Manthena V. Varma, PhD 1 and Ayman F. El-Kattan, PhD 2

Transcription

1 Supplement Article Transporter-Enzyme Interplay: Deconvoluting Effects of Hepatic Transporters and Enzymes on Drug Disposition Using Static and Dynamic Mechanistic Models The Journal of Clinical Pharmacology (2016), 56(S7) S99 S109 C 2016, The American College of Clinical Pharmacology DOI: /jcph.695 Manthena V. Varma, PhD 1 and Ayman F. El-Kattan, PhD 2 Abstract 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. Keywords cytochrome 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. 3,4 Drug 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 )isa function of transporter-enzyme interplay (equation 1) involving several processes such as hepatic intrinsic active uptake clearance (PS active ), passive transport clearance (PS passive ), active basolateral efflux clearance (PS efflux ), and intrinsic biliary secretory (CL bile )and metabolic clearance (CL met ). 6 9 CL int,h = ( PS active + PS passive ) (CL met + CL bile ) ( PSpassive + PS efflux + CL met + CL bile ) (1) Under a limiting condition where PS passive + PS efflux is smaller than CL met + CL bile, hepatic intrinsic clearance is determined primarily by the hepatic uptake (PS active + PS passive ). On the other hand, when 1 Pharmacokinetics, Dynamics and Metabolism, Worldwide Research and Development, Pfizer Inc, Groton, CT, USA 2 Pharmacokinetics, Dynamics and Metabolism, Worldwide Research and Development, Pfizer Inc, Cambridge, MA, USA Submitted for publication 9 November 2015; accepted 14 December Corresponding Author: Manthena V. Varma, MS, PhD, Pharmacokinetics, Dynamics and Metabolism, Pfizer Global Research & Development, Groton, CT manthena.v.varma@pfizer.com

2 S100 The Journal of Clinical Pharmacology / Vol 56 No S7 (2016) PS passive + PS efflux is appreciable in relation to CL met +CL bile, all the individual processes (equation 1) are required to estimate the overall hepatic clearance. Therefore, it should be emphasized that irrespective of their passive permeability (high or low), uptake transport clearance determines the hepatic clearance of uptake transporter substrates, partially or completely. Drug transporters in the liver include members of the solute carrier family (SLCs), which primarily mediate influx or bidirectional transport of substrate drugs, and the members of the ATP-binding cassette family (ABCs), which functionally efflux drugs out of the hepatocytes into blood or bile compartments Biotransformation of drugs to metabolites occurs primarily by phase I and phase II metabolism, with the liver possessing a high abundance of the enzymes. Clinical evidence thus far suggests that the liver-specific SLC transporters, organic anion-transporting polypeptide (OATP)1B1 and OATP1B3 play a prominent role in the hepatic disposition and thus pharmacokinetics of several drugs, including statins, sartans, and certain glinides Due to the clinical relevance of safety and possibly of efficacy, recent EMA and USFDA regulatory guidances recommend appropriate assessment of investigational drugs for potential drug interactions involving OATP1B1 and OATP1B3 (as victims and/or perpetrators). 10,12,15 18 Practices are emerging or evolving in the drug discovery and development settings to assess OATP substrate and inhibition activity for new molecular entities (NMEs) in order to facilitate prediction/evaluation of pharmacokinetics and DDI liability. Although hepatic uptake could be the ratedetermining process in the systemic clearance of several OATP transporter substrates, enzymatic metabolism and/or biliary efflux may also contribute to the systemic clearance and elimination from the body. For instance, atorvastatin is largely metabolized by the CYP3A4, and repaglinide and cerivastatin are metabolized by CYP2C8 and CYP3A4. The transporter-enzyme interplay may determine the disposition of such dual substrates, and these multiple processes should be simultaneously considered using mechanistic models for the pharmacokinetic and DDI predictions. It is therefore important to adopt extended clearance concepts (equation 1) that frame these multiple processes in a mechanistic sense for clearance predictions and in deconvoluting the individual components of clearance (transporter-enzyme interplay) and thus build confidence in the absorption, distribution, metabolism, and elimination (ADME) attributes of NMEs. In this review theoretical concepts behind