Toward improved predictions of pharmacokinetics of transported drugs in hepatic impairment: Insights from the extended clearance model.

Hepatic impairment (HI) moderately (<5-fold) affects the systemic exposure (i.e., area under the plasma concentration-time curve [AUC]) of drugs that are substrates of the hepatic sinusoidal organic anion transporting polypeptide (OATP) transporters and are excreted unchanged in the bile and/or urine. However, the effect of HI on their AUC is much greater (>10-fold) for drugs that are also substrates of cytochrome P450 (CYP) 3A enzymes. Using the extended clearance model, through simulations, we identified the ratio of sinusoidal efflux clearance (CL) over the sum of metabolic and biliary CLs as important in predicting the impact of HI on the AUC of dual OATP/CYP3A substrates. Because HI may reduce hepatic CYP3A-mediated CL to a greater extent than biliary efflux CL, the greater the contribution of the former versus the latter, the greater the impact of HI on drug AUC ratio (AUCRHI ). Using physiologically-based pharmacokinetic modeling and simulation, we predicted relatively well the AUCRHI of OATP substrates that are not significantly metabolized (pitavastatin, rosuvastatin, valsartan, and gadoxetic acid). However, there was a trend toward underprediction of the AUCRHI of the dual OATP/CYP3A4 substrates fimasartan and atorvastatin. These predictions improved when the sinusoidal efflux CL of these two drugs was increased in healthy volunteers (i.e., before incorporating the effect of HI), and by modifying the directionality of its modulation by HI (i.e., increase or decrease). To accurately predict the effect of HI on AUC of hepatobiliary cleared drugs it is important to accurately predict all hepatobiliary pathways, including sinusoidal efflux CL.

Evaluation of Pupillometry for CYP2D6 Phenotyping in Children Treated with Tramadol.

Following the contraindication of codeine use in children, increasing use of tramadol has been observed in pain management protocols. However, tramadol's pharmacokinetics (PK) and pharmacodynamics are influenced by cytochrome P450 (CYP)2D6 activity, similarly to codeine. Previous studies in adults have demonstrated a correlation between pupillary response and tramadol PK. Our objective was to evaluate pupillometry as a phenotyping method to assess CYP2D6 activity in children treated with tramadol. We included 41 children (mean age 11 years) receiving a first dose of tramadol (2 mg/kg) in the emergency room (ER) as part of their routine care. CYP2D6 phenotyping and genotyping were performed. The concentrations of tramadol and its active metabolite, M1, were measured, and static and dynamic pupillometry was conducted using a handheld pupillometer at the time of tramadol administration and during the ER stay. Pupillometric measurements were obtained for 37 children. Tramadol affected pupillary parameters, with a decrease in pupil diameter in 83.8% of children (p = 0.002) (mean decrease 14.1 ± 16.7%) and a decrease in reflex amplitude constriction in 78.4% (p = 0.011) (mean decrease 17.7 ± 34.5%) at T150 compared to T0. We were unable to identify a correlation between pupillometry measurements and CYP2D6 activity. Likely confounding factors include light intensity, pain, and stress, making the procedure less feasible in paediatric emergency settings.

Predicting changes in the pharmacokinetics of CYP3A-metabolized drugs in hepatic impairment and insights into factors driving these changes.

Physiologically based pharmacokinetic models, populated with drug-metabolizing enzyme and transporter (DMET) abundance, can be used to predict the impact of hepatic impairment (HI) on the pharmacokinetics (PK) of drugs. To increase confidence in the predictive power of such models, they must be validated by comparing the predicted and observed PK of drugs in HI obtained by phenotyping (or probe drug) studies. Therefore, we first predicted the effect of all stages of HI (mild to severe) on the PK of drugs primarily metabolized by cytochrome P450 (CYP) 3A enzymes using the default HI module of Simcyp Version 21, populated with hepatic and intestinal CYP3A abundance data. Then, we validated the predictions using CYP3A probe drug phenotyping studies conducted in HI. Seven CYP3A substrates, metabolized primarily via CYP3A (fraction metabolized, 0.7-0.95), with low to high hepatic availability, were studied. For all stages of HI, the predicted PK parameters of drugs were within twofold of the observed data. This successful validation increases confidence in using the DMET abundance data in HI to predict the changes in the PK of drugs cleared by DMET for which phenotyping studies in HI are not available or cannot be conducted. In addition, using CYP3A drugs as an example, through simulations, we identified the salient PK factors that drive the major changes in exposure (area under the plasma concentration-time profile curve) to drugs in HI. This theoretical framework can be applied to any drug and DMET to quickly determine the likely magnitude of change in drug PK due to HI.

