Effects of imatinib (Glivec) on the pharmacokinetics of metoprolol, a CYP2D6 substrate, in Chinese patients with chronic myelogenous leukaemia

Physiologically based pharmacokinetic modeling of imatinib and N-desmethyl imatinib for drug-drug interaction predictions

The first-generation tyrosine kinase inhibitor imatinib has revolutionized the development of targeted cancer therapy and remains among the frontline treatments, for example, against chronic myeloid leukemia. As a substrate of cytochrome P450 (CYP) 2C8, CYP3A4, and various transporters, imatinib is highly susceptible to drug-drug interactions (DDIs) when co-administered with corresponding perpetrator drugs. Additionally, imatinib and its main metabolite N-desmethyl imatinib (NDMI) act as inhibitors of CYP2C8, CYP2D6, and CYP3A4 affecting their own metabolism as well as the exposure of co-medications. This work presents the development of a parent-metabolite whole-body physiologically based pharmacokinetic (PBPK) model for imatinib and NDMI used for the investigation and prediction of different DDI scenarios centered around imatinib as both a victim and perpetrator drug. Model development was performed in PK-Sim® using a total of 60 plasma concentration-time profiles of imatinib and NDMI in healthy subjects and cancer patients. Metabolism of both compounds was integrated via CYP2C8 and CYP3A4, with imatinib additionally transported via P-glycoprotein. The subsequently developed DDI network demonstrated good predictive performance. DDIs involving imatinib and NDMI were simulated with perpetrator drugs rifampicin, ketoconazole, and gemfibrozil as well as victim drugs simvastatin and metoprolol. Overall, 12/12 predicted DDI area under the curve determined between first and last plasma concentration measurements (AUClast) ratios and 12/12 predicted DDI maximum plasma concentration (Cmax) ratios were within twofold of the respective observed ratios. Potential applications of the final model include model-informed drug development or the support of model-informed precision dosing.

Clinical pharmacokinetics and drug-drug interactions of tyrosine-kinase inhibitors in chronic myeloid leukemia: A clinical perspective

In the past decade, numerous tyrosine kinase inhibitors (TKIs) have been introduced in the treatment of chronic myeloid leukemia. Given the significant interpatient variability in TKIs pharmacokinetics, potential drug-drug interactions (DDIs) can greatly impact patient therapy. This review aims to discuss the pharmacokinetic characteristics of TKIs, specifically focusing on their absorption, distribution, metabolism, and excretion profiles. Additionally, it provides a comprehensive overview of the utilization of TKIs in special populations such as the elderly, children, and patients with liver or kidney dysfunction. We also highlight known or suspected DDIs between TKIs and other drugs, highlighting various clinically relevant interactions. Moreover, specific recommendations are provided to guide haemato-oncologists, oncologists, and clinical pharmacists in managing DDIs during TKI treatment in daily clinical practice.

The Application of Virtual Therapeutic Drug Monitoring to Assess the Pharmacokinetics of Imatinib in a Chinese Cancer Population Group

PURPOSE

Imatinib is used in gastrointestinal stromal tumours (GIST) and chronic myeloid leukaemia (CML). Oncology patients demonstrate altered physiology compared to healthy adults, e.g. reduced haematocrit, increased α-1 acid glycoprotein, decreased albumin and reduced glomerular filtration rate (GFR), which may influence imatinib pharmacokinetics. Given that Chinese cancer patients often report raised imatinib plasma concentrations and wider inter-individual variability reported in trough concentration when compared to Caucasian cancer patients, therapeutic drug monitoring (TDM) has been advocated.

METHOD

This study utilised a previously validated a Chinese cancer population and assessed the impact of imatinib virtual-TDM in Chinese and Caucasian cancer populations across a dosing range from 200-800 mg daily.

RESULTS

Staged dose titration to 800 mg daily, resulted in recapitulation to within the target therapeutic range for 50 % (Chinese) and 42.1% (Caucasian) subjects possessing plasma concentration < 550 ng/mL when dosed at 400 mg daily. For subjects with plasma concentrations >1500 ng/mL when dosed at 400 mg daily, a dose reduction to 200 mg once daily was able to recover 67 % (Chinese) and 87.4 % (Caucasian) patients to the target therapeutic range.

CONCLUSION

Virtual TDM highlights the benefit of pharmacokinetic modelling to optimising treatments in challenging oncology population groups.

Drug-Drug-Gene Interactions in Cardiovascular Medicine

Cardiovascular disease remains a leading cause of both morbidity and mortality worldwide. It is widely accepted that both concomitant medications (drug-drug interactions, DDIs) and genomic factors (drug-gene interactions, DGIs) can influence cardiovascular drug-related efficacy and safety outcomes. Although thousands of DDI and DGI (aka pharmacogenomic) studies have been published to date, the literature on drug-drug-gene interactions (DDGIs, cumulative effects of DDIs and DGIs) remains scarce. Moreover, multimorbidity is common in cardiovascular disease patients and is often associated with polypharmacy, which increases the likelihood of clinically relevant drug-related interactions. These, in turn, can lead to reduced drug efficacy, medication-related harm (adverse drug reactions, longer hospitalizations, mortality) and increased healthcare costs. To examine the extent to which DDGIs and other interactions influence efficacy and safety outcomes in the field of cardiovascular medicine, we review current evidence in the field. We describe the different categories of DDIs and DGIs before illustrating how these two interact to produce DDGIs and other complex interactions. We provide examples of studies that have reported the prevalence of clinically relevant interactions and the most implicated cardiovascular medicines before outlining the challenges associated with dealing with these interactions in clinical practice. Finally, we provide recommendations on how to manage the challenges including but not limited to expanding the scope of drug information compendia, interaction databases and clinical implementation guidelines (to include clinically relevant DDGIs and other complex interactions) and work towards their harmonization; better use of electronic decision support tools; using big data and novel computational techniques; using clinically relevant endpoints, preemptive genotyping; ensuring ethnic diversity; and upskilling of clinicians in pharmacogenomics and personalized medicine.

