Population approach software

Aarons, L, Balant, 1.. P., Mentre, F., et al. Population approaches in drug development. Report on an expert meeting to discuss population pharmacoki-netic/pharmacodynamic software. Eur J Clin Pharmacol 1994, 46 389-391. 

Jonsson F, Johanson G. 2003. The Bayesian population approach to physiological toxicoki-netic-toxicodynamic models—an example using the MCSim software. Toxicol Lett 138 143-150. 

Maitre et al. (15) proposed an improvement on the traditional approach. The approach consists of using individual Bayesian posthoc PK or PK/PD parameters from a population modeling software such as NONMEM and plotting these parameter estimates against covariates to look for any possible model parameter covariate relationship. The individual model parameter estimates are obtained using a base model—a model without covariates. The covariates are in turn tested to determine individual significant covariate predictors, which are in turn used to form a full model. The final irreducible model is obtained by backward elimination. The drawback for this approach is the same as that for the traditional approach. 

In addition to these three major methods mentioned, several other computational approaches can also be used to deal with population stratification. For example, ADMIXMAP (22-26) is a model-based method that estimates the individual history of admixture. It can be applied to an admixed population with two or more ancestral populations. It also tests the association of a trait with ancestry at a marker locus with control for population structure. Wu et al. developed a software package in R (PSMIX) for the inference of population stratification and admixture (27). PSMIX is based on the maximum likelihood method. It performs as well as model-based methods such as STRUCTURE and is more computationally efficient. 

The most familiar estimation procedure is to assume that the population mean and variance are equal to the sample mean and variance. More generally, the method of moments (MOM) approach is to equate sample moments (mean, variance, skewness, and kurtosis) to the corresponding population. Software such as Crystal Ball (Oracle Corporation, Redwood Shores, CA) uses MOM to fit the gamma and beta distributions (see also Johnson et al. 1994). Use of higher moments is exemplified by fitting of the... 

Ho vever this approach does not address inter-individual variability in CYP expression nor the apparent substrate specificity of RAFs. This may be overcome through the use of intersystem extrapolation factors (ISEFs) vhich compare the intrinsic activities of rCYP versus liver microsomes and provide CYP abundance scaling by mathematical means. This employs the RAF approach and adjusts for the actual amount of liver microsomes CYP present (measured by immunochemistry) rather than a theoretical amount (Equation 8.4). Such corrections can be made using nominal specific contents of individual CYP proteins in liver microsomes or more appropriately employ modeling and simulation software (e.g., SIMCYP www.simcyp.com) which takes into account population-based variability in CYP content. 

Software algorithms for estimating the proportion of S-phase cells are approximations. They can be refined mathematically in the hope of better approaching the biological truth. A mathematical model will always, however, have trouble coping with a biological situation that is disturbed or contains mixed populations behaving in erratic... 

The definitions and statistical theory of PPK, advantages, and disadvantages of PPK have been discussed in this chapter. Models, data type, methods, and software programs for estimating population pharmacokinetic parameters, design, and analysis of population pharmacokinetic studies have been reviewed, as well as its application in biopharmaceutics. The use of population methods continues to increase while there is a shortage of those who can implement the approach. 

This ability is available in many software programs. NONMEM (Iconus, EUicott City, MD) has been widely used to estimate population models arising from both sparse and intensely sampled data. Other programs include WinNonMix (Pharsight Corp., Palo Alto, CA), Kinetica 2000 (Innaphase Corp, Philadelphia, PA), and Pop-Kinetics (SAAM Institute, Seattle, WA). ADAPT II and WinNonlin have focused on PK/PD models and have been combined with Bayesian approaches to estimate population models. 

Alternatively, the complete population balance can be solved dynamically using efficient ODE solvers [70, 71], The versatile commercial software PREDICI can solve population balances that describe polymerizations with coordination catalysts and many other polymerization mechanisms [72]. In this approach, the complete microstructural distributions are modeled, leading to a detailed description of the polymer microstructure. 

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