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.     

In vitro assessment of the roles of drug transporters in the disposition and drug–drug interaction potential of olaparib

1.?In vitro assessments were conducted to examine interactions between olaparib (a potent oral inhibitor of poly[ADP-ribose] polymerase) and drug transporters.

2.?Olaparib showed inhibition of the hepatic drug uptake transporters OATP1B1 (IC₅₀ values of 20.3?μM and 27.1?μM) and OCT1 (IC₅₀ 37.9?μM), but limited inhibition of OATP1B3 (25% at 100?μM); inhibition of the renal uptake transporters OCT2 (IC₅₀ 19.9?μM) and OAT3 (IC₅₀ 18.4?μM), but limited inhibition of OAT1 (13.5% at 100?μM); inhibition of the renal efflux transporters MATE1 and MATE2K (IC₅₀s 5.50?μM and 47.1?μM, respectively); inhibition of the efflux transporter MDR1 (IC₅₀ 76.0?μM), but limited inhibition of BCRP (47% at 100?μM) and no inhibition of MRP2. At clinically relevant exposures, olaparib has the potential to cause pharmacokinetic interactions via inhibition of OCT1, OCT2, OATP1B1, OAT3, MATE1 and MATE2K in the liver and kidney, as well as MDR1 in the liver and GI tract. Olaparib was found to be a substrate of MDR1 but not of several other transporters.

3.?Our assessments indicate that olaparib is a substrate of MDR1 and may cause clinically meaningful inhibition of MDR1, OCT1, OCT2, OATP1B1, OAT3, MATE1 and MATE2K.     

Identification and evaluation of drug–supplement interactions in Hungarian hospital patients

Background The increasing number of patients taking supplementary products together with prescribed medicines has become a new challenge for health care systems. These products may influence therapy outcomes by inducing unwanted effects. Particularly concerning is the potential for harmful interactions between prescribed medicines and supplementary products. Objective The aims of the study were to evaluate supplement use, to identify and analyse potential interactions, and to assess the efficiency of computerised interaction screening. Setting Participants of the study were inpatients and outpatients of a Hungarian university hospital. Method A cross-sectional point-of-care survey of 200 patients was carried out. Data was collected through personal interviews and a review of the medical records. Drug–drug, drug–supplement and supplement–supplement interactions were analysed with three interaction databases (Lexi-Interact Online, Medscape Drug Interaction Checker and Mediris). Main outcome measure Prevalence of supplementary product use, number of medicines and supplementary products per patient, procurement sources of products, number of potentially severe interactions. Results There was a marked difference between data obtained from patient interviews and the medical records. 85.5 % of the surveyed patients took supplementary products during the 2 weeks prior to the interview. The average number of prescribed medicines and supplementary products were 7.8 and 2.5, respectively. Women were more likely to take supplements than men. There was no significant difference in supplement use between patients under or over 60 years, between inpatients and outpatients and among patients in various wards. 39.4 % of supplementary products were purchased outside a regulated pharmacy environment. Potentially severe drug–supplement interactions were detected with 45.2 % of supplement users; however the majority of interactions were not included in one or the other of the three databases. In addition to that the risk ratings of the same interactions varied greatly between databases. Conclusion A significant number of patients are exposed to potential drug interactions with supplementary products; however interagreement among interaction databases is poor. Our data suggest that a full medication history should specifically address the intake of supplements.     

Building a drug–target network and its applications

Background: One of the most recent and important developments in drug discovery is a new drug development approach of building and analyzing networks that contain relationships among drugs and targets, diseases, genes and other components. These networks and their integrations provide useful information for finding new targets as well as new drugs. Objective: This review article aims to review recent developments in various types of networks and suggest the future direction of these network studies for drug discovery. Methods: Databases and networks are integrated into a more complete network to better present the relationships among drugs, targets, genes, phenotypes and diseases. After discussing the limitations and obstacles of the recent research, we suggest several strategies to build a successful and practical drug–target network. Results/conclusion: A useful, integrated network can be built from various databases and networks by resolving several issues, such as limited coverage and inconsistency. This integrated network can be completed by the prediction of missing links, biological network comparison and drug target identification. Possible applications are multi-target drug development, drug repurposing, estimation of drug effect on target perturbations in the whole system and extraction of the suitable purpose of the drug–target sub-network.     

