Potential drug-drug interaction and its determinants among patients with cancer receiving chemotherapy in oncology centres of Northwest Ethiopia: an institutional-based cross-sectional study

OBJECTIVE

The study was conducted to assess potential drug-drug interactions (PDDIs) and its determinants among patients with cancer receiving chemotherapy.

DESIGN AND SETTING

An institutional-based cross-sectional study was used. This study was conducted from 1 June 2021 to 15 December 2021, in Northwest Ethiopia oncology centres.

PARTICIPANTS

All eligible patients with cancer received a combination of chemotherapy.

OUTCOMES

The prevalence and severity of PDDIs were evaluated using three drug interaction databases. Characteristics of participants were presented, arranged and summarised using descriptive statistics. The predictors and outcome variables were examined using logistic regression. The cut-off point was a p value of 0.05.

RESULTS

Of 422 patients included in the study, 304 patients were exposed to at least one PDDI with a prevalence of 72.1% (95 % CI: 68% to 76%) using three drug interaction databases. There were varied reports of the severity of PDDI among databases, but the test agreement using the kappa index was 0.57 (95% CI: 0.52 to 0.62, p=0.0001) which is interpreted as a moderate agreement among three databases. Patients aged ≥50 years old had the risk to be exposed to PDDI by odds of 3.1 times (adjusted OR (AOR)=3.1, 95% CI (1.8 to 5.3); p=0.001) as compared with patients <50 years old. Similarly, patients with polypharmacy and comorbidity were more likely to be exposed to PDDI than their counterparts (AOR=2.4, 95% CI (1.4 to 4.1); p=0.002 and AOR=1.9, 95% CI (1.1 to 3.4); p=0.02, respectively).

CONCLUSION

The main finding of this study is the high prevalence of PDDI, signifying the need for strict patient monitoring for PDDIs among patients with cancer receiving chemotherapy. We suggest the use of at least three drug databases for quality screening. Patients with an age ≥50 years old, polypharmacy and comorbidity were significantly associated with PDDIs. The establishment of oncology clinical pharmacists and computerised reminder mechanisms for PDDIs through drug utilisation review is suggested.

The Prevalence and Severity of Potential Drug-Drug Interactions in Internal Medicine Ward at Soba Teaching Hospital

Background

Multiple drug therapies are commonly used to achieve a desired therapeutic goal, especially in hospitalized patients. However, drug-drug interactions might occur and threaten the patients' safety.

Objective

This study aims to assess the prevalence and severity of potential drug-drug interactions (PDDIs) in the internal medicine ward at Soba Teaching Hospital.

Methods

A retrospective cross-sectional hospital-based study was carried out in the internal medicine ward at Soba Teaching Hospital from June 2021 to December 2021. The data was collected from patients' medical records. PDDIs were identified using Lexicomp® drug interaction software.

Results

A total of 377 patients were included in this study, and overall prevalence of PDDIs was 62.9%. We have identified 989 potential DDIs and 345 pairs of interacting drugs, the mean of the PDDIs per patient was 4.17 ± 4.079. Among 345 PDDIs most were of moderate interactions 70.1% (n=242) followed by Minor interactions 19.1% (n=66). The most common type of interaction was of category C representing 63.5% (n=219). A significant association was observed between the occurrence of PDDIs with patients' age, presence of chronic diseases, length of hospital stay, and number of medications received by the patients.

Conclusion

Drug-drug interactions were highly prevalent in the internal medicine ward. Therefore, certain attempts are required to increase the awareness of the physicians about these interactions and minimize their occurrence.

Potential drug-drug interactions in patients with non-small cell lung cancer at a university hospital in Turkey

BACKGROUND

The aim of this study was to determine the drug profile of patients with non-small cell lung cancer (NSCLC) and to identify potential drug-drug interactions (PDDIs) during hospitalization. In particular, PDDIs in categories X and D were determined.

METHODS

This retrospective cross-sectional study was conducted in the oncology services of a university hospital between 2018 and 2021. PDDIs were evaluated using Lexicomp Drug Interactions® software included in UpToDate®.

RESULTS

A total of 199 patients were included in the study. Polypharmacy was present in 92.5% of the patients and the median (min-max) number of drugs used was 8 (2-16). 32% of the patients had D and X PDDIs. A total of 16 PDDIs at risk grade X were found in 15 (7.5%) patients. A total of 81 PDDIs of risk grade D were found in 54 (27.1%) patients and a total of 276 PDDIs of risk grade C were identified in 97 (48.7%) patients. Anticancer drugs (p = 0.008), opioids (p = 0.046), steroids (p = 0.003), 5-HT3 receptor antagonists (p = 0.012), aprepitant (p = 0.025) and antihistamines (p < 0.001) were statistically more frequent among patients with PDDIs than among those without.

CONCLUSION

The results of our study indicated that polypharmacy and PDDIs are common in hospitalized patients with NSCLC cancer. The monitoring of medications is critical for maximizing therapeutic effects and minimizing side effects related to PDDIs. As a part of multidisciplinary team, clinical pharmacists can contribute significantly to preventing, detecting and managing PDDIs.

