Effects of Gemfibrozil and Atorvastatin on the Pharmacokinetics of Repaglinide in Relation to SLCO1B1 Polymorphism

Mechanistic Account of Distinct Change in Organic Anion Transporting Polypeptide 1B (OATP1B) Substrate Pharmacokinetics during OATP1B-Mediated Drug-Drug Interactions Using Physiologically Based Pharmacokinetic Modeling

The role of transporters in drug clearance is widely acknowledged, directly and indirectly by facilitating tissue/enzyme exposure. Through the latter, transporters also affect volume of distribution. Drug-drug interactions (DDIs) involving organic anion transporting polypeptides (OATPs) 1B1/1B3 and SLCO1B1 pharmacogenetics lead to altered pharmacokinetics of OATP1B substrates; however, several factors may confound direct interpretation of pharmacokinetic parameters from these clinical studies using noncompartmental analysis (NCA). A review of clinical data herein indicates a single dose of OATP1B inhibitor rifampin almost never leads to increased substrate half-life but often a decrease and that most clinical OATP1B substrates are CYP3A4 substrates and/or undergo enterohepatic cycling (EHC). Using hypothetically simple OATP1B substrate physiologically based pharmacokinetic (PBPK) models, simulated effect of rifampin differed from specific OATP1B inhibition due to short rifampin half-life causing dissipation of OATP1B inhibition over time combined with CYP3A4 induction. Calculated using simulated tissue data, volume of distribution indeed decreased with OATP1B inhibition and was expectedly limited to the contribution of liver volume. However, an apparent and counterintuitive effect of rifampin on volume greater than that on clearance resulted for CYP3A4 substrates using NCA. The effect of OATP1B inhibition and rifampin on OATP1B substrate models incorporating EHC plus or minus renal clearance was distinct compared with simpler models. Using PBPK models incorporating reversible lactone metabolism for clinical OATP1B substrates atorvastatin and pitavastatin, DDIs reporting decreased half-life with rifampin were reproduced. These simulations provide an explanation for the distinct change in OATP1B substrate pharmacokinetics observed in clinical studies, including changes in volume of distribution and additional mechanisms. SIGNIFICANCE STATEMENT: Transporters are involved in drug clearance and volume of distribution, and distinct changes in OATP1B substrate pharmacokinetics are observed with OATP1B inhibitor rifampin. Using hypothetical and validated PBPK models and simulations, this study addresses the limitations of single-dose rifampin and complicated clinical OATP1B substrate disposition in evaluating the pharmacokinetic parameters of OATP1B substrates during rifampin drug-drug interactions (DDIs). These models account for change in volume of distribution and identify additional mechanisms underlying apparent pharmacokinetic changes in OATP1B DDIs.

Fluorescence-based methods for studying activity and drug-drug interactions of hepatic solute carrier and ATP binding cassette proteins involved in ADME-Tox

In humans, approximately 70% of drugs are eliminated through the liver. This process is governed by the concerted action of membrane transporters and metabolic enzymes. Transporters mediating hepatocellular uptake of drugs belong to the SLC (Solute carrier) superfamily of transporters. Drug efflux either toward the portal vein or into the bile is mainly mediated by active transporters of the ABC (ATP Binding Cassette) family. Alteration in the function and/or expression of liver transporters due to mutations, disease conditions, or co-administration of drugs or food components can result in altered pharmacokinetics. On the other hand, drugs or food components interacting with liver transporters may also interfere with liver function (e.g., bile acid homeostasis) and may even cause liver toxicity. Accordingly, certain transporters of the liver should be investigated already at an early stage of drug development. Most frequently radioactive probes are applied in these drug-transporter interaction tests. However, fluorescent probes are cost-effective and sensitive alternatives to radioligands, and are gaining wider application in drug-transporter interaction tests. In our review, we summarize our current understanding about hepatocyte ABC and SLC transporters affected by drug interactions. We provide an update of the available fluorescent and fluorogenic/activable probes applicable in in vitro or in vivo testing of these ABC and SLC transporters, including near-infrared transporter probes especially suitable for in vivo imaging. Furthermore, our review gives a comprehensive overview of the available fluorescence-based methods, not directly relying on the transport of the probe, suitable for the investigation of hepatic ABC or SLC-type drug transporters.

Development of predictive QSAR models for the substrates/inhibitors of OATP1B1 by deep neural networks

The organic anion transporting polypeptide 1B1 (OATP1B1) is an important hepatic uptake transporter. Inhibition of its normal function could lead to drug-drug interactions. In silico prediction is an effective means to identify potential OATP1B1 inhibitors and quantitative structure-activity relationship (QSAR) modeling is extensively used. As the structures of OATP1B1 substrates/inhibitors are quite diverse, machine learning based methods should be a good option for their QSAR analysis. In the present study, deep neural networks (DNNs) were employed to develop QSAR models for the substrates/inhibitors of OATP1B1 with different molecular fingerprints. Our results showed that QSAR models based on 4-hidden layer DNNs and ECFP4/FCFP4 fingerprints had the best generalization performance. The correlation coefficients (R²) of test set for ECFP4 and FCFP4 models were 0.641 and 0.653, respectively. Model application domain (AD) was calculated with Euclidean distance-based method, and AD could improve the performance of ECFP4 model but has little effect on FCFP4 model. Finally, the prediction of additional 8 compounds that not included in the data set further demonstrated that our QSAR models had a good predictive ability (averaged prediction accuracy >92%). The developed QSAR models could be used to screen large data sets and discover novel inhibitors for OATP1B1.

Factors Influencing Plasma Coproporphyrin-I Concentration as Biomarker of OATP1B Activity in Patients With Rheumatoid Arthritis

Organic anion transporting polypeptides (OATPs) 1B are drug transporters mainly expressed in the sinusoidal membrane. In previous reports, genetic factor, 3-carboxy-4-methyl-5-propyl-2-furanpropanoic acid (CMPF), which is one of the uremic toxins, inflammatory cytokines such as tumor necrosis factor-α (TNF-α) and interleukin-6 (IL-6) decreased OATP1B1 activity in vitro, but in vivo effects of these factors have not been elucidated. Plasma coproporphyrin-I (CP-I) is spotlighted as a highly accurate endogenous substrate of OATP1B. This study focused on patients with rheumatoid arthritis (RA) and evaluated the influence of several factors comprising gene polymorphisms, uremic toxins, and inflammatory cytokines on OATP1B activity using plasma CP-I concentration. Thirty-seven outpatients with RA who satisfied the selection criteria were analyzed at the time of recruitment (baseline) and at the next visit. OATP1B1*15 carriers tended to have higher CP-I concentration compared with noncarriers. Plasma CP-I correlated positively with CMPF concentration, but did not correlate with IL-6 or TNF-α concentration. Multiple logistic regression analysis by stepwise selection identified plasma CMPF concentration and OATP1B1*15 allele as significant factors independently affecting plasma CP-I concentration at baseline and at the next visit, respectively. In conclusion, the present results suggest that inflammatory cytokines do not have clinically significant effects on OATP1B activity, whereas the effects of genetic polymorphisms and uremic toxins should be considered.

