions: (1) In individuals at high risk of type 1 diabetes, does tepelizumab have a substantial effect on β-cell function within 3 months after a 14-day treatment course, and if so, does this effect persist for at least 6 months? (2) If tepelizumab is not administered, is there a likelihood of a rapid decline in β-cells within 3 months in high-risk individuals? We used two composite glucose and C-peptide markers, namely Index60 (8,9) and the C-peptide AUC / glucose AUC ratio (AUC ratio), to address these questions. In addition, we used OGTT-derived glucose and C-peptide response curves (GCRC) on a two-dimensional grid (2d grid). This recently introduced method extends the concept of combined glucose and C-peptide measurements by leveraging combined qualitative and quantitative approaches. It has facilitated the study of the metabolic natural history of type 1 diabetes (12), the metabolic heterogeneity of disorders at diagnosis (13), and the metabolic effects of potential preventive treatments (14). In another study, GCRC (from MMTT) was the basis for investigating the relationship between metabolic changes and microRNAs after the diagnosis of type 1 diabetes (15).

[0137] The study results will show that tepelizumab effectively preserves and improves β-cell function shortly after treatment in individuals at high risk of type 1 diabetes, and this effect persists. They will also show that, although none of the placebo group was diagnosed within 3 months after randomization, there was already significant metabolic deterioration during this period. In addition, the study results demonstrate that the use of combined glucose and C-peptide endpoints in conjunction with GCRC provides qualitative and quantitative insights into the timing and magnitude of preventive treatment effects missed in type 1 diabetes prevention trials due to the use of only diagnostic endpoints. Study Design and Methods Trial Procedures and Design

[0138] The design of the Phase 2 randomized, placebo-controlled, double-blind TrialNet TN10 anti-CD3 prevention study (NCT01030861) has been previously reported in detail (2). Each participating site obtained institutional review board approval and written informed consent and authorization prior to trial entry. Inclusion criteria were age ≥8 years at randomization, a family history of type 1 diabetes, stage 2 diabetes [positive titers of ≥ two islet autoantibodies (anti-glutamic acid decarboxylase 65, IA-2, anti-islet antigen 2, anti-zinc transporter 8, and / or islet cell antibody) and glucose dysmetabolism]. HbA1c levels were within the normal range [median (IQR) teplizumab: 5.2% (4.9%-5.4%); placebo: 5.3% (5.1%-5.4%)]. Participants were randomly assigned to the teplizumab or saline group and received a 14-day outpatient course administered as an intravenous infusion. The Northwest Lipids Research Laboratories tested OGTT C-peptide and glucose values using TOSOH C-peptide and Roche glucose assays. OGTTs with glucose values in the diabetic range were excluded from this analysis.

[0139] AUC values for C-peptide and glucose were calculated using the trapezoidal rule (16). C-peptide AUC / glucose AUC ratio values were obtained by calculating the ratio x 100. Index60 was calculated as 0.3695 × (log fasting C-peptide [ng / mL]) + 0.0165 × 60-min glucose (mg / dL) - 0.3644 × 60-min C-peptide (ng / mL) (17). Glucose and C-peptide response curves (GCRC) were generated by plotting the mean glucose values (y-axis) and C-peptide values (x-axis) of the OGTT (30, 60, 90, and 120 min) on a 2-d grid. Analysis components

[0140] Figures 3A - 3C Included six key components used in the metabolic change analysis: a 2-d grid with glucose on the y-axis and C-peptide on the x-axis, GCRC, centroid (center point of the GCRC), vector, direction quadrant of the vector, and angle. Figure 3AShows two hypothetical GCRCs of mean glucose and C-peptide values at 30, 60, 90, and 120 minutes from the same individual plotted on a 2D grid. One GCRC is a typical GCRC within 6 months before diagnosis, while the other is a typical GCRC at diagnosis. Calculate the centroid of each GCRC (see formula). From 6 months before diagnosis to diagnosis, there are significant changes in the GCRC position and shape, indicating a metabolic decline. The arrow is the vector of the position change from the centroid of the GCRC 6 months before diagnosis to the centroid of the GCRC at diagnosis. Note that the direction and magnitude of the vector change with the decrease in C-peptide and increase in glucose from baseline to 6 months.

[0141] In Figure 3B a right triangle is overlaid on Figure 3A it. The right triangle is formed by the changes in the vector (hypotenuse) and glucose (vertical side) and C-peptide (horizontal side). The formation of the right triangle provides a means for calculating the angle of the vector. As shown, the calculated angle for this analysis is defined as the angle between the vector (hypotenuse) and the horizontal side of the right triangle.

[0142] Figure 3C Hypothetical examples of vectors in each of the four direction quadrants are shown: lower right quadrant; upper right quadrant; upper left quadrant; lower left quadrant. They all originate from the baseline centroid, where the changes in glucose and C-peptide are fixed at 0. Thus, the direction quadrant of the vector depends on whether the changes in glucose and C-peptide are positive or negative. Also shown is the calculated angle between the horizontal line and the vector.

