A Tree-Based Scan Statistic for Detecting Signals of Drug-Drug Interactions in Spontaneous Reporting Databases.

Patients with Alzheimer's disease (AD) and other dementias are more frequently exposed to polymedication, mainly due to the presence of comorbidities, are particularly vulnerable to drug-related problems, and present greater risk of adverse effects due to drug-drug interactions (DDIs). To assess the prevalence of clinically relevant interactions in dementia patients using a routine database, we describe the most frequent interactions and risk factors associated with them to facilitate specific interventions and programs to prevent and minimize them. An observational, descriptive, and cross-sectional study that included patients with AD and other types of dementia (n = 100, 64% female) was conducted to identify potential DDI in their treatment using the Lexi-Interact/Lexicomp® database. A total of 769 drugs were prescribed, involving 190 different active ingredients; 83% of the treatments included five or more drugs. DDI occurred in 87% of the patients, of which 63.2% were female. A total of 689 DDIs were found, grouped in 448 drug pairs, with a mean of 6.9 ± 7.1 (range, 0-31) DDIs per patient, and 680 DDIs were considered clinically relevant. It was observed that 89.8% of the DDIs had a moderate level of severity, 23.5% had a good level of relevance, and pharmacodynamic-based DDIs accounted for 89.5%. The drugs most frequently involved in DDIs were quetiapine (24.5%) and acetylsalicylic acid (10%). A total of 97 DDIs were detected between the acetylcholinesterase inhibitors (AChEIs), and the remaining drugs were administered concomitantly. One of the most frequent DDIs was between AChEIs and beta-blocking agents (n = 29, 4.3%). The most important factors that showed the strongest association with the presence of drug interactions were the use of AChEIs (p = 0.01) and the total number of drugs (p = 0.014) taken by the patient. Patients with dementia present increased risk of DDIs. Among the most common drugs are psychotropic drugs, which are involved in pharmacodynamic interactions caused by the concomitant use of CNS-targeted drugs. The results highlight the difficulty to evaluate DDIs in clinical practice due to polymedication and variety of comorbidities. Therefore, it is important to review their treatment and consider metabolism inhibition or induction, and potentially P450 substrate overlapping.

Introduction:Concomitant use of multiple drugs is often indicated to manage comorbid conditions and enhance efficacy. Such concomitant use of multiple drugs (five or more drugs) has been defined as “polypharmacy.” Polypharmacy has been associated with adverse consequences such as greater healthcare costs, increased risk of adverse drug events, drug–drug interactions (DDIs), medication nonadherence, reduced functional capacity, and multiple geriatric syndromes. This study evaluated number of potential harmful DDIs due to polypharmacy.Materials and Methods:A prospective, cross-sectional, observational study was performed from July 2011 to June 2012. Approval was obtained from the Institutional Ethics Committee, Goa Medical College. Drug interactions were identified using a computerized DDI database system Lexi-Comp version: 2.4.1. Quantitative data analysis was done by the SPSS for Windows version 17.0.Results:Seven hundred and fifty-one out of 5424 (13.85%) prescriptions were observed to have polypharmacy with highest rates observed in the Department of Medicine. The median age of patients was 55.60 ± 13.86 (range 10–108 years). A total number of drugs per prescription ranged from minimum of 5 to maximum of 16 drugs, with an average of 7.96 ± 1.75. A large number of 596 prescriptions contained 6–9 drugs per prescription. Drugs involved in potential DDIs in our study included aspirin, antacids, beta-blockers, 3-hydroxy-3-methylglutaryl-coenzyme reductase inhibitors, calcium channel blockers, angiotensin-converting enzyme inhibitors, ondansetron, and H2 blockers.Conclusion:Patients taking multiple medications experience unique pharmacotherapy. Personalized drug prescribing strategies and close monitoring of patients taking drugs with potential DDIs are keys to optimal therapeutic result.

