Validation of a Drug Transporter Probe Cocktail Using the Prototypical Inhibitors Rifampin, Probenecid, Verapamil, and Cimetidine

Predicting OCT2/MATEs-Mediated Drug Interactions in Healthy Volunteers and Patients with Chronic Kidney Disease: Insights from Extended Clearance Concept, Endogenous Biomarkers, and In Vitro Inhibition Studies (Perspectives from the International Transporter Consortium)

Organic cation transporter (OCT) 2 and multidrug and toxin extrusion (MATE) transporters play significant roles in the renal secretion of organic cations and drug-drug interactions (DDIs). Recent in vitro studies indicate that the Ki values for OCT2 exhibit substrate dependency and increase in potency with pre-incubation. However, consensus is lacking on whether these factors should be considered in predicting in vivo inhibition. Physiologically based pharmacokinetic models, combined with the extended clearance concept, have been used and are discussed here for OCT2/MATEs probes. In addition to modeling, early clinical studies use endogenous biomarkers to evaluate transporter-mediated DDI risk, with the aim of avoiding unnecessary clinical DDI studies. Identified biomarkers for OCT2/MATEs, such as creatinine, N1-methylnicotinamide, and N1-methyladenosine, have proven useful in confirming clinically relevant OCT2/MATEs-mediated DDIs when renal clearance (CLr) is used as an endpoint; their application is discussed further. From a clinical perspective, the intact nephron hypothesis (INH), which postulates that the decrease in CLr in chronic kidney disease (CKD) is proportional to that in nephron numbers, has been proposed. However, reports suggest that the secretion clearance of creatinine and substrates of organic anion transporters (OATs) does not follow this proportionality in patients with CKD. This state-of-the-art review highlights key developments in predicting OCT2/MATEs-mediated DDIs in healthy volunteers and explores the prediction of clinical OCT2/MATEs DDI risk in patients with CKD by comparing substrate-dependent changes in secretion clearance for substrates of OCT2/MATEs and OATs. Recommendations for the prediction of OCT2/MATEs-mediated DDI risk, together with the current knowledge gaps and future directions, are discussed.

A Systematic Review and Classification of the Effects of P-glycoprotein Inhibitors and Inducers in Humans, Using Digoxin, Fexofenadine, and Dabigatran as Probe Drugs

P-glycoprotein is a critical efflux transporter that may significantly affect the pharmacokinetics of various drugs by influencing their absorption, distribution and elimination. While European and American regulatory guidelines provide lists of P-glycoprotein modulators, they lack specificity concerning in vivo studies and clear guidance on inducers, creating uncertainty in their clinical relevance. A systematic search on in vivo clinical studies involving healthy volunteers using fexofenadine, dabigatran and digoxin as P-glycoprotein substrates has been performed in accordance with the PRISMA guidelines. A total of 151 studies assessing the impact of P-glycoprotein modulators on the concentration-time profile of P-glycoprotein substrates were retrieved. Additionally, data on the P-glycoprotein modulators' effect on cytochrome P450 3A4 induction or inhibition were also collected. P-gp modulators were classified as potent, moderate, weak or non-interactors for P-glycoprotein, with or without cytochrome P450 3A4 impact, on the basis of the area under the concentration-time curve ratio. This classification was adapted from the Food and Drug Administration criteria for cytochrome interactions. This systematic review identified 49 area under the plasma concentration-time curve ratio values corresponding to P-glycoprotein inhibitors, 23 to P-glycoprotein inducers and 131 to non-interactors. Of these, only 32.5% and 41.1% were classified as weak to potent, respectively. Only 0.7% of inhibitors and no inducers were classified as potent. This suggests that most P-glycoprotein modulators have a limited impact on drug exposure. The potential for interaction increases when P-glycoprotein modulators also affect cytochrome P450 3A4, which is the case for 59.9% of P-glycoprotein modulators. However, some moderate P-glycoprotein modulators may have clinically significant effects depending on the therapeutic margin of the substrate and the clinical context.

Significance of gut breast cancer resistance protein versus organic anion transporting polypeptide 2B1 inhibition on rosuvastatin clinical drug-drug interactions

Organic anion transporting polypeptide (OATP)2B1 facilitates oral absorption of many drugs including celiprolol, fexofenadine, and rosuvastatin. The present study aimed to examine the relevance of gut OATP2B1 and breast cancer resistance protein (BCRP) inhibition in rosuvastatin drug-drug interactions (DDIs). We first characterized OATP2B1-mediated transport of rosuvastatin in transfected cells as a function of extracellular pH 6.0 and 7.4. Rosuvastatin transporter-specific uptake was found to be pH sensitive with 2-fold higher Vmax at acidic pH; however, OATP2B1 affinity (Km = 8-10 μM) was similar at both conditions. We next studied the effect of 26 inhibitor drugs on rosuvastatin OATP2B1-specific transport at 2 pH conditions. Measured IC50s were generally consistent between the 2 pHs (∼88% with 2-fold). For an additional 23 drugs, OATP2B1 IC50 was obtained only at pH 7.4 due to observed limited pH dependency. Inhibition of BCRP-mediated rosuvastatin transport was also acquired at pH 7.4 for 40 compounds using membrane vesicles assay. Finally, the static model for gut interactions (G-value, I2/IC50) was employed to project in vivo DDI potential. A significant relationship was observed between the BCRP G-value and rosuvastatin area under the curve (AUC) ratio; however, no correlation was apparent with the OATP2B1 G-value. The majority of inhibitors with BCRP G-values >100 perpetrated a "positive" DDI (AUC ratio >1.25). Ronacaleret and elagolix reduced rosuvastatin AUC by 40%-50%, likely due to stronger OATP2B1 inhibition compared with BCRP inhibition. The present study indicates that the "net-effect" of BCRP and OATP2B1 lead to a "positive" DDI, whereas a "negative" DDI (AUC ratio <0.8) is possible for "OATP2B1-alone" inhibitors. SIGNIFICANCE STATEMENT: Gut organic anion transporting polypeptide (OATP)2B1 and breast cancer resistance protein (BCRP) play key roles in rosuvastatin oral absorption and may determine its drug-drug interactions (DDIs). Based on a comprehensive dataset, it was found that rosuvastatin area under the curve ratios correlate significantly with BCRP inhibition, but not with OATP2B1 inhibition. Strong BCRP inhibition, with a G-value >100, translated to "positive" DDIs, whereas "OATP2B1-alone" inhibitors may lead to "negative" DDIs. For example, ronacaleret and elagolix significantly reduced rosuvastatin area under the curve due to strong OATP2B1 inhibition but weak BCRP inhibition.

Quantitative Contributions of Hepatic and Renal Organic Cation Transporters to the Clinical Pharmacokinetic Cimetidine-Metformin Interaction

The widely prescribed oral anti-diabetic drug metformin is eliminated unchanged in the urine primarily through active tubular secretion. This process is mediated by organic cation transporter 2 (OCT2), an uptake transporter expressed on the basolateral membrane of renal proximal tubule cells. Metformin uptake into the liver, the site of action, is mediated by organic cation transporter 1 (OCT1), which is expressed on the sinusoidal membrane of hepatocytes. Sixteen healthy adults participated in a clinical pharmacokinetic drug-drug interaction study in which they were orally administered metformin (50 mg) as a dual OCT1/2 substrate alone (baseline) and with cimetidine (400 mg) as an OCT inhibitor. Relative to baseline, metformin systemic plasma exposure increased by 24% (p < 0.05) in the presence of cimetidine, which was accompanied by a disproportional decrease (8%) in metformin renal clearance (p = 0.005). Genetic variants of OCT1 and OCT2 moderately impacted the significance and magnitude of the interaction. Collectively, we hypothesized that the cimetidine-metformin interaction involves inhibition of hepatic OCT1 as well as renal OCT2. We tested this hypothesis by developing a physiologically based pharmacokinetic (PBPK) model and assessing potential OCT biomarkers in plasma and urine to gain mechanistic insight into the transporters involved in this interaction. The PBPK model predicted that cimetidine primarily inhibits hepatic OCT1 and, to a lesser extent, renal OCT2. The unchanged renal clearance of potential OCT2 biomarkers following cimetidine exposure supports a minimal role for renal OCT2 in this interaction.