Prevalence of Potential Drug-Drug Interactions Among Hypertensive Pregnant Women Admitted to a Tertiary Care Hospital

AIMS AND OBJECTIVES

The aim is to determine the frequency of potential drug-drug interactions (pDDIs) and to analyze the clinically relevant drug interactions among hypertensive pregnant women.

MATERIALS AND METHODS

This was an observational, cross-sectional study conducted at a tertiary care hospital. The prescriptions of the hypertensive pregnant women admitted to the hospital from June 2021 to December 2021 were analyzed for potential drug-drug interactions using the database from Lexicomp ® Solutions android mobile application version 7.5.4 (Wolters Kluwer, The Netherlands).

RESULTS

A total of 127 patients were evaluated during the study period of 6 months, of which 70 (55.12%) had pDDIs. The total number of pDDIs was 85, of which 70 (82.35 %) were clinically relevant interactions with the majority of them having moderate severity (81.17%) followed by minor severity (17.65%) and major severity (1.18%). The most frequently interacting pDDIs were between Labetalol and Lornoxicam (42.35%), followed by Labetalol and Diclofenac (22.35%).

CONCLUSION

This study highlights the high prevalence of potential drug interactions among hypertensive pregnant women and the need for rational drug use and strict vigilance in their monitoring.

Human Engineered Heart Tissue Models for Disease Modeling and Drug Discovery

The emergence of human induced pluripotent stem cells (hiPSCs) and efficient differentiation of hiPSC-derived cardiomyocytes (hiPSC-CMs) induced from diseased donors have the potential to recapitulate the molecular and functional features of the human heart. Although the immaturity of hiPSC-CMs, including the structure, gene expression, conduct, ion channel density, and Ca²⁺ kinetics, is a major challenge, various attempts to promote maturation have been effective. Three-dimensional cardiac models using hiPSC-CMs have achieved these functional and morphological maturations, and disease models using patient-specific hiPSC-CMs have furthered our understanding of the underlying mechanisms and effective therapies for diseases. Aside from the mechanisms of diseases and drug responses, hiPSC-CMs also have the potential to evaluate the safety and efficacy of drugs in a human context before a candidate drug enters the market and many phases of clinical trials. In fact, novel drug testing paradigms have suggested that these cells can be used to better predict the proarrhythmic risk of candidate drugs. In this review, we overview the current strategies of human engineered heart tissue models with a focus on major cardiac diseases and discuss perspectives and future directions for the real application of hiPSC-CMs and human engineered heart tissue for disease modeling, drug development, clinical trials, and cardiotoxicity tests.

Comparing Potential Drug-Drug Interactions in Companion Animal Medications Using Two Electronic Databases

Multiple-drug prescriptions can cause drug–drug interactions (DDIs), which increase risks associated with healthcare in veterinary medicine. Moreover, many human medicines are used in canine patients under the responsibility of veterinarians and may cause severe problems due to off-label use. Currently, many electronic databases are being used as tools for potential DDI prediction, for example, Micromedex and Drugs.com, which may benefit the prediction of potential DDIs for drugs used in canine. The purpose of this study was to examine different abilities for the identification of potential DDIs in companion animal medicine, especially in canine patients, by Micromedex and Drugs.com. Micromedex showed 429 pairs of potential DDIs, while Drugs.com showed 842 pairs of potential DDIs. The analysis comparing results between the two databases showed 139 pairs (12.28%) with the same severity and 993 pairs (87.72%) with different severities. The major mechanisms of contraindicated and major potential DDIs were cytochrome P450 induction–inhibition and QT interval prolongation. Veterinarians should interpret potential DDIs from several databases with caution and keep in mind that the results might not be reliable due to differences in sensitivity to drugs, drug-metabolizing enzymes, and elimination pathway between animals and humans.

Drug-drug interactions in neonatal intensive care: A prospective cohort study

BACKGROUND

To characterize the prevalence and profile of drug-drug interactions (DDIs), the drugs most related to major DDIs and risk factors of their prescription in a neonatal intensive care unit (NICU).

METHODS

Neonates admitted to a NICU who had at least one medication prescribed and a hospital stay >24 h were included in a prospective cohort study (August 2017 to July 2018). All medications prescribed during the hospitalization were collected from all neonates (n = 220), with the screening for DDIs. Prevalence and type of DDIs was identified. Network analysis was used to identify the drugs more implicated with DDIs. Logistic regression was used for the analysis of risk factors (p < 0.05).

RESULTS

Over 70% of neonates were exposed to DDIs and 29% were exposed to major DDIs. The network analysis identified furosemide, fentanyl, aminophylline and fluconazole as most implicated with DDI, fentanyl was especially associated with major DDIs. The number of drugs (OR 1.60, p < 0.01), caesarean delivery (OR 2.68, p < 0.05), gestational age (OR 1.03, p < 0.01) and APGAR score (OR 0.78, p < 0.01) were identified as risk factors for exposure to DDI.

CONCLUSION

Neonates in intensive care have a high exposure to DDIs and the occurrence of major DDIs is related specifically to the prescription of fentanyl. The number of prescribed drugs, gestational age, cesarean delivery and low APGAR score in the first minute were identified as risk factors for DDIs in NICU.

