Contemporary models in the field of pharmacokinetic-pharmacodynamic (PK-PD) modeling often incorporate the fundamental principles of capacity limitation and operation of turnover processes to describe the time course of pharmacological effects in mechanistic terms. This permits the identification of drug-and system-specific factors that govern drug responses. There is considerable interest in utilizing mechanism-based PK-PD models in translational pharmacology, whereby in silico, in vitro, and preclinical data may be effectively coupled with relevant models to streamline the discovery and development of new therapeutic agents. These translational PK-PD models form the subject of this review. BASIC TENETS OF PHARMACODYNAMICSThe basic principles of pharmacokinetics, pharmacology, and physiology form the foundation of mechanism-based pharmacokinetic-pharmacodynamic (PK-PD) modeling. A summary of these components is shown as a diagram in Figure 1. Pharmacokinetics encompasses the factors affecting the time course of drug/metabolite concentrations in relevant biological fluids and tissues after various routes of administration and represents the driving force for pharmacological and most toxicological effects. Noncompartmental (i.e., area/moment analysis) and mammillary plasma-clearance models that quantitatively assess pharmacokinetic processes (i.e., absorption, distribution, metabolism, and excretion) are the most common methods used for PK data analysis. At the very minimum, the primary parameters of drug distribution and elimination should be identified (volume of distribution and clearance). Despite the widespread use of these assessment techniques in studies using various animal species, the relatively empirical and hybrid nature of the parameters derived from such techniques do not readily allow for extrapolation of the PK properties across species and compounds, a highly desirable feature of translational models. In contrast, physiology-based PK (PBPK) models seek to emulate physiological pathways and processes that control plasma and tissue drug concentrations, and this approach is regarded as the state-of-the-art technique in advanced PK systems analysis. 1,2 As stated by Dedrick, "Physiologic modeling enables us to examine the joint effect of a number of complex interrelated processes and assess the relative significance of each." 3 The compartments in PBPK models represent organs and tissues of interest and are arranged and connected according to anatomical and physiological relationships (Figure 1, top left). A series of mass-balance differential equations that extend from Fick's law of perfusion/diffusion describe the rate of change of drug concentrations within each tissue. Other major processes may be incorporated, including drug metabolism and/or Correspondence: DE Mager (E-mail: dmager@buffalo.edu). CONFLICT OF INTERESTThe authors declared no conflict of interest. The law of mass action and the relatively low concentration of pharmacological receptors or targets impart capacity limitation i...
The distribution of digoxin in the myocardium, skeletal muscle, erythrocytes, and plasma (or serum) was studied in 19 infants. There was a linear relationship between myocardium and serum concentrations and no saturation was observed over the serum concentration range of 0.5-8.6 ng/ml. Myocardium uptake of digoxin was nearly twice as great in infants as in adults at any given serum concentration. Erythrocyte: plasma concentration ratios of digoxin were one-third smaller during digitalization than during maintenance digoxin therapy. The latter ratios were also three times greater in infants than found previously in adults. Their findings are consistent with a greater apparent volume of distribution of digoxin in infants and may partly explain the unusually large therapeutic doses needed in infants.
1 The independent as well as interactive effects of chronic (> 6 months) oral contraceptive (OC) use and cigarette smoking on single-dose (4 mg/kg) theophylline disposition were assessed in 49 young, healthy women. 2 Significant elevations (40%) in theophylline plasma clearance were found in women who smoked. OC use resulted in decreases in clearance of a similar magnitude (28%). These factors do not appear to interact with respect to theophylline disposition. The combination of main effects tended to cancel one another (clearance of49.1 ml h-I kg-'ideal body weight for OC non-user, non-smoker, vs 49.7 ml h-' kg-'for OC user-smoker). 3 Single dose exposure to OC in non-users did not significantly alter theophylline pharmacokinetics for the group as a whole. However, in the subgroup of smoking subjects, significant decreases in clearance were evident (P < 0.05). Analogous results were found for half-life. Volume of distribution was slightly diminished in smokers, but was unaffected in OC users.4 Areas under the serum concentration-time (AUC) profiles of norgestrel and ethinyloestradiol were examined in 27 women as indices of OC exposure. The smallest values of theophylline clearance were found in the subjects with largest AUC of both OC steroids. 5 Appropriate statistical analyses of data which are influenced by multiple factors are discussed.Special concern is needed when the factor partitioning process yields subgroups of unequal sizes.
Thiocyanate (SCN) concentrations were determined in serum samples from 130 young healthy persons (60 smokers) and related to their smoking and physiologic characteristics. Serum thiocyanate correlated strongly and approximately equally with the number of cigarettes/d X kg of ideal body weight (IBW) (r = 0.748), total nicotine intake in mg/d X kg IBW (r = 0.735), and total tar intake in mg/d X kg IBW (r = 0.731). Multiple linear regression analysis that included these factors as well as sex, marijuana use, menthol, and degree of inhalation only increased the multiple r to 0.803. A more sensitive statistical method (NYBAID) was also used to determine the most significant influences of these variables on serum SCN. The association with depth of inhalation (i.e., smoking versus nonsmoking) was dominant among the relationships considered. The highest SCN levels were exhibited in heavy nicotine users (8.58 +/- 3.00 mg/l), while average users had slightly lower concentrations (6.49 +/- 2.37 mg/l) (p less than 0.03). In nontobacco smokers, those who smoked marijuana several times weekly had higher SCN levels (4.66 +/- 2.16 mg/l) than noncannabis users (2.38 +/- 1.38 mg/l) (p less than 0.03). These studies confirm the utility of serum SCN as an index of smoking rate and demonstrate the role of secondary variables in accounting for the chemical in serum.
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