Edoxaban drug–drug interactions with ketoconazole,erythromycin, and cyclosporine

AimsEdoxaban, a novel factor Xa inhibitor, is a substrate of cytochrome P450 3 A4 (CYP3A4) and the efflux transporter P‐glycoprotein (P‐gp). Three edoxaban drug–drug interaction studies examined the effects of P‐gp inhibitors with varying degrees of CYP3A4 inhibition.MethodsIn each study, healthy subjects received a single oral dose of 60 mg edoxaban with or without an oral dual P‐gp/CYP3A4 inhibitor as follows: ketoconazole 400 mg once daily for 7 days, edoxaban on day 4; erythromycin 500 mg four times daily for 8 days, edoxaban on day 7; or single dose of cyclosporine 500 mg with edoxaban. Serial plasma samples were obtained for pharmacokinetics and pharmacodynamics. Safety was assessed throughout the study.ResultsCoadministration of ketoconazole, erythromycin, or cyclosporine increased edoxaban total exposure by 87%, 85%, and 73%, respectively, and the peak concentration by 89%, 68%, and 74%, respectively, compared with edoxaban alone. The half‐life did not change appreciably. Exposure of M4, the major active edoxaban metabolite, was consistent when edoxaban was administered alone or with ketoconazole and erythromycin. With cyclosporine, M4 total exposure increased by 6.9‐fold and peak exposure by 8.7‐fold, suggesting an additional interaction. Pharmacodynamic effects were reflective of increased edoxaban exposure. No clinically significant adverse events were observed.ConclusionsAdministration of dual inhibitors of P‐gp and CYP3A4 increased edoxaban exposure by less than two‐fold. This effect appears to be primarily due to inhibition of P‐gp. The impact of CYP3A4 inhibition appears to be less pronounced, and its contribution to total clearance appears limited in healthy subjects.     

Merging drug companies, submerging creativity?

The Discussion Forum provides a medium for airing your views on any issues related to the pharmaceutical industry and obtaining feedback and discussion on these views from others in the field. You can discuss issues that get you hot under the collar, practical problems at the bench, recently published literature, or just something bizarre or humorous that you wish to share. Publication of letters in this section is subject to editorial discretion and company-promotional letters will be rejected immediately. Furthermore, the views provided are those of the authors and are not intended to represent the views of the companies they work for. Moreover, these views do not reflect those of Elsevier, Drug Discovery Today or its editorial team. Please submit all letters to Dr Rebecca Lawrence, News & Features Editor, Drug Discovery Today, e-mail: Rebecca.Lawrence@current-trends.com     

Clinical disposition,metabolism and in vitro drug–drug interaction properties of omadacycline

1.?Absorption, distribution, metabolism, transport and elimination properties of omadacycline, an aminomethylcycline antibiotic, were investigated in vitro and in a study in healthy male subjects.

2.?Omadacycline was metabolically stable in human liver microsomes and hepatocytes and did not inhibit or induce any of the nine cytochrome P450 or five transporters tested. Omadacycline was a substrate of P-glycoprotein, but not of the other transporters.

3.?Omadacycline metabolic stability was confirmed in six healthy male subjects who received a single 300?mg oral dose of [¹⁴C]-omadacycline (36.6 μCi). Absorption was rapid with peak radioactivity (～610 ngEq/mL) between 1–4?h in plasma or blood. The AUC_last of plasma radioactivity (only quantifiable to 8?h due to low radioactivity) was 3096 ngEq?h/mL and apparent terminal half-life was 11.1?h. Unchanged omadacycline reached peak plasma concentrations (～563?ng/mL) between 1–4?h. Apparent plasma half-life was 17.6?h with biphasic elimination. Plasma exposure (AUC_inf) averaged 9418?ng?h/mL, with high clearance (CL/F, 32.8?L/h) and volume of distribution (Vz/F 828?L). No plasma metabolites were observed.

4.?Radioactivity recovery of the administered dose in excreta was complete (>95%); renal and fecal elimination were 14.4% and 81.1%, respectively. No metabolites were observed in urine or feces, only the omadacycline C4-epimer.     

Biotransformation and in vitro assessment of metabolism-associated drug–drug interaction for CRx-102, a novel combination drug candidate

