Modeling population pharmacokinetics

FIGURE 4.4 A Lithium plasma concentration time profile based on a population pharmacokinetics model (Taright et al., 1994). Closed circles are the actual measured lithium concentrations broken lines represent the therapeutic range (0.6-1.2 mmol/L). B Individualized lithium plasma concentration time profile based on the population model with feedback of measured concentrations (Bayesian recalculation). Closed circles are the measured lithium concentrations. The second part of the curve is the predicted lithium concentration profile after increasing the dose to 1000 mg lithium carbonate twice daily, based on a target of 0.6-1.2 mmol/L (broken lines). 

Various methods are available to estimate population parameters, but today the nonlinear mixed effects modeling approach is the most common one employed. Population analyses have been performed for mAbs such as basiliximab, daclizu-mab and trastuzumab, as well as several others in development, including clenolixi-mab and sibrotuzumab. Population pharmacokinetic models comprise three submodels the structural the statistical and covariate submodels (Fig. 3.13). Their development and impact for mAbs will be discussed in the following section. 

In conclusion, robust population pharmacokinetic models may contribute to the mechanistic understanding of the fate of mAbs in the body. Factors that may influence the pharmacokinetics of mAbs should be investigated for their potential influence on dosage regimen design in clinical trials and therapeutic use. 

Fig. 12.6 (A) Predicted serum concentrationtime profiles for etanercept following subcutaneous administration of the low (25 mg once weekly), medium (25 mg twice weekly), and high (50 mg twice weekly) dose in rheumatoid arthritis patients using the population pharmacokinetic model described previously...

Objective The objective of this analysis was to develop a population pharmacokinetic model for NS2330 and its major metabolite Ml, based on data from a 14-week proof of concept study in Alzheimer s disease patients, including a screening for covariates that might influence the pharmacokinetic characteristics of the drug and/or its metabolite. Subsequently, several simulations should be performed to assess the influence of the covariates on the plasma concentration-time profiles of NS2330 and its metabolite. 

Lehr, T., Staab, A., Tillmann, C., Trommes-hauser, T., Schafer, H. G., Kloft, C. Population pharmacokinetic modelling of... 

Ezzet F, Krishna G, Wexler DB, Statkevich P, Kosoglou T, Batra VK (2001) A population pharmacokinetic model that describes multiple peaks due to enterohepatic recirculation of ezetimibe. Clin Ther 23 871—885. 

EIGURE 32.13 Correlation betwreen observed and predicted erythropoietin concentration values analyzing sparse sampling data Auth a population pharmacokinetic model (no r value given). (Reproduced wnth permission from Hayashi W et al. Br J Clin Pharmacol 1998 46 11-9.)... 

Nestorov I, Zitnik R, Ludden T (2004) Population pharmacokinetic modeling of subcutaneously administered etanercept in patients witli psoriasis. J Pharmacokirretic Pharmacodyn 31(6) 463 90. 

A second type of study involves applying food as a covariate in population pharmacokinetic models. "" ... 

There are many approaches used for PPK model development in the literature. These range from modeling population pharmacokinetic data without exploratory data analysis to approaches that incorporate the latter. Excellent examples of population pharmacokinetic model development, which incorporate exploratory data analysis into population pharmacokinetic model development, can be found in the articles by Ette and Ludden and Mandema, Verotta, and Sheiner Excellent reviews on the validation of PPK models are available in the literature. Thus, validation will not be discussed. 

Forrest, A. Sallow, C.H. Nix, D.E. Birmingham, M.C. Schentag, J.J. Development of a population pharmacokinetic model and optimal sampling strategy for intravenous ciprofloxacin in seriously ill patients. Antimicrob. Agents Chemother. 1993, 57, 1065-1072. 

Dodge, W.F. Jellife, R.W. Richardson, C.J. McCleery, R.A. Hokanson, J.A. Snodgrass, W.R. Gentamicin population pharmacokinetic models for low birth weight infants using a new non-parmaetric method. Clin. Pharmacol Ther. 1991, 50 (1), 25-31. 

Ette, E.I. Ludden, T.M. Population pharmacokinetic modeling the importance of informative graphics. Pharm. Res. 1995, 12 2), 1845-1855. 

Variol P, Nguyen L, Tranchand B, Puozzo C. A simultaneous oral/intravenous population pharmacokinetic model for vinorelbine. Eur J Clin Pharmacol 2002 58(7) 467-76. 

J. Wakefield, The Bayesian analysis of population pharmacokinetic models. J Am Stat Assoc 91 62-75 (1996). 

C. Dansirikul, R. G. Morris, S. E. Tett, and S. B. DuffuU, A Bayesian approach for population pharmacokinetic modeling of sirolimus. Br J Clin Pharmacol 62 420-434 (2006). 

M. O. Karlsson, E. N. Jonsson, C. G. WUtse, and J. R. Wade, Assumption testing in population pharmacokinetic models illustrated with an analysis of moxonidine data from congestive heart failure patients. J Pharmacokinet Biopharm 26 207-246 (1998). 

E. Ette, P. Williams, J. Lane, Y. K. Kim, and E. V. Capparelli, The determination of population pharmacokinetic model appropriateness. / Clin Pharamcol 43 2-15 (2003). V. F. Cosson, E. Fuseau, C. Efthymiopouios, and A. Bye, Mixed effect modeling of sumatriptan pharmacokinetics during drug development. I. Interspecies allometric scaling. / Pharmacokinet Biopharm 25 149-167 (1997). 

E. I. Ette, Stability and performance of a population pharmacokinetic model. J Clin Pharmacol 37 486-495 (1997). 

W. F. Dodge, R. W. Jellife, C. J. Richardson, R. A. McCleery, J. A. Hokanson, and W. R. Snodgrass, Gentamicin population pharmacokinetic models for low birth weight infants using a new nonparamaetric method. Clin Pharmacol Ther 50 25-31 (1991). 

A. Rousseau, F. Leger, Y. Le Meur, F. Saint-Marcoux, G. Paintaud, M. Buchler, and P. Marquet, Population pharmacokinetic modeling of oral cyclosporin using NONMEM comparison of absorption pharmacokinetic models and design of a Bayesian estimator. 

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