Drug-Drug Interactions in Recently Approved Antibacterials: A Mini Review.

Opioid Metabolism

The ever-increasing introduction of new therapeutic agents means that the potential for drug interactions is likely to escalate. Numerous different classes of drugs are currently used to treat hypertension. The angiotensin receptor blockers offer one of the newest approaches to the management of patients with high blood pressure. Compared with other classes of antihypertensive agents, the angiotensin receptor blockers appear overall to have a low potential for drug interactions, but variations within the class have been detected. Losartan and irbesartan have a greater affinity for cytochrome p450 (CYP) isoenzymes and, thus, are more likely to be implicated in drug interactions. There is pharmacokinetic evidence to suggest that such interactions could have a clinical impact. Candesartan cilexetil, valsartan and eprosartan have variable but generally modest affinity and telmisartan has no affinity for any of the CYP isoenzymes. In vitro studies and pharmacokinetic/pharmacodynamic evaluation can provide evidence for some interactions, but only a relatively small number of drug combinations are usually studied in this way. The absence of any pharmacokinetic evidence of drug interaction, however, should not lead to complacency. Patients should be made aware of possible interactions, especially involving the concurrent use of over-the-counter products, and it may be prudent for all patients receiving antihypertensive treatment to be monitored for possible drug interactions at their regular check-ups. The physician can help by prescribing agents with a low potential for interaction, such as angiotensin receptor blockers.

