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.

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.     

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.     

Potential drug–drug interactions in internal medicine wards in hospital setting in Pakistan

Background Multiple drugs therapies may be the potential source of drug–drug interactions that can result in alteration of therapeutic response and/or increase untoward effects of many drugs. Objective To identify the frequency and levels of potential drug–drug interactions (pDDIs) in internal medicine wards and their association with patients’ age, gender, length of hospital stay and number of prescribed medications; and to describe management of frequently identified major or moderate pDDIs. Setting Internal medicine wards of two major tertiary care hospitals of Khyber Pakhtunkhwa, Pakistan. Method Micromedex Drug-Reax system was used to screen patient’s profiles for pDDIs. Logistic regression was applied to determine the odds ratio for specific risk factors of pDDIs i.e., age, gender, hospital-stay and number of medications. Main outcome measure Overall prevalence and prevalence of contraindicated, major, moderate and minor pDDIs; levels of pDDIs; frequently identified major or moderate interactions; and odds ratios for risk factors. Results Total, 188 interacting drug-combinations were identified that contributed to 675 pDDIs. Of 400 patients, 52.8 % patients were presented with at least one pDDI (overall prevalence), 21.3 % with at least one major-pDDI, and 44.3 % with at least one moderate-pDDI. Of 675 pDDIs, most were of moderate (63.6 %) or major severity (23 %); good (61.2 %) or fair (25.5 %) type of scientific evidence; and delayed onset (50.2 %). Most frequently identified major or moderate interactions resulted in 45.3 % of all pDDIs. Their potential adverse outcomes included hepatotoxicity, bleeding, ototoxicity, nephrotoxicity, hypoglycemia, hyperglycemia, risk of thrombosis, hypotension, cardiac arrhythmias and reduction in therapeutic-effectiveness. There was significant association of the occurrence of pDDIs with patients’ age of 60 years or more (OR = 2.1; 95 % CI = 1.3–3.3; p = 0.003), hospital stay of 6 days or longer (OR = 2.6; 95 % CI = 1.5–4.5; p = 0.001), and seven or more number of prescribed medications (OR = 5.9; 95 % CI = 3.6–9.6; p < 0.001). Conclusion The present study has recorded a high prevalence of pDDIs in internal medicine wards. Patients with old age, longer hospital stay and increased number of prescribed medications were at higher risk.     

LC–NMR and LC–MS identification of an impurity in a novel antifungal drug icofungipen

Successful use of LC–NMR and LC–MS for rapid identification of an impurity in a novel antifungal drug icofungipen has been demonstrated. Complementary information obtained from the two methods made it possible to determine the structure of A1 prior to its isolation and purification. Stop-flow LC–NMR (¹H and DQFCOSY), LC–MS and LC–MS/MS spectra have shown that A1 is structurally related to icofungipen. It was later isolated and prepared synthetically and its structure was corroborated by high-resolution NMR spectroscopy.     

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.     

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.     

Microsomal metabolism of calycosin,formononetin and drug–drug interactions by dynamic microdialysis sampling and HPLC–DAD–MS analysis

A dynamic microdialysis sampling method with liquid chromatography–diode array detection and time-of-flight mass spectrometry (LC–DAD–TOF/MS) analysis was developed to investigate rat microsomal metabolisms of calycosin and formononetin, and their drug–drug interactions. Two hydroxylated metabolites from calycosin, and three hydroxylated or 4′-O-demethylated derivatives from formononetin were detected and identified after co-incubation with microsomes. Calibration curves offered linear ranges of two orders of magnitude with r² > 0.999 for calycosin, formononetin and daidzein. The quantitative LC method provides a range of 0.028–0.034 μg/mL for limits of detection, overall precision less than 5% and accuracy less than 3% by RSD. Besides, calycosin and formononetin were found to produce the depressive effect on the CYP450 enzyme reaction, and inhibit phase I enzyme reaction of each other when they are concurrent. Dynamic microdialysis sampling with LC–DAD–TOF/MS analysis developed in this work is a powerful tool for in vitro metabolism studies of drugs and metabolic interactions.     

Evaluation of drug–drug interactions for oncology therapies: in vitro–in vivo extrapolation model-based risk assessment

Aims

Understanding drug–drug interactions (DDI) is a critical part of the drug development process as polypharmacy has become commonplace in many therapeutic areas including the cancer patient population. The objectives of this study were to investigate cytochrome P450 (CYP)-mediated DDI profiles available for therapies used in the oncology setting and evaluate how models based on in vitro–in vivo extrapolation performed in predicting CYP-mediated DDI risk.