Organic cation transporters 2: Structure, regulation, functions, and clinical implications

The SLC22A2 gene encodes organic cation transporter 2 (OCT2), which is predominantly expressed in renal proximal tubule cells. OCT2 is critical for the active renal excretion of various cationic drugs and endogenous metabolites. OCT2 expression varies across species, with higher levels in mice and monkeys compared with humans and rats. The human OCT2 protein consists of 555 amino acids and contains 12 transmembrane domains. OCT2 functions as a uniporter, facilitating the bidirectional transport of organic cations into renal tubular cells, driven by the inside-negative membrane potential. Its expression is regulated by sex hormones, contributing to potential sex differences in Oct2 activity in rodents. OCT2 has been linked to tissue toxicity, such as cisplatin-induced nephrotoxicity. Factors such as genetic variants, age, disease states, and the coadministration of drugs, including tyrosine kinase inhibitors, contribute to interindividual variability in OCT2 activity. This, in turn, impacts the systemic exposure and elimination of drugs and endogenous substances. Regulatory agencies recommend evaluating the potential of a drug to inhibit OCT2 through in vitro and clinical drug-drug interaction (DDI) studies, often using metformin as a probe substrate. Emerging tools like transporter biomarkers and physiologically based pharmacokinetic modeling hold promise in predicting OCT2-mediated DDIs. While several OCT2 biomarkers, such as N1-methylnicotinamide, have been proposed, their reliability in predicting renal DDIs remains uncertain and requires further study. Ultimately, a better understanding of the factors influencing OCT2 activity is essential for achieving precision medicine and minimizing renal and systemic toxicity. SIGNIFICANCE STATEMENT: Organic cation transporter 2 (OCT2) is essential for the active tubular secretion of xenobiotics and endogenous cationic substances in the kidneys. This article offers a comprehensive overview of the tissue distribution, interspecies differences, and factors affecting its activity-critical for evaluating tissue toxicity and systemic exposure to cationic substances. Using OCT2 biomarkers and integrating OCT2 activity and expression data into physiologically based pharmacokinetic models are valuable tools for predicting OCT2 function and its clinical implications.

Effects of Cimetidine and Dolutegravir on the Endogenous Drug-Drug Interaction Biomarkers for Organic Cation Transporter 2 and Multidrug and Toxin Extrusion Protein 1 in Healthy Volunteers

This study was designed to assess the quantitative performance of endogenous drug-drug interaction (DDI) biomarkers (N1-methylnicotinamide (1-NMN), N1-methyladenosine (m1A), and creatinine) for the organic cation transporters, OCT2 and MATE1/2K in the kidney. Ten healthy volunteers received cimetidine (400 and 800 mg, single dose) or dolutegravir (50 mg, twice a day) together with metformin (500 mg). Cimetidine and dolutegravir were considered to act mainly as MATE1/2K and OCT2 inhibitors, respectively. The renal clearance (CLr) of metformin was decreased by 15.5% and 42.5% by cimetidine 400 and 800 mg, and by 26.8% and 56.9% by dolutegravir first and fifth doses, respectively. CLr ratio (CLrR) of 1-NMN were 0.93 and 0.64 for cimetidine 400 and 800 mg, and 0.87 and 0.47 for dolutegravir first and fifth doses, respectively. CLrR of m1A was less than that of 1-NMN: 1.0 and 0.80 for cimetidine 400 and 800 mg, and 0.77 and 0.71 for dolutegravir first and fifth doses, respectively. CLr of creatinine was significantly decreased only by cimetidine 800 mg. Individual CLrR of 1-NMN and m1A showed a positive correlation with the corresponding CLrR of metformin with r2 of 0.58 and 0.55, respectively. When evaluated individually, m1A showed a better correlation during cimetidine periods (r2 0.64) than 1-NMN (r2 0.36), but vice versa during dolutegravir periods (r2 1-NMN, 0.80; m1A, 0.32). These results suggest that 1-NMN and m1A might be more promising than creatinine as endogenous biomarkers for quantitatively assessing the DDI potential of investigational drugs for OCT2 and MATE1/2K based on their CLrR change.

New Biomarkers for Renal Transporter-Mediated Drug-Drug Interactions: Metabolomic Effects of Cimetidine, Probenecid, Verapamil, and Rifampin in Humans

The inhibition of renal transport proteins organic cation transporter 2 (OCT2), multidrug and toxin extrusion proteins (MATE1, MATE2-K), and organic anion transporters (OAT1, OAT3) causes clinically relevant drug-drug interactions (DDI). Endogenous biomarkers could be used to improve risk prediction of such renal DDIs. While a number of biomarkers for renal DDIs have been described so far, multiple criteria for valid biomarkers have frequently not been investigated, for example, specificity, metabolism, or food effects. Therefore, there is a need for novel biomarkers of renal DDIs. Here, we investigated the global metabolomic effects following the administration of two classical inhibitors of renal transport proteins [cimetidine (OCT2/MATEs), probenecid (OATs)] in human plasma and urine of healthy volunteers. Additionally, we investigated metabolomic effects of two inhibitors of other transporters [verapamil (P-glycoprotein), rifampin (organic anion transporting polypeptides)] as controls. This analysis shows that both cimetidine and probenecid affect compounds involved in caffeine metabolism, carnitines, and sulfates. Hierarchical cluster analysis of the effects of all four inhibitors on endogenous compounds identified multiple promising new sensitive and specific biomarker candidates for OCT2/MATE- or OAT-mediated DDIs. For OCT2/MATEs, 5-amino valeric acid betaine (median log2-fold change of estimated renal elimination: -3.62) presented itself as a promising candidate. For OATs, estimated renal elimination of 7-methyluric acid and cinnamoylglycine (median log2-fold changes -3.10 and -1.92, respectively) was both sensitive and specific. This study provides comprehensive information on metabolomic effects of transport protein inhibition in humans and identifies putative new sensitive and specific biomarkers for renal transporter-mediated DDIs.

Quantitative contributions of hepatic and renal organic cation transporters to the clinical pharmacokinetic cimetidine-metformin interaction

The widely prescribed oral anti-diabetic drug metformin is eliminated unchanged in the urine primarily through active tubular secretion. This process is mediated by organic cation transporter 2 (OCT2), an uptake transporter expressed on the basolateral membrane of renal proximal tubule cells. Metformin uptake into the liver, the site of action, is mediated by OCT1, which is expressed on the sinusoidal membrane of hepatocytes. Sixteen healthy adults participated in a clinical pharmacokinetic drug-drug interaction study in which they were orally administered metformin (50 mg) as a dual OCT1/2 substrate alone (baseline) and with cimetidine (400 mg) as an OCT inhibitor. Relative to baseline, metformin systemic plasma exposure increased by 24% ( p <0.05) in the presence of cimetidine, which was accompanied by a disproportional decrease (8%) in metformin renal clearance ( p =0.005). Genetic variants of OCT1 and OCT2 moderately impacted the significance and magnitude of the interaction. Collectively, we hypothesized that the cimetidine-metformin interaction involves inhibition of hepatic OCT1 as well as renal OCT2. We tested this hypothesis by developing a physiologically based pharmacokinetic (PBPK) model and assessing potential OCT biomarkers in plasma and urine to gain mechanistic insight into the transporters involved in this interaction. The PBPK model predicted that cimetidine primarily inhibits hepatic OCT1 and, to a lesser extent, renal OCT2. The unchanged renal clearance of potential OCT2 biomarkers following cimetidine exposure supports a minimal role for renal OCT2 in this interaction.