The next frontier in ADME science: Predicting transporter-based drug disposition, tissue concentrations and drug-drug interactions in humans.

Predicting transporter-based drug clearance (CL) and tissue concentrations (TC) in humans is important to reduce the risk of failure during drug development. In addition, when transporters are present at the tissue:blood interface (e.g., in the liver, blood-brain barrier), predicting TC is important to predict the drug's efficacy and safety. With the advent of quantitative targeted proteomics, in vitro to in vivo extrapolation (IVIVE) of transporter-based drug CL and TC is now possible using transporter-expressing models (cells lines, membrane vesicles) and the in vivo to in vitro relative expression of transporters (REF) as a scaling factor. Unlike other approaches based on physiological scaling, the REF approach is not dependent on the availability of primary cells. Here, we review the REF approach and compare it with other IVIVE approaches such as the relative activity factor approach and physiological scaling. For each of these scaling approaches, we review their underlying principles, assumptions, methodology, predictive performance, as well as advantages and limitations. Finally, we discuss current gaps in IVIVE of transporter-based CL and TC and propose possible reasons for these gaps as well as areas to investigate to bridge these gaps.

Is the Protein-Mediated Uptake of Drugs by Organic Anion Transporting Polypeptides a Real Phenomenon or an Artifact?

Plasma proteins or human serum albumin (HSA) have been reported to increase the in vitro intrinsic uptake clearance (CLint,uptake) of drugs by hepatocytes or organic anion transporting polypeptide (OATP)-transfected cell lines. This so-called protein-mediated uptake effect (PMUE) is thought to be due to an interaction between the drug-protein complex and the cell membrane causing an increase in the unbound drug concentration at the cell surface, resulting in an increase in the apparent CLint,uptake of the drug. To determine if the PMUE on OATP-mediated drug uptake is an artifact or a real phenomenon, we determined the effect of 1%, 2%, and 5% HSA on OATP1B1-mediated [human embryonic kidney (HEK)293 transfected cells] and passive CLint,uptake (mock HEK293 cells) on a cocktail of five statins. In addition, we determined the non-specific binding (NSB) of the statin-HSA complex to the cells/labware. The increase in uptake of atorvastatin, fluvastatin, and rosuvastatin in the presence of HSA was completely explained by the extent of NSB of the statin-HSA complex, indicating that the PMUE for these statins is an artifact. In contrast, this was not the case for OATP1B1-mediated uptake of pitavastatin and passive uptake of cerivastatin, suggesting that the PMUE is a real phenomenon for these drugs. Additionally, the PMUE on OATP1B1-mediated uptake of pitavastatin was confirmed by a decrease in its unbound IC50 in the presence of 5% HSA versus Hank's balanced salt solution buffer (HBSS). These data question the utility of routinely including plasma proteins or HSA in uptake experiments and the previous findings on PMUE on OATP-mediated drug uptake. SIGNIFICANCE STATEMENT: Here we report, for the first time, that the protein-mediated uptake effect (PMUE) on organic anion transporting polypeptide (OATP)-transported drugs could be an artifact of the non-specific binding (NSB) of the drug-albumin complex to cells/labware. Future experiments on PMUE must take into consideration such NSB. In addition, mechanisms other than PMUE need to be explored to explain the underprediction of in vivo OATP-mediated hepatic drug clearance from in vitro uptake studies.

Prediction of Hepatobiliary Clearances and Hepatic Concentrations of Transported Drugs in Humans Using Rosuvastatin as a Model Drug.