Published in Clinical Pharmacology and Therapeutics in 2001, the National Institutes of Health Biomarkers Definitions Working Group provided an early definition of a biomarker as “a characteristic that is objectively measured and evaluated as an indicator of normal biological processes, pathogenic processes, or pharmacologic responses to a therapeutic intervention.”1 Since then, several organizations have expanded on the definition, including the World Health Organization (WHO); however, the essential features of a biomarker remain the same: it must be measurable and reflect the interaction between a biological system and a potential perturbation, which may be chemical, physical, or biological.2 Currently, the US Food and Drug Administration (FDA) defines seven biomarker categories: susceptibility/risk, diagnostic, monitoring, prognostic, predictive, pharmacodynamic/response, and safety.3 In this issue of Clinical Pharmacology and Therapeutics, several papers focus on biomarkers for pharmacokinetic transporters, which may be categorized as safety biomarkers. Rodrigues describes the current state of the art methods for assessing transporter-mediated drug–drug interaction (DDI) risk in a paper entitled “Reimagining the Framework Supporting the Static Analysis of Transporter Drug Interaction Risk; Integrated Use of Biomarkers to Generate Pan-Transporter Inhibition Signatures.”4 The author makes the point that current methods, which rely on in vitro studies in cells expressing transporters along with criteria from regulatory bodies, are “static” and result in many false positive predictions of DDI risk. In contrast, they describe the use of biomarkers for solute carrier (SLC) transporters in the liver and kidneys. Beginning with a referenced list of biomarkers for liver transporters, including OATP1B1 (SLCO1B1), OATP1B3 (SLCO1B3), and OCT1 (SLC22A1), as well as renal transporters OCT2 (SLC22A2), MATE1 (SLC47A1), MATE2K (SLC47A2), OAT3 (SLC22A6), and OAT3 (SLC22A8), Rodrigues demonstrates that quantitative changes in these biomarkers predict quantitative changes in drug exposure as indicated by an area under the curve (AUC) ratio (AUCR = AUC of the drug in the presence of a transporter inhibitor divided by control AUC of the drug).3 Clearly preferable to the current “static” methods, transporter biomarkers coupled with physiologically-based pharmacokinetic (PBPK) models, provide a quantitative prediction of transporter-mediated DDIs. Expanding on the use of biomarkers for predicting DDIs, Lin et al.5 in their paper entitled “Effect of Hepatic Impairment on OATP1B1 Activity: Quantitative Pharmacokinetic Analysis of Endogenous Biomarker and Substrate Drugs,” describe the use of biomarkers in predicting the effect of hepatic impairment on the activity of OATP1B. Notably, in a group of patients with varying degrees of hepatic impairment, the authors establish that mean levels of the OATP1B1 biomarker, coproporphyrin-I (CPI) significantly increase as hepatic function decreases (severe > moderate > mild). Similarly, mean levels of drugs that are OATP1B1 substrates, indicated as an AUCR (AUC of the drug in the presence of hepatic impairment divided by AUC of the drug in healthy controls) also significantly increase as hepatic function deteriorates. A PBPK model is developed to predict AUC change of CPI in moderate and severe hepatic impairment. Their elegant study combining biomarkers, hepatic disease, and PBPK modeling underscores how biomarkers can be used to assess transporter activity, which may be modulated by both DDIs and hepatic impairment. Whereas Rodrigues4 and Lin et al.5 focus on the use of circulating biomarkers of transporters to predict transporter activity changes either through DDIs or human disease, transporter activity may also be modified by genetic polymorphisms. For example, OATP1B1 activity is modulated by several reduced function genetic polymorphisms. Last month's paper from the Pharmacogene Variation Consortium (PharmVar) for the first time extends the star allele nomenclature, commonly used to identify distinct haplotypes (i.e., the combination of variants on the same chromosome within a defined region) of drug metabolizing enzymes, to a transporter (i.e., OATP1B1).6 Standardized SLCO1B1 allele definitions, which are compliant with PharmVar standards and allele definition criteria, are presented in this paper. The new allele definitions should replace legacy definitions, which are complicated and confusing. Many of the alleles are associated with changes in OATP1B1 activity. As such, the relationship between alleles of OATP1B1 and levels of circulating biomarkers have been the subject of several early studies published in Clinical Pharmacology and Therapeutics and in Clinical and Translational Science. In particular, Yee et al.7 exploited publicly available genomewide association studies to discover and validate several plasma biomarkers of OATP1B1. This team previously showed highly significant associations between the OATP1B1 polymorphism p.Val171Ala (rs4149056) and levels of a number of endogenous biomarkers, including tetradecanedioic acid, glycochenodeoxycholate glucuronide, and glycodeoxycholate sulfate.8 In addition, isobutyryl-L-carnitine and N1-methyladenosine have been shown by other groups to be biomarkers for drug interaction studies involving hepatic OCT1 transporter and renal OCT2 transporter, respectively.9 Polymorphisms in these transporters have been shown to have significant associations with the levels of these biomarkers.10 These studies demonstrate that circulating biomarkers can be used to predict polymorphisms of transporters and vice versa, that is, polymorphisms (which are genetic biomarkers) predict levels of circulating biomarkers as well as levels of drugs that are substrates of the transporter. The fourth manuscript by Muller et al.11 goes beyond measurements of circulating levels of biomarkers to predict DDIs; instead, the paper entitled “N1-Methylnicotinamide as Biomarker for MATE-Mediated Renal Drug–Drug Interactions: Impact of Cimetidine, Rifampin, Verapamil, and Probenecid,” focuses on the use of renal clearance (CLR) of a biomarker, which can be used to predict DDIs caused by altered activity of transporters in the kidneys.11 The paper concerns N1-methylnicotinamide (NMN) and DDIs mediated by the multidrug and toxin extrusion proteins (MATEs). The study demonstrates that rather than circulating levels of NMN, CLR of NMN is the biomarker for DDIs involving MATEs. In their elegant studies, they show that plasma levels of NMN increase in the presence of four drugs, which are not inhibitors of MATEs: digoxin, metformin, furosemide, and rosuvastatin, as well as a cocktail of the four drugs. However, CLR (0–12 hours) does not change significantly in the presence of any of the four drugs or the cocktail. In contrast, with the addition of the established MATE inhibitor, cimetidine, CLR of NMN was significantly reduced, consistent with reduced MATE-mediated renal secretion of this endogenous biomarker. The investigators conclude that NMN CLR, but not circulating levels, is a specific and sensitive marker for MATE-mediated DDIs in the kidneys. One final paper reminds us that in addition to refining and expanding the use of biomarkers for clinical DDIs, in vitro predictions also need to be continuously refined. In their paper entitled “Beyond the Michaelis–Menten: Accurate Prediction of Drug Interactions through Cytochrome P450 3A4 Induction,” Vu et al.12 point out that the FDA has established guidelines for DDIs mediated by induction of cytochrome P450 enzymes (CYPs). Challenging the assumptions of the FDA guidelines and refining equations, Vu et al. provide new equations that more accurately predict the effects of CYP inducers especially for drugs that have high affinities for CYPs. These studies highlight the need for improving the analysis of in vitro and computational methods in parallel with other methods, such as measurements and use of circulating biomarkers, to continuously increase the accuracy of predictions of DDIs. Collectively, the papers in this issue of Clinical Pharmacology and Therapeutics advance and refine our current methods for predicting DDIs mediated by drug transporters through the use of measured biomarkers or refinement of analysis of data obtained from in vitro methodologies. The papers go beyond DDIs, highlighting new advances in the methodologies used to predict perturbations in the activity of transporters and enzymes on pharmacokinetics, which are caused by human disease, genetic polymorphisms, and concomitant medications. No funding was received for this work. The author declared no competing interests for this work.

