Hepatobiliary Disposition of Atovaquone: A Case of Mechanistically Unusual Biliary Clearance.

Atovaquone, an antiprotozoal and antipneumocystic agent, is predominantly cleared by biliary excretion of unchanged parent drug. Atovaquone is ≥10,000-fold concentrated in human bile relative to unbound plasma. Even after correcting for apparent nonspecific binding and incomplete solubility in bile, atovaquone is still concentrated ≥100-fold in bile, consistent with active biliary excretion. Mechanisms of atovaquone hepatobiliary disposition were studied using a multiexperimental in vitro and in vivo approach. Atovaquone uptake was not elevated in HEK293 cells singly overexpressing OATP1B1, OATP1B3, OATP2B1, OCT1, NTCP, or OAT2. Hepatocyte uptake of atovaquone was not impaired by OATP and OCT inhibitor cocktail (rifamycin and imipramine). Atovaquone liver-to-blood ratio at distributional equilibrium was not reduced in Oatp1a/1b and Oct1/2 knockout mice. Atovaquone exhibited efflux ratios of approximately unity in P-gp and BCRP overexpressing MDCK cell monolayers and did not display enhanced uptake in MRP2 vesicles. Biliary and canalicular clearance were not decreased in P-gp, Bcrp, Mrp2, and Bsep knockout rats. In the present study, we rule out the involvement of major known basolateral uptake and bile canalicular efflux transporters in the hepatic uptake and biliary excretion of atovaquone. This is the first known example of a drug cleared by biliary excretion in humans, with extensive biliary concentration, which is not transported by the mechanisms investigated herein.

Derivation of a System-Independent Ki for P-glycoprotein Mediated Digoxin Transport from System-Dependent IC50 Data.

It has been previously demonstrated that IC50 values for inhibition of digoxin transport across confluent polarized cell monolayers are system-dependent. Digoxin IC50 data from five laboratories participating in the P-glycoprotein (P-gp) IC50 Initiative, using Caco-2, MDCKII-hMDR1 or LLC-PK1-hMDR1 cells, were fitted by the structural mass action kinetic model for P-gp-mediated transport across confluent cell monolayers. We determined their efflux-active P-gp concentration [T(0)], inhibitor elementary dissociation rate constant from P-gp (krQ), digoxin basolateral uptake clearance (kB), and inhibitor binding affinity to the digoxin basolateral uptake transporter (KQB). We also fitted the IC50 data for inhibition of digoxin transport through monolayers of primary human proximal tubule cells (HPTCs). All cell systems kinetically required a basolateral uptake transporter for digoxin, which also bound to all inhibitors. The inhibitor krQ was cell system-independent, thereby allowing calculation of a system-independent Ki. The variability in efflux-active P-gp concentrations and basolateral uptake clearances in the five laboratories was about an order of magnitude. These laboratory-to-laboratory variabilities can explain more than 60% of the IC50 variability found in the principal component analysis plot in a previous study, supporting the hypothesis that the observed IC50 variability is primarily due to differences in expression levels of efflux-active P-gp and the basolateral digoxin uptake transporter. HPTCs had 10- to 100-fold lower efflux-active P-gp concentrations than the overexpressing cell lines, whereas their digoxin basolateral uptake clearances were similar. HPTC basolateral uptake of digoxin was inhibited 50% by 10 µM ouabain, suggesting involvement of OATP4C1.

Extrapolation of Elementary Rate Constants of P-glycoprotein-Mediated Transport from MDCKII-hMDR1-NKI to Caco-2 Cells.

The best parameters for incorporation into mechanistic physiologically based pharmacokinetic models for transporters are system-independent kinetic parameters and active (not total) transporter levels. Previously, we determined the elementary rate constants for P-glycoprotein (P-gp)-mediated transport (on- and off-rate constants from membrane to P-gp binding pocket and efflux rate constant into the apical chamber) using the structural mass action kinetic model in confluent MDCKII-hMDR1-NKI cell monolayers. In the present work, we extended the kinetic analysis to Caco-2 cells for the first time and showed that the elementary rate constants are very similar compared with MDCKII-hMDR1-NKI cells, suggesting they primarily depend on the interaction of the compound with P-gp and are therefore mostly independent of the in vitro system used. The level of efflux active (not total) P-gp is also fitted by our model. The estimated level of efflux active P-gp was 5.0 ± 1.4-fold lower in Caco-2 cells than in MDCKII-hMDR1-NKI cells. We also kinetically identified the involvement of a basolateral uptake transporter for both digoxin and loperamide in Caco-2 cells, as found previously in MDCKII-hMDR1-NKI cells, due to their low passive permeability. This demonstrates the value of our P-gp structural model as a diagnostic tool in detecting the importance of other transporters, which cannot be unambiguously done by the Michaelis-Menten approach. The system-independent elementary rate constants for P-gp obtained in vitro are more fundamental parameters than those obtained using Michaelis-Menten steady-state equations. This suggests they will be more robust mechanistic parameters for incorporation into physiologically based pharmacokinetic models for transporters.

