Supplement 7 for the manuscript: “Development and application of a
mechanistic pharmacokinetic model for simvastatin and its active
metabolite simvastatin acid using an integrated population PBPK
approach”
Nikolaos Tsamandouras1, Gemma Dickinson2, Yingying Guo2, Stephen Hall2, Amin RostamiHodjegan1,3, Aleksandra Galetin1, Leon Aarons1
1. Centre for Applied Pharmacokinetic Research, Manchester Pharmacy School, University of
Manchester, Manchester, UK.
2. Eli Lilly and Company, Indianapolis, IN, USA
3. Simcyp Limited, Blades Enterprise Centre, Sheffield, UK.
Contents
1. Uncertainty in the inter-individual variability estimate related to SVA CYP3A intrinsic clearance .. 2
2. Parameter estimates assessment in terms of physiological plausibility .............................................. 3
2.1. SV muscle tissue to unbound plasma partition coefficient .......................................................... 3
2.2. SVA active uptake clearance into the hepatocytes ...................................................................... 4
3. Supplementary Figures ....................................................................................................................... 5
3.1. Goodness of fit plots .................................................................................................................... 5
Figure S7.1 ...................................................................................................................................... 5
Figure S7.2 ...................................................................................................................................... 6
3.2. External model validation ............................................................................................................ 7
Figure S7.3 ...................................................................................................................................... 7
3.3. Typical SV/SVA concentration profiles in plasma, liver and muscle tissue ............................... 8
Figure S7.4 ...................................................................................................................................... 8
3.4. Effect of the OATP1B1 c.521T>C polymorphism on KPuuliver .................................................. 9
Figure S7.5 ...................................................................................................................................... 9
3.5. Model predicted SV/SVA concentration profiles for the investigated DDIs ............................. 10
Figure S7.6 .................................................................................................................................... 10
Figure S7.7 .................................................................................................................................... 11
1
Figure S7.8 .................................................................................................................................... 12
3.6. Fraction of active CYP3A in small intestinal wall and liver for the investigated DDIs ............ 13
Figure S7.9 .................................................................................................................................... 13
Figure S7.10 .................................................................................................................................. 14
Figure S7.11 .................................................................................................................................. 15
4. Supplementary Tables ....................................................................................................................... 16
4.1. Inter-conversion rate constants applied in different model compartments ................................ 16
Table S7.1 ..................................................................................................................................... 16
4.2. Model predictions regarding the DDI effect on SV/SVA plasma concentrations ..................... 17
Table S7.2 ..................................................................................................................................... 17
5. References ......................................................................................................................................... 18
1. Uncertainty in the inter-individual variability estimate related to SVA CYP3A
intrinsic clearance
Very good agreement was observed between NONMEM estimates/standard errors from the original
dataset and the bootstrapping procedure (Manuscript Table 3), providing additional evidence
regarding model stability and absence of bias. The only exception to this agreement was the standard
error of the inter-individual variability variance term on SVA CYP3A-mediated intrinsic clearance
(CLintʹCYP3A,vitro). This variability term seems to be relatively precisely estimated (0.139, RSE=49.7%)
according to NONMEM’s covariance step. However, in few of the bootstrapped datasets this random
effect converged to substantially higher values and therefore although the median estimate was 0.137,
the bootstrap RSE was much higher than the one reported from the covariance step (Manuscript Table
3). It should be also noted that this was the only random effect associated with substantial η-shrinkage
(>50%). The difficulty associated with the estimation of this random effect is completely
understandable when its physiological interpretation is considered. The motivation for the
incorporation of a variability term in SVA metabolic intrinsic clearance is derived from the
knowledge that this parameter can be associated with substantial population variability (e.g., due to
genetic polymorphisms in metabolic enzymes, different expression levels etc.). However the overall
SVA hepatic clearance is predominantly determined by the active uptake clearance into the
2
hepatocytes rather than the metabolic clearance. The former process is particularly described in the
current model with a separate parameter (CLuact) which is also associated with a variability term
(given the knowledge that SVA active uptake is substantially variable in the population [1]).
