Supplement 5 for the Manuscript: Development and Application of a Mechanistic Pharmacokinetic

Supplement 5 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 Rostami- Hodjegan1,3, 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 1. Model evaluation regarding description of the fitted data

Typical goodness of fit plots such as observations versus population (DV vs PRED) and individual

(DV vs IPRED) predictions and conditionally weighted residuals versus time (CWRES vs TIME) and population predictions (CWRES vs PRED) were used to detect any misspecifications in the structural and statistical model. In addition for both SV and SVA visual predictive checks (n=1000) stratified by study were used to test the overall predictive performance of the model. A limited non-parametric bootstrapping procedure (n=114) was performed with PsN 3.5.3 (Perl-speaks-NONMEM) [1] for the final model in order to evaluate the robustness of the parameter estimates and the accuracy of the asymptotic standard errors provided by NONMEM covariance step.

2. External model validation

An independent (not fitted) dataset [2] consisting of intensively sampled SV/SVA plasma concentrations after a single 40 mg simvastatin dose in 28 healthy volunteers was used for external model validation. The demographic characteristics of the participants, the sampling times design and the methodology used to determine SV and SVA plasma concentrations (assay) have been extensively described elsewhere [2]. The developed population model and the external validation data were used to generate visual predictive checks (n=1000) for both SV and SVA plasma concentrations. It should be noted that these data come from a population with different demographic characteristics (e.g. ethnic background), different study design and different analytical methodology compared to the studies used for model development. Therefore, a certain degree of disagreement between model predictions and the validation dataset is expected. However, the motivation for this validation process derives from the need to assess the ability of the developed model for generalisation and description of SV and SVA plasma concentrations and their variability outside the studied population. As the parameters of the physiological model are informed from clinical data (and they do not solely derive from physiology knowledge or in vitro/ in silico experiments), they should be considered as population-specific and the ability of the model to be generalised outside the studied population and

2 experimental conditions needs to examined. Hence, this validation step can provide confidence regarding the universality of the population model parameters and was considered pre-requisite before proceeding to model extrapolation and prediction of DDI and polymorphisms effects.

3. Investigation of additional parameters of mechanistic interest

Simvastatin bioavailability (F) and its constituent components i.e. the fraction absorbed into gut wall

(Fa), the fraction reaching gut wall that escapes intestinal first-pass metabolism (F g) and the fraction reaching liver that escapes hepatic first-pass metabolism (Fh) were calculated under a model-based in silico approach. This approach takes advantage of the mechanistic model structure and the description of several processes occurring during absorption / metabolism, in order to allow predictions for the fraction of drug that is being lost in each of them. More specifically, the typical population parameter estimates of the model were used to simulate a typical SV concentration profile after an oral dose of

40mg and calculate the plasma AUC∞, oral. Subsequently a typical SV concentration profile after a virtual IV dose of 40mg was simulated to calculate the plasma AUC ∞, IV. This in silico experiment was performed by setting the differential equations initial condition of the stomach compartment to zero and of the systemic blood compartment to 40mg. Hence, SV bioavailability (F) was calculated with

Eq.S5.1 as the ratio of AUC∞, oral to AUC∞, IV. Similarly, additional in silico experiments were performed in which the 40mg virtual doses were inputted directly in the small intestinal wall (Isiw administration) or the liver vascular compartment (Ilv administration) and the plasma exposure for each route of administration was calculated (AUC∞,Isiw and AUC∞,Ilv respectively). All the components of bioavailability (Fa, Fg, Fh) can then be determined from Eqs.S5.2-S5.4.

3 However, the advantage of the developed physiological mixed effects model is that apart from the typical values of bioavailability and its constituent components (as calculated above), population variability in these parameters can be also investigated. This was performed by applying the procedure described above for 1000 individuals (number adequate to obtain credible population prediction intervals) instead of a typical individual taking advantage of the stochastic part of the developed population model. Using Eqs.S5.1-S5.4 bioavailability and its consisting components were determined for each individual and then 90% population prediction intervals were calculated.

Finally, in order to quantify the magnitude of the recycling (inter-conversion) process two auxiliary mass balance differential equations were included (see NONMEM code in Supplement 9) that monitor the accumulative amount of SV that was converted to SVA through hydrolysis in the several model compartments (Ahydr) and the accumulative amount of SVA that is back-converted to SV through lactonisation in the liver (Alact). The recycling fraction (FREC) was then determined with

Eq.S5.5.

Similarly as described above, population variability in the recycled fraction was also determined by applying this procedure in 1000 simulated individuals and 90% prediction intervals for FREC were calculated.

4. References

1. Lindbom L, Pihlgren P, Jonsson N. PsN-Toolkit--a collection of computer intensive statistical methods for non-linear mixed effect modeling using NONMEM. Comput Methods Programs Biomed. 2005;79(3):241-257. 2. 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.