Commentary Pharmacokinetic Theory Must Consider Published Experimental Data.

Recently, we have proposed simple methodology to derive clearance and rate constant equations, independent of differential equations, based on Kirchhoff's Laws, a common methodology from physics used to describe rate-defining processes either in series or parallel. Our approach has been challenged in three recent publications, two published in this journal, but notably what is lacking is that none evaluate experimental pharmacokinetic data. As reviewed here, manuscripts from our laboratory have evaluated published experimental data, demonstrating that the Kirchhoff's Laws approach explains (1) why all of the experimental perfused liver clearance data appear to fit the equation that was previously believed to be the well-stirred model, (2) why linear pharmacokinetic systemic bioavailability determinations can be greater than 1, (3) why renal clearance can be a function of drug input processes, and (4) why statistically different bioavailability measures may be found for urinary excretion versus systemic concentration measurements. Our most recent paper demonstrates (5) how the universally accepted steady-state clearance approach utilized by the field for the past 50 years leads to unrealistic outcomes concerning the relationship between liver-to-blood Kpuu and hepatic availability FH , highlighting the potential for errors in pharmacokinetic evaluations based on differential equations. The Kirchhoff's Laws approach is applicable to all pharmacokinetic analyses of quality experimental data, those that were previously adequately explained with present pharmacokinetic theory, and those that were not. The publications that have attempted to rebut our position do not address unexplained experimental data, and we show here why their analyses are not valid. Significance Statement The Kirchhoff's Laws approach to deriving clearance equations for linear systems in parallel or in series, independent of differential equations, successfully describes published pharmacokinetic data that has previously been unexplained. Three recent publications claim to refute our proposed methodology; these publications only make theoretical arguments, do not evaluate experimental data; never demonstrate that the Kirchhoff methodology provides incorrect interpretations of experimental pharmacokinetic data, including statistically significant data not explained by present pharmacokinetic theory. We demonstrate why these analyses are invalid.

An Explanation of Why Dose-Corrected Area Under the Curve for Alternate Administration Routes Can Be Greater than for Intravenous Dosing.

It is generally believed that bioavailability (F) calculated based on systemic concentration area under the curve (AUC) measurements cannot exceed 1.0, yet some published studies report this inconsistency. We teach and believe, based on differential equation derivations, that rate of absorption has no influence on measured systemic clearance following an oral dose, i.e., determined as available dose divided by AUC. Previously, it was thought that any difference in calculating F from urine data versus that from systemic concentration AUC data was due to the inability to accurately measure urine data. A PubMed literature search for drugs exhibiting F > 1.0 and studies for which F was measured using both AUC and urinary excretion dose-corrected analyses yielded data for 35 drugs. We show and explain, using Kirchhoff's Laws, that these universally held concepts concerning bioavailability may not be valid in all situations. Bioavailability, determined using systemic concentration measurements, for many drugs may be overestimated since AUC reflects not only systemic elimination but also absorption rate characteristics, which is most easily seen for renal clearance measures. Clearance of drug from the absorption site must be significantly greater than clearance following an iv bolus dose for F(AUC) to correctly correspond with F(urine). The primary purpose of this paper is to demonstrate that studies resulting in F > 1.0 and/or greater systemic vs urine bioavailability predictions may be accurate. Importantly, these explications have no significant impact on current regulatory guidance for bioequivalence testing, nor on the use of exposure (AUC) measures in making drug dosing decisions.

Solubility-Permeability Interplay in Facilitating the Prediction of Drug Disposition Routes, Extent of Absorption, Food Effects, Brain Penetration and Drug Induced Liver Injury Potential.

