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.

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.     

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.     

TDM: therapeutic drug measuring or therapeutic drug monitoring?

The third round of the International Interlaboratory Quality Control Program for Therapeutic Drug Monitoring in HIV infection (QC-program) consisted of the analysis not only of plasma samples but also of patient cases. The case was composed of different topics related to the therapeutic drug monitoring of antiretroviral drugs. The participants were asked to give recommendations concerning dose adjustments, changes to the regimen, and drug-drug interactions to observe whether the expert recommendations were comparable. Of the 30 participants of the QC-program, 16 returned their comments and recommendations with regard to the patient case. The drug level was easy to judge: approximately 90% were able to correctly do so. Almost half of the recommendations (44%) given were satisfactory. Levels of knowledge regarding HIV treatment appeared to be variable among the respondents and for this reason were partly incomparable.     

Evaluation of drug–drug interactions in drug metabolism: Differences and harmonization in guidance/guidelines

The U.S. Drugs and Food Administration (FDA) and the Ministry of Health, Labor and Welfare of Japan (MHLW) issued the drastically revised draft guidance and final guideline on drug-drug interactions (DDI) in 2017 and 2018, respectively. One of the most drastic changes for the evaluation of inhibition potential of drug metabolizing enzymes in the liver using a basic model in these guidance and guideline are represented by the concept to use the unbound maximum concentration in the systemic circulation as the investigational drug concentration instead of the total maximum concentration and the corresponding cutoff values are applied in harmonization with the current DDI guideline of Europe. In this review, the current DDI guidance and guidelines of the three regions are compared and the points which are in common are described. In addition, several issues to be considered and/or clarified such as a criterion for the metabolites to be evaluated as perpetrator drugs, details of in vitro study design etc. are also briefly summarized. Based on further accumulation of data and information, and their continuous international scientific discussion, these issues are expected to be solved to make the current DDI guidance and guidelines be much more harmonized and practically available standards.     

Does inhibition of P-glycoprotein lead to drug–drug interactions?

Permeability-glycoprotein (Pgp) is a drug transporter responsible for the efflux of xenobiotics out of cells that influence the pharmacokinetics of numerous drugs. However, the role of this transporter in drug-drug interactions is still poorly studied even though a lot of P-glycoprotein substrates and P-glycoprotein inhibitors are identified among drugs of standard usage. On one hand, Pgp is distributed within a lot of organs and tissues implicated in the absorption or excretion of xenobiotics, and drug-drug interactions with this protein may increase the bioavailability of simultaneously administered active drugs. On the other hand, Pgp is linked to the integrity of blood-tissue barriers, such as the blood-brain barrier or placenta, and a partial blockage of Pgp could be responsible for a new drug distribution in the organism with possible increase of drug rates in organs behind these barriers. Therefore, concomitant administration of substrates and Pgp inhibitors would modify drug pharmacokinetics by increasing bioavailability and organ uptake, leading to more adverse drug reactions and toxicities. Consequently, the identification and comprehension of these drug-drug interactions remain important keys to risk assessment.     

Assessment of non-linear combination effect terms for drug–drug interactions

Drugs interact with their targets in different ways. A diversity of modeling approaches exists to describe the combination effects of two drugs. We investigate several combination effect terms (CET) regarding their underlying mechanism based on drug-receptor binding kinetics, empirical and statistical summation principles and indirect response models. A list with properties is provided and the interrelationship of the CETs is analyzed. A method is presented to calculate the optimal drug concentration pair to produce the half-maximal combination effect. This work provides a comprehensive overview of typically applied CETs and should shed light into the question as to which CET is appropriate for application in pharmacokinetic/pharmacodynamic models to describe a specific drug–drug interaction mechanism.     

Development of an ADME and drug–drug interactions knowledge database for the acceleration of drug discovery and development

It is widely recognised that predicting or determining the absorption, distribution, metabolism and excretion (ADME) properties of a compound as early as possible in the drug discovery process helps to prevent costly late-stage failures. Although in recent years high-throughput in vitro absorption distribution metabolism excretion toxicity (ADMET) screens have been implemented, more efficient in silico filters are still highly needed to predict and model the most relevant metabolic and pharmacokinetic end points, and thereby accelerate drug discovery and development. The usefulness of the data generated and published for the chemist, biologist or project manager who ultimately wants to understand and optimise the ADME properties of lead compounds cannot be argued with. Collecting and comparing data is an overwhelming task for the time-pressed scientist. Aureus Pharma provides a uniquely specialised solution for knowledge generation in drug discovery. AurSCOPE^® ADME/DDI (drug–drug interaction) is a fully annotated, structured knowledge database containing all the pertinent biological and chemical information on the metabolic properties of drugs. This Aureus knowledge database has proven to be highly useful in designing predictive models and identifying potential drug–drug interactions.     