[0143] To provide a quantitative comparison that complements the visual comparison of GCRCs, we developed two new endpoints that indicate the change in GCRC movement over a 6-month period based on the vector and angle: within-quadrant endpoint (WQE) and ordered direction endpoint (ODE). Both endpoints are modeled using Cox regression, which is based on the movement of the GCRC into four potential quadrants that reflect changes in glucose and C-peptide. For information on the development of these 2 new endpoints, see Figure 4 . Formula for calculating the centroid CPEP centroid = (1 / 3) * ((((CPEP30 + CPEP60) * ((CPEP30 * GLUC60) - (CPEP60 * GLUC30))) + ((CPEP60 + CPEP90) * ((CPEP60 * GLUC90) - (CPEP90 * GLUC60))) + ((CPEP90 + CPEP120) * ((CPEP90 * GLUC120) - (CPEP120 * GLUC90))) + ((CPEP120 + CPEP30) * ((CPEP120 * GLUC30) - (CPEP30 * GLUC120)))) / ((((CPEP30 * GLUC60) - (CPEP60 * GLUC30)) + ((CPEP60 * GLUC90) - (CPEP90 * GLUC60)) + ((CPEP90 * GLUC120) - (CPEP120 * GLUC90)) + ((CPEP120 * GLUC30) - (CPEP30 * GLUC120)))) GLUC centroid = (1 / 3) * ((((GLUC30 + GLUC60) * ((CPEP30 * GLUC60) - (CPEP60 * GLUC30))) + ((GLUC60 + GLUC90) * ((CPEP60 * GLUC90) - (CPEP90 * GLUC60))) + ((GLUC90 + GLUC120) * ((CPEP90 * GLUC120) - (CPEP120 * GLUC90))) + ((GLUC120 + GLUC30) * ((CPEP120 * GLUC30) - (CPEP30 * GLUC120)))) / ((((CPEP30 * GLUC60) - (CPEP60 * GLUC30)) + ((CPEP60 * GLUC90) - (CPEP90 * GLUC60)) + ((CPEP90 * GLUC120) - (CPEP120 * GLUC90)) + ((CPEP120 * GLUC30) - (CPEP30 * GLUC120)))) Development of Endpoints within the 6 - Month Quadrant and Endpoints in the 6 - Month Ordinal Direction

[0144] The following section will describe the development and formulation of two new endpoints used together in the analysis. Basis for selecting endpoints for analysis

[0145] The prediction of type 1 diabetes for potential endpoints was first tested. Then, the performance of those endpoints that predicted type 1 diabetes was evaluated in the oral insulin treatment effect in the DPT-1 and Trial Net oral insulin trial cohorts of the detection combination (n = 208; no interaction between trials and treatments). Among the endpoints studied, the within-quadrant endpoint (WQE) and the ordered direction endpoint (ODE) predicted type 1 diabetes in 281 DPT-1 participants in the oral insulin or parenteral insulin control groups (unadjusted, for WQE, p < 0.001, and for ODE, p = 0.001; after adjustment for AUC glucose AUC, C-peptide AUC, age, and BMI, for both, p < 0.001). Although other endpoints also predicted type 1 diabetes, WQE and ODE were superior to other endpoints for detecting the effect of oral insulin. Table 4 shows the comparison of WQE (p < 0.001) and ODE (p = 0.005) between the placebo group and the oral insulin group after adjustment for baseline glucose AUC, C-peptide AUC, age, and BMI. Based on these findings, we used WQE and ODE to statistically evaluate the early effect of teplizumab. Within - quadrant endpoint (WQE)

[0146] The complete 360° continuum had poor prediction for type 1 diabetes. Therefore, we explored the possibility of enhancing risk prediction by dividing the 360° continuum into 4 directional quadrants from 0° to 90°. Each directional quadrant would be considered to have its own characteristic risk, which, when included in the model, would predict the overall risk. The directional quadrants were named: Right upper quadrant (RUQ) Left upper quadrant (LUQ) Right lower quadrant (RLQ) Left lower quadrant (LLQ)

[0147] As Figure 4 shown, the angle was calculated from the right triangle formed by the directional quadrant according to the individual change vector. Since the angles calculated in the LUQ and RLQ were negative, they were converted to positive values. The percentage change in centroid glucose from baseline to 6 months on the y-axis and the percentage change in centroid C-peptide from baseline to 6 months on the x-axis were used for the calculation of the angle. The hypotenuse of the triangle represents the distance from the baseline to the centroid of the GCRC at 6 months from the baseline (i.e., the change vector). The formulas for calculating the angle and the hypotenuse are shown below: Radians = arctan(percent change in glucose / percent change in C-peptide) Angle = radians * 57.296 Hypotenuse = sqrt((percent change in glucose * percent change in glucose) + (percent change in C-peptide * percent change in C-peptide)) (The formula uses the percentage change in glucose and C-peptide instead of the actual values to standardize the units of the sides of the triangle.)

