A novel high-performance liquid chromatographic method combined with fluorescence detection for determination of ertugliflozin in rat plasma: Assessment of pharmacokinetic drug interaction potential of ertugliflozin with mefenamic acid and ketoconazole.

Ertugliflozin (ERTU) is a novel, potent, and highly selective sodium glucose cotransporter 2 inhibitor that has been recently approved for the treatment of type 2 diabetes mellitus. We describe a novel bioanalytical method using high-performance liquid chromatography (HPLC) coupled with fluorescence detection for quantitative determination of ERTU in rat plasma. Acetonitrile-based protein precipitation method was used for sample preparation, and chromatographic separation was performed on a Kinetex® C18 column with an isocratic mobile phase comprising acetonitrile and 10 mM potassium phosphate buffer (pH 6.0). The eluent was monitored by a fluorescence detector at an optimized excitation/emission wavelength pair of 277/320 nm. The method was validated to demonstrate the selectivity, linearity (ranging from 4 to 2000 ng/mL), precision, accuracy, recovery, matrix effect, and stability in line with the current FDA guidelines. The newly developed method was successfully applied to investigate the pharmacokinetic interactions of ERTU with mefenamic acid (MEF) and ketoconazole (KET). The findings of the present study revealed that the pharmacokinetics of ERTU may be altered by concurrent administration of MEF and KET in rats. To our knowledge, the present study is the first to develop a validated bioanalytical method for quantification of ERTU using HPLC coupled with fluorescence detection and to assess the drug interaction potential of ERTU with non-steroidal anti-inflammatory (MEF) and azole antifungal (KET) drugs.

In Vitro Assessment of Supersaturation/Precipitation and Biological Membrane Permeation of Poorly Water-Soluble Drugs: A Case Study With Albendazole and Ketoconazole.

This study aimed to elucidate the relationship between supersaturation and precipitation and the effect of a supersaturated state on drug membrane permeation. Stock solutions of albendazole (ALB) and ketoconazole (KTZ) dissolved in dimethyl sulfoxide (0.1-50 mg/mL) were diluted 100-fold with buffer solution (pH 6.8, 37°C). In the case of ALB, a supersaturated state and immediate precipitation were observed at 10 µg/mL or less and 20 µg/mL or higher, respectively. When KTZ was used, at an initial concentration of 200 µg/mL or higher, precipitation was observed, although the dissolved concentration remained at approximately 120 µg/mL for at least 30 min. These dissolved concentrations of ALB and KTZ related to approximately 10-fold and 14-fold over the saturated solubility from respective bulk powder. An in vitro permeation study implied that the rate of drug permeation across a biological membrane increased with increasing supersaturation. These results suggested favorable strategies for development of a supersaturable formulation could depend on the precipitation properties of the drug. Immediate- and controlled-release forms might be suitable for supersaturable formulations for KTZ and ALB, respectively.

Assessment of effect of CYP3A inhibition, CYP induction, OATP1B inhibition, and high-fat meal on pharmacokinetics of the JAK1 inhibitor upadacitinib.

AIMS: Upadacitinib (ABT-494) is a selective Janus kinase 1 inhibitor being developed for treatment of auto-immune inflammatory disorders. This work evaluated effects of high-fat meal, cytochrome P450 (CYP) 3A inhibition, CYP induction, and organic anion transporting polypeptide (OATP) 1B inhibition on upadacitinib pharmacokinetics. METHODS: Two Phase 1 evaluations were conducted, each in 12 healthy subjects. In Study 1, using a randomized, two-sequence crossover design, a 3 mg dose of upadacitinib (immediate-release capsules) was administered alone under fasting conditions, after high-fat meal, or on Day 4 of a 6-day regimen of 400 mg once-daily ketoconazole. In Study 2, a 12 mg upadacitinib dose was administered alone, with the first, and with the eighth dose of a 9-day regimen of rifampin 600 mg once daily. Upadacitinib plasma concentrations were characterized. RESULTS: Administration of upadacitinib immediate-release capsules after a high-fat meal decreased upadacitinib Cmax by 23% and had no impact on upadacitinib AUC relative to the fasting conditions. Ketoconazole (strong CYP3A inhibitor) increased upadacitinib Cmax and AUC by 70% and 75%, respectively. Multiple doses of rifampin (broad CYP inducer) decreased upadacitinib Cmax and AUC by approximately 50% and 60%, respectively. A single dose of rifampin (also an OATP1B inhibitor) had no effect on upadacitinib AUC. Upadacitinib was well tolerated when co-administered with ketoconazole, rifampin, or after a high-fat meal. CONCLUSIONS: Strong CYP3A inhibition and broad CYP induction result in a weak and moderate effect, respectively, on upadacitinib exposures. OATP1B inhibition and administration of upadacitinib immediate-release formulation with food does not impact upadacitinib exposure.