IntroductionWhile direct-acting antivirals (DAA) are effective treatment for hepatitis C virus (HCV) patients, concerns about drug-drug interactions (DDIs) remain a significant challenge. Although there are several studies investigating the risk of DDIs associated with DAA therapy, there is limited research evaluating DDIs of DAA therapy in real-world settings in Japan. We investigated prescription patterns of comedication associated with DDIs risk in HCV patients receiving DAA therapy using a large Japanese database.MethodsThis was a descriptive epidemiological study, using the Japanese administrative claims database provided by DeSC Healthcare, Inc. Patients who initiated sofosbuvir/velpatasvir (SOF/VEL) or glecaprevir/pibrentasvir (GLE/PIB) between April 2017 and August 2023 were identified from the data. The primary outcome was DDIs associated with comedications which were assessed based on both Japanese package inserts and the Liverpool HEP Drug Interaction Checker (Liverpool HEP checker).ResultsPatients included in this study were 7,338, with 467 prescribed SOF/VEL and 6,871 prescribed GLE/PIB. The mean age of the patients was 69.9 years (SD = 13.1), with 50% being male. The median number of comedications was higher in the SOF/VEL group (14.0; IQR = 14.0) than in the GLE/PIB group (9.0; IQR = 12.0) and based on package insert and Liverpool HEP checker, the DDI risk was present in 59.3% (277) of the SOF/VEL group and 51.5% (3,542) of the GLE/PIB group. DDI risk involving two or more medications in combination with a DAA was 14.1% (66) in the SOF/VEL group and 24.0% (1,648) in the GLE/PIB group. In terms of DDI severity, in the SOF/VEL group there were no patients identified under the level “Contraindication (Red)” category, indicating medications that do not co-administered, in contrast with the 1.7% (115) in the GLE/PIB group who were identified as “contraindication (red)”.ConclusionA considerable proportion of patients were prescribed medications with DDI risk during DAA treatment. A small but notable proportion of patients were on “Contraindication (Red)” medications. Consideration of the potential DDI risks associated with comedications by healthcare professionals is advised, referring not only to package inserts but also tools such as Liverpool HEP checker to guide safe prescribing when initiating DAA therapy for HCV patients.

Are We Getting the Best Return on Investment From Clinical Drug-Drug Interaction Studies?

Food and Drug Administration (FDA) regulations mandate that package inserts (PIs) include observed or predicted clinically significant drug-drug interactions (DDIs), as well as the results of pharmacokinetic studies that establish the absence of effect. To quantify how frequently observed metabolic inhibition DDIs affecting US-marketed psychotropics are present in FDA-approved PIs and what influence the source of DDI information has on agreement between 3 DDI screening programs. The scientific literature and PIs were reviewed to determine all drug pairs for which there was rigorous evidence of a metabolic inhibition interaction or noninteraction. The DDIs were tabulated noting the source of evidence and the strength of agreement over chance. Descriptive statistics were used to examine the influence of source of DDI information on agreement among 3 DDI screening tools. Logistic regression was used to assess the influence of drug class, indication, generic status, regulatory approval date, and magnitude of effect on agreement between the literature and PI as well as agreement among the DDI screening tools. Thirty percent (13/44) of the metabolic inhibition DDIs affecting newer psychotropics were not mentioned in PIs. Drug class, indication, regulatory approval date, generic status, or magnitude of effect did not appear to be associated with more complete DDI information in PIs. DDIs found exclusively in PIs were 3.25 times more likely to be agreed upon by all 3 DDI screening tools than were those found exclusively in the literature. Generic status was inversely associated with agreement among the DDI screening tools (odds ratio 0.11; 95% CI 0.01 to 0.89). The presence in PIs of DDI information for newer psychotropics appears to have a strong influence on agreement among DDI screening tools. Users of DDI screening software should consult more than 1 source when considering interactions involving generic psychotropics.