[Development and Application for Drug-safety and Efficacy Using a Large Claims Data]

Recent years, evidences for medical safety and efficacy are accelerated-developing using medical big data. Medical big data were adequate for analyzing 1) rare events that difficult for finding in each hospital, 2) for comparison of bench marks obtained routine work between average data in large number of hospitals and specific hospital data and 3) prescription surveys etc. As so far, these analyses using medical big data were conducted by academia and/or researcher. However, in these days, evidences using medical big data were focused on hospital pharmacists little by little. In this review, we show 3 researches using large claims data such as 1) risk factors assessing for failed low-density lipoprotein level achievement in members of the working-age population, 2) prevalence of drug-drug interaction in atrial fibrillation patients and 3) assessment of "look-alike" packaging designs related to medication errors using information technology and large claims data. Medical big data such as large claims data analysis is useful and suitable for building evidences according to medical staffs-needs.

Assessment of Potential Drug–Drug Interactions and Their Predictors in Chronic Outpatient Department of Dessie Referral Hospital, Dessie, Northeast Ethiopia

Objective

To assess the prevalence and predictors of Potential drug–drug interactions (DDIs) at the chronic outpatient department of Dessie Referral Hospital, Dessie, Northeast Ethiopia.

Patients and Methods

A cross-sectional study was carried out on the medical records of patients treated in the chronic ambulatory department of Dessie Referral Hospital (DRH), from March 1/2019 to May 30/2019. Ethical clearance was granted from the department of pharmacy, college of medicine, and health sciences, Wollo University. Lexi-comp computer program database was used to detect pDDIs. SPSS version 22 was used to produce a descriptive analysis of the background data and logistic regression to identify predictors of pDDIs.

Results

In this study, the medical record of 300 patients has been reviewed and 489 pDDIs have been identified. The prevalence of pDDIs per patient was 1.63. Of all the identified pDDIs, the moderate severity interactions were the majority, 88.55% (n=433) followed by 8.38% (n=41) of minor, 2.66% (n=13) of major, and 0.41% (n=2) of contraindicated drug interactions. Taking three or more drugs at a time has been found as a statistically significant predictor of the occurrence of pDDIs.

Conclusion

A high rate of moderate severity pDDIs have been recorded. A system of checks and balances should be developed and executed for all those who are involved in prescribing, dispensing, and administration of medications for effective identification and prevention of pDDIs.

Prevalence and significance of potential drug-drug interactions among cancer patients receiving chemotherapy

Background

Cancer patients often receive multiple drugs to maximize their therapeutic benefit, treat co-morbidities and counter the adverse effects of chemotherapy. Concomitant administration of multiple drugs increases the risk of drug interactions leading to compromised therapeutic efficacy or safety of therapy. The purpose of this study was to identify the prevalence, levels and predictors of potential drug-drug interactions (pDDIs) among cancer patients.

Methods

Six hundred and 78 patients receiving chemotherapy from two tertiary care hospitals were included in this cross-sectional study. Patient medication profiles were screened for pDDIs using the Micromedex® database. Logistic regression analysis was performed to identify the predictors of pDDIs.

Results

The overall prevalence of pDDIs was 78%, majority of patients had 1–2 pDDIs (39.2%). A total of 1843 pDDIs were detected. Major-pDDIs were most frequent (67.3%) whereas, a significant association of pDDIs was found between > 7 all prescribed drugs (p < 0.001) and ≥ 3 anti-cancer drugs (p < 0.001). Potential adverse outcomes of these interactions include reduced therapeutic effectiveness, QT interval prolongation, tendon rupture, bone marrow suppression and neurotoxicity.

Conclusions

Major finding of this study is the high prevalence of pDDIs signifying the need of strict patient monitoring for pDDIs among cancer patients. Patients at higher risk to pDDIs include those prescribed with > 7 any types of drugs or ≥ 3 anticancer drugs. Moreover, list of most frequently identified major and moderate interactions will aid health care professional in timely identification and prevention of pDDIs.

Prevalence of drug-drug interaction in atrial fibrillation patients based on a large claims data

This study aimed to compare and determine the prevalence of drug–drug interaction (DDI) and bleeding rate in atrial fibrillation (AF) patients receiving anticoagulants in a clinical setting. We used large claims data of AF patients obtained from the Japan Medical Data Center. The prevalence of DDIs and cases leading to bleeding events were surveyed clinically relevant DDIs extracted from 1) reported from a spontaneous adverse event reporting system (Japanese Adverse Drug Events Report system; JADER) ≥4 patients; 2) DDIs cited in the package inserts of each anticoagulant (each combination assessed according to “Drug interaction 2015” list; 3) warfarin and quinolone antibiotics DDIs. DDIs were categorized the mechanisms for pharmacokinetic DDI (Cytochrome P450 (CYP) or transporter etc. that modulate blood concentration of anticoagulants)/pharmacodynamic DDI (combination with similar pharmacological actions) or both in the analysis for each patients’ prescriptions obtained from a claims data. AF patients were compared between cases with and without bleeding after administered of anticoagulants. Bleeding was observed in 220/3290 (6.7%) AF patients. The bleeding rate in patients with both pharmacokinetic and pharmacodynamic DDI mechanisms (26.3%) was higher than that in patients with either mechanism (8.6% and 9.2%, respectively) or without DDIs (4.9%). The odds ratio for bleeding in AF patients with both of pharmacokinetic and pharmacodynamic was (7.18 [4.69–11.00], p<0.001). Our study concluded multi mechanism based DDIs leads serious outcome as compared to that of single mechanism based DDIs in AF patients. We determined the prevalence and frequency of bleeding for anticoagulant-related DDIs. To manage DDIs, both pharmacokinetic and pharmacodynamic DDI mechanisms should be closely monitored for initial symptoms of bleeding within the first 3 months.