Here I detail the use of measures of permeability rate and solubility in predicting drug disposition characteristics through the utilization of the Biopharmaceutics Drug Disposition Classification System (BDDCS) and the Extended Clearance Classification System (ECCS) as well as the accuracy of the systems in predicting the major route of elimination and the extent of oral absorption of a new small molecule therapeutics. I compare the BDDCS and ECCS with the FDA Biopharmaceutics Classification System (BCS). I also detail the use of the BCS in predicting food effects and the BDDCS in predicting brain disposition of small molecule therapeutics and in validating DILI predictive metrics. This review provides an update of the current status of these classification systems and their uses in the drug development process.

The Uses and Advantages of Kirchhoff's Laws vs. Differential Equations in Pharmacology, Pharmacokinetics, and (Even) Chemistry.

In chemistry, rate processes are defined in terms of rate constants, with units of time-1, and are derived by differential equations from amounts. In contrast, when considering drug concentrations in biological systems, particularly in humans, rate processes must be defined in terms of clearance, with units of volume/time, since biological volumes, which are highly dependent on drug partition into biological tissues, cannot be easily determined. In pharmacology, pharmacokinetics, and in making drug dosing decisions, drug clearance and changes in drug clearance are paramount. Clearance is defined as the amount of drug eliminated or moved divided by the exposure driving that elimination or movement. Historically, all clearance derivations in pharmacology and pharmacokinetics have been based on the use of differential equations in terms of rate constants and amounts, which are then converted into clearance equations when multiplied/divided by a hypothesized volume of distribution. Here, we show that except for iv bolus dosing, multiple volumes may be relevant. We have recently shown that clearance relationships, as well as rate constant relationships, may be derived independent of differential equations using Kirchhoff's Laws from physics. Kirchhoff's Laws may be simply translated to recognize that when two or more rate-defining processes operate in parallel, the total value of the overall reaction parameter is equal to the sum of those rate-defining processes. In contrast, when two or more rate-defining processes operate in series, the inverse of the total reaction parameter is equal to the sum of the inverse of those rate-defining steps.

Using an animal model to predict the effective human dose for oral multiple sclerosis drugs.

The objective of this study was to determine the potential usefulness of an animal model to predict the appropriate dose of newly developed drugs for treating relapsing remitting multiple sclerosis (RRMS). Conversion of the lowest effective dose (LEffD) for mice and rats in the experimental autoimmune encephalomyelitis (EAE) model was used to predict the human effective dose utilizing the body surface area correction factor found in the 2005 US Food and Drug Administration (FDA) Guidance for Industry in selecting safe starting doses for clinical trials. Predictions were also tested by comparison with doses estimated by scaling up the LEffD in the model by the human to animal clearance ratio. Although initial proof-of-concept studies of oral fingolimod tested the efficacy and safety of 1.25 and 5 mg in treating RRMS, the EAE animal model predicted the approved dose of this drug, 0.5 mg daily. This approach would have also provided useful predictions of the approved human oral doses for cladribine, dimethyl fumarate, ozanimod, ponesimod, siponimod, and teriflunomide, drugs developed with more than one supposed mechanism of action. The procedure was not useful for i.v. dosed drugs, including monoclonal antibodies. We maintain that drug development scientists should always examine a simple allometric method to predict the therapeutic effective dose in humans. Then, following clinical studies, we believe that the animal model might be expected to yield useful predictions of other drugs developed to treat the same condition. The methodology may not always be predictive, but the approach is so simple it should be investigated.

Review of the application of Kirchhoff's Laws of series and parallel flows to pharmacology: Defining organ clearance.

