Understanding the critical disposition pathways of statins to assess drug-drug interaction risk during drug development: it's not just about OATP1B1

Phase 1 Studies to Assess Inhaled Seralutinib as a Perpetrator or a Victim of Drug-Drug Interactions in Healthy Participants

Seralutinib, an inhaled, small-molecule tyrosine kinase inhibitor in clinical development for the treatment of pulmonary arterial hypertension (PAH), was evaluated for its potential as a perpetrator or victim of a metabolic and transporter-based drug-drug interactions in 2 phase 1 studies. In study 1, 24 participants received a cocktail of probe substrates: caffeine (CYP1A2), montelukast (CYP2C8), flurbiprofen (CYP2C9), midazolam (CYP3A), and pravastatin (OATP1B1/1B3), plus digoxin (P-gp) with or without seralutinib. In study 2, 19 participants received seralutinib with/without itraconazole, a strong CYP3A inhibitor, or fosaprepitant, a weak CYP3A inhibitor. Geometric least-squares mean ratios and 90% confidence intervals for maximum observed concentration (Cmax) and area under the plasma concentration-time curve (AUC) were obtained. Safety was monitored throughout the studies. All adverse events were mild or moderate in severity. Seralutinib coadministration increased AUC for midazolam 3.03-fold and caffeine 1.32-fold. The coadministration increased digoxin Cmax 1.28-fold. Seralutinib did not meaningfully alter Cmax and AUC for montelukast, flurbiprofen, or pravastatin. Fosaprepitant and itraconazole increased seralutinib AUC 1.08- and 1.84-fold, respectively. Seralutinib is a moderate CYP3A inhibitor and a weak CYP1A2 inhibitor; it slightly inhibits P-gp. Seralutinib exposure is minimally affected by a weak CYP3A inhibitor but is substantially increased by a strong CYP3A inhibitor.

Impact of genetic polymorphisms and drug-drug interactions mediated by carboxylesterase 1 on remimazolam deactivation

Remimazolam (Byfavo, Acacia Pharma), a recent Food and Drug Administration-approved ester-linked benzodiazepine, offers advantages in sedation, such as rapid onset and predictable duration, making it suitable for broad anesthesia applications. Its favorable pharmacological profile is primarily attributed to rapid hydrolysis, the primary metabolism pathway for its deactivation. Thus, understanding remimazolam hydrolysis determinants is essential for optimizing its clinical use. This study aimed to identify the enzyme(s) and tissue(s) responsible for remimazolam hydrolysis and to evaluate the influence of genetic polymorphisms and drug-drug interactions on its hydrolysis in the human liver. An initial incubation study with remimazolam and PBS, human serum, and the S9 fractions of human liver and intestine demonstrated that remimazolam was exclusively hydrolyzed by human liver S9 fractions. Subsequent incubation studies utilizing a carboxylesterase inhibitor (bis(4-nitrophenyl) phosphate), recombinant human carboxylesterase 1 (CES1) and carboxylesterase 2 confirmed that remimazolam is specifically hydrolyzed by CES1 in human liver. Furthermore, in vitro studies with wild-type CES1 and CES1 variants transfected cells revealed that certain genetic polymorphisms significantly impair remimazolam deactivation. Notably, the impact of CES1 G143E was verified using individual human liver samples. Moreover, our evaluation of the drug-drug interactions between remimazolam and several other substrates/inhibitors of CES1-including simvastatin, enalapril, clopidogrel, and sacubitril-found that clopidogrel significantly inhibited remimazolam hydrolysis at clinically relevant concentrations, with CES1 genetic variants potentially influencing the interactions. In summary, CES1 genetic variants and its interacting drugs are crucial factors contributing to interindividual variability in remimazolam hepatic hydrolysis, holding the potential to serve as biomarkers for optimizing remimazolam use. SIGNIFICANCE STATEMENT: This investigation demonstrates that remimazolam is deactivated by carboxylesterase 1 (CES1) in the human liver, with CES1 genetic variants and drug-drug interactions significantly influencing its metabolism. These findings emphasize the need to consider CES1 genetic variability and potential drug-drug interactions in remimazolam use, especially in personalized pharmacotherapy to achieve optimal anesthetic outcomes.

Coproporphyrin-I as a Selective OATP1B Biomarker Can Be Used to Delineate the Mechanisms of Complex Drug-Drug Interactions: Cedirogant Case Study

Cedirogant is an inverse agonist of retinoic acid-related orphan receptor gamma thymus developed for the treatment of chronic plaque psoriasis. Cedirogant induces cytochrome P450 (CYP) 3A4 while inhibiting P-glycoprotein (P-gp), breast cancer resistance protein (BCRP), organic anion transporting polypeptide (OATP) 1B1, and OATP1B3 in vitro. Static drug-drug interactions (DDIs) predictions suggested possible clinical induction of CYP3A4, and inhibition of P-gp, BCRP, and OATP1B1, leading to challenges in interpreting DDI studies between cedirogant and substrates of CYP3A, P-gp, BCRP, and OATP1B1/3. Here the effects of cedirogant on the pharmacokinetics of two statin drugs were investigated in healthy participants. Coproporphyrin-I (CP-I), a selective endogenous OATP1B biomarker, was used to assess the impact of cedirogant on OATP1B. Cedirogant (375 mg once daily) increased rosuvastatin maximum plasma concentration (Cmax) and area under the plasma concentration curve (AUCtau) by 141% and 55%, respectively when co-administered, whereas atorvastatin Cmax increased by 40% with no effect on its AUCtau compared with administration of rosuvastatin/atorvastatin alone. Cedirogant did not increase CP-I exposures, indicating no clinical OATP1B inhibition. The increased rosuvastatin exposure and minimal change in atorvastatin exposure with co-administration of cedirogant is attributed to BCRP inhibition and interplay between P-gp/BCRP inhibition and CYP3A induction, respectively. Correlation analysis with data from two investigational drugs (glecaprevir and flubentylosin) demonstrated that OATP1B1 R-value of > 1.5 and [Cmax,u]/[OATP1B1 IC50] of > 0.1 are associated with > 1.25-fold increase in CP-I Cmax ratio. This demonstrates the utility of CP-I in disentangling mechanisms underlying a complex DDI involving multiple transporters and enzymes and proposes refined criteria for static OATP1B inhibition predictions.

From hazard to risk prioritization: a case study to predict drug-induced cholestasis using physiologically based kinetic modeling

Cholestasis is characterized by hepatic accumulation of bile acids. Clinical manifestation of cholestasis only occurs in a small proportion of exposed individuals. The present study aims to develop a new approach methodology (NAM) to predict drug-induced cholestasis as a result of drug-induced hepatic bile acid efflux inhibition and the resulting bile acid accumulation. To this end, hepatic concentrations of a panel of drugs were predicted by a generic physiologically based kinetic (PBK) drug model. Their effects on hepatic bile acid efflux were incorporated in a PBK model for bile acids. The predicted bile acid accumulation was used as a measure for a drug's cholestatic potency. The selected drugs were known to inhibit hepatic bile acid efflux in an assay with primary suspension-cultured hepatocytes and classified as common, rare, or no for cholestasis incidence. Common cholestasis drugs included were atorvastatin, chlorpromazine, cyclosporine, glimepiride, ketoconazole, and ritonavir. The cholestasis incidence of the drugs appeared not to be adequately predicted by their Ki for inhibition of hepatic bile acid efflux, but rather by the AUC of the PBK model predicted internal hepatic drug concentration at therapeutic dose level above this Ki. People with slower drug clearance, a larger bile acid pool, reduced bile salt export pump (BSEP) abundance, or given higher than therapeutic dose levels were predicted to be at higher risk to develop drug-induced cholestasis. The results provide a proof-of-principle of using a PBK-based NAM for cholestasis risk prioritization as a result of transporter inhibition and identification of individual risk factors.

In Vivo Activity of Intestinal P-Glycoprotein and Hepatic Organic Anion Transporters Polypeptide in Pregnancy and Postpartum

This study investigates the influence of pregnancy on the in vivo activity of the intestinal P-glycoprotein (P-gp) and hepatic organic anion transporters polypeptide (OATP/BCRP) using, respectively, fexofenadine and rosuvastatin as probe drugs. Eleven healthy participants were investigated during the third trimester of pregnancy (Phase 1, 28 to 38 weeks of gestation) and in the postpartum period (Phase 2, 8 to 12 weeks postpartum). In both phases, after administration of a single oral dose of fexofenadine (60 mg) and rosuvastatin (5 mg), serial blood samples were collected for up to 24 h. Rosuvastatin and fexofenadine in plasma were analyzed by LC-MS/MS using previously validated methods. The pharmacokinetic parameters of fexofenadine and rosuvastatin (Phoenix WinNonLin software) with normal distribution (Shapiro-Wilk test) are presented as geometric mean and 90% confidence interval. Phases 1 and 2 were compared using the t test (P < .05). Fexofexadine AUC0-24 values do not differ (P-value: .0715) between Phase 1 (641.9 ng h/mL [500.6-823.1]) and Phase 2 (823.8 ng h/mL [641.5-1057.6]) showing that pregnancy (third trimester) does not alter intestinal P-gp activity. However, rosuvastatin AUC0-24 values are higher (P-value: .00005) in Phase 1 (18.7 ng h/mL [13.3-26.4]) when compared to Phase 2 (9.5 ng h/mL [6.7-13.4]), suggesting inhibition of OATP1B1/OATP1B3 transporters. In conclusion, pregnancy assessed during the third trimester does not alter the intestinal P-gp activity but reduces the activity of hepatic OATP1B1/OATP1B3 transporters. Therefore, adjustments in dosage regimens may be necessary for drugs with low therapeutic index, substrates of the OATP1B1/OATP1B3 transporters, administered during the third trimester of pregnancy.

