Transcript Fasting Glucose Model-Based Meta-Analysis: A Tool for

W-045
Fasting Glucose Model-Based Meta-Analysis: A Tool for Designing and Interpreting
Early Diabetes Studies
Contact information:
William S. Denney, PhD
Pfizer, PTx Clinical Pharmacology
620 Memorial Drive,
Cambridge, MA 02139
[email protected] (e-mail)
William S. Denney, Beesan Tan, and Gianluca Nucci
Clinical Pharmacology, Pfizer PharmaTherapeutics R&D,
Cambridge Laboratories, Pfizer Inc, Cambridge, MA 02139
OBJECTIVES
RESULTS
RESULTS (cont’d)
Fasting Glucose (FG) is a key parameter of
the overall diabetes diagnosis, progression,
and
treatment
ideally
complementing
postprandial glucose, weighted mean glucose
and
the
gold
standard,
long
term
measurements of glycemic control: HbA1c [12]. FG is also a critical parameter for the
assessment of glucose control in short term
trials in the early clinical development phases
of novel anti-diabetic treatments [3] to
estimate initial dose–response and predict
efficacy in longer term studies. As such it is
important to quantify the time course and dose
response of FG effects of available antidiabetic agents. This offers a quantitative
framework for decision making of novel agents
undergoing early signal of efficacy studies,
informing
study
duration,
comparative
effectiveness, and potentially washout time of
previous anti-diabetic agents. To this end, we
have undertaken a model-based longitudinal
meta-analysis (MBMA, [4, 5]) of FG in
published clinical studies with sulfonylureas
(SU), thiazolidinediones (TZD), metformin
(MET), dipeptidyl peptidase-4 inhibitors (DPP4), sodium/glucose cotransporter 2 inhibitors
(SGLT-2), and GLP-1 analogs.
The dose and time response relationship was
best described using the following model:
The model-estimated placebo FPG changes A longitudinal MBMA was developed and used
by representative week are shown in the forest to assess the dose and time response of
various classes of anti-diabetic agents:
plot in Figure 3.
metformin, TZD, DPP-4, GLP-1, SU, and
SGLT2.
METHODS
A database of 194 publicly-available (journal
articles, conference abstracts / posters, FDA
reviews and packages), double-blind, clinical
trials of anti-diabetic treatments with a
combined 71694 subjects was constructed by
Pfizer and Quantitative Solutions. Given the
focus on early studies, the meta-analysis was
limited to longitudinal summary level data from
0 to 26 weeks. Treatments were assessed to
be titrated if, within a single period, the dose
did not remain constant or the targeted dose
was a range. The dataset is summarized in
Table 1.
Table 1. Database Summary
# Trials
30
31
72
63
15
47
194
Figure 3. Model-Estimated Placebo FPG
Change
Week 2
Treatment effects were baseline-adjusted with
a power function.
Inclusion of
titration
significantly improved model fitting.
Goodness of fit plots indicated that the model
adequately described the trial data allowing for
characterization of the longitudinal dose
response of different anti-diabetic agents and
classes.
See for example in Figure 1 the dose
response for representative agents within the
different treatment classes: dapagliflozin
(SGLT-2), glimepiride (SU), pioglitazone
(TZD), sitagliptin (DPP-4), liraglutide (GLP-1)
and metformin. Figure 2 reports the FG time
course at an efficacious dose of each agent.
The treatment effect was highly dependent on
baseline FG with similar dependence
observed in all drug classes.
# Subjects
5382
8394
14255
18786
4513
11453
71694
*Total may not be the sum of individual mechanisms as
trials and subjects may have included multiple
mechanisms.
Figure 1. Model-estimated and observed
dose response for FPG lowering
0
Dapagliflozin
Glimepiride
Pioglitazone
-10
-20
-30
-40
-50
0
2
4
6
8
10 0
2
4
6
8
0
10
20
30
40
Models were fit using the nonlinear mixed
0
Sitagliptin
Liraglutide
Metformin
effect modeling (nlme library) in R 2.15.2.
-10
Baseline fasting glucose (FGbase) was reported
-20
in most studies; when not reported, it was
-30
estimated as a function of baseline HbA1c and
study time. The modeled inverse standard
-40
error squared of FG was used as the per-50
measurement weighting. During stepwise0
20 40 60 80 100 0.0
0.5
1.0
1.5
0 5001000
2000
3000
selection, models were selected based on the
Daily Dose (mg/day)
Akaike information criterion (AIC) and Circles are pred-corrected observations sized by model
physiological plausibility.
weight. Lines are median estimates and shaded regions
The effect of titration on T50 was tested and
potential differences in dose response in
titrated studies were attempted.
are 90% CI.