Microvilli Morphology Can Affect Efflux Active P-Glycoprotein in Confluent MDCKII -hMDR1-NKI and Caco-2 Cell Monolayers.

From fits of drug transport kinetics across confluent MDCKII-hMDR1-NKI and Caco-2 cell monolayers we estimated the levels of efflux active P-glycoprotein (P-gp) in these two cell lines (companion paper). In the present work, we compared the efflux active P-gp number to the total P-gp level, using liquid chromatography-tandem mass spectrometry, and showed that in Caco-2 cells total P-gp is about 10-fold greater than efflux active P-gp, whereas in MDCKII-hMDR1-NKI cells these values are within twofold. We further visualized the microvilli in MDCKII-hMDR1-NKI and Caco-2 cells using three-dimensional structured illumination super-resolution microscopy and found that the microvilli in Caco-2 cells are taller and more densely packed than those in MDCK-hMDR1-NKI cells. We hypothesized over 10 years ago that only P-gp at the tips of the microvilli contribute significantly to efflux activity, whereas the remaining P-gp are involved in a futile cycle of efflux of amphipathic drugs from the microvillus membrane, followed by their reabsorption into the same or nearby microvillous membranes. The difference between the levels of total and efflux active P-gp in Caco-2 cells can be explained by the more densely packed microvilli in Caco-2 cells, which would lead to a substantial fraction of P-gp not contributing to final release of drug into the apical chamber. Our results suggest that the effect of microvilli morphology differences between in vitro and in vivo systems must be considered when scaling transporter activity for efflux transporters of amphipathic compounds, for example, P-gp.

Inhibition of Intestinal OATP2B1 by the Calcium Receptor Antagonist Ronacaleret Results in a Significant Drug-Drug Interaction by Causing a 2-Fold Decrease in Exposure of Rosuvastatin.

Rosuvastatin is a widely prescribed antihyperlipidemic which undergoes limited metabolism, but is an in vitro substrate of multiple transporters [organic anion transporting polypeptide 1B1 (OATP1B1), OATP1B3, OATP1A2, OATP2B1, sodium-taurocholate cotransporting polypeptide, breast cancer resistance protein (BCRP), multidrug resistance protein 2 (MRP2), MRP4, organic anion transporter 3]. It is therefore frequently used as a probe substrate in clinical drug-drug interaction (DDI) studies to investigate transporter inhibition. Although each of these transporters is believed to play a role in rosuvastatin disposition, multiple pharmacogenetic studies confirm that OATP1B1 and BCRP play an important role in vivo. Ronacaleret, a drug-development candidate for treatment of osteoporosis (now terminated), was shown to inhibit OATP1B1 in vitro (IC50 = 11 µM), whereas it did not inhibit BCRP. Since a DDI risk through inhibition of OATP1B1 could not be discharged, a clinical DDI study was performed with rosuvastatin before initiation of phase II trials. Unexpectedly, coadministration with ronacaleret decreased rosuvastatin exposure by approximately 50%, whereas time of maximal plasma concentration and terminal half-life remained unchanged, suggesting decreased absorption and/or enhanced first-pass elimination of rosuvastatin. Of the potential in vivo rosuvastatin transporter pathways, two might explain the observed results: intestinal OATP2B1 and hepatic MRP4. Further investigations revealed that ronacaleret inhibited OATP2B1 (in vitro IC50 = 12 µM), indicating a DDI risk through inhibition of absorption. Ronacaleret did not inhibit MRP4, discharging the possibility of enhanced first-pass elimination of rosuvastatin (reduced basolateral secretion from hepatocytes into blood). Therefore, a likely mechanism of the observed DDI is inhibition of intestinal OATP2B1, demonstrating the in vivo importance of this transporter in rosuvastatin absorption in humans.

Prediction of the Transporter-Mediated Drug-Drug Interaction Potential of Dabrafenib and Its Major Circulating Metabolites.