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.

Physiologically based pharmacokinetic modelling to predict the pharmacokinetics of metoprolol in different CYP2D6 genotypes

Metoprolol, a selective β₁-adrenoreceptor blocking agent used in the treatment of hypertension, angina, and heart failure, is primarily metabolized by the CYP2D6 enzyme, which catalyzes α-hydroxylation and O-desmethylation. As CYP2D6 is genetically highly polymorphic and the enzymatic activity differs greatly depending on the presence of the mutant allele(s), the pharmacokinetic profile of metoprolol is highly variable depending on the genotype of CYP2D6. The aim of study was to develop the physiologically based pharmacokinetic (PBPK) model of metoprolol related to CYP2D6 genetic polymorphism for personalized therapy with metoprolol. For PBPK modelling, our previous pharmacogenomic data were used. To obtain kinetic parameters (K_m, V_max, and CL_int) of each genotype, the recombinant CYP enzyme of each genotype was incubated with metoprolol and metabolic rates were assayed. Based on these data, the PBPK model of metoprolol was developed and validated in different CYP2D6 genotypes using PK-Sim^® software. As a result, the input values for various parameters for the PBPK model were presented and the PBPK model successfully described the pharmacokinetics of metoprolol in each genotype group. The simulated values were within the acceptance criterion (99.998% confidence intervals) compared with observed values. The PBPK model developed in this study can be used for personalized pharmacotherapy with metoprolol in individuals of various races, ages, and CYP2D6 genotypes.

Drug Interactions Affecting Antiarrhythmic Drug Use

Antiarrhythmic drugs (AAD) play an important role in the management of arrhythmias. Drug interactions involving AAD are common in clinical practice. As AADs have a narrow therapeutic window, both pharmacokinetic as well as pharmacodynamic interactions involving AAD can result in serious adverse drug reactions ranging from arrhythmia recurrence, failure of device-based therapy, and heart failure, to death. Pharmacokinetic drug interactions frequently involve the inhibition of key metabolic pathways, resulting in accumulation of a substrate drug. Additionally, over the past 2 decades, the P-gp (permeability glycoprotein) has been increasingly cited as a significant source of drug interactions. Pharmacodynamic drug interactions involving AADs commonly involve additive QT prolongation. Amiodarone, quinidine, and dofetilide are AADs with numerous and clinically significant drug interactions. Recent studies have also demonstrated increased morbidity and mortality with the use of digoxin and other AAD which interact with P-gp. QT prolongation is an important pharmacodynamic interaction involving mainly Vaughan-Williams class III AAD as many commonly used drug classes, such as macrolide antibiotics, fluoroquinolone antibiotics, antipsychotics, and antiemetics prolong the QT interval. Whenever possible, serious drug-drug interactions involving AAD should be avoided. If unavoidable, patients will require closer monitoring and the concomitant use of interacting agents should be minimized. Increasing awareness of drug interactions among clinicians will significantly improve patient safety for patients with arrhythmias.

Physiologically-based pharmacokinetic model predictions of inter-ethnic differences in imatinib pharmacokinetics and dosing regimens

AIMS

This study implements a physiologically-based pharmacokinetic (PBPK) modelling approach to investigate inter-ethnic differences in imatinib pharmacokinetics and dosing regimens.

METHODS

A PBPK model of imatinib was built in the Simcyp Simulator (version 17) integrating in vitro drug metabolism and clinical pharmacokinetic data. The model accounts for ethnic differences in body size and abundance of drug-metabolising enzymes and proteins involved in imatinib disposition. Utility of this model for prediction of imatinib pharmacokinetics was evaluated across different dosing regimens and ethnic groups. The impact of ethnicity on imatinib dosing was then assessed based on the established range of trough concentrations (C_ss,min ).

RESULTS

The PBPK model of imatinib demonstrated excellent predictive performance in describing pharmacokinetics and the attained C_ss,min in patients from different ethnic groups, shown by prediction differences that were within 1.25-fold of the clinically-reported values in published studies. PBPK simulation suggested a similar dose of imatinib (400-600 mg/d) to achieve the desirable range of C_ss,min (1000-3200 ng/mL) in populations of European, Japanese and Chinese ancestry. The simulation indicated that patients of African ancestry may benefit from a higher initial dose (600-800 mg/d) to achieve imatinib target concentrations, due to a higher apparent clearance (CL/F) of imatinib compared to other ethnic groups; however, the clinical data to support this are currently limited.

CONCLUSION

PBPK simulations highlighted a potential ethnic difference in the recommended initial dose of imatinib between populations of European and African ancestry, but not populations of Chinese and Japanese ancestry.

Acute pancreatitis in a patient treated with imatinib and gefitinib

INTRODUCTION

Use of oral antineoplastic agents (OAAs) has increased significantly in recent years. OAAs currently represent 30-50% of all cancer treatments. Drug interactions are the most frequent drug-related problem affecting OAAs. We describe the case of a patient who presented acute pancreatitis, possibly induced by the concomitant use of imatinib and gefitinib.

CASE REPORT

A female patient received imatinib and gefitinib for the treatment of chronic myeloid leukemia and lung adenocarcinoma, respectively. Liver function and pancreatic enzyme values gradually worsened after initiation of imatinib, and the patient was diagnosed with acute pancreatitis.