Implementation of pharmacogenomics: Where are we now?

Pharmacogenomics (PGx), examining the effect of genetic variation on interpatient variation in drug disposition and response, has been widely studied for several decades. However, as cost, as well as turnaround time associated with PGx testing, has significantly improved, the use of PGx in the clinical setting has been gaining momentum. Nevertheless, challenges have emerged in the broader clinical implementation of PGx. In this review, we will outline current models of PGx delivery and methodologies of evaluation, and discuss clinically relevant PGx tests and associated medications. Additionally, we will describe our approach for the broad implementation of pre-emptive DPYD genotyping in patients taking fluoropyrimidines in Ontario, Canada, as an example of clinically actionable PGx testing with sufficient clinical evidence of patient benefit that can become a new standard of patient care. We will highlight challenges associated with PGx testing, including a lack of diversity in PGx studies as well as general limitations that impact the broad adoption of PGx testing. Lastly, we examine the future of PGx, discussing new clinical targets, methodologies and analysis approaches.

Detection of muscular system adverse reaction signals in sacubitril/valsartan treatment combined with statins

Objective

To detect muscular system adverse reaction signals of sacubitril/valsartan treatment combined with statins (atorvastatin, rosuvastatin, simvastatin) to provide a reference for clinical administration.

Methods

Multiplicative and additive models were used to mine the FDA's spontaneous reports database to detect signals of drug-drug interactions between sacubitril/valsartan and statins. SAS 9.4 software was used to conduct statistical tests for suspicious signals to determine whether the signals were statistically significant.

Results

A total of 8,883,870 adverse reaction reports were analyzed. The combinations "sacubitril/valsartan - simvastatin - musculoskeletal muscle pain" had statistically significant correlation signals in both models (P < 0.05). The combination "sacubitril/valsartan - atorvastatin - myopathy" and "sacubitril/valsartan-simvastatin - myopathy" had statistically significant correlation signal in the multiplicative model (P < 0.05).

Conclusion

Compared with a single drug, coadministration of sacubitril/valsartan with atorvastatin may increase safety risks to myopathy, with simvastatin may increase safety risks to the musculoskeletal pain and myopathy, which should be closely monitored in clinical practice.

Physiologically based pharmacokinetics modeling and transporter proteomics to predict systemic and local liver and muscle disposition of statins

Statins are used to reduce liver cholesterol levels but also carry a dose-related risk of skeletal muscle toxicity. Concentrations of statins in plasma are often used to assess efficacy and safety, but because statins are substrates of membrane transporters that are present in diverse tissues, local differences in intracellular tissue concentrations cannot be ruled out. Thus, plasma concentration may not be an adequate indicator of efficacy and toxicity. To bridge this gap, we used physiologically based pharmacokinetic (PBPK) modeling to predict intracellular concentrations of statins. Quantitative data on transporter clearance were scaled from in vitro to in vivo conditions by integrating targeted proteomics and transporter kinetics data. The developed PBPK models, informed by proteomics, suggested that organic anion-transporting polypeptide 2B1 (OATP2B1) and multidrug resistance-associated protein 1 (MRP1) play a pivotal role in the distribution of statins in muscle. Using these PBPK models, we were able to predict the impact of alterations in transporter function due to genotype or drug-drug interactions on statin systemic concentrations and exposure in liver and muscle. These results underscore the potential of proteomics-guided PBPK modeling to scale transporter clearance from in vitro data to real-world implications. It is important to evaluate the role of drug transporters when predicting tissue exposure associated with on- and off-target effects.

Quantitative prediction of transporter-mediated drug-drug interactions using the mechanistic static pharmacokinetic (MSPK) model

Guidance/guidelines on drug-drug interactions (DDIs) have been issued in Japan, the United States, and Europe. These guidance/guidelines provide decision trees for conducting metabolizing enzyme-mediated clinical DDI studies; however, the decision trees for transporter-mediated DDIs lack quantitative prediction methods. In this study, the accuracy of a net-effect mechanistic static pharmacokinetics (MSPK) model containing the fraction transported (ft) of transporters was examined to predict transporter-mediated DDIs. This study collected information on 25 oral drugs with new active reagents that were used in clinical DDI studies as perpetrators (42 cases) from drugs approved in Japan between April 2016 and June 2020. The AUCRs (AUC ratios with and without perpetrators) of victim drugs were predicted using the net-effect MSPK model. As a result, 83 and 95% of the predicted AUCRs were within 1.5- and 2-fold error in the observed AUCRs, respectively. In cases where the victims were statins in which pharmacokinetics several transporters are involved, 70 and 91% of the predicted AUCRs were within 1.5- and 2-fold errors, respectively. Therefore, the net-effect MSPK model was applicable for predicting the AUCRs of victims, which are substrates for multiple transporters.

Ticagrelor Increases Exposure to the Breast Cancer Resistance Protein Substrate Rosuvastatin

Ticagrelor and rosuvastatin are often used concomitantly after atherothrombotic events. Several cases of rhabdomyolysis during concomitant ticagrelor and rosuvastatin have been reported, suggesting a drug-drug interaction. We showed recently that ticagrelor inhibits breast cancer resistance protein (BCRP) and organic anion transporting polypeptide (OATP) 1B1, 1B3, and 2B1-mediated rosuvastatin transport in vitro. The aim of this study was to investigate the effects of ticagrelor on rosuvastatin pharmacokinetics in humans. In a randomized, crossover study, 9 healthy volunteers ingested a single dose of 90 mg ticagrelor or placebo, followed by a single 10 mg dose of rosuvastatin 1 hour later. Ticagrelor 90 mg or placebo were additionally administered 12, 24, and 36 hours after their first dose. Ticagrelor increased rosuvastatin area under the plasma concentration-time curve (AUC) and peak plasma concentration 2.6-fold (90% confidence intervals: 1.8-3.8 and 1.7-4.0, P = 0.001 and P = 0.003), and prolonged its half-life from 3.1 to 6.6 hours (P = 0.009). Ticagrelor also decreased the renal clearance of rosuvastatin by 11% (3%-19%, P = 0.032). The N-desmethylrosuvastatin:rosuvastatin AUC0-10h ratio remained unaffected by ticagrelor. Ticagrelor had no effect on the plasma concentrations of the endogenous OATP1B substrates glycodeoxycholate 3-O-glucuronide, glycochenodeoxycholate 3-O-glucuronide, glycodeoxycholate 3-O-sulfate, and glycochenodeoxycholate 3-O-sulfate, or the sodium-taurocholate cotransporting polypeptide substrate taurocholic acid. These data indicate that ticagrelor increases rosuvastatin concentrations more than twofold in humans, probably mainly by inhibiting intestinal BCRP. Because the risk for rosuvastatin-induced myotoxicity increases along with rosuvastatin plasma concentrations, using ticagrelor concomitantly with high doses of rosuvastatin should be avoided.

Interindividual variability in statin pharmacokinetics and effects of drug transporters

INTRODUCTION

Statins are HMG-CoA reductase inhibitors that primarily lower plasma cholesterol levels. It has been suggested that the myotoxic response is a direct result of hydroxymethylglutaryl-CoA reductase inhibition and dose-dependent. Therefore, an accurate understanding of the combination of drugs that inhibit statin metabolism and factors that cause interindividual variability in the pharmacokinetics of statin is important to avoid serious side effects of statins. Relevant articles included in this review were identified through a PubMed search (through May 2023).

AREAS COVERED

This review provides an overview of hepatic and intestinal metabolism of statins, followed by a discussion of drug-drug interactions and interindividual variables that influence statin pharmacokinetics: gut bacteria, disease, and pharmacokinetics-related genetic polymorphisms.

EXPERT OPINION

Drug-drug interactions have a strong influence on statin pharmacokinetics, and gut microbiota, disease, and genetic polymorphisms all contribute significantly to interindividual variation in statin pharmacokinetics. Individual optimization of statin treatment requires studies that consider the progression of the disease and associated changes in concomitant medications.

The Evaluation and Management of Coronary Artery Disease in the Lung Transplant Patient

Lung transplantation can greatly improve quality of life and extend survival in those with end-stage lung disease. In order to derive the maximal benefit from such a procedure, patients must be carefully selected and be otherwise healthy enough to survive a high-risk surgery and sometimes prolonged immunosuppressive therapy following surgery. Patients therefore must be critically assessed prior to being listed for transplantation with close attention paid towards assessment of cardiovascular health and operative risk. One of the biggest dictators of this is coronary artery disease. In this review article, we discuss the assessment and management of coronary artery disease in the potential lung transplant candidate.

Utilization of Rosuvastatin and Endogenous Biomarkers in Evaluating the Impact of Ritlecitinib on BCRP, OATP1B1, and OAT3 Transporter Activity

PURPOSE

Ritlecitinib, an inhibitor of Janus kinase 3 and tyrosine kinase expressed in hepatocellular carcinoma family kinases, is in development for inflammatory diseases. This study assessed the impact of ritlecitinib on drug transporters using a probe drug and endogenous biomarkers.