Drug transporters in drug disposition - highlights from the year 2023

Drug transporter field is rapidly evolving with significant progress in in vitro and in vivo tools and, computational models to assess transporter-mediated drug disposition and drug-drug interactions (DDIs) in humans. On behalf of all coauthors, I am pleased to share the fourth annual review highlighting articles published and deemed influential in the field of drug transporters in the year 2023. Each coauthor independently selected peer-reviewed articles published or available online in the year 2023 and summarized them as shown previously (Chothe et al. 2021; Chothe et al. 2022, 2023) with unbiased perspectives. Based on selected articles, this review was categorized into four sections: (1) transporter structure and in vitro evaluation, (2) novel in vitro/ex vivo models, (3) endogenous biomarkers, and (4) PBPK modeling for evaluating transporter DDIs (Table 1). As the scope of this review is not to comprehensively review each article, readers are encouraged to consult original paper for specific details. Finally, I appreciate all the authors for their time and continued support in writing this review.

Prediction of Inhibitory Activity against the MATE1 Transporter via Combined Fingerprint- and Physics-Based Machine Learning Models

Renal secretion plays an important role in excretion of drug from the kidney. Two major transporters known to be highly involved in renal secretion are MATE1/2 K and OCT2, the former of which is highly related to drug-drug interactions. Among published in silico models for MATE inhibitors, a previous model obtained a ROC-AUC value of 0.78 using high throughput percentage inhibition data [J. Med. Chem. 2013, 56(3), 781-795] which we aimed to improve upon here using a combined fingerprint and physics-based approach. To this end, we collected 225 publicly available compounds with pIC50 values against MATE1. Subsequently, on the one hand, we performed a physics-based approach using an Alpha-Fold protein structure, from which we obtained MM-GB/SA scores for those compounds. On the other hand, we built Random Forest (RF) and message passing neural network models using extended-connectivity fingerprints with radius 4 (ECFP4) and chemical structures as graphs, respectively, which also included MM-GB/SA scores as input variables. In a five-fold cross-validation with a separate test set, we found that the best predictivity for the hold-out test was observed in the RF model (including ECFP4 and MM-GB/SA data) with an ROC-AUC of 0.833 ± 0.036; while that of the MM-GB/SA regression model was 0.742. However, the MM-GB/SA model did not show a dependency of the performance on the particular chemical space being predicted. Additionally, via structural interaction fingerprint analysis, we identified interacting residues with inhibitor as identical for those with noninhibitors, including substrates, such as Gln49, Trp274, Tyr277, Tyr299, Ile303, and Tyr306. The similar binding modes are consistent with the observed similar IC50 value inhibitor when using different substrates experimentally, and practically, this can release the experimental scientists from bothering of selecting substrates for MATE1. Hence, we were able to build highly predictive classification models for MATE1 inhibitory activity with both ECFP4 and MM-GB/SA score as input features, which is fit-for-purpose for use in the drug discovery process.

Overview of preclinical and clinical studies investigating pharmacokinetics and drug-drug interactions of padsevonil

BACKGROUND

Padsevonil is an antiseizure medication candidate intended to benefit patients with drug-resistant epilepsy. Our investigations aimed at characterizing pharmacokinetics and drug-drug interaction (DDI) profile of padsevonil.

RESEARCH DESIGN AND METHODS

An overview of preclinical and clinical pharmacology studies conducted during padsevonil development is provided.

RESULTS

In preclinical studies, cytochrome (CYP) 3A4 was identified as the main P450 isoform involved in padsevonil metabolism, with potential minor contribution from CYP2C19. Padsevonil was shown to be a time-dependent CYP2C19-inhibitor, weak CYP3A4-inducer, weak inhibitor of P-gp/OCT1/MATE2-K, and potent OCT2-inhibitor. Initial clinical pharmacology studies in healthy participants showed that padsevonil had (i) good absorption, (ii) clearance mediated mainly by metabolism, and (iii) time-dependent kinetics. A study in genotyped participants confirmed the role of CYP2C19 in clearance and time-dependent kinetics; the major contribution of CYP3A4 was confirmed in DDI studies with CYP3A4-inducers (carbamazepine, oxcarbazepine) and -inhibitor (erythromycin). Padsevonil did not affect pharmacokinetics of valproate/lamotrigine/levetiracetam/oxcarbazepine or oral contraceptives. In a cocktail clinical study, padsevonil showed moderate CYP2C19 inhibition (omeprazole) and weak CYP3A4 induction (oral midazolam). No specific effects on CYP1A2 (caffeine), CYP2C9 (S-warfarin), and CYP2D6 (dextromethorphan) were observed.

CONCLUSIONS

The studies presented helped in understanding padsevonil disposition and risks of DDIs, which would inform dosing and prescribing.

CLINICAL TRIAL REGISTRATION

https://www.clinicaltrials.gov identifiers are NCT04131517, NCT03480243, NCT03695094, NCT04075409.

Innovative Approaches to Optimize Clinical Transporter Drug-Drug Interaction Studies

Of the 450 cell membrane transporters responsible for shuttling substrates, nutrients, hormones, neurotransmitters, antioxidants, and signaling molecules, approximately nine are associated with clinically relevant drug-drug interactions (DDIs) due to their role in drug and metabolite transport. Therefore, a clinical study evaluating potential transporter DDIs is recommended if an investigational product is intestinally absorbed, undergoes renal or hepatic elimination, or is suspected to either be a transporter substrate or perpetrator. However, many of the transporter substrates and inhibitors administered during a DDI study also affect cytochrome P450 (CYP) activity, which can complicate data interpretation. To overcome these challenges, the assessment of endogenous biomarkers can help elucidate the mechanism of complex DDIs when multiple transporters or CYPs may be involved. This perspective article will highlight how creative study designs are currently being utilized to address complex transporter DDIs and the role of physiology-based -pharmacokinetic (PBPK) models can play.

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

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

Transporter-mediated drug-drug interactions: regulatory guidelines, in vitro and in vivo methodologies and translation, special populations, and the blood-brain barrier

This review, part of a special issue on drug-drug interactions (DDIs) spearheaded by the International Society for the Study of Xenobiotics (ISSX) New Investigators, explores the critical role of drug transporters in absorption, disposition, and clearance in the context of DDIs. Over the past two decades, significant advances have been made in understanding the clinical relevance of these transporters. Current knowledge on key uptake and efflux transporters that affect drug disposition and development is summarized. Regulatory guidelines from the FDA, EMA, and PMDA that inform the evaluation of potential transporter-mediated DDIs are discussed in detail. Methodologies for preclinical and clinical testing to assess potential DDIs are reviewed, with an emphasis on the utility of physiologically based pharmacokinetic (PBPK) modeling. This includes the application of relative abundance and expression factors to predict human pharmacokinetics (PK) using preclinical data, integrating the latest regulatory guidelines. Considerations for assessing transporter-mediated DDIs in special populations, including pediatric, hepatic, and renal impairment groups, are provided. Additionally, the impact of transporters at the blood-brain barrier (BBB) on the disposition of CNS-related drugs is explored. Enhancing the understanding of drug transporters and their role in drug disposition and toxicity can improve efficacy and reduce adverse effects. Continued research is essential to bridge remaining gaps in knowledge, particularly in comparison with cytochrome P450 (CYP) enzymes.

Understanding adefovir pharmacokinetics as a component of a transporter phenotyping cocktail

PURPOSE

Adefovir (as dipivoxil) was selected as a probe drug in a previous transporter cocktail phenotyping study to assess renal organic anion transporter 1 (OAT1), with renal clearance (CLR) as the primary parameter describing renal elimination. An approximately 20% higher systemic exposure of adefovir was observed when combined with other cocktail components (metformin, sitagliptin, pitavastatin, and digoxin) compared to sole administration. The present evaluation applied a population pharmacokinetic (popPK) modeling approach to describe adefovir pharmacokinetics as a cocktail component in more detail.

METHODS

Data from 24 healthy subjects were reanalyzed. After establishing a base model, covariate effects, including the impact of co-administered drugs, were assessed using forward inclusion then backward elimination.