Dosing rate decisions for drugs and changes in dosing in a patient due to disease states, drug interactions and pharmacogenomics are all based on clearance, a measure of the body's ability to eliminate drug. The primary organs of elimination are the liver and the kidney. Clearance for each of these organs is a summative composition of biologic processes. In 1857, Gustav Kirchhoff first developed his laws to describe the "motion of electricity in conductors... [and] ...in wires", recognizing that summative processes occur either in parallel or in series. Since then, Kirchhoff's Laws have also been applied to heat transfer, diffusion and drag force on falling objects, but not to pharmacology. Although not previously recognized, renal clearance always follow Kirchhoff's Laws, as does hepatic clearance for drugs where basolateral transporters are not clinically relevant. However, when basolateral transporters are clinically relevant, we demonstrate that the present accepted approach is inconsistent with recognized drug disposition processes. However, this clearance relationship can be easily corrected using Kirchhoff's Laws. The purpose of this review is to demonstrate that Kirchhoff's Laws, which define how to approach rate processes that occur in parallel versus processes that occur in series, can be applicable to pharmacology in addition to the over 160-year recognition of their use in physical sciences. We anticipate that the application to clearance will be only the first of many such pharmacological analyses.

State of the Art and Uses for the Biopharmaceutics Drug Disposition Classification System (BDDCS): New Additions, Revisions, and Citation References.

The Biopharmaceutics Drug Disposition Classification system (BDDCS) is a four-class approach based on water solubility and extent of metabolism/permeability rate. Based on the BDDCS class to which a drug is assigned, it is possible to predict the role of metabolic enzymes and transporters on the drug disposition of a new molecular entity (NME) prior to its administration to animals or humans. Here, we report a total of 1475 drugs and active metabolites to which the BDDCS is applied. Of these, 379 are new entries, and 1096 are revisions of former classification studies with the addition of references for the approved maximum dose strength, extent of the systemically available drug excreted unchanged in the urine, and lowest solubility over the pH range 1.0-6.8 when such information is available in the literature. We detail revised class assignments of previously misclassified drugs and the literature analyses to classify new drugs. We review the process of solubility assessment for NMEs prior to drug dosing in humans and approved dose classification, as well as the comparison of Biopharmaceutics Classification System (BCS) versus BDDCS assignment. We detail the uses of BDDCS in predicting, prior to dosing animals or humans, disposition characteristics, potential brain penetration, food effect, and drug-induced liver injury (DILI) potential. This work provides an update on the current status of the BDDCS and its uses in the drug development process.

Does Addition of Protein to Hepatocyte or Microsomal In Vitro Incubations Provide a Useful Improvement in In Vitro-In Vivo Extrapolation Predictability?

Accurate prediction of in vivo hepatic clearance is an essential part of successful and efficient drug development; however, many investigators have recognized that there are significant limitations in the predictability of clearance with a tendency for underprediction for primarily metabolized drugs. Here, we examine the impact of adding serum or albumin into hepatocyte and microsomal incubations on the predictability of in vivo hepatic clearance. The addition of protein into hepatocyte incubations has been reported to improve the predictability for high clearance (extraction ratio) drugs and highly protein-bound drugs. Analyzing published data for 60 different drugs and 97 experimental comparisons (with 17 drugs being investigated from two to seven) we confirmed the marked underprediction of clearance. However, we could not validate any relevant improved predictability within twofold by the addition of serum to hepatocyte incubations or albumin to microsomal incubations. This was the case when investigating all measurements, or when subdividing analyses by extraction ratio, degree of protein binding, Biopharmaceutics Drug Disposition Classification System class, examining Extended Clearance Classification System class 1B drugs only, or drug charge. Manipulating characteristics of small data sets of like compounds and adding scaling factors can appear to yield good predictability, but the carryover of these methods to alternate drug classes and different laboratories is not evident. Improvement in predictability of poorly soluble compounds is greater than that for soluble compounds, but not to a meaningful extent. Overall, we cannot confirm that protein addition improves in vitro-in vivo extrapolation predictability to any clinically meaningful degree when considering all drugs and different subsets. SIGNIFICANCE STATEMENT: The addition of protein into microsomal or hepatocyte incubations has been widely proposed to improve hepatic clearance predictions. To date, studies examining this phenomenon have not included appropriate negative controls where predictability is achieved without protein addition and have been conducted with small data sets of similar compounds that don't apply to alternate drug classes. Here, an extensive analysis of published data for 60 drugs and 97 experimental comparisons couldn't validate any relevant clinically improved clearance predictability with protein addition.

