Clarithromycin substantially increases steady-state bosentan exposure in healthy volunteers

Simple Determination of Bosentan in Plasma Samples by Reversed-Phase High-Performance Liquid Chromatography

Background

In order to measure the plasma levels of Losartan and Bosentan, a sensitive Reverse Phase-High Performance Liquid Chromatography (RP-HPLC) technique was developed.

Methods

To compare bioavailability, the Area Under the Curve (AUC), peak plasma concentration (Cmax), and time to Cmax (Tmax) were employed. The standard curve (150-2400 ng/ml) was linear (R2=0.999), relative errors were between 2.4 to 10.05% and the coefficient of variation (CV%) ranged from 1.52 to 10.88. A single dosage (test and reference) was used for the in vivo investigation, which involved 16 healthy individuals.

Results

The AUC0-48, AUC0-, Cmax, and Tmax of the test and reference had no statistically significant differences. The Cmax and 95% confidence intervals of the ratio of Cmax of the two formulations were 0.93-0.96 and 97.6-135%, respectively.

Conclusion

Therefore, it was established that generic Bosentan was equivalent to Bosentan from Actelion and that both medications could be regarded as equally effective in clinical settings. The blood level of Bosentan could be measured using this straightforward procedure in all hospital laboratories.

Improving antibacterial prescribing safety in the management of COPD exacerbations: systematic review of observational and clinical studies on potential drug interactions associated with frequently prescribed antibacterials among COPD patients

Background

Guidelines advise the use of antibacterials (ABs) in the management of COPD exacerbations. COPD patients often have multiple comorbidities, such as diabetes mellitus and cardiac diseases, leading to polypharmacy. Consequently, drug–drug interactions (DDIs) may frequently occur, and may cause serious adverse events and treatment failure.

Objectives

(i) To review DDIs related to frequently prescribed ABs among COPD patients from observational and clinical studies. (ii) To improve AB prescribing safety in clinical practice by structuring DDIs according to comorbidities of COPD.

Methods

We conducted a systematic review by searching PubMed and Embase up to 8 February 2018 for clinical trials, cohort and case–control studies reporting DDIs of ABs used for COPD. Study design, subjects, sample size, pharmacological mechanism of DDI and effect of interaction were extracted. We evaluated levels of DDIs and quality of evidence according to established criteria and structured the data by possible comorbidities.

Results

In all, 318 articles were eligible for review, describing a wide range of drugs used for comorbidities and their potential DDIs with ABs. DDIs between ABs and co-administered drugs could be subdivided into: (i) co-administered drugs altering the pharmacokinetics of ABs; and (ii) ABs interfering with the pharmacokinetics of co-administered drugs. The DDIs could lead to therapeutic failures or toxicities.

Conclusions

DDIs related to ABs with clinical significance may involve a wide range of indicated drugs to treat comorbidities in COPD. The evidence presented can support (computer-supported) decision-making by health practitioners when prescribing ABs during COPD exacerbations in the case of co-medication.

Interpretation of Drug Interaction Using Systemic and Local Tissue Exposure Changes

Systemic exposure of a drug is generally associated with its pharmacodynamic (PD) effect (e.g., efficacy and toxicity). In this regard, the change in area under the plasma concentration-time curve (AUC) of a drug, representing its systemic exposure, has been mainly considered in evaluation of drug-drug interactions (DDIs). Besides the systemic exposure, the drug concentration in the tissues has emerged as a factor to alter the PD effects. In this review, the status of systemic exposure, and/or tissue exposure changes in DDIs, were discussed based on the recent reports dealing with transporters and/or metabolic enzymes mediating DDIs. Particularly, the tissue concentration in the intestine, liver and kidney were referred to as important factors of PK-based DDIs.

Clinical Aspects of Transporter-Mediated Drug-Drug Interactions

Drug transporters play an essential role in disposition and effects of multiple drugs. Plasma concentrations of the victim drug can be modified by drug-drug interactions occurring in enterocytes (e.g., P-glycoprotein), hepatocytes (e.g., organic anion-transporting polypeptide 1B1 (OATP1B1)), and/or renal proximal tubular cells (e.g., organic cation transporter 2 (OCT2)/multidrug and toxin extrusion 1 and 2-K (MATE1/MATE2-K)). In addition, transporter-mediated drug-drug interactions can cause altered local tissue concentrations and possibly altered effects/toxicity (e.g., in liver and kidneys). During drug development, there is now an intensive in vitro screening of new molecular entities as transporter substrates and inhibitors, followed if necessary by drug-drug interaction studies in healthy volunteers. Nevertheless, there are still unresolved issues, which will also be discussed in this review article (e.g., the clinical significance of transporter-mediated drug-drug interactions of particular relevance to the elderly who are prescribed multiple drugs, with additional impaired liver or kidney function, and the extent to which medication safety in real life could be improved by a reduction of those interactions).