[0148] Using data from the control groups (n = 281) of the DPT-1 and TrialNet parenteral and oral insulin trials, we developed Cox regression models that included the angular change over 6 months calculated within each quadrant as independent variables for predicting type 1 diabetes. Since the change vector for an individual can only fall into one of the quadrants, the values for the other quadrants were assigned 0. Based on this paradigm, we found that the models significantly predicted type 1 diabetes. However, we also found that for some quadrants, other models could also predict if the calculated angle was subtracted from 90°.

[0149] Figure 4 Shows how the vectors, quadrants, and angles are defined. It also hypothetically shows that the angles used in the final model include the percentage change in the angular change calculated in the LUQ and RLQ, and the percentage change in the angular change calculated in the LLQ and RLQ subtracted from 90°. In addition, we observed that by adjusting the model coefficients for baseline risk using the DPT-1 risk score (10) (DPTRS), these models could better predict type 1 diabetes. Therefore, the final model coefficients were based on the adjustment of the DPTRS. The following equation shows the coefficients for the directional quadrant angles of the model used to detect the maximum difference (p < 0.001) between the detectable oral insulin group and the placebo group. WQE = 0.02455*qangle1 + 0.01464*qangle2 + 0.00831*qangle3 - 0.00465*qangle4 where qangle1 = RUQ, qangle2 = LUQ, qangle3 = LLQ, and qangle4 = RLQ (Since 3 of the 4 directional quadrants are always negative for an individual, indicator variables coded as "0" were used.)

[0150] The following summary shows the steps required to calculate the WQE: -- Calculate the glucose and C-peptide coordinates of the baseline and 6-month GCRC centroids. -- Convert the change in the glucose and C-peptide centroid coordinates to percentage change. -- Determine the directional quadrant of the vector between the baseline and 6-month coordinates. -- Calculate the angle within the quadrant between the horizontal line and the vector using the standard formula based on a right triangle. -- Convert the negative angles in directional quadrants 2 and 4 (qangle2 and qangle4) to positive angles. ----Use the calculated angle for qangle1 and qangle2. Use (90° - calculated angle) for qangle3 and (90 - qangle4) in the model.

[0151] The process of angle conversion from negative to positive and subtracting the angle from 90° is as follows: If the vector of centroid change is in RUQ, then qangle1 = calculated angle If the vector of centroid change is in LUQ, then qangle2 = calculated angle * (-1) If the vector of centroid change is in LLQ, then qangle3 = 90 - calculated angle If the vector of centroid change is in LLQ, then qangle4 = 90 - calculated angle * (-1)

[0152] Example of WQE: The directionality of an individual from the baseline GCRC centroid to the 6 - month GCRC centroid is in LLQ, and the qangle is 32°. WQE = 0.00831 * (32) = 0.266 (0.00831 is the regression coefficient for the LLQ direction quadrant) 6 - month ordered - direction endpoint (ODE)

[0153] Using a 360° scale, based on previous evidence of sequential directionality during the progression of type 1 diabetes longitudinally (15), we assign values to the 4 quadrants as follows. Lower - right quadrant (RLQ): 0° Lower - left quadrant (LLQ): 90° Upper - right quadrant (RUQ): 180° Upper - left quadrant (LUQ): 270°

[0154] Add the value to the qangle value obtained from the WQE model. Then divide the sum by 360 to produce a scale with a maximum value of 1.00.

[0155] The summary of the steps required for ODE calculation is the same as the summary of the steps for WQE calculation, except that instead of inserting the qangle value into the model, the qangle is added to the specified value indicated above for the direction quadrant.

[0156] Example of ODE: The vector directionality of an individual from the baseline GCRC centroid to the 6 - month GCRC centroid is in LUQ, and the qangle is 49°. The 6M - ODE for this individual is: 270° + 49° = 319° / 360° = 0.87 unit Where 270° represents the specified quadrant value of the LUQ, and 49° represents the angular value within the LUQ.

[0157] The prediction of type 1 diabetes for potential endpoints was first tested. Then the performance of those endpoints predicting type 1 diabetes in the oral insulin treatment effect in the DPT-1 and Trial Net oral insulin trial cohorts of the detection combination (n = 208; no interaction between trials and treatments) was evaluated. Among the endpoints studied, the within-quadrant endpoint (WQE) and the ordered direction endpoint (ODE) predicted type 1 diabetes in 281 DPT-1 participants in the oral insulin or parenteral insulin control groups (unadjusted, for WQE, p < 0.001, and for ODE, p = 0.001; after adjustment for AUC glucose AUC, C-peptide AUC, age, and BMI, for both, p < 0.001). Although other endpoints also predicted type 1 diabetes, WQE and ODE were superior to other endpoints in detecting the effect of oral insulin. Table 4 shows the comparison of WQE (p < 0.001) and ODE (p = 0.005) between the placebo group and the oral insulin group after adjustment for baseline glucose AUC, C-peptide AUC, age, and BMI. Based on these findings, we used WQE and ODE for statistical evaluation of the early effect of teplizumab. Table 4 Comparison of endpoints between the placebo group and the oral insulin group using changes in metabolic parameters from baseline to 1-year visit* + Adjusted for age, BMI, baseline AUC C-peptide, and AUC glucose values * The 1-year visit was used to evaluate the endpoints because the peak effect of oral insulin was at this time point in a previous study (22) Statistical analysis

