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. 2022 Jul;11(7):805-821.
doi: 10.1002/psp4.12791. Epub 2022 Apr 18.

Guide to development of compound files for PBPK modeling in the Simcyp population-based simulator

Affiliations

Guide to development of compound files for PBPK modeling in the Simcyp population-based simulator

Udoamaka Ezuruike et al. CPT Pharmacometrics Syst Pharmacol. 2022 Jul.

Abstract

The Simcyp Simulator is a software platform for population physiologically-based pharmacokinetic (PBPK) modeling and simulation. It links in vitro data to in vivo absorption, distribution, metabolism, excretion and pharmacokinetic/pharmacodynamic outcomes to explore clinical scenarios and support drug development decisions, including regulatory submissions and drug labels. This tutorial describes the different input parameters required, as well as the considerations needed when developing a PBPK model within the Simulator, for a small molecule intended for oral administration. A case study showing the development and application of a PBPK model for ondansetron is herein used to aid the understanding of different PBPK model development concepts.

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Conflict of interest statement

All authors are paid employees of Certara UK Limited (Simcyp Division) and may hold shares in Certara.

Figures

FIGURE 1
FIGURE 1
The input parameters required for a Simcyp compound file are arranged in tabs as shown in (a). The first tab (b) has the physicochemical and blood binding parameters for the compound, some of which can either be user‐input or predicted. For a drug being administered orally, the absorption parameters for the GI tract tab (c) provides the flexibility to select the absorption model to be used, a Peff,man prediction option depending on what input parameters are available and the option to enter input parameters to describe formulation effects when applicable. The distribution tab (d) has the input parameters and the different model options that can be selected to describe the drug's distribution, whereas the elimination tab (e) has the model and input options to describe the drug's clearance from the body. When a compound is identified as a perpetrator, the interaction tab (f) is used to include input parameters in the model to enable the simulation of DDIs against either enzymes or transporters. Separate entry boxes for the same transporters in different organs are considered in the Simulator to enable independent modeling of the effect of the transporter in each organ. DDI, drug‐drug interactions; GI, gastrointestinal; Peff,man, effective permeability of the compound in the human jejunum
FIGURE 2
FIGURE 2
Automated sensitivity analysis (ASA) was done to investigate the impact of changing values of f uGut on (a) the predicted fraction escaping gut metabolism (F g), (b) the predicted C max, and (c) the predicted T max values. The simulations showed that changing the f uGut from (d) the predicted value of 0.03 to (e) the default value of one had little effect on the predicted F g, and hence C max and T max. ADAM, advanced dissolution, absorption, and metabolism; C max, maximum concentration; f uGut, fraction unbound in the gut; T max, time to maximum concentration
FIGURE 3
FIGURE 3
Workflow of ondansetron model development. The model was initially developed using a bottom‐up approach, incorporating physicochemical, in vitro permeability, and in vitro metabolism data. However, this base model underpredicted the reported clinical CLiv and was refined using the RTT tool with CLiv and the in vitro derived percentage of hepatic metabolism as inputs. The optimized model was verified with clinical studies in which ondansetron was administered as single doses both i.v. and orally as well as to a phenotyped CYP2D6 population. The model was further verified as a CYP3A4 substrate with a clinical DDI with rifampicin, as well as an inhibitor of OCT2 and MATE transporters with a clinical DDI with metformin. The verified model can be further applied in exploring other “what‐if” scenarios. CLiv, intravenous clearance; DDI, drug‐drug interaction; Hep Met, hepatic metabolism; MD, multiple dose; RTT, reverse translational tool; SD, single dose
FIGURE 4
FIGURE 4
Simulated and observed (open circles) mean plasma concentration–time profiles of ondansetron after (a) single dose of 4 mg administered i.v. (10 trials × 12 HVs, 32–57 years, 0.58 women); (b) single dose of 8 mg administered orally under fasted and (c) fed states (10 trials × 12 male HVs, 18–40 years); and (d) single dose of 8 mg administered orally before and after the administration of multiple doses of 600 mg rifampicin for 5 days (10 trials × 10 HVs, 21–41 years, 0.8 women); as performance verification of the developed ondansetron PBPK model. The dark lines represent the mean plasma concentration–time profiles, the gray lines represent the predictions from individual trials, whereas the dashed lines represent the 5th and 95th percentiles. The dashed lines in d represent the predictions after the administration of rifampicin. HVs, healthy volunteers

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