Multi-factorial pharmacokinetic interactions: unraveling complexities in precision drug therapy

Introduction: Precision drug therapy requires accounting for pertinent factors in pharmacokinetic (PK) inter-individual variability (i.e., pharmacogenetics, diseases, polypharmacy, and natural product use) that can cause sub-therapeutic or adverse effects. Although each of these individual factors can alter victim drug PK, multi-factorial interactions can cause additive, synergistic, or opposing effects. Determining the magnitude and direction of these complex multi-factorial effects requires understanding the rate-limiting redundant and/or sequential PK processes for each drug.Areas covered: Perturbations in drug-metabolizing enzymes and/or transporters are integral to single- and multi-factorial PK interactions. Examples of single factor PK interactions presented include gene-drug (pharmacogenetic), disease-drug, drug-drug, and natural product-drug interactions. Examples of multi-factorial PK interactions presented include drug-gene-drug, natural product-gene-drug, gene-gene-drug, disease-natural product-drug, and disease-gene-drug interactions. Clear interpretation of multi-factorial interactions can be complicated by study design, complexity in victim drug PK, and incomplete mechanistic understanding of victim drug PK.Expert opinion: Incorporation of complex multi-factorial PK interactions into precision drug therapy requires advances in clinical decision tools, intentional PK study designs, drug-metabolizing enzyme and transporter fractional contribution determinations, systems and computational approaches (e.g., physiologically-based pharmacokinetic modeling), and PK phenotyping of progressive diseases.

Physiologically Based Pharmacokinetic Models for Prediction of Complex CYP2C8 and OATP1B1 (SLCO1B1) Drug-Drug-Gene Interactions: A Modeling Network of Gemfibrozil, Repaglinide, Pioglitazone, Rifampicin, Clarithromycin and Itraconazole

Background

Drug–drug interactions (DDIs) and drug–gene interactions (DGIs) pose a serious health risk that can be avoided by dose adaptation. These interactions are investigated in strictly controlled setups, quantifying the effect of one perpetrator drug or polymorphism at a time, but in real life patients frequently take more than two medications and are very heterogenous regarding their genetic background.

Objectives

The first objective of this study was to provide whole-body physiologically based pharmacokinetic (PBPK) models of important cytochrome P450 (CYP) 2C8 perpetrator and victim drugs, built and evaluated for DDI and DGI studies. The second objective was to apply these models to describe complex interactions with more than two interacting partners.

Methods

PBPK models of the CYP2C8 and organic-anion-transporting polypeptide (OATP) 1B1 perpetrator drug gemfibrozil (parent–metabolite model) and the CYP2C8 victim drugs repaglinide (also an OATP1B1 substrate) and pioglitazone were developed using a total of 103 clinical studies. For evaluation, these models were applied to predict 34 different DDI studies, establishing a CYP2C8 and OATP1B1 PBPK DDI modeling network.

Results

The newly developed models show a good performance, accurately describing plasma concentration–time profiles, area under the plasma concentration–time curve (AUC) and maximum plasma concentration (C_max) values, DDI studies as well as DGI studies. All 34 of the modeled DDI AUC ratios (AUC during DDI/AUC control) and DDI C_max ratios (C_max during DDI/C_max control) are within twofold of the observed values.

Conclusions

Whole-body PBPK models of gemfibrozil, repaglinide, and pioglitazone have been built and qualified for DDI and DGI prediction. PBPK modeling is applicable to investigate complex interactions between multiple drugs and genetic polymorphisms.

Electronic supplementary material

The online version of this article (10.1007/s40262-019-00777-x) contains supplementary material, which is available to authorized users.

Pharmacokinetic Interaction between Sorafenib and Atorvastatin, and Sorafenib and Metformin in Rats

The tyrosine kinase inhibitor sorafenib is the first-line treatment for patients with hepatocellular carcinoma (HCC), in which hyperlipidemia and type 2 diabetes mellitus (T2DM) may often coexist. Protein transporters like organic cation (OCT) and multidrug and toxin extrusion (MATE) are involved in the response to sorafenib, as well as in that to the anti-diabetic drug metformin or atorvastatin, used in hyperlipidemia. Changes in the activity of these transporters may lead to pharmacokinetic interactions, which are of clinical significance. The study aimed to assess the sorafenib−metformin and sorafenib−atorvastatin interactions in rats. The rats were divided into five groups (eight animals in each) that received sorafenib and atorvastatin (I_SOR+AT), sorafenib and metformin (II_SOR+MET), sorafenib (III_SOR), atorvastatin (IV_AT), and metformin (V_MET). Atorvastatin significantly increased the maximum plasma concentration (C_max) and the area under the plasma concentration–time curve (AUC) of sorafenib by 134.4% (p < 0.0001) and 66.6% (p < 0.0001), respectively. Sorafenib, in turn, caused a significant increase in the AUC of atorvastatin by 94.0% (p = 0.0038) and its metabolites 2−hydroxy atorvastatin (p = 0.0239) and 4−hydroxy atorvastatin (p = 0.0002) by 55.3% and 209.4%, respectively. Metformin significantly decreased the AUC of sorafenib (p = 0.0065). The AUC ratio (II_SOR+MET group/III_SOR group) for sorafenib was equal to 0.6. Sorafenib did not statistically significantly influence the exposure to metformin. The pharmacokinetic interactions observed in this study may be of clinical relevance in HCC patients with coexistent hyperlipidemia or T2DM.

Drug-drug-gene interactions and adverse drug reactions

The economic and health burden caused by adverse drug reactions has increased dramatically in the last few years. This is likely to be mediated by increasing polypharmacy, which increases the likelihood for drug–drug interactions. Tools utilized by healthcare practitioners to flag potential adverse drug reactions secondary to drug–drug interactions ignore individual genetic variation, which has the potential to markedly alter the severity of these interactions. To date there have been limited published studies on impact of genetic variation on drug–drug interactions. In this review, we establish a detailed classification for pharmacokinetic drug–drug–gene interactions, and give examples from the literature that support this approach. The increasing availability of real-world drug outcome data linked to genetic bioresources is likely to enable the discovery of previously unrecognized, clinically important drug–drug–gene interactions.

