Involvement of CYP2D6 activity in the N-oxidation of procainamide in man

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.

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.

Metabolism and pharmacokinetics of novel selective vascular endothelial growth factor receptor-2 inhibitor apatinib in humans

Apatinib is a new oral antiangiogenic molecule that inhibits vascular endothelial growth factor receptor-2. The present study aimed to determine the metabolism, pharmacokinetics, and excretion of apatinib in humans and to identify the enzymes responsible for its metabolism. The primary routes of apatinib biotransformation included E- and Z-cyclopentyl-3-hydroxylation, N-dealkylation, pyridyl-25-N-oxidation, 16-hydroxylation, dioxygenation, and O-glucuronidation after 3-hydroxylation. Nine major metabolites were confirmed by comparison with reference standards. The total recovery of the administered dose was 76.8% within 96 hours postdose, with 69.8 and 7.02% of the administered dose excreted in feces and urine, respectively. About 59.0% of the administered dose was excreted unchanged via feces. Unchanged apatinib was detected in negligible quantities in urine, indicating that systemically available apatinib was extensively metabolized. The major circulating metabolite was the pharmacologically inactive E-3-hydroxy-apatinib-O-glucuronide (M9-2), the steady-state exposure of which was 125% that of the apatinib. The steady-state exposures of E-3-hydroxy-apatinib (M1-1), Z-3-hydroxy-apatinib (M1-2), and apatinib-25-N-oxide (M1-6) were 56, 22, and 32% of parent drug exposure, respectively. Calculated as pharmacological activity index values, the contribution of M1-1 to the pharmacology of the drug was 5.42 to 19.3% that of the parent drug. The contribution of M1-2 and M1-6 to the pharmacology of the drug was less than 1%. Therefore, apatinib was a major contributor to the overall pharmacological activity in humans. Apatinib was metabolized primarily by CYP3A4/5 and, to a lesser extent, by CYP2D6, CYP2C9, and CYP2E1. UGT2B7 was the main enzyme responsible for M9-2 formation. Both UGT1A4 and UGT2B7 were responsible for Z-3-hydroxy-apatinib-O-glucuronide (M9-1) formation.

Metabolomics reveals the metabolic map of procainamide in humans and mice

Procainamide, a type I antiarrhythmic agent, is used to treat a variety of atrial and ventricular dysrhythmias. It was reported that long-term therapy with procainamide may cause lupus erythematosus in 25-30% of patients. Interestingly, procainamide does not induce lupus erythematosus in mouse models. To explore the differences in this side-effect of procainamide between humans and mouse models, metabolomic analysis using ultra-performance liquid chromatography coupled with electrospray ionization quadrupole time-of-flight mass spectrometry (UPLC-ESI-QTOFMS) was conducted on urine samples from procainamide-treated humans, CYP2D6-humanized mice, and wild-type mice. Thirteen urinary procainamide metabolites, including nine novel metabolites, derived from P450-dependent, FMO-dependent oxidations and acylation reactions, were identified and structurally elucidated. In vivo metabolism of procainamide in CYP2D6-humanized mice as well as in vitro incubations with microsomes and recombinant P450s suggested that human CYP2D6 plays a major role in procainamide metabolism. Significant differences in N-acylation and N-oxidation of the drug between humans and mice largely account for the interspecies differences in procainamide metabolism. Significant levels of the novel N-oxide metabolites produced by FMO1 and FMO3 in humans might be associated with the development of procainamide-induced systemic lupus erythematosus. Observations based on this metabolomic study offer clues to understanding procainamide-induced lupus in humans and the effect of P450s and FMOs on procainamide N-oxidation.

Identification of multiple glutathione conjugates of 8-amino- 2-methyl-4-phenyl-1,2,3,4-tetrahydroisoquinoline maleate (nomifensine) in liver microsomes and hepatocyte preparations: evidence of the bioactivation of nomifensine

8-Amino-2-methyl-4-phenyl-1,2,3,4-tetrahydroisoquinoline maleate (nomifensine), an antidepressant drug, was withdrawn from the market because of increased incidence of hemolytic anemia, as well as kidney and liver toxicity. Although the nature of the potentially reactive metabolites formed after nomifensine metabolism remains unknown and no glutathione (GSH) adducts of these nomifensine reactive metabolites have been reported, bioactivation has been postulated as a potential mechanism for the toxicity of nomifensine. This study was conducted to probe the potential bioactivation pathways of nomifensine in human and animal hepatocytes and in liver microsomes using GSH as a trapping agent. Two types of GSH conjugates were characterized by liquid chromatography/tandem mass spectrometry: 1) aniline oxidation followed by GSH conjugation leading to the formation of nomifensine-GSH sulfinamides (M1 and M2); and 2) arene oxidation followed by GSH conjugation yielding a range of arene C-linked GSH adducts (M3-M9). Nine GSH adducts (M1-M9) were identified in liver microsomes of humans, dogs, monkeys, and rats and in human and rat hepatocytes. In dog hepatocyte preparations, six GSH adducts (M1-M6) were identified. The GSH adducts in dog and rat liver microsomes were formed primarily through aniline and arene oxidation, respectively. Both pathways contributed significantly to the formation of the GSH adducts in human and monkey liver microsomes. The bioactivation pathways proposed here account for the formation of the observed GSH conjugates. These investigations have confirmed the aniline and the arene groups in nomifensine as potential toxicophores capable of generating reactive intermediates.

