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

Letter to the FDA Proposing Major Changes in the US Clozapine Package Insert Supported by Clozapine Experts Worldwide. Part I: A Review of the Pharmacokinetic Literature and Proposed Changes

PURPOSE/BACKGROUND

Clozapine was approved in the United States (US) using 1989 regulations and knowledge. After 30 years, many sections of the US package insert (PI) are outdated.

METHODS

We comprehensively reviewed the literature to propose PI updates. We present the information in 2 articles. In Part I, we focus on basic pharmacology based on 407 relevant articles. Part II focuses on clinical aspects and pharmacovigilance.

FINDINGS/RESULTS

Based on more recent expectations of Food and Drug Administration regulations, we reviewed clozapine basic pharmacology including the following: 1) clearance, 2) pharmacokinetics and pharmacodynamics, and 3) monitoring tools. We identified 9 major problems in the basic pharmacological sections of the PI including the following: 1) in vivo studies indicate that clozapine is dependent on CYP1A2 for its metabolism, 2) the minor role of CYP2D6 in clozapine metabolism requires removing the PI recommendation to lower clozapine doses in CYP2D6 poor metabolizers, 3) in nontoxic concentrations CYP3A4 has a minor role in clozapine metabolism and potent CYP3A4 inhibitors lack clinically relevant effects, 4) several drug-drug interactions need to be updated based on recent literature, 5) systemic inflammation may decrease clozapine metabolism and increase the risk of clozapine intoxication, 6) obesity may decrease clozapine metabolism, 7) patients of Asian and Indigenous American ancestry need lower clozapine doses, 8) personalized titration and c-reactive protein monitoring should be considered until prospective studies are available, and 9) the half-life section needs to be modified to acknowledge that single dosing at night is frequent in the US.

IMPLICATIONS/CONCLUSIONS

An improvement in the US clozapine PI may lead to improvement in PIs worldwide.

Insights into therapeutic approaches for the treatment of neurodegenerative diseases targeting metabolic syndrome

Due to the significant energy requirements of nerve cells, glucose is rapidly oxidized to generate ATP and works in conjunction with mitochondria in metabolic pathways, resulting in a combinatorial impact. The purpose of this review is to show how glucose metabolism disorder invariably disrupts the normal functioning of neurons, a phenomenon commonly observed in neurodegenerative diseases. Interventions in these systems may alleviate the degenerative load on neurons. Research on the concepts of metabolic adaptability during disease progression has become a key focus. The majority of the existing treatments are effective in mitigating some clinical symptoms, but they are unsuccessful in preventing neurodegeneration. Hence, there is an urgent need for breakthrough and highly effective therapies for neurodegenerative diseases. Here, we summarise the interactions that various neurodegenerative diseases have with abnormalities in insulin signalling, lipid metabolism, glucose control, and mitochondrial bioenergetics. These factors have a crucial role in brain activity and cognition, and also significantly contribute to neuronal degeneration in pathological conditions. In this article, we have discussed the latest and most promising treatment methods, ranging from molecular advancements to clinical trials, that aim at improving the stability of neurons.

Physiologically based pharmacokinetic modeling of CYP2C8 substrate rosiglitazone and its metabolite to predict metabolic drug-drug interaction

Rosiglitazone is an activator of nuclear peroxisome proliferator-activated (PPAR) receptor gamma used in the treatment of type 2 diabetes mellitus. The elimination of rosiglitazone occurs mainly via metabolism, with major contribution by enzyme cytochrome P450 (CYP) 2C8. Primary routes of rosiglitazone metabolism are N-demethylation and hydroxylation. Modulation of CYP2C8 activity by co-administered drugs lead to prominent changes in the exposure of rosiglitazone and its metabolites. Here, we attempt to develop mechanistic parent-metabolite physiologically based pharmacokinetic (PBPK) model for rosiglitazone. Our goal is to predict potential drug-drug interaction (DDI) and consequent changes in metabolite N-desmethyl rosiglitazone exposure. The PBPK modeling was performed in the PKSim® software using clinical pharmacokinetics data from literature. The contribution to N-desmethyl rosiglitazone formation by CYP2C8 was delineated using vitro metabolite formation rates from recombinant enzyme system. Developed model was verified for prediction of rosiglitazone DDI potential and its metabolite exposure based on observed clinical DDI studies. Developed model exhibited good predictive performance both for rosiglitazone and N-desmethyl rosiglitazone respectively, evaluated based on commonly acceptable criteria. In conclusion, developed model helps with prediction of CYP2C8 DDI using rosiglitazone as a substrate, as well as changes in metabolite exposure. In vitro data for metabolite formation can be successfully utilized to translate to in vivo conditions.

Exploring low clozapine C/D ratios, inverted clozapine-norclozapine ratios and undetectable concentrations as measures of non-adherence in clozapine patients: A literature review and a case series of 17 patients from 3 studies

BACKGROUND

Up to 1/2 of outpatients prescribed clozapine may be partially/fully non-adherent, based on therapeutic drug monitoring (TDM). Three indices for measuring partial/full non-adherence are proposed a: 1) clozapine concentration/dose (C/D) ratio which drops to half or more of what is expected in the patient; 2) clozapine/norclozapine ratio that becomes inverted; and 3) clozapine concentration that becomes non-detectable.

METHODS

These 3 proposed indices are based on a literature review and 17 cases of possible non-adherence from 3 samples: 1) an inpatient study in a Chinese hospital, 2) an inpatient randomized clinical trial in a United States hospital, and 3) and a Uruguayan outpatient study.

RESULTS

The first index of non-adherence is a clozapine C/D ratio which is less than half the ratio corresponding to the patient's specific ancestry group and sex-smoking subgroup. Knowing the minimum therapeutic dose of the patient based on repeated TDM makes it much easier to establish non-adherence. The second index is inverted clozapine/norclozapine ratios in the absence of alternative explanations. The third index is undetectable concentrations. By using half-lives, the chronology of the 3 indices of non-adherence was modeled in two patients: 1) the clozapine C/D ratio dropped to ≥1/2 of what is expected from the patient (around day 2); 2) the clozapine/norclozapine ratio became inverted (around day 3); and 3) the clozapine concentration became undetectable by the laboratory (around days 9-11).

