Effect of the potent CYP2D6 inhibitor sarpogrelate on the pharmacokinetics and pharmacodynamics of metoprolol in healthy male Korean volunteers

Clinical Pharmacokinetics of Metoprolol: A Systematic Review

BACKGROUND

Metoprolol is recommended for therapeutic use in multiple cardiovascular conditions, thyroid crisis, and circumscribed choroidal hemangioma. A detailed systematic review on the metoprolol literature would be beneficial to assess all pharmacokinetic parameters in humans and their respective effects on patients with hepatic, renal, and cardiovascular diseases. This review combines all the pharmacokinetic data on metoprolol from various accessible studies, which may assist in clinical decision making.

METHODOLOGY

The Google Scholar and PubMed databases were searched to screen articles associated with the clinical pharmacokinetics of metoprolol. The comprehensive literature search retrieved 41 articles including data on plasma concentration-time profiles after intravenous and oral (immediate-release, controlled-release, slow-release, or extended-release) routes of administration, and at least one pharmacokinetic parameter was reported in all studies included.

RESULTS

Out of 41 retrieved articles, six were after intravenous and 12 were after oral administration in healthy individuals. The oral studies depict a dose-dependent increase in maximum plasma concentration (C_max), time to reach maximum plasma concentration (T_max), and area under the concentration-time curve (AUC). Two studies were conducted in R- and S-enantiomers, in which one study reported the gender differences, depicting greater C_max and AUC among women, whereas in another study S-metoprolol was found to have higher values of C_max, T_max, and AUC in comparison with R-metoprolol. Results in different diseases depicted that after IV administration of 20 mg, patients with renal impairment showed an increase in clearance (CL) (60 L/h vs 48 L/h) compared with healthy subjects, whereas a decrease in CL (36.6 ± 7.8 L/h vs 48 ± 6.6 L/h) was seen in patients with hepatic cirrhosis at a similar dose. In comparison with a single oral dose following administration of 15 mg IV in three divided doses, patients having an acute myocardial infarction (AMI) showed an increase in C_max (823 nmol/L vs 248 nmol/L) at a steady state. Twenty different studies have reported significant changes in CL, C_max, and AUC of metoprolol when it is co-administered with other drugs. One study has reported a drug-food interaction for metoprolol but no significant changes were seen in the C_max and AUC.

CONCLUSION

This review summarizes all the pharmacokinetic parameters of metoprolol after pooling up-to-date data from all the studies available. The summarized pharmacokinetic data presented in this review can assist in developing and evaluating pharmacokinetic models of metoprolol. Moreover, this data can provide practitioners with an insight into dosage adjustments among the diseased populations and can assist in preventing potential adverse drug reactions. This review can also help avoid side effects and drug-drug interactions.

Development of a Korean-specific virtual population for physiologically based pharmacokinetic modelling and simulation

Physiologically based pharmacokinetic (PBPK) modelling and simulation is a useful tool in predicting the PK profiles of a drug, assessing the effects of covariates such as demographics, ethnicity, genetic polymorphisms and disease status on the PK, and evaluating the potential of drug-drug interactions. We developed a Korean-specific virtual population for the SimCYP® Simulator (version 15 used) and evaluated the population's predictive performance using six substrate drugs (midazolam, S-warfarin, metoprolol, omeprazole, lorazepam and rosuvastatin) of five major drug metabolizing enzymes (DMEs) and two transporters. Forty-three parameters including the proportion of phenotypes in DMEs and transporters were incorporated into the Korean-specific virtual population. The simulated concentration-time profiles in Koreans were overlapped with most of the observed concentrations for the selected substrate drugs with a < 2-fold difference in clearance. Furthermore, we found some drug models within the SimCYP® library can be improved, e.g., the minor allele frequency of ABCG2 and the fraction metabolized by UGT2B15 should be incorporated for rosuvastatin and lorazepam, respectively. The Korean-specific population can be used to evaluate the impact of ethnicity on the PKs of a drug, particularly in various stages of drug development.