Comparison of potential drug-drug interactions with metabolic syndrome medications detected by two databases

Background

Drug-drug interactions (DDIs) are one of the most common drug-related problems. Recently, electronic databases have drug interaction tools to search for potential DDIs, for example, Micromedex and Drugs.com. However, Micromedex and Drugs.com have different abilities in detecting potential DDIs, and this might cause misinformation to occur between patients and health care providers.

Methods and findings

The aim of this study was to compare the ability of Micromedex and Drugs.com to detect potential DDIs with metabolic syndrome medications using the drug list from the U-central database, King Chulalongkorn Memorial Hospital in April 2019. There were 90 available drugs for the treatment of the metabolic syndrome and its associated complications, but six were not found in the Micromedex and Drugs.com databases; therefore, only 84 items were used in the present study. There were 1,285 potential DDI pairs found by the two databases. Micromedex reported DDIs of 724 pairs, while, Drugs.com reported 1,122 pairs. For the severity of the potential DDI reports, the same severity occurred between the two databases of 481 pairs (37.43%) and a different severity for 804 pairs (62.57%).

Conclusion

Drugs.com had a higher sensitivity to detect potential DDIs by approximately 1.5-fold, but Micromedex supplied more informative documentation for the severity classification. Therefore, pharmacists should use at least two databases to evaluate potential DDIs and determine the appropriate drug regimens for physician communications and patient consultations.

Impact of Clinical Pharmacist's Interventions on Potential Drug-Drug Interactions in the Cardiac Care Units of Two University Hospitals in Shiraz, South of Iran

Objective:

The main objective of this study aimed to assess drug–drug interactions (DDIs) in the cardiac care unit (CCU) and cardiac surgery units and the role of a clinical pharmacist in detecting and preventing the expected DDIs.

Methods:

This cross-sectional study was conducted in the CCU Units of Nemazee and Shahid Faghihi Hospitals, two referral hospitals in Shiraz, South of Iran, from August to February 2016. Patients older than 18 years, who were admitted and had received >24 h of inpatient services in these wards with two or more medication orders, were included in this study. All medication orders were evaluated by a pharmacist and DDIs were examined based on the Lexi-Interact™ software. In cases with serious DDIs (D or X), the physicians and nurses were informed, and intervention was conducted by a clinical pharmacist.

Findings:

A total of 3706 medical orders were evaluated. 6478 DDIs were detected, of which, 446 (6.88%) belonged to Classes D and X, and a total of 43.43% of all hospitalizations had at least one DDI. Factors with the most considerable influence on DDIs included an increased number of prescribed medications and patients underlying disease. The physicians accepted 62% of the interventions. The most frequent drugs responsible for interactions of Classes C, D, and X were aspirin, warfarin, and clopidogrel, respectively.

Conclusion:

This study shows that a significant number of clinical DDIs exist in hospitalized patients, especially among consumers of warfarin and aspirin. The role of a clinical pharmacist in preventing such interactions and safer pharmacotherapy management for hospitalized patients is essential.

Potential drug-drug interactions in outpatient department of a tertiary care hospital in Pakistan: a cross-sectional study

BACKGROUND

Potential drug-drug interactions (pDDIs) are one of the preventable drug related problems having the risk of serious adverse events or therapeutic failure. In developing countries like Pakistan, this issue remains poorly addressed. The objective of this study was to explore prevalence of pDDIs in the Outpatient Department (OPD) of a tertiary care hospital in Pakistan. The secondary aim was to describe the levels of reported pDDIs and develop a list of widespread clinically relevant interactions.

METHODS

Prescriptions of 2400 OPD patients were analyzed for pDDIs through Micromedex Drug-Reax®. Prevalence, severity- and documentation-levels and widespread clinically relevant interactions were reported.

RESULTS

Of total 2400 prescriptions, pDDIs were present in 22.3%. Whereas, moderate- and major-pDDIs were found in 377 (15.7%) and 225 (9.4%), respectively. PDDIs were more prevalent in Medicine (9.2%) and Cardiology (2.6%) as compared with other OPD specialties. Total 942 pDDIs were identified, of which, the majority were either moderate- (61.9%) or major-pDDIs (32.1%). Some of the most common interactions were ibuprofen + levofloxacin (n = 50), ciprofloxacin + diclofenac (32), aspirin + atenolol (24), and diclofenac + levofloxacin (19). The potential adverse outcomes of widespread interactions were seizures, bleeding, QT-interval prolongation, arrhythmias, tendon rupture, hypoglycemia/hyperglycemia, serotonin syndrome, drug toxicity, and decreased therapeutic response.

