Simulation, population pharmacokinetics

Clinical pharmacology carries out all phase 1 smdies and in some companies also proof of principle smdies. Usually clinical pharmacokinetics (including PK/PD modeling, simulation and population pharmacokinetics) also belongs to the domain of... 

Fig. 14.6 Simulation of clearance (CL) for cetuximab versus concentration, based on the results of the integrated population pharmacokinetic analysis.

N., Armstrong, M., Aerssens, J. Galan-tamine population pharmacokinetics in patients with Alzheimer s disease modeling and simulations. J Clin Pharmacol 2003,43 514-523. 

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. 

This approach is called the first order (FO) method in NONMEM. This is the most widely used approach in population pharmacokinetic and pharmacodynamic data analysis, and has been evaluated by simulation. The use of the first-order Taylor series expansion to approximate the non-linear model in r], and, possibly,... 

K. G. Kowalski and M. M. Hutmacher, Design evaluation for a population pharmacokinetic study using clinical trial simulations a case study. Stat Med 20 75-91 (2001). 

FIGURE 37.1 A Une plot of the effect of sample size and intersubject variability on the precision (expressed as percent mean absolute error—%MAE) with which central volume of distribution (VI) was estimated in a simulated population pharmacokinetic study in which a balanced sampling design was used. 

E. I. Ette, A. W. Kelman, C. W. Howie, and B. Whiting, Interpretation of simulation studies for efficient estimation of population pharmacokinetic parameters. Ann Pharma-cother 27 1034-1039 (1993) and Correction 27 1548 (1993). 

Holford, N.H.G. The population pharmacokinetic screen— How well does it work Presented at 3rd Annual Clinical Trial Simulation in Drug Development Best Practices for Creating Cost Effective Modeling and Simulation Practices, 2002. 

Lindstrom, F.T. and Birkes, D.S. Estimation of population pharmacokinetic parameters using destructively obtained experimental data A simulation study of the 1-compartment open model. Drug Metabolism Reviews 1984 15 195-264. 

Roe, D.J. Comparison of population pharmacokinetic modeling methods using simulated data Results from the population modeling workgroup. Statistics in Medicine 1997 16 1241-1262. 

Hooker et al. [65] 2003 Computer- generated design An evaluation of population D-optImal designs via pharmacokinetic simulation... 

Hooker AC, Foracchia M, Dodds MG, Vicini P. An evaluation of population D-optimal designs via pharmacokinetic simulation. Ann Biomed Eng 2003 31 98-111. 

Flooker, A.C., Foracchia, M., Dodds, M.G., and Vicini, P. 2003. An evaluation of population D-optimal designs via pharmacokinetic simulations. Ann. Biomed. Eng. 31 98-111. lacquez,l.A. 1996. Compartmental Analysis in Biology and Medicine. 3rded.,Biomedware,AnnArbor,MI. lacquez, l.A. and Simon, C.P. 1993. QuaUtative theory of compartmental systems. Siam. Rev., 35 43-79. Landaw, E.M. and DiStefano III, LI. 1984. Multiexponential, multicompartmental, and noncompartmental modeling. II. Data analysis and statistical considerations. Am. J. Physiol 246 R665-R677. 

Once key pharmacokinetic parameters are estimated (based on population data) or calculated, they can be used to simulate the plasma concentrationtime profile of the drug for the patient and to ascertain how much drug to administer and when. 

Pharmacokinetic models are used as tools to extrapolate from the results obtained in studies with experimental animals to predict effects in human populations that generally are exposed at lower environmental exposure levels compared to the generally higher exposure levels used in animal experiments. In such models, target tissue doses in different animal species under a variety of exposure conditions are predicted, using computer simulation. 

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