Figure 2. Model-estimated and observed
time course for FPG lowering
0
FPG Change (mg/dL)
Model forms tested included a combination of
Emax time to effect (collapsed to step-changes
in time when T50 could not be estimated given
publicly-available data) with Emax dose
response (collapsed to step-changes in dose
when ED50 was not acceptably estimated).
Pioglitazone 30 mg
-10
-20
-30
0
Placebo effect
was tested both as
nonparametric (unique fixed effects estimated
at each time point for each study) as well as
Emax in time with random effect on Emax by
trial.
Dapagliflozin 10 mg
Glimepiride 4 mg
Metformin 2000 mg (non-titrated)
Sitagliptin 100 mg
Metformin (titrated)
2
4
6
8
10
12
0
2
4
6
8
10
12
0.0
0.5
1.0
1.5
2.0
Time (weeks)
The left two panels are mechanisms with longer T50s.
Circles are pred-corrected observations at the given dose
sized by model weight. Lines (including the right panel
with short T50s are median estimates and shaded regions
are 90% CI.
x
x
xx
xx
xxx
xxx
xx
x
x
xxx
xxx
xx
xx
xxx
x
x
x xx
xx
x
Week 4
Study
ΔFG is the change in FG for a specific arm of
a specific study; E0 is the non-parametric
placebo effect at a given time point in a study;
FGbase is the baseline FG for a given study
arm (mg/dL); pbase is the power adjustment for
efficacy due to baseline FG; Emax,class is the
maximum effect for a given drug class (also
differs by titration for metformin); Edrug is the
drug effect as predicted by having a
combination of dose and time responses
where ED50,drug is the dose required for halfmaximal effect and T50,class is the time required
for half-maximal effect at a given dose.
x
xx
x
x
FPG Change (mg/dL)
Mechanism
SU
Metformin
TZD
DPP-4
SGLT-2
GLP-1
Total*
DISCUSSION
x
xx x
xx
xxx
xxx
xxxx
xxx
xxxx
xxxx
xx
xx
xxx
x xx
xxx
xx
x
xxx
xxx
xxx
xx
xxx
x
x xx
x xx
xxx
x
Week 8
x
x
-40
-20
x
x xx
xx
xx x
xx
xx
xxx
xxx
xx
xx x
xxx
x
x
xxxx
xxx
xxx
xx
xx
xxx
xxx
xx
xx
x xx
xx
0
x
20
40
Placebo FPG Change (mg/dL)
Longitudinal dose response parameters are
reported in Table 2. Dose response
parameters were estimated with low relative
standard errors. For the time course, when
the rate of FG change was faster then the first
time point (usually 1 or 2 weeks), the model
estimated T50 had wider relative confidence
intervals and was reported as less than one
week. The model estimated a significant
longer onset of action for titrated metformin
and TZD. The inclusion of titration changed
the metformin model significantly and only
TZD remained with slow onset.
There was no significant within-class
difference in Emax across anti-diabetic
agents. Large within formulation differences in
ED50 were noted for exenatide (BID vs
QW/QM).
Table 2. Estimated Model Parameters
Section
Parameter
Metformin T50.met
Emax.met
ED50.met
T50.met.titrated
e.met
TZD
T50.tzd
Emax.tzd
ED50.pio
ED50.rosi
SU
T50.su
Emax.su
ED50.glibenclamide
ED50.gliclazide
ED50.glimepiride
ED50.glipizide
DPP-4
T50.dpp4
Emax.dpp4
ED50.linagliptin
ED50.saxagliptin
ED50.sitagliptin
ED50.alogliptin
ED50.vildagliptin
GLP-1
T50.GLP-1
Emax.glp1
ED50.liraglutide
ED50.taspoglutide
ED50.lixisenatide
ED50.exenatide
ED50.exenatide.bid
ED50.albiglutide
ED50.dulaglutide
SGLT-2 T50.sglt2
Emax.sglt2
ED50.canagliflozin
ED50.dapagliflozin
ED50.empagliflozin
Covariate p.base
Error Model Compound Sym Corr
Estimate [90% CI]
< 1 week
-59.4 [-85.4, -33.5]
1950 [856, 4420]
4.03 [2.87, 5.66]
-32.4 [-35.6, -29.3]
2.49 [2.17, 2.85]
-49.2 [-53.5, -45.0]
17 [12.8, 22.5]
2.00 [1.43, 2.81]
0.311 [0.186, 0.521]
-43.6 [-46.3, -40.9]
0.561 [0.283, 1.11]
18.6 [8.51, 40.9]
0.748 [0.485, 1.15]
3.99 [2.56, 6.22]
< 1 week
-22.7 [-25.4, -20.0]
1.32 [0.545, 3.18]
3.17 [1.93, 5.21]
13.2 [4.91, 35.3]
5.58 [2.52, 12.4]
35.5 [20.1, 62.6]
< 1 week
-49.2 [-52.8, -45.5]
0.254 [0.176, 0.366]
5.13 [3.48, 7.57]
53.6 [37.6, 76.3]
0.850 [0.554, 1.30]
23.0 [17.9, 29.7]
32.0 [15.7, 64.9]
0.787 [0.281, 2.20]
0.210 [0.113, 0.389]
-40.5 [-45.2, -35.7]
57.1 [30.5, 107]
4.12 [2.77, 6.12]
2.47 [1.13, 5.39]
1.75 [1.56, 1.95]
0.658 [0.626, 0.689]
T50 estimates in weeks, when <1 week and not well estimated,
uncertainty is not reported for T50 as the time estimate may be
due to minimal data at <2 weeks.