[0158] The groups were compared using the t-test and chi-square test. Linear regression was used to adjust for variables, while proportional hazards regression was used to develop models for the endpoints. Gender was not included as a covariate in the analysis. All analyses were performed using the statistical program SAS (version 9.4). A two-sided p-value < 0.05 was considered statistically significant. Results

[0159] The relevant baseline characteristics of the participants are shown in Table 5. There were no significant differences between the placebo group and the teprotumumab group for any demographic measurement or glucose or C-peptide levels of the baseline OGTT. Among the 76 participants, there was sufficient OGTT data to analyze 29 placebo-treated individuals and 41 teprotumumab-treated individuals at 3 months after randomization, and 24 placebo-treated individuals and 44 teprotumumab-treated individuals at 6 months after randomization. Five individuals in the placebo group were excluded from the OGTT analysis at 6 months because of a diagnosis of diabetes (3 individuals), or a glucose value of one OGTT was in the diabetic range but did not yet meet the diagnostic criteria for two consecutive diabetic-range OGTTs (2 individuals). Table 5. Participant characteristics at baseline Metabolic responses were evaluated at 3 months and 6 months after treatment using the GCRC

[0160] To determine whether there was early evidence that teprotumumab had an effect on β-cell function after treatment, we compared the changes in GCRC from randomization to 3 months between the placebo group and the teprotumumab group. In Figure 1A and Figure 1B for each individual in the placebo group and the teprotumumab group, the centroid change vectors from randomization to the 3-month visit were plotted. In these vector plots, the vectors of 11 / 29 individuals (37.9%) in the placebo group and 6 / 41 individuals (14.6%) in the teprotumumab group pointed to the upper left quadrant (decreasing C-peptide and increasing glucose), indicating worsening metabolic function. In contrast, the vectors of 11 / 41 individuals (26.8%) in the teprotumumab group and 2 / 29 individuals (6.9%) in the placebo group pointed to the lower right quadrant (increasing C-peptide and decreasing glucose), indicating improved metabolic function. The frequencies of placebo-treated participants and teprotumumab-treated participants were significantly different in all four quadrants (overall p = 0.045) (Table 1). Table 1. Frequencies of individual GCRC change vectors according to direction quadrants from baseline to 3 months and from baseline to 6 months in treatment groups *Chi-square test showed significantly different distributions between treatment groups at the 3-month interval (p = 0.045) and the 6-month interval (p = 0.004).

[0161] Figures 1C - 1DDepicts the GCRC constructed from the mean glucose and C-peptide values at each OGTT time point, and the vector indicating the change in GCRC centroid from randomization to 3 months. In the placebo group, there was a marked progressive metabolic dysfunction by 3 months, with the vector of GCRC centroid change during this period pointing to the upper left quadrant (decreasing C-peptide and increasing glucose). In contrast, the vector of GCRC centroid change in the teplizumab group had an almost opposite directionality, i.e., towards the lower right quadrant (increasing C-peptide and decreasing glucose). The magnitude of the teplizumab effect was evident when there was a 153° (maximum 180°) difference in direction between the placebo and teplizumab vectors.

[0162] The changes in the AUC ratio and Index60 from baseline to 3 months were consistent with the large difference in vector directionality. There were significant differences in the changes between placebo and teplizumab for both (p < 0.01 after adjustment for baseline parameters, age, and BMI; Table 2). Additionally, there was evidence of improvement in the teplizumab group: an increase in the AUC ratio (p < 0.01) and a decrease in Index60 (p < 0.05). (Adjustment could not be made in the paired analysis due to multicollinearity.) Table 2. Comparison of endpoints between the placebo and teplizumab groups based on metabolic changes from baseline to 3 months and from baseline to 6 months + Adjust ODE and WQE for age, BMI, and baseline AUC C-peptide and AUC glucose; adjust ΔAUC C-peptide / AUC glucose and ΔIndex60 for age, BMI, and baseline Index60.

[0163] It is also notable in Figures 1C - 1D the marked morphological change in the GCRC of the placebo group from randomization to 3 months. Of particular note is the increased upward slope between 30 and 60 minutes, which is a typical change during the progression to type 1 diabetes (12). In contrast, the slope in the teplizumab group was almost the same.