Can In Vitro-In Vivo Extrapolation Be Successful? Recognizing the Incorrect Clearance Assumptions.

For a number of years, our laboratory has been investigating the underlying reasons for the published poor in vitro-in vivo extrapolation (IVIVE) predictability of human clearance both from a theoretical and from an experimental perspective. Here, we critically examine clearance concepts and commonly employed IVIVE approaches, concluding that there is no theoretical reason that IVIVE should work, just as it does not. Our analysis, however, has identified 10 misconceptions and/or poorly understood aspects of clearance that are listed in the Conclusion section of this manuscript. Chief among these are that all published human drug clearance values are arterial clearances-clearance calculated as organ blood flow multiplied by the extraction ratio is the arterial clearance of the organ of elimination (and not the published drug clearance value)-and that the well-stirred model equation taught in all pharmacokinetic courses that relates organ blood flow, fraction unbound in blood, and intrinsic clearance has no validity. We further list 10 conclusions relating to the IVIVE process. The primary IVIVE-related conclusions are that the intrinsic clearance value determined from an in vitro incubation is an arterial intrinsic clearance, there is no theoretical basis upon which an arterial intrinsic clearance can be related to a whole-body arterial clearance to accomplish IVIVE, there are no published data demonstrating that in vitro intrinsic metabolic clearance can predict in vivo organ clearance as IVIVE assumes, and the scientific basis for the hypothesized albumin-mediated hepatic uptake phenomenon is invalid. We further propose three IVIVE process recommendations.

Analyzing Potential Intestinal Transporter Drug-Drug Interactions: Reevaluating Ticagrelor Interaction Studies.

PURPOSE: Previous studies evaluating ticagrelor drug-drug interactions have not differentiated intestinal versus systemic mechanisms, which we do here. METHODS: Using recently published methodologies from our laboratory to differentiate metabolic- from transporter-mediated drug-drug interactions, a critical evaluation of five published ticagrelor drug-drug interactions was carried out to investigate the purported clinical significance of enzymes and transporters in ticagrelor disposition. RESULTS: The suggested CYP3A4 inhibitors, ketoconazole and diltiazem, displayed unchanged mean absorption time (MAT) and time of maximum concentration (Tmax) values as was expected, i.e., the interactions were mainly mediated by metabolic enzymes. The potential CYP3A4/P-gp inhibitor cyclosporine also showed an unchanged MAT value. Further analysis assuming there was no P-gp effect suggested that the increased AUC and unchanged t1/2 for ticagrelor after cyclosporine administration were attributed to the inhibition of intestinal CYP3A4 rather than P-gp. Rifampin, an inducer of CYP3As after multiple dosing, unexpectedly showed decreased MAT and Tmax values, which cannot be completely explained. In contrast, grapefruit juice, an intestinal CYP3A/P-gp/OATP inhibitor, significantly increased MAT and Tmax values for ticagrelor, which may be due to activation of P-gp or inhibition of OATPs expressed in intestine. CONCLUSIONS: This study provides new insight into the role of transporter pathways in ticagrelor intestinal absorption by examining potential MAT and Tmax changes mediated by drug-drug interactions.

Batch Selection via In Vitro/In Vivo Correlation in Pharmacokinetic Bioequivalence Testing.