Emax in mg/dL, ED50 in mg (except exenatide BID in ug)
Interestingly, all the mechanism tested, with
the exception of metformin and TZDs, have a
rapid onset of action when considering dosing
regimens as per clinical practice. When
accounting for titration, also the time-to-halfeffect for metformin becomes <1 week. These
results justify designing short duration of
treatment trials (2-4 weeks) for early signal of
efficacy studies with most novel mechanisms
of action. The longer T50 for TZDs suggests
that at least 6-10 weeks may be needed to
assess 70-80% of glucose lowering potential
of a TZD-like sensitizer. Similar considerations
could be extended to guide the washout in
short term glucose studies with different
agents.
In this longitudinal FG MBMA, we included the
effect of anti-diabetic agent titration which has
not been accounted for in previous analyses.
Titration parameters significantly improved the
quality of fitting and improved model
parameter precision. Explicitly modeling dose
adjustments may further improve the
understanding of true drug effects and is a
planned update to this model.
The only significant covariate introduced in the
model was baseline FG. Future refinements
will test additional demographic and disease
characteristics as well as explicitly accounting
for
background
and
co-randomized
treatments.
CONCLUSIONS
A FG MBMA was developed to quantify the
time course and dose response of available
anti-diabetic agents. The MBMA allows the
comparison of individual drug therapies
between and within classes, offering insight in
the relative efficacy and onset of action of
various mechanisms. This analysis offers a
quantitative framework to leverage external
data in the efficient design and interpretation
of early signal of efficacy trials informing the
development of novel anti-diabetic agents in
the landscape of current therapies.
ACKNOWLEDGEMENTS
The authors would like to extend our sincere
thanks to Ted Rieger for his confirmation of
time to effect with systems pharmacology
simulations and Jaap Mandema of Quantitative
Solutions for helpful discussions on the
application of meta-analysis techniques.
REFERENCES
[1] Stumvoll M, Goldstein BJ, van Haeften TW, Type 2 diabetes:
principles of pathogenesis and therapy. Lancet 2005; 365:
1333–46
[2]Monnier L, Lapinski H, Colette C. Contributions of Fasting and
Postprandial Plasma Glucose Increments to the Overall Diurnal
Hyperglycemia of Type 2 Diabetic Patients. Variations with
increasing levels of HbA1c. Diabetes Care March 2003 vol. 26
no. 3 881-885.
[3] Chien JY, Sinha VP. The Application of Drug-Disease Models
in the Development of Anti-Hyperglycemic Agents. In : Clinical
Trial Simulations Applications and Trends, Kimko, HHC and
Peck CC. (Eds.). AAPS Advances in the Pharmaceutical
Sciences Series, 2011.
[4] Mandema JW, Gibbs M, Boyd RA, Wada DR, Pfister M.
Model-based meta-analysis for comparative efficacy and
safety: application in drug development and beyond. Clin
Pharmacol Ther. 90(6):766-769, 2011.
[5] Mandema JW, Sweeney K, Terra S, Sahasrabudhe V. ModelBased Meta-Analysis of the HbA1c Lowering Effect of PF04971729, a Sodium Glucose Co-Transporter-2 Inhibitor
(SGLT2i), in Comparison with Other SGLT2i and Anti-Diabetic
Agents(ADA). American Diabetes Association’s 72nd Scientific
Sessions, June 6-12, Philadelphia, PA, 2012