The drug–drug interaction of sorafenib mediated by P-glycoprotein and CYP3A4

1.?The aim of this study was to investigate the potential drug–drug interaction of sorafenib mediated by P-glycoprotein (P-gp) and cytochrome P450 3A4 (CYP3A4).

2.?In this research, a sensitive and reliable LC-MS/MS method was developed and applied for the determination of sorafenib in rat plasma. The pharmacokinetic profiles of orally administered sorafenib from rats with and without verapamil pretreatment were investigated.

3.?The results indicated that when the rats were pretreated with verapamil, the C_max of sorafenib increased from 55.73?ng/ml to 87.72?ng/ml (57.40%), and the AUC_(0?t) increased by approximately 58.2% when sorafenib was co-administered with verapamil. Additionally, the effects of verapamil on the absorption of sorafenib were investigated using the Caco-2 cell transwell model, and the effects of verapamil on the metabolic stability of sorafenib were also studied using rat liver microsomes incubation systems. A markedly higher transport of sorafenib across the Caco-2 cells was observed in the basolateral-to-apical direction and was abrogated in the presence of the P-gp inhibitor, verapamil. The results indicated that P-gp was involved in the transport of sorafenib, and verapamil could increase its absorption in the Caco-2 cell model, and the metabolic stability of sorafenib was prolonged by the pretreatment with verapamil.

4.?In conclusion, the drug–drug interaction of sorafenib might happen when sorafenib was co-administered with P-gp or CYP3A4 inhibitors.     

The metabolism and drug–drug interaction potential of the selective prostacyclin receptor agonist selexipag

1.?The metabolism of selexipag has been studied in vivo in man and the main excreted metabolites were identified. Also, metabolites circulating in human plasma have been structurally identified and quantified.

2.?The main metabolic pathway of selexipag in man is the formation of the active metabolite ACT-333679. Other metabolic pathways include oxidation and dealkylation reactions. All primary metabolites undergo subsequent hydrolysis of the sulphonamide moiety to their corresponding acids. ACT-333679 undergoes conjugation with glucuronic acid and aromatic hydroxylation to P10, the main metabolite detected in human faeces.

3.?The formation of the active metabolite ACT-333679 is catalysed by carboxylesterases, while the oxidation and dealkylation reactions are metabolized by CYP2C8 and CYP3A4. CYP2C8 is the only P450 isoform catalysing the aromatic hydroxylation to P10. CYP2C8 together with CYP3A4 are also involved in the formation of several minor ACT-333679 metabolites. UGT1A3 and UGT2B7 catalyse the glucuronidation of ACT-333679.

4.?The potential of selexipag to inhibit or induce cytochrome P450 enzymes or drug transport proteins was studied in vitro. Selexipag is an inhibitor of CYP2C8 and CYP2C9 and induces CYP3A4 and CYP2C9 in vitro. Also, selexipag inhibits the transporters OATP1B1, OATP1B3, OAT1, OAT3, and BCRP. However, due to its low dose and relatively low unbound exposure, selexipag has a low potential for causing drug–drug interactions.     

Prediction of drug–ABC-transporter interaction — Recent advances and future challenges

With the discovery of P-glycoprotein (P-gp), it became evident that ABC-transporters play a vital role in bioavailability and toxicity of drugs. They prevent intracellular accumulation of toxic compounds, which renders them a major defense mechanism against xenotoxic compounds. Their expression in cells of all major barriers (intestine, blood–brain barrier, blood–placenta barrier) as well as in metabolic organs (liver, kidney) also explains their influence on the ADMET properties of drugs and drug candidates. Thus, in silico models for the prediction of the probability of a compound to interact with P-gp or analogous transporters are of high value in the early phase of the drug discovery process. Within this review, we highlight recent developments in the area, with a special focus on the molecular basis of drug–transporter interaction. In addition, with the recent availability of X-ray structures of several ABC-transporters, also structure-based design methods have been applied and will be addressed.     

