Struggles With Clinical Translation of Immune

Diabetes Care Volume 37, May 2014
1173
COMMENTARY
Struggles With Clinical
Translation of Immune
Intervention Trials
Diabetes Care 2014;37:1173–1175 | DOI: 10.2337/dc13-2878
Immune intervention trials in type 1
diabetes have shown very mixed, and
often surprising, results. Despite suggestions of beneﬁt from GAD-Alum vaccine in a phase 2 study (1), further
studies, including two large phase 3 trials, showed no effect (2–4). Phase 3
trials with Anti-CD3 monoclonal antibodies failed to achieve their primary
outcome (5–7), despite very promising
results from multiple phase 2 trials
(8–13). The conﬂicting Anti-CD3 data may
be explained by unfortunate changes in
design (5) or dose (6,7) in phase 3 (14).
Many immunologic strategies have been
tested in phase 2 trials, some with signs
of beneﬁt, such as rituximab (15) and
abatacept (16); others without beneﬁt,
such as mycophenolate mofetil with or
without daclizumab (17) or anti-interleukin
1 blockade with either canakinumab or
anakinra (18); and others with ambiguous effects, such as thymoglobulin (19)
or alefacept (20). In this issue, there are
two articles describing results from the
phase 3 Efﬁcacy Study of DiaPep277 in
Newly Diagnosed Type 1 Diabetes Patients (DIA-AID 1) trial evaluating the
safety and efﬁcacy of a 24 amino acid
peptide derived from heat shock protein
60, called DiaPep277 (21,22). According
to Raz et al. (21) and Pozzilli et al. (22), it
appears as if the study demonstrated a
beneﬁcial effect, and if so, that would
be a very exciting ﬁnding. Yet, as outlined below, there are several aspects
of the study that make that conclusion
less certain. This commentary examines
the studies and explores some clinically
relevant issues readers may want to consider when interpreting the data from
the trial.
The principal measure of efﬁcacy in
immune intervention trials in type 1 diabetes is preservation of C-peptide as an
index of b-cell function. Almost all of the
trials mentioned above have used a
mixed-meal tolerance test (MMTT) to
assess C-peptide response. The DIAAID 1 study used two methods of stimulation of C-peptide, the MMTT and a
glucagon stimulation test (GST) (21).
The sample size was calculated from results of phase 2 studies, which had used
the GST to stimulate C-peptide. Nonetheless, the initial primary outcome
measure for the DIA-AID 1 trial was
MMTT-stimulated C-peptide. As MMTT
was the original primary outcome measure, it was performed at randomization
(month 0) and after 6, 12, 18, and 24
monthsda total of ﬁve measurements.
As GST originally was a secondary outcome measure, it was performed at
month 1 (deﬁned as “baseline” for the
GST, but 1 month after the ﬁrst treatment had been given) and at 12 and 24
monthsda total of three measurements. The authors intended, initially,
to have MMTT be the primary outcome
measure. They performed the ﬁrst
MMTT before initiating treatment
Jay S. Skyler
(a true baseline measurement), and conducted the test at more frequent intervals. However, the primary outcome
measure appears to have been changed
from the MMTT to the GST. Speciﬁcally,
Raz et al. (21) stated that “the study protocol was amended, and the statistical
analysis plan was planned and ﬁnalized
before the study was unblinded, with
the GST clearly deﬁned as the primary
end point.” The study did have two
planned interim analyses to permit reestimation of sample size. However,
according to the study’s history on
ClinicalTrials.gov (NCT00615264), the
primary outcome measure was changed
after the last subject had completed the
trial. One could certainly argue that
there has to be a very good and compelling reason before consideration is
given to change a primary outcome
measure. In this case, and in fairness
to the authors, they did provide a rationale. Speciﬁcally, results from a trial using DiaPep277 in a study of patients
with latent autoimmune diabetes of
adults (LADA) stimulated the change,
as “it became apparent that there might
be discrepancies between the two
methods” (21). Regrettably, a full report
of that study has not been published.
Nonetheless, there are some results included in Pozzilli et al. (22) comparing
GST and MMTT that may have supported the change. In the LADA study,
the correlations between GST and MMTT
Diabetes Research Institute, University of Miami, Miami, FL
Corresponding author: Jay S. Skyler, [email protected].
© 2014 by the American Diabetes Association. See http://creativecommons.org/licenses/by-nc-nd/3.0/ for details.
See accompanying articles, pp. 1384 and 1392.
1174
Commentary
at baseline and at 12 months were 0.9
and 0.85, respectively, which seems like
strong correlations between the two
methods. However, the correlation between GST and MMTT was only 0.48
for change in C-peptide between baseline and 12 months. Unfortunately, the
LADA study was terminated early for futility, and only 46 subjects were available
for analysis. A very relevant question is
whether data from that incomplete
study is sufﬁcient to warrant the change
in the primary outcome measure of a
large phase 3 clinical trial.