Transporter-Mediated Drug-Drug Interactions and Their Significance

Drug transporters are considered to be determinants of drug disposition and effects/toxicities by affecting the absorption, distribution, and excretion of drugs. Drug transporters are generally divided into solute carrier (SLC) family and ATP binding cassette (ABC) family. Widely studied ABC family transporters include P-glycoprotein (P-GP), breast cancer resistance protein (BCRP), and multidrug resistance proteins (MRPs). SLC family transporters related to drug transport mainly include organic anion-transporting polypeptides (OATPs), organic anion transporters (OATs), organic cation transporters (OCTs), organic cation/carnitine transporters (OCTNs), peptide transporters (PEPTs), and multidrug/toxin extrusions (MATEs). These transporters are often expressed in tissues related to drug disposition, such as the small intestine, liver, and kidney, implicating intestinal absorption of drugs, uptake of drugs into hepatocytes, and renal/bile excretion of drugs. Most of therapeutic drugs are their substrates or inhibitors. When they are comedicated, serious drug-drug interactions (DDIs) may occur due to alterations in intestinal absorption, hepatic uptake, or renal/bile secretion of drugs, leading to enhancement of their activities or toxicities or therapeutic failure. This chapter will illustrate transporter-mediated DDIs (including food drug interaction) in human and their clinical significances.

Effects of CYP2C8 and SLCO1B1 Genetic Polymorphisms on Repaglinide Pharmacokinetics: A Systematic Review and Meta-Analysis

Objective:

The purpose of this systematic review and meta-analysis was to summarize the potential impact of CYP2C8 and SLCO1B1 genetic polymorphisms on repaglinide pharmacokinetics.

Methods:

A systematic search was conducted using electronic databases. Eligible studies reported data from pharmacokinetic evaluations of repaglinide in healthy adults according to different categories of CYP2C8 and SLCO1B1 genetic polymorphisms.

Results:

Six studies including a total of 191 participants met the inclusion criteria. We noted that CYP2C8 *1/*3 carriers exhibited lower AUC(0-∞) (SMD: -0.77; 95%CI: -1.23 to -0.30; P=0.001) and Cmax (SMD: -0.94; 95%CI: - 1.41 to -0.47; P<0.001) than CYP2C8 *1/*1 carriers. There were no significant differences in AUC(0-∞), Cmax, t1/2 and mean change in blood glucose concentration between *1/*4 and *1/*1 carriers. Further, *3/*3 carriers had lower Cmax (SMD: -1.42; 95%CI: -2.66 to -0.17; P=0.026) than *1/*1 carriers. Additionally, *3/*3 carriers had lower Cmax than *1/*3 carriers (SMD: -1.20; 95%CI: -2.40 to -0.00; P=0.050). Finally, we noted that repaglinide pharmacokinetics did not differ by SLCO1B1 genotype.

Conclusion:

The current systematic review and meta-analysis indicated that the genotype of CYP2C8, but not SLCO1B1, may affect repaglinide pharmacokinetics. However, because of the comparatively insufficient number of published studies included, our conclusions require support from additional studies.

Organic Anion Transporter Polypeptide 1B1 Polymorphism Modulates the Extent of Drug-Drug Interaction and Associated Biomarker Levels in Healthy Volunteers

Understanding transporter‐mediated drug–drug interactions is an integral part of risk assessment in drug development. Recent studies support the use of hexadecanedioate (HDA), tetradecanedioate (TDA), coproporphyrin (CP)‐I, and CP‐III as clinical biomarkers for evaluating organic anion‐transporting polypeptide (OATP)1B1 (SLCO1B1) inhibition. The current study investigated the effect of OATP1B1 genotype c.521T>C (OATP1B1‐Val174Ala) on the extent of interaction between cyclosporin A (CsA) and pravastatin, and associated endogenous biomarkers of the transporter (HDA, TDA, CP‐I, and CP‐III), in 20 healthy volunteers. The results show that the levels of each clinical biomarker and pravastatin were significantly increased in plasma samples of the volunteers following administration of pravastatin plus CsA compared with pravastatin plus placebo. The overall fold change in the area under the concentration–time curve (AUC) and maximum plasma concentration (C_max) was similar among the four biomarkers (1.8–2.5‐fold, paired t‐test P value < 0.05) in individuals who were homozygotes or heterozygotes of the major allele, c.521T. However, the fold change in AUC and C_max for HDA and TDA was significantly abolished in the subjects who were c.521‐CC, whereas the respective fold change in AUC and C_max for pravastatin and CP‐I and CP‐III were slightly weaker in individuals who were c.521‐CC compared with c.521‐TT/TC genotypes. In addition, this study provides the first evidence that SLCO1B1 c.521T>C genotype is significantly associated with CP‐I but not CP‐III levels. Overall, these results suggest that OATP1B1 genotype can modulate the effects of CsA on biomarker levels; the extent of modulation differs among the biomarkers.

Use of Physiologically Based Pharmacokinetic Modeling to Evaluate the Effect of Chronic Kidney Disease on the Disposition of Hepatic CYP2C8 and OATP1B Drug Substrates

Chronic kidney disease (CKD) differentially affects the pharmacokinetics (PK) of nonrenally cleared drugs via certain pathways (e.g., cytochrome P450 (CYP)2D6); however, the effect on CYP2C8‐mediated clearance is not well understood because of overlapping substrate specificity with hepatic organic anion‐transporting polypeptides (OATPs). This study used physiologically based pharmacokinetic (PBPK) modeling to delineate potential changes in CYP2C8 or OATP1B activity in patients with CKD. Drugs analyzed are predominantly substrates of CYP2C8 (rosiglitazone and pioglitazone), OATP1B (pitavastatin), or both (repaglinide). Following initial model verification, pharmacokinetics (PK) of these drugs were simulated in patients with severe CKD considering changes in glomerular filtration rate (GFR), plasma protein binding, and activity of either CYP2C8 and/or OATP1B in a stepwise manner. The PBPK analysis suggests that OATP1B activity could be decreased up to 60% in severe CKD, whereas changes to CYP2C8 are negligible. This improved understanding of CKD effect on clearance pathways could be important to inform the optimal use of nonrenally eliminated drugs in patients with CKD.