transporter-enzyme interplay in the hepatic disposition of drugs are reviewed, and the utility of integrated mechanistic models for predicting clinical pharmacokinetics and DDIs from in vitro data is presented. Further, the state of the mechanistic understanding of the role of transporters and enzymes in regulating the intracellular free target organ (liver) exposure is discussed. Extended Clearance Classification System: Predicting the Role of Hepatic Transporters in Systemic Clearance A broad range of extensive studies using preclinical species and in vitro human reagents are usually required to define the predominant clearance mechanism, which often limits the efficiency in the drug discovery settings. For instance, definitive uptake studies using primary suspension or cultured hepatocyte systems are needed to determine if hepatic uptake is a predominant clearance pathway. To provide early prediction, we recently proposed an extended clearance classification system (ECCS) a framework that can be applied for the prediction of the predominant ratedetermining clearance mechanism based on simple compound physicochemical and in vitro properties, ionization, molecular weight, and permeability (Figure 1). 5 The major mechanisms that determine the systemic clearance of drug molecules include hepatic metabolism, transporter-mediated hepatic uptake, and renal clearance. According to ECCS, compounds are classified as class 1A high-permeability, lowmolecular-weight (MW 400 Da) acids/zwitterions, for which metabolism is likely the primary systemic clearance mechanism; class 1B high-permeability, high-mw (>400 Da) acids/zwitterions, for which transporter-mediated hepatic uptake is the primary systemic clearance mechanism; class 2 highpermeability bases/neutrals, for which metabolism is the primary clearance mechanism; class 3A lowpermeability, low-mw ( 400 Da) acids/zwitterions subjected to renal clearance; class 3B lowpermeability, high-mw (>400 Da) acids/zwitterions with transporter-mediated hepatic uptake clearance; and class 4 low-permeability bases/neutrals with renal clearance likely the predominant clearance mechanism. Validation of this framework with more than 300 drugs for which the predominant clearance mechanism was identified using clinical data yielded a predictive success of >90%. 5 In general, systemic clearance of class 1B and 3B compounds (acids and zwitterions with MW > 400 Da) is primarily determined by the hepatic uptake mediated by OATP transporters. However, once cleared from the blood compartment to the liver compartment, class 1B compounds are metabolized and excreted in bile and/or urine as phase I and/or phase II metabolites, whereas class 3B OATP substrates are eliminated

3 Varma and El-Kattan S101 MW 400 MW >400 Class 1 Metabolism Class 2 High Permeable Class 1A Metabolism Class 1B Hepatic uptake MW 400 MW >400 Class 3 Class 4 Renal Clearance Low Permeable Class 3A Renal clearance Class 3B Hepatic uptake (or) Renal Acids/Zwitterions Bases/Neutrals Figure 1. Extended clearance classification system (ECCS) for predicting the clearance mechanism (rate-determining process). 5 Hepatic uptake mediated by OATPs is likely the rate-determining step in the clearance of class 1B and 3B compounds. unchanged in the bile. Various clinical DDIs, such as those with class 1B and 3B drugs (eg, statins) and singledose rifampicin or cyclosporine have been attributed to the inhibition of hepatic transport mediated by OATP1B1 and possibly other isoforms, OATP1B3 and OATP2B Polymorphisms in SLCO1B1 (encoding OATP1B1) have been demonstrated to have altered transporter activity leading to significant changes in systemic exposure for class 1B and 3B compounds. For all the other ECCS classes, hepatic transporters are less likely to contribute to the systemic clearance. Overall, the ECCS framework is effective in predicting the potential role of hepatic transporters (class 1B and 3B) and the scope of transporter-enzyme interplay in the systemic clearance and elimination of new molecular entities. This approach facilitates proactive prioritization of preclinical ADME characterization to enable medicinal chemistry design efforts toward clearance optimization. Additionally, ECCS can enable prioritization of DDI and pharmacogenetic studies during drug development. Transporter-Mediated Disposition: Pharmacokinetics Predictions Transport kinetic parameters needed for the mechanistic prediction of the hepatic clearance (equation 1) are often derived using primary or cultured hepatocytes Previous studies have demonstrated that in vitro metabolic clearance alone underpredicts in vivo clearance of several OATP substrate drugs; however, the in vitro in vivo correlation is improved when one considers hepatic uptake clearance 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 ratedetermining process for the clearance of these drugs. 