Quantitative Prediction of Transporter- and Enzyme-Mediated Clinical Drug-Drug Interactions of Organic Anion-Transporting Polypeptide 1B1 Substrates Using a Mechanistic Net-Effect Model

Varma, Manthena V.; Bi, Yi‐an; Kimoto, Emi; Lin, Jian

doi:10.1124/jpet.114.215970

Cited by 60 publications

(104 citation statements)

References 50 publications

(74 reference statements)

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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: 56%

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: 56%

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

et al. 2018

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“…For instance, Watanabe et al showed that the in vitro uptake clearance obtained using human hepatocytes was similar to in vivo hepatic clearance for several statins, suggesting that hepatic uptake is the rate‐determining process for the clearance of these drugs . Similar conclusions were also reported by others . Camenish and Umehara used suspended hepatocytes, liver microsomes, and sandwich‐cultured hepatocytes to estimate the intrinsic sinusoidal uptake and efflux, metabolism, and biliary secretory clearances and showed a good in vitro–in vivo extrapolation of human clearance for 13 selected compounds .…”

Section: Transporter‐mediated Disposition: Pharmacokinetics Predictionssupporting

confidence: 54%

Transporter‐Enzyme Interplay: Deconvoluting Effects of Hepatic Transporters and Enzymes on Drug Disposition Using Static and Dynamic Mechanistic Models

Varma

El‐Kattan

2016

The Journal of Clinical Pharma

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A large body of evidence suggests hepatic uptake transporters, organic anion-transporting polypeptides (OATPs), are of high clinical relevance in determining the pharmacokinetics of substrate drugs, based on which recent regulatory guidances to industry recommend appropriate assessment of investigational drugs for the potential drug interactions. We recently proposed an extended clearance classification system (ECCS) framework in which the systemic clearance of class 1B and 3B drugs is likely determined by hepatic uptake. The ECCS framework therefore predicts the possibility of drug-drug interactions (DDIs) involving OATPs and the effects of genetic variants of SLCO1B1 early in the discovery and facilitates decision making in the candidate selection and progression. Although OATP-mediated uptake is often the rate-determining process in the hepatic clearance of substrate drugs, metabolic and/or biliary components also contribute to the overall hepatic disposition and, more importantly, to liver exposure. Clinical evidence suggests that alteration in biliary efflux transport or metabolic enzymes associated with genetic polymorphism leads to change in the pharmacodynamic response of statins, for which the pharmacological target resides in the liver. Perpetrator drugs may show inhibitory and/or induction effects on transporters and enzymes simultaneously. It is therefore important to adopt models that frame these multiple processes in a mechanistic sense for quantitative DDI predictions and to deconvolute the effects of individual processes on the plasma and hepatic exposure. In vitro data-informed mechanistic static and physiologically based pharmacokinetic models are proven useful in rationalizing and predicting transporter-mediated DDIs and the complex DDIs involving transporter-enzyme interplay. Keywordscytochrome P-450, drug transporters, drug-drug interactions, extended clearance classification system, organic anion-transporting polypeptides, physiologically based pharmacokinetic model, transporter-enzyme interplay Clearance is one of the most critical determinants of drug exposure in the systemic circulation and consequently at the pharmacological target compartment and, as a result, dictates the therapeutic dose.1,2 A considerable amount of attention is made in the early drug discovery in identifying the predominant clearance mechanism, which is the cornerstone for (1) adoption of a reliable mechanism-based approach for clearance and consequently for dose predictions and (2) early assessment of the clinical risks associated with drugdrug interactions (DDIs) and genetic variants of drug transporters and/or metabolizing enzymes. 3,4Drug clearance is often complex and entails more than one contributing process. In the liver, active and/or passive drug transport across the sinusoidal membrane governs the drug availability for subsequent biotransformation by the drug-metabolizing enzymes or efflux across canalicular membrane into bile.5 In a mathematical sense, the hepatic intrinsic clearance (CL int,H ) ...

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“…The major difference between this dataset and what is reported by Lombardo et al [89] is the addition of hepatic uptake as a rate-determining process of clearance [90]. Hepatic uptake was assigned as the primary clearance mechanism through the direct availability of transporter data, or from clinical drugdrug interactions and pharmacogenomic data [7,27,[90][91][92].…”

Section: Renalmentioning

confidence: 97%

“…3b). This predominant clearance mechanism increases the reliance of Class 1B compounds on human in vitro systems such as suspension hepatocytes and sandwich culture human hepatocyte (SCHH) for predicting active hepatic uptake mediated clearance [27,49,91,[160][161][162][163][164]. In vitro tools aligned with predicting the metabolic components of drug elimination, such as human liver microsomes, will underestimate systemic clearance, although they can be critical for modelling liver concentrations [91,165].…”

Section: Class 1bmentioning

confidence: 99%

Predicting Clearance Mechanism in Drug Discovery: Extended Clearance Classification System (ECCS)

et al. 2015

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Early prediction of clearance mechanisms allows for the rapid progression of drug discovery and development programs, and facilitates risk assessment of the pharmacokinetic variability associated with drug interactions and pharmacogenomics. Here we propose a scientific framework--Extended Clearance Classification System (ECCS)--which can be used to predict the predominant clearance mechanism (rate-determining process) based on physicochemical properties and passive membrane permeability. Compounds are classified as: Class 1A--metabolism as primary systemic clearance mechanism (high permeability acids/zwitterions with molecular weight (MW) ≤400 Da), Class 1B--transporter-mediated hepatic uptake as primary systemic clearance mechanism (high permeability acids/zwitterions with MW >400 Da), Class 2--metabolism as primary clearance mechanism (high permeability bases/neutrals), Class 3A--renal clearance (low permeability acids/zwitterions with MW ≤400 Da), Class 3B--transporter mediated hepatic uptake or renal clearance (low permeability acids/zwitterions with MW >400 Da), and Class 4--renal clearance (low permeability bases/neutrals). The performance of the ECCS framework was validated using 307 compounds with single clearance mechanism contributing to ≥70% of systemic clearance. The apparent permeability across clonal cell line of Madin - Darby canine kidney cells, selected for low endogenous efflux transporter expression, with a cut-off of 5 × 10(-6) cm/s was used for permeability classification, and the ionization (at pH7) was assigned based on calculated pKa. The proposed scheme correctly predicted the rate-determining clearance mechanism to be either metabolism, hepatic uptake or renal for ~92% of total compounds. We discuss the general characteristics of each ECCS class, as well as compare and contrast the framework with the biopharmaceutics classification system (BCS) and the biopharmaceutics drug disposition classification system (BDDCS). Collectively, the ECCS framework is valuable in early prediction of clearance mechanism and can aid in choosing the right preclinical tool kit and strategy for optimizing drug exposure and evaluating clinical risk of pharmacokinetic variability caused by drug interactions and pharmacogenomics.

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Quantitative Prediction of Transporter- and Enzyme-Mediated Clinical Drug-Drug Interactions of Organic Anion-Transporting Polypeptide 1B1 Substrates Using a Mechanistic Net-Effect Model

Cited by 60 publications

References 50 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

Transporter‐Enzyme Interplay: Deconvoluting Effects of Hepatic Transporters and Enzymes on Drug Disposition Using Static and Dynamic Mechanistic Models

Predicting Clearance Mechanism in Drug Discovery: Extended Clearance Classification System (ECCS)

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