[0164] The pattern of individual vectors at 6 months was similar to that at 3 months. As Figures 1E - 1FAs shown, the directionality of the individual vectors in the placebo group was leftward and upward, indicating a worsening of metabolism, while in the teplizumab group they moved rightward and downward, indicating an improvement in metabolism. Thus, a higher percentage of individuals in the placebo group had vectors pointing to the upper left quadrant (45.8% in the placebo group compared to 11.4% in the teplizumab group), while a higher percentage of individuals in the teplizumab group had vectors pointing to the lower right quadrant (8.3% in the placebo group compared to 34.1% in the teplizumab group). There were also significant differences in the GCRC centroid displacement vectors derived from the mean OGTT values ( Figures 1G - 1H ). From randomization to 6 months, the difference in direction between the placebo vector and the teplizumab vector was 138°. In all four quadrants, the vector frequency differences between the groups were significant (overall p = 0.004) (Table 1).

[0165] The differences in the AUC ratio and the change in Index60 between the groups were also significant, but the differences were not as large (p = 0.005 and p = 0.021, respectively, after adjustment; Table 2). From baseline to 6 months, the AUC ratio in the teplizumab group also increased significantly (p < 0.01), but Index60 did not decrease significantly.

[0166] In Figures 1G - 1H , it was clear that by 6 months after randomization, the shape of the GCRC had essentially become more pathological, such that its shape resembled the characteristic GCRC shape at diagnosis (12). Specifically, the placebo GCRC had become almost linear from 30 to 90 minutes, which was due to the slope from 60 to 90 minutes having become less downward. The degree of change in the teplizumab GCRC shape was much smaller. Quantitative assessment of treatment effect using the vector angle within the direction quadrants

[0167] The above analysis showed that there were significant differences in the GCRC vectors between the placebo group and the teplizumab group within 3 months after 14 days of treatment and at least up to 6 months. These differences were confirmed by the differences in Index60 and the AUC ratio between the groups. However, those combined glucose and C-peptide measurements were not based on the directionality of the vectors indicating GCRC changes. Therefore, we sought to develop quantitative endpoints that would more directly indicate the vector differences between the placebo group and the treatment group. Two such endpoints were developed: the within-quadrant endpoint (WQE) and the ordered direction endpoint (ODE) (see Research Design and Methods). Both endpoints were derived from the change in the GCRC centroid position in the Diabetes Prevention Trial-Type 1 at 6-month intervals, which was the shortest interval available for analysis between individual OGTTs.

[0168] The visual difference in the movement of GCRC from baseline to 3 months between the placebo and treatment groups was statistically confirmed using ODE and WQE( Figure 2A and Figure 2B ). The ODE value in the teplizumab group was significantly lower (adjusted p < 0.05). The WQE value in the teplizumab group was also significantly lower before adjustment (p < 0.05), but only trended towards a difference after adjustment (p = 0.072).

[0169] The differences in WQE and ODE at 6 months between the placebo and teplizumab groups were significantly greater than those at 3 months: the WQE in the teplizumab group was significantly lower (p < 0.01), and the same was true for ODE (p < 0.01). Table 2 summarizes the differences between the placebo and teplizumab groups according to the endpoints, showing that the differences in Index60 and AUC ratio were greater between groups at 3 months, while the differences in WQE and ODE were greater at 6 months. Conclusion

[0170] The teplizumab trial showed that a single course of treatment could delay the onset of type 1 diabetes (2). However, this trial and other prevention trials using the standard endpoint of time to diagnosis were long. In addition, although teplizumab is highly effective in delaying type 1 diabetes, there is a lack of information on its onset time and its effect on the metabolic state. Therefore, we used existing and new metabolic markers as endpoints to obtain this information. Such early readings could not only help to understand the effect of preventive treatment on β-cell function, but might also shorten prevention trials.

[0171] Measurement of the C-peptide AUC in response to an oral glucose before diagnosis or a mixed meal after diagnosis is commonly used to evaluate insulin responsiveness; however, it is not necessarily the most sensitive measure for identifying changes in β-cell function. Multiple studies have shown that the loss of C-peptide in type 1 diabetes may be best presented and understood in the context of glucose changes (5 - 11). We used a new method in this study that incorporated qualitative and quantitative information to better understand the kinetics of insulin secretion in response to glucose. Using this method, the study results provided strong evidence that teplizumab could prevent severe β-cell decline and potentially improve function in high-risk groups up to 3 months after treatment. In addition, the effect persisted for at least 6 months after treatment. These study results are significant because they show that even in the late stage of metabolic progression before diagnosis, the action of teplizumab is rapid enough to have a highly influential effect.

[0172] The GCRC, its centroid, along with the vectors and their angles play important roles in the analysis. It is evident from the changes in GCRC on the 2D grid that the use of those elements leads to a visual contrast between the weakened metabolic state in the placebo group and the significant improvement in the teplizumab group. Those elements are also used as the basis for developing the new endpoints WQE and ODE, which are used for quantitative comparison of the changes between the placebo group and the teplizumab group. Together with the AUC ratio and Index60, those endpoints confirm the visual impression that teplizumab quickly arrests the rapid metabolic decline in high-risk individuals after 14 days of treatment from baseline. Table 3 summarizes how the GCRC movement and shape-based methods enhance our understanding of the timing, magnitude, and potential clinical importance of the effect of teplizumab. Table 3. The following list shows the substantial contribution of the use of GCRC plotted on a 2D grid to the analysis.