Physiologically Based Pharmacokinetic Modeling of Bosentan Identifies the Saturable Hepatic Uptake As a Major Contributor to Its Nonlinear Pharmacokinetics

Bosentan is a substrate of hepatic uptake transporter organic anion-transporting polypeptides (OATPs), and undergoes extensive hepatic metabolism by cytochrome P450 (P450), namely, CYP3A4 and CYP2C9. Several clinical investigations have reported a nonlinear relationship between bosentan doses and its systemic exposure, which likely involves the saturation of OATP-mediated uptake, P450-mediated metabolism, or both in the liver. Yet, the underlying causes for the nonlinear bosentan pharmacokinetics are not fully delineated. To address this, we performed physiologically based pharmacokinetic (PBPK) modeling analyses for bosentan after its intravenous administration at different doses. As a bottom-up approach, PBPK modeling analyses were performed using in vitro kinetic parameters, other relevant parameters, and scaling factors. As top-down approaches, three different types of PBPK models that incorporate the saturation of hepatic uptake, metabolism, or both were compared. The prediction from the bottom-up approach (models 1 and 2) yielded blood bosentan concentration-time profiles and their systemic clearance values that were not in good agreement with the clinically observed data. From top-down approaches (models 3, 4, 5-1, and 5-2), the prediction accuracy was best only with the incorporation of the saturable hepatic uptake for bosentan. Taken together, the PBPK models for bosentan were successfully established, and the comparison of different PBPK models identified the saturation of the hepatic uptake process as a major contributing factor for the nonlinear pharmacokinetics of bosentan.

Causes and Consequences of Variability in Drug Transporter Activity in Pediatric Drug Therapy

Drug transporters play a key role in mediating the uptake of endo- and exogenous substances into cells as well as their efflux. Therefore, variability in drug transporter activity can influence pharmaco- and toxicokinetics and be a determinant of drug safety and efficacy. In children, particularly in neonates and young infants, the contribution of tissue-specific drug transporters to drug absorption, distribution, and excretion may differ from that in adults. In this review 5 major factors and their interdependence that may influence drug transporter activity in children are discussed: developmental differences, genetic polymorphisms, pediatric comorbidities, interacting comedication, and environmental factors. Even if data are sparse, altered drug transporter activity due to those factors have been associated with clinically relevant differences in drug disposition, efficacy, and safety in pediatric patients. Single nucleotide polymorphisms in drug transporter-encoding genes were the most studied source of drug transporter variability in children. However, in the age group where drug transporter activity has been reported to differ from that in adults, namely neonates and young infants, hardly any studies have been performed. Longitudinal studies in this young population are required to investigate the age- and disease-dependent genotype-phenotype relationships and relevance of drug transporter drug-drug interactions. Physiologically based pharmacokinetic modeling approaches can integrate drug- and patient-specific parameters, including drug transporter ontogeny, and may further improve in silico predictions of pediatric-specific pharmacokinetics.

Simultaneous quantification of endothelin receptor antagonists and phosphodiesterase 5 inhibitors currently used in pulmonary arterial hypertension

Combination treatment with endothelin receptor antagonists (ERA) and phosphodiesterase 5 inhibitors (PDE5I) improved efficacy of pulmonary arterial hypertension (PAH) therapy. However, drug-drug interactions, variable exposure, non-adherence can influence plasma levels. For these reasons, drug quantification may be advantageous particularly in patients with poor treatment responses. We developed, validated, and applied an assay for the simultaneous quantification of ambrisentan, bosentan, macitentan, sildenafil, and tadalafil as well as their main (and partly active) metabolites in human plasma. This method is based on LC-MS/MS separation for a rapid and sensitive quantification with stable isotopically labelled analogues as internal standards for each drug and metabolite. Sample preparation was carried out using a solid phase extraction protocol based on Oasis HLB material. The separation was achieved on a Kinetex C18 column and multiple reaction monitoring in negative ionization mode was used for sensitive detection. The calibrations were linear for all analytes with correlation coefficients >0.99 within the concentration range observed under a therapeutic PAH dosing scheme with lower limits of quantification between 0.34ng/mL (OH-ambrisentan) and 10ng/mL (despropyl-macitentan). Intra- and inter-day precision at LLOQ and QC levels ranged between 2.03% and 19.8%, and 0.65% and 14.0%, respectively. The sample turnover time was 12min. The applicability of this versatile LC/MS/MS assay was verified by the successful analysis of clinical routine samples of patients on PAH medication. This new method allows for the first time to assess trough drug and metabolite levels of the currently approved PDE5I and ERAs in PAH patients, thus enabling for measurement of samples in clinical routine.