Assessment of Bioequivalence of Weak Base Formulations Under Various Dosing Conditions Using Physiologically Based Pharmacokinetic Simulations in Virtual Populations. Case Examples: Ketoconazole and Posaconazole.

Postabsorptive factors which can affect systemic drug exposure are assumed to be dependent on the active pharmaceutical ingredient (API), and thus independent of formulation. In contrast, preabsorptive factors, for example, hypochlorhydria, might affect systemic exposure in both an API and a formulation-dependent way. The aim of this study was to evaluate whether the oral absorption of 2 poorly soluble, weakly basic APIs, ketoconazole (KETO) and posaconazole (POSA), would be equally sensitive to changes in dissolution rate under the following dosing conditions-coadministration with water, with food, with carbonated drinks, and in drug-induced hypochlorhydria. The systems-components of validated absorption and PBPK models for KETO and POSA were modified to simulate the above-mentioned clinical scenarios. Virtual bioequivalence studies were then carried out to investigate whether formulation effects on the plasma profile vary with the dosing conditions. The slow precipitation of KETO upon reaching the upper part of the small intestine renders its absorption more sensitive to the completeness of gastric dissolution and thus to the gastric environment than POSA, which is subject to extensive precipitation in response to a pH shift. The virtual bioequivalence studies showed that hypothetical test and reference formulations containing KETO would be bioequivalent only if the microenvironment in the stomach enables complete gastric dissolution. We conclude that physiologically based pharmacokinetic modeling and simulation has excellent potential to address issues close to bedside such as optimizing dosing conditions. By studying virtual populations adapted to various clinical situations, clinical strategies to reduce therapeutic failures can be identified.

Prediction of pH dependent absorption using in vitro, in silico, and in vivo rat models: Early liability assessment during lead optimization.

Weakly basic compounds which have pH dependent solubility are liable to exhibit pH dependent absorption. In some cases, a subtle change in gastric pH can significantly modulate the plasma concentration of the drug and can lead to sub-therapeutic exposure of the drug. Evaluating the risk of pH dependent absorption and potential drug-drug interaction with pH modulators are important aspects of drug discovery and development. In order to assess the risk around the extent of decrease in the systemic exposure of drugs co-administered with pH modulators in the clinic, a pH effect study is carried out, typically in higher species, mostly dog. The major limitation of a higher species pH effect study is the resource and material requirement to assess this risk. Hence, these studies are mostly restricted to promising or advanced leads. In our current work, we have used in vitro aqueous solubility, in silico simulations using GastroPlus™ and an in vivo rat pH effect model to provide a qualitative assessment of the pH dependent absorption liability. Here, we evaluate ketoconazole and atazanavir with different pH dependent solubility profiles and based on in vitro, in silico and in vivo results, a different extent of gastric pH effect on absorption is predicted. The prediction is in alignment with higher species and human pH effect study results. This in vitro, in silico and in vivo (IVISIV) correlation is then extended to assess pH absorption mitigation strategy. The IVISIV predicts pH dependent absorption for BMS-582949 whereas its solubility enhancing prodrug, BMS-751324 is predicted to mitigate this liability. Overall, the material requirement for this assessment is substantially low which makes this approach more practical to screen multiple compounds during lead optimization.

Clinical assessment of drug-drug interactions of tasimelteon, a novel dual melatonin receptor agonist.