METHODS

In vitro transporter-mediated substrate uptake and inhibition by ritlecitinib and its major metabolite were evaluated. Subsequently, a clinical drug interaction study was conducted in 12 healthy adult participants to assess the effect of ritlecitinib on pharmacokinetics of rosuvastatin, a substrate of breast cancer resistance protein (BCRP), organic anion transporting polypeptide 1B1 (OATP1B1), and organic anion transporter 3 (OAT3). Plasma concentrations of coproporphyrin I (CP-I) and pyridoxic acid (PDA) were assessed as endogenous biomarkers for OATP1B1 and OAT1/3 function, respectively.

RESULTS

In vitro studies suggested that ritlecitinib can potentially inhibit BCRP, OATP1B1 and OAT1/3 based on regulatory cutoffs. In the subsequent clinical study, coadministration of ritlecitinib decreased rosuvastatin plasma exposure area under the curve from time 0 to infinity (AUCinf) by  ~ 13% and maximum concentration (Cmax) by  ~ 27% relative to rosuvastatin administered alone. Renal clearance was comparable in the absence and presence of ritlecitinib coadministration. PK parameters of AUCinf and Cmax for CP-I and PDA were also similar regardless of ritlecitinib coadministration.

CONCLUSION

Ritlecitinib does not inhibit BCRP, OATP1B1, and OAT3 and is unlikely to cause a clinically relevant interaction through these transporters. Furthermore, our findings add to the body of evidence supporting the utility of CP-I and PDA as endogenous biomarkers for assessment of OATP1B1 and OAT1/3 transporter activity.

Understanding Statin-Roxadustat Drug-Drug-Disease Interaction Using Physiologically-Based Pharmacokinetic Modeling

A different drug-drug interaction (DDI) scenario may exist in patients with chronic kidney disease (CKD) compared with healthy volunteers (HVs), depending on the interplay between drug-drug and disease (drug-drug-disease interaction (DDDI)). Physiologically-based pharmacokinetic (PBPK) modeling, in lieu of a clinical trial, is a promising tool for evaluating these complex DDDIs in patients. However, the prediction confidence of PBPK modeling in the severe CKD population is still low when nonrenal pathways are involved. More mechanistic virtual disease population and robust validation cases are needed. To this end, we aimed to: (i) understand the implications of severe CKD on statins (atorvastatin, simvastatin, and rosuvastatin) pharmacokinetics (PK) and DDI; and (ii) predict untested clinical scenarios of statin-roxadustat DDI risks in patients to guide suitable dose regimens. A novel virtual severe CKD population was developed incorporating the disease effect on both renal and nonrenal pathways. Drug and disease PBPK models underwent a four-way validation. The verified PBPK models successfully predicted the altered PKs in patients for substrates and inhibitors and recovered the observed statin-rifampicin DDIs in patients and the statin-roxadustat DDIs in HVs within 1.25- and 2-fold error. Further sensitivity analysis revealed that the severe CKD effect on statins PK is mainly mediated by hepatic BCRP for rosuvastatin and OATP1B1/3 for atorvastatin. The magnitude of statin-roxadustat DDI in patients with severe CKD was predicted to be similar to that in HVs. PBPK-guided suitable dose regimens were identified to minimize the risk of side effects or therapeutic failure of statins when co-administered with roxadustat.

Imeglimin: A Clinical Pharmacology Review

Imeglimin (PXL008, EMD-387008, Twymeeg®) is a first-in-class novel oral hypoglycemic agent, launched in Japan, for the treatment of type 2 diabetes mellitus. Its mechanism of action targets mitochondrial bioenergetics to ameliorate insulin resistance and to enhance β-cell function. This review summarizes the properties underlying the pharmacokinetic profile of imeglimin, a small cationic drug belonging to the tetrahydrotriazine chemical class, with a complex mechanism of absorption involving an active transport through organic cation transporters (OCTs). Imeglimin absorption decreases when dose increases due to the saturation of the active uptake transport. Post absorption, imeglimin is rapidly and primarily distributed to organs and tissues, and has a half-life ranging from 9.03 to 20.2 h. Plasma protein binding of imeglimin is low, which explains the rapid distribution to the organs observed in all species. Imeglimin is excreted unchanged in urine, indicating a low extent of metabolism. Imeglimin is a substrate of multidrug and toxic compound extrusion (MATE) 2-K and a substrate and inhibitor of OCT1, OCT2, and MATE1. Clinical drug-drug interaction studies confirmed the absence of relevant clinical interaction with substrates or inhibitors of these transporters. Overall, the drug-drug interaction potential of imeglimin is low. Its pharmacokinetics profile has also been characterized in special populations, showing no influence of mild and moderate hepatic impairment but an impact of renal function on imeglimin renal clearance. Dosage adjustment is thus required in moderately and severely renally impaired patients. Imeglimin pharmacokinetics was shown to be insensitive to ethnicity and food intake and to have no effect on QTcF interval.

MCFF-MTDDI: multi-channel feature fusion for multi-typed drug-drug interaction prediction

Adverse drug-drug interactions (DDIs) have become an increasingly serious problem in the medical and health system. Recently, the effective application of deep learning and biomedical knowledge graphs (KGs) have improved the DDI prediction performance of computational models. However, the problems of feature redundancy and KG noise also arise, bringing new challenges for researchers. To overcome these challenges, we proposed a Multi-Channel Feature Fusion model for multi-typed DDI prediction (MCFF-MTDDI). Specifically, we first extracted drug chemical structure features, drug pairs' extra label features, and KG features of drugs. Then, these different features were effectively fused by a multi-channel feature fusion module. Finally, multi-typed DDIs were predicted through the fully connected neural network. To our knowledge, we are the first to integrate the extra label information into KG-based multi-typed DDI prediction; besides, we innovatively proposed a novel KG feature learning method and a State Encoder to obtain target drug pairs' KG-based features which contained more abundant and more key drug-related KG information with less noise; furthermore, a Gated Recurrent Unit-based multi-channel feature fusion module was proposed in an innovative way to yield more comprehensive feature information about drug pairs, effectively alleviating the problem of feature redundancy. We experimented with four datasets in the multi-class and the multi-label prediction tasks to comprehensively evaluate the performance of MCFF-MTDDI for predicting interactions of known-known drugs, known-new drugs and new-new drugs. In addition, we further conducted ablation studies and case studies. All the results fully demonstrated the effectiveness of MCFF-MTDDI.

Rhabdomyolysis during concomitant ticagrelor and rosuvastatin: A breast cancer resistance protein-mediated drug interaction?

We present 3 patients diagnosed with rhabdomyolysis 1-6 months after the initiation of concomitant rosuvastatin and ticagrelor medication. A literature review and Food and Drug Administration adverse event reporting system revealed >40 reports of rhabdomyolysis during concomitant ticagrelor and rosuvastatin, including 3 with a fatal outcome. We show that ticagrelor inhibits breast cancer resistance protein-, organic anion transporting polypeptide (OATP) 1B1-, 1B3- and 2B1-mediated transport of rosuvastatin in vitro with half-maximal unbound inhibitory concentrations of 0.36, 4.13, 7.5 and 3.26 μM, respectively. A static drug interaction model predicted that ticagrelor may inhibit intestinal breast cancer resistance protein and thus increase rosuvastatin plasma exposure 2.1-fold, whereas the OATP-mediated hepatic uptake of rosuvastatin should not be inhibited due to relatively low portal ticagrelor concentrations. Taken together, concomitant use of ticagrelor with rosuvastatin may increase the systemic exposure to rosuvastatin and the risk of rosuvastatin-induced rhabdomyolysis. Further studies are warranted to investigate the potential pharmacokinetic interaction between ticagrelor and rosuvastatin in humans.

Mechanistic in vitro studies indicate that the clinical drug-drug interactions between protease inhibitors and rosuvastatin are driven by inhibition of intestinal BCRP and hepatic OATP1B1 with minimal contribution from OATP1B3, NTCP and OAT3

Previous use of a mechanistic static model to accurately quantify the increased rosuvastatin exposure due to drug-drug interaction (DDI) with coadministered atazanavir underpredicted the magnitude of area under the plasma concentration-time curve ratio (AUCR) based on inhibition of breast cancer resistance protein (BCRP) and organic anion transporting polypeptide (OATP) 1B1. To reconcile the disconnect between predicted and clinical AUCR, atazanavir and other protease inhibitors (darunavir, lopinavir and ritonavir) were evaluated as inhibitors of BCRP, OATP1B1, OATP1B3, sodium taurocholate cotransporting polypeptide (NTCP) and organic anion transporter (OAT) 3. None of the drugs inhibited OAT3, nor did darunavir and ritonavir inhibit OATP1B3 or NTCP. All drugs inhibited BCRP-mediated estrone 3-sulfate transport or OATP1B1-mediated estradiol 17β-D-glucuronide transport with the same rank order of inhibitory potency (lopinavir>ritonavir>atazanavir>>darunavir) and mean IC50 values ranging from 15.5 ± 2.80 μM to 143 ± 14.7 μM or 0.220 ± 0.0655 μM to 9.53 ± 2.50 μM, respectively. Atazanavir and lopinavir also inhibited OATP1B3- or NTCP-mediated transport with a mean IC50 of 1.86 ± 0.500 μM or 65.6 ± 10.7 μM and 5.04 ± 0.0950 μM or 20.3 ± 2.13 μM, respectively. Following integration of a combined hepatic transport component into the previous mechanistic static model using the in vitro inhibitory kinetic parameters determined above for atazanavir, the newly predicted rosuvastatin AUCR reconciled with the clinically observed AUCR confirming additional minor involvement of OATP1B3 and NTCP inhibition in its DDI. The predictions for the other protease inhibitors confirmed inhibition of intestinal BCRP and hepatic OATP1B1 as the principal pathways involved in their clinical DDI with rosuvastatin.

