Impact of drug transporter pharmacogenomics on pharmacokinetic and pharmacodynamic variability – considerations for drug development

Lai, Yurong; Varma, Manthena V.; Feng, Bo; Stephens, Joel Clay; Kimoto, Emi; El‐Kattan, Ayman; Ichikawa, Keita; Kikkawa, Hironori; Ono, Chiho; Suzuki, Atsuko; Suzuki, Motoharu; Yamamoto, Yuichi; Tremaine, Larry M.

doi:10.1517/17425255.2012.678048

Cited by 51 publications

(27 citation statements)

References 122 publications

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“…For example, atorvastatin has a high extent of metabolism (>90% of parent eliminated as metabolites), however, active uptake mediated by organic anion transporting polypeptide (OATP) transporters is the rate-determining step of its clearance [12]. Similar evidences were reported with bosentan, cerivastatin, fluvastatin, and repaglinide [5][6][7][8][9][10][11]. In addition, compounds such as valsartan and rosuvastatin are predominantly excreted unchanged in bile while active uptake mediated by OATP transporters is the rate-determining step of their clearance.…”

Section: Introductionmentioning

confidence: 67%

“…It should be emphasized that a drug's predominant elimination process such as metabolism, biliary or renal excretion may not be always its ratedetermining step in systemic clearance -the latter being the determinant of systemic drug exposure [5][6][7][8][9][10][11]. For example, atorvastatin has a high extent of metabolism (>90% of parent eliminated as metabolites), however, active uptake mediated by organic anion transporting polypeptide (OATP) transporters is the rate-determining step of its clearance [12].…”

Section: Introductionmentioning

confidence: 99%

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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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Section: Introductionmentioning

confidence: 67%

Section: Introductionmentioning

confidence: 99%

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

et al. 2015

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“…A recent report showed significant loss in transporter expression and activity in human cryopreserved hepatocytes compared with fresh hepatocytes and may contribute to the in vitro-in vivo disconnect (Lundquist et al, 2014). Genetic polymorphism in SLCO1B1, particularly homozygous OATP1B1*15 variant, was reported to possess reduced transport activity (Nishizato et al, 2003;Niemi et al, 2011;Lai et al, 2012;Tomita et al, 2013), and the hepatocytes with such variant may underpredict in vivo clearance. Further investigation in these areas is warranted to understand the lower functional activity in the in vitro systems (Barton et al, 2013;Zamek-Gliszczynski et al, 2013;Li et al, 2014;Lundquist et al, 2014).…”

Section: Discussionmentioning

confidence: 99%

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

Kimoto

et al. 2014

J Pharmacol Exp Ther

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Quantitative prediction of complex drug-drug interactions (DDIs) involving hepatic transporters and cytochromes P450 (P450s) is challenging. We evaluated the extent of DDIs of nine victim drugs-which are substrates to organic anion-transporting polypeptide 1B1 and undergo P450 metabolism or biliary elimination-caused by five perpetrator drugs, using in vitro data and the proposed extended net-effect model. Hepatobiliary transport and metabolic clearance estimates were obtained from in vitro studies. Of the total of 62 clinical interaction combinations assessed using the net-effect model, 58 (94%) could be predicted within a 2-fold error, with few false-negative predictions. Model predictive performance improved significantly when in vitro active uptake clearance was corrected to recover in vivo clearance. The basic R-value model yielded only 63% predictions within 2-fold error. This study demonstrates that the interactions involving transporter-enzyme interplay need to be mechanistically assessed for quantitative rationalization and prospective prediction.

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“…1980 Di et al through transporter genetic polymorphism and drug interaction studies, where significant changes in the pharmacokinetics of some drugs and consequent clinical responses are observed (Niemi et al, 2005;Giacomini et al, 2010;Niemi et al, 2011;Elsby et al, 2012;Lai et al, 2012). A number of studies suggested that hepatic uptake could be a rate-determining step in hepatic clearance, not only for the compounds that are metabolically stable, such as rosuvastatin (Bergman et al, 2006) and pravastatin, (Yamazaki et al, 1996;Watanabe et al, 2009a;Varma et al, 2012c), but also for compounds that are extensively metabolized, e.g., repaglinide (Kalliokoski et al, 2008;Varma et al, 2013b) and atorvastatin .…”

Section: Predicting Clearancementioning

confidence: 99%

A Perspective on the Prediction of Drug Pharmacokinetics and Disposition in Drug Research and Development

Feng

Goosen

et al. 2013

Drug Metab Dispos

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Prediction of human pharmacokinetics of new drugs, as well as other disposition attributes, has become a routine practice in drug research and development. Prior to the 1990s, drug disposition science was used in a mostly descriptive manner in the drug development phase. With the advent of in vitro methods and availability of human-derived reagents for in vitro studies, drugdisposition scientists became engaged in the compound design phase of drug discovery to optimize and predict human disposition properties prior to nomination of candidate compounds into the drug development phase. This has reaped benefits in that the attrition rate of new drug candidates in drug development for reasons of unacceptable pharmacokinetics has greatly decreased. Attributes that are predicted include clearance, volume of distribution, halflife, absorption, and drug-drug interactions. In this article, we offer our experience-based perspectives on the tools and methods of predicting human drug disposition using in vitro and animal data.

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Impact of drug transporter pharmacogenomics on pharmacokinetic and pharmacodynamic variability – considerations for drug development

Cited by 51 publications

References 122 publications

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

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

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

A Perspective on the Prediction of Drug Pharmacokinetics and Disposition in Drug Research and Development

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