Concomitant use of multiple drugs for therapeutic purposes is known as “polypharmacy situations,” which has been recognized as an important social problem recently. In polypharmacy situations, each drug not only induces adverse events (AEs) but also increases the risk of AEs due to drug–drug interactions (DDIs). The proportion of AEs caused by DDIs is estimated to be around 30% of unexpected AEs. The randomized clinical trials in pre-marketing typically focus emphasis on the verification of single drug safety and efficacy rather than the surveys of DDI, and therefore, patients on multiple drugs are usually excluded. However, unlike pre-marketing randomized clinical trials, in clinical practice (= post marketing), many patients use multiple drugs. The spontaneous reporting system is one of the significant sources drug safety surveillance in post-marketing. Commonly, signals of potential drug-induced AEs detected from this source are validated in real-world settings. Recently, not only methodological studies on signal detection of “single” drug, but also on several methodological studies on signal detection of DDIs have been conducted. On the other hand, there are few articles that systematically summarize the statistical methodology for signal detection of DDIs. Therefore, this article reviews the studies on the latest statistical methodologies from classical methodologies for signal detection of DDIs using spontaneous reporting system. This article describes how to calculate for each detection method and the major findings from the published literatures about DDIs. Finally, this article presented several limitations related to the currently used methodologies for signal detection of DDIs and suggestions for further studies.

Utility of physiologically based pharmacokinetic modeling in predicting and characterizing clinical drug interactions.

Aim: Polypharmacy may cause life-threatening adverse effects due to drug-drug interactions (DDIs). It is possible to observe DDIs due to polypharmacy in obese patients who is known to have many co-morbid diseases that necessitates multiple drug use. The aim of the present study is to determine the frequency and severity of potential DDIs (pDDIs) in obese patients. Material and Methods: This cross-sectional study analyzed the patient charts that admitted to obesity outpatient clinic of tertiary care hospital from April 1, 2016 to July 1, 2017. The severity of DDIs was interpreted using the Lexi-comp® drug interaction database. A chisquare test was performed for the comparison of the presence of DDIs based on patients’ demographic characteristics [gender (male/ female), age categories (18-44, 45-64 and ≥65 years) and BMI (30-34.9, 35-39.9 and ≥40 kg/m2)], co-morbid clinical conditions and number of drugs. The comparisons were considered as statistically significant at p< 0.05. Results: Out of 476 patient data evaluated, a total of 781 drugs were prescribed. Among 190 patients who were prescribed two or more drugs, 35 (18.4%) patients had one or more pDDIs. We determined 48 (70.6%) C, 12 (17.6%) B, 7 (10.3%) D and 1 (1.5%) X risk category interactions. The most common pDDIs were between metformin and nonsteroidal anti-inflammatory drugs (7.4%). The presence of pDDIs was significantly associated with the number of prescribed drugs (p

Minimization of drug interactions in polypharmacy treatments of diabetes mellitus type 2 with cardiovascular comorbidities by using the decision support tool PM-TOM

e20656 Background: Previous studies have shown that cancer patients are at risk of drug interactions. But the proportion of potential adverse events that turn into clinical consequences is unknown. We sought to evaluate how many hospital admissions in oncology are due to drug-drug interactions (DDI) or adverse drug reactions (ADR). Methods: All cancer patients admitted to an oncology ward during an 8-month period had their charts retrospectively evaluated for reasons of hospitalization. Clinical trial patients were excluded. Each hospital admission was independently evaluated by two blinded investigators using a 4-point scale that was developed to classify sadmissions by their probability to be associated with either a DDI or an ADR (definitely, probably, possibly or unlikely associated). All medical records were thoroughly reviewed and discussed by experts. Results: From September 2007 to May 2008, there were 550 hospital admissions and 458 were eligible. Among unplanned admissions (N=298), 39 (13.0%, 95% CI 9.4 - 17.4%) were considered to be associated with an adverse drug event: 33 (11.0%, 95% CI 7.7 - 15.2%) were associated with an ADR and 6 (2.0%, 95% CI 0.7 - 4.3%) with a DDI. The most common DDI involved warfarin, captopril and anti-inflammatory agents and the most frequent ADR was neutropenic fever post chemotherapy. Most patients were discharged completely recovered but 2 patients died. Use of chemotherapy within 4 weeks of hospital admission (Odds Ratio 10.8, 95% CI 5.3 - 22.1; p < 0.0001) and presence of hematological tumors (Odds Ratio 12.1, 95% CI 5.9 - 25; p < 0.0001) were risk factors for being hospitalized to treat an ADR. Conclusions: Approximately one in 10 unplanned hospitalizations of cancer patients is associated with an adverse drug event. Prospective and population-based studies are warranted to evaluate their magnitude in oncology. [Table: see text]