Important Drug Interactions in Hematopoietic Stem Cell Transplantation: What Every Physician Should Know

To compare comprehensiveness and accuracy of drug interaction information in the German summary of product characteristics (SPC) with current evidence from the literature and to evaluate the SPC's usefulness with respect to management of drug interactions. Information on clinically relevant drug interactions was compared between the SPC and three standard information sources on drug interactions (DRUGDEX, Hansten/Horn's Drug Interactions Analysis and Management, Stockley's Drug Interactions) according to five consecutive criteria (inclusion, appropriateness of class labelling, effect description, management recommendation, explicit dose adjustment). Using medication data of an outpatient population (n=4,949), we determined what percentage of insufficiently characterized combinations indeed occurred in outpatients treated with combination drug therapy. Only for 33% (192/579) of the evaluated combinations did SPCs provide drug interaction information equivalent to the evidence from the published literature. Of the clinically relevant drug interactions, 16% were completely missing and 51% were insufficiently characterized compared with standard sources. Explicit management recommendations were either missing or differed from standard sources in 18% of the evaluated pairs of compounds. Of these missing or insufficiently characterized combinations, 12% (47/387) were indeed prescribed to outpatients. Those drug combinations for which the interaction potential was not mentioned in the SPC were received by 0.6% (32/4,949) of patients, and 4% (192/4,949) of patients received combinations that had insufficiently characterized drug interactions. If physicians only rely on SPC information for drug interactions, adverse events due to lacking management recommendations may occur. To meet the SPCs claim of being the basis of information for health professionals on how to use medicinal products safely and effectively, information on drug interactions should be thoroughly up-dated and expanded.

New oral anticoagulants (OACs) that directly inhibit Factor Xa (FXa) or thrombin have been developed for the long-term prevention of thromboembolic disorders. These novel agents provide numerous benefits over older vitamin K antagonists (VKAs) due to major pharmacological differences. VKAs are economical and very well characterized, but have important limitations that can outweigh these advantages, such as slow onset of action, narrow therapeutic window and unpredictable anticoagulant effect. VKA-associated dietary precautions, monitoring and dosing adjustments to maintain international normalized ratio (INR) within therapeutic range, and bridging therapy, are inconvenient for patients, expensive, and may result in inappropriate use of VKA therapy. This may lead to increased bleeding risk or reduced anticoagulation and increased risk of thrombotic events. The new OACs have rapid onset of action, low potential for food and drug interactions, and predictable anticoagulant effect that removes the need for routine monitoring. FXa inhibitors, e.g. rivaroxaban and apixaban, are potent, oral direct inhibitors of prothrombinase-bound, clot-associated or free FXa. Both agents have a rapid onset of action, a wide therapeutic window, little or no interaction with food and other drugs, minimal inter-patient variability, and display similar pharmacokinetics in different patient populations. Since both are substrates, co-administration of rivaroxaban and apixaban with strong cytochrome P450 (CYP) 3A4 and permeability glycoprotein (P-gp) inhibitors and inducers can result in substantial changes in plasma concentrations due to altered clearance rates; consequently, their concomitant use is contraindicated and caution is required when used concomitantly with strong CYP3A4 and P-gp inducers. Although parenteral oral direct thrombin inhibitors (DTIs), such as argatroban and bivalirudin, have been on the market for years, DTIs such as dabigatran are novel synthetic thrombin antagonists. Dabigatran etexilate is a low-molecular-weight non-active pro-drug that is administered orally and converted rapidly to its active form, dabigatran--a potent, competitive and reversible DTI. Dabigatran has an advantage over the indirect thrombin inhibitors, unfractionated heparin and low-molecular-weight heparin, in that it inhibits free and fibrin-bound thrombin. The reversible binding of dabigatran may provide safer and more predictable anticoagulant treatment than seen with irreversible, non-covalent thrombin inhibitors, e.g. hirudin. Dabigatran shows a very low potential for drug-drug interactions. However, co-administration of dabigatran etexilate with other anticoagulants and antiplatelet agents can increase the bleeding risk. Although the new agents are pharmacologically better than VKAs--particularly in terms of fixed dosing, rapid onset of action, no INR monitoring and lower risk of drug interactions--there are some differences between them: the bioavailability of dabigatran is lower than rivaroxaban and apixaban, and so the dabigatran dosage required is higher; lower protein binding of dabigatran reduces the variability related to albuminaemia. The risk of metabolic drug-drug interactions also appears to differ between OACs: VKAs > rivaroxaban > apixaban > dabigatran. The convenience of the new OACs has translated into improvements in efficacy and safety as shown in phase III randomized trials. The new anticoagulants so far offer the greatest promise and opportunity for the replacement of VKAs.