CONCLUSIONS

OPD patients were at risk to pDDIs, particularly to major- and moderate-pDDIs. Screening of prescriptions for pDDIs and monitoring of pharmacotherapy in terms of response and associated adverse drug events will contribute to patient safety.

Challenges in designing and executing clinical trials in a dish studies

The ever-increasing cost of drug discovery and development represents a significant challenge for the pharmaceutical industry and new strategies to bridge studies between preclinical testing and clinical trials are needed to reduce the knowledge gap prior to first human exposures, and to allow earlier decisions to be made on the further development of drugs. A number of studies have demonstrated that various cell types differentiated from human induced pluripotent stem cells (iPSCs) do not just respond similarly to human tissues in general, but rather recapitulate the drug response of their specific donor's, when exposed to the same drug in vivo. This recapitulation opens the doors to Clinical Trials in a Dish (CTiD), a platform which involves testing, in vitro, medical therapies for safety on cells collected from a sample of human patients, before moving into clinical trials. However, the science behind CTiD is complex, and every element of the process from tissue acquisition to data generation must be assessed and designed to meet quality metrics and standards. Without such rigorous assessment and design, the basic scientific integrity of CTiD constructs is likely compromised, and the results questionable. Given the lack of standard process and/or quality metrics in place for the use of stem cell-based products for in vitro testing per se, we discuss here the key elements that one needs to consider when designing, implementing and executing CTiD studies, in order to ensure an approach that will reliably mimic clinical trials, and allow obtaining reproducible and reliable experimental data.

Assessment of Consistency of Drug Interaction Information in Drug Labels Among the United States, the United Kingdom, China, Japan, and Korea

Information gap in drug labeling among countries create challenges in therapeutic use of drugs. We aimed to evaluate the consistency of drug interaction information in drug labels among five countries. The study drugs were chosen from the commonly approved drug list in the US, UK, China, Japan, and Korea. The degree of agreement of drug interaction data was evaluated by kappa coefficient. Thirty-eight drugs were evaluated, and moderate degree of agreement was observed among all countries' labeling (κ = 0.43, 95% confidence interval (CI) = 0.41-0.46). The degree of agreement was the highest for the UK and Korea (κ = 0.71, 95% CI = 0.67-0.75) and the lowest for the UK and Japan (κ = 0.02, 95% CI = 0.00-0.04). Information regarding drug interactions listed in the studied drug labels was not in high agreement. International standardization of drug labeling is required to ensure safe drug therapy.

Potential Drug-Drug Interactions in a Cohort of Elderly, Polymedicated Primary Care Patients on Antithrombotic Treatment

INTRODUCTION

Drug-drug interactions (DDIs) are an important risk factor for adverse drug reactions. Older, polymedicated patients are particularly affected. Although antithrombotics have been detected as high-risk drugs for DDIs, data on older patients exposed to them are scarce.

METHODS

Baseline data of 365 IDrug study outpatients (≥ 60 years, use of an antithrombotic and one or more additional long-term drug) were analyzed regarding potential drug-drug interactions (pDDIs) with a clinical decision support system. Data included prescription and self-medication drugs.

RESULTS

The prevalence of having one or more pDDI was 85.2%. The median number of alerts per patient was three (range 0-17). For 58.4% of the patients, potential severe/contraindicated interactions were detected. Antiplatelets and non-steroidal anti-inflammatory drugs (NSAIDs) showed the highest number of average pDDI alert involvements per use (2.9 and 2.2, respectively). For NSAIDs, also the highest average number of severe/contraindicated alert involvements per use (1.2) was observed. 91.8% of all pDDI involvements concerned the 25 most frequently used drug classes. 97.5% of the severe/contraindicated pDDIs were attributed to only nine different potential clinical manifestations. The most common management recommendation for severe/contraindicated pDDIs was to intensify monitoring. Number of drugs was the only detected factor significantly associated with increased number of pDDIs (p < 0.001).

CONCLUSION

The findings indicate a high risk for pDDIs in older, polymedicated patients on antithrombotics. As a consequence of patients' frequently similar drug regimens, the variety of potential clinical manifestations was small. Awareness of these pDDI symptoms and the triggering drugs as well as patients' self-medication use may contribute to increased patient safety.

Clinical Trials in a Dish: A Perspective on the Coming Revolution in Drug Development

The pharmaceutical industry is facing unprecedented challenges as the cost of developing new drugs has reached unsustainable levels, fueled in large parts by a high attrition rate in clinical development. Strategies to bridge studies between preclinical testing and clinical trials are needed to reduce the knowledge gap and allow earlier decisions to be made on the continuation or discontinuation of further development of drugs. The discovery and development of human induced pluripotent stem cells (hiPSCs) have opened up new avenues that support the concept of screening for cell-based safety and toxicity at the level of a population. This approach, termed “Clinical Trials in a Dish” (CTiD), allows testing medical therapies for safety or efficacy on cells collected from a representative sample of human patients, before moving into actual clinical trials. It can be applied to the development of drugs for specific populations, and it allows predicting not only the magnitude of effects but also the incidence of patients in a population who will benefit or be harmed by these drugs. This, in turn, can lead to the selection of safer drugs to move into clinical development, resulting in a reduction in attrition. The current article offers a perspective of this new model for “humanized” preclinical drug development.

