(2008) thus proposed the global sensitivity analysis for robust experimental design based on the modified Morris method ( Morris, 1991), but it still requires an initial guess or prior knowledge concerning the underlying relation of the parameters. However, the local sensitivity analysis is likely to make a wrong decision if the estimate is far from the true value or the model has very complicated dynamics. The local sensitivity analysis in the statistical identification uses the first partial derivatives of the differential equations with respect to the parameters, and depends on the non-singularity of the Fisher information matrix, which is equivalent to the Taylor series method and differential algebra method ( Hidalgo and Ayesa, 2001 Wynn and Parkin, 2001). One of the empirical approaches to assessing the statistical identifiabilty is the local sensitivity analysis. The deterministic identifiablity is concerned with whether the model parameters can be identified with noise-free data, while the statistical identifiability is the possibility of identifying the model parameters with noise data.Īlthough numerous methods have been presented to detect the non-identifiable parameters deterministically, such as the Laplace transform ( Godfrey and DiStefano, 1987), the similarity transformation approach ( Vajda et al., 1989), the Voterra and generating power series approaches ( Lecourtier et al., 1987), the differential algebra approach ( Saccomani et al., 2003), and the alternating conditional expectation algorithm ( Hengl et al., 2007), there has been much less development in statistical identifiability analysis of PK models. The deterministic and statistical identifiabilities of parameters in the MM equation have been examined ( Tong and Metzler, 1980 Metzler and Tong, 1981 Godfrey and Fitch, 1984). Km is also called the MM constant having the units of C( t). Where Vmax is the maximum enzyme activity Km is an inverse function of the affinity between drug and enzyme C( t) is an unbound drug concentration.

Nonmem weighted optimization