Drug interactions of meglitinide antidiabetics involving CYP enzymes and OATP1B1 transporter

Meglitinides such as repaglinide and nateglinide are useful to treat type 2 diabetes patients who follow a flexible lifestyle. They are short-acting insulin secretagogues and are associated with less risk of hypoglycemia, weight gain and chronic hyperinsulinemia compared with sulfonylureas. Meglitinides are the substrates of cytochrome P450 (CYP) enzymes and organic anion transporting polypeptide 1B1 (OATP1B1 transporter) and the coadministration of the drugs affecting them will result in pharmacokinetic drug interactions. This article focuses on the drug interactions of meglitinides involving CYP enzymes and OATP1B1 transporter. To prevent the risk of hypoglycemic episodes, prescribers and pharmacists must be aware of the adverse drug interactions of meglitinides.

Progress in Prediction and Interpretation of Clinically Relevant Metabolic Drug-Drug Interactions: a Minireview Illustrating Recent Developments and Current Opportunities

PURPOSE OF REVIEW

This review gives a perspective on the current "state of the art" in metabolic drug-drug interaction (DDI) prediction. We highlight areas of successful prediction and illustrate progress in areas where limits in scientific knowledge or technologies prevent us from having full confidence.

RECENT FINDINGS

Several examples of success are highlighted. Work done for bitopertin shows how in vitro and clinical data can be integrated to give a model-based understanding of pharmacokinetics and drug interactions. The use of interpolative predictions to derive explicit dosage recommendations for untested DDIs is discussed using the example of ibrutinib, and the use of DDI predictions in lieu of clinical studies in new drug application packages is exemplified with eliglustat and alectinib. Alectinib is also an interesting case where dose adjustment is unnecessary as the activity of a major metabolite compensates sufficiently for changes in parent drug exposure. Examples where "unusual" cytochrome P450 (CYP) and non-CYP enzymes are responsible for metabolic clearance have shown the importance of continuing to develop our repertoire of in vitro regents and techniques. The time-dependent inhibition assay using human hepatocytes suspended in full plasma allowed improved DDI predictions, illustrating the importance of continued in vitro assay development and refinement.

SUMMARY

During the past 10 years, a highly mechanistic understanding has been developed in the area of CYP-mediated metabolic DDIs enabling the prediction of clinical outcome based on preclinical studies. The combination of good quality in vitro data and physiologically based pharmacokinetic modeling may now be used to evaluate DDI risk prospectively and are increasingly accepted in lieu of dedicated clinical studies.

Role of Cytochrome P450 2C8 in Drug Metabolism and Interactions

During the last 10-15 years, cytochrome P450 (CYP) 2C8 has emerged as an important drug-metabolizing enzyme. CYP2C8 is highly expressed in human liver and is known to metabolize more than 100 drugs. CYP2C8 substrate drugs include amodiaquine, cerivastatin, dasabuvir, enzalutamide, imatinib, loperamide, montelukast, paclitaxel, pioglitazone, repaglinide, and rosiglitazone, and the number is increasing. Similarly, many drugs have been identified as CYP2C8 inhibitors or inducers. In vivo, already a small dose of gemfibrozil, i.e., 10% of its therapeutic dose, is a strong, irreversible inhibitor of CYP2C8. Interestingly, recent findings indicate that the acyl-β-glucuronides of gemfibrozil and clopidogrel cause metabolism-dependent inactivation of CYP2C8, leading to a strong potential for drug interactions. Also several other glucuronide metabolites interact with CYP2C8 as substrates or inhibitors, suggesting that an interplay between CYP2C8 and glucuronides is common. Lack of fully selective and safe probe substrates, inhibitors, and inducers challenges execution and interpretation of drug-drug interaction studies in humans. Apart from drug-drug interactions, some CYP2C8 genetic variants are associated with altered CYP2C8 activity and exhibit significant interethnic frequency differences. Herein, we review the current knowledge on substrates, inhibitors, inducers, and pharmacogenetics of CYP2C8, as well as its role in clinically relevant drug interactions. In addition, implications for selection of CYP2C8 marker and perpetrator drugs to investigate CYP2C8-mediated drug metabolism and interactions in preclinical and clinical studies are discussed.

Altered pharmacokinetics and pharmacodynamics of repaglinide by ritonavir in rats with healthy, diabetic and impaired hepatic function

BACKGROUND

Ritonavir is an antiretroviral drug to treat HIV AIDS and inhibits cytochrome P450 3A4. To treat diabetes mellitus in HIV, repaglinide is coadministered with ritonavir in the clinic. Multiple cytochrome P450 (CYP) isoforms are involved in the metabolism of repaglinide like CYP2C8 and CYP 3A4. In order to predict and understand drug-drug interactions of these two drugs, the pharmacokinetics and pharmacodynamics (PK/PD) of repaglinide and ritonavir were studied in normal, diabetic and hepatic impaired rats. The purpose of the study was to assess the influence of ritonavir on the PK/PD of repaglinide in rats with normal, diabetic and impaired hepatic function.

METHODS

Human oral therapeutic doses of ritonavir and repaglinide were extrapolated to rats based on the body surface area. Ritonavir (20 mg/kg, p.o.), alone and along with repaglinide (0.5 mg/kg, p.o.), was given to normal, diabetic and hepatic impaired rats, and the PK/PD were studied.

RESULTS

The pharmacokinetic parameters like peak plasma concentration (Cmax), area under the plasma concentration time profile (AUC) and elimination half life of repaglinide were significantly (p<0.0001) increased when compared to repaglinide control rats. The repaglinide clearance (CL) was significantly (p<0.0001) decreased in the presence of ritonavir treatment. In the presence of ritonavir, repaglinide hypoglycemic activity was increased significantly (p<0.0005) when compared with repaglinide control group.

CONCLUSIONS

The significant difference in the PK/PD changes have been due to the increased plasma exposure and decreased total body clearance of repaglinide, which may be due to the inhibition of the CYP P450 metabolic system and organic anion-transporting polypeptide transporter by ritonavir.