Pharmacokinetic differences between manufacturing batches, well established for inhaled drug products, preclude control of patient risk in the customary two-way (single batch) pharmacokinetic bioequivalence crossover design if batches are randomly chosen. European regulators have recommended selecting a "typical" in vitro batch to represent each product in pharmacokinetic bioequivalence testing. We explored the feasibility of this approach to control patient risk (the "false equivalence", or Type I, error rate). The probability of achieving a Test/Reference 90% confidence interval within (0.80, 1.25) for a true (non-equivalent) value of 1.25 was simulated for a two-way crossover design using the median in vitro batch across a range of number of in vitro batches, in vitro/in vivo correlation (IVIVC) quality (correlation coefficient, r, of zero to one), and within-subject between-batch pharmacokinetic variability. Even under extremely optimistic conditions, e.g., r=0.95 and >100 batches per product screened in vitro, patient risk for typical between-batch variability levels remained at least threefold higher than the 5% regulatory expectation for the significance level (the false equivalence error rate) of the pharmacokinetic bioequivalence test. This elevated error rate in bioequivalence decision-making occurs because of incomplete confidence that the true product average has been identified, and, importantly, omission of this uncertainty from the bioequivalence confidence interval.

Examination of Urinary Excretion of Unchanged Drug in Humans and Preclinical Animal Models: Increasing the Predictability of Poor Metabolism in Humans.

PURPOSE: A dataset of fraction excreted unchanged in the urine (fe) values was developed and used to evaluate the ability of preclinical animal species to predict high urinary excretion, and corresponding poor metabolism, in humans. METHODS: A literature review of fe values in rats, dogs, and monkeys was conducted for all Biopharmaceutics Drug Disposition Classification System (BDDCS) Class 3 and 4 drugs (n=352) and a set of Class 1 and 2 drugs (n=80). The final dataset consisted of 202 total fe values for 135 unique drugs. Human and animal data were compared through correlations, two-fold analysis, and binary classifications of high (fe ≥30%) versus low (<30%) urinary excretion in humans. Receiver Operating Characteristic curves were plotted to optimize animal fe thresholds. RESULTS: Significant correlations were found between fe values for each animal species and human fe (p<0.05). Sixty-five percent of all fe values were within two-fold of human fe with animals more likely to underpredict human urinary excretion as opposed to overpredict. Dogs were the most reliable predictors of human fe of the three animal species examined with 72% of fe values within two-fold of human fe and the greatest accuracy in predicting human fe ≥30%. ROC determined thresholds of ≥25% in rats, ≥19% in dogs, and ≥10% in monkeys had improved accuracies in predicting human fe of ≥30%. CONCLUSIONS: Drugs with high urinary excretion in animals are likely to have high urinary excretion in humans. Animal models tend to underpredict the urinary excretion of unchanged drug in humans.

There is Only One Valid Definition of Clearance: Critical Examination of Clearance Concepts Reveals the Potential for Errors in Clinical Drug Dosing Decisions.

Drug dosing decisions in clinical medicine and in introducing a drug to market for the past 60 years are based on the pharmacokinetic/clinical pharmacology concept of clearance. We used chemical reaction engineering models to demonstrate the limitations of presently employed clearance measurements based upon systemic blood concentration in reflecting organ clearance. The belief for the last 49 years that in vivo clearance is independent of the mechanistic model for organ clearance is incorrect. There is only one valid definition of clearance. Defining organ clearance solely on the basis of systemic blood concentrations can lead to drug dosing errors when drug effect sites reside either in an eliminating organ exhibiting incremental clearance or in a non-eliminating organ where intraorgan concentration is governed by transporter actions. Attempts to predict clearance are presently hampered by the lack of recognition that what we are trying to predict is a well-stirred model clearance.

Investigating Intestinal Transporter Involvement in Rivaroxaban Disposition through Examination of Changes in Absorption.

