Transcript Document

Separating Signal from Noise:
PK/PD modelling of QT-interval prolongation
Anne Chain1, Lutz Harnisch2 & Oscar Della Pasqua1
1 CPK
M&S Research Group , Clinical Pharmacology & Discovery Medicine, GlaxoSmithKline, Greenford, UK.
2 Clinical
Pharmacokinetics M&S, Clinical Pharmacology & Discovery Medicine, GlaxoSmithKline, Greenford, UK.
BACKGROUND
RESULTS
The presence of a prolonged QT-interval has become an
identifier for the risk of a unique from of polymorphic ventricular
tachycardia, Torsade de Pointes (TdP). Since this finding can be
a serious safety issue, policies and guidelines have been
proposed to ensure that the effects of non-cardiovascular drugs
on QT-interval are accurately characterised. Such policies have
assumed that ECG measurements are highly reproducible.
However, there is convincing evidence from clinical research that
QT-interval assessments can show high variability if considered
over a widely spanned time course. Therefore, any meaningful
attempt to characterise drug-induced changes in ECG
parameters requires identification of variability sources, as they
will have major impact on clinical study design and sample size.
We have derived a population-based correction factor to estimate
the QT/RR relation and subsequently quantify drug effect on
corrected QT-interval. Initially, we found clusters derived from
automated measurements which cause major discrepancies in
the reproducibility of recordings. An iterative mixture model was
implemented to account for data clustering and estimate a
QT/RR relation for each subject. A direct effect model using an
Emax function was sufficient to characterise the QT response on
d,l-sotalol exposure.
PK/PD
Pharmacokinetics
Posterior distribution presented as background
density
Red (10% around mean), green (95% CI)
OBJECTIVE
The primary objective of this investigation was to develop a
pharmacokinetic / pharmacodynamic (PK/PD) model to describe
the time course and variability of QT-interval in healthy subjects.
In addition, it was our aim to establish the relevance of external
factors on the accuracy and reproducibility of ECG
measurements.
CLINICAL STUDY
30 healthy subjects were given a single oral dose of 160 mg d,lsotalol (SOTACORTM), a beta-blocker well known to produce
clinically significant QT-interval prolongation, according to a
double-blind, randomised, placebo-controlled, crossover study
design. Pharmacokinetic sampling was performed at various
times up to 24h after dosing. 12-lead ECG was monitored
continuously throughout the study and recording were made at
different time points before and after dosing. QT-intervals were
read from automated recordings and manual assessments, as
defined by a cardiographer. The following factors have been
specifically controlled during the study to minimise the effect of
variability of QT-interval assessments:
- Wake-up time
- Meal time
- Blood sampling time relative to ECG assessments
- Temperature of all meals and beverages (room temperature
20° - 25°)
- Room temperature
- Assignment of nurse
- Skin preparation
- Location of ECG lead placement on chest
- Supine position (angle = 35°)
- Resting time (5 min in supine position, prior to ECG recording)
Extrinsic Factors
Extrinsic factors are defined as procedural factors that could
have an effect on the outcome of an ECG interval measurement.
They are divided into two categories:
1.The way the ECG is performed and measured.
- training of nurses
- accuracy of lead placement
2. The way the ECG is read.
- quality of ECG printouts
- automated readings vs. manual readings
Parameters
CL [L/hr]
VSS [L]
KA [hr-1]
Median
9.39
147
1.27
CV%**
8.79
7.58
143
95% CI
6.33, 13.9
69, 313
0.647, 2.47
IIV %*
17.15
12.73
57.36
95% CI
3.16, 24.04
0, 38.39
0, 85.15
– Inter-individual variability.
** CV% refers to the accuracy of the parameter estimates.
*IIV
Fig. 2 PK modelling results.
Pharmacodynamics
2. IOV
+
1. Slope &
Intercept –
Iterative Model
INCPT
[ms]
EC50
[ng/mL]
EMAX[
%]
0.306
389
3460
40 fix
HILL
1.66
CV%
3.02
0.517
24.5
0
17.7
95% CI
0.288, 0.324
385, 393
1760, 5160
-
1.07, 2.25
IIV %
19.75
3.332
114.9
180.6
-
IOV %
7.823
1.233
-
-
-
Final Evaluation – Effect size
Fig. 3 Raw QT data showing clustering in automated readings
(black) and its inconsistency compared to manual readings (red).
Iterative Model – Placebo automated ECG
Using the final model estimates and the following equation,
EMAX
QT  INCPT (1 
) - INCPT
HILL
1  EC 50
CONC