The BRAF inhibitor dabrafenib was recently approved for the treatment of certain BRAF V600 mutation-positive tumors, either alone or in combination therapy with the mitogen-activated extracellular signal regulated kinase 1 (MEK1) and MEK2 inhibitor, trametinib. This article presents the dabrafenib transporter-mediated drug-drug interaction (DDI) risk assessment, which is currently an important part of drug development, regulatory submission, and drug registration. Dabrafenib and its major circulating metabolites (hydroxy-, carboxy-, and desmethyl-dabrafenib) were investigated as inhibitors of the clinically relevant transporters P-gp, BCRP, OATP1B1, OATP1B3, OCT2, OAT1, and OAT3. The DDI Guidance risk assessment decision criteria for inhibition of BCRP, OATP1B1 and OAT3 were slightly exceeded and therefore a minor DDI effect resulting from inhibition of these transporters remained possible. Biliary secretion is the major excretion pathway of dabrafenib-related material (71.1% of orally administered radiolabeled dose recovered in feces), whereas urinary excretion was observed as well (22.7% of the dose). In vitro uptake into human hepatocytes of the dabrafenib metabolites, but not of dabrafenib parent compound, was mediated, at least in part, by hepatic uptake transporters. The transporters responsible for uptake of the pharmacologically active hydroxy- and desmethyl dabrafenib could not be identified, whereas carboxy-dabrafenib was a substrate of several OATPs. Dabrafenib, hydroxy-, and desmethyl-dabrafenib were substrates of P-gp and BCRP, whereas carboxy-dabrafenib was not. Although a small increase in exposure to carboxy-dabrafenib upon inhibition of OATPs and an increase in exposure to desmethyl-dabrafenib upon inhibition of P-gp or BCRP cannot be excluded, the clinical significance of such increases is likely to be low.

Breast cancer resistance protein (ABCG2) in clinical pharmacokinetics and drug interactions: practical recommendations for clinical victim and perpetrator drug-drug interaction study design.

Breast cancer resistance protein (BCRP; ABCG2) limits intestinal absorption of low-permeability substrate drugs and mediates biliary excretion of drugs and metabolites. Based on clinical evidence of BCRP-mediated drug-drug interactions (DDIs) and the c.421C>A functional polymorphism affecting drug efficacy and safety, both the US Food and Drug Administration and European Medicines Agency recommend preclinical evaluation and, when appropriate, clinical assessment of BCRP-mediated DDIs. Although many BCRP substrates and inhibitors have been identified in vitro, clinical translation has been confounded by overlap with other transporters and metabolic enzymes. Regulatory recommendations for BCRP-mediated clinical DDI studies are challenging, as consensus is lacking on the choice of the most robust and specific human BCRP substrates and inhibitors and optimal study design. This review proposes a path forward based on a comprehensive analysis of available data. Oral sulfasalazine (1000 mg, immediate-release tablet) is the best available clinical substrate for intestinal BCRP, oral rosuvastatin (20 mg) for both intestinal and hepatic BCRP, and intravenous rosuvastatin (4 mg) for hepatic BCRP. Oral curcumin (2000 mg) and lapatinib (250 mg) are the best available clinical BCRP inhibitors. To interrogate the worst-case clinical BCRP DDI scenario, study subjects harboring the BCRP c.421C/C reference genotype are recommended. In addition, if sulfasalazine is selected as the substrate, subjects having the rapid acetylator phenotype are recommended. In the case of rosuvastatin, subjects with the organic anion-transporting polypeptide 1B1 c.521T/T genotype are recommended, together with monitoring of rosuvastatin's cholesterol-lowering effect at baseline and DDI phase. A proof-of-concept clinical study is being planned by a collaborative consortium to evaluate the proposed BCRP DDI study design.

Disposition and metabolism of darapladib, a lipoprotein-associated phospholipase A2 inhibitor, in humans.