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.

A comprehensive pharmacogenomic study indicates roles for SLCO1B1, ABCG2 and SLCO2B1 in rosuvastatin pharmacokinetics

AIMS

The aim was to comprehensively investigate the effects of genetic variability on the pharmacokinetics of rosuvastatin.

METHODS

We conducted a genome-wide association study and candidate gene analyses of single dose rosuvastatin pharmacokinetics in a prospective study (n = 159) and a cohort of previously published studies (n = 88).

RESULTS

In a genome-wide association meta-analysis of the prospective study and the cohort of previously published studies, the SLCO1B1 c.521 T > C (rs4149056) single nucleotide variation (SNV) associated with increased area under the plasma concentration-time curve (AUC) and peak plasma concentration of rosuvastatin (P = 1.8 × 10^-12 and P = 3.2 × 10^-15 ). The candidate gene analysis suggested that the ABCG2 c.421C > A (rs2231142) SNV associates with increased rosuvastatin AUC (P = .0079), while the SLCO1B1 c.388A > G (rs2306283) and SLCO2B1 c.1457C > T (rs2306168) SNVs associate with decreased rosuvastatin AUC (P = .0041 and P = .0076). Based on SLCO1B1 genotypes, we stratified the participants into poor, decreased, normal, increased and highly increased organic anion transporting polypeptide (OATP) 1B1 function groups. The OATP1B1 poor function phenotype associated with 2.1-fold (90% confidence interval 1.6-2.8, P = 4.69 × 10^-5 ) increased AUC of rosuvastatin, whereas the OATP1B1 highly increased function phenotype associated with a 44% (16-62%; P = .019) decreased rosuvastatin AUC. The ABCG2 c.421A/A genotype associated with 2.2-fold (1.5-3.0; P = 2.6 × 10^-4 ) increased AUC of rosuvastatin. The SLCO2B1 c.1457C/T genotype associated with 28% decreased rosuvastatin AUC (11-42%; P = .01).

CONCLUSION

These data suggest roles for SLCO1B1, ABCG2 and SLCO2B1 in rosuvastatin pharmacokinetics. Poor SLCO1B1 or ABCG2 function genotypes may increase the risk of rosuvastatin-induced myotoxicity. Reduced doses of rosuvastatin are advisable for patients with these genotypes.

ABCG2 and SLCO1B1 gene polymorphisms in the Croatian population

BACKGROUND

Organic anion-transporting polypeptide 1B1 (OATP1B1) and the ATP-binding cassette subfamily G member 2, ABCG2, are important transporters involved in the transport of endogenous substrates and xenobiotics, including drugs. Genetic polymorphisms of these transporters have effect on transporter activity. There is significant interethnic variability in the frequency of allele variants.

AIM

To determined allele and genotype frequencies of ABCG2 and SLCO1B1 genes in Croatian populations of European descent.

SUBJECTS AND METHODS

A total of 905 subjects (482 women) were included. Genotyping for ABCG2 c.421C > A (rs2231142) and for SLCO1B1 c.521T > C (rs4149056), was performed by real-time polymerase chain reaction (PCR) using TaqMan^® DME Genotyping Assays.

RESULTS

For ABCG2 c.421C > A, the frequency of CC, CA and AA genotypes was 81.4%, 17.8% and 0.8% respectively. The frequency of variant ABCG2 421 A allele was 9.7%. For SLCO1B1 c.521T > C, the frequency of TT, TC and CC genotypes was 61.7%, 34.8% and 3.5% respectively. The frequency of variant SLCO1B1 521 C allele was 20.9%.

CONCLUSION

The frequency of the ABCG2 and SLCO1B1 allelic variants and genotypes in the Croatian population is in accordance with other European populations. Pharmacogenetic analysis can serve to individualise drug therapy and minimise the risk of developing adverse drug reactions.

Clinical conundrums involving statin drug-drug interactions

Statins are the cornerstone of pharmacologic therapy for the prevention and treatment of atherosclerotic cardiovascular disease. While they are generally considered safe, statins can be affected by drug-drug interactions (DDIs) that increase their systemic exposure increasing the risk for statin-associated muscle symptoms. These interactions are primarily mediated through metabolizing enzymes such as cytochrome P450 isoenzymes and membrane-bound drug transporting proteins including P-glycoprotein and organic ion transporting polypeptide. Recognition and avoidance of clinically significant statin DDIs is important to ensure their safe use. Conversely, concern over statin DDIs that are not clinically significant may lead to inappropriate underutilization or avoidance of statins in patients who would benefit from them. While many statin DDIs are well-characterized, we present several others that are less-well-established which may warrant clinical attention.

Studying the right transporter at the right time: an in vitro strategy for assessing drug-drug interaction risk during drug discovery and development

INTRODUCTION

Transporters are significant in dictating drug pharmacokinetics, thus inhibition of transporter function can alter drug concentrations resulting in drug-drug interactions (DDIs). Because they can impact drug toxicity, transporter DDIs are a regulatory concern for which prediction of clinical effect from in vitro data is critical to understanding risk.

AREA COVERED

The authors propose in vitro strategies to assist mitigating/removing transporter DDI risk during development by frontloading specific studies, or managing patient risk in the clinic. An overview of clinically relevant drug transporters and observed DDIs are provided, alongside presentation of key considerations/recommendations for in vitro study design evaluating drugs as inhibitors or substrates. Guidance on identifying critical co-medications, clinically relevant disposition pathways and using mechanistic static equations for quantitative prediction of DDI is compiled.

EXPERT OPINION

The strategies provided will facilitate project teams to study the right transporter at the right time to minimise development risks associated with DDIs. To truly alleviate or manage clinical risk, the industry will benefit from moving away from current qualitative basic static equation approaches to transporter DDI hazard assessment towards adopting the use of mechanistic models to enable quantitative DDI prediction, thereby contextualising risk to ascertain whether a transporter DDI is simply pharmacokinetic or clinically significant requiring intervention.

Cluster Gauss-Newton method analyses of PBPK model parameter combinations of coproporphyrin-I based on OATP1B-mediated rifampicin interaction studies

Coproporphyrin I (CP-I) is an endogenous biomarker supporting the prediction of drug-drug interactions (DDIs) involving hepatic organic anion transporting polypeptide 1B (OATP1B). We previously constructed a physiologically-based pharmacokinetic (PBPK) model for CP-I using clinical DDI data with an OATP1B inhibitor, rifampicin (RIF). In this study, PBPK model parameters for CP-I were estimated using the cluster Gauss-Newton method (CGNM), an algorithm used to find multiple approximate solutions for nonlinear least-squares problems. Eight unknown parameters including the hepatic overall intrinsic clearance (CL_int,all ), the rate of biosynthesis (v_syn ), and the OATP1B inhibition constant of RIF(K_i,u,OATP ) were estimated by fitting to the observed CP-I blood concentrations in two different clinical studies involving changing the RIF dose. Multiple parameter combinations were obtained by CGNM that could well capture the clinical data. Among those, CL_int,all , K_i,u,OATP , and v_syn were sensitive parameters. The obtained K_i,u,OATP for CP-I was 5.0- and 2.8-fold lower than that obtained for statins, confirming our previous findings describing substrate-dependent K_i,u,OATP values. In conclusion, CGNM analyses of PBPK model parameter combinations enables estimation of the three essential parameters for CP-I to capture the DDI profiles, even if the other parameters remain unidentified. The CGNM also clarified the importance of appropriate combinations of other unidentified parameters to enable capture of the CP-I concentration time course under the influence of RIF. The described CGNM approach may also support the construction of robust PBPK models for additional transporter biomarkers beyond CP-I.

Does the choice of applied physiologically‐based pharmacokinetics platform matter? A case study on simvastatin disposition and drug–drug interaction

Physiologically‐based pharmacokinetic (PBPK) models have an important role in drug discovery/development and decision making in regulatory submissions. This is facilitated by predefined PBPK platforms with user‐friendly graphical interface, such as Simcyp and PK‐Sim. However, evaluations of platform differences and the potential implications for disposition‐related applications are still lacking. The aim of this study was to assess how PBPK model development, input parameters, and model output are affected by the selection of PBPK platform. This is exemplified via the establishment of simvastatin PBPK models (workflow, final models, and output) in PK‐Sim and Simcyp as representatives of established whole‐body PBPK platforms. The major finding was that the choice of PBPK platform influenced the model development strategy and the final model input parameters, however, the predictive performance of the simvastatin models was still comparable between the platforms. The main differences between the structure and implementation of Simcyp and PK‐Sim were found in the absorption and distribution models. Both platforms predicted equally well the observed simvastatin (lactone and acid) pharmacokinetics (20–80 mg), BCRP and OATP1B1 drug–gene interactions (DGIs), and drug–drug interactions (DDIs) when co‐administered with CYP3A4 and OATP1B1 inhibitors/inducers. This study illustrates that in‐depth knowledge of established PBPK platforms is needed to enable an assessment of the consequences of PBPK platform selection. Specifically, this work provides insights on software differences and potential implications when bridging PBPK knowledge between Simcyp and PK‐Sim users. Finally, it provides a simvastatin model implemented in both platforms for risk assessment of metabolism‐ and transporter‐mediated DGIs and DDIs.