Evaluation of Ketoconazole and Its Alternative Clinical CYP3A4/5 Inhibitors as Inhibitors of Drug Transporters: The In Vitro Effects of Ketoconazole, Ritonavir, Clarithromycin, and Itraconazole on 13 Clinically-Relevant Drug Transporters

Ketoconazole is a potent CYP3A4/5 inhibitor and, until recently, recommended by the Food and Drug Administration (FDA) and the European Medicines Agency as a strong CYP3A4/5 inhibitor in clinical drug-drug interaction (DDI) studies. Ketoconazole sporadically causes liver injury or adrenal insufficiency. Because of this, the FDA and European Medicines Agency recommended suspension of ketoconazole use in DDI studies in 2013. The FDA specifically recommended use of clarithromycin or itraconazole as alternative strong CYP3A4/5 inhibitors in clinical DDI studies, but many investigators have also used ritonavir as an alternative. Although the effects of these clinical CYP3A4/5 inhibitors on other CYPs are largely established, reports on the effects on the broad range of drug transporter activities are sparse. In this study, the inhibitory effects of ketoconazole, clarithromycin, ritonavir, and itraconazole (and its CYP3A4-inhibitory metabolites, hydroxy-, keto-, and N-desalkyl itraconazole) toward 13 drug transporters (OATP1B1, OATP1B3, OAT1, OAT3, OCT1, OCT2, MATE1, MATE2-K, P-gp, BCRP, MRP2, MRP3, and BSEP) were systematically assessed in transporter-expressing HEK-293 cell lines or membrane vesicles. In vitro findings were translated into clinical context with the basic static model approaches outlined by the FDA in its 2012 draft guidance on DDIs. The results indicate that, like ketoconazole, the alternative clinical CYP3A4/5 inhibitors ritonavir, clarithromycin, and itraconazole each have unique transporter inhibition profiles. None of the alternatives to ketoconazole provided a clean inhibition profile toward the 13 drug transporters evaluated. The results provide guidance for the selection of clinical CYP3A4/5 inhibitors when transporters are potentially involved in a victim drug's pharmacokinetics.

In Silico Modeling for the Prediction of Dose and Pathway-Related Adverse Effects in Humans From In Vitro Repeated-Dose Studies

Long-term repeated-dose toxicity is mainly assessed in animals despite poor concordance of animal data with human toxicity. Nowadays advanced human in vitro systems, eg, metabolically competent HepaRG cells, are used for toxicity screening. Extrapolation of in vitro toxicity to in vivo effects is possible by reverse dosimetry using pharmacokinetic modeling. We assessed long-term repeated-dose toxicity of bosentan and valproic acid (VPA) in HepaRG cells under serum-free conditions. Upon 28-day exposure, the EC50 values for bosentan and VPA decreased by 21- and 33-fold, respectively. Using EC(10) as lowest threshold of toxicity in vitro, we estimated the oral equivalent doses for both test compounds using a simplified pharmacokinetic model for the extrapolation of in vitro toxicity to in vivo effect. The model predicts that bosentan is safe at the considered dose under the assumed conditions upon 4 weeks exposure. For VPA, hepatotoxicity is predicted for 4% and 47% of the virtual population at the maximum recommended daily dose after 3 and 4 weeks of exposure, respectively. We also investigated the changes in the central carbon metabolism of HepaRG cells exposed to orally bioavailable concentrations of both drugs. These concentrations are below the 28-day EC(10) and induce significant changes especially in glucose metabolism and urea production. These metabolic changes may have a pronounced impact in susceptible patients such as those with compromised liver function and urea cycle deficiency leading to idiosyncratic toxicity. We show that the combination of modeling based on in vitro repeated-dose data and metabolic changes allows the prediction of human relevant in vivo toxicity with mechanistic insights.

Interaction of ambrisentan with clarithromycin and its modulation by polymorphic SLCO1B1

PURPOSE

We assessed the effect of cytochrome P450 (CYP) 3A4 and the OATP1B1 inhibitor clarithromycin on ambrisentan steady-state kinetics and its relationship to the SLCO1B1 15 haplotype in healthy volunteers.

METHODS

In this open-label, monocenter, one-sequence crossover clinical trial ten male healthy participants were stratified according to CYP2C19 and SLCO1B1 (encoding for OATP1B1) genotype into two groups: group 1 (n = 6), with CYP2C19 1/1 (extensive metabolizer, EM) and SLCO1B1 wild-type; group 2 (n = 4), with CYP2C19 EM and homozygous (n = 3) or heterozygous for SLCO1B1 15 (n = 1). The participants were administered a once-daily oral dose of 5 mg ambrisentan on study days 1 and days 3-14 and twice-daily oral doses of 500 mg clarithromycin on study days 11-14. To monitor CYP3A activity 3 mg midazolam was given orally 1 day before the first ambrisentan administration and on days 1, 10, and 14 of ambrisentan treatment. Ambrisentan plasma kinetics was assessed on days 1 (single dose), 10 (steady-state), and 14 (CYP3A4/OATP1B1 inhibition by clarithromycin).

RESULTS

Consistent with the expectation that ambrisentan does not induce its own metabolism, ambrisentan exposure and peak concentration (Cmax) were similar after the first dose and at steady-state. Clarithromycin increased the area under the plasma concentration-time curve of ambrisentan by 41 % and Cmax by 27 % (n = 10, both p < 0.05). No contribution of SLCO1B1*15 to the extent of this interaction was observed.

CONCLUSIONS

Clarithromycin increased ambrisentan exposure to a similar extent to ketoconazole, namely, clinically minor and likely irrelevant.