The absorption, metabolism, and excretion of darapladib, a novel inhibitor of lipoprotein-associated phospholipase A2, was investigated in healthy male subjects using [(14)C]-radiolabeled material in a bespoke study design. Disposition of darapladib was compared following single i.v. and both single and repeated oral administrations. The anticipated presence of low circulating concentrations of drug-related material required the use of accelerator mass spectrometry as a sensitive radiodetector. Blood, urine, and feces were collected up to 21 days post radioactive dose, and analyzed for drug-related material. The principal circulating drug-related component was unchanged darapladib. No notable metabolites were observed in plasma post-i.v. dosing; however, metabolites resulting from hydroxylation (M3) and N-deethylation (M4) were observed (at 4%-6% of plasma radioactivity) following oral dosing, indicative of some first-pass metabolism. In addition, an acid-catalyzed degradant (M10) resulting from presystemic hydrolysis was also detected in plasma at similar levels of ∼5% of radioactivity post oral dosing. Systemic exposure to radioactive material was reduced within the repeat dose regimen, consistent with the notion of time-dependent pharmacokinetics resulting from enhanced clearance or reduced absorption. Elimination of drug-related material occurred predominantly via the feces, with unchanged darapladib representing 43%-53% of the radioactive dose, and metabolites M3 and M4 also notably accounting for ∼9% and 19% of the dose, respectively. The enhanced study design has provided an increased understanding of the absorption, distribution, metabolism and excretion (ADME) properties of darapladib in humans, and substantially influenced future work on the compound.

The importance of villous physiology and morphology in mechanistic physiologically-based pharmacokinetic models.

PURPOSE: Existing PBPK models incorporating intestinal first-pass metabolism account for effect of drug permeability on accessible absorption surface area by use of "effective" permeability, P eff , without adjusting number of enterocytes involved in absorption or proportion of intestinal CYP3A involved in metabolism. The current model expands on existing models by accounting for these factors. METHODS: The PBPK model was developed using SAAM II. Midazolam clinical data was generated at GlaxoSmithKline. RESULTS: The model simultaneously captures human midazolam blood concentration profile and previously reported intestinal availability, using values for CYP3A CLu int , permeability and accessible surface area comparable to literature data. Simulations show: (1) failure to distinguish absorbing from non-absorbing enterocytes results in overestimation of intestinal metabolism of highly permeable drugs absorbed across the top portion of the villous surface only; (2) first-pass extraction of poorly permeable drugs occurs primarily in enterocytes, drugs with higher permeability are extracted by enterocytes and hepatocytes; (3) CYP3A distribution along crypt-villous axes does not significantly impact intestinal metabolism; (4) differences in permeability of perpetrator and victim drugs results in their spatial separation along the villous axis and intestinal length, diminishing drug-drug interaction magnitude. CONCLUSIONS: The model provides a useful tool to interrogate intestinal absorption/metabolism of candidate drugs.

Variability in P-glycoprotein inhibitory potency (IC50) using various in vitro experimental systems: implications for universal digoxin drug-drug interaction risk assessment decision criteria.

A P-glycoprotein (P-gp) IC50 working group was established with 23 participating pharmaceutical and contract research laboratories and one academic institution to assess interlaboratory variability in P-gp IC50 determinations. Each laboratory followed its in-house protocol to determine in vitro IC50 values for 16 inhibitors using four different test systems: human colon adenocarcinoma cells (Caco-2; eleven laboratories), Madin-Darby canine kidney cells transfected with MDR1 cDNA (MDCKII-MDR1; six laboratories), and Lilly Laboratories Cells--Porcine Kidney Nr. 1 cells transfected with MDR1 cDNA (LLC-PK1-MDR1; four laboratories), and membrane vesicles containing human P-glycoprotein (P-gp; five laboratories). For cell models, various equations to calculate remaining transport activity (e.g., efflux ratio, unidirectional flux, net-secretory-flux) were also evaluated. The difference in IC50 values for each of the inhibitors across all test systems and equations ranged from a minimum of 20- and 24-fold between lowest and highest IC50 values for sertraline and isradipine, to a maximum of 407- and 796-fold for telmisartan and verapamil, respectively. For telmisartan and verapamil, variability was greatly influenced by data from one laboratory in each case. Excluding these two data sets brings the range in IC50 values for telmisartan and verapamil down to 69- and 159-fold. The efflux ratio-based equation generally resulted in severalfold lower IC50 values compared with unidirectional or net-secretory-flux equations. Statistical analysis indicated that variability in IC50 values was mainly due to interlaboratory variability, rather than an implicit systematic difference between test systems. Potential reasons for variability are discussed and the simplest, most robust experimental design for P-gp IC50 determination proposed. The impact of these findings on drug-drug interaction risk assessment is discussed in the companion article (Ellens et al., 2013) and recommendations are provided.

Application of receiver operating characteristic analysis to refine the prediction of potential digoxin drug interactions.

