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.     

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.     

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.     

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.     

Cytochrome P450 3A and P-glycoprotein drug–drug interactions with voclosporin

Aims

Voclosporin is a novel calcineurin inhibitor intended for prevention of organ graft rejection and treatment of lupus nephritis. Pharmacokinetic drug interactions between voclosporin and a CYP3A inhibitor, inducer and substrate and a P-glycoprotein inhibitor and substrate were evaluated.

Methods

Voclosporin 0.4 mg kg⁻¹ was administered to 24 subjects in each of five studies, as follows: every 12 h (Q12H) alone and concomitantly with ketoconazole 400 mg once daily (QD); single dose before and single dose after rifampin 600 mg QD; Q12H where midazolam 7.5 mg was administered as a single dose alone before voclosporin and with last the dose of voclosporin; Q12H alone and concomitantly with verapamil 80 mg every 8 h; and Q12H with digoxin 0.25 mg QD. The noncompartmental pharmacokinetic parameters maximal concentration (C_max) and area under the concentration–time curve (AUC) were obtained, and geometric least squares mean ratios and 90% confidence intervals were evaluated.

Results

Ketoconazole increased voclosporin C_max (6.4-fold) and AUC (18-fold); rifampin reduced voclosporin AUC (0.9-fold); voclosporin did not change exposure of midazolam or α-hydroxy-midazolam; verapamil increased voclosporin C_max (2.1-fold) and AUC (2.7-fold); and voclosporin increased digoxin C_max (0.5-fold), AUC (0.25-fold) and urinary excretion (0.2-fold).

Conclusions

Administration of voclosporin concomitantly with strong inhibitors and inducers of CYP3A resulted in increased and decreased exposures, respectively, and should be considered contraindicated. Drug–drug interactions involving voclosporin and CYP3A substrates are not expected. Administration of voclosporin concomitantly with inhibitors and substrates of P-glycoprotein resulted in increased voclosporin and substrate exposures, respectively. Appropriate concentration and safety monitoring is recommended with co-administration of voclosporin and P-glycoprotein substrates and inhibitors.     

Patient- and physician-related risk factors for hyperkalaemia in potassium-increasing drug–drug interactions

Purpose

Hyperkalaemia due to potassium-increasing drug–drug interactions (DDIs) is a clinically important adverse drug event. The purpose of this study was to identify patient- and physician-related risk factors for the development of hyperkalaemia.

Methods

The risk for adult patients hospitalised in the University Hospital Zurich between 1 December 2009 and 31 December 2011 of developing hyperkalaemia was correlated with patient characteristics, number, type and duration of potassium-increasing DDIs and frequency of serum potassium monitoring.

Results

The 76,467 patients included in this study were prescribed 8,413 potentially severe potassium-increasing DDIs. Patient-related characteristics associated with the development of hyperkalaemia were pulmonary allograft [relative risk (RR) 5.1; p?<?0.0001), impaired renal function (RR 2.7; p?<?0.0001), diabetes mellitus (RR 1.6; p?=?0.002) and female gender (RR 1.5; p?=?0.007). Risk factors associated with medication were number of concurrently administered potassium-increasing drugs (RR 3.3 per additional drug; p?<?0.0001) and longer duration of the DDI (RR 4.9 for duration ≥6 days; p?<?0.0001). Physician-related factors associated with the development of hyperkalaemia were undetermined or elevated serum potassium level before treatment initiation (RR 2.2; p?<?0.001) and infrequent monitoring of serum potassium during a DDI (interval >48 h: RR 1.6; p?<?0.01).

Conclusion

Strategies for reducing the risk of hyperkalaemia during potassium-increasing DDIs should consider both patient- and physician-related risk factors.     

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.     

Polymer–drug interactions and wetting of solid dispersions

We demonstrate the ability of drugs to influence the wetting of solid dispersion tablets in unexpected ways. Five model drugs of different water solubility and ability to interact with the involved polymers were incorporated in hydrophilic polymer matrices, made of either hydroxypropyl methylcellulose (HPMC) or polyvinyl pyrrolidone (PVP). The physical mixtures of all combinations of drug and polymer presented surface hydrophobicities, as measured by the equilibrium advancing contact angle of water, which are expected for materials that do not influence the interactions of each other with water. However, the solid dispersions containing HPMC deviated from this regular behaviour and displayed contact angles below those of the pure compounds involved, either drug or polymer. This behaviour is explained by changed surface exposure of HPMC side groups, as a result of changes in intermolecular hydrogen bonds. In addition to water contact angle measurements, we employed NMR imaging to monitor the time course of water ingress and swelling.     

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.     