CONCLUSION

Prospective studies should further explore these proposed clozapine indices in average patients, poor metabolizers (3 presented) and ultrarapid metabolizers (2 presented).

Mechanistic Determinants of Daprodustat Drug-Drug Interactions and Pharmacokinetics in Hepatic Dysfunction and Chronic Kidney Disease: Significance of OATP1B-CYP2C8 Interplay

Daprodustat is the first oral hypoxia-inducible factor prolyl hydroxylase inhibitor approved recently for the treatment of anemia caused by chronic kidney disease (CKD) in adults receiving dialysis. We evaluated the role of organic anion transporting polypeptide (OATP)1B-mediated hepatic uptake transport in the pharmacokinetics (PKs) of daprodustat using in vitro and in vivo studies, and physiologically-based PK (PBPK) modeling of its drug-drug interactions (DDIs) with inhibitor drugs. In vitro, daprodustat showed specific transport by OATP1B1/1B3 in the transfected cell systems and primary human and monkey hepatocytes. A single-dose oral rifampin (OATP1B inhibitor) reduced daprodustat intravenous clearance by a notable 9.9 ± 1.2-fold (P < 0.05) in cynomolgus monkeys. Correspondingly, volume of distribution at steady-state was also reduced by 5.0 ± 1.1-fold, whereas the half-life change was minimal (1.5-fold), corroborating daprodustat hepatic uptake inhibition by rifampin. A PBPK model accounting for OATP1B-CYP2C8 interplay was developed, which well described daprodustat PK and DDIs with gemfibrozil (CYP2C8 and OATP1B inhibitor) and trimethoprim (weak CYP2C8 inhibitor) within 25% error of the observed data in healthy subjects. About 18-fold increase in daprodustat area under the curve (AUC) following gemfibrozil treatment was found to be associated with strong CYP2C8 inhibition and moderate OATP1B inhibition. Moreover, PK modulation in hepatic dysfunction and subjects with CKD, in comparison to healthy control, was well-captured by the model. CYP2C8 and/or OATP1B inhibitor drugs (e.g., gemfibrozil, clopidogrel, rifampin, and cyclosporine) were predicted to perpetrate moderate-to-strong DDIs in healthy subjects, as well as, in target CKD population. Daprodustat can be used as a sensitive CYP2C8 index substrate in the absence of OATP1B modulation.

Neuroprotective effects of gemfibrozil in neurological disorders: Focus on inflammation and molecular mechanisms

BACKGROUND

Gemfibrozil (Gem) is a drug that has been shown to activate PPAR-α, a nuclear receptor that plays a key role in regulating lipid metabolism. Gem is used to lower the levels of triglycerides and reduce the risk of coronary heart disease in patients. Experimental studies in vitro and in vivo have shown that Gem can prevent or slow the progression of neurological disorders (NDs), including cerebral ischemia (CI), Alzheimer's disease (AD), Parkinson's disease (PD), and multiple sclerosis (MS). Neuroinflammation is known to play a significant role in these disorders.

METHOD

The literature review for this study was conducted by searching Scopus, Science Direct, PubMed, and Google Scholar databases.

RESULT

The results of this study show that Gem has neuroprotective effects through several cellular and molecular mechanisms such as: (1) Gem has the ability to upregulate pro-survival factors (PGC-1α and TFAM), promoting the survival and function of mitochondria in the brain, (2) Gem strongly inhibits the activation of NF-κB, AP-1, and C/EBPβ in cytokine-stimulated astroglial cells, which are known to increase the expression of iNOS and the production of NO in response to proinflammatory cytokines, (3) Gem protects dopamine neurons in the MPTP mouse model of PD by increasing the expression of PPARα, which in turn stimulates the production of GDNF in astrocytes, (4) Gem reduces amyloid plaque pathology, reduces the activity of glial cells, and improves memory, (5) Gem increases myelin genes expression (MBP and CNPase) via PPAR-β, and (6) Gem increases hippocampal BDNF to counteract depression.

CONCLUSION

According to the study, Gem was investigated for its potential therapeutic effect in NDs. Further research is needed to fully understand the therapeutic potential of Gem in NDs.

Use of physiologically-based pharmacokinetic modeling to understand the effect of omeprazole administration on the pharmacokinetics of oral extended-release nifedipine

Proton pump inhibitors (PPIs) can affect the release of drugs from their dosage forms in vivo by elevating the gastric pH. Our recent clinical study has demonstrated that drug-drug interactions (DDIs) exist between a PPI, omeprazole, and nifedipine extended-release formulations, where systemic exposure of nifedipine was increased in subjects after multiple-dose pretreatment of omeprazole. However, the mechanism of the observed DDIs between omeprazole and nifedipine has not been well-understood, as the DDI may also be mediated through CYP3A4 enzyme inhibition in addition to the elevated gastric pH caused by omeprazole. This study used physiologically-based pharmacokinetic (PBPK) modeling and simulations to investigate the underlying mechanism of these complex DDIs. A formulation exhibiting differences in in vitro dissolution across physiological pH range and another formulation where pH does not impact dissolution appreciably (e.g., an osmotic pump) were chosen to characterize the potential impact of pH. The PBPK models incorporated two-stage in vitro release profiles via US Pharmacopeia 2 apparatus. PBPK simulations suggest that the elevated gastric pH following multiple-dose administration of omeprazole has a minimal effect on nifedipine pharmacokinetics (PKs), whereas CYP3A4-mediated DDI is likely the main driver to the observed change of nifedipine PKs in the presence of omeprazole. Compared to the osmotic formulation, the slightly increased exposure of nifedipine can be accounted for by the enhanced drug release in the pH-dependent formulation. The reported model-based approach may be useful in DDI risk assessments, product formulation designs, and bioequivalence evaluations.

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.

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.

