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Pharmacokinetics
Published in Samuel C. Morris, Cancer Risk Assessment, 2020
There are basically two approaches to pharmacokinetic modeling. The classic approach is empirical and focuses on data fitting; the newer approach, physiologically based pharmacokinetic modeling (PB-PK), is predictive. O’Flaherty (1987) describes the role of each class of model and how the need for interspecies conversion of animal data led to the development of PB-PK models.
Methods in Physiologically Based Pharmacokinetic Modeling
Published in Francis N. Marzulli, Howard I. Maibach, Dermatotoxicology Methods: The Laboratory Worker’s Vade Mecum, 2019
Another important reason for using physiologically based pharmacokinetic modeling of dermal absorption is to acquire the experience necessary to extrapolate to other species. Classical pharmacokinetic modeling assumes that the body can be adequately described by one to three compartments based on the shape of the semilogarithmic plot of plasma concentration versus time (Gibaldi and Perrier, 1982). The most common classical description is a two-compartment linear system where one compartment is the plasma and the other is all remaining body water and tissues. Using this type of model, the plasma concentration curve can be fit by a distributive phase (a) and a post-distributive phase (b). This type of model is useful in clinical situations for determining dose or dose regimen. Classical modeling has occasionally been used in skin penetration studies (Cooper, 1976; Wallace and Barnett, 1978; Peck et al., 1981; Chandrasekaran et al., 1978; Birmingham et al., 1979; Guy et al., 1982; Kubota and Ishizake, 1986).
Toxicokinetics of Nerve Agents
Published in Brian J. Lukey, James A. Romano, Salem Harry, Chemical Warfare Agents, 2019
Marcel J. van der Schans, Hendrik P. Benschop, Christopher E. Whalley
Eventually, the toxicokinetic data, together with the distribution data, is very useful for the validation of physiologically based pharmacokinetic modeling (PBPK) (Gearhart et al., 1990; Langenberg et al., 1997; Maxwell et al., 1988; Ramsey and Andersen, 1984). These models are needed because the experiments that were discussed in this chapter can never be performed in humans, while extrapolation of the results obtained in these animal experiments to humans is still the ultimate goal of these investigations.
ABCG2 as a therapeutic target candidate for gout
Published in Expert Opinion on Therapeutic Targets, 2018
Kyoko Fujita, Kimiyoshi Ichida
The ABCG2 mediated drug–drug interactions is important for the clinical use of ABCG2-targeting drugs [38]. To verify and characterize the mechanisms, physiologically based pharmacokinetic modeling has been developed [39]. For example, rosuvastin, a 3-hydroxy-3-methyl coenzyme A reductase inhibitor, is a synthetic statin used to reduce low-density lipoprotein cholesterol levels in the treatment of hyperlipidemia. Rosuvastatin is a frequently used probe in transporter-mediated drug-drug interaction studies. The interaction of rosuvastin with rifampin, bactericidal agent, and cyclosporine, immunosuppressant, were reported to mediate via inhibition of ABCG2 [40]. Intestinal ABCG2 is inhibited when rifampin is administered orally, resulting in decreased intestinal efflux of rosuvastatin with a concomitant increase in intestinal absorption. By contrast, absorption of rosuvastatin was unaffected by intravenous administration of rifampin. The effect of cyclosporine on intestinal ABCG2 resulted in an increased fraction of rosuvastatin absorbed in most gastrointestinal segments, except the colon. The decreased fraction of rosuvastatin absorbed from the colon can be attributed to the fact that, in the presence of cyclosporine, most of the rosuvastatin dose was absorbed from the gastrointestinal segments before the colon, which was not the case in the absence of cyclosporine.
Comparison of predicted intrinsic hepatic clearance of 30 pharmaceuticals in canine and feline liver microsomes
Published in Xenobiotica, 2019
Marike Visser, Matthew J. Zaya, Charles W. Locuson, Dawn M. Boothe, Dawn A. Merritt
The CLhep,ws under predicted the reported vivo clearance, a recognized problem with the use of this model in the metabolic stability assay (Obach, 1999). In addition, it is difficult to compare the CLhep to the reported in vivo clearance due to the impact of bioavailability on drugs administrated via an extravenous route. The prediction improves with drugs considered to be high extraction (E), defined as an E > 0.7. Drugs classified as high extraction in humans include midazolam, carvedilol (Rasool et al., 2015), fluoxetine (Altamura et al., 1994), praziquantel (Mandour et al., 1990), quinidine (Sugihara et al., 1993), sildenafil (Mehrota et al., 2007) and verapamil. The CLhep,ws model is one of the most frequently used models, and assumes that the substrate is instantly and homogeneously distributed through the liver water, the unbound drug concentration in the plasma is identical to unbound drug concentrations in the liver water, there is no active transport involved and that CYP is the main route for metabolism. In contrast, the CLhep,pt model assumes that the liver consists of parallel tubes with enzymes evenly distributed in each section and the concentration of the drug decreases along the direction of blood flow in the sinusoids (Chao et al., 2010). Both models assume that mixing occurs between the hepatic portal and arterial blood prior to drug partition in the sinusoids, that only unbound drug traverses the cellular membranes, the rate of distribution is perfusion limited without the influence of transporters and the rate of drug elimination is dependent on the concentration of unbound drug at the enzyme location (Pang & Rowland, 1977). The well-stirred model is the most frequently cited model and is used in physiologically based pharmacokinetic modeling (PBPK). However, this model is reported to predict lower CLhep compared to the parallel tube model, which was observed in this study (Figure 3). The CLhep,pt predictions were higher compared to the well-stirred model, but the fold difference between the species remained similar (Table 4). Additional research and pharmacokinetic studies in both species are necessary to determine the best model for integration with PBPK for each species (Kuepfer et al., 2016).