Pozzilli et al. (22) compared the GST
and MMTT, noting a number of differences between these two stimuli. They
highlighted the fact that the C-peptide
response during an MMTT varies depending on the fasting plasma glucose,
and they wondered whether variations
in gastric motility or incretin hormone
response might inﬂuence the outcome
of an MMTT. In point of fact, people
consume meals and need b-cell function
to respond to thesedan MMTT evaluates that. But again, one could argue
that no one consumes or injects glucagon routinely and that the GST response may not be the clinically most
relevant parameter. Moreover, a multitude of recent clinical trials of immune interventions in recent-onset
type 1 diabetes have been reported
and almost all have used the MMTT
as the primary outcome measure (1–
4,6–9,12,13,15–20). Each reader will
have to make his or her own decision
on what is the best test, as data comparing the two stimuli are not available
from other large trials.
The conﬂicting results from the two
outcome measures used in the DIA-AID
1 trial creates a difﬁcult conundrum, as
depending on what parameter is truly
the best one to use, we could now ask
as to whether there was or was not a
beneﬁcial effect. The interpretation is
even more complicated by additional
issues. First, there were 457 subjects
randomized. However, there were only
330 (72% of those randomized) included in the analysis of the primary
efﬁcacy end point (21) and only 297
(65% of those randomized) of these
were available for the comparison of
MMTT and GST (22). Second, the outcome measures needed to be imputed
for a fair number of subjects due to
missing data. Moreover, although it is
Diabetes Care Volume 37, May 2014
stated that there was a clinical beneﬁt
of DiaPep277 in terms of hypoglycemia, it appears the only statistical support provided is for rate of change in
hypoglycemic events from month 3 to
study end. Yet, the absolute difference
between groups in hypoglycemia
event rates in the modiﬁed intentionto-treat cohort at 24 months is only
0.14 hypoglycemic event per month,
or about 1.7 events per year. Since
many of the hypoglycemic events included are the typical mild events that
are readily treated, even if statistically
signiﬁcant, such a difference may not
have clinical meaning.
Thus, as outlined, DIA-AID 1 (21,22)
has strengths and weaknesses. One
strength lies in the importance of the
question regarding type 1 diabetes prevention and the difﬁculty in conducting
such prevention studies for type 1 diabetes. Another strength is that there are
no safety issues. A third strength is that
this intervention is antigen based, and
thus should not impair overall immune
responses. One weakness is that data
were available for only a relatively
small proportion of the subjects enrolled.
The major weakness is the concern on
the end point used, as depending on
whether one uses the GST or the
MMTT, DIA-AID 1 either does or does
not show a beneﬁt of the intervention.
Although the GST difference was positive statistically, it should be noted
that the difference in C-peptide is relatively minor as there is a large drop in
C-peptide in both the treated and control groups.
I see nothing wrong with having a
negative primary outcome and discussing insights and results from secondary
outcomes, mechanistic studies, and subgroup analyses. As a matter of fact, such
insights provide important information
for the design of further studies, even if
the primary outcome is negative. In the
case of DIA-AID 1, the results do indicate
that the ﬁeld could beneﬁt from further
comparisons of the MMTT and the GST
within future trials of interventions in
type 1 diabetes. The question at hand
is whether that is in the context of a
positive trial (based on GST) or a negative trial (based on MMTT).
Duality of Interest. No potential conﬂicts of
interest relevant to this article were reported.
References
1. Ludvigsson J, Faresj¨o M, Hjorth M, et al. GAD
treatment and insulin secretion in recent-onset
type 1 diabetes. N Engl J Med 2008;359:1909–
1920
2. Wherrett DK, Bundy B, Becker DJ, et al.; Type 1
Diabetes TrialNet GAD Study Group. Antigenbased therapy with glutamic acid decarboxylase
(GAD) vaccine in patients with recent-onset
type 1 diabetes: a randomised double-blind trial.