Results From Drug-Drug Interaction Studies In Vitro and In Vivo Investigating the Inhibitory Effect of Finerenone on the Drug Transporters BCRP, OATP1B1, and OATP1B3

BACKGROUND AND OBJECTIVES

In vitro and in vivo studies were performed with the novel, selective, nonsteroidal mineralocorticoid receptor antagonist finerenone to assess the relevance of inhibitory effects on the transporters breast cancer resistance protein (BCRP), organic anion transporting polypeptide 1B1 (OATP1B1), and OATP1B3. These transporters are involved in the disposition of a number of drugs, including statins. Statins are also a frequent comedication in patients receiving finerenone. Therefore, inhibitory effects on BCRP and OATPs are of potential clinical relevance.

METHODS

The effect on the transport of specific substrates of BCRP and OATP1B1/1B3 was assessed in cell-based in vitro assays with finerenone or its metabolites. A fixed-sequence crossover study in 14 healthy male volunteers investigated the effects of finerenone (40 mg once daily) on the pharmacokinetics of the index substrate rosuvastatin (5 mg) administered alone, simultaneously with, or approximately 4 h before finerenone. The effect of finerenone on the endogenous OATP substrates coproporphyrin I and III was also assessed.

RESULTS

Based on in vitro findings and threshold values proposed in regulatory guidelines, finerenone appeared to be a potentially relevant inhibitor of all three transporters. Relevant inhibition could also not be ruled out for the finerenone metabolites M1a (OATP1B1) and M3a (OATP1B1 and OAT1B3), which prompted an investigation into the relevance of these findings in vivo. After administration on a background of finerenone 40 mg, all point estimates of area under the curve ratios (114.47% [rosuvastatin], 99.62% [coproporphyrin I; simultaneous], and 105.28% [rosuvastatin; 4 h separation]) and maximum concentration ratios (111.24% [rosuvastatin], 101.22% [coproporphyrin I], 89.14% [coproporphyrin III; simultaneous], and 96.84% [rosuvastatin; 4 h separation]) of the investigated substrates were within 80.0-125%. In addition, the 90% confidence intervals of the ratios were within the conventional no-effect boundaries of 80.0% and 125% for rosuvastatin after temporally separated administration, and for coproporphyrin I and III.

CONCLUSION

Administration of finerenone 40 mg once daily confers no risk of clinically relevant drug-drug interactions with substrates of BCRP, OATP1B1, or OATP1B3. The potential for relevant inhibition of these transporters suggested by in vitro findings was not confirmed in vivo.

Coproporphyrin I as an Endogenous Biomarker to Detect Reduced OATP1B Activity and Shift in Elimination Route in Chronic Kidney Disease

Coproporphyrin I (CPI) is an endogenous biomarker of organic anion transporting polypeptide 1B transporter (OATP1B). CPI plasma baseline was reported to increase with severity of chronic kidney disease (CKD). Further, ratio of CPI area under the plasma concentration-time curve (AUCR) in the presence/absence of OATP1B inhibitor rifampin was higher in patients with CKD compared with healthy participants, in contrast to pitavastatin (a clinical OATP1B probe). This study investigated mechanism(s) contributing to altered CPI baseline in patients with CKD by extending a previously developed physiologically-based pharmacokinetic (PBPK) model to this patient population. CKD-related covariates were evaluated in a stepwise manner on CPI fraction unbound in plasma (f_u,p ), OATP1B-mediated hepatic uptake clearance (CL_active ), renal clearance (CL_R ), and endogenous synthesis (k_syn ). The CPI model successfully recovered increased baseline and rifampin-mediated AUCR in patients with CKD by accounting for the following disease-related changes: 13% increase in f_u,p , 29% and 39% decrease in CL_active in mild and moderate to severe CKD, respectively, decrease in CL_R proportional to decline in glomerular filtration rate, and 27% decrease in k_syn in severe CKD. Almost complete decline in CPI renal elimination in severe CKD increased its fraction transported by OATP1B, rationalizing differences in the CPI-rifampin interaction observed between healthy participants and patients with CKD. In conclusion, mechanistic modeling performed here supports CKD-related decrease in OATP1B function to inform prospective PBPK modeling of OATP1B-mediated drug-drug interaction in these patients. Monitoring of CPI allows detection of CKD-drug interaction risk for OATP1B drugs with combined hepatic and renal elimination which may be underestimated by extrapolating the interaction risk based on pitavastatin data in healthy participants.

Quantitative prediction of OATP1B-mediated drug-drug interactions using endogenous biomarker coproporphyrin I

1. Evaluation of the organic anion transporting polypeptide (OATP) 1B-mediated drug-drug interaction (DDI) potential is important for drug development. The focus of this study was coproporphyrin I (CP-I), an endogenous OATP1B biomarker.2. We investigated a new approach to OATP1B-mediated DDI prediction based on the mechanistic static pharmacokinetics (MSPK) model.3. The ratio of the area under the plasma concentration-time curve (AUCR) with and without co-administration of rifampicin (a typical OATP1B inhibitor) was found for CP-I and OATP1B substrate, respectively, and was then used to derive the correlation curve equation. The AUCR with and without co-administration of another OATP1B inhibitor than rifampicin was then predicted for the OATP1B substrates by substituting the AUCR of CP-I in the correlation curve equation to verify the predictability of the AUCR of the OATP1B substrates.4. The derived correlation curve equation between CP-I and the OATP1B substrates of the AUCRs with and without co-administration of rifampicin matched the observed AUCRs well. Regarding pitavastatin, rosuvastatin and pravastatin, 92.9% of the predicted AUCR values were within a two-fold range of the observed values, indicating that this approach may be a good way to quantitatively predict DDI potential.

Association of Drug-Metabolizing Enzyme and Transporter Gene Polymorphisms and Lipid-Lowering Response to Statins in Thai Patients with Dyslipidemia

Purpose

Statins are increasingly widely used in the primary and secondary prevention of cardiovascular disease. However, there is an inter-individual variation in statin response among patients. The study aims to determine the association between genetic variations in drug-metabolizing enzyme and transporter (DMET) genes and lipid-lowering response to a statin in Thai patients with hyperlipidemia.

Patients and Methods

Seventy-nine patients who received statin at steady-state concentrations were recruited. Serum lipid profile was measured at baseline and repeated after 4-month on a statin regimen. The genotype profile of 1936 DMET markers was obtained using Affymetrix DMET Plus genotyping microarrays.

Results

In this DMET microarray platform, five variants; SLCO1B3 (rs4149117, rs7311358, and rs2053098), QPRT (rs13331798), and SLC10A2 (rs188096) showed a suggestive association with LDL-cholesterol-lowering response. HDL-cholesterol-lowering responses were found to be related to CYP7A1 gene variant (rs12542233). Seven variants, SLCO1B3 (rs4149117, rs7311358, and rs2053098); SULT1E1 (rs3736599 and rs3822172); and ABCB11 (rs4148768 and rs3770603), were associated with the total cholesterol-lowering response. One variant of the ABCB4 gene (rs2109505) was significantly associated with triglyceride-lowering response.

Conclusion

This pharmacogenomic study identifies new genetic variants of DMET genes that are associated with the lipid-lowering response to statins. Genetic polymorphisms in DMET genes may impact the pharmacokinetics and lipid-lowering response to statin. The validation studies confirmations are needed in future pharmacogenomic studies.

Physiologically Based Pharmacokinetic Modeling in Pregnant Women Suggests Minor Decrease in Maternal Exposure to Olanzapine

Pregnancy is accompanied by significant physiological changes that might affect the in vivo drug disposition. Olanzapine is prescribed to pregnant women with schizophrenia, while its pharmacokinetics during pregnancy remains unclear. This study aimed to develop a physiologically based pharmacokinetic (PBPK) model of olanzapine in the pregnant population. With the contributions of each clearance pathway determined beforehand, a full PBPK model was developed and validated in the non-pregnant population. This model was then extrapolated to predict steady-state pharmacokinetics in the three trimesters of pregnancy by introducing gestation-related alterations. The model adequately simulated the reported time-concentration curves. The geometric mean fold error of Cmax and AUC was 1.14 and 1.09, respectively. The model predicted that under 10 mg daily dose, the systematic exposure of olanzapine had minor changes (less than 28%) throughout pregnancy. We proposed that the reduction in cytochrome P4501A2 activity is counteracted by the induction of other enzymes, especially glucuronyltransferase1A4. In conclusion, the PBPK model simulations suggest that, at least at the tested stages of pregnancy, dose adjustment of olanzapine can hardly be recommended for pregnant women if effective treatment was achieved before the onset of pregnancy and if fetal toxicity can be ruled out.