Inhibition of CYP2C8 by Acyl Glucuronides of Gemfibrozil and Clopidogrel: Pharmacological Significance, Progress and Challenges

The lipid-regulating drug gemfibrozil is a useful medication for reducing high cholesterol and triglycerides in the blood. In addition to oxidation, it undergoes extensive glucuronidation to produce gemfibrozil acyl glucuronide, which is a known mechanism-based inactivator of cytochrome P450 (CYP) 2C8. Such selective and time-dependent inhibition results in clinically important drug–drug interactions (DDI) with the drugs metabolized by CYP2C8. Similarly, the acyl glucuronide of clopidogrel, a widely used antiplatelet agent, is a potent time-dependent inhibitor of CYP2C8 that demonstrated significant DDI with the substrates of CYP2C8. Current progress in atomic-level understanding mostly involves studying how different drugs bind and undergo oxidation in the active site of CYPs. It is not clear how an acyl glucuronide metabolite of the drug gemfibrozil or clopidogrel interacts in the active site of CYP2C8 and selectively inhibit the enzyme. This mini-review summarizes the current knowledge on some of the important clinical DDI caused by gemfibrozil and clopidogrel due to the inhibition of CYP2C8 by acyl glucuronide metabolites of these drugs. Importantly, it examines recent developments and potential applications of structural biology tools to elucidate the binding and orientation of gemfibrozil acyl glucuronide and clopidogrel acyl glucuronide in the active site near heme that contributes to the inhibition and inactivation of CYP2C8.

Predictive In Vitro-In Vivo Extrapolation for Time Dependent Inhibition of CYP1A2, CYP2C8, CYP2C9, CYP2C19, and CYP2D6 Using Pooled Human Hepatocytes, Human Liver Microsomes, and a Simple Mechanistic Static Model

Inactivation of Cytochrome P450 (CYP450) enzymes can lead to significant increases in exposure of comedicants. The majority of reported in vitro to in vivo extrapolation (IVIVE) data have historically focused on CYP3A, leaving the assessment of other CYP isoforms insubstantial. To this end, the utility of human hepatocytes (HHEP) and human liver microsomes (HLM) to predict clinically relevant drug-drug interactions was investigated with a focus on CYP1A2, CYP2C8, CYP2C9, CYP2C19, and CYP2D6. Evaluation of IVIVE for CYP2B6 was limited to only weak inhibition. A search of the University of Washington Drug-Drug Interaction Database was conducted to identify a clinically relevant weak, moderate, and strong inhibitor for selective substrates of CYP1A2, CYP2C8, CYP2C9, CYP2C19, and CYP2D6, resulting in 18 inhibitors for in vitro characterization against 119 clinical interaction studies. Pooled human hepatocytes and HLM were preincubated with increasing concentrations of inhibitors for designated timepoints. Time dependent inhibition was detected in HLM for four moderate/strong inhibitors, suggesting that some optimization of incubation conditions (i.e., lower protein concentrations) is needed to capture weak inhibition. Clinical risk assessment was conducted by incorporating the in vitro derived kinetic parameters maximal rate of enzyme inactivation (min-1) (kinact) and concentration of inhibitor resulting in 50% of the maximum enzyme inactivation (KI) into static equations recommended by regulatory authorities. Significant overprediction was observed when applying the basic models recommended by regulatory agencies. Mechanistic static models, which consider the fraction of metabolism through the impacted enzyme, using the unbound hepatic inlet concentration lead to the best overall prediction accuracy with 92% and 85% of data from HHEPs and HLM, respectively, within twofold of the observed value. SIGNIFICANCE STATEMENT: Coupling time-dependent inactivation parameters derived from pooled human hepatocytes and human liver microsomes (HLM) with a mechanistic static model provides an easy and quantitatively accurate means to determine clinical drug-drug interaction risk from in vitro data. Optimization is needed to evaluate time-dependent inhibition (TDI) for weak and moderate inhibitors using HLM. Recommendations are made with respect to input parameters for in vitro to in vivo extrapolation (IVIVE) of TDI with non-CYP3A enzymes using available data from HLM and human hepatocytes.

A Quantitative Approach to the Prediction of Drug-Drug Interactions Mediated by Cytochrome P450 2C8 Inhibition

BACKGROUND

Ohno and Colleagues proposed an approach for predicting drug-drug interactions (DDIs) mediated by cytochrome P450 (CYP) 3A4 based on the use of the ratio of the inhibited to non-inhibited area under the plasma concentration time curve (AUC) of substrates to estimate the fraction of the dose metabolized via CYP3A4 (contribution ratio, CR) and the in vivo inhibitory potency of a perpetrator (inhibition ratio, IR). This study evaluated the performance of this approach on DDIs mediated by CYP2C8 inhibitors.

RESEARCH DESIGN AND METHODS

Initial estimates of CR and IR of CYP2C8 substrates and inhibitors were calculated for 33 DDI in vivo studies. The approach was externally validated with 17 additional studies. Bayesian orthogonal regression was used to refine the estimates of the parameters. Assessment of prediction success was conducted by plotting observed versus predicted AUC ratios.

RESULTS

Final estimates of CRs and IRs were obtained for 19 CYP2C8 substrates and 23 inhibitors, respectively. The method demonstrated good predictive capacity, with only two values outside of the prespecified limits.

CONCLUSIONS

The approach may help to adapt dose regimens for CYP2C8 substrates when given in combination with CYP2C8 inhibitors and to map the potential DDIs of new molecular entities.