CRx-102 is an oral synergistic combination drug which contains the cardiovascular agent, dipyridamole (DP) and a very low dose of the glucocorticoid, prednisolone (PRED). CRx-102 works through a novel mechanism of action in which DP selectively amplifies the anti-inflammatory activity of PRED without replicating its side effects. CRx-102 is in clinical trials for the treatment of osteoarthritis. Here we delineate the in vitro metabolism and explore the potential for a drug–drug interaction between the active agents in CRx-102. Our study using human hepatocyte suspensions showed that both DP and PRED were metabolized by CYP3A4 isozymes, resulting in the formation of diverse arrays of both oxidative and oxidative-reduced metabolites. Within phase 1 biotransformation, CYP3A4 was one of the pathways responsible for the metabolism of PRED, while phase 2 biotransformation played a significant role in the metabolism of DP. Glucuronidation of DP was substantial and was catalyzed by many UGT members, specifically those in the UGT1A subfamily. Based on the tandem mass (MS/MS) product ion spectra (PIS) acquired, the major metabolites of both agents, namely, monooxygenated, mono-N-deethanolaminated, dehydrogenated and O-glucuronidated metabolites of DP and the monooxygenated (e.g., 6-hydroxyl), dehydrogenated (prednisone) and reduced (20-hydroxyl) metabolites of PRED, were identified and elucidated. The affinities for DP biotransformation, including CYP3A4-mediated oxidative pathways and UGT-mediated O-glucuronidation, appeared high (K_m < 10 μM), as compared with the modest affinities of PRED biotransformation catalyzed by CYP3A4 (K_m ∼ 40–170 μM). DP, but not PRED, exerted a minimal inhibitory effect on the drug-metabolizing CYP isoforms, including CYP3A4, which was determined using a panel of CYP isoform-preferred substrate activities in pooled human liver microsomal (HLM) preparations and microsomal preparations containing the recombinant enzymes (K_i ∼ 2–12 μM). Using the DP maximal plasma concentration (C_max) observed in the clinic and a predictive mathematical model for metabolism-associated drug–drug interaction (DDI), we have demonstrated that there is little likelihood of a pharmacokinetic interaction between the two active agents in CRx-102.     

Drug information systems, early warning, and new drug trends: can drug monitoring systems become more sensitive to emerging trends in drug consumption?

Drug Information Systems (DIS) are called upon to provide an early warning of emerging trends in drug use. However, little theoretical attention is directed toward exploring conceptual issues in this area. In this paper a typology of existing DIS is offered. Among the features that distinguish DIS are their structure (human network or organization systems) and the range of information sources used. Indicators of drug use can be placed on a continuum of sensitivity ranging from leading edge indicators to lagged indicators. Sensitivity implies volatility as sensitive indicators also react to fluctuations that do not become trends. DIS conventionally are largely reliant upon lagged indicators. What is required are DIS that combine a critical information processing function with the ongoing systematic collection of data from a range of data sources.     

Population pharmacokinetic analysis of drug–drug interactions among risperidone,bupropion, and sertraline in CF1 mice

Rationale Accumulating evidence indicates that modulation of the activity of cytochrome P450 (CYP) enzymes and the multidrug resistance transporter P-glycoprotein (P-gp) is responsible for many drug–drug interactions. Objectives The potential interaction of risperidone (RISP), which is metabolized by 2D6 and transported across the blood brain barrier (BBB) by P-gp, was studied in combination with bupropion (BUP) and also with sertraline (SERT). Methods BUP, SERT, and RISP were administered intraperitoneally into CF1 mice at doses of 100, 10, and 1 μg/g mouse, respectively. Plasma and brain samples were collected at timed intervals from 0.5 to 6 h. A pharmacokinetic analysis was performed using both traditional compartmental modeling and a population pharmacokinetic approach. Results BUP increased the RISP plasma (5.9-fold, P<0.01) and brain (2.2-fold, P<0.01) area under the drug concentration vs time curve (AUC), but did not alter the brain-to-plasma concentration ratio. SERT did not significantly change the plasma AUC of RISP and 9-hydroxy-RISP, but increased the brain AUC of RISP and 9-hydroxy-RISP 1.5-fold (P<0.05) and 5-fold (P<0.01), respectively. RISP did not produce significant alterations of plasma or brain concentrations of BUP. It increased the plasma AUC and elimination half-life (T _1/2e) of desmethyl-SERT 12.5-fold (P<0.01) and 107-fold (P<0.01), respectively. Conclusions These results suggest that pharmacokinetic interactions exist among these three psychoactive drugs involving inhibition of drug metabolizing enzymes and/or P-gp and other drug transporters present in the BBB. The mechanisms and consequences of these interactions require further study in humans to establish clinical relevance.     

Consistency of psychotropic drug–drug interactions listed in drug monographs

Background

With an increasing prevalence of psychotropic polypharmacy, clinicians depend on drug–drug interaction (DDI) references to ensure safe regimens, but the consistency of such information is frequently questioned.

The “iatrogenic triad”: polypharmacy,drug–drug interactions,and potentially inappropriate medications in older adults

International Journal of Clinical Pharmacy - Background The elderly population is often in continuous use of several medications and is more subject to the “iatrogenic triad” of...     

Epidemiology and characteristics of adverse drug reactions caused by drug–drug interactions

Introduction: Drug–drug interactions (DDIs) arise in numerous different ways, involving pharmacokinetic or pharmacodynamic mechanisms. Adverse drug reactions are a possible consequence of DDIs and health operators are often unaware of the clinical risks of certain drug combinations. Many papers on drug interactions have been published in recent years, but most of them focused on potential DDIs while few studies have been conducted on actual interactions.

Areas covered: This paper reviews the epidemiology of actual DDIs in outpatients as well as in hospital settings and in spontaneous reporting databases. The incidence of actual DDIs is consistently lower than that of potential DDIs. However, the absolute number of patients involved is high, representing a significant proportion of adverse drug reactions. The importance of risk factors such as age, polypharmacy and genetic polymorphisms is also evaluated. The relevance and efficacy of tools for recognizing and preventing DDIs are discussed.