MANAGEMENT AND OUTCOMES

Imatinib was discontinued owing to pancreatic toxicity. Gefitinib was subsequently discontinued owing to tumor progression. The patient received supportive measures for pancreatitis, although she eventually died 3 months after the onset of symptoms.

DISCUSSION

To our knowledge, this is the first case in the medical literature of acute pancreatitis possibly induced by an interaction between imatinib and gefitinib. The interaction most likely arose because imatinib is a CYP2D6 inhibitor and could therefore impair the metabolism of gefitinib (a CYP2D6 substrate) and increase its serum concentration. This interaction is extremely rare. However, due to its severity, hepatic and pancreatic function should be carefully monitored in patients treated with imatinib and/or gefitinib and other inhibitors or inducers of CYP2D6 and CYP3A4.

Onco-cardiology: Drug-drug interactions of antineoplastic and cardiovascular drugs

Cardiovascular diseases (CVD) and cancer are still the leading causes of death. There are many common etiologic factors, especially smoking and obesity. Therefore, it is not uncommon for CVD and cancer to coexist. Drug-drug interactions (DDIs) inevitably occur in this group of patients, where polypharmacy is increasing due to older age and multiple comorbidities. However, multidisciplinary studies, especially close collaboration of medical oncologists and cardiologists, who deals with the diagnosis and treatment of these diseases, awareness and preventive approaches to DDIs may reduce serious morbidity and mortality. In this review, information about the common treatments used in cardiology and oncology and possible DDIs are discussed.

Clinically relevant drug interactions with multikinase inhibitors: a review

Multikinase inhibitors (MKIs), including the tyrosine kinase inhibitors (TKIs), have rapidly become an established factor in daily (hemato)-oncology practice. Although the oral route of administration offers improved flexibility and convenience for the patient, challenges arise in the use of MKIs. As MKIs are prescribed extensively, patients are at increased risk for (severe) drug–drug interactions (DDIs). As a result of these DDIs, plasma pharmacokinetics of MKIs may vary significantly, thereby leading to high interpatient variability and subsequent risk for increased toxicity or a diminished therapeutic outcome. Most clinically relevant DDIs with MKIs concern altered absorption and metabolism. The absorption of MKIs may be decreased by concomitant use of gastric acid-suppressive agents (e.g. proton pump inhibitors) as many kinase inhibitors show pH-dependent solubility. In addition, DDIs concerning drug (uptake and efflux) transporters may be of significant clinical relevance during MKI therapy. Furthermore, since many MKIs are substrates for cytochrome P450 isoenzymes (CYPs), induction or inhibition with strong CYP inhibitors or inducers may lead to significant alterations in MKI exposure. In conclusion, DDIs are of major concern during MKI therapy and need to be monitored closely in clinical practice. Based on the current knowledge and available literature, practical recommendations for management of these DDIs in clinical practice are presented in this review.

Copy number variation analysis in cytochromes and glutathione S-transferases may predict efficacy of tyrosine kinase inhibitors in chronic myeloid leukemia

Chronic myeloid leukemia (CML) is a myeloproliferative disease characterized by the presence of BCR/ABL fusion gene in leukemic cells, which promotes uncontrolled cell proliferation. Up to 20% of CML patients show primary resistance or non-optimal response to tyrosine kinase inhibitor (TKI) therapy. We investigated the association between copy number variation (CNV) in glutathione S-transferases (GST) and cytochromes (CYP) and the response rate to TKI. We enrolled 47 patients with CML: 31 with an optimal response and 16 with failure at 6 months in accordance with European LeukemiaNet 2013 recommendations. CNV detection was performed using SALSA MLPA P128-C1 Cytochrome P450 probe mix. Patients with optimal response and with failure of TKI therapy showed different frequencies of wild type and mutated CYPs and GST (p<0.0013). Validation in the group of 15 patients proved high prognostic value (p = 0.02): positive and negative predictive value 83% and 78%; sensitivity and specificity 71% and 88%. Wild type genotypes of CYP and GST associate with a worse response to TKI treatment in CML patients. This test can be recommended for further clinical trials.

Pharmacogenetics of drug-drug interaction and drug-drug-gene interaction: a systematic review on CYP2C9, CYP2C19 and CYP2D6

Currently, most guidelines on drug-drug interaction (DDI) neither consider the potential effect of genetic polymorphism in the strength of the interaction nor do they account for the complex interaction caused by the combination of DDI and drug-gene interaction (DGI) where there are multiple biotransformation pathways, which is referred to as drug-drug-gene interaction (DDGI). In this systematic review, we report the impact of pharmacogenetics on DDI and DDGI in which three major drug-metabolizing enzymes - CYP2C9, CYP2C19 and CYP2D6 - are central. We observed that several DDI and DDGI are highly gene-dependent, leading to a different magnitude of interaction. Precision drug therapy should take pharmacogenetics into account when drug interactions in clinical practice are expected.

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.

Metabolism-related pharmacokinetic drug-drug interactions with tyrosine kinase inhibitors: current understanding, challenges and recommendations

Drug-drug interactions (DDIs) occur when a patient's response to the drug is modified by administration or co-exposure to another drug. The main cytochrome P450 (CYP) enzyme, CYP3A4, is implicated in the metabolism of almost all of the tyrosine kinase inhibitors (TKIs). Therefore, there is a substantial potential for interaction between TKIs and other drugs that modulate the activity of this metabolic pathway. Cancer patients are susceptible to DDIs as they receive many medications, either for supportive care or for treatment of toxicity. Differences in DDI outcomes are generally negligible because of the wide therapeutic window of common drugs. However for anticancer agents, serious clinical consequences may occur from small changes in drug metabolism and pharmacokinetics. Therefore, the objective of this review is to highlight the current understanding of DDIs among TKIs, with a focus on metabolism, as well as to identify challenges in the prediction of DDIs and provide recommendations.