In the 2012 Food and Drug Administration (FDA) draft guidance on drug-drug interactions (DDIs), a new molecular entity that inhibits P-glycoprotein (P-gp) may need a clinical DDI study with a P-gp substrate such as digoxin when the maximum concentration of inhibitor at steady state divided by IC50 ([I1]/IC50) is ≥0.1 or concentration of inhibitor based on highest approved dose dissolved in 250 ml divide by IC50 ([I2]/IC50) is ≥10. In this article, refined criteria are presented, determined by receiver operating characteristic analysis, using IC50 values generated by 23 laboratories. P-gp probe substrates were digoxin for polarized cell-lines and N-methyl quinidine or vinblastine for P-gp overexpressed vesicles. Inhibition of probe substrate transport was evaluated using 15 known P-gp inhibitors. Importantly, the criteria derived in this article take into account variability in IC50 values. Moreover, they are statistically derived based on the highest degree of accuracy in predicting true positive and true negative digoxin DDI results. The refined criteria of [I1]/IC50 ≥ 0.03 and [I2]/IC50 ≥ 45 and FDA criteria were applied to a test set of 101 in vitro-in vivo digoxin DDI pairs collated from the literature. The number of false negatives (none predicted but DDI observed) were similar, 10 and 12%, whereas the number of false positives (DDI predicted but not observed) substantially decreased from 51 to 40%, relative to the FDA criteria. On the basis of estimated overall variability in IC50 values, a theoretical 95% confidence interval calculation was developed for single laboratory IC50 values, translating into a range of [I1]/IC50 and [I2]/IC50 values. The extent by which this range falls above the criteria is a measure of risk associated with the decision, attributable to variability in IC50 values.

Preclinical strategy to reduce clinical hepatotoxicity using in vitro bioactivation data for >200 compounds.

Drug-induced liver injury is the most common cause of market withdrawal of pharmaceuticals, and thus, there is considerable need for better prediction models for DILI early in drug discovery. We present a study involving 223 marketed drugs (51% associated with clinical hepatotoxicity; 49% non-hepatotoxic) to assess the concordance of in vitro bioactivation data with clinical hepatotoxicity and have used these data to develop a decision tree to help reduce late-stage candidate attrition. Data to assess P450 metabolism-dependent inhibition (MDI) for all common drug-metabolizing P450 enzymes were generated for 179 of these compounds, GSH adduct data generated for 190 compounds, covalent binding data obtained for 53 compounds, and clinical dose data obtained for all compounds. Individual data for all 223 compounds are presented here and interrogated to determine what level of an alert to consider termination of a compound. The analysis showed that 76% of drugs with a daily dose of <100 mg were non-hepatotoxic (p < 0.0001). Drugs with a daily dose of ≥100 mg or with GSH adduct formation, marked P450 MDI, or covalent binding ≥200 pmol eq/mg protein tended to be hepatotoxic (∼ 65% in each case). Combining dose with each bioactivation assay increased this association significantly (80-100%, p < 0.0001). These analyses were then used to develop the decision tree and the tree tested using 196 of the compounds with sufficient data (49% hepatotoxic; 51% non-hepatotoxic). The results of these outcome analyses demonstrated the utility of the tree in selectively terminating hepatotoxic compounds early; 45% of the hepatotoxic compounds evaluated using the tree were recommended for termination before candidate selection, whereas only 10% of the non-hepatotoxic compounds were recommended for termination. An independent set of 10 GSK compounds with known clinical hepatotoxicity status were also assessed using the tree, with similar results.

Digoxin is not a substrate for organic anion-transporting polypeptide transporters OATP1A2, OATP1B1, OATP1B3, and OATP2B1 but is a substrate for a sodium-dependent transporter expressed in HEK293 cells.