Polypharmacy and potential drug–drug interactions in emergency department patients in the Caribbean

Background Potential Drug–Drug Interactions (DDI) account for many emergency department visits. Polypharmacy, as well as herbal, over-the-counter (OTC) and combination medication may compound this, but these problems are not well researched in low-and-middle-income countries. Objective To compare the incidence of drug–drug interactions and polypharmacy in older and younger patients attending the Emergency Department (ED). Setting The adult ED of a tertiary teaching hospital in Trinidad. Methods A 4 month cross sectional study was conducted, comparing potential DDI in older and younger patients discharged from the ED, as defined using Micromedex 2.0. Main outcome measure The incidence and severity of DDI and polypharmacy (defined as the use of ≥5 drugs simultaneously) in older and younger patients attending the ED. Results 649 patients were included; 275 (42.3%) were ≥65 years and 381 (58.7%) were female. There were 814 DDIs, of which 6 (.7%) were contraindications and 148 (18.2%) were severe. Polypharmacy was identified in 244 (37.6%) patients. Older patients were more likely to have potential DDI (67.5 vs 48.9%) and polypharmacy (56 vs 24.1%). Herbal products, OTC and combination drugs were present in 8, 36.7 and 22.2% of patients, respectively. On multivariate analysis, polypharmacy and the presence of hypertension and ischaemic heart disease were associated with an increased risk of potential DDI. Conclusion Polypharmacy and potential drug–drug interactions are common in ED patients in the Caribbean. Older patients are particularly at risk, especially as they are more likely to be on multiple medications. The association between herbal medication and polypharmacy needs further investigation. This study indicates the need for a more robust system of drug reconciliation in the Caribbean.     

Potential drug–drug interactions associated with clinical and laboratory findings at hospital admission

International Journal of Clinical Pharmacy - Background Drug–drug interactions represent one of the causes of adverse therapy outcomes through deteriorated efficacy or safety. However, the...     

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.     

Organic anion transporters also mediate the drug–drug interaction between imipenem and cilastatin

This study aimed to clarify that organic anion transporters(OATs)mediate the drug–drug interaction(DDI)between imipenem and cilastatin.After co-administration with imipenem,the plasma concentrations and the plasma concentration-time curve(AUC)of cilastatin were significantly increased,while renal clearance and cumulative urinary excretion of cilastatin were decreased.At the same time,imipenem significantly inhibited the uptake of cilastatin in rat kidney slices and in human OAT1(hOAT1)-HEK293 and human OAT3(hOAT3)-HEK293 cells.Probenecid,p-aminohippurate,and benzylpenicillin inhibited the uptake of imipenem and cilastatin in rat kidney slices and in hOAT1-and hOAT3-HEK 293 cells,respectively.The uptakes of imipenem and cilastatin in hOAT1-and hOAT3-HEK 293 cells were significantly higher than that in mock-HEK-293 cells.Moreover,the K m values of cilastatin were increased in the presence of imipenem with unchanged V max,indicating that imipenem inhibited the uptake of cilastatin in a competitive manner.When imipenem and cilastatin were co-administered,the level of imipenem was higher compared with imipenem alone both in vivo and in vitro.But,cilastatin significantly inhibited the uptake of imipenem when dehydropeptidase-1(DPEP1)was silenced by RNAi technology in hOAT1-and hOAT3-HEK 293 cells.In conclusion,imipenem and cilastatin are the substrates of OAT1 and OAT3.OAT1 and OAT3 mediate the DDI between imipenem and cilastatin.Meanwhile,cilastatin also reduces the hydrolysis of imipenem by inhibiting the uptake of imipenem mediated by OAT1 and OAT3 in the kidney as a complement.     

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.

Recognition and management of potential drug–drug interactions in patients on internal medicine wards

INTRODUCTION: Our aim was to study and possibly improve the clinical management of potential drug-drug interactions (pDDIs) in hospitalized patients by specific interventions. METHODS: During the initial period, inpatients on three medical wards were screened for major and moderate pDDIs using the interaction screening program Pharmavista. During the second period, patients at discharge were screened similarly. After assessment of the detected pDDIs for clinical relevance, written recommendations and/or information about the pDDIs were sent to the treating physicians. Feedback from the physicians and implementation of the recommendations were analyzed. RESULTS: During the initial period, 502 inpatients were exposed to 567 pDDIs, of which 419 (74%) were judged to be clinically relevant. Three hundred and forty-nine substantiated recommendations and 70 simple information leaflets were handed out to the physicians. Eighty percent (278 of 349) of the recommendations were accepted and implemented. During the second period, 792 patients at hospital discharge were exposed to 392 pDDIs, of which 258 (66%) were judged to be clinically relevant. Two hundred and forty-seven substantiated recommendations and 11 simple information leaflets were sent to the physicians. Seventy-three percent (180 of 247) of the recommendations were accepted. At hospital discharge, 47 of 71 interventions recommending checkable medication changes were implemented. One year after hospital discharge, 11 of 13 checked medication changes were still in place. CONCLUSIONS: Clinically relevant pDDIs are common in patients on medical wards, and their management can be influenced by providing substantiated recommendations to physicians. Most changes in medication following such recommendations are still in place 1 year after discharge.     

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.