Pharmacokinetics individual processes

Therapeutic drug monitoring The process of using drug concentrations, pharmacokinetic principles, and pharmacodynamic criteria to optimize drug therapy in individual patients. 

The pharmacokinetic term clearance (CT) best describes the efficiency of the elimination process. Clearance by an elimination organ (e.g., liver, kidney) is defined as the volume of blood, serum, or plasma that is totally cleared of drug per unit time. This term is additive the total body or systemic clearance of a drug is equal to the sum of the clearances by individual eliminating organs. Usually this is represented as the sum of renal and hepatic clearances CT = CT renal -I- CL hepatic. Clearance is constant and independent of serum concentration for drugs that are eliminated by first-order processes, and therefore may be considered proportionally constant between the rate of drug elimination and serum concentration. 

Chemical clastogenesis and mutagenesis both involve a complex series of processes, including pharmacokinetic mechanisms (uptake, transport, diffusion, excretion), metabolic activation and inactivation, production of DNA lesions and their incomplete repair or misrepair, and steps leading to the subsequent expression of mutations in surviving cells or individuals (Thble 7.1). Each of the steps in these processes might conceivably involve first order kinetics at low doses (e.g., diffusion, MichaeUs-Menten enzyme kinetics) and hence be linear. In principle, therefore, the overall process edso might be linear and without threshold. 

In the physiological sense, one can divide the body into compartments that represent discrete parts of the whole-blood, liver, urine, and so on, or use a mathematical model describing the process as a composite that pools together parts of tissues involved in distribution and bioactivation. Usually pharmacokinetic compartments have no anatomical or physiological identity they represent all locations within the body that have similar characteristics relative to the transport rates of the particular toxicant. Simple first-order kinetics is usually accepted to describe individual... 

In pharmaceutical research and drug development, noncompartmental analysis is normally the first and standard approach used to analyze pharmacokinetic data. The aim is to characterize the disposition of the drug in each individual, based on available concentration-time data. The assessment of pharmacokinetic parameters relies on a minimum set of assumptions, namely that drug elimination occurs exclusively from the sampling compartment, and that the drug follows linear pharmacokinetics that is, drug disposition is characterized by first-order processes (see Chapter 7). Calculations of pharmacokinetic parameters with this approach are usually based on statistical moments, namely the area under the concentration-time profile (area under the zero moment curve, AUC) and the area under the first moment curve (AUMC), as well as the terminal elimination rate constant (Xz) for extrapolation of AUC and AUMC beyond the measured data. Other pharmacokinetic parameters such as half-life (t1/2), clearance (CL), and volume of distribution (V) can then be derived. 

We have discussed both target receptors and pharmacokinetics in this book. Protein manufacture is under direct genetic control, and two factors are of particular relevance here. First, the precise structure and function of protein macromolecules (receptors) targeted by a specific drug molecule will vary in different individuals. Since the structure and function of the protein are directly related to how the drug molecule will interact with that protein, individuals responses to the drug will vary. Second, there are genetic variations in metabolic enzymes (proteins) and hence metabolism. Both of these processes fall neatly into the domain of pharmacoproteomics (see Section 14.8). 

Poulin and Krishnan (1995) developed a method to predict tissue blood PCs for incorporation into physiologically based pharmacokinetic (PBPK) models. Tissue blood partitioning was calculated as an additive function of partitioning into the water, neutral lipids and phospholipids constituent of individual tissues. These were calculated using published values for lipid and water content of tissues and the octanol-water PC of the compounds. Poulin and Krishnan (1998 1999) used this method to predict tissue blood PCs that were subsequently incorporated into a quantitative structure-toxicokinetic model. The prediction of tissue plasma PCs to describe distribution processes and as input parameters for PBPK models has been extensively researched by Poulin and coworkers a great deal of further information can be obtained from their references (Poulin and Theil, 2000 Poulin et al., 2001 Poulin and Theil, 2002a Poulin and Theil, 2002b). 

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]. 

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