Static and dynamic (PBPK) prediction models were applied to estimate the drug-drug interaction (DDI) risk of AZD2066. The predictions were compared to the results of an in vivo cocktail study. Various in vivo measures for tolbutamide as a probe agent for cytochrome P450 2C9 (CYP2C9) were also compared. In vitro inhibition data for AZD2066 were obtained using human liver microsomes and CYP-specific probe substrates. DDI prediction was performed using PBPK modelling with the SimCYP simulator™ or static model. The cocktail study was an open label, baseline, controlled interaction study with 15 healthy volunteers receiving multiple doses of AD2066 for 12days. A cocktail of single doses of 100mg caffeine (CYP1A2 probe), 500mg tolbutamide (CYP2C9 probe), 20mg omeprazole (CYP2C19 probe) and 7.5mg midazolam (CYP3A probe) was simultaneously applied at baseline and during the administration of AZD2066. Bupropion as a CYP2B6 probe (150mg) and 100mg metoprolol (CYP2D6 probe) were administered on separate days. The pharmacokinetic parameters for the probe drugs and their metabolites in plasma and urinary recovery were determined. In vitro AZD2066 inhibited CYP1A2, CYP2B6, CYP2C9, CYP2C19 and CYP2D6. The static model predicted in vivo interaction with predicted AUC ratio values of >1.1 for all CYP (except CYP3A4). The PBPK simulations predicted no risk for clinical relevant interactions. The cocktail study showed no interaction for the CYP2B6 and CYP2C19 enzymes, a possible weak inhibition of CYP1A2, CYP2C9 and CYP3A4 activities and a slight inhibition (29%) of CYP2D6 activity. The tolbutamide phenotyping metrics indicated that there were significant correlations between CLform and AUCTOL, CL, Aemet and LnTOL24h. The MRAe in urine showed no correlation to CLform. DDI prediction using the static approach based on total concentration indicated that AZD20066 has a potential risk for inhibition. However, no DDI risk could be predicted when a more in vivo-like dynamic prediction method with the PBPK with SimCYP™ software based on early human PK data was used and more parameters (i.e. free fraction in plasma, no DDI risk) were taken into account. The clinical cocktail study showed no or low risks for clinical relevant DDI interactions. Our findings are in line with the hypothesis that the dynamic prediction method predicts DDI in vivo in humans better than the static model based on total plasma concentrations.

Interactions médicamenteuses et hypolipémiants

Polypharmacy increase the risk of drug-drug interactions (DDIs) in the elderly population living with human immunodeficiency virus (HIV). Several expert databases can be used to evaluate DDIs. The aim of the study was to describe actual DDIs between antiretroviral drugs and comedications in an elderly population and to compare grading of the DDIs in 3 databases. All treatments of HIV-infected subjects aged 65 years and older were collected in 6 French HIV centres. Summary of Product Characteristic (SPC), French DDI Thesaurus (THES), and Liverpool HIV DDI website (LIV) were used to define each DDI and specific grade. DDIs were classified in yellow flag interaction (undefined grade in SPC and THES or potential weak interaction in LIV), amber flag interaction (to be considered/precaution of use in SPC and THES and potential interaction in LIV) and red flag interaction (not recommended/contraindication in SPC and THES and do not administer/contraindication in LIV). Among 239 subjects included, 60 (25.1%) had at least 1 DDI for a total of 126 DDIs: 23/126 red flag DDIs were identified in 17 patients. All these 23 DDIs were identified in LIV. THES and SPC missed 6 and 1 red flag DDIs, respectively. Seven of 23 red flag DDIs were identified in the 3 databases concomitantly. Polypharmacy is frequent in this elderly HIV population leading to DDI in a quarter of the subjects. The discrepancies between databases can be explained by differences in analysis methods. A consensus between databases would be helpful for clinicians.

The LTC View on Rasagiline and Sitagliptin

The Clinical Pharmacology Sections in Drug Package Inserts: Do We Need to Reexamine the Basis?

Predicting the probability of experiencing clinically significant drug-drug interactions involving boceprevir-containing hepatitis C therapy among patients coinfected with hepatitis C and HIV.