Expert opinion: Potential DDIs far outnumber actual drug interactions. The potential for an adverse interaction to occur is often theoretical, and clinically important adverse effects occur only in the presence of specific risk factors. Several studies have shown the efficacy of computers in early detection of DDIs. However, a correct risk–benefit evaluation by the prescribing physician, together with a careful clinical, physiological and biochemical monitoring of patients, is essential. Future directions of drug interaction research include the increasing importance of pharmacogenetics in preventing DDIs and the evaluation of interactions with biological drugs.     

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.     

Evaluation of drug–drug interaction between henagliflozin,a novel sodium-glucose co-transporter 2 inhibitor,and metformin in healthy Chinese males

1.?Henagliflozin is a novel sodium-glucose transporter 2 inhibitor and presents a complementary therapy to metformin for patients with T2DM due to its insulin-independent mechanism of action. This study evaluated the potential pharmacokinetic drug-drug interaction between henagliflozin and metformin in healthy Chinese male subjects.

2.?In open-label, single-center, single-arm, two-period, three-treatment self-control study, 12 subjects received 25?mg henagliflozin, 1000?mg metformin or the combination. Lack of PK interaction was defined as the ratio of geometric means and 90% confidence interval (CI) for combination: monotherapy being within the range of 0.80-1.25.

3.?Co-administration of henagliflozin with metformin had no effect on henagliflozin area under the plasma concentration-time curve (AUC_0–24) (GRM: 1.08; CI: 1.05, 1.10) and peak plasma concentration (C_max) (GRM: 0.99; CI: 0.92, 1.07). Reciprocally, co-administration of metformin with henagliflozin had no clinically significant on metformin AUC_0–24 (GRM: 1.09, CI: 1.02, 1.16) although there was an 11% increase in metformin C_max (GRM 1.12; CI 1.02, 1.23). All monotherapies and combination therapy were well tolerated.

4.?Henagliflozin can be co-administered with metformin without dose adjustment of either drug.     

Towards ‘Sensible’ drug information: critically exploring drug intersectionalities, ‘Just Say No,’ normalisation and harm reduction

Abstract

Aim: This article examines the impact of new psychoactive substances (NPS) on drug service interventions using a case study of professional practitioners in South East England. We assess how professionals seek to develop an innovative approach towards providing ‘sensible drug information.’

Methodology: The research methods include observations, and individual and collective ethnographic interviews with 13 professionals who work with young people across the region.

Results: The article theorises sensible because it is a key element in contemporary drug education with a harm reduction approach. Therefore, we take up this challenge and use the ideas of Gilles Deleuze, which according to Mazzei and McCoy ‘prompts the possibilities of new questions and different ways of thinking research’. We identify a series of drug intersectionalities between ‘traditional’ illegal drugs and NPS and through social class differences between young affluent and more socially disenfranchised drug users. This article assesses the delivery of ‘sensible drug information’ as part of a harm reduction approach, which may not always be supported by other agencies. In responding to these challenges we explore Deleuze’s ideas as a foundation for ‘sensible’ drug information which incorporates Matza?s theory of drift, to explain young people?s changing pattern of drug consumption.     

Improvement in the handling of drug–drug interactions

Approximately one in 200 hospitalised patients has a serious adverse drug effect caused by drug–drug interactions (DDIs). Such adverse effects should be avoidable, but current information provided on DDIs is often incomplete and difficult or even impossible to translate into true risk and appropriate tangible action. Clinicians need to know the mean and maximal expected effect of a DDI on clinical endpoints, any dose adjustments required, and how to monitor tolerability and efficacy in patients subject to a DDI. To this end, improved study designs should take the objective of improving treatment explicitly into account, and any existing DDI data should be publicly accessible. Modelling needs to be used more extensively in order to quantitatively predict the effects of DDIs on clinical endpoints in patients and to relate clinical endpoint effects considered as acceptable to respective changes in experimental and clinical studies. Computer-based expert systems will be required to convert such DDI data into recommendations applicable to the individual patient. Therefore, the incorporation of DDIs in a more general procedure for personalisation of drug therapy is desirable.     

Pharmacokinetic drug–drug interactions with methotrexate in oncology

Methotrexate is an antifolate agent used in the treatment of various cancers and some autoimmune diseases. In oncology, methotrexate is frequently administered at a high dose (>1 g/m²) and comes with various procedures to reduce the occurrence of toxicity and particularly to ensure optimal renal elimination. Drug–drug interactions involving methotrexate are the origin of severe side effects owing to delayed elimination of the antifolate and, more rarely, of decreased efficacy in relation to suboptimal exposure. Most of these interactions are driven by membrane drug transporters whose activity/expression can be inhibited by the interacting medication. In the last 10 years, research on drug transporters has permitted retrospective identification of the molecular mechanisms underlying drug–drug interactions with methotrexate. This article summarizes reported drug–drug interactions involving methotrexate in clinical oncology with reference to the role of drug transporters that control the disposition of the antifolate agent.