Therefore, the observed population variability in plasma SVA concentrations is affected to a much
higher degree by the population variability in active uptake clearance rather than the variability in
intrinsic metabolic clearance. We have recently specifically illustrated this concept through
simulations in the case of repaglinide [2]. Given that in the current model the variability terms related
to these two parameters are estimated solely from plasma data and in the absence of any prior
knowledge or SVA liver concentrations, it is understandable that the estimation of the intrinsic
metabolic clearance random effect might be relatively problematic. This difficulty was not so
apparent with the current dataset (the RSE from NONMEM covariance step was 49.7%) but it became
evident in a few of the bootstrapped datasets. Overall, the above discussion highlights the importance
of careful stochastic model parameterisation in a physiological model and the need for identifiability
analyses with regard to random effect variability terms.
2. Parameter estimates assessment in terms of physiological plausibility
2.1. SV muscle tissue to unbound plasma partition coefficient
In the current work, the model parameters provided with informative priors converged to estimates
which are within the prior knowledge range. The only parameter that converged in a region not
covered particularly well by the prior distribution was the SV muscle tissue to unbound plasma
partition coefficient (KPuT:P,m), (see Manuscript Figure 2). However, the estimate (4315.6) of this
parameter is not physiologically unrealistic. After consideration of the SV fraction unbound in plasma
(0.0134), the estimate above corresponds to a muscle tissue to plasma partition coefficient (KPT:P,m) of
57.8. This value, although higher than the in silico [3] prediction for this tissue (15.4), is not
unrealistic given the high lipophilicity of the compound (logP of 4.68) and the fact that partition
coefficients of comparable (bone, brain) or even higher range (adipose) were predicted for SV in
3
some other tissues. In addition, the limitations regarding the in silico prediction of partition
coefficients in such highly lipophilic compounds have been clearly highlighted in the literature [4].
Finally, it could be argued that this estimated high partition of SV into the muscle tissue is in
agreement with the observed main adverse effect of the compound, which is muscle toxicity.
However, such an argument cannot be solidly supported without experimental work as this estimate is
conditional on the model structure and assumptions.
2.2. SVA active uptake clearance into the hepatocytes
Special consideration should be also applied with regard to the structural model parameters which
were not supported by informative priors, as their estimates can converge outside their true
physiological range in order to provide an adequate plasma fit. An example of such parameter that
needs to be assessed in terms of physiological plausibility is the SVA active uptake clearance into the
hepatocytes (CLuact). The estimate of SVA in vivo uptake clearance determined in this work (8266.8
L/h) is within the range of the in vivo uptake clearances determined before for a range of other OATP
substrates: pravastatin 406 L/h, rosuvastatin 1190 L/h, valsartan 2463 L/h, bosentan 8489 L/h,
cerivastatin 12827 L/h, and fluvastatin 76513 L/h [5]. This provides additional confidence with
regard to the estimate determined in the current work.
4
3. Supplementary Figures
3.1. Goodness of fit plots
Figure S7.1: Goodness of fit plots with regard to SV observed data. DV vs PRED and DV vs
IPRED denote observations versus population and individual predictions respectively.
CWRES vs TIME and CWRES vs PRED denote conditionally weighted residuals versus time
and population predictions respectively.
5
Figure S7.2: Goodness of fit plots with regard to SVA observed data. DV vs PRED and DV vs
IPRED denote observations versus population and individual predictions respectively.
CWRES vs TIME and CWRES vs PRED denote conditionally weighted residuals versus time
and population predictions respectively.
6
3.2. External model validation
(a)
(b)
Figure S7.3: Visual predictive check (VPC) of the developed population model for both SV (a)
and SVA (b) plasma concentrations of the non-fitted external validation dataset [6]. Closed
circles represent the observed plasma concentrations; highlighted with grey are the areas
between the 5th and 95th percentiles of model simulations whereas the black solid line
represents the median; the horizontal dashed black line represents the limit of
quantification.
7
3.3. Typical SV/SVA concentration profiles in plasma, liver and muscle tissue
(a)
(b)
Figure S7.4: Model simulated typical concentration profiles for both SV (a) and SVA (b) in plasma, liver
tissue and muscle tissue.
8
3.4. Effect of the OATP1B1 c.521T>C polymorphism on KPuuliver
Figure S7.5: Effect of the OATP1B1 c.521T>C polymorphism on the ratio between the SVA unbound liver
tissue concentrations and SVA unbound liver plasma concentrations (a dynamic representation of the
SVA KPuuliver). Black and red lines correspond to the homozygous wild-type (TT) and homozygous variant
(CC) genotypes respectively.