To assess efficacy and toxicity of a drug in humans, it is important to measure the tissue concentration of a drug at the target site. For a drug that is transported into or out of the tissue, the tissue unbound steady-state concentration can be dramatically different from its corresponding unbound steady-state plasma concentration. Because routine measurement of drug tissue concentrations is not possible, using rosuvastatin as a model transporter substrate drug, we compared the ability of the proteomics-informed relative expression factor (REF) approach and sandwich-cultured human hepatocytes (SCH) to accurately predict rosuvastatin human hepatobiliary clearances and hepatic concentrations. REF-predicted rosuvastatin biliary clearance (CLbile ), estimated using BCRP-overexpressing, MDR1-overexpressing, and MRP2-overexpressing vesicles, together with our previously published REF-predicted rosuvastatin hepatic sinusoidal uptake clearance (CLuptake ) and physiologically scaled sinusoidal passive uptake and efflux clearance (CLs,efflux ), were used to predict rosuvastatin hepatic concentrations. For SCH, the estimated rosuvastatin CLbile , CLuptake , and CLs,efflux were scaled using physiological scaling. The REF-predicted CLbile (6.39 ± 1.56 mL/minute) and hepatic rosuvastatin area under the concentration-time curve (AUC) fell within our a priori defined success criterion, i.e., within twofold of the observed positron emission tomography-imaged values. In contrast, as expected, SCH dramatically overpredicted (predicted/observed ratio P/O = 8.38-10.41) rosuvastatin CLbile , and underpredicted hepatic AUC (P/O = 0.08-0.14). For both approaches, predictions were improved by using the parallel tube model vs. well-stirred model. Overall, using rosuvastatin as a model drug, this study demonstrates the success of the REF approach in predicting in vivo CLbile and hepatic concentration of drugs, and highlights the shortcomings of the SCH approach in making such predictions.

Successful Prediction of Human Fetal Exposure to P-Glycoprotein Substrate Drugs Using the Proteomics-Informed Relative Expression Factor Approach and PBPK Modeling and Simulation.

Many women take drugs during their pregnancy to treat a variety of clinical conditions. To optimize drug efficacy and reduce fetal toxicity, it is important to determine or predict fetal drug exposure throughout pregnancy. Previously, we developed and verified a maternal-fetal physiologically based pharmacokinetic (m-f PBPK) model to predict fetal Kp,uu (unbound fetal plasma AUC/unbound maternal plasma AUC) of drugs that passively cross the placenta. Here, we used in vitro transport studies in Transwell, in combination with our m-f PBPK model, to predict fetal Kp,uu of drugs that are effluxed by placental P-glycoprotein (P-gp)-namely, dexamethasone, betamethasone, darunavir, and lopinavir. Using Transwell, we determined the efflux ratio of these drugs in hMDR1-MDCKcP-gpKO cells, in which human P-gp was overexpressed and the endogenous P-gp was knocked out. Then, using the proteomics-informed efflux ratio-relative expressive factor approach, we predicted the fetal Kp,uu of these drugs at term. Finally, to verify our predictions, we compared them with the observed in vivo fetal Kp,uu at term. The latter was estimated using our m-f PBPK model and published fetal [umbilical vein (UV)]/maternal plasma drug concentrations obtained at term (UV/maternal plasma). Fetal Kp,uu predictions for dexamethasone (0.63), betamethasone (0.59), darunavir (0.17), and lopinavir (0.08) were successful, as they fell within the 90% confidence interval of the corresponding in vivo fetal Kp,uu (0.30-0.66, 0.29-0.71, 0.11-0.22, 0.04-0.19, respectively). This is the first demonstration of successful prediction of fetal Kp,uu of P-gp drug substrates from in vitro studies. SIGNIFICANCE STATEMENT: For the first time, using in vitro studies in cells, this study successfully predicted human fetal Kp,uu of P-gp substrate drugs. This success confirms that the m-f PBPK model, combined with the ER-REF approach, can successfully predict fetal drug exposure to P-gp substrates. This success provides increased confidence in the use of the ER-REF approach, combined with the m-f PBPK model, to predict fetal Kp,uu of drugs (transported by P-gp or other transporters), both at term and at earlier gestational ages.

Successful Prediction of Human Steady-State Unbound Brain-to-Plasma Concentration Ratio of P-gp Substrates Using the Proteomics-Informed Relative Expression Factor Approach.