In Vitro Evidence of Potential Interactions between CYP2C8 and Candesartan Acyl-β-D-glucuronide in the Liver

Growing evidence suggests that certain glucuronides function as potent inhibitors of CYP2C8. We previously reported the possibility of drug-drug interactions between candesartan cilexetil and paclitaxel. In this study, we evaluated the effects of candesartan N2-glucuronide and candesartan acyl-β-D-glucuronide on pathways associated with the elimination of paclitaxel, including those involving organic anion-transporting polypeptide (OATP) 1B1, OATP1B3, CYP2C8, and CYP3A4. UDP-glucuronosyltransferase (UGT) 1A10 and UGT2B7 were found to increase candesartan N2-glucuronide and candesartan acyl-β-D-glucuronide formation in a candesartan concentration-dependent manner. Additionally, the uptake of candesartan N2-glucuronide and candesartan acyl-β-D-glucuronide by cells stably expressing OATPs is a saturable process with K m of 5.11 and 12.1 μM for OATP1B1 and 28.8 and 15.7 μM for OATP1B3, respectively; both glucuronides exhibit moderate inhibition of OATP1B1/1B3. Moreover, the hydroxylation of paclitaxel was evaluated using recombinant CYP3A4 and CYP3A5. Results show that candesartan, candesartan N2-glucuronide, and candesartan acyl-β-D-glucuronide inhibit the CYP2C8-mediated metabolism of paclitaxel, with candesartan acyl-β-D-glucuronide exhibiting the strongest inhibition (IC50 is 18.9 µM for candesartan acyl-β-D-glucuronide, 150 µM for candesartan, and 166 µM for candesartan N2-glucuronide). However, time-dependent inhibition of CYP2C8 by candesartan acyl-β-D-glucuronide was not observed. Conversely, the IC50 values of all the compounds are comparable for CYP3A4. Taken together, these data suggest that candesartan acyl-β-D-glucuronide is actively transported by OATPs into hepatocytes, and drug-drug interactions may occur with coadministration of candesartan and CYP2C8 substrates, including paclitaxel, as a result of the inhibition of CYP2C8 function. SIGNIFICANCE STATEMENT: This study demonstrates that the acyl glucuronidation of candesartan to form candesartan acyl-β-D-glucuronide enhances CYP2C8 inhibition while exerting minimal effects on CYP3A4, organic anion-transporting polypeptide (OATP) 1B1, and OATP1B3. Thus, candesartan acyl-β-D-glucuronide might represent a potential mediator of drug-drug interactions between candesartan and CYP2C8 substrates, such as paclitaxel, in clinical settings. This work adds to the growing knowledge regarding the inhibitory effects of glucuronides on CYP2C8.

Evaluation of Pharmacokinetic Drug-Drug Interactions: A Review of the Mechanisms, In Vitro and In Silico Approaches

Pharmacokinetic drug–drug interactions (DDIs) occur when a drug alters the absorption, transport, distribution, metabolism or excretion of a co-administered agent. The occurrence of pharmacokinetic DDIs may result in the increase or the decrease of drug concentrations, which can significantly affect the drug efficacy and safety in patients. Enzyme-mediated DDIs are of primary concern, while the transporter-mediated DDIs are less understood but also important. In this review, we presented an overview of the different mechanisms leading to DDIs, the in vitro experimental tools for capturing the factors affecting DDIs, and in silico methods for quantitative predictions of DDIs. We also emphasized the power and strategy of physiologically based pharmacokinetic (PBPK) models for the assessment of DDIs, which can integrate relevant in vitro data to simulate potential drug interaction in vivo. Lastly, we pointed out the future directions and challenges for the evaluation of pharmacokinetic DDIs.

Prediction of Cyclosporin-Mediated Drug Interaction Using Physiologically Based Pharmacokinetic Model Characterizing Interplay of Drug Transporters and Enzymes

Uptake transporter organic anion transporting polypeptides (OATPs), efflux transporters (P-gp, BCRP and MRP2) and cytochrome P450 enzymes (CYP450s) are widely expressed in the liver, intestine or kidney. They coordinately work to control drug disposition, termed as "interplay of transporters and enzymes". Cyclosporine A (CsA) is an inhibitor of OATPs, P-gp, MRP2, BCRP and CYP3As. Drug-drug interaction (DDI) of CsA with victim drugs occurs via disordering interplay of transporters and enzymes. We aimed to establish a whole-body physiologically-based pharmacokinetic (PBPK) model which predicts disposition of CsA and nine victim drugs including atorvastatin, cerivastatin, pravastatin, rosuvastatin, fluvastatin, simvastatin, lovastatin, repaglinide and bosentan, as well as drug-drug interactions (DDIs) of CsA with nine victim drugs to investigate the integrated effect of enzymes and transporters in liver, intestinal and kidney on drug disposition. Predictions were compared with observations. Most of the predictions were within 0.5-2.0 folds of observations. Atorvastatin was represented to investigate individual contributions of transporters and CYP3As to atorvastatin disposition and their integrated effect. The contributions to atorvastatin disposition were hepatic OATPs >> hepatic CYP3A > intestinal CYP3As ≈ efflux transporters (P-gp/BCRP/MRP2). The results got the conclusion that the developed PBPK model characterizing the interplay of enzymes and transporters was successfully applied to predict the pharmacokinetics of 10 OATP substrates and DDIs of CsA with 9 victim drugs.

Current progress in identifying endogenous biomarker candidates for drug transporter phenotyping and their potential application to drug development

Drug transporters play important roles in the elimination of various compounds from the blood. Genetic variation and drug-drug interactions underlie the pharmacokinetic differences for the substrates of drug transporters. Some endogenous substrates of drug transporters have emerged as biomarkers to assess differences in drug transporter activity-not only in animals, but also in humans. Metabolomic analysis is a promising approach for identifying such endogenous substrates through their metabolites. The appropriateness of metabolites is supported by studies in vitro and in vivo, both in animals and through pharmacogenomic or drug-drug interaction studies in humans. This review summarizes current progress in identifying such endogenous biomarkers and applying them to drug transporter phenotyping.