9
3.5. Model predicted SV/SVA concentration profiles for the investigated DDIs
(a)
(b)
Figure S7.6: Model simulated typical concentration profiles for both SV (a) and SVA (b) in plasma, liver tissue
and muscle tissue, with (red line) and without (black line) the itraconazole (ITZ) interaction.
10
(a)
(b)
Figure S7.7: Model simulated typical concentration profiles for both SV (a) and SVA (b) in plasma, liver tissue
and muscle tissue, with (red line) and without (black line) the erythromycin (ERY) interaction. The small
irregularities observed in the liver SVA concentration profiles occur at times that ERY doses were
administered.
11
(a)
(b)
Figure S7.8: Model simulated typical concentration profiles for both SV (a) and SVA (b) in plasma, liver tissue
and muscle tissue, with (red line) and without (black line) the diltiazem (DTZ) interaction.
12
3.6. Fraction of active CYP3A in small intestinal wall and liver for the investigated DDIs
Figure S7.9: Fraction of active CYP3A enzyme (y-axis) in the small intestinal wall (left) and liver (right) against
time since the 1st dose of erythromycin (ERY).
13
Figure S7.10: Fraction of active CYP3A enzyme (y-axis) in the small intestinal wall (left) and liver (right) against
time since the 1st dose of clarithromycin (CLR).
14
Figure S7.11: Fraction of active CYP3A enzyme (y-axis) in the small intestinal wall (left) and liver (right) against
time since the 1st dose of diltiazem (DTZ).
15
4. Supplementary Tables
4.1. Inter-conversion rate constants applied in different model compartments
Table S7.1: Inter-conversion rate constants applied in different model compartments
Buffer (pH=7.4)
Small intestinal wall
Liver vascular
Liver tissue
Systemic blood
Muscle
Rest of body
Hydrolysis (h-1)
0.026 (a)
2.77 (b)
9.36 (c)
107.97 (d)
9.36 (f)
0.026 (g)
2.77 (h)
Lactonisation (h-1)
1.16 (e)
-
(a) SV to SVA hydrolysis rate constant as determined in buffer (pH=7.4) in vitro. It is reported in this table to aid
comparison with the conversion rate constants applied in the different model compartments.
(b) SV to SVA hydrolysis rate constant in the small intestinal wall compartment (k hydr,hybrid estimate)
(c) SV to SVA hydrolysis rate constant in the liver vascular compartment (the k hydr,pl estimate was scaled up
with Eq.S4.29 (Supplement 4) to hydrolysis intrinsic clearance in the liver vascular compartment and then
was divided by the liver vascular volume)
(d) SV to SVA hydrolysis rate constant in the liver tissue compartment (the k hydr,S9 estimate was scaled up with
Eq.S4.30 (Supplement 4) to hydrolysis intrinsic clearance in the liver tissue compartment and then was
divided by the liver tissue volume)
(e) SVA to SV lactonisation rate constant in the liver tissue compartment (the CLintgluc,vitro estimate was scaled
up with Eqs.S4.34 and S4.41 (Supplement 4) to total lactonisation intrinsic clearance in the liver tissue
compartment and then was divided by the liver tissue volume)
(f) SV to SVA hydrolysis rate constant in the systemic blood compartment (the khydr,pl estimate was scaled up
with Eq.S4.28 (Supplement 4) to hydrolysis intrinsic clearance in the systemic blood compartment and then
was divided by the systemic blood volume)
(g) SV to SVA hydrolysis rate constant in the muscle compartment (k hydr,buff estimate). Note that it has been
assumed that the lactone to acid hydrolysis in the muscle is mediated only chemically without an enzymatic
contribution (see Supplement 1)
(h) SV to SVA hydrolysis rate constant in the rest of body compartment (k hydr,hybrid estimate)
16
4.2. Model predictions regarding the DDI effect on SV/SVA plasma concentrations
Table S7.2: Model predictions regarding the DDI effect on SV/SVA plasma concentrations
SV alone (a)
SV + inhibitor (a)
Predicted ratio
Observed ratio (b)
Itraconazole (ITZ)
SV AUC (nmol·h/L)
SV Cmax (nmol/L)
SVA AUC (nmol·h/L)
SVA Cmax (nmol/L)
77.93
14.60
40.13
4.39
619.85
84.25
342.47
31.72
7.95
5.77
8.53
7.23
10 (c)
10 (c)
- (c)
- (c)
Erythromycin (ERY)
SV AUC (nmol·h/L)
SV Cmax (nmol/L)
SVA AUC (nmol·h/L)
SVA Cmax (nmol/L)
77.63
14.59
39.82
4.37
434.20
63.54
230.94
21.95
5.59
4.36
5.80
5.02
6.21
3.46
3.92
5.0
Clarithromycin (CLR)
SV AUC (nmol·h/L)
SV Cmax (nmol/L)
SVA AUC (nmol·h/L)
SVA Cmax (nmol/L)
102.54
15.81
53.13
5.02
1027.90
102.54
608.99
44.98
10.02
6.49
11.46
8.97
9.95
7.14
12.17
10.0
Diltiazem (DTZ)
SV AUC (nmol·h/L)
SV Cmax (nmol/L)
SVA AUC (nmol·h/L)
SVA Cmax (nmol/L)
42.85
9.16
22.39
2.50
166.05
28.14
88.59
8.84
3.88
3.07
3.96
3.54
4.8
4.1
- (d)
3.7
(a) Plasma exposures (AUC) and maximum concentrations (Cmax) are predicted by simulation with the developed
model until 24 hours post-SV-dose. For all inhibitors the SV dose was 40mg apart from DTZ where a 20mg dose
was given. For CLR where multiple SV doses where given, the reported plasma exposures and maximum
concentrations refer to the last dosing interval.