Lancet 2011;378:319–327
3. Ludvigsson J, Krisky D, Casas R, et al. GAD65
antigen therapy in recently diagnosed type 1 diabetes mellitus. N Engl J Med 2012;366:433–442
4. Diamyd initiates closure of US Phase III study
[Internet], press release 23 June 2011. Available
from http://www.diamyd.com/docs/pressClip
.aspx?section5investor&ClipID5584435. Accessed
1 February 2014
5. Sherry N, Hagopian W, Ludvigsson J, et al.;
Prot e´ g e´ Trial Investigators. Teplizumab for
treatment of type 1 diabetes (Prote´ ge´ study):
1-year results from a randomised, placebocontrolled trial. Lancet 2011;378:487–497
6. Gottlieb P, Pozzilli P. DEFEND-1: durable response therapy evaluation for early or newonset type 1 diabetes. Presented at the 71st
Scientiﬁc Sessions of the American Diabetes Association, 24–28 June 2011, San Diego, CA
7. Ambery P, Donner TW, Biswas N, Donaldson
J, Parkin J, Dayan CM. Efﬁcacy and safety of
low-dose otelixizumab anti-CD3 monoclonal
antibody in preserving C-peptide secretion in
adolescent type 1 diabetes: DEFEND-2, a randomized, placebo-controlled, double-blind,
multi-centre study. Diabet Med. 16 November
2013 [Epub ahead of print]
8. Herold KC, Hagopian W, Auger JA, et al. AntiCD3 monoclonal antibody in new-onset type 1 diabetes mellitus. N Engl J Med 2002;346:1692–1698
9. Herold KC, Gitelman SE, Masharani U, et al.
A single course of anti-CD3 monoclonal antibody hOKT3g1(Ala-Ala) results in improvement
in C-peptide responses and clinical parameters
for at least 2 years after onset of type 1 diabetes. Diabetes 2005;54:1763–1769
10. Keymeulen B, Vandemeulebroucke E,
Ziegler AG, et al. Insulin needs after CD3antibody therapy in new-onset type 1 diabetes.
N Engl J Med 2005;352:2598–2608
11. Keymeulen B, Walter M, Mathieu C, et al.
Four-year metabolic outcome of a randomised
controlled CD3-antibody trial in recent-onset
type 1 diabetic patients depends on their age
and baseline residual beta cell mass. Diabetologia
2010;53:614–623
12. Herold KC, Gitelman SE, Willi SM, et al.
Teplizumab treatment may improve C-peptide
responses in participants with type 1 diabetes
after the new-onset period: a randomised controlled trial. Diabetologia 2013;56:391–400
13. Herold KC, Gitelman SE, Ehlers MR, et al.;
AbATE Study Team. Teplizumab (anti-CD3
mAb) treatment preserves C-peptide responses
in patients with new-onset type 1 diabetes in a
randomized controlled trial: metabolic and
immunologic features at baseline identify a
subgroup of responders. Diabetes 2013;62:
3766–3774
14. Skyler JS. The compelling case for anti-CD3
in type 1 diabetes. Diabetes 2013;62:3656–
3657
care.diabetesjournals.org
15. Pescovitz MD, Greenbaum CJ, KrauseSteinrauf H, et al.; Type 1 Diabetes TrialNet
Anti-CD20 Study Group. Rituximab, B-lymphocyte
depletion, and preservation of beta-cell function.
N Engl J Med 2009;361:2143–2152
16. Orban T, Bundy B, Becker DJ, et al.; Type 1
Diabetes TrialNet Abatacept Study Group. Costimulation modulation with abatacept in patients
with recent-onset type 1 diabetes: a randomised,
double-blind, placebo-controlled trial. Lancet
2011;378:412–419
17. Gottlieb PA, Quinlan S, Krause-Steinrauf H,
et al.; Type 1 Diabetes TrialNet MMF/DZB Study
Group. Failure to preserve beta-cell function
with mycophenolate mofetil and daclizumab
combined therapy in patients with new-onset
Skyler
type 1 diabetes. Diabetes Care 2010;33:826–
832
18. Moran A, Bundy B, Becker DJ, et al.; Type 1
Diabetes TrialNet Canakinumab Study Group;
AIDA Study Group. Interleukin-1 antagonism in
type 1 diabetes of recent onset: two multicentre, randomised, double-blind, placebocontrolled trials. Lancet 2013;381:1905–1915
19. Gitelman SE, Gottlieb PA, Rigby MR, et al.;
START Study Team. Antithymocyte globulin
therapy for patients with recent-onset type 1
diabetes: 12-month results of a randomized
double-blind phase 2 trial. Lancet Diabetes Endocrinol 2013;1:306–316
20. Rigby MR, DiMeglio LA, Rendell MS, et al.;
T1DAL Study Team. Targeting of memory
T cells with alefacept in new-onset type 1 diabetes (T1DAL study): 12 month results of a
randomised, double-blind, placebo-controlled
phase 2 trial. Lancet Diabetes Endocrinol
2013;1:284–294
21. Raz I, Ziegler AG, Linn T, et al.; DIA-AID 1
Writing Group. Treatment of recent-onset type
1 diabetic patients with DiaPep277: results of a
double-blind, placebo-controlled, randomized
phase 3 trial. Diabetes Care 2014;37:1392–
1400
22. Pozzilli P, Raz I, Peled D, et al. Evaluation of
long-term treatment effect in a type 1 diabetes
intervention trial: differences after stimulation
with glucagon or a mixed meal. Diabetes Care
2014;37:1384–1391
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