Digoxin, an orally administered cardiac glycoside cardiovascular drug, has a narrow therapeutic window. Circulating digoxin levels (maximal concentration of ∼1.5 ng/ml) require careful monitoring, and the potential for drug-drug interactions (DDI) is a concern. Increases in digoxin plasma exposure caused by inhibition of P-glycoprotein (P-gp) have been reported. Digoxin has also been described as a substrate of various organic anion-transporting polypeptide (OATP) transporters, posing a risk that inhibition of OATPs may result in a clinically relevant DDI similar to what has been observed for P-gp. Although studies in rats have shown that Oatps contribute to the disposition of digoxin, the role of OATPs in the disposition of digoxin in humans has not been clearly defined. Using two methods, Boehringer Ingelheim, GlaxoSmithKline, Pfizer, and Solvo observed that digoxin is not a substrate of OATP1A2, OATP1B1, OATP1B3, and OATP2B1. However, digoxin inhibited the uptake of probe substrates of OATP1B1 (IC(50) of 47 µM), OATP1B3 (IC(50) > 8.1 µM), and OATP2B1 (IC(50) > 300 µM), but not OATP1A2 in transfected cell lines. It is interesting to note that digoxin is a substrate of a sodium-dependent transporter endogenously expressed in HEK293 cells because uptake of digoxin was significantly greater in cells incubated with sodium-fortified media compared with incubations conducted in media in which sodium was absent. Thus, although digoxin is not a substrate for the human OATP transporters evaluated in this study, in addition to P-gp-mediated efflux, its uptake and pharmacokinetic disposition may be partially facilitated by a sodium-dependent transporter.

Case Study 8: Status of the Structural Mass Action Kinetic Model of P-gp-Mediated Transport Through Confluent Cell Monolayers.

In the first edition of this book, we presented the basics of explicitly incorporating the lipid biochemistry into a confluent cell monolayer transport model and the novel findings of this model up to 2013, including the use of global optimization to fit the elementary rate constants and the efflux active P-glycoprotein (P-gp) membrane concentrations for the transport of four P-gp substrates across MDCKII-hMDR1-NKI confluent cell monolayers. This chapter is an update on that model, which has been focused primarily on discovering how microvilli morphology regulates the efflux active P-gp and the existence of, as yet, unidentified uptake transporters of P-gp substrates in all of the commonly used P-gp expressing cell lines used in the pharmaceutical industry, thereby adding new players to DDI predictions and IVIVE. The structural mass action kinetic model uses the general mass action reactions for P-gp binding and efflux, with the membrane structural parameters for the confluent cell monolayer to predict drug transport over time. Binding of drug to P-gp occurs within the cytosolic monolayer of the apical membrane, according to (a) the molar partition coefficient of the drug to the cytosolic monolayer and (b) the association rate constant, k1 (M-1 s-1), of the drug from the basolateral or apical outer monolayers into the P-gp binding site. Release of substrate from P-gp back into the cytosolic monolayer occurs with a dissociation rate constant kr (s-1) or, much less frequently, into the apical aqueous chamber with an efflux rate constant k2 (s-1). The model fits the efflux active P-gp concentration, T(0), i.e., the P-gp whose effluxed drug actually reaches the apical aqueous chamber, as opposed to the majority of P-gp whose effluxed drug is reabsorbed back into the same or neighboring microvilli prior to reaching the apical aqueous chamber. Efflux active P-gp largely resides near the tips of the microvilli. We have shown using kinetics and structured illumination microscopy that: (a) efflux active P-gp is controlled by microvilli morphology; (b) there are apical (AT) and basolateral (BT) uptake transporters for P-gp substrates in most, if not all, P-gp expressing cell lines used in the pharmaceutical industry, which exist, but which remain unidentified; (c) the lab-to-lab variability in P-gp IC50 values observed in the P-gp IC50 initiative was due to the conflated inhibition of P-gp and the basolateral digoxin uptake transporters by all 15 P-gp substrates tested in that study; (d) even the IC50 values for P-gp inhibition alone do not obey the Cheng-Prusoff relationship; (e) the fitted elementary rate constants and the molecular dissociation constant Ki for this kinetic model are system independent; and (f) the time dependence of product formation for these confluent cell monolayers is correlated with the P-gp Vmax/Km, when defined by its fitted elementary rate constants and uptake transporter clearances, without any steady-state assumptions.

A mechanism-based mathematical model of aryl hydrocarbon receptor-mediated CYP1A induction in rats using beta-naphthoflavone as a tool compound.

