Transport vs. Metabolism: What Determines the Pharmacokinetics and Pharmacodynamics of Drugs? Insights From the Extended Clearance Model

Patilea‐Vrana, Gabriela; Unadkat, Jashvant D.

doi:10.1002/cpt.437

Cited by 82 publications

(93 citation statements)

References 9 publications

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“…Similarly, Kusuhara and Sugiyama (7) and Yoshikado et al (15, 16) describe a PBPK extended clearance model with 5 consecutive in-series liver compartments where each liver compartment mimics the dispersion model. However, as shown here, and as recognized by Pang, Caminesch, Varma, Unadkat, Riley and their co-workers (5, 9–14, 17, 20, 22), the extended clearance relationship is derived from the well-stirred model. Nevertheless, the dispersion clearance approach appears to be universally used to model organ clearance as described throughout the PBPK literature.…”

Section: Resultsmentioning

confidence: 57%

“…Pfizer scientists (12, 13) also presented a similar equation recognizing that the relationship was derived based on the well-stirred model, but not explicitly indicating that the transporter parameters were intrinsic clearances, not total drug transporter clearances. Recently, Patilea-Vrana and Unadkat (14) also presented the equation, recognizing that the relationship was derived based on the well-stirred model, but not substituting blood for plasma parameters. Yoshikado et al (15) also presented a form of Eq.…”

Section: Methodsmentioning

confidence: 99%

“…1 as liver plasma [Liu and Pang (17), with the hepatocyte compartment in Fig. 1 designated liver tissue], plasma [Patilea-Vrana and Unadkat (14)], or simply blood [El-Kattan and Varma (13) and many others]. In these alternate depictions, what we are designating as hepatocyte in Fig.…”

Section: Methodsmentioning

confidence: 99%

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The Extended Clearance Concept Following Oral and Intravenous Dosing: Theory and Critical Analyses

et al. 2018

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Purpose: To derive the theoretical basis for the extended clearance model of organ elimination following both oral and IV dosing, and critically analyze the approaches previously taken. Methods: We derived from first principles the theoretical basis for the extended clearance concept of organ elimination following both oral and IV dosing and critically analyzed previous approaches. Results: We point out a number of critical characteristics that have either been misinterpreted or not clearly presented in previously published treatments. First, the extended clearance concept is derived based on the well-stirred model. It is not appropriate to use alternative models of hepatic clearance. In analyzing equations, clearance terms are all intrinsic clearances, not total drug clearances. Flow and protein binding parameters should reflect blood measurements, not plasma values. In calculating the AUCR-factor following oral dosing, the AUC terms do not include flow parameters. We propose that calculations of AUCR may be a more useful approach to evaluate drug-drug and pharmacogenomic interactions than evaluating rate-determining steps. Through analyses of cerivastatin and fluvastatin interactions with cyclosporine we emphasize the need to characterize volume of distribution changes resulting from transporter inhibition/induction that can affect rate constants in PBPK models. Finally, we note that for oral doses, prediction of systemic and intrahepatic drug-drug interactions do not require knowledge of fu,H or Kp,uu for substrates/victims. Conclusions: The extended clearance concept is a powerful tool to evaluate drug-drug interactions, pharmacogenomic and disease state variance but evaluating the AUCR-factor may provide a more valuable approach than characterizing rate-determining steps.

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Section: Resultsmentioning

confidence: 57%

Section: Methodsmentioning

confidence: 99%

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The Extended Clearance Concept Following Oral and Intravenous Dosing: Theory and Critical Analyses

et al. 2018

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“…Thus, a major unanswered question is whether the subgroup of participants with low plasma concentrations of SVA represents patients likely to respond better than anticipated because of enhanced hepatic uptake. Ultimately, though, therapeutic response is a function of SVA concentration within hepatocytes and is dependent on both uptake into the cells and clearance via biotransformation and cellular efflux; whichever of these components is rate limiting with respect to hepatocyte exposure will represent the primary determinant of pharmacodynamic response …”

