Simulation-based simplification of target-mediated drug disposition model of denosumab

doi:10.5246/jcps.2018.11.077

Abstract

Abstract:

Target-mediated drug disposition (TMDD) model is one of the main modeling theories for studying nonlinear pharmacokinetics (PK) of monoclonal antibodies. However, there are too many parameters in full TMDD model to be estimated based on limited clinical data, leading to instability of the final model. In the present study, we analyzed the predictive ability and applicability of a simplified quasi-steady state (QSS) model with the assumption that the total target concentration was a constant parameter during treatment with monoclonal antibody in clinical data modeling. Based on the parameters of a published TMDD model of denosumab, simulations were performed at population and individual levels. Then, a simplified TMDD model, QSS model, was used to examine the effects of hypotheses, in which the total receptor concentration was constant or variable on model fit and stability of parameter estimation. Both simulations at the population level and model fit results of simulated individual data showed that at the therapeutic doses, the total receptor concentration had little influence on changes in drug concentration, and the model with constant total receptor concentration had the same predictive power. The validated hypothesis could be applied to clinical trial design and selection of the optimal PK model in the development of monoclonal antibodies.

Key words: Target-mediated drug disposition model, Monoclonal antibody, Nonlinear pharmacokinetics, Denosumab, Simulation

CLC Number:

R943.5

Supporting:

Figure S1. Goodness-of-fit plots of R_tot constant model. (A) Simulated observation vs. individual predicted concentration; (B) Observed vs. population predicted concentration; (C) Conditional weighted residual error vs. population predicted concentration; (D) Conditional weighted residual error vs. time after dose.

Figure S2. Goodness-of-fit plots of R_tot variable model. (A) Simulated observation vs. individual predicted concentration; (B) Observed vs. population predicted concentration; (C) Conditional weighted residual error vs. population predicted concentration; (D) Conditional weighted residual error vs. time after dose.

Figure S3. Goodness-of-fit plot of two-compartment model. (A) Simulated observation vs. individual predicted concentration; (B) Observed vs. population predicted concentration; (C) Conditional weighted residual error vs. population predicted concentration; (D) Conditional weighted residual error vs. time after dose.

Yu Fu, Ye Yao, Peiming Ma, Xuan Zhou, Wei Lu, Tianyan Zhou. Simulation-based simplification of target-mediated drug disposition model of denosumab[J]. Journal of Chinese Pharmaceutical Sciences, 2018, 27(11): 767-776.

References

[1] Dirks, N.L.; Meibohm, B. Population pharmacokinetics of therapeutic monoclonal antibodies. Clin. Pharmacokinet. 2010, 49, 633–659.

[2] Levy, G. Pharmacologic target-mediated drug disposition. Clin. Pharmacol. Ther. 1994, 56, 248–252.

[3] Bauer, R.J; Dedrick, R.L; White, M.L; Murray, M.J. Garovoy MR. Population pharmacokinetics and pharma-codynamics of the anti-CD11a antibody hu1124 in human subjects with psoriasis. J. Pharmacokinet. Biopharm. 1999, 27, 397–420.

[4] Mager, D.E; Jusko, W.J. General pharmacokinetic model for drugs exhibiting target-mediated drug disposition. J. Pharmacokinet. Pharmacodyn. 2001, 28, 507–532.

[5] Aston, P.J; Derks, G.; Raji, A.; Agoram, B.M.; van der Graaf, P.H. Mathematical analysis of the pharmacokinetic-pharmacodynamic (PKPD) behaviour of monoclonal antibodies: predicting in vivo potency. J. Theor. Biol. 2011, 281, 113–121.

[6] Peletier, L.A. Gabrielsson J. Dynamics of target-mediated drug disposition: characteristic profiles and parameter identification. J. Pharmacokinet. Pharmacodyn. 2012, 39, 429–451.

[7] Mager, D.E.; Jusko, W.J. Receptor-mediated pharmacokinetic/pharmacodynamic model of interferon-beta 1a in humans. Pharm. Res. 2002, 19, 1537–1543.

[8] Meijer, R.T.; Koopmans, R.P.; ten Berge, I.J.; Schellekens, P.T. Pharmacokinetics of murine anti-human CD3 antibodies in man are determined by the disappearance of target antigen. J. Pharm. Exp. Ther. 2002, 300, 346–353.

[9] Mager, D.E.; Krzyzanski, W. Quasi-equilibrium pharma-cokinetic model for drugs exhibiting target-mediated drug disposition. Pharm. Res. 2005, 22, 1589–1596.

[10] Woo, S.; Krzyzanski, W.; Jusko, W.J. Target-mediated pharmacokinetic and pharmacodynamic model of recombinant human erythropoietin (rHuEPO). J. Pharmacokinet. Pharmacodyn. 2007, 34, 849–868.

[11] Ma, P. Theoretical considerations of target-mediated drug disposition models: simplifications and approximations. Pharm. Res. 2012, 29, 866–882.

[12] Gibiansky, L.; Gibiansky, E. Target-mediated drug disposition model: approximations, identifiability of model parameters and applications to the population pharmacokinetic-pharmacodynamic modeling of biologics. Expert. Opin. Drug Metabolism Toxicol. 2009, 5, 803–812.

[13] Gibiansky, L.; Sutjandra, L.; Doshi, S.; Zheng, J; Sohn W; Peterson MC; et al. Population pharmacokinetic analysis of denosumab in patients with bone metastases from solid tumours. Clin. Pharmacokinet. 2012, 51, 247–260.

[14] Gibiansky, L.; Gibiansky, E.; Kakkar, T.; Ma, P. Approximations of the target-mediated drug disposition model and identifiability of model parameters. J. Pharmacokinet. Pharmacodyn. 2008, 35, 573–591.

[15] Pan, W. Akaike’s information criterion in generalized estimating equations. Biometrics. 2001, 57, 120–125.

[16] Jacquez, J.A; Greif, P. Numerical parameter identifiability and estimability: integrating identifiability, estimability, and optimal sampling design. Math. Biosci. 1985, 77, 201–227.

[17] Eudy, R.J; Riggs, M.M.; Gastonguay, M.R. A Priori Identifiability of Target-Mediated Drug Disposition Models and Approximations. AAPS J. 2015, 17, 1280–1284.

[18] Lamberton, T.O; Condon, N.D.; Stow, J.L.; Hamilton, N.A. On linear models and parameter identifiability in experimental biological systems. J. Theor. Biol. 2014, 358, 102–121.

[19] Abraham, A.K.; Krzyzanski, W.; Mager, D.E. Partial derivative-based sensitivity analysis of models describing target-mediated drug disposition. AAPS J. 2007, 9, E181–189.

[20] Gabrielsson, J.; Peletier, L.A,; Hjorth, S. In vivo potency revisited-Keep the target in sight. Pharmacol. Ther. 2018, 184, 177–188.