In order to optimize central nervous system (CNS) drug development, accurate prediction of the drug's human steady-state unbound brain interstitial fluid-to-plasma concentration ratio (Kp,uu,brain ) is critical, especially for drugs that are effluxed by the multiple drug resistance transporters (e.g., P-glycoprotein, P-gp). Due to lack of good in vitro human blood-brain barrier models, we and others have advocated the use of a proteomics-informed relative expressive factor (REF) approach to predict Kp,uu,brain . Therefore, we tested the success of this approach in humans, with a focus on P-gp substrates, using brain positron emission tomography imaging data for verification. To do so, the efflux ratio (ER) of verapamil, N-desmethyl loperamide, and metoclopramide was determined in human P-gp-transfected MDCKII cells using the Transwell assay. Then, using the ER estimate, Kp,uu,brain of the drug was predicted using REF (ER approach). Alternatively, in vitro passive and P-gp-mediated intrinsic clearances (CLs) of these drugs, estimated using a five-compartmental model, were extrapolated to in vivo using REF (active CL) and brain microvascular endothelial cells protein content (passive CL). The ER approach successfully predicted Kp,uu,brain of all three drugs within twofold of observed data and within 95% confidence interval of the observed data for verapamil and N-desmethyl loperamide. Using the in vitro-to-in vivo extrapolated clearance approach, Kp,uu,brain was reasonably well predicted but not the brain unbound interstitial fluid drug concentration-time profile. Therefore, we propose that the ER approach be used to predict Kp,uu,brain of CNS candidate drugs to enhance their success in development.

Abundance of P-Glycoprotein and Other Drug Transporters at the Human Blood-Brain Barrier in Alzheimer's Disease: A Quantitative Targeted Proteomic Study.

The human blood-brain barrier (BBB) transporter P-gp can efflux amyloid-ß (Aß) out of the central nervous system (CNS). Aß is thought to be the causative agent for Alzheimer's disease (AD). Using positron emission tomography imaging, we have shown that BBB P-gp activity is reduced in AD, as quantified by the in vivo brain distribution of the P-gp probe [11 C]-verapamil. Therefore, the aim of this study was to determine whether this reduced BBB P-gp activity in AD was due to decreased P-gp abundance at the BBB. Using targeted proteomics, we quantified the abundance of P-gp and other drug transporters in gray matter brain microvessels isolated from 43 subjects with AD and 38 age-matched controls (AMCs) from regions affected by AD (hippocampus and the parietal lobe of the brain cortex) and not affected by AD (cerebellum). First, P-gp abundance was decreased in the BBB of the hippocampus vs. the cerebellum in both subjects with AD and AMCs, and therefore was not AD-related. In addition, gray matter BBB abundance of P-gp (and of other transporters) in the hippocampus and the parietal lobe was not different between AD and AMC. The gray matter BBB abundance of all drug transporters decreased with age, likely due to age-dependent decrease in the density of brain microvessels. Collectively, the observed reduced in vivo cerebral BBB P-gp activity in AD cannot be explained by reduced abundance of P-gp at the BBB. Nevertheless, the drug transporter abundance at the human gray matter BBB data provided here can be used to predict brain distribution of drugs targeted to treat CNS diseases, including AD.

Physiologically-Based Pharmacokinetic Modeling for the Prediction of CYP2D6-Mediated Gene-Drug-Drug Interactions.

The aim of this work was to predict the extent of Cytochrome P450 2D6 (CYP2D6)-mediated drug-drug interactions (DDIs) in different CYP2D6 genotypes using physiologically-based pharmacokinetic (PBPK) modeling. Following the development of a new duloxetine model and optimization of a paroxetine model, the effect of genetic polymorphisms on CYP2D6-mediated intrinsic clearances of dextromethorphan, duloxetine, and paroxetine was estimated from rich pharmacokinetic profiles in activity score (AS)1 and AS2 subjects. We obtained good predictions for the dextromethorphan-duloxetine interaction (Ratio of predicted over observed area under the curve (AUC) ratio (Rpred/obs ) 1.38-1.43). Similarly, the effect of genotype was well predicted, with an increase of area under the curve ratio of 28% in AS2 subjects when compared with AS1 (observed, 33%). Despite an approximately twofold underprediction of the dextromethorphan-paroxetine interaction, an Rpred/obs of 0.71 was obtained for the effect of genotype on the area under the curve ratio. Therefore, PBPK modeling can be successfully used to predict gene-drug-drug interactions (GDDIs). Based on these promising results, a workflow is suggested for the generic evaluation of GDDIs and DDIs that can be applied in other situations.