PURPOSE: The involvement of the intestinally expressed xenobiotic transporters P-glycoprotein (P-gp) and Breast Cancer Resistance Protein (BCRP) have been implicated in rivaroxaban disposition based on in vitro studies, similar to what had previously been proposed for apixaban. We recently showed that these efflux transporters were not clinically relevant for apixaban disposition and examine here their relevance for this second Factor Xa inhibitor. METHODS: Using recently published methodologies to discern metabolic- from transporter- mediated drug interactions, a critical evaluation was undertaken of 9 rivaroxaban studies reporting 12 DDIs, one study of food effects and one study of hepatic function. RESULTS: Rationale examination of these clinical studies using basic pharmacokinetic theory finds little support for the clinical significance of intestinal efflux transporters in rivaroxaban disposition. Drug-drug interactions are most likely adequately predicted based on the level of CYP 3A metabolism. CONCLUSION: These analyses indicate that inhibition of efflux transporters appears to have negligible, clinically insignificant effects on the rivaroxaban absorption process, which is consistent with the concern that predictions based on in vitro measures may not translate to a clinically relevant interaction in vivo. We emphasize the need to evaluate gastric emptying, dissolution and other processes related to absorption when using MAT changes to indicate efflux transporter inhibition.

Effects of Single Dose Rifampin on the Pharmacokinetics of Fluvastatin in Healthy Volunteers.

The objective of this study was to determine the effects of the OATP inhibitor rifampin on pharmacokinetic of Biopharmaceutics Drug Disposition Classification System Class 1 compound fluvastatin. A crossover study was carried out in 10 healthy subjects who were randomized to 2 phases to receive fluvastatin 20 mg orally alone and following a 30-minute 600 mg i.v. infusion of rifampin. The results demonstrated that i.v. rifampin increased the mean area under the plasma fluvastatin concentration-time curve (AUC0-∞ ) by 255%, mean peak plasma concentration (Cmax ) by 254%, decreased oral volume of distribution by 71%, whereas the mean elimination terminal half-life (T1/2 ), mean absorption time (MAT), and time to peak concentration (Tpeak ) of fluvastatin did not significantly change. The study demonstrated that rifampin exhibited a significant drug interaction with fluvastatin. The mechanism of the increased plasma concentrations is likely due to inhibition of OATP transporters in hepatocytes.

Successful and Unsuccessful Prediction of Human Hepatic Clearance for Lead Optimization.

Development of new chemical entities is costly, time-consuming, and has a low success rate. Accurate prediction of pharmacokinetic properties is critical to progress compounds with favorable drug-like characteristics in lead optimization. Of particular importance is the prediction of hepatic clearance, which determines drug exposure and contributes to projection of dose, half-life, and bioavailability. The most commonly employed methodology to predict hepatic clearance is termed in vitro to in vivo extrapolation (IVIVE) that involves measuring drug metabolism in vitro, scaling-up this in vitro intrinsic clearance to a prediction of in vivo intrinsic clearance by reconciling the enzymatic content between the incubation and an average human liver, and applying a model of hepatic disposition to account for limitations of protein binding and blood flow to predict in vivo clearance. This manuscript reviews common in vitro techniques used to predict hepatic clearance as well as current challenges and recent theoretical advancements in IVIVE.

Tenofovir and emtricitabine concentrations in hair are comparable between individuals on tenofovir disoproxil fumarate versus tenofovir alafenamide-based ART.

Tenofovir disoproxil fumarate (TDF) in combination with emtricitabine (FTC) is the backbone for both human immunodeficiency virus (HIV) treatment and pre-exposure prophylaxis (PrEP) worldwide. Tenofovir alafenamide (TAF) with FTC is increasingly used in HIV treatment and was recently approved for PrEP among men-who-have-sex-with-men. TDF and TAF are both metabolized into tenofovir (TFV). Antiretrovirals in plasma are taken up into hair over time, with hair levels providing a long-term measure of adherence. Here, we report a simple, robust, highly sensitive, and validated high-performance liquid chromatography coupled with tandem mass spectrometry (LC/MS/MS)-based analytical method for analyzing TFV and FTC from individuals on either TDF/FTC or TAF/FTC in small hair samples. TFV/FTC are extracted from ~5 mg hair and separated on a column using a gradient elution. The lower quantification limits are 0.00200 (TFV) and 0.0200 (FTC) ng/mg hair; the assay is linear up to 0.400 (TFV) and 4.00 (FTC) ng/mg hair. The intra-day and inter-day coefficients of variance (CVs) are 5.39-12.6% and 6.40-13.5% for TFV and 0.571-2.45% and 2.45-5.16% for FTC. TFV concentrations from participants on TDF/FTC-based regimens with undetectable plasma HIV RNA were 0.0525 ± 0.0295 ng/mg, whereas those from individuals on TAF/FTC-based regimens were 0.0426 ± 0.0246 ng/mg. Despite the dose of TFV in TDF being 10 times that of TAF, hair concentrations of TFV were not significantly different for those on TDF versus TAF regimens. Pharmacological enhancers (ritonavir and cobicistat) did not boost TFV concentrations in hair. In summary, we developed and validated a sensitive analytical method to analyze TFV and FTC in hair and found that hair concentrations of TFV were essentially equivalent among those on TDF and TAF.