Mechanisms of CYP450 Inhibition: Understanding Drug-Drug Interactions Due to Mechanism-Based Inhibition in Clinical Practice

In an ageing society, polypharmacy has become a major public health and economic issue. Overuse of medications, especially in patients with chronic diseases, carries major health risks. One common consequence of polypharmacy is the increased emergence of adverse drug events, mainly from drug–drug interactions. The majority of currently available drugs are metabolized by CYP450 enzymes. Interactions due to shared CYP450-mediated metabolic pathways for two or more drugs are frequent, especially through reversible or irreversible CYP450 inhibition. The magnitude of these interactions depends on several factors, including varying affinity and concentration of substrates, time delay between the administration of the drugs, and mechanisms of CYP450 inhibition. Various types of CYP450 inhibition (competitive, non-competitive, mechanism-based) have been observed clinically, and interactions of these types require a distinct clinical management strategy. This review focuses on mechanism-based inhibition, which occurs when a substrate forms a reactive intermediate, creating a stable enzyme–intermediate complex that irreversibly reduces enzyme activity. This type of inhibition can cause interactions with drugs such as omeprazole, paroxetine, macrolide antibiotics, or mirabegron. A good understanding of mechanism-based inhibition and proper clinical management is needed by clinicians when such drugs are prescribed. It is important to recognize mechanism-based inhibition since it cannot be prevented by separating the time of administration of the interacting drugs. Here, we provide a comprehensive overview of the different types of mechanism-based inhibition, along with illustrative examples of how mechanism-based inhibition might affect prescribing and clinical behaviors.

Physiologically based pharmacokinetic modelling of oxycodone drug-drug interactions

Oxycodone is an opioid analgesic with several pharmacologically active metabolites and relatively narrow therapeutic index. Cytochrome P450 (CYP) 3A4 and CYP2D6 play major roles in the metabolism of oxycodone and its metabolites. Thus, inhibition and induction of these enzymes may result in substantial changes in the exposure of both oxycodone and its metabolites. In this study, a physiologically based pharmacokinetic (PBPK) model was built using GastroPlus™ software for oxycodone, two primary metabolites (noroxycodone, oxymorphone) and one secondary metabolite (noroxymorphone). The model was built based on literature and in house in vitro and in silico data. The model was refined and verified against literature clinical data after oxycodone administration in the absence of drug-drug interactions (DDI). The model was further challenged with simulations of oxycodone DDI with CYP3A4 inhibitors ketoconazole and itraconazole, CYP3A4 inducer rifampicin and CYP2D6 inhibitor quinidine. The magnitude of DDI (AUC ratio) was predicted within 1.5-fold error for oxycodone, within 1.8-fold and 1.3-4.5-fold error for the primary metabolites noroxycodone and oxymorphone, respectively, and within 1.4-4.5-fold error for the secondary metabolite noroxymorphone, when compared to the mean observed AUC ratios. This work demonstrated the capability of PBPK model to simulate DDI of the administered compounds and the formed metabolites of both DDI victim and perpetrator. However, the predictions for the formed metabolites tend to be associated with higher uncertainty than the predictions for the administered compound. The oxycodone model provides a tool for forecasting oxycodone DDI with other CYP3A4 and CYP2D6 DDI perpetrators that may be co-administered with oxycodone.

Physiologically-Based Pharmacokinetic Models for Evaluating Membrane Transporter Mediated Drug-Drug Interactions: Current Capabilities, Case Studies, Future Opportunities, and Recommendations

Physiologically-based pharmacokinetic (PBPK) modeling has been extensively used to quantitatively translate in vitro data and evaluate temporal effects from drug-drug interactions (DDIs), arising due to reversible enzyme and transporter inhibition, irreversible time-dependent inhibition, enzyme induction, and/or suppression. PBPK modeling has now gained reasonable acceptance with the regulatory authorities for the cytochrome-P450-mediated DDIs and is routinely used. However, the application of PBPK for transporter-mediated DDIs (tDDI) in drug development is relatively uncommon. Because the predictive performance of PBPK models for tDDI is not well established, here, we represent and discuss examples of PBPK analyses included in regulatory submission (the US Food and Drug Administration (FDA), the European Medicines Agency (EMA), and the Pharmaceuticals and Medical Devices Agency (PMDA)) across various tDDIs. The goal of this collaborative effort (involving scientists representing 17 pharmaceutical companies in the Consortium and from academia) is to reflect on the use of current databases and models to address tDDIs. This challenges the common perceptions on applications of PBPK for tDDIs and further delves into the requirements to improve such PBPK predictions. This review provides a reflection on the current trends in PBPK modeling for tDDIs and provides a framework to promote continuous use, verification, and improvement in industrialization of the transporter PBPK modeling.

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

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

Simultaneous Assessment of Transporter-Mediated Drug-Drug Interactions Using a Probe Drug Cocktail in Cynomolgus Monkey

We aim to establish an in vivo preclinical model to enable simultaneous assessment of inhibition potential of an investigational drug on clinically relevant drug transporters, organic anion-transporting polypeptide (OATP)1B, breast cancer resistance protein (BCRP), P-glycoprotein (P-gp), and organic anion transporter (OAT)3. Pharmacokinetics of substrate cocktail consisting of pitavastatin (OATP1B substrate), rosuvastatin (OATP1B/BCRP/OAT3), sulfasalazine (BCRP), and talinolol (P-gp) were obtained in cynomolgus monkey-alone or in combination with transporter inhibitors. Single-dose rifampicin (30 mg/kg) significantly (P < 0.01) increased the plasma exposure of all four drugs, with a marked effect on pitavastatin and rosuvastatin [area under the plasma concentration-time curve (AUC) ratio ∼21-39]. Elacridar, BCRP/P-gp inhibitor, increased the AUC of sulfasalazine, talinolol, as well as rosuvastatin and pitavastatin. An OAT1/3 inhibitor (probenecid) significantly (P < 0.05) impacted the renal clearance of rosuvastatin (∼8-fold). In vitro, rifampicin (10 µM) inhibited uptake of pitavastatin, rosuvastatin, and sulfasalazine by monkey and human primary hepatocytes. Transport studies using membrane vesicles suggested that all probe substrates, except talinolol, are transported by cynoBCRP, whereas talinolol is a cynoP-gp substrate. Elacridar and rifampicin inhibited both cynoBCRP and cynoP-gp in vitro, indicating potential for in vivo intestinal efflux inhibition. In conclusion, a probe substrate cocktail was validated to simultaneously evaluate perpetrator impact on multiple clinically relevant transporters using the cynomolgus monkey. The results support the use of the cynomolgus monkey as a model that could enable drug-drug interaction risk assessment, before advancing a new molecular entity into clinical development, as well as providing mechanistic insights on transporter-mediated interactions.