Loss of function polymorphisms in SLCO1B1 (c.521T>C, rs4149056) and ABCG2 (c.421C>A, rs2231142) genes are associated with adverse events of rosuvastatin: a case-control study

PURPOSE

The study aims to evaluate relationship between polymorphisms associated with a reduced function of two transporter proteins resulting in increased exposure to rosuvastatin - organic anion transporter 1B1 (OATP1B1) (SLCO1B1 c.521T>C) and ATP binding cassette subfamily G member 2 (ABCG2) (ABCG2 c.421C>A) and occurrence of rosuvastatin related myotoxicity/hepatotoxicity.

METHODS

In a case-control study, cases (rosuvastatin treated patients developing myotoxicity or hepatotoxicity) and controls (concurrent rosuvastatin treated patients free of adverse events) were prospectively recruited over a 2 year period in a single tertiary center specialized in treatment of metabolic disorders. Subjects were evaluated for clinical, comorbidity, and comedication characteristics and for genotype predicted metabolizing phenotypes regarding cytochrome P450 enzymes CYP2C9 and CYP2C19. Standard regression analysis and analysis in matched sets of cases and controls (optimal full matching) were undertaken by fitting frequentist and Bayesian models (covariates/matching variables: age, sex, diabetes, liver/renal disease, hypertension, CYP2C9 and C19 phenotype, use of CYP or transporter inhibitors, non evaluated transporter genotype).

RESULTS

A total of 88 cases (81 with myotoxicity, 6 with hepatotoxicity, 1 with both) and 129 controls were recruited. Odds of variant SLCO1B1 c.521T>C allele were 2.2-2.5 times higher in cases than in controls (OR = 2.45, 95% CI 1.34-4.48; Bayesian OR = 2.59, 95% CrI 1.42-4.90 in regression analysis; OR = 2.20, 1.10-4.42; Bayesian OR = 2.26, 1.28-4.41 in matched analysis). Odds of variant ABCG2 c.421C>A allele were 2.1-2.3 times higher in cases than in controls (OR = 2.24, 1.04-4.83; Bayesian OR = 2.35, 1.09-4.31 in regression analysis; OR = 2.10, 0.83-5.31; Bayesian OR = 2.17, 1.07-4.35 in matched analysis).

CONCLUSION

Loss of function polymorphisms in SLCO1B1 c.521T>C and ABCG2 c.421C>A genes are associated with the presence of rosuvastatin related myotoxicity and/or hepatotoxicity.

Biomarker-Informed Model-Based Risk Assessment of Organic Anion Transporting Polypeptide 1B Mediated Drug-Drug Interactions

Quantitative prediction of drug-drug interactions (DDIs) involving organic anion transporting polypeptide (OATP)1B1/1B3 inhibition is limited by uncertainty in the translatability of experimentally determined in vitro inhibition potency (half-maximal inhibitory concentration (IC₅₀ )). This study used an OATP1B endogenous biomarker-informed physiologically-based pharmacokinetic (PBPK) modeling approach to predict the effect of inhibitor drugs on the pharmacokinetics (PKs) of OATP1B substrates. Initial static analysis with about 42 inhibitor drugs, using in vitro IC₅₀ values and unbound liver inlet concentrations (I_in,max,u ), suggested in vivo OATP1B inhibition risk for drugs with R-value (1+ I_in,max,u /IC₅₀ ) above 1.5. A full-PBPK model accounting for transporter-mediated hepatic disposition was developed for coproporphyrin I (CP-I), an endogenous OATP1B biomarker. For several inhibitors (cyclosporine, diltiazem, fenebrutinib, GDC-0810, itraconazole, probenecid, and rifampicin at 3 different doses), PBPK models were developed and verified against available CP-I plasma exposure data to obtain in vivo OATP1B inhibition potency-which tend to be lower than the experimentally measured in vitro IC₅₀ by about 2-fold (probenecid and rifampicin) to 37-fold (GDC-0810). Models verified with CP-I data are subsequently used to predict DDIs with OATP1B probe drugs, rosuvastatin and pitavastatin. The predicted and observed area under the plasma concentration-time curve ratios are within 20% error in 55% cases, and within 30% error in 89% cases. Collectively, this comprehensive study illustrates the adequacy and utility of endogenous biomarker-informed PBPK modeling in mechanistic understanding and quantitative predictions of OATP1B-mediated DDIs in drug development.

Islatravir Is Not Expected to Be a Victim or Perpetrator of Drug-Drug Interactions via Major Drug-Metabolizing Enzymes or Transporters

Islatravir (MK-8591) is a nucleoside reverse transcriptase translocation inhibitor in development for the treatment and prevention of HIV-1. The potential for islatravir to interact with commonly co-prescribed medications was studied in vitro. Elimination of islatravir is expected to be balanced between adenosine deaminase-mediated metabolism and renal excretion. Islatravir did not inhibit uridine diphosphate glucuronosyltransferase 1A1 or cytochrome p450 (CYP) enzymes CYP1A2, 2B6, 2C8, 2C9, 2C19, 2D6, or 3A4, nor did it induce CYP1A2, 2B6, or 3A4. Islatravir did not inhibit hepatic transporters organic anion transporting polypeptide (OATP) 1B1, OATP1B3, organic cation transporter (OCT) 1, bile salt export pump (BSEP), multidrug resistance-associated protein (MRP) 2, MRP3, or MRP4. Islatravir was neither a substrate nor a significant inhibitor of renal transporters organic anion transporter (OAT) 1, OAT3, OCT2, multidrug and toxin extrusion protein (MATE) 1, or MATE2K. Islatravir did not significantly inhibit P-glycoprotein and breast cancer resistance protein (BCRP); however, it was a substrate of BCRP, which is not expected to be of clinical significance. These findings suggest islatravir is unlikely to be the victim or perpetrator of drug-drug interactions with commonly co-prescribed medications, including statins, diuretics, anti-diabetic drugs, proton pump inhibitors, anticoagulants, benzodiazepines, and selective serotonin reuptake inhibitors.

Evaluation of Normothermic Machine Perfusion of Porcine Livers as a Novel Preclinical Model to Predict Biliary Clearance and Transporter-Mediated Drug-Drug Interactions Using Statins

There is a lack of translational preclinical models that can predict hepatic handling of drugs. In this study, we aimed to evaluate the applicability of normothermic machine perfusion (NMP) of porcine livers as a novel ex vivo model to predict hepatic clearance, biliary excretion, and plasma exposure of drugs. For this evaluation, we dosed atorvastatin, pitavastatin, and rosuvastatin as model drugs to porcine livers and studied the effect of common drug-drug interactions (DDIs) on these processes. After 120 minutes of perfusion, 0.104 mg atorvastatin (n = 3), 0.140 mg pitavastatin (n = 5), or 1.4 mg rosuvastatin (n = 4) was administered to the portal vein, which was followed 120 minutes later by a second bolus of the statin coadministered with OATP perpetrator drug rifampicin (67.7 mg). After the first dose, all statins were rapidly cleared from the circulation (hepatic extraction ratio > 0.7) and excreted into the bile. Presence of human-specific atorvastatin metabolites confirmed the metabolic capacity of porcine livers. The predicted biliary clearance of rosuvastatin was found to be closer to the observed biliary clearance. A rank order of the DDI between the various systems upon coadministration with rifampicin could be observed: atorvastatin (AUC ratio 7.2) > rosuvastatin (AUC ratio 3.1) > pitavastatin (AUC ratio 2.6), which is in good agreement with the clinical DDI data. The results from this study demonstrated the applicability of using NMP of porcine livers as a novel preclinical model to study OATP-mediated DDI and its effect on hepatic clearance, biliary excretion, and plasma profile of drugs. SIGNIFICANCE STATEMENT: This study evaluated the use of normothermic machine perfusion (NMP) of porcine livers as a novel preclinical model to study hepatic clearance, biliary excretion, plasma (metabolite) profile of statins, and OATP-mediated DDI. Results showed that NMP of porcine livers is a reliable model to study OATP-mediated DDI. Overall, the rank order of DDI severity indicated in these experiments is in good agreement with clinical data, indicating the potential importance of this new ex vivo model in early drug discovery.

Drug-drug-gene interactions as mediators of adverse drug reactions to diclofenac and statins: a case report and literature review

Concomitant treatment with drugs that inhibit drug metabolising enzymes and/or transporters, such as commonly prescribed statins and nonsteroidal anti-inflammatory drugs (NSAIDs), has been associated with prolonged drug exposure and increased risk of adverse drug reactions (ADRs) due to drug-drug interactions. The risk is further increased in patients with chronic diseases/comorbidities who are more susceptible because of their genetic setup or external factors. In that light, we present a case of a 46-year-old woman who had been experiencing acute renal and hepatic injury and myalgia over two years of concomitant treatment with diclofenac, atorvastatin, simvastatin/fenofibrate, and several other drugs, including pantoprazole and furosemide. Our pharmacogenomic findings supported the suspicion that ADRs, most notably the multi-organ toxicity experienced by our patient, may be owed to drug-drug-gene interactions and increased bioavailability of the prescribed drugs due to slower detoxification capacity and decreased hepatic and renal elimination. We also discuss the importance of CYP polymorphisms in the biotransformation of endogenous substrates such as arachidonic acid and their modulating role in pathophysiological processes. Yet even though the risks of ADRs related to the above mentioned drugs are substantially evidenced in literature, pre-emptive pharmacogenetic analysis has not yet found its way into common clinical practice.