Pharmacokinetic interactions among imatinib, bosentan and sildenafil, and their clinical implications in severe pulmonary arterial hypertension

AIMS

This study characterized the population pharmacokinetics (PK) of imatinib in patients with severe pulmonary arterial hypertension (PAH), investigated drug-drug interactions (DDI) among imatinib, sildenafil and bosentan, and evaluated their clinical implications.

METHODS

Plasma concentrations of imatinib, bosentan and sildenafil were collected in a phase III study and were used to characterize the PK of imatinib in this population. DDIs among the three drugs were quantified using a linear mixed model and log-transformed drug concentrations.

RESULTS

The population mean estimates of apparent clearance (CL/F) and volume (V/F) were 10.8 l h(-1) (95% CI 9.2, 12.4 l h(-1) ) and 267 l (95% CI 208, 326 l), respectively. It was estimated that sildenafil concentrations increased, on average, by 64% (95% CI 32%, 103%) and bosentan concentrations by 51% (95% CI 12%, 104%), in the presence of imatinib. Despite increased concentrations of co-medications, treatment differences between imatinib and placebo for change in 6 min walk distance and pulmonary vascular resistance were relatively constant across the entire concentration range for sildenafil and bosentan. Overall, higher concentrations of imatinib and bosentan were not associated with increasing liver enzymes (serum glutamic oxaloacetic transaminases [SGOT]/serum glutamic-pyruvic transaminase [SGPT]).

CONCLUSIONS

Population PKs of imatinib in patients with severe PAH were found comparable with those of patients with chronic myeloid leukemia. Imatinib was found effective regardless of the co-medications and showed intrinsic efficacy beyond merely elevating the concentrations of the co-medications due to DDIs. There was no evidence of increased risk of liver toxicity upon co-administration with bosentan.

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

1. Recently, we demonstrated that sarpogrelate is a potent and selective CYP2D6 inhibitor in vitro. Here, we evaluated the effect of sarpogrelate on the pharmacokinetics and pharmacodynamics of metoprolol in healthy subjects. 2. Nine healthy male subjects genotyped for CYP2D6*1/*1 or *1/*2 were included in an open-label, randomized, three treatment-period and crossover study. A single oral dose of metoprolol (100 mg) was administered with water (treatment A) and sarpogrelate (100 mg bid.; a total dose of 200 mg and treatment B), or after pretreatment of sarpogrelate for three days (100 mg tid.; treatment C). Plasma levels of metoprolol and α-hydroxymetoprolol were determined using a validated LC-MS/MS method. Changes in heart rate and blood pressure were monitored as pharmacodynamic responses to metoprolol. 3. Metoprolol was well tolerated in the three treatment groups. In treatment B and C groups, the AUCt of metoprolol increased by 53% (GMR, 1.53; 90% CI, 1.17-2.31) and by 51% (1.51; 1.17-2.31), respectively. Similar patterns were observed for the increase in Cmax of metoprolol by sarpogrelate. However, the pharmacodynamics of metoprolol did not differ significantly among the three treatment groups. 4. Greater systemic exposure to metoprolol after co-administration or pretreatment with sarpogrelate did not result in clinically relevant effects. Co-administration of both agents is well tolerated and can be employed without the need for dose adjustments.

Drug-drug interactions with tyrosine-kinase inhibitors: a clinical perspective

In the past decade, many tyrosine-kinase inhibitors have been introduced in oncology and haemato-oncology. Because this new class of drugs is extensively used, serious drug-drug interactions are an increasing risk. In this Review, we give a comprehensive overview of known or suspected drug-drug interactions between tyrosine-kinase inhibitors and other drugs. We discuss all haemato-oncological and oncological tyrosine-kinase inhibitors that had been approved by Aug 1, 2013, by the US Food and Drug Administration or the European Medicines Agency. Various clinically relevant drug interactions with tyrosine-kinase inhibitors have been identified. Most interactions concern altered bioavailability due to altered stomach pH, metabolism by cytochrome P450 isoenzymes, and prolongation of the QTc interval. To guarantee the safe use of tyrosine-kinase inhibitors, a drugs review for each patient is needed. This Review provides specific recommendations to guide haemato-oncologists, oncologists, and clinical pharmacists, through the process of managing drug-drug interactions during treatment with tyrosine-kinase inhibitors in daily clinical practice.

Simultaneous determination of metoprolol and its metabolites, α-hydroxymetoprolol and O-desmethylmetoprolol, in human plasma by liquid chromatography with tandem mass spectrometry: Application to the pharmacokinetics of metoprolol associated with CYP2D6 genotypes

A rapid and simple LC with MS/MS method for the simultaneous determination of metoprolol and its two CYP2D6-derived metabolites, α-hydroxy- and O-desmethylmetoprolol, in human plasma was established. Metoprolol (MET), its two metabolites, and the internal standard chlorpropamide were extracted from plasma (50 μL) using ethyl acetate. Chromatographic separation was performed on a Luna CN column with an isocratic mobile phase consisting of distilled water and methanol containing 0.1% formic acid (60:40, v/v) at a flow rate of 0.3 mL/min. The total run time was 3.0 min per sample. Mass spectrometric detection was conducted by ESI in positive ion selected-reaction monitoring mode. The linear ranges of concentration for MET, α-hydroxymetoprolol, and O-desmethylmetoprolol were 2-1000, 2-500, and 2-500 ng/mL, respectively, with a lower limit of quantification of 2 ng/mL for all analytes. The coefficient of variation for the assay's precision was ≤ 13.2%, and the accuracy was 89.1-110%. All analytes were stable under various storage and handling conditions and no relevant cross-talk and matrix effect were observed. Finally, this method was successfully applied to assess the influence of CYP2D6 genotypes on the pharmacokinetics of MET after oral administration of 100 mg to healthy Korean volunteers.