[0173] The findings of these analyses highlight the advantages of using vectors as well as scalars as metabolic endpoints for analyzing treatment effects. Previous analyses of preventive measures relied on scalars, which only provide information about magnitude, while vectors provide information about direction in addition to magnitude. The vectors of GCRC movement on the 2D grid clearly add information that cannot be determined solely from scalar endpoints.

[0174] We previously utilized the GCRC change vector as evidence of the effect of oral insulin for preserving β-cell function in post hoc analyses in high-risk individuals with type 1 diabetes in the DPT-1 and TrialNet oral insulin trials (14). Interestingly, compared with the oral insulin analysis, the vectors in the current teplizumab analysis are much farther apart between the placebo group and the treatment group. In the teplizumab trial, the difference in vector direction between the placebo group and the teplizumab group spans from the upper left quadrant (placebo) to the lower right quadrant (teplizumab), while the difference between the placebo and oral insulin vectors is mainly confined to the upper right quadrant. The greater vector separation in the teplizumab trial may be based on differences in the treatment mechanisms and / or the magnitude of the effects between teplizumab and oral insulin. It is shown that the analysis of preventive treatment effects would be incomplete if vectors were ignored and only scalars were relied upon.

[0175] The detection of treatment effects by WQE and ODE endpoints may not generalize to trials in other stages of disease, as those in the teplizumab trial were selected for high risk. In addition, it is likely that other endpoints that correlate with the GCRC centroid and its vector of change will be found to be superior to the WQE and ODE. However, the performance of those endpoints in the analysis validated the visual impression derived from the GCRC, suggesting that the combined qualitative and quantitative use of vectors to examine metabolic changes can be a valuable approach to assessing the effects of preventive treatments.

[0176] Notably, this is also the first reported analysis of a T1D prevention study using changes in Index60 as an endpoint to demonstrate metabolic effects. Taken together, these and previous results (14) suggest that multiple metabolic endpoints, including Index60, AUC ratio, and WQE and ODE combined with changes in glucose and C-peptide, can provide a readout of treatment effects in type 1 diabetes prevention studies. The subtle variability in treatment group differences detected between the oral insulin and teplizumab trials may reflect differences in study populations or treatment interventions. The changes in Index60 and AUC ratio from baseline to 3 months between the placebo and teplizumab groups were greater than those for WQE and ODE, whereas the opposite was true from baseline to 6 months. As both the WQE and ODE were developed based on 6-month data, it is not surprising that they performed better from baseline to 6 months. Although the analysis of the teplizumab study suggests that Index60 and AUC ratio may be more sensitive for detecting early effects, this may not be generalizable to other trials. The choice of a specific metabolic endpoint for a prevention trial will depend on factors that may influence its relevance and performance, such as the target population, objectives, intervention, and trial design. The advantages of the WQE and ODE over other measures are that they directly complement the compelling visual impressions derived from the GCRC (summarized in Table 3).

[0177] The 0 to 30 minute interval was not included in the GCRC because the magnitude of change in metabolic measures from 0 to 30 minutes was much greater than the magnitude of change in each of the other time intervals. Therefore, its incorporation would have minimized the 30 to 120 minute visualization. Similarly, from a quantitative perspective, the 0 to 30 minute interval would have had an outsized impact on the calculation of the centroid and vector, which would have further complicated the analysis. Although the 0 and 30 minute C-peptide and glucose measurements were not included in the GCRC, we did adjust for baseline AUC C-peptide and AUC glucose values ​​in our analysis. Now that quantitative measures have been developed to complement the visualization of the GCRC and its centroid and vector, an important future direction will be to investigate the impact of changes from 0 to 30 minutes on changes in the form and position of the GCRC.

[0178] The applicability of short-term and intermediate metabolic endpoints for prevention trials will require further study. The primary metabolic endpoints have been successfully used in trials of new-onset type 1 diabetes, typically with a defined follow-up of one year to assess the treatment effect on delaying further loss of insulin secretion. Positive results for metabolic outcomes in these new-onset trials have been used to justify treatment in pre-diagnostic prevention trials (18 - 21). Our findings suggest that information from short-term pre-diagnostic prevention trials can also be used to decide whether a larger pre-diagnostic prevention trial of a specific treatment is necessary.

[0179] Short-term metabolic endpoints may have several other utilities for evaluating pre-diagnostic preventive treatment. If the pre-specified effect is not achieved, they can be used for stopping rules in the trial. A new way of using metabolic endpoints is to combine them with diagnostic endpoints. It is even conceivable that metabolic endpoints could serve as the primary endpoints for some trials. These endpoints can also provide important pharmacological information from the trial, such as the time of onset of treatment, as seen in this study.