Quantitative Analysis of Complex Drug-Drug Interactions between Cerivastatin and Metabolism/Transport Inhibitors Using Physiologically Based Pharmacokinetic Modeling

Cerivastatin (CER) was withdrawn from the world market because of lethal rhabdomyolysis. Coadministrations of CER and cyclosporine A (CsA) or gemfibrozil (GEM) have been reported to increase the CER blood concentration. CsA is an inhibitor of organic anion transporting polypeptide (OATP)1B1 and CYP3A4, and GEM and its glucuronide (GEM-glu) inhibit OATP1B1 and CYP2C8. The purpose of this study was to describe the transporter-/enzyme-mediated drug-drug interactions (DDIs) of CER with CsA or GEM based on unified physiologically based pharmacokinetic (PBPK) models and to investigate whether the DDIs can be quantitatively analyzed by a bottom-up approach. Initially, the PBPK models for CER and GEM/GEM-glu were constructed based on the previously reported standard protocols. Next, the drug-dependent parameters were optimized by Cluster Newton Method. Thus, described concentration-time profiles for CER and GEM/GEM-glu agreed well with the clinically observed data. The DDIs were then simulated using the established PBPK models with previously obtained in vitro inhibition constants of CsA or GEM/GEM-glu against the OATP1B1 and cytochrome P450s. DDIs with the inhibitors were underestimated compared with observed data using the geometric means of reported values. To search for better described parameters within the range of in vitro values, sensitivity analyses were performed for DDIs of CER. Using the in vitro parameter sets selected by sensitivity analyses, these DDIs were well reproduced, indicating that the present PBPK models were able to describe adequately the clinical DDIs based on a bottom-up approach. The approaches in this study would be applicable to the prediction of other DDIs involving both transporters and metabolic enzymes.

Navigating Transporter Sciences in Pharmacokinetics Characterization Using the Extended Clearance Classification System

Membrane transporters play an important role in the absorption, distribution, clearance, and elimination of drugs. Supported by the pharmacokinetics data in human, several transporters including organic anion transporting polypeptide (OATP)1B1, OATP1B3, organic anion transporter (OAT)1, OAT3, organic cation transporter (OCT)2, multidrug and toxin extrusion (MATE) proteins, P-glycoprotein and breast cancer resistance protein are suggested to be of clinical relevance. An early understanding of the transporter role in drug disposition and clearance allows reliable prediction/evaluation of pharmacokinetics and changes due to drug-drug interactions (DDIs) or genetic polymorphisms. We recently proposed an extended clearance classification system (ECCS) based on simple drug properties (i.e., ionization, permeability, and molecular weight) to predict the predominant clearance mechanism. According to this framework, systemic clearance of class 1B and 3B drugs is likely determined by the OATP-mediated hepatic uptake. Class 3A and 4 drugs, and certain class 3B drugs, are predominantly cleared by renal, wherein, OAT1, OAT3, OCT2, and MATE proteins could contribute to their active renal secretion. Intestinal efflux and uptake transporters largely influence the oral pharmacokinetics of class 3A, 3B, and 4 drugs. We discuss the paradigm of applying the ECCS framework in mapping the role of clinically relevant drug transporters in early discovery and development; thereby implementing the right strategy to allow optimization of drug exposure and evaluation of clinical risk due to DDIs and pharmacogenomics.

Implications of intercorrelation between hepatic CYP3A4-CYP2C8 enzymes for the evaluation of drug-drug interactions: a case study with repaglinide

AIMS

Statistically significant positive correlations are reported for the abundance of hepatic drug-metabolizing enzymes. We investigate, as an example, the impact of CYP3A4-CYP2C8 intercorrelation on the predicted interindividual variabilities of clearance and drug-drug interactions (DDIs) for repaglinide using physiologically based pharmacokinetic (PBPK) modelling.

METHODS

PBPK modelling and simulation were employed using Simcyp Simulator (v15.1). Virtual populations were generated assuming intercorrelations between hepatic CYP3A4-CYP2C8 abundances derived from observed values in 24 human livers. A repaglinide PBPK model was used to predict PK parameters in the presence and absence of gemfibrozil in virtual populations, and the results were compared with a clinical DDI study.

RESULTS

Coefficient of variation (CV) of oral clearance was 52.5% in the absence of intercorrelation between CYP3A4-CYP2C8 abundances, which increased to 54.2% when incorporating intercorrelation. In contrast, CV for predicted DDI (as measured by AUC ratio before and after inhibition) was reduced from 46.0% in the absence of intercorrelation between enzymes to 43.8% when incorporating intercorrelation: these CVs were associated with 5th/95th percentiles (2.48-11.29 vs. 2.49-9.69). The range of predicted DDI was larger in the absence of intercorrelation (1.55-77.06) than when incorporating intercorrelation (1.79-25.15), which was closer to clinical observations (2.6-12).

CONCLUSIONS

The present study demonstrates via a systematic investigation that population-based PBPK modelling incorporating intercorrelation led to more consistent estimation of extreme values than those observed in interindividual variabilities of clearance and DDI. As the intercorrelations more realistically reflect enzyme abundances, virtual population studies involving PBPK and DDI should avoid using Monte Carlo assignment of enzyme abundance.

Drug interactions of meglitinide antidiabetics involving CYP enzymes and OATP1B1 transporter

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

Physiologically Based Pharmacokinetic Modeling Suggests Limited Drug-Drug Interaction Between Clopidogrel and Dasabuvir

Dasabuvir, a nonnucleoside NS5B polymerase inhibitor, is a sensitive substrate of cytochrome P450 (CYP) 2C8 with a potential for drug-drug interaction (DDI) with clopidogrel. A physiologically based pharmacokinetic (PBPK) model was developed for dasabuvir to evaluate the DDI potential with clopidogrel, the acyl-β-D glucuronide metabolite of which has been reported as a strong mechanism-based inhibitor of CYP2C8 based on an interaction with repaglinide. In addition, the PBPK model for clopidogrel and its metabolite were updated with additional in vitro data. Sensitivity analyses using these PBPK models suggested that CYP2C8 inhibition by clopidogrel acyl-β-D glucuronide may not be as potent as previously suggested. The dasabuvir and updated clopidogrel PBPK models predict a moderate increase of 1.5-1.9-fold for Cmax and 1.9-2.8-fold for AUC of dasabuvir when coadministered with clopidogrel. While the PBPK results suggest there is a potential for DDI between dasabuvir and clopidogrel, the magnitude is not expected to be clinically relevant.