Pharmacokinetic Variability of Drugs Used for Prophylactic Treatment of Migraine

In this review, we evaluate the variability in the pharmacokinetics of 11 drugs with established prophylactic effects in migraine to facilitate 'personalized medicine' with these drugs. PubMed was searched for 'single-dose' and 'steady-state' pharmacokinetic studies of these 11 drugs. The maximum plasma concentration was reported in 248 single-dose and 115 steady-state pharmacokinetic studies, and the area under the plasma concentration-time curve was reported in 299 single-dose studies and 112 steady-state pharmacokinetic studies. For each study, the coefficient of variation was calculated for maximum plasma concentration and area under the plasma concentration-time curve, and we divided the drug variability into two categories; high variability, coefficient of variation >40%, or low or moderate variability, coefficient of variation <40%. Based on the area under the plasma concentration-time curve in steady-state studies, the following drugs have high pharmacokinetic variability: propranolol in 92% (33/36), metoprolol in 85% (33/39), and amitriptyline in 60% (3/5) of studies. The following drugs have low or moderate variability: atenolol in 100% (2/2), valproate in 100% (15/15), topiramate in 88% (7/8), and naproxen and candesartan in 100% (2/2) of studies. For drugs with low or moderate pharmacokinetic variability, treatment can start without initial titration of doses, whereas titration is used to possibly enhance tolerability of topiramate and amitriptyline. The very high pharmacokinetic variability of metoprolol and propranolol can result in very high plasma concentrations in a small minority of patients, and those drugs should therefore be titrated up from a low initial dose, depending mainly on the occurrence of adverse events.

Application of physiologically based pharmacokinetic modeling in predicting drug-drug interactions for sarpogrelate hydrochloride in humans

Background

Evaluating the potential risk of metabolic drug–drug interactions (DDIs) is clinically important.

Objective

To develop a physiologically based pharmacokinetic (PBPK) model for sarpogrelate hydrochloride and its active metabolite, (R,S)-1-{2-[2-(3-methoxyphenyl)ethyl]-phenoxy}-3-(dimethylamino)-2-propanol (M-1), in order to predict DDIs between sarpogrelate and the clinically relevant cytochrome P450 (CYP) 2D6 substrates, metoprolol, desipramine, dextromethorphan, imipramine, and tolterodine.

Methods

The PBPK model was developed, incorporating the physicochemical and pharmacokinetic properties of sarpogrelate hydrochloride, and M-1 based on the findings from in vitro and in vivo studies. Subsequently, the model was verified by comparing the predicted concentration-time profiles and pharmacokinetic parameters of sarpogrelate and M-1 to the observed clinical data. Finally, the verified model was used to simulate clinical DDIs between sarpogrelate hydrochloride and sensitive CYP2D6 substrates. The predictive performance of the model was assessed by comparing predicted results to observed data after coadministering sarpogrelate hydrochloride and metoprolol.

Results

The developed PBPK model accurately predicted sarpogrelate and M-1 plasma concentration profiles after single or multiple doses of sarpogrelate hydrochloride. The simulated ratios of area under the curve and maximum plasma concentration of metoprolol in the presence of sarpogrelate hydrochloride to baseline were in good agreement with the observed ratios. The predicted fold-increases in the area under the curve ratios of metoprolol, desipramine, imipramine, dextromethorphan, and tolterodine following single and multiple sarpogrelate hydrochloride oral doses were within the range of ≥1.25, but <2-fold, indicating that sarpogrelate hydrochloride is a weak inhibitor of CYP2D6 in vivo. Collectively, the predicted low DDIs suggest that sarpogrelate hydrochloride has limited potential for causing significant DDIs associated with CYP2D6 inhibition.

Conclusion

This study demonstrated the feasibility of applying the PBPK approach to predicting the DDI potential between sarpogrelate hydrochloride and drugs metabolized by CYP2D6. Therefore, it would be beneficial in designing and optimizing clinical DDI studies using sarpogrelate as an in vivo CYP2D6 inhibitor.