Comparative Hepatic and Intestinal Efflux Transport of Statins

Previous studies have shown that lipid-lowering statins are transported by various ATP-binding cassette (ABC) transporters. However, because of varying methods, it is difficult to compare the transport profiles of statins. Therefore, we investigated the transport of 10 statins or statin metabolites by six ABC transporters using human embryonic kidney cell-derived membrane vesicles. The transporter protein expression levels in the vesicles were quantified with liquid chromatography-tandem mass spectrometry and used to scale the measured clearances to tissue levels. In our study, apically expressed breast cancer resistance protein (BCRP) and P-glycoprotein (P-gp) transported atorvastatin, fluvastatin, pitavastatin, and rosuvastatin. Multidrug resistance-associated protein 3 (MRP3) transported atorvastatin, fluvastatin, pitavastatin, and, to a smaller extent, pravastatin. MRP4 transported fluvastatin and rosuvastatin. The scaled clearances suggest that BCRP contributes to 87%-91% and 84% of the total active efflux of rosuvastatin in the small intestine and the liver, respectively. For atorvastatin, the corresponding values for P-gp-mediated efflux were 43%-79% and 66%, respectively. MRP3, on the other hand, may contribute to 23%-26% and 25%-37% of total active efflux of atorvastatin, fluvastatin, and pitavastatin in jejunal enterocytes and liver hepatocytes, respectively. These data indicate that BCRP may play an important role in limiting the intestinal absorption and facilitating the biliary excretion of rosuvastatin and that P-gp may restrict the intestinal absorption and mediate the biliary excretion of atorvastatin. Moreover, the basolateral MRP3 may enhance the intestinal absorption and sinusoidal hepatic efflux of several statins. Taken together, the data show that statins differ considerably in their efflux transport profiles. SIGNIFICANCE STATEMENT: This study characterized and compared the transport of atorvastatin, fluvastatin, pitavastatin, pravastatin, rosuvastatin, and simvastatin acid and four atorvastatin metabolites by six ABC transporters (BCRP, MRP2, MRP3, MRP4, MRP8, P-gp). Based on in vitro findings and protein abundance data, the study concludes that BCRP, MRP3, and P-gp have a major impact in the efflux of various statins. Together with in vitro metabolism, uptake transport, and clinical data, our findings are applicable for use in comparative systems pharmacology modeling of statins.

The Interface of Therapeutics and Genomics in Cardiovascular Medicine

Pharmacogenomics has a burgeoning role in cardiovascular medicine, from warfarin dosing to antiplatelet choice, with recent developments in sequencing bringing the promise of personalised medicine ever closer to the bedside. Further scientific evidence, real-world clinical trials, and economic modelling are needed to fully realise this potential. Additionally, tools such as polygenic risk scores, and results from Mendelian randomisation analyses, are only in the early stages of clinical translation and merit further investigation. Genetically targeted rational drug design has a strong evidence base and, due to the nature of genetic data, academia, direct-to-consumer companies, healthcare systems, and industry may meet in an unprecedented manner. Data sharing navigation may prove problematic. The present manuscript addresses these issues and concludes a need for further guidance to be provided to prescribers by professional bodies to aid in the consideration of such complexities and guide translation of scientific knowledge to personalised clinical action, thereby striving to improve patient care. Additionally, technologic infrastructure equipped to handle such large complex data must be adapted to pharmacogenomics and made user friendly for prescribers and patients alike.

Potential drug-drug interaction between sodium-glucose co-transporter 2 inhibitors and statins: pharmacological and clinical evidence

INTRODUCTION

Recent case reports suggested that concomitant use of sodium-glucose co-transporter 2 (SGLT2) inhibitors with statins could lead to increased statin toxicity. We provide a comprehensive overview of the available pharmacological and clinical evidence on this potential drug-drug interaction (DDI).

AREAS COVERED

We searched MEDLINE PubMed until November 2020 for (i) pharmacokinetic studies on SGLT2 inhibitors, statins, and their potential interaction, and (ii) case reports and clinical studies assessing the safety of concomitant use of SGLT2 inhibitors and statins. We also searched regulatory documents submitted to the United States Food and Drug Administration for unpublished data on this potential DDI.

EXPERT OPINION

SGLT2 inhibitors are increasingly used for type 2 diabetes, chronic heart failure, and chronic kidney disease, and concomitant use with statins is common given the comorbidity of indications. While pharmacokinetic studies in healthy subjects showed no clinically relevant changes in statin levels during SGLT2 inhibitor co-administration, the published case reports and pharmacologic reasoning support the possibility of an interaction. Underlying mechanisms could be pharmacokinetic or pharmacodynamic, and canagliflozin appears to be the SGLT2 inhibitor with the highest interaction potential. Further research including 'real-world' pharmacoepidemiologic studies is needed to better understand the clinical significance of this DDI.

Evaluation of the drug-drug interaction potential for trazpiroben (TAK-906), a D2/D3 receptor antagonist for gastroparesis, towards cytochrome P450s and transporters

Trazpiroben (TAK-906), a peripherally selective dopamine D2/D3 receptor antagonist, is being developed for the treatment of patients with gastroparesis. The potential of trazpiroben to act as a perpetrator or a victim for cytochrome P450 (CYP)- or transporter- mediated drug-drug interactions (DDIs) was evaluated following the latest regulatory guidelines.In vitro studies revealed that trazpiroben is metabolised mainly through a non-CYP pathway (56.7%) by multiple cytosolic, NADPH-dependent reductase, such as aldo-keto reductase and short-chain dehydrogenase/reductase including carbonyl reductases. Remaining metabolism occurs through CYP3A4 and CYP2C8 (43.3%). Trazpiroben is neither an inhibitor nor an inducer of major CYP enzymes at a clinically relevant dose. It is a substrate of P-glycoprotein (P-gp) and organic anion transporting polypeptide (OATP) 1B1/1B3, but is not an inhibitor of transporters listed in the DDI guidelines at a clinically relevant dose. This is consistent with findings from CYP3A and P-gp-based clinical assessment showing no substantial change (≤2-fold) in trazpiroben exposure when co-administered with itraconazole.Collectively, trazpiroben has low potential of enzyme-mediated DDIs and is unlikely to act as a perpetrator of transporter-mediated DDIs but there may be a potential to act as a victim of OATP1B1/1B3 DDI that will be evaluated clinically.

Evaluation of the Utility of PXB Chimeric Mice for Predicting Human Liver Partitioning of Hepatic Organic Anion-Transporting Polypeptide Transporter Substrates

The ability to predict human liver-to-plasma unbound partition coefficient (K_puu) is important to estimate unbound liver concentration for drugs that are substrates of hepatic organic anion-transporting peptide (OATP) transporters with asymmetric distribution into the liver relative to plasma. Herein, we explored the utility of PXB chimeric mice with humanized liver that are highly repopulated with human hepatocytes to predict human hepatic disposition of OATP substrates, including rosuvastatin, pravastatin, pitavastatin, valsartan, and repaglinide. In vitro total uptake clearance and transporter-mediated active uptake clearance in C57 mouse hepatocytes were greater than in PXB chimeric mouse hepatocytes for rosuvastatin, pravastatin, pitavastatin, and valsartan. Consistent with in vitro uptake data, enhanced hepatic uptake and resulting total systemic clearance were observed with the above four compounds in severely compromised immune-deficient (SCID) control mice compared with the PXB chimeric mice, which suggest that mouse has a stronger transporter-mediated hepatic uptake than human. In vivo liver-to-plasma K_puu from PXB chimeric and SCID control mice were also compared, and rosuvastatin and pravastatin K_puu in SCID mice were more than 10-fold higher than that in PXB chimeric mice, whereas pitavastatin, valsartan, and repaglinide K_puu in SCID mice were comparable with K_puu in PXB chimeric mice. Finally, PXB chimeric mouse liver-to-plasma K_puu values were compared with the reported human K_puu, and a good correlation was observed as the PXB K_puu vales were within 3-fold of human K_puu Our results indicate that PXB mice could be a useful tool to delineate hepatic uptake and enable prediction of human liver-to-plasma K_puu of hepatic uptake transporter substrates. SIGNIFICANCE STATEMENT: We evaluated PXB mouse with humanized liver for its ability to predict human liver disposition of five organic anion-transporting polypeptide transporter substrates. Both in vitro and in vivo data suggest that mouse liver has a stronger transporter-mediated hepatic uptake than the humanized liver in PXB mouse. More importantly, PXB liver-to-plasma unbound partition coefficient (K_puu) values were compared with the reported human K_puu, and a good correlation was observed. PXB mice could be a useful tool to project human liver-to-plasma K_puu of hepatic uptake transporter substrates.

Statin-induced rhabdomyolysis from azithromycin interaction in a patient with heterozygous SLCO1B1 polymorphism

WHAT IS KNOWN AND OBJECTIVE

Unlike other macrolide antibiotics, azithromycin is considered safe to co-prescribe with simvastatin. We aim to elucidate the mechanism of a rare azithromycin-simvastatin interaction.

CASE DESCRIPTION

We report a case of simvastatin-induced rhabdomyolysis caused by an azithromycin drug interaction in a patient with heterozygous SLCO1B1 loss-of-function polymorphism. We propose a dual-hit mechanism for this drug-drug-genome interaction. Azithromycin mildly inhibits simvastatin's CYP 3A4 hepatic metabolism, and the SLCO1B1 polymorphism reduces simvastatin hepatic uptake. The combination increases simvastatin serum concentrations significantly, inducing rhabdomyolysis.