(b) Observed values as reported in Neuvonen et al [7], Kantola et al [8], Jacobson [9] and Mousa et al [10] for ITZ,
ERY, CLR and DTZ respectively.
(c) Note that due to assay limitations in the reported DDI study [7], SV was not quantifiable in the placebo phase,
therefore the authors reported an at least approximate 10-fold increase. In addition, again due to assay
limitations, the SVA measurements in the same study represented naïve SVA plus SV that was converted to the
acid form. Therefore, the reported DDI effects on SVA cannot be directly compared to the model predicted
values.
(d) In the reported DDI study the effect on SVA AUC was not available due to assay limitations (numerous
concentrations below the limit of quantification)
17
5. References
1.
2.
3.
4.
5.
6.
7.
8.
9.
10.
Pasanen MK, Neuvonen M, Neuvonen PJ, Niemi M. SLCO1B1 polymorphism markedly affects
the pharmacokinetics of simvastatin acid. Pharmacogenet Genomics. 2006;16(12):873-879.
Gertz M, Tsamandouras N, Sall C, Houston JB, Galetin A. Reduced physiologically-based
pharmacokinetic model of repaglinide: Impact of OATP1B1 and CYP2C8 genotype and source
of in vitro data on the prediction of drug-drug interaction risk. Pharm Res. 2014 Mar 13.
[Epub ahead of print].
Rodgers T, Rowland M. Physiologically based pharmacokinetic modelling 2: Predicting the
tissue distribution of acids, very weak bases, neutrals and zwitterions. J Pharm Sci.
2006;95(6):1238-1257.
Rodgers T, Rowland M. Mechanistic approaches to volume of distribution predictions:
Understanding the processes. Pharm Res. 2007;24(5):918-933.
Jones HM, Barton HA, Lai Y, Bi Y-a, Kimoto E, Kempshall S, et al. Mechanistic
pharmacokinetic modeling for the prediction of transporter-mediated disposition in humans
from sandwich culture human hepatocyte data. Drug Metab Dispos. 2012;40(5):1007-1017.
Polli JW, Hussey E, Bush M, Generaux G, Smith G, Collins D, et al. Evaluation of drug
interactions of GSK1292263 (a GPR119 agonist) with statins: From in vitro data to clinical
study design. Xenobiotica. 2013;43(6):498-508.
Neuvonen PJ, Kantola T, Kivisto KT. Simvastatin but not pravastatin is very susceptible to
interaction with the CYP3A4 inhibitor itraconazole. Clin Pharmacol Ther. 1998;63(3):332-341.
Kantola T, Kivisto KT, Neuvonen PJ. Erythromycin and verapamil considerably increase serum
simvastatin and simvastatin acid concentrations. Clin Pharmacol Ther. 1998;64(2):177-182.
Jacobson TA. Comparative pharmacokinetic interaction profiles of pravastatin, simvastatin,
and atorvastatin when coadministered with cytochrome P450 inhibitors. Am J Cardiol.
2004;94(9):1140-1146.
Mousa O, Brater DC, Sundblad KJ, Hall SD. The interaction of diltiazem with simvastatin. Clin
Pharmacol Ther. 2000;67(3):267-274.
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