Drug-drug interactions and their harmful effects in hospitalised patients: a systematic review and meta-analysis

PURPOSE

Drug-drug interactions (DDIs) are often avoidable and, if undetected, can lead to patient harm. This review aimed to determine the prevalence of potential DDIs (pDDIs), clinically relevant DDIs (DDIs that could lead to measurable patient harm, taking into account the patient's individual clinical profile) and DDIs that resulted in actual patient harm during hospitalisation.

METHOD

Four databases were scanned for English papers published from 2000 to 2016. Papers that reported prevalence of DDIs in the outpatient setting, at admission or discharge, involving only specific drugs, or in specific disease populations or age groups were excluded.

RESULTS

Twenty-seven papers met the inclusion criteria and were graded for quality using the Critical Appraisal Skills Programme (CASP) cohort study checklist. Ten papers were rated as 'poor', 14 as 'fair' and only three papers as 'good'. Overall, the meta-analysis revealed that 33% of general patients and 67% of intensive care patients experienced a pDDI during their hospital stay. It was not possible to determine the prevalence of clinically relevant DDIs or DDIs that resulted in actual patient harm as data on these categories were limited. Of the very few studies that reported on harm, only a small proportion of DDIs were found to have resulted in actual patient harm.

CONCLUSIONS

Standardisation of DDI definitions and research methods are required to allow meaningful prevalence rates to be obtained and compared. Studies that go further than measuring pDDIs are critically needed to determine the impact of DDIs on patient safety.

Incidence of Potential Drug-Drug Interactions in a Limited and Stereotyped Prescription Setting - Comparison of Two Free Online Pharmacopoeias

BACKGROUND

Drug-drug interactions (DDIs) are very common adverse events in health care delivery settings. The use of electronic pharmacopeias can potentially reduce the incidence of DDIs, but they are often thought to be cumbersome to use. This study is aimed at studying the incidence of potential DDIs in a surgical department, where a limited number of drugs are used in stereotyped combinations. We also compared two popular drug compendia in detecting potential DDIs.

METHODS

The prescriptions of selected patients were entered into Epocrates® and Medscape® for Android smartphones. Potential DDIs were generated and their categories were noted. The warnings generated by Epocrates® were compared with those generated by Medscape® and an agreement index was calculated.

RESULTS

Three hundred and thirty-one patients were included for analysis who had received a total of 2,878 drug orders. The incidence of potential DDIs was very high - 89% of all prescriptions. Phenytoin was the drug most commonly implicated, followed by furosemide. Of the DDIs detected, 0.14% were potentially serious and the drug combinations were contraindicated. There was a significant discrepancy between the categories of potential DDIs detected by Epocrates® and Medscape®. No clinically significant DDI was detected in any patient in this cohort.

CONCLUSIONS

Despite routinely using only a limited number of drugs in stereotyped combinations, prescriptions in surgical departments may not be immune from a significant incidence of DDIs. The use of free apps could reduce the incidence of DDIs, enhance patient safety, and also aid in educating trainees.

Potential drug-drug interactions in hospitalised haematological patients

Background Frequently, haematological patients undergo highly complex and intensive treatment protocols, so a high risk of drug-drug interactions could be expected. Objectives To determine prevalence of clinically relevant drug-drug interactions, to identify the most frequent drug-drug interactions and associated risk factors. Methods A prospective, observational and descriptive study was carried out from November 2012 to February 2013. Twice a week, every patient's treatment sheet was collected. Each medication list was screened through two databases: Thomson MicromedexTM and Drug Interaction FactsTM. All identified potential drug-drug interactions with a moderate or higher severity rating were recorded. Summary statistics were used to describe patient and disease characteristics, most often prescribed drugs, and frequency, types and classification of drug-drug interactions. Multiple logistic regression models were used to identify risk factors associated with drug-drug interactions. Results A total of 2061 drug-drug interactions were detected in 317 treatment sheets from 58 patients. The prevalence of treatment sheets with drug-drug interactions by Micromedex and Drug Interaction Facts databases were 74.1% and 56.8%, respectively. Azole antifungals, immunosuppressive drugs, antiemetics, antidepressants, acid suppressants and corticosteroids were the most frequent involved drugs. In multivariate analysis, the main risk factor associated with increased odds for drug-drug interactions was a higher number of non-antineoplastic drugs. Conclusions The prevalence of drug-drug interactions was common, with immunosuppressant and azole antifungal agents being the most commonly involved drugs. The factor having the greatest influence on drug-drug interactions was a higher number of non-antineoplastic drugs.