Drug–drug interaction of rabeprazole and clopidogrel in healthy Chinese volunteers

Purpose

This study was aimed to determine the impact of rabeprazole (RBRZ) on the antiplatelet efficacy of clopidogrel (CPG) in healthy Chinese volunteers, and further to predict the effect of CYP2C19 genetic polymorphism on the efficacy of rabeprazole and clopidogrel.

Methods

The open-label, two period cross-over study was conducted in 20 healthy Chinese subjects with different CYP2C19 genotypes receiving clopidogrel, rabeprazole or the two drugs, respectively. All the volunteers were divided into two groups, poor metabolizers (PMs) and extensive metabolizers (EMs), depending on CYP2C19 genotypes. Blood samples were collected at baseline and at 0.5, 1, 2, 3, 4, 6, 8, 10, and 12 h after administration. The plasma concentrations of rabeprazole and clopidogrel were analyzed by LC-MS/MS and ADP-induced platelet aggregation was detected by the optical turbidimetric method.

Results

There were no significant differences in the mean plasma concentration–time curves of clopidogrel (CPG), the inactive metabolite clopidogrel carboxylic acid (CPG-CA), the active metabolite clopidogrel-MP-Derivative (MP-AM), and rabeprazole (RBRZ) according to the co-administration of CPG and RBRZ. There were no major changes in the pharmacokinetics of CPG and RBRZ. The maximal ADP-induced platelet aggregation (2 μmol/L) was decreased in EMs compared with PMs.

Conclusion

Co-administration of rabeprazol and clopidogrel did not affect the antiplatelet efficacy of clopidogrel. The CYP2C19 genetic polymorphism may impact the efficacy of clopidogrel.     

Protein–protein interaction modulator drug discovery: past efforts and future opportunities using a rich source of low- and high-throughput screening assays

Introduction: Historically, small-molecule drug discovery projects have largely focused on the G-protein-coupled receptor, ion-channel and enzyme target classes. More recently, there have been successes demonstrating that protein–protein interactions (PPIs) can be targeted by small-molecules and that this strategy has the potential to provide appropriate specificity and selectivity. However, a disadvantage is that compounds that modulate PPIs are often associated with relatively weak affinities as the targeted interaction surfaces are often relatively large. Moreover, from a small-molecule screening perspective, a large proportion of the initial screening Hits are often false positives and these need to be identified and excluded in order to focus on genuine modulators of the PPI being investigated.

Areas covered: The authors review previous efforts on PPI modulator drug discovery. Furthermore, they review assays that can be employed in small-molecule screening and/or Hit validation. The PPI assays are categorized as: i) low-throughput target-based biochemical assays, which are primarily employed for Hit validation at the post-screening stage; ii) high-throughput target-based biochemical assays that are suitable for screening campaigns; and iii) cell-based assays, which are suitable for high-throughput screening campaigns and/or Hit validation.

Expert opinion: Modulating the interaction of PPIs offers the potential to develop novel drugs to treat a wide range of diseases. New assay technologies are continually being developed and it is anticipated that these will be able to be directly used for small-molecule screening campaigns in the future.     