Concomitant use of multiple drugs in most patients with cancer may result in drug-drug interactions (DDIs), potentially causing serious adverse effects. These patients often experience unrelieved cancer-related pain (CRP) during and after cancer treatment, which can lead to a reduced quality of life. Opioids can be used as part of a multimodal pain management strategy when non-opioid analgesics are not providing adequate pain relief, not tolerated, or are contraindicated. However, due to their narrow therapeutic window, opioids are more susceptible to adverse events when a DDI occurs. Clinically relevant DDIs with opioids are usually pharmacokinetic, mainly occurring via metabolism by cytochrome P450 (CYP). This article aims to provide an overview of potential DDIs with opioids often used in the treatment of moderate-to-severe CRP and commonly used anticancer drugs such as chemotherapeutics, tyrosine kinase inhibitors (TKIs), or biologics. A DDI-checker tool was used to contextualize the tool-informed DDI assessment outcomes with clinical implications and practice. The findings were compared to observations from a literature search conducted in Embase and PubMed to identify clinical evidence for these potential DDIs. The limited results mainly included case studies and retrospective reviews. Some potential DDIs on the DDI-checker were aligned with literature findings, while others were contradictory. In conclusion, while DDI-checkers are useful tools in identifying potential DDIs, it is necessary to incorporate literature verification and comprehensive clinical assessment of the patient before implementing tool-informed decisions in clinical practice.

In Sub-Saharan Africa, the cancer burden is predicted to increase by > 85% by 2030, the largest increase worldwide. This region has a large HIV-positive population. Drug-drug interactions (DDIs) from concomitant use of multiple drugs increase the risk of drug toxicities, sub-optimal therapy, and drug resistance. With the increase in polypharmacy, involving antiretroviral (ARV), and anticancer drugs, there is a greater need for an appreciation of clinically relevant DDIs. Anticancer and ARV drugs studied in this review were from The World Health Organization's Model List of Essential Medicines 2017. We reviewed; drug package inserts, www.drugbank.ca and www.UpToDate.com, to evaluate pharmacokinetic interactions with cytochrome P450 (CYP450) and ABCB1. The DDIs between drugs were assessed using the University Of Liverpool, UK HIV Drug Interactions Checker, and the LexiComp Drug Interaction tool of www.UpToDate.com. About 70% of ARVs studied interact with CYP450, all involve CYP3A4, and 55% interact with ABCB1. About 65% of anticancer drugs interact with CYP450, 44% of which do so through CYP3A4. About 75% of anticancer drugs interact with ARV drugs, with nine absolute contraindications to concomitant therapy. There exist a substantial number of DDIs between ARV and anticancer drugs, primarily mediated through CYP450 enzymes. Dolutegravir based regimens offer the safest DDI profile for concurrent use with anticancer drugs. However, there are substantial gaps in our knowledge, and this study serves to highlight the need for additional research to better define these interactions and their effect on drug exposure, as attention to these DDIs is a relatively simple intervention that could lead to optimizing disease treatment.