Genotype-sensitive reversible and time-dependent CYP2D6 inhibition in human liver microsomes.

Cytochrome P450 (CYP) 2D6 metabolizes a wide range of xenobiotics and is characterized by a huge interindividual variability. A recent clinical study highlighted differential magnitude of CYP inhibition as a function of CYP2D6 genotype. The aim of this study was to investigate the effect of CYP2D6 genotype on the inhibition of dextromethorphan O-demethylation by duloxetine and paroxetine in human liver microsomes (HLMs). The study focused on genotypes defined by the combination of two fully functional alleles (activity score 2, AS 2, n = 6), of one fully functional and one reduced allele (activity score 1.5, AS 1.5, n = 4) and of one fully functional and one non-functional allele (activity score 1, AS 1, n = 6), which all predict extensive metabolizer phenotype. Kinetic experiments showed that maximal reaction velocity was affected by CYP2D6 genotype, with a decrease in 33% of Vmax in AS 1 HLMs compared to AS 2 (P = 0.06). No difference in inhibition parameters Ki , KI and kinact was observed neither with the competitive inhibitor duloxetine nor with the time-dependent inhibitor paroxetine. Among the genotypes tested, we found no difference in absolute CYP2D6 microsomal levels with ELISA immunoquantification. Therefore, our results suggest that genotype-sensitive magnitude of drug-drug interactions recently observed in vivo is likely to be due to differential amounts of functional enzymes at the microsomal level rather than to a difference in inhibition potencies across genotypes, which motivates for further quantitative proteomic investigations of functional and variant CYP2D6 alleles.

Complex Drug-Drug-Gene-Disease Interactions Involving Cytochromes P450: Systematic Review of Published Case Reports and Clinical Perspectives.

Drug pharmacokinetics (PK) is influenced by multiple intrinsic and extrinsic factors, among which concomitant medications are responsible for drug-drug interactions (DDIs) that may have a clinical relevance, resulting in adverse drug reactions or reduced efficacy. The addition of intrinsic factors affecting cytochromes P450 (CYPs) activity and/or expression, such as genetic polymorphisms and diseases, may potentiate the impact and clinical relevance of DDIs. In addition, greater variability in drug levels and exposures has been observed when such intrinsic factors are present in addition to concomitant medications perpetrating DDIs. This variability results in poor predictability of DDIs and potentially dramatic clinical consequences. The present review illustrates the issue of complex DDIs using systematically searched published case reports of DDIs involving genetic polymorphisms, renal impairment, cirrhosis, and/or inflammation. Current knowledge on the impact of each of these factors on drug exposure and DDIs is summarized and future perspectives for the management of such complex DDIs in clinical practice are discussed, including the use of advanced Computerized Physician Order Entry (CPOE) systems, the development of model-based dose optimization strategies, and the education of healthcare professionals with respect to personalized medicine.

Impact of CYP2D6 Functional Allelic Variations on Phenoconversion and Drug-Drug Interactions.

We investigated whether CYP2D6 extensive metabolizers carrying a nonfunctional allele are at higher risk of phenoconversion to poor metabolizers in the presence of CYP2D6 inhibitors. Seventeen homozygous carriers of two fully-functional alleles and 17 heterozygous carriers of one fully-functional and one nonfunctional allele participated in this trial. Dextromethorphan 5 mg and tramadol 10 mg were given at each of the three study sessions. CYP2D6 was inhibited by duloxetine 60 mg (session 2) and paroxetine 20 mg (session 3). A higher rate of phenoconversion to intermediate metabolizers with duloxetine (71% vs. 25%, P = 0.009) and to poor metabolizers with paroxetine (94% vs. 56%, P = 0.011) was observed in heterozygous than homozygous extensive metabolizers. The magnitude of drug-drug interaction between dextromethorphan and paroxetine was higher in homozygous than in heterozygous subjects (14.6 vs. 8.5, P < 0.028). Our study suggests that genetic extensive metabolizers may not represent a homogenous population and that available genetic data should be considered when addressing drug-drug interactions in clinical practice.