Transfusion Patterns in All Patients Admitted to the Intensive Care Unit and in Those Who Die in Hospital: A Descriptive Analysis

While it is known that the use of health care resources increases at the end of life in patients admitted to the Intensive Care Unit (ICU), the allocation of blood products at the end of life has not been described. The objective of this study was to describe overall transfusion patterns in the ICU, and specifically in patients who die in hospital. We conducted a retrospective cohort study of adult patients admitted to the ICU of a university-affiliated hospital, who were discharged or died between November 1, 2006 and June 30, 2012. During the study period, 10,642 patients were admitted at least once to the ICU. Of these patients, 4079 (38.3%) received red blood cells (RBCs), plasma or platelets in the ICU. The ICU mortality rate was 28.1% and in-hospital mortality rate was 32.3%. Among 39,591 blood product units transfused over the course of the study in the ICU (18,144 RBC units, 16,920 plasma units and 4527 platelet units), 46.2% were administered to patients who later died within the same hospitalization (41.2% of RBCs, 50.4% of plasma and 50.8% of platelets). Of all blood product units (RBCs, plasma and platelets) administered in the ICU over the study period, 11% were given within the last 24 hours before death. A large proportion of blood products used in the ICU are administered to patients who ultimately succumb to their illness in hospital, and many of these blood units are given in close proximity to death.

Evaluation of Potential Drug-Drug Interactions with Antidepressants in Two Tertiary Care Hospitals

BACKGROUND

Limited resources of healthcare system and high use of antidepressants have raised some serious concerns regarding proper surveillance system of prescribed medicines. Not much literature is available from Pakistan regarding the potential drug-drug interactions (pDDIs) associated with antidepressants.

OBJECTIVE

The objective of this study was to assess the frequency of pDDIs associated with antidepressants, their severity, significance and their association with patient characteristics.

MATERIALS AND METHODS

A prospective, observational study was conducted in two major hospitals of Karachi for the period of three months. Patient profiles, medication charts, and physician notes were thoroughly reviewed to gather all the relevant information. Inclusion and exclusion criteria were set prior to data collection. The collected data was then analysed using Micromedex Drug-REAX System. Descriptive and binomial logistic regression analysis was used to express results.

RESULTS

Of 245 prescriptions reviewed, 141 prescriptions had at least one pDDI (57.5%). A total of 181 pDDIs were identified in prescription containing antidepressant. The ratio of pDDI per prescriptions was 0.78. 42.5% interactions were moderate in severity, 30% of interactions were rapid in onset, and 43% were considered as significant interactions. Polypharmacy (OR=3.41, p< 0.001) and presence of chronic problems (OR=2.14, p=0.002) were significantly associated with the occurrence of pDDIs. Citalopram and diclofenac (11.6%) was commonly prescribed interacting pair in this study.

CONCLUSION

The findings of this study recorded high frequency of antidepressants associated pDDIs. Our results confirm the significant association of polypharmacy with the occurrence of pDDIs with antidepressants. Future studies are warranted to establish these results by including hospitals in different parts of the country.

Pharmacoepidemiological study of drug-drug interactions in onco-hematological pediatric patients

BACKGROUND

Onco-hematological patients are particularly susceptible to drug-drug interactions (DDIs) because they often undergo multiple combined treatments. Some studies have analyzed the frequency of DDIs in adult patients with cancer; however, the prevalence of DDIs in children, and especially among pediatric cancer patients, remains unknown.

OBJECTIVE

To determine the prevalence of DDIs in treatment sheets comparing two commonly used drug interaction databases, to describe the most common clinically relevant DDIs (CR-DDIs) and to investigate the risk factors associated with them.

SETTING

An onco-hematological pediatric unit from a tertiary hospital in Spain.

METHOD

A prospective, observational and descriptive study was carried out from November 2012 to February 2013. Twice a week, every patient's treatment sheet was collected. Each medication list was screened through two databases: Thomson Micromedex™ and Drug Interaction Facts™. All identified DDIs were graded by their level of severity. Summary statistics were used to describe patient and disease characteristics, most often prescribed drugs, and frequency, types and classification of CR-DDIs. Multivariate analysis was used to identify risk factors associated with CRDDIs.

MAIN OUTCOME MEASURE

Prevalence of CR-DDIs was measured as percentage.

RESULTS

A total of 506 potential DDIs were detected in 150 treatment sheets. The prevalence of CR-DDIs by Micromedex database and Drug Interaction Facts database were 44.7 and 51.3% respectively. Amikacin, azole antifungals, antiemetics and cyclosporine were the most frequent drugs involved in CR-DDIs. In multivariate analysis, the main risk factor associated with increased odds for CR-DDIs was a higher number of drugs.

CONCLUSION

The frequency of potential DDIs was related to a higher number of drugs, being immunosuppressant and azole antifungal agents the most commonly involved drugs. The lack of agreement between different databases enhances the complexity to detect drug interactions in clinical practice.

A study of potential drug-drug interactions among hospitalized cardiac patients in a teaching hospital in Western Nepal

AIM

Drug-drug interaction (DDI) is of major concern in patients with complex therapeutic regimens. The involvement of cardiovascular medicines in drug interaction is even higher. However, reports of DDI between these groups of drugs are few. The study aims to identify the potential DDI among hospitalized cardiac patients. Furthermore, we assessed the possible risk factors associated with these interactions.

SUBJECTS AND METHODS

The Type of study prospective observational study was conducted from May 2012 to August 2012 among hospitalized cardiac patients. Cardiac patients who were taking at least two drugs and who had a hospital stay of at least 24 h were enrolled. The medications of the patients were analyzed for possible interactions using the standard drug interaction database - Micromedex -2 (Thomson Reuters) × 2.0.