Dosing Recommendations for Concomitant Medications During 3D Anti-HCV Therapy

The development of direct-acting antiviral (DAA) agents has reinvigorated the treatment of hepatitis C virus infection. The availability of multiple DAA agents and drug combinations has enabled the transition to interferon-free therapy that is applicable to a broad range of patients. However, these DAA combinations are not without drug-drug interactions (DDIs). As every possible DDI permutation cannot be evaluated in a clinical study, guidance is needed for healthcare providers to avoid or minimize drug interaction risk. In this review, we evaluated the DDI potential of the novel three-DAA combination of ombitasvir, paritaprevir, ritonavir, and dasabuvir (the 3D regimen) with more than 200 drugs representing 19 therapeutic drug classes. Outcomes of these DDI studies were compared with the metabolism and elimination routes of prospective concomitant medications to develop mechanism-based and drug-specific guidance on interaction potential. This analysis revealed that the 3D regimen is compatible with many of the drugs that are commonly prescribed to patients with hepatitis C virus infection. Where interaction is possible, risk can be mitigated by paying careful attention to concomitant medications, adjusting drug dosage as needed, and monitoring patient response and/or clinical parameters.

Quantitative Rationalization of Gemfibrozil Drug Interactions: Consideration of Transporters-Enzyme Interplay and the Role of Circulating Metabolite Gemfibrozil 1-O-β-Glucuronide

Gemfibrozil has been suggested as a sensitive cytochrome P450 2C8 (CYP2C8) inhibitor for clinical investigation by the U.S. Food and Drug Administration and the European Medicines Agency. However, gemfibrozil drug-drug interactions (DDIs) are complex; its major circulating metabolite, gemfibrozil 1-O-β-glucuronide (Gem-Glu), exhibits time-dependent inhibition of CYP2C8, and both parent and metabolite also behave as moderate inhibitors of organic anion transporting polypeptide 1B1 (OATP1B1) in vitro. Additionally, parent and metabolite also inhibit renal transport mediated by OAT3. Here, in vitro inhibition data for gemfibrozil and Gem-Glu were used to assess their impact on the pharmacokinetics of several victim drugs (including rosiglitazone, pioglitazone, cerivastatin, and repaglinide) by employing both static mechanistic and dynamic physiologically based pharmacokinetic (PBPK) models. Of the 48 cases evaluated using the static models, about 75% and 98% of the DDIs were predicted within 1.5- and 2-fold of the observed values, respectively, when incorporating the interaction potential of both gemfibrozil and its 1-O-β-glucuronide. Moreover, the PBPK model was able to recover the plasma profiles of rosiglitazone, pioglitazone, cerivastatin, and repaglinide under control and gemfibrozil treatment conditions. Analyses suggest that Gem-Glu is the major contributor to the DDIs, and its exposure needed to bring about complete inactivation of CYP2C8 is only a fraction of that achieved in the clinic after a therapeutic gemfibrozil dose. Overall, the complex interactions of gemfibrozil can be quantitatively rationalized, and the learnings from this analysis can be applied in support of future predictions of gemfibrozil DDIs.

Comparative Bioavailability and Tolerability of a Single 2-mg Dose of 2 Repaglinide Tablet Formulations in Fasting, Healthy Chinese Male Volunteers: An Open-Label, Randomized-Sequence, 2-Period Crossover Study

Background

Repaglinide, an oral insulin secretagogue, was the first meglitinide analogue to be approved for use in patients with type 2 diabetes mellitus.

Objective

In our study, the bioavailability and tolerability of the proposed generic formulation with the established reference formulation of repaglinide 2 mg were compared in a fasting, healthy Chinese male population.

Methods

This 2-week, open-label, randomized-sequence, single-dose, 2-period crossover study was conducted in 22 healthy native Han Chinese male volunteers. Eligible subjects were randomly assigned in a 1:1 ratio to receive a single 2-mg dose of the test or reference formulation, followed by a 7-day washout period and administration of the alternate formulation. After an overnight fast, subjects received a single oral dose of repaglinide (2 mg). Blood samples were drawn at predetermined time points (0, 0.25, 0.5, 0.75, 1, 1.25, 1.5, 2.0, 2.5, 3.0, 4.0, 5.0, and 6.0 hours). All plasma concentrations of repaglinide were measured by LC-MS/MS. The observed C_max, T_max, t_1/2, and AUC were assessed. The formulations were to be considered bioequivalent if the ln-transformed ratios of C_max and AUC were within the predetermined bioequivalence range of 80% to 125% established by the State Food and Drug Administration of the People’s Republic of China. Tolerability was assessed throughout the study via subject interview, vital signs, and blood sampling.

Results

The mean (SD) age of the subjects was 24.2 (2.3) years; their mean (SD) weight was 62.6 (5.8) kg, their mean (SD) height was 172 (5.7) cm, and their mean (SD) body mass index was 21.0 (1.1). The mean (SD) C_max for repaglinide with the test and reference formulations were 20.0 (5.1) and 18.7 (8.7) ng/mL. The AUC_0–t for the test formulation was 46.3 (15.1) and AUC_0–∞ was 47.9 (16.5) ng^•h/mL. With the reference formulation, the corresponding values were 46.4 (26.1) and 49.0 (31.3) ng^•h/mL. The mean (SD) T_max values with the test and reference formulations were 1.2 (0.7) hours and 1.5 (0.8) hours and the mean (SD) values t_1/2 values were 1.0 (0.3), and 0.9 (0.3) hours, respectively. The ln-transformed ratios of C_max, AUC_0–t, and AUC_0–∞ were 113.6:1, 105.6:1, and 104.7:1. The corresponding 90% CIs were 99.8 to 129.2, 93.4 to 119.5, and 91.8 to 119.5, respectively.

Conclusions

This single-dose study found that the test and reference formulations of repaglinide met the regulatory criteria for bioequivalence in these fasting, healthy Chinese male volunteers. Both formulations appeared to be well tolerated. ClinicalTrials.gov identifier: 2012L01684.

Optimization of cancer chemotherapy on the basis of pharmacokinetics and pharmacodynamics: from patients enrolled in clinical trials to those in the 'real world'

  Cytotoxic anticancer drugs are the most challenging therapeutic agents among all medicines with relatively narrow efficacy profiles. Therefore, medical oncologists have to practically manage the risk of severe toxic effects to optimize treatment outcomes. Dose and treatment-schedule recommendations for cytotoxic anticancer agents are determined on the basis of clinical trials. Patients enrolled in clinical trials are those likely to receive the drug in clinical practice, excluding those with conditions such as organ dysfunction, obesity, advanced age, or comorbidity. On the other hand, the 'real world' includes large numbers of such patients who do not meet the eligibility criteria of clinical trials. However, there is a paucity of data from sufficiently powered pharmacokinetic and pharmacodynamic studies to support dosage recommendations in such patients. Consequently, dose levels and treatment schedules for chemotherapy in these subjects are somewhat arbitrary and not evidence-based. Pharmacokinetic and pharmacodynamic studies of patients in the 'real world' are needed to address this issue. In this review article, we describe general aspects of clinical pharmacology in cancer patients enrolled in clinical trials and those in the 'real world,' and introduce recent findings regarding the pharmacokinetic and pharmacodynamic properties of irinotecan and S-1 in 'real world' cancer patients.