Volume of Distribution is Unaffected by Metabolic Drug-Drug Interactions.

INTRODUCTION: It has been recognized that significant transporter interactions result in volume of distribution changes in addition to potential changes in clearance. For drugs that are not clinically significant transporter substrates, it is expected that drug-drug interactions would not result in any changes in volume of distribution. METHODS: An evaluation of this hypothesis proceeded via an extensive analysis of published intravenous metabolic drug-drug interactions, based on clinically recommended index substrates and inhibitors of major cytochrome P450 (CYP) isoforms. RESULTS: Seventy-two metabolic drug interaction studies were identified where volume of distribution at steady-state (Vss) values were available for the CYP index substrates caffeine (CYP1A2), metoprolol (CYP2D6), midazolam (CYP3A4), theophylline (CYP1A2), and tolbutamide (CYP2C9). Changes in exposure (area under the curve) up to 5.1-fold were observed; however, ratios of Vss changes have a range of 0.70-1.26, with one outlier displaying a Vss ratio of 0.57. DISCUSSION: These results support the widely held founding tenant of pharmacokinetics that clearance and Vss are independent parameters. Knowledge that Vss is unchanged in metabolic drug-drug interactions can be helpful in discriminating changes in clearance from changes in bioavailability (F) when only oral dosing data are available, as we have recently demonstrated. As Vss remains unchanged for intravenous metabolic drug-drug interactions, following oral dosing changes in Vss/F will reflect changes in F alone. This estimation of F change can subsequently be utilized to assess changes in clearance alone from calculations of apparent clearance. Utilization of this simple methodology for orally dosed drugs will have a significant impact on how drug-drug interactions are interpreted from drug development and regulatory perspectives.

Intestinal Efflux Transporters P-gp and BCRP Are Not Clinically Relevant in Apixaban Disposition.

PURPOSE: The involvement of the intestinally expressed xenobiotic transporters P-glycoprotein (P-gp) and Breast Cancer Resistance Protein (BCRP) have been implicated in apixaban disposition based on in vitro studies. Recommendations against co-administration of apixaban with inhibitors of these efflux transporters can be found throughout the literature as well as in the apixaban FDA label. However, the clinical relevance of such findings is questionable due to the high permeability and high solubility characteristics of apixaban. METHODS: Using recently published methodologies to discern metabolic- from transporter- mediated drug-drug interactions, a critical evaluation of all published apixaban drug-drug interaction studies was conducted to investigate the purported clinical significance of efflux transporters in apixaban disposition. RESULTS: Rational examination of these clinical studies using basic pharmacokinetic theory does not support the clinical significance of intestinal efflux transporters in apixaban disposition. Further, there is little evidence that efflux transporters are clinically significant determinants of systemic clearance. CONCLUSIONS: Inhibition or induction of intestinal CYP3A4 can account for exposure changes of apixaban in all clinically significant drug-drug interactions, and lack of intestinal CYP3A4 inhibition can explain all studies with no exposure changes, regardless of the potential for these perpetrators to inhibit intestinal or systemic efflux transporters.