Tasimelteon ([1R-trans]-N-[(2-[2,3-dihydro-4-benzofuranyl] cyclopropyl) methyl] propanamide), a novel dual melatonin receptor agonist that demonstrates specificity and high affinity for melatonin receptor types 1 and 2 (MT1 and MT2 receptors), is the first treatment approved by the US Food and Drug Administration for Non-24-Hour Sleep-Wake Disorder. Tasimelteon is rapidly absorbed, with a mean absolute bioavailability of approximately 38%, and is extensively metabolized primarily by oxidation at multiple sites, mainly by cytochrome P450 (CYP) 1A2 and CYP3A4/5, as initially demonstrated by in vitro studies and confirmed by the results of clinical drug-drug interactions presented here. The effects of strong inhibitors and moderate or strong inducers of CYP1A2 and CYP3A4/5 on the pharmacokinetics of tasimelteon were evaluated in humans. Coadministration with fluvoxamine resulted in an approximately 6.5-fold increase in tasimelteon's area under the curve (AUC), whereas cigarette smoking decreased tasimelteon's exposure by approximately 40%. Coadministration with ketoconazole resulted in an approximately 54% increase in tasimelteon's AUC, whereas rifampin pretreatment resulted in a decrease in tasimelteon's exposure of approximately 89%.

Assessment of the drug interaction risk for remogliflozin etabonate, a sodium-dependent glucose cotransporter-2 inhibitor: evidence from in vitro, human mass balance, and ketoconazole interaction studies.

Remogliflozin etabonate is the ester prodrug of remogliflozin, a selective sodium-dependent glucose cotransporter-2 inhibitor. This work investigated the absorption, metabolism, and excretion of [(14)C]remogliflozin etabonate in humans, as well as the influence of P-glycoprotein (Pgp) and cytochrome P450 (P450) enzymes on the disposition of remogliflozin etabonate and its metabolites to understand the risks for drug interactions. After a single oral 402 ± 1.0 mg (106 ± 0.3 µCi) dose, [(14)C]remogliflozin etabonate is rapidly absorbed and extensively metabolized. The area under the concentration-time curve from 0 to infinity [AUC((0-∞))] of plasma radioactivity was approximately 14-fold higher than the sum of the AUC((0-∞)) of remogliflozin etabonate, remogliflozin, and 5-methyl-4-({4-[(1-methylethyl)oxy]phenyl}methyl)-1H-pyrazol-3-yl-ß-d-glucopyranoside (GSK279782), a pharmacologically active N-dealkylated metabolite. Elimination half-lives of total radioactivity, remogliflozin etabonate, and remogliflozin were 6.57, 0.39, and 1.57 h, respectively. Products of remogliflozin etabonate metabolism are eliminated primarily via renal excretion, with 92.8% of the dose recovered in the urine. Three glucuronide metabolites made up the majority of the radioactivity in plasma and represent 67.1% of the dose in urine, with 5-methyl-1-(1-methylethyl)-4-({4-[(1-methylethyl)oxy]phenyl}methyl)-1H-pyrazol-3-yl-ß-d-glucopyranosiduronic acid (GSK1997711) representing 47.8% of the dose. In vitro studies demonstrated that remogliflozin etabonate and remogliflozin are Pgp substrates, and that CYP3A4 can form GSK279782 directly from remogliflozin. A ketoconazole clinical drug interaction study, along with the human mass balance findings, confirmed that CYP3A4 contributes less than 50% to remogliflozin metabolism, demonstrating that other enzyme pathways (e.g., P450s, UDP-glucuronosyltransferases, and glucosidases) make significant contributions to the drug's clearance. Overall, these studies support a low clinical drug interaction risk for remogliflozin etabonate due to the availability of multiple biotransformation pathways.

Predictions of metabolic drug-drug interactions using physiologically based modelling: Two cytochrome P450 3A4 substrates coadministered with ketoconazole or verapamil.