The effect of apigenin on pharmacokinetics of imatinib and its metabolite N-desmethyl imatinib in rats

The purpose of this study was to determine the effect of apigenin on the pharmacokinetics of imatinib and N-desmethyl imatinib in rats. Healthy male SD rats were randomly divided into four groups: A group (the control group), B group (the long-term administration of 165 mg/kg apigenin for 15 days), C group (a single dose of 165 mg/kg apigenin), and D group (a single dose of 252 mg/kg apigenin). The serum concentrations of imatinib and N-desmethyl imatinib were measured by HPLC, and pharmacokinetic parameters were calculated using DAS 3.0 software. The parameters of AUC_(0−t), AUC_(0−∞), T_max, V_z/F, and CL_z/F for imatinib in group B were different from those in group A (P < 0.05). Besides, MRT_(0−t) and MRT_(0−∞) in groups C and D differed distinctly from those in group A as well. The parameters of AUC_(0−t) and C_max for N-desmethyl imatinib in group C were significantly lower than those in group A (P < 0.05); however, compared with groups B and D, the magnitude of effect was modest. Those results indicated that apigenin in the short-term study inhibited the metabolism of imatinib and its metabolite N-desmethyl imatinib, while in the long-term study the metabolism could be accelerated.

Drug interactions with solid tumour-targeted therapies

Drug interactions are an on-going concern in the treatment of cancer, especially when targeted therapies, such as tyrosine kinase inhibitors (TKI) or mammalian target of rapamycin (mTOR) inhibitors, are being used. The emergence of elderly patients and/or patients with both cancer and other chronic co-morbidities leads to polypharmacy. Therefore, the risk of drug-drug interactions (DDI) becomes a clinically relevant issue, all the more so as TKIs and mTOR inhibitors are essentially metabolised by cytochrome P450 enzymes. These DDIs can result in variability in anticancer drug exposure, thus favouring the selection of resistant cellular clones or the occurrence of toxicity. This review provides a comprehensive overview of DDIs that involve targeted therapies approved by the FDA for the treatment of solid tumours for more than 3 years (sorafenib, sunitinib, erlotinib, gefitinib, imatinib, lapatinib, everolimus, temsirolimus) and medicinal herb or drugs. This review also provides some guidelines to help oncologists and pharmacists in their clinical practice.

A meta-analysis of CYP2D6 metabolizer phenotype and metoprolol pharmacokinetics

Metoprolol, a commonly prescribed beta-blocker, is primarily metabolized by cytochrome P450 2D6 (CYP2D6), an enzyme with substantial genetic heterogeneity. Several smaller studies have shown that metoprolol pharmacokinetics is influenced by CYP2D6 genotype and metabolizer phenotype. To increase robustness of metoprolol pharmacokinetic estimates, a systematic review and meta-analysis of pharmacokinetic studies that administered a single oral dose of immediate release metoprolol was performed. Pooled analysis (n= 264) demonstrated differences in peak plasma metoprolol concentration, area under the concentration-time curve, elimination half-life, and apparent oral clearance that were 2.3-, 4.9-, 2.3-, and 5.9-fold between extensive and poor metabolizers, respectively, and 5.3-, 13-, 2.6-, and 15-fold between ultra-rapid and poor metabolizers (all p<0.001). Enantiomer-specific analysis revealed genotype-dependent enantio-selective metabolism, with nearly 40% greater R- vs S-metoprolol metabolism in ultra-rapid and extensive metabolizers. This study demonstrates a marked effect of CYP2D6 metabolizer phenotype on metoprolol pharmacokinetics and confirms enantiomer specific metabolism of metoprolol.

Interactions between oral antineoplastic agents and concomitant medication: a systematic review

INTRODUCTION

In recent years, the number of oral antitumoral agents has considerably increased. Oral administration increases the risk of interactions, because most oral anticancer drugs are taken on a daily basis. Interactions can increase exposure to antitumoral agents or cause treatment failure. Many antitumoral drugs undergo enzymatic metabolism by cytochrome P450. As some act as inducers or inhibitors of one or more isoenzymes, they can lead to decreases or increases in plasma concentrations of concomitant drugs. Hence, cytostatic drugs can act not only as victims but also as perpetrators. P-glycoprotein, an efflux transporter, can also be involved in pharmacokinetic interactions.

AREAS COVERED

A Medline search was performed to summarize the available evidence of the most clinically relevant interactions between oral chemotherapy agents and other drugs. The search covered the period from 1966 until August 2012 for each antitumoral drug using the medical subject headings 'Drug Interactions' OR 'Pharmacokinetics'. While the present review is not exhaustive, it aims to increase clinicians' awareness of potential drug-drug interactions.

EXPERT OPINION

As cancer patients are often polymedicated and treated by different physicians, the risk of drug interactions between antitumoral agents and other medications is high. More clinical interaction studies are encouraged to ensure appropriate antineoplastic pharmacokinetics in clinical practice.

SLC22A1-ABCB1 haplotype profiles predict imatinib pharmacokinetics in Asian patients with chronic myeloid leukemia

OBJECTIVE

This study aimed to explore the influence of SLC22A1, PXR, ABCG2, ABCB1 and CYP3A5 3 genetic polymorphisms on imatinib mesylate (IM) pharmacokinetics in Asian patients with chronic myeloid leukemia (CML).