26 Similar conclusions were also reported by others. 28,30 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. 27 On the other hand, mechanistic approaches were proposed wherein the in vitro transport data have been integrated either into semimechanistic or whole-body PBPK models to simulate in vivo pharmacokinetics for OATP substrates in rats and humans A majority of these studies suggested a systematic underprediction of in vivo uptake clearance and therefore proposed the need for empirical scaling factors (SFs) for the uptake and efflux transport to accurately recapitulate

4 S102 The Journal of Clinical Pharmacology / Vol 56 No S7 (2016) human pharmacokinetics. 29,30,32 34 In some sense, the approach of empirical correction is similar to that proposed earlier for recovering in vivo hepatic clearance determined by metabolism (typical ECCS class 1A and 2 drugs), using in vitro metabolic stability assays based on human liver microsomes and hepatocytes. 35 To increase the confidence in the prospective application of SFs obtained by fitting observed human pharmacokinetics, Jones et al estimated SFs for 7 compounds individually and proposed to use the geometric mean value of these SFs for the novel compounds. 29 Further, to manage the parameter identifiability concern, which possibly occurred due to fitting individual plasma profiles, Li et al applied a nonnumerical global optimization method (ie, brute-force grid search method) that simultaneously leverages data for 7 compounds in searching for a unique set of SFs. 36 The globally fitted SFs have an overall better fitting on the pharmacokinetics of 7 compounds, compared with the geometric mean of individually fitted SFs, and further allowed construction of confidence intervals for globally optimized SFs as well as prediction intervals for the predicted plasma concentration-time courses. 36 However, because the fraction of each transporter s contribution to the total active uptake may be different for each compound, the underlying assumption that all transporters share a similar scaling factor may add to the uncertainty in applying the refined model for projecting pharmacokinetics of NMEs. In order to estimate transporter-specific scaling factors, the fractional contribution that each transporter made to the total active uptake needs to be better understood. 37 Alternatively, identifying selective substrates with clinical pharmacokinetic data could facilitate determination of transporter-specific SFs. Nevertheless, this PBPK platform was successfully applied to evaluate the tissue exposure and pharmacogenetic effects on the plasma pharmacokinetics of OATPs substrates. 38,39 It should be noted that the empirical SFs are influenced by the implementation of in vitro methodologies to estimate transporter kinetics, and thus, the derived SFs could vary from laboratory to laboratory. A potential reason for the need for SFs is differing transporter abundances between in vitro and in vivo systems, which may be accounted for by using the concept of relative expression factor (REF, ie, ratio between in vivo and in vitro protein expression). 37 After accounting for the experimentally determined abundance differences in their HEK-based transfected cells and the human hepatocytes or liver samples, Bosgra et al showed a reasonable prediction of the rosuvastatin plasma concentration-time profile using a PBPK model. 40 Although the REF methodology was shown to recover rosuvastatin pharmacokinetics, only minimal differences in the protein expression noted between the in vitro systems (eg, hepatocytes) and liver tissue (based on the LC-MS based quantitative proteomics of OATPs) imply that REFs cannot completely explain the in vitro in vivo disconnect in the hepatic uptake for OATP substrate drugs. The middleout PBPK models built and refined using in vitro and clinical pharmacokinetic data of a set of OATP substrates have proven useful for pharmacokinetic predictions in drug discovery and in assessing DDIs, and additional studies are warranted in this space to build further confidence in the prospective predictions. Transporter-Enzyme Interplay: Quantitative Predictions of DDIs As discussed, the overall hepatic intrinsic clearance involving transporter-enzyme interplay is defined by the extended clearance concept (equation 1). Therefore, the magnitude of change in the clearance of the victim drug when dosed with a perpetrator or a combination of perpetrator drugs can be mechanistically described by an extended net effect model. 