The increased risk of adverse events in patients receiving potentially interacting drugs has long been recognized. The purpose of the present study was to evaluate the change in the risk of receiving potentially interacting drugs during a period covering three decades and to examine the relative risk of actual drug combinations. The prescriptions from all individuals (about 8,000) with two or more prescriptions during three periods of 15 months, October to December 1983-1984, 1993-1994 and 2003-2004, were collected from an ongoing cohort study in the county of Jämtland, Sweden. The potential interactions were detected by a computerized system. The relative risk (RR) of receiving potentially interacting drugs increased for type C interactions [RR: 1.177, 95% confidence interval (CI): 1.104-1.256] and decreased for type D interactions (RR: 0.714, 95% CI: 0.587-0.868) from the period 1983-1984 to 2003-2004. Polypharmacy for the participants increased by 61%, from 9.05 filled prescriptions per subject in 1983-1984 to 10.6 in 1993-1994 and 14.6 in 2003-2004. The RR was positively correlated to the pronounced increase in polypharmacy; in addition, an exponential relationship was found for the more severe type D interactions. Few interacting drug combinations were responsible for a large proportion of the risk. We conclude that the risk of receiving potentially interacting drugs was strongly correlated to the concomitant use of multiple drugs. The pronounced increase in polypharmacy over time implies a growing reason for prescribers and pharmacists to be aware of drug interactions. Recently established national prescription registers should be evaluated for drug interaction vigilance, both clinically and epidemiologically.

The thesis aimed to study the developments, in the area of pharmacoinformatics, of the electronic prescribing and dispensing processes of drugs - in medical praxis, follow-up, and research. For hundreds of years, the written prescription has been the method of choice for physicians to communicate decisions on drug therapy and for pharmacists to dispense medication. Successively the prescription has also become a source of information for the patient about how to use the medication to maximize its benefit. Currently, the medical prescription is at a transitional stage between paper and web, and to adapt a traditional process to the new electronic era offers both opportunities and challenges The studies in the thesis have shown that the exposure of prescribed drugs in the general population has increased considerably over three decades. The risk of receiving potentially interacting drugs was also strongly correlated to the concomitant use of multiple drugs, polypharmacy. The pronounced increase in polypharmacy over time constitutes a growing reason for prescribers and pharmacists to be aware of drug interactions. Still, there were relatively few severe potential drug interactions. Recently established national prescription registers should be evaluated for drug interaction vigilance, both clinically and epidemiologically. The Swedish National Pharmacy Register provides prescription dispensing information for the majority of the population. The medication history in the register may be accessed online to improve drug utilization, by registered individuals, prescribers, and pharmacists in a safe and secure way. Lack of widespread secure digital signatures in healthcare may delay general availability. With a relatively high prevalence of dispensed drugs in the population, the National Pharmacy Register seems justified in evaluating individual medication history. With a majority of prescriptions transferred as ePrescriptions, the detected increased risk for prescription errors warrants quality improvement, if the full potential of ePrescriptions is to be fulfilled. The main conclusion of the studies was that ePrescribing with communication of prescribed drug information, storing and retrieving dispensed drug information, offers new opportunities for clinical and scientific

PurposeAlthough the concomitant use of multiple drugs often increases therapeutic effectiveness, certain combinations result in unwanted drug–drug interactions (DDIs). Most interactions go unnoticed by physicians due to the absence of new clinical signs and symptoms, and because they often produce a worsening of already existing symptoms. Quantification of the occurrence of the potential DDIs is essential to prevent the harmful effects associated with interactions. This study was launched to assess the prevalence of potential DDIs in the Internal Medicine ward of Tikur Anbessa Specialized Hospital, Addis Ababa, Ethiopia.Patients and methodsCross-sectional data were gathered from the medical charts of 252 randomly selected patients who were admitted to the Internal Medicine ward during August 23 to October 23, 2013, and exposed to at least two concomitant drugs. Potential DDIs were identified using Medscape Drug Interaction Checker. The data were analyzed using SPSS software. Logistic regression analysis was used to determine the presence of association between variables and p-value <0.05 was considered statistically significant.ResultsAt least one potential DDI was found in 78.2% of the patients. The mean number of potential interactions per patient was 3.7±3.4. Out of the 719 potential interactions identified, 49.8% were pharmacokinetic type, 44.6% were pharmacodynamic and the remaining 5.6% were unknown mechanisms. Major potential DDIs accounted for 13.1% of the whole interactions; 53.5% were moderate interactions; and the remaining 33.4% were minor interactions. Ceftriaxone, cimetidine and heparin were the three most involved drugs in major potential interactions. Prescription of five or more concomitant drugs was associated with high risk of encountering potential DDIs.ConclusionThe findings of this study showed that the prevalence of potential DDIs among inpatients was high. Pharmacists should closely review drugs prescribed for patients and avoid dispensing combinations of drugs that may have serious DDIs.

