Pharmacokinetic parameters condition

In most models developed for pharmacokinetic and pharmacodynamic data it is not possible to obtain a closed form solution of E(yi) and var(y ). The simplest algorithm available in NONMEM, the first-order estimation method (FO), overcomes this by providing an approximate solution through a first-order Taylor series expansion with respect to the random variables r i,Kiq, and Sij, where it is assumed that these random effect parameters are independently multivariately normally distributed with mean zero. During an iterative process the best estimates for the fixed and random effects are estimated. The individual parameters (conditional estimates) are calculated a posteriori based on the fixed effects, the random effects, and the individual observations using the maximum a posteriori Bayesian estimation method implemented as the post hoc option in NONMEM [10]. 

Disadvantages arise mainly from the complexity of the statistical algorithms and the fact that fitting models to data is time consuming. The first-order (EO) method used in NONMEM also results in biased estimates of parameters, especially when the distribution of inter individual variability is specified incorrectly. The first-order conditional estimation (EOCE) procedure is more accurate but is even more time consuming. The objective function and adequacy of the model are based in part on the residuals, which for NONMEM are determined based on the predicted concentrations for the mean pharmacokinetic parameters rather than on the predicted concentrations for each individual. Therefore, the residuals are confounded by intraindividual, inter individual, and linearization errors. 

The plasma samples are stored for later analysis. The samples are analyzed using validated analytical methods. The most commonly used methods are chromatographic, i.e., HPLC or gas chromatography. These methods, including the storage conditions, must be validated so that accurate and precise results are assured. Pharmacokinetic parameters from the plasma drug release profiles are determined for individual volunteers. The average values of these parameters reflect the BA of the product. 

Conceptual models of percutaneous absorption which are rigidly adherent to general solutions of Pick s equation are not always applicable to in vivo conditions, primarily because such models may not always be physiologically relevant. Linear kinetic models describing percutaneous absorption in terms of mathematical compartments that have approximate physical or anatomical correlates have been proposed. In these models, the various relevant events, including cutaneous metabolism, considered to be important in the overall process of skin absorption are characterized by first-order rate constants. The rate constants associated with diffusional events in the skin are assumed to be proportional to mass transfer parameters. Constants associated with the systemic distribution and elimination processes are estimated from pharmacokinetic parameters derived from plasma concentration-time profiles obtained following intravenous administration of the penetrant. 

TABLE 5—4. Theophylline Pharmacokinetic Parameters for Selected Disease States/Conditions... 

In the case where the patient has received enough doses to be at steady state, pharmacokinetic parameters can be computed using a predose minimum concentration and a postdose maximum concentration. Under steady-state conditions, serum concentrations after each dose are identical, so the predose minimum concentration is the same before each dose (Fig. 5-10). This situation allows the predose concentration to be used to compute both the patient s 0/2 and V. If the drug was given extravascularly or has a significant distribution... 

Inference is the act of drawing conclusions from a model, be it making a prediction about a concentration at a particular time, such as the maximal concentration at the end of an infusion, or the average of some pharmacokinetic parameter, like clearance. These inferences are referred to as point estimates because they are estimates of the true value. Since these estimates are not known with certainty they have some error associated with them. For this reason confidence intervals, prediction intervals, or simply the error of the point estimate are included to show what the degree of precision was in the estimation. With models that are developed iteratively until some final optimal model is developed, the estimate of the error associated with inference is conditional on the final model. When inferences from a model are drawn, modelers typically act as though this were the true model. However, because the final model is uncertain (there may be other equally valid models, just this particular one was chosen) all point estimates error predicated on the final model will be underestimated (Chatfield, 1995). As such, the confidence interval or prediction interval around some estimate will be overly optimistic, as will the standard error of all parameters in a model. 

Base model development proceeded from a 1-compartment model (ADVAN1 TRANS2) estimated using first-order conditional estimation with interaction (FOCE-I) in NONMEM (Version 5.1 with all bug fixes as of April 2005). All pharmacokinetic parameters were treated as random effects and residual error was modeled using an additive and exponential (sometimes called an additive and proportional) error model. Initial values for the fixed effects were obtained from the literature (Xuan et al., 2000) systemic clearance (CL) of 4.53 L/h and volume of distribution (VI) of 27.3 L. Initial values for the variance components was set to 32% for all, except the additive term in the residual error which was set equal to 1 mg/L. The model successfully converged with an OFV of 20.141. The results are shown in Table 9.4. 

Bioequivalence (BE) is defined as the similarity in the rate and extent of therapeutic and toxic effects observed by the administration of equivalent doses of the pharmaceutical formulations or pharmaceutical alternatives, under similar conditions. It is estimated by comparing the analytical and the above-mentioned pharmacokinetic parameters at different doses for the pharmaceutical preparation under study. 

CE was early identified as a powerful experimental technique to monitor the partitioning of neutral solutes and the neutral form of ionizable compounds [67]. In the search of partitioning parameters closer than log Poa to the pharmacokinetic behavior of dmg compounds [68] several different CE experimental conditions were explored and recently reviewed in detail [5, 69]. 

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