44 In the presence of a perpetrator, the expected net effect of reversible inhibition of uptake or biliary efflux and reversible inhibition, time-dependent inhibition, and induction of CYPs on the intrinsic hepatic clearance can be estimated by equation 2: ( ) CL PS active int,h = + PS passive R OATP ( CL CYP + CL ) bile R CYP.TDI CYP.IND CYP. ( PS passive + R efflux CL CYP R CYP.TDI CYP.IND CYP + CL bile R efflux where R OATP and R efflux are the competitive inhibition terms against OATP-mediated uptake and ABC-mediated biliary efflux, respectively. R CYP is the competitive inhibition term, TDI CYP is the timedependent inhibition term, and IND CYP is the induction term against enzyme-based metabolism. 30,45,46 Figure 2 illustrates the influence of transport and metabolism components on the magnitude of DDIs of victim drug (AUC ratio). It should be emphasized that an OATP substrate drug with high active and low metabolic/biliary intrinsic clearance with reference to passive uptake will show higher DDIs. However, a substrate drug with a low active and high metabolic and biliary components will show lower change in plasma exposure, even when the coadministered perpetrator drug is a potent inhibitor of both active and metabolic/biliary processes (eg, R-value of 10) (Figure 2). Although comprehensive experimental data ) (2)

5 Varma and El-Kattan S103 When, R OATP = 1+I/KI OATP = 10 R CYP = 1+I/KI CYP = 10 At a fixed passive (as a function of activeto-passive ratio and metabolic-to-passive ratio) Low active uptake and high metabolism Low/No DDI risk Low active uptake and low metabolism DDI risk on inhibition of metabolism High active uptake and high metabolism DDI risk on inhibition of active uptake High active uptake and low metabolism High DDI risk Figure 2. Surface plot of the effect of 90% inhibition of both active uptake and metabolic/biliary clearance of victim drugs that are substrates to hepatic uptake transporter (eg, OATP1B1: atorvastatin and pravastatin) and enzyme (eg, CYP3A4: atorvastatin) or biliary efflux pump (eg, MRP2: pravastatin). Gray surface represents R-value (= 10, assuming 90% inhibition of uptake or biliary/metabolic clearance), and light gray surface represents R-value product (= 100,assuming 90% inhibition of both uptake and biliary/metabolic clearance).the magnitude of AUC change depends on the intrinsic victim characteristics along with the perpetrator interaction potential, as described by equation 2. Predicted AUC ratios were based on extended-clearance term assuming liver as the only clearing organ. PS passive was kept constant at 10 ml/(min kg). Based on the mechanistic predictions (extended neteffect model), it should be noted that the DDI is highest for victim drugs with CL met+bile < PS passive and PS active > PS passive (depicted as hypothetical drug 4). on the individual intrinsic clearances are required for applying the extended net effect model, they integrate the mechanistic aspects of transporter-enzyme interplay and are expected to produce quantitative predictions. When the perpetrator drug does not inhibit or induce metabolic or biliary pathways of the victim drug, the change in its hepatic clearance can be simplified to equation 3. CL int,h CL int,h ( PSactive + PS passive ) = ( );where, PS active + PS passive R OATP ( R OATP = 1 + [I ) u,max,in] Ki OATP (3) In addition to this, if PS active >> PS passive,the change in hepatic clearance, which signifies the AUC ratio, will be further simplified to R OATP (also referred as R-value). Here, I u,max,in is the relevant inhibitor unbound concentration at the inlet to the liver or portal vein concentration (equation 3), and K i is the unbound inhibition constant determined in vitro. 10,16,22,47,48 Generally, static models have the advantage of being simple and more transparent and can be valuable in quantitative predictions of DDI scenarios in drug discovery and development. The applicability of the extended net effect model in the prediction of complex DDIs associated with multiple enzyme- and transportermediated processes was presented earlier. 