Section: Discussionmentioning

confidence: 99%

Impact of SLCO1B1 Genotype on Pediatric Simvastatin Acid Pharmacokinetics

Wagner

Abdel‐Rahman

Haandel

et al. 2018

The Journal of Clinical Pharma

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This study investigated the impact of allelic variation in SLCO1B1, a gene encoding for the liver-specific solute carrier organic anion transporter family member 1B1 protein (SLCO1B1), on simvastatin and simvastatin acid (SVA) systemic exposure in children and adolescents. Participants (8-20 years old) with at least 1 variant SLCO1B1 c.521T>C allele (521TC, n = 15; 521CC, n = 2) and 2 wild-type alleles (521TT, n = 15) completed a single oral dose pharmacokinetic study. At equivalent doses, SVA exposure was 6.3- and 2.5-fold greater in 521CC and TC genotypes relative to 521TT (C , 2.1 ± 0.2 vs 1.0 ± 0.5 vs 0.4 ± 0.3 ng/mL; P < .0001; and AUC, 12.1 ± 0.3 vs 4.5 ± 2.5 vs 1.9 ± 1.8 ng·h/mL; P < .0001). The impact of the SLCO1B1 c.521 genotype was more pronounced in children, although considerable interindividual variability in SVA exposure was observed within genotype groups. In addition, SVA systemic exposure was negligible in 25% of pediatric participants. Further investigation of the ontogeny and genetic variation of SVA formation and SLCO1B1-mediated hepatic uptake is necessary to better understand the variability in SVA exposure in children and its clinical consequences.

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“…As illustrated by the extended clearance model, inhibition of efflux transporters in the kidney epithelial cells (e.g., MATE1) could result in a Interindividual Variability in Renal Drug Transporter Protein Expression significant increase in drug concentration in these cells (and therefore potential toxicity) without a concurrent change in the concentration of the drug in the systemic circulation (Patilea-Vrana and Unadkat, 2016). That is, these drug-drug interactions (DDIs) cannot be detected by the classic DDI studies in which the plasma concentration of a drug is measured in the presence and absence of an inhibitor.…”

Section: Discussionmentioning

confidence: 99%

Abundance of Drug Transporters in the Human Kidney Cortex as Quantified by Quantitative Targeted Proteomics

Prasad

Johnson

Billington³

et al. 2016

Drug Metab Dispos

122

111

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Protein expression of renal uptake and efflux transporters was quantified by quantitative targeted proteomics using the surrogate peptide approach. Renal uptake transporters assessed in this study included organic anion transporters (OAT1-OAT4), organic cation transporter 2 (OCT2), organic/carnitine cation transporters (OCTN1 and OCTN2), and sodium-glucose transporter 2 (SGLT2); efflux transporters included P-glycoprotein, breast cancer resistance protein, multidrug resistance proteins (MRP2 and MRP4), and multidrug and toxin extrusion proteins (MATE1 and MATE2-K). Total membrane was isolated from the cortex of human kidneys (N = 41). The isolated membranes were digested by trypsin and the digest was subjected to liquid chromatography-tandem mass spectrometry analysis. The mean expression of surrogate peptides was as follows (given with the standard deviation, in picomoles per milligram of total membrane protein): OAT1 (5.3 6 1.9), OAT2 (0.9 6 0.3), OAT3 (3.5 6 1.6), OAT4 (0.5 6 0.2), OCT2 (7.4 6 2.8), OCTN1 (1.3 6 0.6), OCTN2 (0.6 6 0.2), P-glycoprotein (2.1 6 0.8), MRP2 (1.4 6 0.6), MRP4 (0.9 6 0.6), MATE1 (5.1 6 2.3), and SGLT2 (3.7 6 1.8). Breast cancer resistance protein (BCRP) and MATE2-K proteins were detectable but were below the lower limit of quantification. Interestingly, the protein expression of OAT1 and OAT3 was significantly correlated (r > 0.8). A significant correlation was also observed between expression of multiple other drug transporters, such as OATs/OCT2 or OCTN1/OCTN2, and SGLT2/OCTNs, OCT, OATs, and MRP2. These renal transporter data should be useful in deriving in vitro to in vivo scaling factors to accurately predict renal clearance and kidney epithelial cell exposure to drugs or their metabolites.

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Transport vs. Metabolism: What Determines the Pharmacokinetics and Pharmacodynamics of Drugs? Insights From the Extended Clearance Model

Cited by 82 publications

References 9 publications

The Extended Clearance Concept Following Oral and Intravenous Dosing: Theory and Critical Analyses

The Extended Clearance Concept Following Oral and Intravenous Dosing: Theory and Critical Analyses

Impact of SLCO1B1 Genotype on Pediatric Simvastatin Acid Pharmacokinetics

Abundance of Drug Transporters in the Human Kidney Cortex as Quantified by Quantitative Targeted Proteomics

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