RESULTS

A one-compartment model with first-order absorption (including lag time) and a combination of nonlinear renal and linear nonrenal elimination best described the data. A significantly higher apparent bioavailability (73.6% vs. 59.0%) and a lower apparent absorption rate constant (2.29 h-1 vs. 5.18 h-1) were identified in the combined period compared to the sole administration period, while no difference was seen in renal elimination. The population estimate for the Michaelis-Menten constant (Km) of the nonlinear renal elimination was 170 nmol/L, exceeding the observed range of adefovir plasma maximum concentration, while the maximum rate (Vmax) of nonlinear renal elimination was 2.40 µmol/h at the median absolute estimated glomerular filtration rate of 105 mL/min.

CONCLUSION

The popPK modeling approach indicated that the co-administration primarily affected the apparent absorption and/or prodrug conversion of adefovir dipivoxil, resulting in the minor drug-drug interaction observed for adefovir as a victim. However, renal elimination remained unaffected. The high Km value suggests that assessing renal OAT1 activity by CLR has no relevant misspecification error with the cocktail doses used.

Membrane transporters in drug development and as determinants of precision medicine

The effect of membrane transporters on drug disposition, efficacy and safety is now well recognized. Since the initial publication from the International Transporter Consortium, significant progress has been made in understanding the roles and functions of transporters, as well as in the development of tools and models to assess and predict transporter-mediated activity, toxicity and drug-drug interactions (DDIs). Notable advances include an increased understanding of the effects of intrinsic and extrinsic factors on transporter activity, the application of physiologically based pharmacokinetic modelling in predicting transporter-mediated drug disposition, the identification of endogenous biomarkers to assess transporter-mediated DDIs and the determination of the cryogenic electron microscopy structures of SLC and ABC transporters. This article provides an overview of these key developments, highlighting unanswered questions, regulatory considerations and future directions.

The Effect of BI 730357 (Retinoic Acid-Related Orphan Receptor Gamma t Antagonist, Bevurogant) on the Pharmacokinetics of a Transporter Probe Cocktail, Including Digoxin, Furosemide, Metformin, and Rosuvastatin: An Open-Label, Non-randomized, 2-Period Fixed-Sequence Trial in Healthy Subjects

Evaluating Drug-Drug Interactions (DDIs) for new investigational compounds requires several trials evaluating different drugs with different transporter specificities. By using a cocktail of drugs with different transporter specificities, a single trial could evaluate the pharmacokinetics (PKs) of each cocktail drug simultaneously, reducing the number of clinical DDI trials required for clinical development. We aimed to investigate the effect of steady-state Boehringer Ingelheim (BI) 730357 (bevurogant) on the PKs of a validated and optimized 4-component transporter cocktail. This open-label, non-randomized, 2-period fixed-sequence phase I trial compared transporter cocktail (0.25 mg digoxin/1 mg furosemide/10 mg metformin hydrochloride/10 mg rosuvastatin) with and without BI 730357 in healthy subjects aged 18-55 years with body mass index 18.5-29.9 kg/m2 . During reference treatment/period 1, transporter cocktail was administered 90 minutes after breakfast. After a washout period, during test treatment/period 2, BI 730357 was dosed twice daily for 13 days, with transporter cocktail administered on day 1. The primary endpoints were the area under the concentration-time curve of the analyte in plasma over the time interval from 0 extrapolated to infinity (AUC0-∞ ) and the maximum measured concentration of the analyte in plasma (Cmax ), and the secondary endpoint was the area under the concentration-time curve of the analyte in plasma over the time interval from 0 to the last quantifiable data point (AUC0-tz ). Steady-state BI 730357 increased digoxin (+48% to +94%), minimally affected metformin (-2% to -9%), furosemide (+12% to +18%), and rosuvastatin (+19% to +39%) exposure. Therefore, no clinically relevant inhibition of transporters OCT2/MATE-1/MATE-2K, OAT1/OAT3, OATP1B1/OATP1B3 was observed. Potential inhibition of breast cancer resistance protein noted as PK parameters of coproporphyrin I/III (OATP1B1/OATP1B3 biomarkers) remained within bioequivalence boundaries while rosuvastatin PK parameters (AUC0-∞ /Cmax /AUC0-tz ) exceeded the bioequivalence boundary. BI 730357 was safe and well tolerated. This trial confirms the usefulness and tolerability of the transporter cocktail consisting of digoxin, furosemide, metformin, and rosuvastatin in assessing drug-transporter interactions in vivo.

Effect of Cimetidine on Metformin Pharmacokinetics and Endogenous Metabolite Levels in Rats

Tubular secretion is a primary mechanism along with glomerular filtration for renal elimination of drugs and toxicants into urine. Organic cation transporters (OCTs) and multidrug and toxic extrusion (MATE) transporters facilitate the active secretion of cationic substrates, including drugs such as metformin and endogenous cations. We hypothesized that administration of cimetidine, an Oct/Mate inhibitor, will result in increased plasma levels and decreased renal clearance of metformin and endogenous Oct/Mate substrates in rats. A paired rat pharmacokinetic study was carried out in which metformin (5 mg/kg, intravenous) was administered as an exogenous substrate of Oct/Mate transporters to six Sprague-Dawley rats with and without cimetidine (100 mg/kg, intraperitoneal). When co-administered with cimetidine, metformin area under the curve increased significantly by 3.2-fold, and its renal clearance reduced significantly by 73%. Untargeted metabolomics was performed to investigate the effect of cimetidine on endogenous metabolome in the blood and urine samples. Over 8,000 features (metabolites) were detected in the blood, which were shortlisted using optimized criteria, i.e., a significant increase (P value < 0.05) in metabolite peak intensity in the cimetidine-treated group, reproducible retention time, and quality of chromatogram peak. The metabolite hits were classified into three groups that can potentially distinguish inhibition of i) extra-renal uptake transport or catabolism, ii) renal Octs, and iii) renal efflux transporters or metabolite formation. The metabolomics approach identified novel putative endogenous substrates of cationic transporters that could be tested as potential biomarkers to predict Oct/Mate transporter mediated drug-drug interactions in the preclinical stages. SIGNIFICANCE STATEMENT: Endogenous substrates of renal transporters in animal models could be used as potential biomarkers to predict renal drug-drug interactions in early drug development. Here we demonstrated that cimetidine, an inhibitor of organic cation transporters (Oct/Mate), could alter the pharmacokinetics of metformin and endogenous cationic substrates in rats. Several putative endogenous metabolites of Oct/Mate transporters were identified using metabolomics approach, which could be tested as potential transporter biomarkers to predict renal drug-drug interaction of Oct/Mate substrates.

A Metabolomic Analysis of Sensitivity and Specificity of 23 Previously Proposed Biomarkers for Renal Transporter-Mediated Drug-Drug Interactions

Endogenous biomarkers are discussed as tools for detection of drug-drug interactions (DDIs) mediated by renal transport proteins, such as organic cation transporter 2 (OCT2), multidrug and toxin extrusion proteins (MATE1 and MATE2-K) and organic anion transporters (OAT1 and OAT3). Whereas sensitivity of some endogenous biomarkers against at least one clinical transporter inhibitor has frequently been shown, intra-study comparisons of the extent of effects of inhibitors on different biomarkers are frequently lacking. Moreover, in vivo specificity of such discussed biomarkers has frequently not been studied. We therefore investigated changes of 10 previously described putative biomarkers for inhibition of OCT2/MATEs, as well as 15 previously described putative biomarkers for OATs in human plasma and urine samples of healthy volunteers in response to treatment with 4 inhibitors of transport proteins [verapamil (P-glycoprotein), rifampin (organic anion transporting polypeptides), cimetidine (OCT2/MATEs), and probenecid (OATs)]. Two of the putative biomarkers had been suggested for both OCT2/MATEs and OATs. All substances were unequivocally identified in an untargeted metabolomics assay. The OCT2/MATE biomarkers choline and trimethylamine N-oxide were both sensitive and specific (median log2-fold changes -1.18 in estimated renal elimination and -0.85 in urinary excretion, respectively). For renal OATs, indoleacetyl glutamine and indoleacetic acid (median log2-fold changes -3.77 and -2.85 in estimated renal elimination, respectively) were the candidates for sensitive and specific biomarkers with the most extensive change, followed by taurine, indolelactic acid, and hypoxanthine. This comprehensive study adds further knowledge on sensitivity and specificity of 23 previously described biomarkers of renal OCT2/MATE- and OAT-mediated DDIs.