Drug‐drug interaction (DDI) is a common clinical problem that has occurred as a result of the concomitant use of multiple drugs. DDI may occur in patients under treatment with medications used for coronavirus disease 2019 (COVID‐19; i.e., chloroquine, lopinavir/ritonavir, ribavirin, tocilizumab, and remdesivir) and increase the risk of serious adverse reactions such as QT‐prolongation, retinopathy, increased risk of infection, and hepatotoxicity. This review focuses on summarizing DDIs for candidate medications used for COVID‐19 in order to minimize the adverse reactions.

Adverse drug reactions due to drug-drug interactions (DDIs) are important in drug safety. The aim of this study was to check potential DDIs (pDDIs) on hospital admission and discharge and to evaluate admissions due to DDIs in medical departments of a primary city and tertiary referral hospital. Age, sex, presence of renal and liver failure, drug information, diagnosis, and urgency and reason for admission were retrospectively recorded in 520 randomly selected patients in medical departments of the University Medical Center Ljubljana. The screening program Drug-Reax was used to check for pDDIs in patients with drug information on both admission and discharge home, and the proportion of patients admitted as the consequence of a DDI was estimated. Overall, 14.6% (76/520) of patients had incomplete information on drug names in their medical documentation on admission; at the end of treatment 12.5% (52/416) of patients were discharged home with incomplete information on drug names in their discharge letters. A total of 323 patients had complete information on drug names on both admission and discharge and were included in the analysis of pDDIs: 51% (166/323) of patients on admission and 63% (204/323) on discharge had at least one pDDI (P = 0.001). Major pDDIs were found in 13% (41/323) of patients on admission and 18% (59/323) on discharge (P = 0.001). An ACE inhibitor combined with spironolactone was the most common major pDDI, representing 20.0% of all pDDIs on admission and 25.6% on discharge. Among patients with pDDI on admission, 2.4% (4/166) of were admitted because of an ADR caused by a DDI. Overall, 1.2% (4/323) of patients were admitted as the consequence of a DDI. The information on patient medication on hospital admission and discharge is incomplete. Half of patients on admission and almost two-thirds on discharge had pDDIs. ADRs due to DDIs caused 1.2% of admissions to medical departments in Ljubljana's primary city and tertiary referral hospital.

The high prevalence of concomitant chronic illnesses and the resulting higher number of medications in the elderly population increase the risk of adverse drug reactions due to drug-drug interactions (DDIs) and potentially inappropriate medications (PIMs). Therefore, the aim of this study was to investigate the prevalence and factors associated with DDIs and PIMs in outpatient geriatrics. In this cross-sectional study, 1512 prescriptions belonging to patients aged ≥65 years from five public pharmacies in Tehran were evaluated. Clinically relevant (C, D, and X) and significant DDIs (D and X) were documented according to the Lexicomp®. Additionally, Zhan criteria were used to detect PIMs. At least one clinically relevant DDI was detected in 61.7% of the prescriptions containing ≥2 medications. The largest percentage of prescriptions with DDIs was prescribed by cardiologists (74.3%). The number of medications in prescriptions and the specialty of the prescriber significantly affected both clinically relevant and significant DDIs in a logistic regression model. At least one PIM was identified in 16.3% of the prescriptions. General practitioners (GPs) were the largest prescribers of PIMs. The mean number of medications was significantly higher in prescriptions with PIMs. In conclusion, clinically relevant DDIs are frequent in the elderly. In terms of PIMs, more attention should be paid to the education of GPs.