A perspective on the prediction of drug pharmacokinetics and disposition in drug research and development

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, drug-disposition 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, half-life, 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.

Clinical significance of organic anion transporting polypeptides (OATPs) in drug disposition: their roles in hepatic clearance and intestinal absorption

Organic anion transporting polypeptide (OATP) family transporters accept a number of drugs and are increasingly being recognized as important factors in governing drug and metabolite pharmacokinetics. OATP1B1 and OATP1B3 play an important role in hepatic drug uptake while OATP2B1 and OATP1A2 might be key players in intestinal absorption and transport across blood-brain barrier of drugs, respectively. To understand the importance of OATPs in the hepatic clearance of drugs, the rate-determining process for elimination should be considered; for some drugs, hepatic uptake clearance rather than metabolic intrinsic clearance is the more important determinant of hepatic clearances. The importance of the unbound concentration ratio (liver/blood), K(p,uu) , of drugs, which is partly governed by OATPs, is exemplified in interpreting the difference in the IC(50) of statins between the hepatocyte and microsome systems for the inhibition of HMG-CoA reductase activity. The intrinsic activity and/or expression level of OATPs are affected by genetic polymorphisms and drug-drug interactions. Their effects on the elimination rate or intestinal absorption rate of drugs may sometimes depend on the substrate drug. This is partly because of the different contribution of OATP isoforms to clearance or intestinal absorption. When the contribution of the OATP-mediated pathway is substantial, the pharmacokinetics of substrate drugs should be greatly affected. This review describes the estimation of the contribution of OATP1B1 to the total hepatic uptake of drugs from the data of fold-increases in the plasma concentration of substrate drugs by the genetic polymorphism of this transporter. To understand the importance of the OATP family transporters, modeling and simulation with a physiologically based pharmacokinetic model are helpful.

Effects of Fluvastatin on the Pharmacokinetics of Repaglinide: Possible Role of CYP3A4 and P-glycoprotein Inhibition by Fluvastatin

The purpose of this study was to investigate the effects of fluvastatin on the pharmacokinetics of repaglinide in rats. The effect of fluvastatin on P-glycoprotein and CYP3A4 activity was evaluated. The pharmacokinetic parameters and blood glucose concentrations were also determined after oral and intravenous administration of repaglinide to rats in the presence and absence of fluvastatin. Fluvastatin inhibited CYP3A4 activity in a concentration-dependent manner with a 50% inhibition concentration(IC₅₀) of 4.1 µM and P-gp activity. Compared to the oral control group, fluvastatin significantly increased the AUC and the peak plasma level of repaglinide by 45.9% and 22.7%, respectively. Fluvastatin significantly decreased the total body clearance (TBC) of repaglinide compared to the control. Fluvastatin also significantly increased the absolute bioavailability (BA) of repaglinide by 46.1% compared to the control group. Moreover, the relative BA of repaglinide was 1.14- to 1.46-fold greater than that of the control. Compared to the i.v. control, fluvastatin significantly increased the AUC_0-∞ of i.v. administered repaglinide. The blood glucose concentrations showed significant differences compared to the oral controls. Fluvastatin enhanced the oral BA of repaglinide, which may be mainly attributable to the inhibition of the CYP3A4-mediated metabolism of repaglinide in the small intestine and/or liver, to the inhibition of the P-gp efflux transporter in the small intestine and/or to the reduction of TBC of repaglinide by fluvastatin. The study has raised the awareness of potential interactions during concomitant use of repaglinide with fluvastatin. Therefore, the concurrent use of repaglinide and fluvastatin may require close monitoring for potential drug interactions.

Transporters and drug-drug interactions: important determinants of drug disposition and effects

Uptake and efflux transporters determine plasma and tissue concentrations of a broad variety of drugs. They are localized in organs such as small intestine, liver, and kidney, which are critical for drug absorption and elimination. Moreover, they can be found in important blood-tissue barriers such as the blood-brain barrier. Inhibition or induction of drug transporters by coadministered drugs can alter pharmacokinetics and pharmacodynamics of the victim drugs. This review will summarize in particular clinically observed drug-drug interactions attributable to inhibition or induction of intestinal export transporters [P-glycoprotein (P-gp), breast cancer resistance protein (BCRP)], to inhibition of hepatic uptake transporters [organic anion transporting polypeptides (OATPs)], or to inhibition of transporter-mediated [organic anion transporters (OATs), organic cation transporter 2 (OCT2), multidrug and toxin extrusion proteins (MATEs), P-gp] renal secretion of xenobiotics. Available data on the impact of nutrition on transport processes as well as genotype-dependent, transporter-mediated drug-drug interactions will be discussed. We will also present and discuss data on the variable extent to which information on the impact of transporters on drug disposition is included in summaries of product characteristics of selected countries (SPCs). Further work is required regarding a better understanding of the role of the drug metabolism-drug transport interplay for drug-drug interactions and on the extrapolation of in vitro findings to the in vivo (human) situation.

Analysis of the repaglinide concentration increase produced by gemfibrozil and itraconazole based on the inhibition of the hepatic uptake transporter and metabolic enzymes

The plasma concentration of repaglinide is reported to increase greatly when given after repeated oral administration of itraconazole and gemfibrozil. The present study analyzed this interaction based on a physiologically based pharmacokinetic (PBPK) model incorporating inhibition of the hepatic uptake transporter and metabolic enzymes involved in repaglinide disposition. Firstly, the plasma concentration profiles of inhibitors (itraconazole, gemfibrozil, and gemfibrozil glucuronide) were reproduced by a PBPK model to obtain their pharmacokinetic parameters. The plasma concentration profiles of repaglinide were then analyzed by a PBPK model, together with those of the inhibitors, assuming a competitive inhibition of CYP3A4 by itraconazole, mechanism-based inhibition of CYP2C8 by gemfibrozil glucuronide, and inhibition of organic anion transporting polypeptide (OATP) 1B1 by gemfibrozil and its glucuronide. The plasma concentration profiles of repaglinide were well reproduced by the PBPK model based on the above assumptions, and the optimized values for the inhibition constants (0.0676 nM for itraconazole against CYP3A4; 14.2 μM for gemfibrozil against OATP1B1; and 5.48 μM for gemfibrozil glucuronide against OATP1B1) and the fraction of repaglinide metabolized by CYP2C8 (0.801) were consistent with the reported values. The validity of the obtained parameters was further confirmed by sensitivity analyses and by reproducing the repaglinide concentration increase produced by concomitant gemfibrozil administration at various timings/doses. The present findings suggested that the reported concentration increase of repaglinide, suggestive of synergistic effects of the coadministered inhibitors, can be quantitatively explained by the simultaneous inhibition of the multiple clearance pathways of repaglinide.

