Prevalence Of Drug-drug Interactions Research Articles

Prevalence of potential drug − drug interactions in the cardiothoracic intensive care unit patients in a Chinese tertiary care teaching hospital

BackgroundWith an increasing number of reviews describing clinically significant drug–drug interactions (DDIs), the scope and severity of interactions involving commonly used drugs in cardiothoracic intensive care units (CCUs) remain unclear. This study aims to identify risk factors and determine the incidence of potential DDIs in intensive care units.MethodsDDIs were identified based on the profile of the prescribed drug and classified according to the Micromedex drug interaction database. Potential risk factors associated with DDIs have been identified.ResultsA total of 3193 medication episodes were evaluated, and 680 DDIs (21.3%) were found. A total of 203 patients were recruited into the study, with an average of 3.4 DDIs per patient [95% confidence interval (3.2 − 3.6)]. A total of 84.2% of the patients experienced at least one DDI. Anticoagulant and antiplatelet agents were involved in 33.5% (228/680) of the potential drug − drug interactions in the CCU. Univariate analysis and multiple logistic regression analysis showed that the age of the patient and the number of medications prescribed were significantly correlated with the occurrence of DDIs. In multiple linear regression analysis, the number of DDIs had a significant correlation only with the number of prescription drugs.ConclusionsA high prevalence of DDIs was observed, especially in intensive care units without pharmacist intervention and computerized drug monitoring systems, highlighting the need for active surveillance to prevent potential adverse events.

Comparing Important and Well-documented Potential Drug-Drug Interactions between Emergency, Medical, and Surgical ICUs of a Respiratory Referral Center.

IntroductionDrug–drug interaction (DDI) is one of the major healthcare challenges in intensive care units (ICUs). The prevalence of DDIs and interacting drug pairs may vary between different types of ICUs. This study aimed to compare the frequency and nature of important and well-documented potential DDIs (pDDIs) in three types of ICUs.Materials and methodsA prospective study was conducted in medical (M), surgical (S), and emergency (E) ICUs of a tertiary referral center for respiratory diseases. A pharmacist checked the patients’ files three days in a week for 6 months. The pDDIs were identified using the Lexi-Interact database. Interactions with a severity rating of D (modify regimen) and X (avoid combination) and with a reliability rating of good and excellent were considered important and well-documented. These pDDIs were evaluated in terms of drug combinations, mechanisms of interaction, and clinical management.ResultsOne hundred eighty-nine patients admitted to MICU, SICU, and EICU were included in the study. The percentage of patients who experienced at least one important and well-documented pDDI was 18.8% in MICU, 11.1% in SICU, and 11.8% in EICU. The most common drug pairs causing important and well-documented interactions were atracurium + hydrocortisone in MICU, meropenem + valproic acid in MICU and EICU, and aspirin + warfarin in SICU.ConclusionThe current study shows different frequency and nature of pDDIs between three types of ICUs. We recommend conducting similar studies in other settings to develop evidence-based guidance on clinically relevant pDDIs in different types of ICUs.How to cite this articleHosseinpoor Z, Farzanegan B, Baniasadi S. Comparing Important and Well-documented Potential Drug–Drug Interactions between Emergency, Medical, and Surgical ICUs of a Respiratory Referral Center. Indian J Crit Care Med 2022;26(5):574–578.

A comprehensive evaluation of potentially significant drug-drug, drug-herb, and drug-food interactions among cancer patients receiving anticancer drugs

IntroductionDuring the cancer treatment path, cancer patients use numerous drugs, including anticancer, supportive, and other prescribed medications, along with herbs and certain products. This puts them at risk of significant drug interactions (DIs). This study describes DIs in cancer patients and their prevalence and predictors.MethodsA cross-sectional study design was used to achieve the study objectives. The study was carried out in two centers in the northern West Bank, Palestine. The Lexicomp® Drug Interactions tool (Lexi-Comp, Hudson OH, USA) was applied to check the potential DIs. In addition, the Statistical Package for the Social Sciences (SPSS) was used to show the results and find the associations.ResultsThe final analysis included 327 patients. Most of the participants were older than 50 years (61.2%), female (68.5%), and had a solid tumor (74.6%). The total number of potential DIs was 1753, including 1510 drug-drug interactions (DDIs), 24 drug-herb interactions, and 219 drug-food interactions. Importantly, the prevalence of DDIs was 88.1%. In multivariate analysis, the number of potential DDIs significantly decreased with the duration of treatment (p = 0.007), while it increased with the number of comorbidities (p < 0.001) and the number of drugs used (p < 0.001).ConclusionsWe found a high prevalence of DIs among cancer patients. This required health care providers to develop a comprehensive protocol to monitor and evaluate DIs by improving doctor-pharmacist communication and supporting the role of clinical pharmacists.