Evaluation of the Selectivity of Several Organic Anion Transporting Polypeptide 1B Biomarkers Using Relative Activity Factor Method

In recent years, some endogenous substrates of organic anion transporting polypeptide 1B (OATP1B) have been identified and characterized as potential biomarkers to assess OATP1B-mediated clinical drug-drug interactions (DDIs). However, quantitative determination of their selectivity to OATP1B is still limited. In this study, we developed a relative activity factor (RAF) method to determine the relative contribution of hepatic uptake transporters OATP1B1, OATP1B3, OATP2B1, and sodium-taurocholate co-transporting polypeptide (NTCP) on hepatic uptake of several OATP1B biomarkers, including coproporphyrin I (CPI), coproporphyrin I CPIII, and sulfate conjugates of bile acids: glycochenodeoxycholic acid sulfate (GCDCA-S), glycodeoxycholic acid sulfate (GDCA-S), and taurochenodeoxycholic acid sulfate (TCDCA-S). RAF values for OATP1B1, OATP1B3, OATP2B1, and NTCP were determined in cryopreserved human hepatocytes and transporter transfected cells using pitavastatin, cholecystokinin, resveratrol-3-O-β-D-glucuronide, and taurocholic acid (TCA) as reference compounds, respectively. OATP1B1-specific pitavastatin uptake in hepatocytes was measured in the absence and presence of 1 µM estropipate, whereas NTCP-specific TCA uptake was measured in the presence of 10 µM rifampin. Our studies suggested that CPI was a more selective biomarker for OATP1B1 than CPIII, whereas GCDCA-S and TCDCA-S were more selective to OATP1B3. OATP1B1 and OATP1B3 equally contributed to hepatic uptake of GDCA-S. The mechanistic static model, incorporating the fraction transported of CPI/III estimated by RAF and in vivo elimination data, predicted several perpetrator interactions with CPI/III. Overall, RAF method combined with pharmacogenomic and DDI studies is a useful tool to determine the selectivity of transporter biomarkers and facilitate the selection of appropriate biomarkers for DDI evaluation. SIGNIFICANCE STATEMENT: The authors developed a new relative activity factor (RAF) method to quantify the contribution of hepatic uptake transporters organic anion transporting polypeptide (OATP)1B1, OATP1B3, OATP2B1, and sodium taurocholate co-transporting polypeptide (NTCP) on several OATP1B biomarkers and evaluated their predictive value on drug-drug interactions (DDI). These studies suggest that the RAF method is a useful tool to determine the selectivity of transporter biomarkers. This method combined with pharmacogenomic and DDI studies will mechanistically facilitate the selection of appropriate biomarkers for DDI prediction.

Pharmacokinetic and Pharmacodynamic Drug-Drug Interactions: Research Methods and Applications

Because of the high research and development cost of new drugs, the long development process of new drugs, and the high failure rate at later stages, combining past drugs has gradually become a more economical and attractive alternative. However, the ensuing problem of drug-drug interactions (DDIs) urgently need to be solved, and combination has attracted a lot of attention from pharmaceutical researchers. At present, DDI is often evaluated and investigated from two perspectives: pharmacodynamics and pharmacokinetics. However, in some special cases, DDI cannot be accurately evaluated from a single perspective. Therefore, this review describes and compares the current DDI evaluation methods based on two aspects: pharmacokinetic interaction and pharmacodynamic interaction. The methods summarized in this paper mainly include probe drug cocktail methods, liver microsome and hepatocyte models, static models, physiologically based pharmacokinetic models, machine learning models, in vivo comparative efficacy studies, and in vitro static and dynamic tests. This review aims to serve as a useful guide for interested researchers to promote more scientific accuracy and clinical practical use of DDI studies.

Impact of Direction of Transport on the Evaluation of Substrate Recognition of Mouse Multidrug and Toxin Extrusion Protein 1

Multidrug and toxin extrusion protein (MATE/SLC47A) secretes metabolites and xenobiotics into the urine in the proximal tubules of the kidney. Uptake assays have been commonly used for evaluating MATE-mediated transport of new chemical entities in drug development. The purpose of this study was to examine the relationship between in vitro uptake activities by MATEs and the impact of MATE-mediated transport in in vivo renal secretion. In vitro uptake in mouse Mate1 (mMate1)-expressing human embryonic kidney 293 (HEK293) cells and several in vivo parameters from mMate1 knockout and wild-type mice were compared using nine cationic compounds (almotriptan, naratriptan, talinolol, sumatriptan, alogliptin, sitagliptin, rivaroxaban, saxagliptin, and vildagliptin). Compounds that showed statistically significant decrease in secretory clearances with respect to kidney concentrations (CLR,kidney) in mMate1 knockout mice were categorized as in vivo substrates in this study. A good correlation (R2 = 0.637) was observed between the in vitro uptake ratio and the in vivo ratio of CLR,kidney of mMate1 knockout mice and wild-type mice. This study supported the rationale of using an uptake assay to determine whether investigational compounds are the substrate of MATEs and to predict drug-drug interaction risk via renal secretion by MATE from the viewpoint of drug development in pharmaceutical companies. SIGNIFICANCE STATEMENT: We revealed that substrates judged by in vitro experiments using mouse multidrug and toxin extrusion (mMate)1-expressing cells were excreted in urine via mMate1 in vivo, and a good correlation (R2 = 0.637) was observed between in vitro uptake ratio and in vivo ratio of secretory clearance with respect to the kidney concentrations (CLR,kidney) of mMate1 knockout and wild-type mice. This study supported the rationale of using an uptake assay to predict potential human MATE1-mediated drug-drug interaction as a victim.

Construction and Evaluation of a Novel Organic Anion Transporter 1/3 CRISPR/Cas9 Double-Knockout Rat Model

BACKGROUND

Organic anion transporter 1 (OAT1) and OAT3 have an overlapping spectrum of substrates such that one can exert a compensatory effect when the other is dysfunctional. As a result, the knockout of either OAT1 or OAT3 is not reflected in a change in the excretion of organic anionic substrates. To date, only the mOAT1 and mOAT3 individual knockout mouse models have been available.

METHODS

In this study, we successfully generated a Slc22a6/Slc22a8 double-knockout (KO) rat model using CRISPR/Cas9 technology and evaluated its biological properties.

RESULTS

The double-knockout rat model did not expression mRNA for rOAT1 or rOAT3 in the kidneys. Consistently, the renal excretion of p-aminohippuric acid (PAH), the classical substrate of OAT1/OAT3, was substantially decreased in the Slc22a6/Slc22a8 double-knockout rats. The relative mRNA level of Slco4c1 was up-regulated in KO rats. No renal pathological phenotype was evident. The renal elimination of the organic anionic drug furosemide was nearly abolished in the Slc22a6/Slc22a8 knockout rats, but elimination of the organic cationic drug metformin was hardly affected.

CONCLUSIONS

These results demonstrate that this rat model is a useful tool for investigating the functions of OAT1/OAT3 in metabolic diseases, drug metabolism and pharmacokinetics, and OATs-mediated drug interactions.