PATIENTS AND METHODS

Healthy subjects belonging to three Asian populations (Chinese, Malay, Indian; n = 70 each) and CML patients (n = 38) were enrolled in a prospective pharmacogenetics study. Imatinib trough (C(0h)) and clearance (CL) were determined in the patients at steady state. Haplowalk method was applied to infer the haplotypes and generalized linear model (GLM) to estimate haplotypic effects on IM pharmacokinetics. Association of haplotype copy numbers with IM pharmacokinetics was defined by Mann-Whitney U test.

RESULTS

Global haplotype score statistics revealed a SLC22A1 sub-haplotypic region encompassing three polymorphisms (rs3798168, rs628031 and IVS7+850C>T), to be significantly associated with IM clearance (p = 0.013). Haplotype-specific GLM estimated that the haplotypes AGT and CGC were both associated with 22% decrease in clearance compared to CAC [CL (10(-2) L/hr/mg): CAC vs AGT: 4.03 vs 3.16, p = 0.017; CAC vs CGC: 4.03 vs 3.15, p = 0.017]. Patients harboring 2 copies of AGT or CGC haplotypes had 33.4% lower clearance and 50% higher C(0h) than patients carrying 0 or 1 copy [CL (10(-2) L/hr/mg): 2.19 vs 3.29, p = 0.026; C(0h) (10(-6) 1/ml): 4.76 vs 3.17, p = 0.013, respectively]. Further subgroup analysis revealed SLC22A1 and ABCB1 haplotypic combinations to be significantly associated with clearance and C(0h) (p = 0.002 and 0.009, respectively).

CONCLUSION

This exploratory study suggests that SLC22A1-ABCB1 haplotypes may influence IM pharmacokinetics in Asian CML patients.

Potent mechanism-based inhibition of CYP3A4 by imatinib explains its liability to interact with CYP3A4 substrates

BACKGROUND AND PURPOSE

Imatinib, a cytochrome P450 2C8 (CYP2C8) and CYP3A4 substrate, markedly increases plasma concentrations of the CYP3A4/5 substrate simvastatin and reduces hepatic CYP3A4/5 activity in humans. Because competitive inhibition of CYP3A4/5 does not explain these in vivo interactions, we investigated the reversible and time-dependent inhibitory effects of imatinib and its main metabolite N-desmethylimatinib on CYP2C8 and CYP3A4/5 in vitro.

EXPERIMENTAL APPROACH

Amodiaquine N-deethylation and midazolam 1'-hydroxylation were used as marker reactions for CYP2C8 and CYP3A4/5 activity. Direct, IC(50) -shift, and time-dependent inhibition were assessed with human liver microsomes.

KEY RESULTS

Inhibition of CYP3A4 activity by imatinib was pre-incubation time-, concentration- and NADPH-dependent, and the time-dependent inactivation variables K(I) and k(inact) were 14.3 µM and 0.072 in(-1) respectively. In direct inhibition experiments, imatinib and N-desmethylimatinib inhibited amodiaquine N-deethylation with a K(i) of 8.4 and 12.8 µM, respectively, and midazolam 1'-hydroxylation with a K(i) of 23.3 and 18.1 µM respectively. The time-dependent inhibition effect of imatinib was predicted to cause up to 90% inhibition of hepatic CYP3A4 activity with clinically relevant imatinib concentrations, whereas the direct inhibition was predicted to be negligible in vivo.

CONCLUSIONS AND IMPLICATIONS

Imatinib is a potent mechanism-based inhibitor of CYP3A4 in vitro and this finding explains the imatinib-simvastatin interaction and suggests that imatinib could markedly increase plasma concentrations of other CYP3A4 substrates. Our results also suggest a possibility of autoinhibition of CYP3A4-mediated imatinib metabolism leading to a less significant role for CYP3A4 in imatinib biotransformation in vivo than previously proposed.

What do kinase inhibition profiles tell us about tyrosine kinase inhibitors used for the treatment of CML?

Cancer treatment has long been based upon cytotoxic therapies that affect all rapidly dividing cells, and as such, is necessarily associated with significant toxicity. More recently, drugs targeted toward pathways critical for tumor cell survival have been developed. With limited off-target activity, such therapies are expected to be better tolerated than broad-acting cytotoxic chemotherapies. BCR-ABL inhibitors in chronic myeloid leukemia are reviewed as a model to investigate the concept of targeted cancer therapies and evaluate how the kinase inhibition profiles of these agents may contribute to their toxicity profiles.

Reduced exposure of imatinib after coadministration with acetaminophen in mice

Purpose:

Imatinib is an efficacious drug against chronic myeloid leukemia (CML) and gastrointestinal stromal tumor (GIST) due to selective inhibition of c-KIT and BCR-ABL kinases. It presents almost complete bioavailability, is eliminated via P450-mediated metabolism and is well tolerated. However, a few severe drug-drug interactions have been reported in cancer patients taking acetaminophen.

Materials and Methods:

Male ICR mice were given 100 mg/kg single dose of imatinib orally or imatinib 100 mg/kg (orally) coadministered with acetaminophen intraperitoneally (700 mg/kg). Mice were euthanized at predetermined time points, blood samples collected, and imatinib plasma concentration measured by HPLC.

Results:

Imatinib AUC_0-12 was 27.04 ± 0.38 mg·h/ml, C_max was 7.21 ± 0.99 mg/ml and elimination half-life was 2.3 hours. Acetaminophen affected the imatinib disposition profile: AUC_0-12 and C_max decreased 56% and 59%, respectively and a longer half-life was observed (5.6 hours).