Nowadays, evaluation of potential risk of metabolic drug-drug interactions (mDDIs) is of high importance within the pharmaceutical industry, in order to improve safety and reduce the attrition rate of new drugs. Accurate and early prediction of mDDIs has become essential for drug research and development, and in vitro experiments designed to evaluate potential mDDIs are systematically included in the drug development plan prior to clinical assessment. The aim of this study was to illustrate the value and limitations of the classical and new approaches available to predict risks of DDIs in the research and development processes. The interaction of cytochrome P450 (CYP) 3A4 inhibitors (ketoconazole and verapamil) with midazolam was predicted using the inhibitor concentration/inhibition constant ([I]/K(i)) approach, the static approach with added variability (Simcyp(R)), and whole-body physiologically based pharmacokinetic (WB-PBPK) modelling (acslXtreme(R)). Then an in-house reference drug was used to challenge the different approaches based on the midazolam experience. Predicted values (pharmacokinetic parameters, the area under the plasma concentration-time curve [AUC] ratio and plasma concentrations) were compared with observed values obtained after intravenous and oral administration in order to assess the accuracy of the prediction methods. With the [I]/K(i) approach, the interaction risk was always overpredicted for the midazolam substrate, regardless of its route of administration and the coadministered inhibitor. However, the predictions were always satisfactory (within 2-fold) for the reference drug. For the Simcyp(R) calculations, two of the three interaction results for midazolam were overpredicted, both when midazolam was given orally, whereas the prediction obtained when midazolam was administered intravenously was satisfactory. For the reference drug, all predictions could be considered satisfactory. For the WB-PBPK approach, all predictions were satisfactory, regardless of the substrate, route of administration, dose and coadministered inhibitor. DDI risk predictions are performed throughout the research and development processes and are now fully integrated into decision-making processes. The regulatory approach is useful to provide alerts, even at a very early stage of drug development. The 'steady state' approach in Simcyp(R) improves the prediction by using physiological knowledge and mechanistic assumptions. The DDI predictions are very useful, as they provide a range of AUC ratios that include individuals at the extremes of the population, in addition to the 'average tendency'. Finally, the WB-PBPK approach improves the predictions by simulating the concentration-time profiles and calculating the related pharmacokinetic parameters, taking into account the time of administration of each drug - but it requires a good understanding of the absorption, distribution, metabolism and excretion properties of the compound.

A new probabilistic rule for drug-dug interaction prediction.

An innovative probabilistic rule is proposed to predict the clinical significance or clinical insignificance of DDI. This rule is coupled with a hierarchical Bayesian model approach to summarize substrate/inhibitor's PK models from multiple published resources. This approach incorporates between-subject and between-study variances into DDI prediction. Hence, it can predict both population-average and subject-specific AUCR. The clinically significant DDI, weak DDI, and clinically insignificant inhibitions are decided by the probabilities of predicted AUCR falling into three intervals, (-infinity, 1.25), (1.25, 2), and (2, infinity). The main advantage of this probabilistic rule to predict clinical significance of DDI over the deterministic rule is that the probabilistic rule considers the sample variability, and the decision is independent of sampling variation; while deterministic rule based decision will vary from sample to sample. The probabilistic rule proposed in this paper is best suited for the situation when in vivo PK studies and models are available for both the inhibitor and substrate. An early decision on clinically significant or clinically insignificant inhibition can avoid additional DDI studies. Ketoconazole and midazolam are used as an interaction pair to illustrate our idea. AUCR predictions incorporating between-subject variability always have greater variances than population-average AUCR predictions. A clinically insignificant AUCR at population-average level is not necessarily true when considering between-subject variability. Additional simulation studies suggest that predicted AUCRs highly depend on the interaction constant K(i) and dose combinations.

Drug-drug interaction prediction assessment.

Model-based drug-drug interaction (DDI) is an important in-silico tool to assess the in vivo consequences of in vitro DDI. Before its general application to new drug compounds, the DDI model is always established from known interaction data. For the first time, tests for difference and equivalent tests are implemented to compare reported and model-base simulated DDI (log AUCR) in the sample mean and variance. The biases and predictive confidence interval coverage probabilities are introduced to assess the DDI prediction performance. Sample size and power guidelines are developed for DDI model simulations. These issues have never been discussed in trial simulation studies to investigate DDI prediction. A ketoconazole (KETO)/midazolam (MDZ) example is employed to demonstrate these statistical methods. Based on published KETO and MDZ pharmacokinetics data and their in vitro inhibition rate constant data, current model-based DDI prediction underpredicts the area under concentration curve ratio (AUCR) and its between-subject variance compared to the reported study.

A Bayesian meta-analysis on published sample mean and variance pharmacokinetic data with application to drug-drug interaction prediction.

