Supplementary Information

Physiologically-based Pharmacokinetic Modeling of Target-Mediated Drug Disposition of Bortezomib in Mice

Li Zhang · Donald E. Mager

Department of Pharmaceutical Sciences, University at Buffalo, State University of New York, NY 14214, USA

Corresponding Author:

Dr. Donald E. Mager

Department of Pharmaceutical Sciences,

University at Buffalo, State University of New York, Buffalo, NY, 14214

Tel: (716)645-2903

Fax: (716)829-6569

Email:

Table S1. Final PK Parameters for Bortezomib in Humans

Tissue / Permeability
() / Target density
() / Nonspecific
binding () / Clearance
L/hr / nM / -- / L/hr
Erythrocyte / 208.1 / 349.8 / 7.57 / --
Lung / 0.16 / 107.4 / 2.54 / --
Spleen / 0.12 / 275.7 / 3.68 / --
Liver / 3.51 / 315.0 / 1.54 / 0.15
Kidney / 0.84 / 146.2 / 9.60 / 7.5
Muscle / 1.51 / 8.60 / 1.17 / --
Bone / 0.88 / 48.12 / 1.06 / --
Remainder / 51.0 / 970 / 96.76 / --

Model validation

A model simulation was performed to assess the fidelity to whichthe final bortezomib PBPK model could recapitulate an independent dataset of bortezomib blood PK following a single intravenous (IV) dose (0.8 mg/kg) in mice [28]. The final PK parameters (Tables 2 and 3) were integrated with the bortezomib PBPK model to simulate the time-course of bortezomib blood concentrations. Figure S1 shows that the model simulations reasonably capture the reported bortezomib blood concentrations in mice.

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Sensitivity analysis

A sensitivity analysis of the final PBPK model was conducted using the Matlab toolbox PottersWheel 2.0.59[36, 37]. Sensitivity analysis was performed by simulating the model for 1000 times using the built-in 3-D sensitivity analysis method in PottersWheel, which evaluated the sensitivity of the mean concentrations of plasma and tissues with respect to all parameters perturbed simultaneously. The model parameters were perturbed by 10% of their nominal estimated values, and normalized sensitivity indices were assessed for each parameter in each tissue. Model output following perturbations in all parameters is shown in Figure S2. As expected, tissue output was primarily sensitive to the tissue target density, wherein the extent of bortezomib partitioning in each tissue was highly dependent on the capacity of proteasome in corresponding tissues. Moreover, tissue permeability coefficient terms exert a positive influence on corresponding tissue concentrations. As the drug-specific term, target binding affinity exerted a subtle influence on all the tissues. This could be attributed to the fact that the tissue drug concentrations were much higher than the nominal value, resulting in model outputs that were relatively insensitive to the value.

References

1.Kupperman E, Lee EC, Cao Y, Bannerman B, Fitzgerald M, Berger A, et al. Evaluation of the proteasome inhibitor MLN9708 in preclinical models of human cancer. Cancer Res. 2010;70(5):1970-80.

2.Maiwald T, Timmer J. Dynamical modeling and multi-experiment fitting with PottersWheel. Bioinformatics. 2008;24(18):2037-43.

3.Mager DE, Mody V, Xu C, Forrest A, Lesniak WG, Nigavekar SS, et al. Physiologically based pharmacokinetic model for composite nanodevices: effect of charge and size on in vivo disposition. Pharm Res. 2012;29(9):2534-42.

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