Conclusions:

The study shows a pharmacokinetic interaction between acetaminophen and imatinib which may render further human studies necessary if both drugs are administered concurrently to cancer patients.

In vitro synergism between LFA-1 targeting leukotoxin (Leukothera™) and standard chemotherapeutic agents in leukemia cells

Leukotoxin (Leukothera™; LtxA) is a bacterial protein and experimental therapeutic that binds leukocyte function antigen (LFA-1) on white blood cells (WBCs) and induces cell death via apoptosis or necrosis. We previously found that LtxA preferentially targets WBCs with high levels of activated LFA-1, which is characteristic of many leukemias and lymphomas, and showed that LtxA exhibits significant anti-leukemia activity in vivo using the humanized SCID mouse model. In this report, we demonstrate that LtxA induces very rapid (1h) apoptosis in acute monocytic leukemia THP-1 cells characterized by binding of annexin V to cells, loss of mitochondrial membrane potential, depletion of cellular ATP, and fragmentation of chromosomal DNA. We tested the activity of LtxA in combination with the standard chemotherapeutic agents, etoposide, mitoxantrone, daunorubicin, busulfan, and imatinib against several leukemia cell lines, including THP-1, GDM-1, HL-60, and KU-812 cells. LtxA exhibited synergism with all the drugs, and the levels of synergy were dependent on the doses used and cell lines examined. In general, the greatest level of synergy was observed with LtxA and etoposide or imatinib. Combination index (CI) values were less than 0.1 for many of the combinations, indicating very strong synergism. In addition, LtxA alone was cytotoxic to primary cells from newly diagnosed, relapsed, and refractory patients with different hematological malignancies. Thus, LtxA is highly effective at inducing rapid apoptosis both as a single agent and in combination with approved leukemia therapies.

Effects of imatinib mesylate on the pharmacokinetics of paracetamol (acetaminophen) in Korean patients with chronic myelogenous leukaemia

AIMS

The major objective of the present study was to investigate the effect of imatinib on the pharmacokinetics of paracetamol in patients with chronic myelogenous leukaemia (CML).

METHODS

Patients (n = 12) received a single oral dose of acetaminophen 1000 mg on day 1 (control). On days 2-8, imatinib 400 mg was administered daily. On day 8 (treatment), another 1000 mg dose of paracetamol was administered 1 h after the morning dose of imatinib 400 mg. Blood and urine samples were collected for bioanalytical analyses.

RESULTS

The area under the plasma concentration-time curve (AUC) for paracetamol, paracetamol glucuronide and paracetamol sulphate under control conditions was similar to that after treatment with imatinib; the 90% confidence interval of the log AUC ratio was within 0.8 to 1.25. Urinary excretion of paracetamol, paracetamol glucuronide and paracetamol sulphate was also unaffected by imatinib. The pharmacokinetics of paracetamol and imatinib in Korean patients with CML were similar to previous pharmacokinetic results in white patients with CML. Co-administration of a single dose of paracetamol and multiple doses of imatinib was well tolerated and safety profiles were similar to those of either drug alone.

CONCLUSIONS

The pharmacokinetics of paracetamol and its major metabolites in the presence of imatinib were similar to those of the control conditions and the combination was well tolerated. These findings suggest that imatinib can be safely administered with paracetamol without dose adjustment of either drug.

Participation of CYP2C8 and CYP3A4 in the N-demethylation of imatinib in human hepatic microsomes

BACKGROUND AND PURPOSE

Imatinib is a clinically important inhibitor of tyrosine kinases that are dysregulated in chronic myelogenous leukaemia and gastrointestinal stromal tumours. Inter-individual variation in imatinib pharmacokinetics is extensive, and influences drug safety and efficacy. Hepatic cytochrome P450 (CYP) 3A4 has been implicated in imatinib N-demethylation, but the clearance of imatinib decreases during prolonged therapy. CYP3A phenotype correlates with imatinib clearance at the commencement of therapy, but not at steady state. The present study evaluated the possibility that multiple CYPs may contribute to imatinib oxidation in liver.

EXPERIMENTAL APPROACH

Imatinib biotransformation in human liver microsomes (n= 20) and by cDNA-expressed CYPs was determined by LC-MS. Relationships between imatinib N-demethylation and other drug metabolizing CYPs were assessed.

KEY RESULTS

N-desmethylimatinib formation was correlated with microsomal oxidation of the CYP3A4 substrates testosterone (ρ= 0.60; P < 0.01) and midazolam (ρ= 0.46; P < 0.05), and the CYP2C8 substrate paclitaxel (ρ= 0.58; P < 0.01). cDNA-derived CYPs 2C8, 3A4, 3A5 and 3A7 supported imatinib N-demethylation, but 10 other CYPs were inactive; in kinetic studies, CYP2C8 was a high-affinity enzyme with a catalytic efficiency ∼15-fold greater than those of CYPs 3A4 and 3A5. The CYP3A inhibitors ketoconazole and troleandomycin, and the CYP2C8 inhibitors quercetin and paclitaxel decreased imatinib oxidation. From molecular modelling, the imatinib structure could be superimposed on a pharmacophore for CYP2C8 substrates.

CONCLUSIONS AND IMPLICATIONS

CYP2C8 and CYPs 3A contribute to imatinib N-demethylation in human liver. The involvement of CYP2C8 may account in part for the wide inter-patient variation in imatinib pharmacokinetics observed in clinical practice.