30,44,49 Hepatic transport kinetics and enzymatic (CYPs) stability data of the victim drug obtained from in vitro systems such as sandwich culture human hepatocytes (SCHH) or suspension hepatocytes and human liver microsomes can be used as inputs along with a validated scaling factor for active uptake for prospective predictions of transporter-mediated and complex DDI situations. 30,44,49 PBPK modeling approaches have emerged for a wide array of applications in the drug industry, mainly due to the significant advantages this platform provides compared to more traditional mathematical approaches Indeed, PBPK models provide a mechanistic framework to assess DDI risk during all stages of drug discovery and development. However, there is a level of uncertainty around the human pharmacokinetics and efficacious drug concentration in man prior to first-in-human (FIH) studies Furthermore, in complex situations such as when transporter-enzyme interplay is involved in drug disposition and DDIs, models need to be calibrated with both in vitro parameters ( bottom-up ) and observed in vivo PK or clinical DDI results ( top-down ). 51,52,56 59 Therefore, mechanistic static models are of utility at this stage. Applications of PBPK models for DDIs are generally fully realized once human plasma concentration-time profile data become available to verify or refine the bottomup models, which could be utilized to inform decisions on the inclusion and exclusion of concomitant medications during phase 2 studies and beyond. The application and utility of PBPK models for DDIs involving hepatic transporters and transporterenzyme interplay are now well documented in the literature. 34,49,56,58,60,61 Furthermore, PBPK models can

6 S104 The Journal of Clinical Pharmacology / Vol 56 No S7 (2016) Figure 3. A proposed strategy for model-based predictions of transporter and complex DDIs associated with transporter-enzyme interplay. (a) Different cutoff may be considered for the significance of R-value based on the therapeutic index of the victim drug. (b) Cutoff values for CYP interactions are given as suggested in the US FDA draft guidelines. 16 (c) For complete mathematical expressions of the extended net-effect model refer to Varma et al. 30 employ system parameters (eg, human demographics and genetics, tissue volumes and blood flows, enzyme and transporter expression levels) and drugdependent parameters (eg, tissue partition coefficients, metabolism, or transport rates), which allow prediction of outcomes across population extremes or disease variability beyond the prediction for an average individual. A strategy for model-based predictions of transporter and complex DDIs associated with transporterenzyme interplay has been put forward to facilitate decision making and address specific questions in various stages of NME progression (Figure 3). For evaluating DDI risk, an early assessment of hepatic transporter- or enzyme-mediated DDIs can be achieved with static basic models (R-value) incorporating in vitro and in vivo pharmacokinetic parameters of the perpetrator drug alone. The R-value generally provides an oversimplification of the transporter-mediated DDI risk, assuming that hepatic active transport (uptake and biliary efflux) is responsible individually for 100% of systemic clearance of the victim drug (equation 3). 10,16 A more conservative estimate (R-value product) with theoretical maximum inhibition of hepatic clearance (inhibition of both uptake and metabolism or biliary efflux simultaneously) was also suggested to minimize false-negative predictions. 10,22 Because of its conservative nature, a positive indication of DDI using these basic models would require further assessment using a static extended net effect model or dynamic PBPK models for quantitative prediction. Herein, the predictions are based on transporter-enzyme interplay and are for a given victim-perpetrator combination. In assessing the victim DDI liability of ECCS class 3B NMEs, reversible inhibition of hepatic uptake and/or biliary transporters should be considered simultaneously if the basic models show positive signal against individual mechanisms. Similarly, for ECCS class 1B NMEs, reversible inhibition of uptake transporters and/or enzyme inhibition and induction should be assessed simultaneously.