ß-Naphthoflavone (BNF) is a synthetic flavone that selectively and potently induces CYP1A enzymes via aryl hydrocarbon receptor activation. Mechanism-based mathematical models of CYP1A enzyme induction were developed to predict the time course of enzyme induction and quantitatively evaluate the interrelationship between BNF plasma concentrations, hepatic CYP1A1 and CYP1A2 mRNA levels, and CYP1A enzyme activity in rats in vivo. Male Sprague-Dawley rats received a continuous intravenous infusion of vehicle or 1.5 or 6 mg · kg(-1) · h(-1) BNF for 6 h, with blood and liver sampling. Plasma BNF concentrations were determined by liquid chromatography-tandem mass spectrometry. Hepatic mRNA levels of CYP1A1 and CYP1A2 were determined by TaqMan. Ethoxyresorufin O-deethylation was used to measure the increase in CYP1A enzyme activity as a result of induction. The induction of hepatic CYP1A1/CYP1A2 mRNA and CYP1A activity occurred within 2 h after BNF administration. This caused a rapid increase in metabolic clearance of BNF, resulting in plasma concentrations declining during the infusion. Overall, the enzyme induction models developed in this study adequately captured the time course of BNF pharmacokinetics, CYP1A1/CYP1A2 mRNA levels, and increases in CYP1A enzyme activity data for both dose groups simultaneously. The model-predicted degradation half-life of CYP1A enzyme activity is comparable with previously reported values. The present results also confirm a previous in vitro finding that CYP1A1 is the predominant contributor to CYP1A induction. These physiologically based models provide a basis for predicting drug-induced toxicity in humans from in vitro and preclinical data and can be a valuable tool in drug development.

If the KI is defined by the free energy of binding to P-glycoprotein, which kinetic parameters define the IC50 for the Madin-Darby canine kidney II cell line overexpressing human multidrug resistance 1 confluent cell monolayer?

From previous fits of drug transport kinetics across confluent Madin-Darby canine kidney II cell line overexpressing human multidrug resistance 1 cell monolayers, we found that a drug's binding constant to P-glycoprotein (P-gp) was significantly smaller than its IC(50) when that drug was used as an inhibitor against another P-gp substrate. We tested several IC(50) candidate functions, including the standard function, the Kalvass-Pollack function, and the efflux ratio, to determine whether any of them yielded an IC(50) = K(I), as would be expected for water-soluble enzymes. For the confluent cell monolayer, the IC(50)/K(I) ratio is greater than 1 for all candidate functions tested. From the mass action kinetic model, we have derived a simple approximate equation that shows how the IC(50)/K(I) ratio depends on the elementary rate constants from our mass action model. Thus, the IC(50) will differ between cell lines and tissues, for the same probe substrate and inhibitor, if there are different membrane concentrations of P-gp, or the probe substrate's elementary rate constants, partition coefficient, binding constant to P-gp, passive permeability, and ability to access the other transporters (if any) in the two cell lines. The mass action model and the approximate equation for the IC(50)/K(I) ratio derived here can be used to estimate the elementary rate constants needed to extrapolate in vitro drug-drug interactions for compounds to the in vivo environment.

Kinetic identification of membrane transporters that assist P-glycoprotein-mediated transport of digoxin and loperamide through a confluent monolayer of MDCKII-hMDR1 cells.

A robust screen for compound interaction with P-glycoprotein (P-gp) has some obvious requirements, such as a cell line expressing P-gp and a probe substrate that is transported solely by P-gp and passive permeability. It is actually difficult to prove that a particular probe substrate interacts only with P-gp in the chosen cell line. Using a confluent monolayer of MDCKII-hMDR1 cells, we have determined the elementary rate constants for the P-gp efflux of amprenavir, digoxin, loperamide, and quinidine. For amprenavir and quinidine, transport was fitted with just P-gp and passive permeability. For digoxin and loperamide, fitting required a basolateral transporter (p < 0.01), which was inhibited by the P-gp inhibitor N-(4-[2-(1,2,3,4-tetrahydro-6,7-dimethoxy-2-isoquinolinyl)ethyl]-phenyl)-9,10-dihydro-5-methoxy-9-oxo-4-acridine carboxamide (GF120918). This means that when digoxin is used as a probe substrate and a compound is shown to inhibit digoxin flux, it could be that the inhibition occurs at the basolateral transporter rather than at P-gp. Digoxin basolateral>apical efflux also required an apical importer (p < 0.05). We propose that amprenavir and quinidine are robust probe substrates for assessing P-gp interactions using the MDCKII-hMDR1 confluent cell monolayer. Usage of another cell line, e.g., LLC-hMDR1 or Caco-2, would require the same kinetic validation to ensure that the probe substrate interacts only with P-gp. Attempts to identify the additional digoxin and loperamide transporters using a wide range of substrates/inhibitors of known epithelial transporters (organic cation transporters, organic anion transporters, organic ion-transporting polypeptide, uric acid transporter, or multidrug resistance-associated protein) failed to inhibit the digoxin or loperamide transport through their basolateral transporter.

Mechanistic kinetic modeling generates system-independent P-glycoprotein mediated transport elementary rate constants for inhibition and, in combination with 3D SIM microscopy, elucidates the importance of microvilli morphology on P-glycoprotein mediated efflux activity.