[0180] A previous longitudinal study of teplizumab (3) examined the mean effect of teplizumab treatment on C-peptide AUC over a 6-month period. That study did not use the GCRC method and a composite glucose and C-peptide endpoint to evaluate the specific time of onset of teplizumab, nor did it show the importance of using teplizumab to prevent impending severe loss of β-cell function in high-risk individuals. As previously mentioned, the analysis of the oral insulin trial (14) used the GCRC vector, but to a much lesser extent.

[0181] This study had some limitations. Three individuals in the placebo group had developed diabetes at the 6-month time point and were thus excluded from the 6-month analysis. This not only reduced the sample size but may have led to a less severe metabolic decline in the placebo group. This bias would weaken the effect of teplizumab at 6 months rather than enhance it. Since all teplizumab trial participants had to be positive for multiple islet autoantibodies and metabolically abnormal, future analyses will be needed to determine whether our findings are applicable to high-risk groups with less severe baseline disease, such as individuals positive for a single islet autoantibody test.

[0182] The GCRC vectors in the lower right and upper left quadrants appeared to reflect an improvement or worsening of the metabolic state, while the vectors in the upper right and lower left quadrants could reflect more moderate changes consistent with changes in insulin secretion kinetics or insulin sensitivity. However, a gold-standard measurement of β-cell function, such as the glucose-enhanced arginine clamp, will be needed to truly define these changes. Quantitative treatment group comparisons were adjusted for BMI and age (Table 2), but due to the severity of β-cell dysfunction in this population, we chose not to adjust the insulin resistance measurement based on OGTT modeling (22).

[0183] In DPT-1, both WQE and ODE are significantly associated with diabetes progression; however, the detection of treatment efficacy by metabolic endpoints is not solely a function of its ability to predict type 1 diabetes. For example, an individual may have improved C-peptide in response to treatment but still develop diabetes, while conversely, another individual may not show improved C-peptide secretion in response to treatment but does not develop type 1 diabetes during follow-up. Thus, a key future direction is to analyze the relationship between the ability of an endpoint to detect treatment efficacy and its ability to predict diabetes.

[0184] The development of WQE and ODE endpoints involves some complexities, but their application in research will be mainly based on standard formulas for angular calculations. Thus, while we include in the current manuscript an explanation describing the development of this new method, in practice, the implementation of these measurements will only involve using formulas, similar to other accepted metrics such as Index60(17).

[0185] In summary, the analysis of glucose and C-peptide changes based on the changes in GCRC and its centroid on a 2d network provides visual evidence of the therapeutic efficacy of teplizumab, which is early enough to prevent the rapid decline of β-cell function in high-risk groups for type 1 diabetes and may even improve function. This is statistically confirmed by the composite glucose and C-peptide endpoints and the new endpoints derived from the GCRC change vectors. These findings suggest that changes in the GCRC grid position and shape and the corresponding directional quantitative measurements should be integrated into the analysis of type 1 diabetes prevention trials. In addition, they complement the growing evidence that combining endpoints of both glucose and C-peptide is fundamental to our understanding of treatment efficacy in prevention trials. Future directions will include refining the application of these endpoints to facilitate the assessment of preventive treatments in smaller and larger trials.

[0186] Modifications and variations of the methods and compositions of the present disclosure will be apparent to those skilled in the art without departing from the scope and spirit of the present disclosure. Although the present disclosure has been described in connection with specific embodiments, it should be understood that the present disclosure as claimed should not be unduly limited to such specific embodiments. Indeed, various modifications to the modes for carrying out the present disclosure are intended to be within the scope of the present disclosure as represented by the following claims and will be understood by those skilled in the relevant art to which the present disclosure pertains. Incorporation by reference

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Claims

1. A method for predicting the responsiveness of a therapeutic or prophylactic agent for treating or preventing type 1 diabetes (T1D), the method comprising: administering a therapeutic or prophylactic agent to a subject in need thereof; and determining a glucose and C-peptide response curve (GCRC) change vector by plotting the changes in mean glucose values and mean C-peptide values of an oral glucose tolerance test over a period of time on a two-dimensional grid, wherein the directionality of the change vector that increases towards C-peptide and decreases in glucose indicates metabolic improvement.

2. The method according to claim 1, wherein the oral glucose tolerance test comprises a 1-hour oral glucose tolerance test, a 2-hour oral glucose tolerance test, and a 4-hour oral glucose tolerance test.

3. The method according to claim 1, the method further comprising calculating a within-quadrant endpoint (WQE) and an ordered direction endpoint (ODE) based on the GCRC.

4. The method according to any one of claims 1-3, wherein the therapeutic or prophylactic agent comprises an immunotherapeutic agent.

5. The method according to claim 4, wherein the immunotherapeutic agent comprises an anti-CD3 antibody or an antigen-binding fragment thereof.