Methods

A dataset of 125 oncology therapies was collated using drug label and approval history information, incorporating in vitro and clinical PK data. The predictive accuracy of the basic and net effect mechanistic static models was assessed using this oncology drug dataset, for both victim and perpetrator potential of CYP3A-mediated DDI.

Results

The incidence of CYP3A-mediated interaction potential was 47%, 22% and 11% for substrates, inhibitors and inducers, respectively. The basic models for precipitants gave conservative predictions with no false negatives, whilst the mechanistic static models provided reasonable quantitative predictions (2.3–3-fold error). Further analysis revealed that incorporating DDI at the level of the intestine was in most cases over-predicting interaction magnitude due to overestimates of the rate and extent of oral absorption of the precipitant. Quantifying victim DDI potential was also demonstrated using f_mCYP3A estimates from ketoconazole clinical DDI studies to predict the magnitude of interaction on co-administration with the CYP3A inducer, rifampicin (1.6–3.3 fold error).

Conclusions

This work illustrates the utility and limitations of current DDI risk assessment approaches applied to a range of contemporary anti-cancer agents, and discusses the implications for therapeutic combination strategies.     

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.     

Assessing the potential for drug–drug interactions in an accelerated throughput mode

Drug candidates that cause pharmacokinetic drug drug interactions are less likely to become a commercial success. However, rapid, cost-effective, mechanism-based screens are available for the evaluation of the potential of drug candidates to cause drug&ndashdrug interactions. This review describes experimental designs for, and recent progress in, increasing the throughput of these screens.     

Potential drug–drug interactions in medical intensive care unit of a tertiary care hospital in Pakistan

Background Patients admitted to intensive care unit (ICU) present with severe and life-threatening illnesses. Most of them suffer from various comorbidities. They usually receive complex pharmacotherapy with large number of medicines which increase the risk of drug–drug interactions (DDIs). Objective The present report aimed to investigate prevalence and levels of potential DDIs (pDDIs) in medical ICU. Methods Medications profiles of 416 patients were checked for pDDIs using Micromedex Drug-Reax^®. Prevalence, levels of severity and levels of documentation were reported. Results Of total 416 patients, 310 were exposed to pDDIs (overall prevalence = 74.5 %). Likewise, a prevalence rate of 13.9 % was recorded for contraindicated pDDIs, 52.2 % for major pDDI and 58.4 % for moderate pDDI. This study reported 740 interacting drug pairs that were presented in total 1686 pDDIs. Of 1686 pDDIs, 4.3 % were of contraindicated severity, 33.8 % of major severity and 49.6 % of moderate severity, whereas 45.5 % were of fair scientific evidence and 41.4 % of good scientific evidence. Conclusion In this study, pDDIs were found highly prevalent in ICU patients at a rate of 74.5 %. Most of the pDDIs had moderate severity; however, substantial number of interactions (38.1 %) had major and contraindicated severity.     

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.     

Human hepatocyte assessment of imatinib drug–drug interactions – complexities in clinical translation

Aim

Inducers and inhibitors of CYP3A, such as ritonavir and efavirenz, may be used as part of the highly active antiretroviral therapy (HAART) to treat HIV patients. HIV patients with chronic myeloid leukemia or gastrointestinal stromal tumour may need imatinib, a CYP3A4 substrate with known exposure response–relationships. Administration of imatinib to patients on ritonavir or efavirenz may result in altered imatinib exposure leading to increased toxicity or failure of therapy, respectively. We used primary human hepatocyte cultures to evaluate the magnitude of interaction between imatinib and ritonavir/efavirenz.

Methods

Hepatocytes were pre-treated with vehicle, ritonavir, ketoconazole, efavirenz or rifampicin, and the metabolism of imatinib was characterized over time. Concentrations of imatinib and metabolite were quantitated in combined lysate and medium, using LC-MS.

Results

The predicted changes in imatinib CL_oral (95% CI) with ketoconazole, ritonavir, rifampicin and efavirenz were 4.0-fold (0, 9.2) lower, 2.8-fold (0.04, 5.5) lower, 2.9-fold (2.2, 3.5) higher and 2.0-fold (0.42, 3.5) higher, respectively. These predictions were in good agreement with clinical single dose drug–drug interaction studies, but not with reports of imatinib interactions at steady-state. Alterations in metabolism were similar after acute or chronic imatinib exposure.

Conclusions

In vitro human hepatocytes predicted increased clearance of imatinib with inducers and decreased clearance with inhibitors of CYP enzymes. The impact of HAART on imatinib may depend on whether it is being initiated or has already been dosed chronically in patients. Therapeutic drug monitoring may have a role in optimizing imatinib therapy in this patient population.     

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.