The Association of Combined GSTM1 and CYP2C9 Genotype Status with the Occurrence of Hemorrhagic Cystitis in Pediatric Patients Receiving Myeloablative Conditioning Regimen Prior to Allogeneic Hematopoietic Stem Cell Transplantation.

Hemorrhagic cystitis (HC) is one of the complications of busulfan-cyclophosphamide (BU-CY) conditioning regimen during allogeneic hematopoietic stem cell transplantation (HSCT) in children. Identifying children at high risk of developing HC in a HSCT setting could facilitate the evaluation and implementation of effective prophylactic measures. In this retrospective analysis genotyping of selected candidate gene variants was performed in 72 children and plasma Sulfolane (Su, water soluble metabolite of BU) levels were measured in 39 children following treatment with BU-CY regimen. The cytotoxic effects of Su and acrolein (Ac, water soluble metabolite of CY) were tested on human urothelial cells (HUCs). The effect of Su was also tested on cytochrome P 450 (CYP) function in HepaRG hepatic cells. Cumulative incidences of HC before day 30 post HSCT were estimated using Kaplan-Meier curves and log-rank test was used to compare the difference between groups in a univariate analysis. Multivariate Cox regression was used to estimate hazard ratios with 95% confidence intervals (CIs). Multivariate analysis included co-variables that were significantly associated with HC in a univariate analysis. Cumulative incidence of HC was 15.3%. In the univariate analysis, HC incidence was significantly (p < 0.05) higher in children older than 10 years (28.6 vs. 6.8%) or in children with higher Su levels (>40 vs. <11%) or in carriers of both functional GSTM1 and CYP2C9 (33.3 vs. 6.3%) compared to the other group. In a multivariate analysis, combined GSTM1 and CYP2C9 genotype status was associated with HC occurrence with a hazards ratio of 4.8 (95% CI: 1.3-18.4; p = 0.02). Ac was found to be toxic to HUC cells at lower concentrations (33 µM), Su was not toxic to HUC cells at concentrations below 1 mM and did not affect CYP function in HepaRG cells. Our observations suggest that pre-emptive genotyping of CYP2C9 and GSTM1 may aid in selection of more effective prophylaxis to reduce HC development in pediatric patients undergoing allogeneic HSCT. Article summary: (1) Children carrying functional alleles in GSTM1 and CYP2C9 are at high risk for developing hemorrhagic cystitis following treatment with busulfan and cyclophosphamide based conditioning regimen. (2) Identification of children at high risk for developing hemorrhagic cystitis in an allogeneic HSCT setting will enable us to evaluate and implement optimal strategies for its prevention. Trial registration: This study is a part of the trail "clinicaltrials.gov identifier: NCT01257854."

Pharmacogenomics of Oral Antithrombotic Drugs.

Pharmacogenomics is a relatively recent yet rapidly expanding field of study examining how genetic variations influence responses to drugs. Antithrombotic drugs include the anticoagulant and antiplatelet compounds widely prescribed for the treatment and prevention of cardiovascular diseases. However, there is a large variability in response to antithrombotics, and this can modify the benefit/risk ratio of taking such medications. This variability can be explained by clinical factors such as age, sex, and drug-drug interactions, but also by genetic variants. In recent years, several genetic polymorphisms have been associated with variable biological responses to antithrombotics. Relevant polymorphisms related to antithrombotics have included target genes and genes that participate in the drugs' pharmacokinetics. This article provides a comprehensive review of the published literature about the pharmacogenomics of antithrombotic drugs, including well-studied compounds such as vitamin K antagonists (e.g., warfarin, acenocoumarol, and phenprocoumon), aspirin, and clopidogrel, as well as more recently approved compounds such as prasugrel, ticagrelor, and direct oral anticoagulants.