Potential drug–drug interactions and radiodiagnostic procedures: an in-hospital survey

Objectives To evaluate the type, frequency, severity and predictors of potential Drug-Drug Interactions (DDIs) in a cohort of patients undergoing radiodiagnostic procedures. Setting Eight Radiology wards located in Tuscany (Italy). Methods All participants exposed to at least two medications were included in the analysis. DDIs were grouped according to their severity as ‘minor’, ‘moderate’ or ‘major’. A logistic model was used to estimate Odds Ratios and 95% Confidence Intervals for all predictors of potential DDI. Main outcome measures Type and predictors of potential DDI in a cohort of patients undergoing radiodiagnostic procedures. Results One-thousand-and-two subjects (57.6% females; mean age: 67.3 ± 12.2) entered the analysis, and 46.1% of them incurred in a potential DDI (78.9% ‘moderate’ in severity). The combination of allopurinol and ACE-inhibitors was the most frequent (21/153) among major potential DDIs, while steroids were involved in all cases of potential DDI due to premedication. Co-morbidity, number of co-medications, advanced age and premedication use increased the risk of potential DDI; a protective role was found for positive history of allergy. When the analysis was restricted to subjects with premedication (n = 93), only 12.9% of them reported a potential DDI directly attributable to premedication drugs. Conclusions Among patients undergoing radiological examination, types and predictors of potential DDIs appeared in agreement with other kind of in-hospital populations. Premedication revealed to be a proxy predictor for potential DDIs. Considering the poor capability of the prescriber in recognizing interactions, their systematic evaluation (using an informatics tool) in patients undergoing radiological examination might be helpful in preventing the occurrence of clinically relevant DDIs.     

Target-mediated drug disposition model and its approximations for antibody–drug conjugates

Antibody–drug conjugate (ADC) is a complex structure composed of an antibody linked to several molecules of a biologically active cytotoxic drug. The number of ADC compounds in clinical development now exceeds 30, with two of them already on the market. However, there is no rigorous mechanistic model that describes pharmacokinetic (PK) properties of these compounds. PK modeling of ADCs is even more complicated than that of other biologics as the model should describe distribution, binding, and elimination of antibodies with different toxin load, and also the deconjugation process and PK of the released toxin. This work extends the target-mediated drug disposition (TMDD) model to describe ADCs, derives the rapid binding (quasi-equilibrium), quasi-steady-state, and Michaelis–Menten approximations of the TMDD model as applied to ADCs, derives the TMDD model and its approximations for ADCs with load-independent properties, and discusses further simplifications of the system under various assumptions. The developed models are shown to describe data simulated from the available clinical population PK models of trastuzumab emtansine (T-DM1), one of the two currently approved ADCs. Identifiability of model parameters is also discussed and illustrated on the simulated T-DM1 examples.     

Can intranasal drug use reduce HCV infection among injecting drug users?

Background

Preventing HCV infection among people who inject drugs is a difficult public health challenge. We examined the potential role of intranasal drug use in reducing HCV acquisition.

Methods

Subjects were recruited from IDUs entering the Beth Israel drug detoxification program from 2005 to 2010. A structured interview was administered and serum samples were collected for HCV testing.

Results

726 active injecting drug users were recruited from 2005 to 2010. HCV prevalence was 71%, 90% reported recent heroin injection and 44% reported recent intranasal heroin use. In a multiple logistic regression analysis, being HCV seropositive was associated with more years injecting, Latino ethnicity, previous testing for HCV, and recent injection of speedball, and negatively associated with recent intranasal use of heroin (AOR = 0.52, 95% CI 0.33-0.82) and intranasal use of speedball (AOR = 0.41, 95% CI 0.31-0.80). The association between intranasal heroin use and lower HCV seroprevalance was observed among both new injectors and persons with long injecting histories (16+ years since first injection).

Conclusion

Encouraging intranasal use as an alternative to injection among persons currently injecting drugs may be a viable strategy for reducing HCV transmission.     

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.