the effect size for sotalol in this study is calculated to be 31 ms
(95% CI: 13 - 54) at a concentration of 1500 ng/mL (max conc.)
and
it
is
I and
Type
II 500
errors
6Type
ms (95%
CI: 1.2
- 18) at
ng/mL (mean conc.).
Intrinsic factors are defined as those related to physiological and
pharmacological parameters:
- heart rate
- sympathetic tonus
- blood concentrations of the active treatment
The pharmacokinetics of d,l-sotalol was described according to a
PK
two-compartment model with first order absorption. To account
for the effect of heart rate onMODEL
QT-interval, the relation between
QT and RR was modelled as y = b*x a. Drug effect was then
characterised as a covariate on the intercept. Various models
were explored to define the underlying exposure-response
relation for d,l-sotalol. Data
analysis
was based on non-linear
PK/PD
MODEL
mixed effects modelling (NONMEM V5.1)
SLP
Median
Fig. 6 Final PK/PD modelling results.
Intrinsic Factors
MODELLING STRATEGY
Parameters
Parameters
SLP
INCPT [ms]
Median
0.447
401
15.9
IIV %
4.405
2.347
27.11
IOV* %
19.47
– Inter-occasion variability.
1.992
35.64
*IOV
Delta [ms]
Power calculations were performed using the following equation,
with effect size fixed to 10 ms, to fit simulated datasets with
varying population size (N) from the final model.
INCPT  EMAX  QT 1 HILL
EC 50  CONC (
)
QT
Estimates were obtained comparing two models in which drug
vs. no drug effect were tested for the NULL hypothesis against
the alternative hypothesis, defined as:
HO: -2L(no drug effect) – {-2L(drug effect)} <q
H1: -2L(no drug effect) – {-2L(drug effect)} >=q
where -2L is the log likelihood for each model fit and q is the
difference in the log likelihood between models. Power estimates
for not missing a 10 ms drug-induced change in QTc interval can
be obtained at the vertical line (Fig 7). Additionally, Type I error
was found to be less than 5%.
Fig. 4 Results from the iterative model with placebo automated
ECG.
Iterative Model – Placebo manual ECG
+
qΧ2(p=0.99, df=3) = 11.3
3. Emax
4. Final
PK/PD
Model
Fig. 7 Results from Type II error for assessing power of a given
trial.
CONCLUSIONS
1.
Effect size of sotalol on QT/QTc.
2.
Assessment of Type I and Type II errors.
This PK/PD modelling effort allowed us to describe the QT/RR
and its relationship to d,l-sotalol exposure in an integrated
manner.
 The iterative model used to describe the individual clustering
effect in the automated ECG readings resulted in a similar level
of precision that is obtained from manual readings.
 The precision of the correction factors in estimating the
correlation between QT and RR was best described in the
following order: individual > Fridericia > Bazett > no
transformation.
 Power simulations showed the importance of PK/PD modelling
to optimise trial size and mitigate a potential QTc liability for new
chemical entities.

Final Evaluation
Parameters
SLP
Median
0.337
392
IIV %
20.45
3.45
IOV %
15.43
1.811
INCPT [ms]
Fig. 1 A schematic diagram of the stepwise modelling approach.
Fig. 5 Results from the iterative model with placebo manual ECG.