Prediction of inter-individual variability on the pharmacokinetics of CYP2C8 substrates in human

Inter-individual variability in pharmacokinetics can lead to unexpected side effects and treatment failure, and is therefore an important factor in drug development. CYP2C8 is a major drug-metabolizing enzyme known to be involved in the metabolism of over 100 drugs. In this study, we predicted the inter-individual variability in AUC/Dose of CYP2C8 substrates in healthy volunteers using the Monte Carlo simulation. Inter-individual variability in the hepatic intrinsic clearance of CYP2C8 substrates (CL_int,h,2C8) was estimated from the inter-individual variability in pharmacokinetics of pioglitazone, which is a major CYP2C8 substrate. The coefficient of variation (CV) of CL_int,h,2C8 was estimated to be 40%. Using this value, the CVs of AUC/Dose of other major CYP2C8 substrates, rosiglitazone and amodiaquine, were predicted to validate the estimated CV of CL_int,h,2C8. As a result, the reported CVs of both substrates were within the 2.5-97.5 percentile range of the predicted CVs. Furthermore, the CVs of AUC/Dose of the CYP2C8 substrates loperamide and chloroquine, which are affected by renal clearance, were also successfully predicted. Combining this value with previously reported CVs of other CYPs, we were able to successfully predict the inter-individual variability in pharmacokinetics of various drugs in clinical.

The Application of Physiologically Based Pharmacokinetic Modeling to Predict the Role of Drug Transporters: Scientific and Regulatory Perspectives

Transporters play an important role in drug absorption, disposition, and drug action. The evaluation of drug transporters requires a comprehensive understanding of transporter biology and pharmacology. Physiologically based pharmacokinetic (PBPK) models may offer an integrative platform to quantitatively evaluate the role of drug transporters and its interplay with other drug disposition processes such as passive drug diffusion and elimination by metabolizing enzymes. To date, PBPK modeling and simulations integrating drug transporters lag behind that for drug-metabolizing enzymes. In addition, predictive performance of PBPK has not been well established for predicting the role of drug transporters in the pharmacokinetics of a drug. To enhance overall predictive performance of transporter-based PBPK models, it is necessary to have a detailed understanding of transporter biology for proper representation in the models and to have a quantitative understanding of the contribution of transporters in the absorption and metabolism of a drug. This article summarizes PBPK-based submissions evaluating the role of drug transporters to the Office of Clinical Pharmacology of the US Food and Drug Administration.

Peroxisome Proliferator-Activated Receptor α Activation Suppresses Cytochrome P450 Induction Potential in Mice Treated with Gemfibrozil

Gemfibrozil, a peroxisome proliferator-activated receptor α (PPARα) agonist, is widely used for hypertriglyceridaemia and mixed hyperlipidaemia. Drug-drug interaction of gemfibrozil and other PPARα agonists has been reported. However, the role of PPARα in cytochrome P450 (CYP) induction by fibrates is not well known. In this study, wild-type mice were first fed gemfibrozil-containing diets (0.375%, 0.75% and 1.5%) for 14 days to establish a dose-response relationship for CYP induction. Then, wild-type mice and Pparα-null mice were treated with a 0.75% gemfibrozil-containing diet for 7 days. CYP3a, CYP2b and CYP2c were induced in a dose-dependent manner by gemfibrozil. In Pparα-null mice, their mRNA level, protein level and activity were induced more than those in wild-type mice. So, gemfibrozil induced CYP, and this action was inhibited by activated PPARα. These data suggested that the induction potential of CYPs was suppressed by activated PPARα, showing a potential role of this receptor in drug-drug interactions and metabolic diseases treated with fibrates.

Analysis of the Change in the Blood Concentration-Time Profile Caused by Complex Drug-Drug Interactions in the Liver Considering the Enterohepatic Circulation: Examining Whether the Inhibition Constants for Uptake, Metabolism, and Biliary Excretion Can be Recovered by the Analyses Using Physiologically Based Pharmacokinetic Modeling

Hypothetical substrates undergoing transporter-mediated hepatic uptake, metabolism, and enterohepatic circulation with different rate-determining processes with a combination of inhibition constants (K_i) for hepatic uptake, metabolism, and biliary excretion processes were generated with a constant K_i for uptake and incorporated into a physiologically based pharmacokinetic model. Analyses of the kinetic model suggested that the fraction of substrates excreted in the bile to the total elimination by the liver (f_bile) can be estimated under certain conditions from kinetic analyses of their blood concentration-time profiles. Using the generated time profiles of substrates with and without coadministration of inhibitors, various pharmacokinetic parameters involving f_bile and K_i for the hepatic uptake, metabolism, and biliary excretion of drugs were back-calculated by fitting. Comparing parameters obtained with the original parameter sets by fitting, the K_i were found to be well estimated under the following conditions: the initial estimates for inhibition constants were relatively good, which corresponds to the case for obtaining reliable in vitro inhibition constants. In conclusion, the integration of top-down analyses with bottom-up estimates (experimental determination) of inhibition constants can be used to estimate in vivo inhibition constants and f_bile reliably.