7 Varma and El-Kattan S105 It should be emphasized that situations arise where interactions at uptake and metabolism or biliary levels are small individually, but, when integrated together using mechanistic models, could be significantly higher (Figure 2). Therefore, basic model (eg, R-value) predictions are not necessarily conservative estimates of DDI risk and could result in false-negative predictions when multiple mechanisms are involved. Overall, when hepatic transporters are involved in the disposition of NMEs, mechanistic models integrating transporterenzyme interplay should be implemented (Figure 3). Transporter-Enzyme Interplay: Clinical Relevance of Unbound Concentration in Liver For several substrate drugs (eg, statins), free liver concentrations/exposures are key determinants of efficacy due to localization of pharmacological targets in the hepatocytes PK/PD relationships are typically established assuming plasma concentration and the intracellular concentration at target site are in equilibrium and identical, and therefore, a change in systemic pharmacokinetics is believed to translate to an equivalent effect on the target-site concentrations. However, substrates of hepatic active transport (eg, OATPs) are highly concentrated in the liver, and inhibition of hepatic uptake or metabolism and efflux will have differential effects on plasma and liver concentrations. 7,66 Free liver-to-plasma ratio, also referred as Kp uu,canbe described mathematically as in equation 4: Kp uu = PS uptake + PS passive PS passive + PS efflux + CL met+bile (4) However, the exposure of substrate drug in the liver (area under liver concentration-time profile, AUC h )is a function of metabolic and/or biliary intrinsic clearances, dose, and unbound faction in liver (f h ), when liver is the only eliminating organ (equation 5). 7 AUC h f h = Dose CL met+bile (5) Based on these equations, it can be indicated that the change in active uptake clearance due to drug interactions or polymorphic variants (1) does not alter the overall hepatic exposure (AUC h ), particularly when the nonhepatic clearance is negligible, and (2) possibly alters free hepatic concentration of substrate at a given time, ie, altered liver concentration-time profile shape and Kp uu. On the other hand, altered hepatic metabolic or biliary intrinsic clearance shows relatively less effect on the systemic exposure, whereas it significantly influences the hepatic exposure (Table 1). Although the direct clinical evidence on the potential changes in hepatic exposure is limited, conclusions may be drawn based on PBPK modeling and/or clinical pharmacodynamic readouts. When PBPK model-based sensitivity analyses have been used to assess the influence of transport and metabolic/biliary intrinsic clearances on the systemic and hepatic exposure, lack of change in AUC h was suggested with altered active uptake clearance for several OATP substrates, including pravastatin, 33 repaglinide, 58 rosuvastatin, 67 and glyburide. 60 Rose et al applied PBPK/PD model to assess the impact of genotype-dependent uptake by OATP1B1 on the pharmacodynamic response of rosuvastatin and indicated that reduced OATP1B1 activity translated to relatively large increases in plasma rosuvastatin concentration and a small change in liver exposure, as a result explaining the lack of significant impact on pharmacodynamic response in the clinical studies. 67 However, if the renal clearance is significant, the AUC h is relatively more affected, and plasma AUC is less affected, by the changes in hepatic uptake clearance. 7 This is exemplified by metformin, which is cleared mainly renally, although it is taken up by OCT1 into liver, where it manifests pharmacodynamic responses. 68,69 Polymorphism in SLC22A1 (gene encoding OCT1) showed minimal change in plasma exposure of metformin; however, the pharmacodynamic response (blood glucose and hemoglobin A1c change) is potentiated The SEARCH genome-wide association study found a significant association between c.521t>c single-nucleotide polymorphism in the SLCO1B1 gene and simvastatin-induced myopathy in individuals taking higher simvastatin doses linking higher systemic exposure of simvastatin acid in genetic variants to higher incidence of toxicity. 