Are circulating metabolites important in drug-drug interactions?: Quantitative analysis of risk prediction and inhibitory potency

The potential of metabolites to contribute to drug-drug interactions (DDIs) is not well defined. The aim of this study was to determine the quantitative role of circulating metabolites in inhibitory DDIs in vivo. The area under the plasma concentration-time curve (AUC) data related to at least one circulating metabolite was available for 71% of the 102 inhibitor drugs identified. Of the 80 metabolites characterized at steady state, 78% had AUCs >10% of that of the parent drug. A comparison of the inhibitor concentration/inhibition constant ([I]/K(i)) ratios of metabolites and the respective parent drugs showed that 17 of the 21 (80%) reversible inhibitors studied had metabolites that were likely to contribute to in vivo DDIs, with some metabolites predicted to have inhibitory effects greater than those of the parent drug. The in vivo drug interaction risks associated with amiodarone, bupropion, and sertraline could be identified from in vitro data only, when data pertaining to metabolites were included in the predictions. In conclusion, cytochrome P450 (CYP) inhibitors often have circulating metabolites that contribute to clinically observed CYP inhibition.

Imatinib plasma trough concentration and its correlation with characteristics and response in Chinese CML patients

AIM

To investigate the pharmacokinetics of imatinib in Chinese chronic myelogenous leukemia (CML) patients.

METHODS

Fourty-six naïve Chinese CML patients treated with imatinib (400 and 600 mg daily, n=36 and 10, respectively) were recruited. The correlations of imatinib (400 mg) trough plasma concentrations (C(mins)) with the patients' characteristics and responses were analyzed.

RESULTS

The overall mean (+/-SD, CV%) steady-state C(mins) for imatinib at 400 mg (n=36) and 600 mg (n=10) daily was 1325.61 ng/mL (+/-583.53 ng/mL; 44%) and 1550.90 ng/mL (+/-462.63 ng/mL; 30%), respectively, and no statistically significant differences were found between them (P=0.267). At 400 mg daily, female patients had significantly higher C(mins) than the male patients (P=0.048), and molecular responses were not correlated with imatinib C(mins), but they were correlated with time elapsed before imatinib therapy.

CONCLUSION

The results suggest that Chinese CML patients have higher imatinib C(mins) than their Caucasian counterparts and that the optimal initial imatinib dose for them requires further investigation.

Imatinib and Panax ginseng: a potential interaction resulting in liver toxicity

OBJECTIVE

To report a case of imatinib-induced hepatotoxicity after concurrent ginseng ingestion in a patient with chronic myelogenous leukemia (CML).

CASE SUMMARY

A 26-year-old man with CML who had taken imatinib 400 mg daily for 7 years with no complications presented with right upper quadrant pain. Laboratory test results included alanine aminotransferase 1069 U/L, aspartate aminotransferase 481 U/L, alkaline phosphatase 124 IU/L, total bilirubin 1.4 mg/dL, albumin 4.0 g/dL, and international normalized ratio 1.08. Liver biopsy showed acute lobular hepatitis favoring a drug-induced etiology, and a diagnosis of imatinib-induced hepatotoxicity was made. The patient's only lifestyle modification prior to the diagnosis of hepatotoxicity was daily ingestion of Panax ginseng via energy drinks for the past 3 months. Both imatinib and ginseng were discontinued, and the patient was treated with a short course of corticosteroids. Imatinib was later restarted at the same dose with no recurrent elevations in his liver enzyme levels.

DISCUSSION

Imatinib-associated hepatotoxicity usually presents within 1-2 years of therapy initiation, with the median time to hepatotoxicity being 100 days. Ginseng is an herb that is not known to be hepatotoxic. In vivo, ginseng is known to inhibit CYP3A4, the primary enzyme involved in the metabolism of imatinib. We propose that our patient's late-onset imatinib-associated hepatotoxicity was due to an interaction between ginseng and imatinib through CYP3A4. Based on the Naranjo probability scale, it is probable that imatinib caused this patient's hepatotoxicity, and the Horn drug interaction probability scale also indicates a probable interaction between imatinib and ginseng.

CONCLUSIONS

This case emphasizes the importance of continuous monitoring of liver function tests even after several years of imatinib therapy and the importance of advising patients to avoid ginseng and any other over-the-counter herbal supplements that may interact with imatinib.

Clinical pharmacokinetics of tyrosine kinase inhibitors

In the recent years, eight tyrosine kinase inhibitors (TKIs) have been approved for cancer treatment and numerous are under investigation. These drugs are rationally designed to target specific tyrosine kinases that are mutated and/or over-expressed in cancer tissues. Post marketing study commitments have been made upon (accelerated) approval such as additional pharmacokinetic studies in patients with renal- or hepatic impairment, in children, additional interactions studies and studies on the relative or absolute bioavailability. Therefore, much information will emerge on the pharmacokinetic behavior of these drugs after their approval. In the present manuscript, the pharmacokinetic characteristics; absorption, distribution, metabolism and excretion (ADME), of the available TKIs are reviewed. Results from additional studies on the effect of drug transporters and drug-drug interactions have been incorporated. Overall, the TKIs reach their maximum plasma levels relatively fast; have an unknown absolute bioavailability, are extensively distributed and highly protein bound. The drugs are primarily metabolized by cytochrome P450 (CYP) 3A4 with other CYP-enzymes playing a secondary role. They are predominantly excreted with the feces and only a minor fraction is eliminated with the urine. All TKIs appear to be transported by the efflux ATP binding-cassette transports B1 and G2. Additionally these drugs can inhibit some of their own metabolizing enzymes and transporters making steady-state metabolism and drug-drug interactions both complex and unpredictable. By understanding the pharmacokinetic profile of these drugs and their similarities, factors that influence drug exposure will be better recognized and this knowledge may be used to limit sub- or supra-therapeutic drug exposure.