Prediction of drug–drug interaction potential using physiologically based pharmacokinetic modeling

The occurrence of drug–drug interactions (DDIs) can significantly affect the safety of a patient, and thus assessing DDI risk is important. Recently, physiologically based pharmacokinetic (PBPK) modeling has been increasingly used to predict DDI potential. Here, we present a PBPK modeling concept and strategy. We also surveyed PBPK-related articles about the prediction of DDI potential in humans published up to October 10, 2017. We identified 107 articles, including 105 drugs that fit our criteria, with a gradual increase in the number of articles per year. Studies on antineoplastic and immunomodulatory drugs (26.7%) contributed the most to published PBPK models, followed by cardiovascular (20.0%) and anti-infective (17.1%) drugs. Models for specific products such as herbal products, therapeutic protein drugs, and antibody–drug conjugates were also described. Most PBPK models were used to simulate cytochrome P450 (CYP)-mediated DDIs (74 drugs, of which 85.1% were CYP3A4-mediated), whereas some focused on transporter-mediated DDIs (15 drugs) or a combination of CYP and transporter-mediated DDIs (16 drugs). Full PBPK, first-order absorption modules and Simcyp^® software were predominantly used for modeling. Recently, DDI predictions associated with genetic polymorphisms, special populations, or both have increased. The 107 published articles reasonably predicted the DDI potentials, but further studies of physiological properties and harmonization of in vitro experimental designs are required to extend the application scope, and improvement of DDI predictions using PBPK modeling will be possible in the future.     

Drug–drug interaction with statins

3-hydroxy-3-methyl-glutaryl (HMG)-CoA reductase inhibitors (the so-called statins: atorvastatin, fluvastatin, pravastatin, lovastatin, rosuvastatin and simvastatin) are a well-established class of drugs in the treatment of hypercholesterolemia. Statin monotherapy is generally well tolerated, with a low frequency of adverse events. The most important adverse effects associated with statins are myopathy and an asymptomatic increase in hepatic transaminases, both of which occur infrequently. Since statins are prescribed on a long-term basis, possible interactions with other drugs deserve particular attention, as many patients will typically receive pharmacological therapy for concomitant conditions during the course of statin treatment. Moreover, a combination of therapy between statins and other classes of lipid-lowering agents (e.g., ezetimibe, fibrates, resins and nicotinic acid) is recommended for some patients by current guidelines. Therefore, the potential for drug–drug interactions emerges as a relevant factor in determining the safety profile of statins. This review summarizes the pharmacokinetic properties of statins and emphasizes their clinically relevant drug interactions.     

Concomitant use of mirtazapine and cimetidine: a drug–drug interaction study in healthy male subjects

Objective: The objective of this study was to examine the pharmacokinetics and the tolerability/safety of mirtazapine and cimetidine separately and in combination following oral administration of multiple doses. Methods: This was a double-blind, placebo-controlled, two-period cross-over, multiple-dose pharmacokinetic interaction study in 12 healthy male subjects. They received either cimetidine (800 mg b.i.d.) or placebo in combination with (commercially available, racemic) mirtazapine (30 mg nocte). Cimetidine and placebo were administered for 14 days, with mirtazapine added during days 6–12 of each period. Serial blood samples for kinetic profiling were taken on day 5 and day 12 for cimetidine and on days 12–14 for mirtazapine. Results: The co-administration of cimetidine resulted in a statistically significant increase in the area under the curve (AUC_0–24) and C_max of mirtazapine (54% and 22% respectively). The AUC_0–24 of demethylmirtazapine increased only slightly, and there was no effect on C_max. The elimination half-lives for both mirtazapine and its demethyl metabolite were unaffected by cimetidine co-administration. The trough and average plasma concentrations during the steady state were elevated during cimetidine treatment (62% and 54%, respectively). Mirtazapine had no effect on the pharmacokinetics of cimetidine. Conclusion: Co-administration of cimetidine (800 mg b.i.d.) and mirtazapine (30 mg nocte) resulted in increased steady-state plasma levels of mirtazapine (C_ss,min= +61%, P < 0.05; C_ss,av=+54%, P < 0.05), probably as a result of increased bio-availability. The C_max (+22%, P < 0.05) and AUC_0–24 (+54%, P < 0.05) also increased. Due to the variability of the mirtazapine plasma levels in patients, the clinical meaning of these increases is probably limited. Co-administration of mirtazapine did not alter cimetidine pharmacokinetics. Received: 24 November 1999 / Accepted in revised form: 15 May 2000     

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.