Drug bioactivation, covalent binding to target proteins and toxicity relevance

A number of therapeutic drugs with different structures and mechanisms of action have been reported to undergo metabolic activation by Phase I or Phase II drug-metabolizing enzymes. The bioactivation gives rise to reactive metabolites/intermediates, which readily confer covalent binding to various target proteins by nucleophilic substitution and/or Schiff's base mechanism. These drugs include analgesics (e.g., acetaminophen), antibacterial agents (e.g., sulfonamides and macrolide antibiotics), anticancer drugs (e.g., irinotecan), antiepileptic drugs (e.g., carbamazepine), anti-HIV agents (e.g., ritonavir), antipsychotics (e.g., clozapine), cardiovascular drugs (e.g., procainamide and hydralazine), immunosupressants (e.g., cyclosporine A), inhalational anesthetics (e.g., halothane), nonsteroidal anti-inflammatory drugs (NSAIDSs) (e.g., diclofenac), and steroids and their receptor modulators (e.g., estrogens and tamoxifen). Some herbal and dietary constituents are also bioactivated to reactive metabolites capable of binding covalently and inactivating cytochrome P450s (CYPs). A number of important target proteins of drugs have been identified by mass spectrometric techniques and proteomic approaches. The covalent binding and formation of drug-protein adducts are generally considered to be related to drug toxicity, and selective protein covalent binding by drug metabolites may lead to selective organ toxicity. However, the mechanisms involved in the protein adduct-induced toxicity are largely undefined, although it has been suggested that drug-protein adducts may cause toxicity either through impairing physiological functions of the modified proteins or through immune-mediated mechanisms. In addition, mechanism-based inhibition of CYPs may result in toxic drug-drug interactions. The clinical consequences of drug bioactivation and covalent binding to proteins are unpredictable, depending on many factors that are associated with the administered drugs and patients. Further studies using proteomic and genomic approaches with high throughput capacity are needed to identify the protein targets of reactive drug metabolites, and to elucidate the structure-activity relationships of drug's covalent binding to proteins and their clinical outcomes.

The gastroprokinetic and antiemetic drug metoclopramide is a substrate and inhibitor of cytochrome P450 2D6

Metoclopramide is increasingly prescribed for conditions previously treated with cisapride, but its metabolic enzymology and drug interactions are poorly understood. Using human liver microsomes (HLMs) and recombinant human cytochromes P450 (P450), we identified the major route of metoclopramide oxidation and the P450 isoforms involved. We also documented the ability of metoclopramide to inhibit the P450 system, using isoform-specific substrate reaction probes of CYP1A2, 2C19, 2C9, 2D6, 2E1, and 3A4. Metoclopramide was predominantly N-dealkylated to monodeethylmetoclopramide, a metabolite that has not so far been described in humans. Formation rate of this metabolite followed Michaelis-Menten kinetics (K(m), 68 +/- 16 microM; V(max), 183 +/- 57 pmol/min/mg of protein; n = 3 HLMs). Of the isoform-specific inhibitors tested, 1 microM quinidine was a potent inhibitor of metoclopramide (25 microM) monodeethylation [by an average of 58.2%; range, approximately 38% (HL09-14-99) to 78.7% (HL161)] with K(i) values highly variable among the HLMs tested (K(i), mean +/- S.D., 2.7 +/- 2.8 microM; range, 0.15 microM in HL66, 2.4 microM in HL09-14-99, and 5.7 microM in HLD). Except troleandomycin, which inhibited metoclopramide metabolism in only one HLM (by approximately 23% in HL09-14-99), the effect of other inhibitors was minimal. Among the recombinant human P450 isoforms examined, monodeethylmetoclopramide was formed at the highest rate by CYP2D6 (V = 4.5 +/- 0.3 pmol/min/pmol of P450) and to a lesser extent by CYP1A2 (0.97 +/- 0.15 pmol/min/pmol of P450). The K(m) value derived (approximately 53 microM) was close to that from HLMs (68 microM). Metoclopramide is a potent inhibitor of CYP2D6 at therapeutically relevant concentrations (K(i) = 4.7 +/- 1.3 microM), with negligible effect on other isoforms tested. Further inhibition of CYP2D6 was observed when metoclopramide was preincubated with HLMs and NADPH-generating system before the substrate probe was added (maximum rate of inactivation, K(inact) = 0.02 min(-1), and the concentration required to achieve the half-maximal rate of inactivation, K'(i) = 0.96 microM), suggesting mechanism-based inhibition. Metoclopramide elimination is likely to be slowed in poor metabolizers of CYP2D6 or in patients taking inhibitors of this isoform, whereas metoclopramide itself could reduce the clearance of CYP2D6 substrate drugs.

Summary of information on human CYP enzymes: human P450 metabolism data

This chapter is an update of the data on substrates, reactions, inducers, and inhibitors of human CYP enzymes published previously by Rendic and DiCarlo (1), now covering selection of the literature through 2001 in the reference section. The data are presented in a tabular form (Table 1) to provide a framework for predicting and interpreting the new P450 metabolic data. The data are formatted in an Excel format as most suitable for off-line searching and management of the Web-database. The data are presented as stated by the author(s) and in the case when several references are cited the data are presented according to the latest published information. The searchable database is available either as an Excel file (for information contact the author), or as a Web-searchable database (Human P450 Metabolism Database, www.gentest.com) enabling the readers easy and quick approach to the latest updates on human CYP metabolic reactions.