Current understanding of hepatic and intestinal OATP-mediated drug-drug interactions

At present, many patients are medicated with various drugs, which are, at the same time, associated with an increased risk of drug-drug interactions (DDIs). Detailed analysis of mechanisms underlying DDIs is the basis of a better prediction of adverse drug events caused by drug interactions. In the last few decades, an involvement of transporters in such processes has been more and more recognized. Indeed, uptake transporters belonging to the organic anion-transporting polypeptide (OATP) family have been shown to interact with a variety of drugs in clinical use. Particularly, the subfamily of OATP1B transporters has been extensively studied, identifying several clinical significant DDIs based on those hepatic uptake transporters. By contrast, the role of OATP2B1 in this context is rather underestimated. Therefore, in addition to known interactions based on OATP1B transporters, we have focused on DDIs probably based on OATP2B1 inhibition in the liver and those possibly owing to the inhibition of OATP2B1-mediated drug absorption in the intestine.

Evaluation of exposure change of nonrenally eliminated drugs in patients with chronic kidney disease using physiologically based pharmacokinetic modeling and simulation

Chronic kidney disease, or renal impairment (RI) can increase plasma levels for drugs that are primarily renally cleared and for some drugs whose renal elimination is not a major pathway. We constructed physiologically based pharmacokinetic (PBPK) models for 3 nonrenally eliminated drugs (sildenafil, repaglinide, and telithromycin). These models integrate drug-dependent parameters derived from in vitro, in silico, and in vivo data, and system-dependent parameters that are independent of the test drugs. Plasma pharmacokinetic profiles of test drugs were simulated in subjects with severe RI and normal renal function, respectively. The simulated versus observed areas under the concentration versus time curve changes (AUCR, severe RI/normal) were comparable for sildenafil (2.2 vs 2.0) and telithromycin (1.6 vs 1.9). For repaglinide, the initial, simulated AUCR was lower than that observed (1.2 vs 3.0). The underestimation was corrected once the estimated changes in transporter activity were incorporated into the model. The simulated AUCR values were confirmed using a static, clearance concept model. The PBPK models were further used to evaluate the changes in pharmacokinetic profiles of sildenafil metabolite by RI and of telithromycin by RI and co-administration with ketoconazole. The simulations demonstrate the utility and challenges of the PBPK approach in evaluating the pharmacokinetics of nonrenally cleared drugs in subjects with RI.

Simultaneous assessment of uptake and metabolism in rat hepatocytes: a comprehensive mechanistic model

Kinetic parameters describing hepatic uptake in hepatocytes are frequently estimated without appropriate incorporation of bidirectional passive diffusion, intracellular binding, and metabolism. A mechanistic two-compartment model was developed to describe all of the processes occurring during the in vitro uptake experiments performed in freshly isolated rat hepatocytes plated for 2 h. Uptake of rosuvastatin, pravastatin, pitavastatin, valsartan, bosentan, telmisartan, and repaglinide was investigated over a 0.1 to 300 μM concentration range at 37°C for 2 or 45-90 min; nonspecific binding was taken into account. All concentration-time points were analyzed simultaneously by using a mechanistic two-compartment model describing uptake kinetics [unbound affinity constant (K(m,u)), maximum uptake rate (V(max)), unbound active uptake clearance (CL(active,u))], passive diffusion [unbound passive diffusion clearance (P(diff,u))], and intracellular binding [intracellular unbound fraction (fu(cell))]. When required (telmisartan and repaglinide), the model was extended to account for the metabolism [unbound metabolic clearance (CL(met,u))]. The CL(active,u) ranged 8-fold, reflecting a 11-fold range in uptake K(m,u), with telmisartan and valsartan showing the highest affinity for uptake transporters (K(m,u) <10 μM). Both P(diff,u) and fu(cell) span over two orders of magnitude and reflected the lipophilicity of the drugs in the dataset. An extended incubation time allowed steady state to be reached between media and intracellular compartment concentrations and reduced the error in certain parameter estimates observed with shorter incubation times. Active transport accounted for >70% of total uptake for all drugs investigated and was 4- and 112-fold greater than CL(met,u) for telmisartan and repaglinide, respectively. Modeling of uptake kinetics in conjunction with metabolism improved the precision of the uptake parameter estimates for repaglinide and telmisartan. Recommendations are made for uptake experimental design and modeling strategies.

Influence of SLCO1B1 polymorphisms on the drug-drug interaction between darunavir/ritonavir and pravastatin

The authors investigated whether SLCO1B1 polymorphisms contribute to variability in pravastatin pharmacokinetics when pravastatin is administered alone versus with darunavir/ritonavir. HIV-negative healthy participants were prospectively enrolled on the basis of SLCO1B1 diplotype: group 1 (*1A/*1A, n = 9); group 2 (*1A/*1B, n = 10; or *1B/*1B, n = 2); and group 3 (*1A/*15, n = 1; *1B/*15, n = 5; or *1B/*17, n = 1). Participants received pravastatin (40 mg) daily on days 1 through 4, washout on days 5 through 11, darunavir/ritonavir (600/100 mg) twice daily on days 12 through 18, with pravastatin 40 mg added back on days 15 through 18. Pharmacokinetic studies were conducted on day 4 (pravastatin alone) and day 18 (pravastatin + darunavir/ritonavir). Pravastatin area under the plasma concentration-time curve (AUC(tau)) was 21% higher during administration with darunavir/ritonavir compared with pravastatin alone; however, this difference was not statistically significant (P = .11). Group 3 variants had 96% higher pravastatin AUC(tau) on day 4 and 113% higher pravastatin AUC(tau) on day 18 compared with group 1. The relative change in pravastatin pharmacokinetics was largest in group 3 but did not differ significantly between diplotype groups. In sum, the influence of SLCO1B1*15 and *17 haplotypes on pravastatin pharmacokinetics was maintained in the presence of darunavir/ritonavir. Because OATP1B1 inhibition would be expected to be greater in carriers of normal or high-functioning SLCO1B1 haplotypes, these findings suggest that darunavir/ritonavir is not a potent inhibitor of OATP1B1-mediated pravastatin transport in vivo.