In drug-drug interaction (DDI) research, a two-drug interaction is usually predicted by individual drug pharmacokinetics (PK). Although subject-specific drug concentration data from clinical PK studies on inhibitor or inducer and substrate PK are not usually published, sample mean plasma drug concentrations and their standard deviations have been routinely reported. Hence there is a great need for meta-analysis and DDI prediction using such summarized PK data. In this study, an innovative DDI prediction method based on a three-level hierarchical Bayesian meta-analysis model is developed. The three levels model sample means and variances, between-study variances, and prior distributions. Through a ketoconazle-midazolam example and simulations, we demonstrate that our meta-analysis model can not only estimate PK parameters with small bias but also recover their between-study and between-subject variances well. More importantly, the posterior distributions of PK parameters and their variance components allow us to predict DDI at both population-average and study-specific levels. We are also able to predict the DDI between-subject/study variance. These statistical predictions have never been investigated in DDI research. Our simulation studies show that our meta-analysis approach has small bias in PK parameter estimates and DDI predictions. Sensitivity analysis was conducted to investigate the influences of interaction PK parameters, such as the inhibition constant Ki, on the DDI prediction.

Maraviroc: in vitro assessment of drug-drug interaction potential.

AIMS: To characterize the cytochrome P450 enzyme(s) responsible for the N-dealkylation of maraviroc in vitro, and predict the extent of clinical drug-drug interactions (DDIs). METHODS: Human liver and recombinant CYP microsomes were used to identify the CYP enzyme responsible for maraviroc N-dealkylation. Studies comprised enzyme kinetics and evaluation of the effects of specific CYP inhibitors. In vitro data were then used as inputs for simulation of DDIs with ketoconazole, ritonavir, saquinavir and atazanvir, using the Simcyptrade mark population-based absorption, distribution, metabolism and elimination (ADME) simulator. Study designs for simulations mirrored those actually used in the clinic. RESULTS: Maraviroc was metabolized to its N-dealkylated product via a single CYP enzyme characterized by a K(m) of 21 microM and V(max) of 0.45 pmol pmol(-1) min(-1) in human liver microsomes and was inhibited by ketoconazole (CYP3A4 inhibitor). In a panel of recombinant CYP enzymes, CYP3A4 was identified as the major CYP responsible for maraviroc metabolism. Using recombinant CYP3A4, N-dealkylation was characterized by a K(m) of 13 microM and a V(max) of 3 pmol pmol(-1) CYP min(-1). Simulations therefore focused on the effect of CYP3A4 inhibitors on maraviroc pharmacokinetics. The simulated median AUC ratios were in good agreement with observed clinical changes (within twofold in all cases), although, in general, there was a trend for overprediction in the magnitude of the DDI. CONCLUSION: Maraviroc is a substrate for CYP3A4, and exposure will therefore be modulated by CYP3A4 inhibitors. Simcyptrade mark has successfully simulated the extent of clinical interactions with CYP3A4 inhibitors, further validating this software as a good predictor of CYP-based DDIs.

Transporter-mediated drug interactions: clinical implications and in vitro assessment.

Although they are less frequently compared with the reported cases of CYP-mediated drug interactions, clinically significant transporter-mediated drug interactions, which are mainly based on efflux transporter or P-glycoprotein data, have been reported. Unlike the CYP-mediated drug interactions that can be readily defined by inhibition or induction of CYP enzymes, the evidence for the so-called transporter-mediated drug interactions is often less conclusive. The difficulty in defining transporter-mediated drug interactions is due mainly to the interplay between transporters and drug-metabolizing enzymes in drug disposition, and the lack of specific and potent inhibitors for each transporter and enzyme. An important lesson learned from animal studies is that transporter inhibition has a much greater impact on the tissue distribution of drugs than on the systemic exposure of drugs measured in plasma. The potential risk of transporter-mediated drug interactions might be underestimated if only plasma concentrations are monitored.

Assessment of the hepatic and intestinal first-pass metabolism of midazolam in a CYP3A drug-drug interaction model rats.