WHAT IS NEW AND CONCLUSION

Patients with statin-induced myopathy associated with non-classic CYP inhibitors should be considered for genetic testing and alternative statins with less risk of future interactions.

Pharmacogenomics for Primary Care: An Overview

Most of the prescribing and dispensing of medicines happens in primary care. Pharmacogenomics (PGx) is the study and clinical application of the role of genetic variation on drug response. Mounting evidence suggests PGx can improve the safety and/or efficacy of several medications commonly prescribed in primary care. However, implementation of PGx has generally been limited to a relatively few academic hospital centres, with little adoption in primary care. Despite this, many primary healthcare providers are optimistic about the role of PGx in their future practice. The increasing prevalence of direct-to-consumer genetic testing and primary care PGx studies herald the plausible gradual introduction of PGx into primary care and highlight the changes needed for optimal translation. In this article, the potential utility of PGx in primary care will be explored and on-going barriers to implementation discussed. The evidence base of several drug-gene pairs relevant to primary care will be outlined with a focus on antidepressants, codeine and tramadol, statins, clopidogrel, warfarin, metoprolol and allopurinol. This review is intended to provide both a general introduction to PGx with a more in-depth overview of elements relevant to primary care.

Coproporphyrin I Can Serve as an Endogenous Biomarker for OATP1B1 Inhibition: Assessment Using a Glecaprevir/Pibrentasvir Clinical Study

Organic anion transporting polypeptide (OATP) 1B1 and OATP1B3 are involved in the disposition of a variety of commonly prescribed drugs. The evaluation of OATP1B1/1B3 inhibition potential by investigational drugs is of interest during clinical drug development due to various adverse events associated with increased exposures of their substrates. Regulatory guidance documents on the in vitro assessment of OATP1B1/1B3 inhibition potential are conservative with up to a third of predictions resulting in false positives. This work investigated the utility of OATP1B1/1B3 endogenous biomarkers, coproporphyrin (CP)‐I and CP‐III, to assess clinical inhibition of OATP1B1/1B3 and potentially eliminate the need for prospective clinical drug‐drug interaction (DDI) studies. Correlations between CP‐I exposures and various OATP1B1 static DDI predictions were also evaluated. Glecaprevir/pibrentasvir (GLE/PIB) 300/120 mg fixed‐dose combination is known to cause clinical inhibition of OATP1B1/1B3. In a clinical study evaluating the relative bioavailability of various formulations of GLE/PIB regimen, CP‐I peak plasma concentration (C_max) ratio and 0–16‐hour area under the concentration‐time curve (AUC_0–16) ratio relative to baseline increased with increasing GLE exposures, whereas there was a modest correlation between GLE exposure and CP‐III C_max ratio but no correlation with CP‐III AUC_0–16 ratio. This suggests that CP‐I is superior to CP‐III as an endogenous biomarker for evaluation of OATP1B1 inhibition. There was a significant correlation between CP‐I and GLE C_max (R² = 0.65; P < 0.001) across individual subjects. Correlation analysis between GLE OATP1B1 R values and CP‐I exposures (C_max ratio and AUC_0–16 ratio) suggests that an R value of > 3 can predict a biologically meaningful inhibition of OATP1B1 when the inhibitor clinical pharmacokinetic parameters are available.

Lack of Drug-Drug Interaction Between Cimetidine, a Renal Transporter Inhibitor, and Imeglimin, a Novel Oral Antidiabetic Drug, in Healthy Volunteers

BACKGROUND AND OBJECTIVE: Imeglimin is a novel oral antidiabetic drug to treat type 2 diabetes, targeting the mitochondrial bioenergetics. In vitro, imeglimin was shown to be a substrate of human multidrug and toxic extrusion transporters MATE1 and MATE2-K and organic cation transporters OCT1 and OCT2. The objective of the study was to assess the potential drug-drug interaction between imeglimin and cimetidine, a reference inhibitor of these transporters.

METHODS

A phase 1 study was carried out in 16 subjects who received a single dose of 1500 mg imeglimin alone on day 1 followed by a 6-day treatment (day 5 to day 10) with cimetidine 400 mg twice daily. On day 8, a single dose of imeglimin was co-administered with cimetidine. Blood and urine samples were collected up to 72 h after each imeglimin administration. Pharmacokinetic parameters were determined using non-compartmental methods.

RESULTS

Imeglimin maximum plasma concentration (C_max) and area under the plasma concentration-time curve (AUC) were 1.3-fold [90% CI (1.12-1.62) and (1.10-1.46) for C_max and AUC_0-last, respectively] higher when imeglimin was co-administered with cimetidine but this increase was not considered clinically relevant. This increase could be mainly explained by a reduction in renal elimination, mediated through the cimetidine inhibition of renal MATE1 transporter. Imeglimin taken alone or with cimetidine was safe and well tolerated in all subjects.

CONCLUSIONS

No clinically significant drug-drug interaction exists between imeglimin and cimetidine, a reference inhibitor of MATE1, MATE2-K, OCT1 and OCT2 transporters.

CLINICAL TRIAL REGISTRATION

EudraCT 2018-001103-36.

A Microdose Cocktail to Evaluate Drug Interactions in Patients with Renal Impairment

Renal impairment (RI) is known to influence the pharmacokinetics of nonrenally eliminated drugs, although the mechanism and clinical impact is poorly understood. We assessed the impact of RI and single dose oral rifampin (RIF) on the pharmacokinetics of CYP3A, OATP1B, P-gp, and BCRP substrates using a microdose cocktail and OATP1B endogenous biomarkers. RI alone had no impact on midazolam (MDZ), maximum plasma concentration (C_max ), and area under the curve (AUC), but a progressive increase in AUC with RI severity for dabigatran (DABI), and up to ~2-fold higher AUC for pitavastatin (PTV), rosuvastatin (RSV), and atorvastatin (ATV) for all degrees of RI was observed. RIF did not impact MDZ, had a progressively smaller DABI drug-drug interaction (DDI) with increasing RI severity, a similar 3.1-fold to 4.4-fold increase in PTV and RSV AUC in healthy volunteers and patients with RI, and a diminishing DDI with RI severity from 6.1-fold to 4.7-fold for ATV. Endogenous biomarkers of OATP1B (bilirubin, coproporphyrin I/III, and sulfated bile salts) were generally not impacted by RI, and RIF effects on these biomarkers in RI were comparable or larger than those in healthy volunteers. The lack of a trend with RI severity of PTV and several OATP1B biomarkers, suggests that mechanisms beyond RI directly impacting OATP1B activity could also be considered. The DABI, RSV, and ATV data suggest an impact of RI on intestinal P-gp, and potentially BCRP activity. Therefore, DDI data from healthy volunteers may represent a worst-case scenario for clinically derisking P-gp and BCRP substrates in the setting of RI.

Potential drug-drug interactions associated with drugs currently proposed for COVID-19 treatment in patients receiving other treatments

Patients with COVID-19 are sometimes already being treated for one or more other chronic conditions, especially if they are elderly. Introducing a treatment against COVID-19, either on an outpatient basis or during hospitalization for more severe cases, raises the question of potential drug-drug interactions. Here, we analyzed the potential or proven risk of the co-administration of drugs used for the most common chronic diseases and those currently offered as treatment or undergoing therapeutic trials for COVID-19. Practical recommendations are offered, where possible.

A Genome-wide Association Study of Circulating Levels of Atorvastatin and Its Major Metabolites

Atorvastatin (ATV) is frequently prescribed and generally well  tolerated, but can lead to myotoxicity, especially at higher doses. A genome-wide association study of circulating levels of ATV, 2-hydroxy (2-OH) ATV, ATV lactone (ATV L), and 2-OH ATV L was performed in 590 patients who had been hospitalized with a non-ST elevation acute coronary syndrome 1 month earlier and were on high-dose ATV (80 mg or 40 mg daily). The UGT1A locus (lead single nucleotide polymorphism, rs887829) was strongly associated with both increased 2-OH ATV/ATV (P = 7.25 × 10^-16 ) and 2-OH ATV L/ATV L (P = 3.95 × 10^-15 ) metabolic ratios. Moreover, rs45446698, which tags CYP3A7*1C, was nominally associated with increased 2-OH ATV/ATV (P = 6.18 × 10^-7 ), and SLCO1B1 rs4149056 with increased ATV (P = 2.21 × 10^-6 ) and 2-OH ATV (P = 1.09 × 10^-6 ) levels. In a subset of these patients whose levels of ATV and metabolites had also been measured at 12 months after hospitalization (n = 149), all of these associations remained, except for 2-OH ATV and rs4149056 (P = 0.057). Clinically, rs4149056 was associated with increased muscular symptoms (odds ratio (OR) 3.97; 95% confidence interval (CI) 1.29-12.27; P = 0.016) and ATV intolerance (OR 1.55; 95% CI 1.09-2.19; P = 0.014) in patients (n = 870) primarily discharged on high-dose ATV. In summary, both novel and recognized genetic associations have been identified with circulating levels of ATV and its major metabolites. Further study is warranted to determine the clinical utility of genotyping rs4149056 in patients on high-dose ATV.