Reimagining the Framework Supporting the Static Analysis of Transporter Drug Interaction Risk; Integrated Use of Biomarkers to Generate Pan‐Transporter Inhibition Signatures

Solute carrier (SLC) transporters present as the loci of important drug-drug interactions (DDIs). Therefore, sponsors generate in vitro half-maximal inhibitory concentration (IC₅₀ ) data and apply regulatory agency-guided "static" methods to assess DDI risk and the need for a formal clinical DDI study. Because such methods are conservative and high false-positive rates are likely (e.g., DDI study triggered when liver SLC R value ≥ 1.04 and renal SLC maximal unbound plasma (C_max,u )/IC₅₀ ratio ≥ 0.02), investigators have attempted to deploy plasma- and urine-based SLC biomarkers in phase I studies to de-risk DDI and obviate the need for drug probe-based studies. In this regard, it was possible to generate in-house in vitro SLC IC₅₀ data for various clinically (biomarker)-qualified perpetrator drugs, under standard assay conditions, and then estimate "% inhibition" for each SLC and relate it empirically to published clinical biomarker data (area under the plasma concentration vs. time curve (AUC) ratio (AUCR, AUC_inhibitor /AUC_reference ) and % decrease in renal clearance (ΔCL_renal )). After such a "calibration" exercise, it was determined that only compounds with high R values (> 1.5) and C_max,u /IC₅₀ ratios (> 0.5) are likely to significantly modulate liver (AUCR > 1.25) and renal (ΔCL_renal  > 25%) biomarkers and evoke DDI risk. The % inhibition approach supports integration of liver and renal SLC data and allows one to generate pan-SLC inhibition signatures for different test perpetrators (e.g., SLC % inhibition ranking). In turn, such signatures can guide the selection of the most appropriate individual (or combinations of) biomarkers for testing in phase I studies.

Utilizing Endogenous Biomarkers to Derisk Assessment of Transporter Mediated Drug-Drug Interactions: A Scientific Perspective

Comprehensive characterization of transporter mediated drug-drug interactions (DDIs) is important to formulate clinical management strategies and ensure the safe and effective use of concomitantly administered drugs. The potential of a drug to inhibit transporters is predicted by comparing the ratio of the relevant concentration (depending on the transporter) and the half maximum inhibitory concentration (IC₅₀ ) to a pre-defined "cut off" value. If the ratio is greater than the cut off value, modeling approaches such as Physiologically Based Pharmacokinetic (PBPK) Modeling or a clinical DDI trial may be recommended. Because false positive (in vitro data suggests the potential for a DDI, whereas no significant DDI is observed in vivo) and false negative (in vitro data does not suggest the potential for a DDI, whereas significant DDI is observed in vivo) outcomes have been observed, there is interest in exploring additional approaches to facilitate prediction of transporter mediated DDIs. The idea of assessing changes in the concentration of endogenous biomarkers (which are substrates of clinically relevant transporters) to gain insight on the potential for a drug to inhibit transporter activity has received widespread attention. This brief report describes how endogenous biomarkers may help to expand the DDI assessment toolkit, highlights some current knowledge gaps, and outlines a conceptual framework that may complement the current paradigm of predicting the potential for transporter mediated DDIs. This article is protected by copyright. All rights reserved.

Determining the Effects of Chronic Kidney Disease on Organic Anion Transporter1/3 Activity Through Physiologically Based Pharmacokinetic Modeling

BACKGROUND AND OBJECTIVE

The renal excretion of drugs via organic anion transporters 1 and 3 (OAT1/3) is significantly decreased in patients with renal impairment. This study uses physiologically based pharmacokinetic models to quantify the reduction in OAT1/3-mediated secretion of drugs throughout varying stages of chronic kidney disease.

METHODS

Physiologically based pharmacokinetic models were constructed for four OAT1/3 substrates in healthy individuals: acyclovir, meropenem, furosemide, and ciprofloxacin. Observed data from drug-drug interaction studies with probenecid, a potent OAT1/3 inhibitor, were used to parameterize the contribution of OAT1/3 to the renal elimination of each drug. The models were then translated to patients with chronic kidney disease by accounting for changes in glomerular filtration rate, kidney volume, renal blood flow, plasma protein binding, and hematocrit. Additionally, a relationship was derived between the estimated glomerular filtration rate and the reduction in OAT1/3-mediated secretion of drugs based on the renal extraction ratios of ƿ-aminohippuric acid in patients with varying degrees of renal impairment. The relationship was evaluated in silico by evaluating the predictive performance of each final model in describing the pharmacokinetics (PK) of drugs across stages of chronic kidney disease.

RESULTS

OAT1/3-mediated renal excretion of drugs was found to be decreased by 27-49%, 50-68%, and 70-96% in stage 3, stage 4, and stage 5 of chronic kidney disease, respectively. In support of the parameterization, physiologically based pharmacokinetic models of four OAT1/3 substrates were able to adequately characterize the PK in patients with different degrees of renal impairment. Total exposure after intravenous administration was predicted within a 1.5-fold error and 85% of the observed data points fell within a 1.5-fold prediction error. The models modestly under-predicted plasma concentrations in patients with end-stage renal disease undergoing intermittent hemodialysis. However, results should be interpreted with caution because of the limited number of molecules analyzed and the sparse sampling in observed chronic kidney disease pharmacokinetic studies.

CONCLUSIONS

A quantitative understanding of the reduction in OAT1/3-mediated excretion of drugs in differing stages of renal impairment will contribute to better predictive accuracy for physiologically based pharmacokinetic models in drug development, assisting with clinical trial planning and potentially sparing this population from unnecessary toxic exposures.

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.

Evaluation of the Effect of Abrocitinib on Drug Transporters by Integrated Use of Probe Drugs and Endogenous Biomarkers

Abrocitinib is an oral Janus kinase 1 (JAK1) inhibitor currently approved in the United Kingdom for the treatment of moderate‐to‐severe atopic dermatitis (AD). As patients with AD may use medications to manage comorbidities, abrocitinib could be used concomitantly with hepatic and/or renal transporter substrates. Therefore, we assessed the potential effect of abrocitinib on probe drugs and endogenous biomarker substrates for the drug transporters of interest. In vitro studies indicated that, among the transporters tested, abrocitinib has the potential to inhibit the activities of P‐glycoprotein (P‐gp), breast cancer resistance protein (BCRP), organic anion transporter 3 (OAT3), organic cation transporter 1 (OCT1), and multidrug and toxin extrusion protein 1 and 2K (MATE1/2K). Therefore, subsequent phase I, two‐way crossover, open‐label studies in healthy participants were performed to assess the impact of abrocitinib on the pharmacokinetics of the transporter probe substrates dabigatran etexilate (P‐gp), rosuvastatin (BCRP and OAT3), and metformin (OCT2 and MATE1/2K), as well as endogenous biomarkers for MATE1/2K (N¹‐methylnicotinamide (NMN)) and OCT1 (isobutyryl‐_L‐carnitine (IBC)). Co‐administration with abrocitinib was shown to increase the plasma exposure of dabigatran by ~ 50%. In comparison, the plasma exposure and renal clearance of rosuvastatin and metformin were not altered with abrocitinib co‐administration. Similarly, abrocitinib did not affect the exposure of NMN or IBC. An increase in dabigatran exposure suggests that abrocitinib inhibits P‐gp activity. By contrast, a lack of impact on plasma exposure and/or renal clearance of rosuvastatin, metformin, NMN, or IBC suggests that BCRP, OAT3, OCT1, and MATE1/2K activity are unaffected by abrocitinib.

Cynomolgus Monkey as an Emerging Animal Model to Study Drug Transporters: In Vitro, In Vivo, In Vitro-to-In Vivo Translation

Membrane transporters have been recognized as one of the key determinants of pharmacokinetics and are also known to affect the efficacy and toxicity of drugs. Both qualitatively and quantitatively, however, transporter studies conducted using human in vitro systems have not always been predictive. Consequently, researchers have used cynomolgus monkeys as a model to study drug transporters and anticipate their effects in humans. Burgeoning reports of data in the last few years necessitates a comprehensive review on the topic of drug transporters in cynomolgus monkeys that includes cell-based tools, sequence homology, tissue expression, in vitro studies, in vivo studies, and in vitro-to-in vivo extrapolation. This review highlights the state-of-the-art applications of monkey transporter models to support the evaluation of transporter-mediated drug-drug interactions, clearance predictions, and endogenous transporter biomarker identification and validation. The data demonstrate that cynomolgus monkey transporter models, when used appropriately, can be an invaluable tool to support drug discovery and development processes. Most importantly, they enable an early in vitro-to-in vivo extrapolation assessment, which provides additional context to human in vitro data. Additionally, comprehending species similarities and differences in transporter tissue expression and activity is crucial when translating monkey data to humans. The challenges and limitations when applying such models to inform decision-making must also be considered. SIGNIFICANCE STATEMENT: This paper presents a comprehensive review of currently available published reports describing cynomolgus monkey transporter models. The data indicate that Cynomolgus monkeys provide mechanistic insight regarding the role of intestinal, hepatic, and renal transporters in drug and biomarker disposition and drug interactions. The data generated with cynomolgus monkey models provide mechanistic insight into transporter-mediated drug absorption and disposition. They are valuable to human clearance prediction, drug drug interaction assessment, and endogenous biomarker development related to drug transporters.

