Kate L. Gill (1), Anthonia Afuape (1), Dan Liu (2), Janak Wedagedera (1), Khaled Abduljalil (1), Frederic Y. Bois (1), Masoud Jamei (1)
1. Certara UK Limited, Simcyp Division, Level 2-Acero, 1 Concourse Way, Sheffield, S1 2BJ, United Kingdom; 2. UCB Celltech, 208 Bath road, Slough, SL1 3WE (work performed while employed at Certara UK Limited)
Objectives: The prodrug dabigatran etexilate (DE) is a substrate of intestinal transporter P-gp and interactions are observed with P-gp inhibitors (1). As conversion of DE to the active Dabigatran (Dab) by carboxylesterases (CES1 and CES2) is rapid and complete, DE concentrations are generally not measured in plasma (2, 3). Based on a PBPK model for DE and Dab previously developed in the Simcyp Simulator, and using the newly build-in global sensitivity analysis (GSA) tool in the Simcyp Simulator V19, the aim of this work is to determine parameters with significant impact on AUC, Cmax and Tmax of DE and Dab which should be focussed on for future model refinement.
Methods: The DE and Dab compound files within the Simcyp Simulator V19 were used for this study. The importance of Duodenum and Jejunum I effective permeability (Peff), fraction unbound in enterocytes (fu,gut), CES1 and CES2 maximum metabolism rate (Vmax) and P-gp maximum transport rate (Jmax) on AUC, Cmax, and Tmax were investigated by GSA. Assessment of in vitro and in vivo data suggests that these are the parameters that will be highly influential to the model, and many of them required optimisation during model development. Parameters where robust estimates from in vitro or in vivo data were available were not included in the sensitivity analysis. The GSA tool was used to apply both the Morris and the Sobol methods (4), using uniform distributions and 10-fold ranges around the default parameter values. For Sobol, parameters were ranked based on the total effect sensitivity index (ST). Parameters were considered to have a significant impact on the model outputs when ST > 0.1, i.e. parameters contribute >10% to the total variance of the outputs. For Morris, input parameters were ranked based on the global index metric, a relative comparison index. The results from the Morris and Sobol methods were compared.
Results: The rank order of influential parameters determined by the Sobol method for DE AUC and Cmax was CES1 Vmax > P-gp Jmax > fu,gut > Jejunum Peff > CES2 Vmax > duodenum Peff. Whereas for DE Tmax, the rank order was duodenum Peff > Jejunum Peff > CES1 Vmax > P-gp Jmax > CES2 Vmax > fu,gut. The Sobol analysis showed that all the parameters except duodenum Peff were significant (ST > 0.1) for DE exposure, and duodenum Peff, jejunum Peff and CES1 Vmax were significant for DE Tmax.
The rank order of influential parameters by the Morris method for DE AUC and Cmax was CES1 Vmax > Jejunum Peff > P-gp Jmax > fu,gut > CES2 Vmax > duodenum Peff. For DE Tmax the rank order was duodenum Peff > Jejunum Peff > P-gp Jmax > CES1 Vmax > CES2 Vmax > fu,gut.
For Dab AUC and Cmax, the rank order of influential parameters from the Sobol method was P-gp Jmax > Jejunum Peff > CES2 Vmax > fu,gut > duodenum Peff > CES1 Vmax. The rank order for Dab Tmax was duodenum Peff > Jejunum Peff > P-gp Jmax > fu,gut > CES2 Vmax > CES1 Vmax. P-gp Jmax and jejunum Peff were significant (ST > 0.1) for Dab exposure, and duodenum Peff, jejunum Peff and P-gp Jmax were significant for Dab Tmax.
When using the Morris method the influential parameters for Dab AUC and Cmax was ranked as P-gp Jmax > Jejunum Peff > CES2 Vmax > fu,gut > duodenum Peff > CES1 Vmax. Whereas for Dab Tmax the rank order was Jejunum Peff > duodenum Peff > P-gp Jmax > CES1 Vmax > fu,gut > CES2 Vmax.
Conclusions: The rank order of sensitivity of AUC and Tmax to the chosen parameters was generally similar between Morris and Sobol methods. GSA has shown that all parameters tested were significant to DE and/or Dab exposure and/or Tmax in line with the assessment of the in vitro and in vivo data. Therefore, all the parameters explored for DE-Dab should be considered as important and need robust estimation from in vitro and in vivo data as to support further model development.
GSA that evaluates the entire parameter space can aid in gaining an overall vision of the most influential parameters in a complex system like PBPK models. It can help in guiding and designing future work on model improvement and decision-making.
References:
[1] FDA Clinical Pharmacology Review NDA 22-512, Dabigatran 9/1/2010.
[2] Laizure SC et al. Drug Metab Dispos (2013) 42: 201-206.
[3] Blech S et al. Drug Metab Dispos (2008) 36(2):386-99.
[4] Kucherenko S et al. Computer Physics Communications (2012) 183 (4):937-946.
Reference: PAGE () Abstr 9352 [www.page-meeting.org/?abstract=9352]
Poster: Methodology - Model Evaluation