INTRODUCTION: In vitro transporter kinetics are typically analyzed by steady-state Michaelis-Menten approximations. However, no clear evidence exists that these approximations, applied to multiple transporters in biological membranes, yield system-independent mechanistic parameters needed for reliable in vivo hypothesis generation and testing. Areas covered: The classical mass action model has been developed for P-glycoprotein (P-gp) mediated transport across confluent polarized cell monolayers. Numerical integration of the mass action equations for transport using a stable global optimization program yields fitted elementary rate constants that are system-independent. The efflux active P-gp was defined by the rate at which P-gp delivers drugs to the apical chamber, since as much as 90% of drugs effluxed by P-gp partition back into nearby microvilli prior to reaching the apical chamber. The efflux active P-gp concentration was 10-fold smaller than the total expressed P-gp for Caco-2 cells, due to their microvilli membrane morphology. The mechanistic insights from this analysis are readily extrapolated to P-gp mediated transport in vivo. Expert opinion: In vitro system-independent elementary rate constants for transporters are essential for the generation and validation of robust mechanistic PBPK models. Our modeling approach and programs have broad application potential. They can be used for any drug transporter with minor adaptations.

P-Glycoprotein (P-gp) expressed in a confluent monolayer of hMDR1-MDCKII cells has more than one efflux pathway with cooperative binding sites.

The multidrug resistance transporter P-glycoprotein (P-gp) effluxes a wide range of substrates and can be affected by a wide range of inhibitors or modulators. Many studies have presented classifications for these binding interactions, within either the context of equilibrium binding or the Michaelis-Menten enzyme analysis of the ATPase activity of P-gp. Our approach is to study P-gp transport and its inhibition using a physiologically relevant confluent monolayer of hMDR1-MDCKII cells. We measure the elementary rate constants for P-gp efflux of substrates and study inhibition using pairwise combinations with a different unlabeled substrate acting as the inhibitor. Our current kinetic model for P-gp has only a single binding site, because a previous study proved that the mass-action kinetics of efflux of a single substrate were not sensitive to whether there are one or more substrate-binding and efflux sites. In this study, using this one-site model, we found that, with "high" concentrations of either a substrate or an inhibitor, the elementary rate constants fitted independently for each of the substrates alone quantitatively predicted the efflux curves, simply applying the assumption that binding at the "one site" was competitive. On the other hand, at "low" concentrations of both the substrate and inhibitor, we found no inhibition of the substrate efflux, despite the fact that both the substrate and inhibitor were being well-effluxed. This was not an effect of excess "empty" P-gp molecules, because the competitive efflux model takes site occupancy into account. Rather, it is quantitative evidence that the substrate and inhibitor are being effluxed by multiple pathways within P-gp. Remarkably, increasing the substrate concentration above the "low" concentration, caused the inhibition to become competitive; i.e., the inhibitor became effective. These data and their analysis show that the binding of these substrates must be cooperative, either positive or negative.

A novel application of t-statistics to objectively assess the quality of IC50 fits for P-glycoprotein and other transporters.

Current USFDA and EMA guidance for drug transporter interactions is dependent on IC50 measurements as these are utilized in determining whether a clinical interaction study is warranted. It is therefore important not only to standardize transport inhibition assay systems but also to develop uniform statistical criteria with associated probability statements for generation of robust IC50 values, which can be easily adopted across the industry. The current work provides a quantitative examination of critical factors affecting the quality of IC50 fits for P-gp inhibition through simulations of perfect data with randomly added error as commonly observed in the large data set collected by the P-gp IC50 initiative. The types of errors simulated were (1) variability in replicate measures of transport activity; (2) transformations of error-contaminated transport activity data prior to IC50 fitting (such as performed when determining an IC50 for inhibition of P-gp based on efflux ratio); and (3) the lack of well defined "no inhibition" and "complete inhibition" plateaus. The effect of the algorithm used in fitting the inhibition curve (e.g., two or three parameter fits) was also investigated. These simulations provide strong quantitative support for the recommendations provided in Bentz et al. (2013) for the determination of IC50 values for P-gp and demonstrate the adverse effect of data transformation prior to fitting. Furthermore, the simulations validate uniform statistical criteria for robust IC50 fits in general, which can be easily implemented across the industry. A calibration of the t-statistic is provided through calculation of confidence intervals associated with the t-statistic.