73 Interestingly, all the pharmacogenomic studies of statins showed lack of impact on the pharmacodynamic responses, although the plasma exposure is significantly increased in the population carrying a reduced-activity mutant of SLCO1B1 (Table 1). However, genetic variants of metabolizing enzymes (CYP3A) and biliary efflux transporters (ABCB1 and ABCG2) resulted in significant change in the pharmacodynamic effects of several statins. These clinical findings may suggest that the pharmacological response of statins (HMG-CoA reductase inhibition) is mainly associated with liver exposure (AUC h ) rather than the liver concentrationtime profile or Kp uu. Interestingly, simulations suggested minimal change in the concentration-time profiles in the liver along with minimal change in AUC h for pravastatin and rosuvastatin. 33,67 The possibility of increased systemic and thus hepatic exposure of statins due to reduced intestinal efflux and gut metabolism can not be ruled out. 9,74 Further understanding in these areas is warranted, which may be facilitated by the PBPK models integrated with pharmacodynamic

8 S106 The Journal of Clinical Pharmacology / Vol 56 No S7 (2016) Table 1. Differential Effects of Genotypic Variation in the Uptake Transporter (SLCO1B1) and Biliary Efflux Transporters or Metabolizing Enzymes on the Pharmacokinetics and Liver-Specific Pharmacodynamics of Statins Statin Gene Genotype Reference a Genotype Variant Change in Plasma AUC (%) Significant Change in Pharmacodynamic Effect(s) Genetic Variants of Hepatic Uptake Transporter (OATP1B1) Atorvastatin SLCO1B1 521TT 521TC No TT 521CC No 76 *1A/*1A *15/*15 - No 76 Pravastatin SLCO1B1 521TT 521TC No TT 521CC No 80 *1A/*1A *15/* No 80 Rosuvastatin SLCO1B1 521TT 521TC, 521CC - No TT 521CC No 83 *1A/*1A *15/* AA 388GG No 83 Simvastatin acid SLCO1B1 521TT 521CC No 76 *1A/*1A *15/*15 - No 76 Genetic Variants of Hepatic Efflux Transporters (BCRP and P-gp) and Metabolizing Enzymes Atorvastatin ABCG2 421CC 421AA CC 421CA No 86 ABCB1 3435CC 3435TT - Yes 87 CYP3A4 *1/*1 *1G/*1G 36,88 Yes AA 392GG - Yes 90 Pravastatin ABCG2 421CC 421AA No change CC 421CA No change 91 - Rosuvastatin ABCG2 421CC 421AA 217, ,92 Yes 83,93 ABCB1 2677GA, 2677GG, 2677TT GT Simvastatin acid ABCG2 421CC 421CA, 421AA No change 91 No 81 a Genotype reference: CYP3A4*1/*1, CYP3A5*3/*3, SLCO1B1 521TT; and variant genotype: CYP3A4*1G/*1G, CYP3A5*3/*3, SLCO1B1 521TT. models. More importantly, clinical risk-to-benefit assessment must be weighed in the dose adjustments during comedication. For instance, dose adjustments based on plasma exposure during comedication may avoid systemic adverse events (such as myopathy and rhabdomyolysis with statins), but they also reduce overall hepatic exposure (AUC h ) and could lead to lack of clinical efficacy due to reduced target-site exposure. Conclusion Drug disposition via liver involves several processes including influx transport into hepatocytes, basolateral efflux into blood circulation, hepatocytic metabolism, and canalicular efflux into bile. One or multiples of these processes determine the systemic clearance of a drug, as defined by the extended clearance concept. The understanding of transporter-enzyme interplay is thus important in defining the rate-determining step in the overall hepatic clearance and ultimately in assessing the variability in systemic pharmacokinetics and local tissue concentration associated with DDIs and pharmacogenomics. ECCS provides an early indication of the rate-determining step to the systemic clearance and can greatly facilitate clearance optimization and DDI predictions. Further, through the use of in vitro tools and translational models it is possible to quantitatively predict and rationalize the clinical DDI studies for drug candidates in development. Additionally, mechanistic models verified with early clinical data can be helpful in assessing DDIs associated with polymorphic clearance pathways or in understanding drug disposition and DDIs in disease states or in special populations. Conflict of Interest The authors have no conflicts of interest to declare. References 1. Obach RS, Baxter JG, Liston TE, et al. The prediction of human pharmacokinetic parameters from preclinical and in vitro metabolism data. J Pharmacol Exp Ther. 1997;283(1): van de Waterbeemd H, Gifford E. ADMET in silico modelling: towards prediction paradise? Nat Rev Drug Discov. 2003;2(3): Wienkers LC, Heath TG. Predicting in vivo drug interactions from in vitro drug discovery data. Nat Rev Drug Discov. 2005;4(10): Bjornsson TD, Callaghan JT, Einolf HJ, et al. The conduct of in vitro and in vivo drug-drug interaction studies: a Pharmaceutical Research and Manufacturers of America (PhRMA) perspective. Drug Metab Dispos. 2003;31(7): Varma MV, Steyn SJ, Allerton C, El-Kattan AF. Predicting clearance mechanism in drug discovery: extended clearance classification system (ECCS). Pharm Res. 2015;32(12):

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