Transporter-mediated drug-drug interactions with oral antidiabetic drugs

Uptake transporters (e.g., members of the SLC superfamily of solute carriers) and export proteins (e.g., members of the ABC transporter superfamily) are important determinants for the pharmacokinetics of drugs. Alterations of drug transport due to concomitantly administered drugs that interfere with drug transport may alter the kinetics of drug substrates. In vitro and in vivo studies indicate that many drugs used for the treatment of metabolic disorders and cardiovascular diseases (e.g., oral antidiabetic drugs, statins) are substrates for uptake transporters and export proteins expressed in the intestine, the liver and the kidney. Since most patients with type 2 diabetes receive more than one drug, transporter-mediated drug-drug interactions are important molecular mechanisms leading to alterations in oral antidiabetic drug pharmacokinetics with the risk of adverse drug reactions. This review focuses on uptake transporters of the SLCO/SLC21 (OATP) and SLC22 (OCT/OAT) family of solute carriers and export pumps of the ABC (ATP-binding cassette) transporter superfamily (especially P-glycoprotein) as well as the export proteins of the SLC47 (MATE) family and their role for transporter-mediated drug-drug interactions with oral antidiabetic drugs.

SLCO1B1 polymorphism and oral antidiabetic drugs

Organic anion-transporting polypeptide 1B1 (OATP1B1; gene: SLCO1B1) is an influx transporter expressed on the sinusoidal membrane of human hepatocytes, where it mediates the uptake of its substrates from blood into liver. In vitro, the SLCO1B1 c.521T>C (p.Val174Ala) single-nucleotide polymorphism (SNP) has been associated with reduced and the c.388A>G (p.Asn130Asp) SNP with both enhanced and reduced transport activity of OATP1B1. In vivo in humans, the c.521C allele (present in SLCO1B1*5 and *15 haplotypes) is associated with decreased hepatic uptake and increased plasma concentrations of several OATP1B1 substrates. The SLCO1B1*1B (c.388G-c.521T) haplotype is associated with enhanced hepatic uptake and decreased plasma concentrations of some OATP1B1 substrates. The SLCO1B1 c.521CC genotype has been associated with an about 60-190% increased, and the SLCO1B1*1B/*1B genotype with an about 30% decreased area under the plasma concentration-time curve of repaglinide. Moreover, SLCO1B1 polymorphism can affect the extent of interaction between OATP1B1 inhibitors and repaglinide. Accordingly, SLCO1B1 genotyping may help in choosing the optimal starting dose of repaglinide. In Chinese individuals, the SLCO1B1 c.521C allele has been associated with increased plasma concentrations of nateglinide, but the association could not be replicated in Caucasians. SLCO1B1 genotype has had no effect on the pharmacokinetics of rosiglitazone, pioglitazone or their metabolites. The hepatic uptake of metformin is mediated by organic cation transporters 1 and 3, and the liver is not important for the elimination or action of the dipeptidylpeptidase 4 inhibitors sitagliptin, vildagliptin and saxagliptin. Therefore, SLCO1B1 polymorphism unlikely affects the response to these antidiabetics. Possible effects of SLCO1B1 polymorphism on sulfonylureas remain to be investigated.

Organic anion transporting polypeptide 1B1: a genetically polymorphic transporter of major importance for hepatic drug uptake

The importance of membrane transporters for drug pharmacokinetics has been increasingly recognized during the last decade. Organic anion transporting polypeptide 1B1 (OATP1B1) is a genetically polymorphic influx transporter expressed on the sinusoidal membrane of human hepatocytes, and it mediates the hepatic uptake of many endogenous compounds and xenobiotics. Recent studies have demonstrated that OATP1B1 plays a major, clinically important role in the hepatic uptake of many drugs. A common single-nucleotide variation (coding DNA c.521T>C, protein p.V174A, rs4149056) in the SLCO1B1 gene encoding OATP1B1 decreases the transporting activity of OATP1B1, resulting in markedly increased plasma concentrations of, for example, many statins, particularly of active simvastatin acid. The variant thereby enhances the risk of statin-induced myopathy and decreases the therapeutic indexes of statins. However, the effect of the SLCO1B1 c.521T>C variant is different on different statins. The same variant also markedly affects the pharmacokinetics of several other drugs. Furthermore, certain SLCO1B1 variants associated with an enhanced clearance of methotrexate increase the risk of gastrointestinal toxicity by methotrexate in the treatment of children with acute lymphoblastic leukemia. Certain drugs (e.g., cyclosporine) potently inhibit OATP1B1, causing clinically significant drug interactions. Thus, OATP1B1 plays a major role in the hepatic uptake of drugs, and genetic variants and drug interactions affecting OATP1B1 activity are important determinants of individual drug responses. In this article, we review the current knowledge about the expression, function, substrate characteristics, and pharmacogenetics of OATP1B1 as well as its role in drug interactions, in parts comparing with those of other hepatocyte-expressed organic anion transporting polypeptides, OATP1B3 and OATP2B1.

Effects of diltiazem on pharmacokinetics of tacrolimus in relation to CYP3A5 genotype status in renal recipients: from retrospective to prospective

The impact of CYP3A5*3, a CYP3A5 nonexpresser genotype, on inhibitory effects of diltiazem on tacrolimus metabolism has not been assessed. In retrospective study, when coadministered with diltiazem, mean increments in dose-adjusted C(0D7), C(max) and AUC(0-12 h) for tacrolimus were larger in CYP3A5 expressers than in CYP3A5 nonexpressers (48.7 vs 3.7%, 31.7 vs 17.2% and 38.2 vs 18.5%, respectively). Subsequently, a prospective study was carried out, patients were randomized to algorithm-predicted dosing or standard dosing. For CYP3A5 expressers, an algorithm guided by CYP3A5 and diltiazem significantly reduced tacrolimus maintenance dosage (P=0.009) and improved the accuracy of tacrolimus initial dose, resulting in reduction in out-of-range C(0) after initial dose (P=0.002) and dose adjustments (P=0.004). However, for CYP3A5 nonexpressers, primary end points were not achieved, and tacrolimus-sparing effect of diltiazem was not remarkable. Our study results show that CYP3A5 genotype-guided tacrolimus-diltiazem combination is a promising therapy in renal transplant recipients in the early postoperative stage.