Assessment of drug-drug interactions of CC-90001, a potent and selective inhibitor of c-Jun N-terminal kinase

CC-90001 is predominantly metabolised via glucuronidation, while oxidative metabolism is a minor pathway in human hepatocytes and liver microsomes. In vitro, CC-90001 glucuronidation was catalysed by UGT1A9, UGT1A4, and UGT1A1, while oxidative metabolism was primarily mediated by CYP3A4/5 with minor contributions from CYP1A2, CYP2C9, CYP2B6, and CYP2D6.CC-90001 in vitro inhibits CYP2A6, CYP2B6, CYP2C8, CYP2C9, CYP2C19, and CYP3A4 ≤ 55% at 100 μM, and the inhibition was negligible at ≤30 μM. CC-90001 is not a time-dependent CYP inhibitor.In human hepatocytes CC-90001 is an inducer of CYP2B6 and CYP3A, with mRNA levels increased 34.4% to 52.8% relative to positive controls.In vitro CC-90001 is a substrate of P-gp, and an inhibitor of P-gp, BCRP, OAT3, OATP1B1, OATP1B3, OCT2, MATE1, and MATE2k with IC₅₀ values of 30.3, 25.8, 17.7, 0.417, 19.9, 0.605, 4.17, and 20 μM, respectively.A clinical study demonstrated that CC-90001 has no or little impact on the exposure of warfarin (CYP2C9), omeprazole (CYP2C19), midazolam (CYP3A) or metformin (OCT2, MATE1/2k). CC-90001 co-administration increases the AUC_t and C_max 176% and 339% for rosuvastatin (BCRP/OATP1B1/3), 116% and 171% for digoxin (P-gp), and 266% and 321% for nintedanib (CYP3A & P-gp), respectively.In conclusion, CC-90001 in unlikely to be a victim or perpetrator of clinically relevant interactions involving CYPs or UGTs. Weak to moderate interactions are expected in clinic with substrates of P-gp and OATP1B1 due to CC-90001 inhibition of these transporters.

Physiologically Based Pharmacokinetic Modeling of Rosuvastatin to Predict Transporter-Mediated Drug-Drug Interactions

Purpose

To build a physiologically based pharmacokinetic (PBPK) model of the clinical OATP1B1/OATP1B3/BCRP victim drug rosuvastatin for the investigation and prediction of its transporter-mediated drug-drug interactions (DDIs).

Methods

The Rosuvastatin model was developed using the open-source PBPK software PK-Sim®, following a middle-out approach. 42 clinical studies (dosing range 0.002–80.0 mg), providing rosuvastatin plasma, urine and feces data, positron emission tomography (PET) measurements of tissue concentrations and 7 different rosuvastatin DDI studies with rifampicin, gemfibrozil and probenecid as the perpetrator drugs, were included to build and qualify the model.

Results

The carefully developed and thoroughly evaluated model adequately describes the analyzed clinical data, including blood, liver, feces and urine measurements. The processes implemented to describe the rosuvastatin pharmacokinetics and DDIs are active uptake by OATP2B1, OATP1B1/OATP1B3 and OAT3, active efflux by BCRP and Pgp, metabolism by CYP2C9 and passive glomerular filtration. The available clinical rifampicin, gemfibrozil and probenecid DDI studies were modeled using in vitro inhibition constants without adjustments. The good prediction of DDIs was demonstrated by simulated rosuvastatin plasma profiles, DDI AUC_last ratios (AUC_last during DDI/AUC_last without co-administration) and DDI C_max ratios (C_max during DDI/C_max without co-administration), with all simulated DDI ratios within 1.6-fold of the observed values.

Conclusions

A whole-body PBPK model of rosuvastatin was built and qualified for the prediction of rosuvastatin pharmacokinetics and transporter-mediated DDIs. The model is freely available in the Open Systems Pharmacology model repository, to support future investigations of rosuvastatin pharmacokinetics, rosuvastatin therapy and DDI studies during model-informed drug discovery and development (MID3).

Supplementary Information

The online version contains supplementary material available at 10.1007/s11095-021-03109-6.

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.

Impact of fedratinib on the pharmacokinetics of transporter probe substrates using a cocktail approach

INTRODUCTION

Fedratinib, an oral, selective Janus kinase 2 inhibitor, has been shown to inhibit P-glycoprotein (P-gp), breast cancer resistance protein (BCRP), organic anion transporting polypeptide (OATP) 1B1, OATP1B3, organic cation transporter (OCT) 2, and multidrug and toxin extrusion (MATE) 1 and MATE2-K in vitro. The objective of this study was to evaluate the influence of fedratinib on the pharmacokinetics (PK) of digoxin (P-gp substrate), rosuvastatin (OATP1B1/1B3 and BCRP substrate), and metformin (OCT2 and MATE1/2-K substrate).

METHODS

In this nonrandomized, fixed-sequence, open-label study, 24 healthy adult participants received single oral doses of digoxin 0.25 mg, rosuvastatin 10 mg, and metformin 1000 mg administered as a drug cocktail (day 1, period 1). After a 6-day washout, participants received oral fedratinib 600 mg 1 h before the cocktail on day 7 (period 2). An oral glucose tolerance test (OGTT) was performed to determine possible influences of fedratinib on the antihyperglycemic effect of metformin.

RESULTS

Plasma exposure to the three probe drugs was generally comparable in the presence or absence of fedratinib. Reduced metformin renal clearance by 36% and slightly higher plasma glucose levels after OGTT were observed in the presence of fedratinib. Single oral doses of the cocktail ± fedratinib were generally well tolerated.

CONCLUSIONS

These results suggest that fedratinib has minimal impact on the exposure of P-gp, BCRP, OATP1B1/1B3, OCT2, and MATE1/2-K substrates. Since renal clearance of metformin was decreased in the presence of fedratinib, caution should be exercised in using coadministered drugs that are renally excreted via OCT2 and MATEs.

TRIAL REGISTRATION

Clinicaltrials.gov NCT04231435 on January 18, 2020.

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.

Quantitative prediction of breast cancer resistant protein mediated drug-drug interactions using physiologically-based pharmacokinetic modeling

Quantitative assessment of drug‐drug interactions (DDIs) involving breast cancer resistance protein (BCRP) inhibition is challenged by overlapping substrate/inhibitor specificity. This study used physiologically‐based pharmacokinetic (PBPK) modeling to delineate the effects of inhibitor drugs on BCRP‐ and organic anion transporting polypeptide (OATP)1B‐mediated disposition of rosuvastatin, which is a recommended BCRP clinical probe. Initial static model analysis using in vitro inhibition data suggested BCRP/OATP1B DDI risk while considering regulatory cutoff criteria for a majority of inhibitors assessed (25 of 27), which increased rosuvastatin plasma exposure to varying degree (~ 0–600%). However, rosuvastatin area under plasma concentration‐time curve (AUC) was minimally impacted by BCRP inhibitors with calculated G‐value (= gut concentration/inhibition potency) below 100. A comprehensive PBPK model accounting for intestinal (OATP2B1 and BCRP), hepatic (OATP1B, BCRP, and MRP4), and renal (OAT3) transport mechanisms was developed for rosuvastatin. Adopting in vitro inhibition data, rosuvastatin plasma AUC changes were predicted within 25% error for 9 of 12 inhibitors evaluated via PBPK modeling. This study illustrates the adequacy and utility of a mechanistic model‐informed approach in quantitatively assessing BCRP‐mediated DDIs.