Database of Optimized Proteomic Quantitative Methods for Human Drug Disposition-Related Proteins for Applications in Physiologically Based Pharmacokinetic Modeling

The purpose of this study was to create an open access repository of validated liquid chromatography tandem mass spectrometry (LC‐MS/MS) multiple reaction monitoring (MRM) methods for quantifying 284 important proteins associated with drug absorption, distribution, metabolism, and excretion (ADME). Various in silico and experimental approaches were used to select surrogate peptides and optimize instrument parameters for LC‐MS/MS quantification of the selected proteins. The final methods were uploaded to an online public database (QPrOmics; www.qpromics.uw.edu/qpromics/assay/), which provides essential information for facile method development in triple quadrupole mass spectrometry (MS) instruments. To validate the utility of the methods, the differential tissue expression of 107 key ADME proteins was characterized in the tryptic digests of the pooled subcellular fractions of human liver, kidneys, intestines, and lungs. These methods and the data are critical for development of physiologically based pharmacokinetic (PBPK) models to predict xenobiotic disposition.

Delineating the Role of Various Factors in Renal Disposition of Digoxin through Application of Physiologically Based Kidney Model to Renal Impairment Populations

Development of submodels of organs within physiologically-based pharmacokinetic (PBPK) principles and beyond simple perfusion limitations may be challenging because of underdeveloped in vitro-in vivo extrapolation approaches or lack of suitable clinical data for model refinement. However, advantage of such models in predicting clinical observations in divergent patient groups is now commonly acknowledged. Mechanistic understanding of altered renal secretion in renal impairment is one area that may benefit from such models, despite knowledge gaps in renal pathophysiology. In the current study, a PBPK kidney model was developed for digoxin, accounting for the roles of organic anion transporting peptide 4C1 (OATP4C1) and P-glycoprotein (P-gp) in its tubular secretion, with the aim to investigate the impact of age and renal impairment (moderate to severe) on renal drug disposition. Initial PBPK simulations based on changes in glomerular filtration rate (GFR) underestimated the observed reduction in digoxin renal excretion clearance (CLR) in subjects with moderately impaired renal function relative to healthy. Reduction in either proximal tubule cell number or the OATP4C1 abundance in the mechanistic kidney model successfully predicted 59% decrease in digoxin CLR, in particular when these changes were proportional to reduction in GFR. In contrast, predicted proximal tubule concentration of digoxin was only sensitive to changes in the transporter expression/ million proximal tubule cells. Based on the mechanistic modeling, reduced proximal tubule cellularity and OATP4C1 abundance, and inhibition of OATP4C1-mediated transport, are proposed as possible causes of reduced digoxin renal secretion in renally impaired patients.

Transporter-Mediated Hepatic Uptake Plays an Important Role in the Pharmacokinetics and Drug-Drug Interactions of Montelukast

Montelukast, a leukotriene receptor antagonist commonly prescribed for treatment of asthma, is primarily metabolized by cytochrome P450 (CYP)2C8, and has been suggested as a probe substrate for investigating CYP2C8 activity in vivo. We evaluated the quantitative role of hepatic uptake transport in its pharmacokinetics and drug-drug interactions (DDIs). Montelukast was characterized with significant active uptake in human hepatocytes, and showed affinity towards organic anion transporting polypeptides (OATPs) in transfected cell systems. Single-dose rifampicin, an OATP inhibitor, decreased montelukast clearance in rats and monkeys. Clinical DDIs of montelukast were evaluated using physiologically based pharmacokinetic modeling; and simulation of the interactions with gemfibrozil-CYP2C8 and OATP1B1/1B3 inhibitor, clarithromycin-CYP3A and OATP1B1/1B3 inhibitor, and itraconazole-CYP3A inhibitor, implicated OATPs-CYP2C8-CYP2C8 interplay as the primary determinant of montelukast pharmacokinetics. In conclusion, hepatic uptake plays a key role in the pharmacokinetics of montelukast, which should be taken into account when interpreting clinical interactions.

Quantitative Prediction of Drug-Drug Interactions Involving Inhibitory Metabolites in Drug Development: How Can Physiologically Based Pharmacokinetic Modeling Help?

This subteam under the Drug Metabolism Leadership Group (Innovation and Quality Consortium) investigated the quantitative role of circulating inhibitory metabolites in drug-drug interactions using physiologically based pharmacokinetic (PBPK) modeling. Three drugs with major circulating inhibitory metabolites (amiodarone, gemfibrozil, and sertraline) were systematically evaluated in addition to the literature review of recent examples. The application of PBPK modeling in drug interactions by inhibitory parent-metabolite pairs is described and guidance on strategic application is provided.

Projecting ADME Behavior and Drug-Drug Interactions in Early Discovery and Development: Application of the Extended Clearance Classification System

PURPOSE

To assess the utility of Extended Clearance Classification System (ECCS) in understanding absorption, distribution, metabolism, and elimination (ADME) attributes and enabling victim drug-drug interaction (DDI) predictions.

METHODS

A database of 368 drugs with relevant ADME parameters, main metabolizing enzymes, uptake transporters, efflux transporters, and highest change in exposure (%AUC) in presence of inhibitors was developed using published literature. Drugs were characterized according to ECCS using ionization, molecular weight and estimated permeability.

RESULTS

Analyses suggested that ECCS class 1A drugs are well absorbed and systemic clearance is determined by metabolism mediated by CYP2C, esterases, and UGTs. For class 1B drugs, oral absorption is high and the predominant clearance mechanism is hepatic uptake mediated by OATP transporters. High permeability neutral/basic drugs (class 2) showed high oral absorption, with metabolism mediated generally by CYP3A, CYP2D6 and UGTs as the predominant clearance mechanism. Class 3A/4 drugs showed moderate absorption with dominant renal clearance involving OAT/OCT2 transporters. Class 3B drugs showed low to moderate absorption with hepatic uptake (OATPs) and/or renal clearance as primary clearance mechanisms. The highest DDI risk is typically seen with class 2/1B/3B compounds manifested by inhibition of either CYP metabolism or active hepatic uptake. Class 2 showed a wider range in AUC change likely due to a variety of enzymes involved. DDI risk for class 3A/4 is small and associated with inhibition of renal transporters.

CONCLUSIONS

ECCS provides a framework to project ADME profiles and further enables prediction of victim DDI liabilities in drug discovery and development.