In the current study, to understand the characteristics of dexamethasone (DEX)-treated female rats as an animal model for drug-drug interactions, a double-cannulation method was applied and separately assessed for the intestinal and hepatic first-pass metabolism of midazolam. Midazolam was administered intravenously or orally to the animals, and midazolam concentrations in the portal and systemic plasma were simultaneously determined. Next, the rates of elimination from the intestine and liver were estimated using the AUC values. After oral administration of midazolam, the entire drug was absorbed without intestinal first-pass metabolism, and 93% of the administered midazolam was extracted in the liver of the DEX-treated female rats. Seven per cent of the midazolam administered reached the systemic circulation. When ketoconazole was given orally to the animals, in conjunction with midazolam, the extraction ratio in the liver decreased from 93% to 77% in the control rats, and the bioavailability of midazolam increased to 23%. On the other hand, after intravenous administration, the elimination half-life of midazolam was not changed by ketoconazole pretreatment. These results indicated that midazolam is only extracted in the liver of DEX-treated female rats and that ketoconazole inhibits the hepatic first-pass metabolism, but not the systemic metabolism. In conclusion, DEX-treated female rats can be used as a drug-drug interaction model via CYP3A4 enzyme inhibition, especially for the hepatic first-pass metabolism of orally administered drugs.

Influence of human nail etching for the assessment of topical onychomycosis therapies.

The purpose of this investigation was to study the physico-chemical properties of hot-melt extruded films containing ketoconazole and to determine the influence of 'nail etching' on film bioadhesion and drug permeability for the assessment of topical onychomycosis therapies. Hot-melt extrusion (HME) was used to prepare films containing 20% w/w ketoconazole. Ketoconazole 0.125% gel was also prepared using Carbopol 974P NF. Films were processed at a temperature range of 115-120 degrees C utilizing a Killion extruder (KLB-100), and were evaluated for post-extrusion drug content, content uniformity, bioadhesion, thermal behavior and nail drug permeation. The extruded films demonstrated excellent content uniformity and post-processing drug content. Tensile and peel tests were recorded to determine the bioadhesive profiles. In this study, work of adhesion and peak adhesive force determinations using the peel tests provided more sensitive results for evaluating the bioadhesivity of the HME films than the tensile tests. The in vitro permeability profiles have demonstrated, that nail samples treated with an 'etchant' demonstrated a significant increase in drug permeability compared to control. Differential scanning calorimetry (DSC) thermograms indicated that ketoconazole was in solid solution within the HME films. These findings are encouraging for the future design and formulation of novel drug delivery systems for the topical treatment of onychomycosis.

Concurrent administration of donepezil HCl and ketoconazole: assessment of pharmacokinetic changes following single and multiple doses.

AIM: The aim of this study was to examine the pharmacokinetics of donepezil HCl and ketoconazole separately, and in combination, following administration of single and multiple oral doses. METHODS: This was an open-label, randomized, three-period crossover study in healthy volunteers (n=21). During each treatment period, subjects received single daily doses of either donepezil HCl (5 mg), ketoconazole (200 mg), or a combination of both drugs for 7 consecutive days. Pharmacokinetic comparisons were made between treatment groups for the day 1 and day 7 profiles. Each treatment period was followed by a 3-week, drug-free washout period. RESULTS: On both day 1 and day 7, a statistically significant difference was observed between the donepezil and the donepezil + ketoconazole treatment groups in terms of Cmax and AUC(0-24) of donepezil. The concurrent administration of both drugs resulted in a 12% greater Cmax (9.5 ng ml(-1) versus 8.4 ng ml(-1); P=0.01) and a 12% greater AUC(0-24) (135.2 ng h ml(-1) versus 118.7 ng h ml(-1); P=0.001) than donepezil alone on day 1, and a 26.8% greater Cmax (37.7 ng ml(-1) versus 27.6 ng ml(-1); P < 0.0001) and a 26.4% greater AUC(0-24) (680.9 ng h ml(-1) versus 501.0 ng h ml(-1); P<0.0001) than donepezil alone on day 7. In contrast, ketoconazole plasma concentrations were unaffected by the concurrent administration of donepezil, and there were no statistically significant differences in ketoconazole pharmacokinetics when ketoconazole administered alone was compared with ketoconazole administered with donepezil. CONCLUSIONS: The concurrent administration of ketoconazole and donepezil produces no change in ketoconazole plasma concentrations, but a statistically significant change in donepezil plasma concentrations. These observed changes, which are smaller than those produced by ketoconazole for other agents sharing the CYP-3A4 pathway, are most likely the result of donepezil also being metabolized by CYP-2D6, as well as its slow rate of clearance from plasma.