6. The method according to claim 5, wherein the anti-CD3 antibody is teplizumab, otelixizumab, or frutuximab.

7. The method according to any one of claims 1-3, wherein the effective amount of the therapeutic or prophylactic agent comprises subcutaneous (SC) injection or intravenous (IV) infusion or oral administration of an anti-CD3 antibody at 10-1100 micrograms per square meter (μg / m 2 ) for 10 to 14 days by daily subcutaneous (SC) injection or intravenous (IV) infusion or oral administration.

8. The method according to any one of claims 1 - 3, wherein the effective amount of the therapeutic or prophylactic agent comprises administering the anti - CD3 antibody in a total dose of from about 9000 μg / m 2 to about 14000 μg / m 2 over a period of 10 to 14 days.

9. The method according to any one of claims 1 - 3, the method comprising a process of administering an intravenous infusion of the anti - CD3 antibody to the subject in need thereof for 14 days: on days 1 - 4, administering at 51 μg / m 2 , 103 μg / m 2 , 207 μg / m 2 , and 413 μg / m 2 respectively, and administering one dose of 826 μg / m 2 every day from days 5 - 14.

10. The method according to any one of claims 7-9, wherein the anti-CD3 antibody is teplizumab, otelixizumab, or frutuximab.

11. The method according to any one of claims 7-9, wherein the anti-CD3 antibody is teplizumab.

12. The method according to any one of claims 1-11, wherein the subject is in stage 1, 2, 3, or 4 of T1D.

13. The method according to any one of claims 1-11, wherein the subject is in stage 1 or 2 of T1D, and wherein the method is for the prognosis of a prophylactic agent in preventing or delaying the onset of T1D.

14. A method for predicting the responsiveness of an anti-CD3 antibody in preventing or delaying the onset of type 1 diabetes (T1D), the method comprising: administering a prophylactically effective amount of an anti-CD3 antibody to a non-clinical diabetic subject at risk of clinical T1D; and determining a glucose and C-peptide response curve (GCRC) change vector by plotting the changes in mean glucose values and mean C-peptide values of an oral glucose tolerance test over a period of time on a two-dimensional grid, wherein the directionality of the change vector that increases towards C-peptide and decreases in glucose indicates metabolic improvement.

15. The method according to claim 14, the method further comprising calculating a within-quadrant endpoint (WQE) and an ordered direction endpoint (ODE) based on the GCRC.

16. The method according to claim 14, wherein the non-clinical diabetic subject is a relative of a patient with T1D.

17. The method according to any one of claims 14-16, wherein the non-clinical diabetic subject has two or more diabetes-related autoantibodies selected from the following: islet cell antibody (ICA), insulin autoantibody (IAA), and antibodies against glutamic acid decarboxylase (GAD), tyrosine phosphatase (IA-2 / ICA512), or ZnT8.

18. The method according to any one of claims 14-17, wherein the non-clinical diabetic subject (1) is negative for zinc transporter 8 (ZnT8) antibody, (2) is HLA-DR4+, and / or (3) is not HLA-DR3+.

19. The method according to any one of claims 14-17, wherein the non-clinical diabetic subject is negative for zinc transporter 8 (ZnT8) antibody.

20. The method according to any one of claims 14-17, wherein the non-clinical diabetic subject is HLA-DR4+ and is not HLA-DR3+.

21. The method according to any one of claims 14-20, wherein the non-clinical diabetic subject has abnormal glucose tolerance in an oral glucose tolerance test (OGTT).

22. The method according to claim 21, wherein the abnormal glucose tolerance in the OGTT is a fasting glucose level of 110-125 mg / dL, or a 2-hour plasma glucose level ≥140 and <200 mg / dL, or an intermediate glucose value >200 mg / dL at 30, 60, 90 minutes, or 4 hours of the OGTT.

23. The method according to any one of claims 14-22, the method comprising subcutaneously (SC) injecting or intravenously (IV) infusing or orally administering the anti-CD3 antibody at 10-1100 micrograms per square meter (μg / m 2 ) for a period of 10 to 14 days.

24. The method according to any one of claims 14-23, wherein the effective amount of the therapeutic or prophylactic agent comprises administering the anti-CD3 antibody at a total dose of from about 9000 μg / m 2 to about 14000 μg / m 2 over a period of 10 to 14 days.

25. The method according to any one of claims 14-24, the method comprising a process of administering an intravenous infusion of the anti-CD3 antibody for 14 days: on days 1-4, administering at 51 μg / m 2 , 103 μg / m 2 , 207 μg / m 2 and 413 μg / m 2 respectively, and administering one dose of 826 μg / m 2 each day on days 5-14.

26. The method according to any one of claims 14-25, wherein the anti-CD3 antibody delays the median time to clinical diagnosis of T1D by about 50% to about 90%.

27. The method according to any one of claims 14-26, wherein the anti-CD3 antibody delays the median time to clinical diagnosis of T1D by about 12 months to about 60 months.

28. The method according to any one of claims 14-27, wherein the anti-CD3 antibody is teplizumab, otelixizumab, or frutuximab.

29. The method according to any one of claims 14-27, wherein the anti-CD3 antibody is teplizumab.

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