RESULTS

From a total of 150 enrolled patients, at least one interacting drug combination was identified among 32 patients. The incidence of potential DDI was 21.3%. A total of 48 potentially hazardous drug interactions were identified. Atorvastatin/azithromycin (10.4%), enalapril/metformin (10.4%), enalapril/potassium chloride (10.4%), atorvastatin/clarithromycin (8.3%) and furosemide/gentamicin (6.3%) were the most common interacting pairs. Drugs most commonly involved were atorvastatin, enalapril, digoxin, furosemide, clopidogrel and warfarin. Majority of interactions were of moderate severity (62.5%) and pharmacokinetic (58.3%) in nature. Increased number of medicines, prolonged hospital stays and comorbid conditions were the risk factors found associated with the potential DDI.

CONCLUSIONS

This study highlighted the need of intense monitoring of patients who have identified risk factors to help detect and prevent them from serious health hazards associated with drug interactions.

Analysis of potential drug-drug interactions in medical intensive care unit patients

OBJECTIVE

To describe the frequency and type of potential drug-drug interactions (pDDIs) in a general intensive care unit (ICU) and to make recommendations to improve the management of these pDDIs.

DESIGN

Retrospective observational study.

SETTING

General ICU of a tertiary care hospital.

SUBJECTS

All patients admitted for more than 24 hours between May 2009 and December 2010 who were prescribed at least one medication.

MEASUREMENT AND MAIN RESULTS

Based on the G-Standaard, the Dutch national drug database, pDDIs were identified and classified into categories of potential clinical outcome and management advice. In total, 35,784 medication episodes were identified, resulting in 2887 pDDIs (8.1%). These 2887 pDDIs occurred in 1659 patients for a mean frequency of 1.7 (95% confidence interval [CI] 1.6-1.9) pDDIs per patient. Overall, 54% of the patients experienced at least one pDDI with pDDIs present during 27% of all ICU admission days. All pDDIs could be reconstructed using 81 of the 358 (23%) relevant unique pDDI pairs described in the G-Standaard. The most frequently occurring potential clinical consequence was an increased risk of side effects or toxicity (91% of the pDDIs) such as electrolyte disturbances and masking of hypoglycemia. The most important advised management strategy was monitoring (81%), consisting of monitoring of laboratory values (52%), clinical monitoring of toxicity or effectiveness (48%), or monitoring of physical parameters such as electrocardiogram and blood pressure (11%).

CONCLUSION

Potential drug-drug interactions occur in 54% of all ICU patients, which is two times more than the rate seen in patients on general wards. A limited set of 20 pDDI pairs is responsible for more than 90% of all pDDIs. Therefore, it is worthwhile to develop guidelines for the management of these specific pDDIs. As the vast majority of the interactions can be managed by monitoring, advanced clinical decision support systems linking laboratory data to prescription data may be an effective risk management strategy.

Incidence of potential drug interactions in medication prescriptions for children and adolescents in the University Hospital Olomouc, Czech Republic

Drug interactions are important potential causes of adverse drug reactions. However, studies of their occurrence in children are almost entirely lacking. This study evaluates the incidence of potential drug interactions (PDIs) in medication prescriptions for children. The study was performed at the University Hospital in Olomouc. PDIs in each patient's prescriptions were identified. Multivariate analysis was performed in order to assess the risk factors confounding the potential interactions. Univariate analysis was used to assess which diagnostic groups and medication groups significantly increase or lower the odds of a potential drug-drug interaction. A total of 6,078 patients meeting the inclusion criteria entered the study. They received 19,522 prescriptions. PDIs were identified in 3.83 % of patients (moderate-to-severe cases in 0.47 %). Patient age (p = 0.008), the average number of prescriptions per visit (p < 0.0001), and the number of visits per year (p < 0.0001) were found to increase the risk of drug interaction. The presence of epilepsy, leukemia, or rheumatoid arthritis and related disease diagnoses were discovered to increase the risk of PDIs significantly.

CONCLUSION

The risk of PDIs in children is low, but it increases significantly with age and the number of drugs prescribed, particularly antiepileptics and immunosuppressants. The finding of a potential interaction in 0.47 % of all children in whom any medication was prescribed should not be underestimated since it means a significant risk for one child out of every 200, and it is also substantially higher in the chronically ill. Pediatricians should be aware of relevant interactions and should prevent them by therapeutic drug monitoring or appropriate clinical and laboratory monitoring.

Use of physiologically based pharmacokinetic modeling for assessment of drug-drug interactions

Interactions between co-administered medicines can reduce efficacy or lead to adverse effects. Understanding and managing such interactions is essential in bringing safe and effective medicines to the market. Ideally, interaction potential should be recognized early and minimized in compounds that reach late stages of drug development. Physiologically based pharmacokinetic models combine knowledge of physiological factors with compound-specific properties to simulate how a drug behaves in the human body. These software tools are increasingly used during drug discovery and development and, when integrating relevant in vitro data, can simulate drug interaction potential. This article provides some background and presents illustrative examples. Physiologically based models are an integral tool in the discovery and development of drugs, and can significantly aid our understanding and prediction of drug interactions.