Effect of Pharmaceutical Excipients on Intestinal Absorption of Metformin via Organic Cation-Selective Transporters

Growing evidence has shown that some pharmaceutical excipients can act on drug transporters. The present study was aimed at investigating the effects of 13 commonly used excipients on the intestinal absorption of metformin (MTF) and the underlying mechanisms using Caco-2 cells and an ex vivo mouse non-everted gut sac model. First, the uptake of MTF in Caco-2 cells was markedly inhibited by nonionic excipients including Solutol HS 15, polysorbate 20, polysorbate 40, polysorbate 60, polysorbate 80, and crospovidone. Second, transport profile studies showed that MTF was taken up via multiple cation-selective transporters, among which a novel pyrilamine-sensitive proton-coupled organic cation (H⁺/OC⁺) antiporter played a key role. Third, Solutol HS 15, polysorbate 40, and polysorbate 60 showed cis-inhibitory effects on the uptake of either pyrilamine (prototypical substrate of the pyrilamine-sensitive H⁺/OC⁺ antiporter) or 1-methyl-4-phenylpyridinium (substrate of traditional cation-selective transporters including OCTs, MATEs, PMAT, SERT, and THTR-2), indicating that their suppression on MTF uptake is due to the synergistic inhibition toward multiple influx transporters. Finally, the pH-dependent mouse intestinal absorption of MTF was significantly decreased by Solutol HS 15, polysorbate 20, polysorbate 40, polysorbate 60, polysorbate 80, and pyrilamine. In conclusion, this study revealed that a novel transport process mediated by the pyrilamine-sensitive H⁺/OC⁺ antiporter contributes to the intestinal absorption of MTF in conjunction with the traditional cation-selective transporters. Mechanistic understanding of the interaction of excipients with cation-selective transporters can improve the formulation design and clinical application of cationic drugs.

Effects of Probenecid on Hepatic and Renal Disposition of Hexadecanedioate, an Endogenous Substrate of Organic Anion Transporting Polypeptide 1B in Rats

The aim of the present study was to investigate changes in plasma concentrations and tissue distribution of endogenous substrates of organic anion transporting polypeptide (OATP) 1B, hexadecanedioate (HDA), octadecanedioate (ODA), tetradecanedioate (TDA), and coproporphyrin-III, induced by its weak inhibitor, probenecid (PBD), in rats. PBD increased the plasma concentrations of these four compounds regardless of bile duct cannulation, whereas liver-to-plasma (K_p,liver) and kidney-to-plasma concentration ratios of HDA and TDA were reduced. Similar effects of PBD on plasma concentrations and K_p,liver of HDA, ODA, and TDA were observed in kidney-ligated rats, suggesting a minor contribution of renal disposition to the overall distribution of these three compounds. Tissue uptake clearance of deuterium-labeled HDA (d-HDA) in liver was 16-fold higher than that in kidney, and was reduced by 80% by PBD. This was compatible with inhibition by PBD of d-HDA uptake in isolated rat hepatocytes. Such inhibitory effects of PBD were also observed in the human OATP1B1-mediated uptake of d-HDA. Overall, the disposition of HDA is mainly determined by hepatic OATP-mediated uptake, which is inhibited by PBD. HDA might, thus, be a biomarker for OATPs minimally affected by urinary and biliary elimination in rats.

Physiologically Based Pharmacokinetic Models of Probenecid and Furosemide to Predict Transporter Mediated Drug-Drug Interactions

Purpose

To provide whole-body physiologically based pharmacokinetic (PBPK) models of the potent clinical organic anion transporter (OAT) inhibitor probenecid and the clinical OAT victim drug furosemide for their application in transporter-based drug-drug interaction (DDI) modeling.

Methods

PBPK models of probenecid and furosemide were developed in PK-Sim®. Drug-dependent parameters and plasma concentration-time profiles following intravenous and oral probenecid and furosemide administration were gathered from literature and used for model development. For model evaluation, plasma concentration-time profiles, areas under the plasma concentration–time curve (AUC) and peak plasma concentrations (C_max) were predicted and compared to observed data. In addition, the models were applied to predict the outcome of clinical DDI studies.

Results

The developed models accurately describe the reported plasma concentrations of 27 clinical probenecid studies and of 42 studies using furosemide. Furthermore, application of these models to predict the probenecid-furosemide and probenecid-rifampicin DDIs demonstrates their good performance, with 6/7 of the predicted DDI AUC ratios and 4/5 of the predicted DDI C_max ratios within 1.25-fold of the observed values, and all predicted DDI AUC and C_max ratios within 2.0-fold.

Conclusions

Whole-body PBPK models of probenecid and furosemide were built and evaluated, providing useful tools to support the investigation of transporter mediated DDIs.

Supplementary Information

The online version contains supplementary material available at 10.1007/s11095-020-02964-z.

Detection of Weak Organic Anion-Transporting Polypeptide 1B Inhibition by Probenecid with Plasma-Based Coproporphyrin in Humans

Probenecid (PROB) is a clinical probe inhibitor of renal organic anion transporter (OAT) 1 and OAT3 that inhibits in vitro activity of hepatic drug transporters OATP1B1 and OATP1B3. It was hypothesized that PROB could potentially affect the disposition of OATP1B drug substrates. The plasma levels of the OATP1B endogenous biomarker candidates, including coproporphyrin I (CPI), CPIII, hexadecanedioate (HDA), and tetradecanedioate (TDA), were examined in 14 healthy subjects treated with PROB. After oral administration with 1000 mg PROB alone and in combination with furosemide (FSM), AUC (0-24 h) values were 1.39 ± 0.21-fold and 1.57 ± 0.41-fold higher than predose levels for CPI and 1.34 ± 0.16-fold and 1.45 ± 0.57-fold higher for CPIII. Despite increased systemic exposures, no decreases in CPI and CPIII renal clearance were observed (0.97 ± 0.38-fold and 1.16 ± 0.51-fold for CPI, and 1.34 ± 0.53-fold and 1.50 ± 0.69-fold for CPIII, respectively). These results suggest that the increase of CP systemic exposure is caused by OATP1B inhibition. Consistent with this hypothesis, PROB inhibited OATP1B1- and OATP1B3-mediated transport of CPI in a concentration-dependent manner, with IC50 values of 167 ± 42.0 and 76.0 ± 17.2 µM, respectively, in transporter-overexpressing human embryonic kidney cell assay. The inhibition potential was further confirmed by CPI and CPIII hepatocyte uptake experiments. In contrast, administration of PROB alone did not change AUC (0-24 h) of HDA and TDA relative to prestudy levels, although the administration of PROB in combination with FSM increased HDA and TDA levels compared with FSM alone (1.02 ± 0.18-fold and 0.90 ± 0.20-fold vs. 1.71 ± 0.43-fold and 1.62 ± 0.40-fold). Taken together, these findings indicate that PROB displays weak OATP1B inhibitory effects in vivo and that coproporphyrin is a sensitive endogenous probe of OATP1B inhibition. This study provides an explanation for the heretofore unknown mechanism responsible for PROB's interaction with other xenobiotics. SIGNIFICANCE STATEMENT: This study suggested that PROB is a weak clinical inhibitor of OATP1B based on the totality of evidence from the clinical interaction between PROB and CP and the in vitro inhibitory effect of PROB on OATP1B-mediated CP uptake. It demonstrates a new methodology of utilizing endogenous biomarkers to evaluate complex drug-drug interaction, providing explanation for the heretofore unknown mechanism responsible for PROB's inhibition. It provides evidence to strengthen the claim that CP is a sensitive circulating endogenous biomarker of OATP1B inhibition.