Clinical Opportunities for Germline Pharmacogenetics and Management of Drug-Drug Interactions in Patients With Advanced Solid Cancers.

Precision medicine approaches, including germline pharmacogenetics (PGx) and management of drug-drug interactions (DDIs), are likely to benefit patients with advanced cancer who are frequently prescribed multiple concomitant medications to treat cancer and associated conditions. Our objective was to assess the potential opportunities for PGx and DDI management within a cohort of adults with advanced cancer. Medication data were collected from the electronic health records for 481 subjects since their first cancer diagnosis. All subjects were genotyped for variants with clinically actionable recommendations in Clinical Pharmacogenetics Implementation Consortium guidelines for 13 pharmacogenes. DDIs were defined as concomitant prescription of strong inhibitors or inducers with sensitive substrates of the same drug-metabolizing enzyme and were assessed for six major cytochrome P450 (CYP) enzymes. Approximately 60% of subjects were prescribed at least one medication with Clinical Pharmacogenetics Implementation Consortium recommendations, and approximately 14% of subjects had an instance for actionable PGx, defined as a prescription for a drug in a subject with an actionable genotype. The overall subject-level prevalence of DDIs and serious DDIs were 50.3% and 34.8%, respectively. Serious DDIs were most common for CYP3A, CYP2D6, and CYP2C19, occurring in 24.9%, 16.8%, and 11.7% of subjects, respectively. When assessing PGx and DDIs together, approximately 40% of subjects had at least one opportunity for a precision medicine-based intervention and approximately 98% of subjects had an actionable phenotype for at least one CYP enzyme. Our findings demonstrate numerous clinical opportunities for germline PGx and DDI management in adults with advanced cancer.

Patient’s Own Medication Use During Hospitalization

Medication wasting has been adding to the cost burden on the healthcare system. Sustainable interventions such as using patients’ own medications have been implemented to upgrade the medicine management system. This review aimed to describe the studies related to patients’ own medication in hospital settings to explore its positive impacts. Current literature has studied the impact of using patients’ own medicines from cost-effectiveness, clinical, and patient safety perspectives. The economic impact was represented by determining the cost saved after implementing the patient’s own medications intervention. On the other hand, the efficacy of patient care was proven by reducing the frequency of medication error and prevalence of drug-drug interactions as the intervention was implemented. Patients’ knowledge of their medications was also proven to be improved with their own medicines during hospitalization, improving their adherence to their medication treatment. Improving patients' adherence to and knowledge of their prescriptions during hospitalization may also aid in the reduction of hospital readmissions, which could result in a net economic return.

Potential Drug-drug Interaction among the Patients Admitted in Intensive Care Units of a Tertiary Care Centre: A Descriptive Cross-sectional Study.

Drug interactions are one of the major contributors to increase hospital stay, inflate health care expenses, and cause serious adverse events and end-organ damage. Patients admitted to the intensive care unit are already critically sick and are at greater risk of these adverse outcomes. The study aimed to find out the prevalence of potential drug-drug interactions in the Intensive Care Units of a tertiary care centre. A descriptive cross-sectional study was conducted among the patients admitted in the Intensive Care Unit of a tertiary care hospital from April-June 2019. Ethical approval was taken from the Institutional Review Board at the institute (Reference number: 399). Convenience sampling method was used. Data was collected using proforma and potential drug-drug interactions were identified using Lexicomp® drug-interactions version 1.1 (Wolters Kluwer). All the drug interactions identified were classified and the severity scale of interactions was also defined. Statistical Package for the Social Sciences version 17.0 was used for data analysis. Point estimate at 95% Confidence Interval was calculated along with frequency, percentage, mean, standard deviation, and mode. Out of 101 patients, the prevalence of the drug-drug interaction was found to be 90 (89.11%) (83.04-95.18 at 95% Confidence Interval). A total of 490 drug-drug interactions were identified. In severity scale, it was seen that 311 (63.46%) were of moderate severity and 303 (61.83%) of drug interactions were categorised as category C in risk rating. Prevalence of potential drug-drug interactions was higher compared to similar published literature. The most common drug with potential interaction was fentanyl and among pairs was fentanyl plus paracetamol. drug interactions; intensive care units; Nepal; software.

Impact of polypharmacy and aging on the risk of multiple drug-drug interactions (DDIs) in HCV patients treated with pangenotypic direct-acting antivirals (pDAA)

Background: Recent studies suggest that sofosbuvir-based regimens are preferred in HCV elderly patients for their slightly more user-friendly DDI profile compared to protease inhibitor-based regimens. Our aim was to explore the impact of polymedication and aging on multiple DDIs prevalence in patients treated with sofosbuvir/velpatasvir (SOF/VEL) or glecaprevir/pibrentasvir (GLE/PIB).

Evaluation of drug-drug interactions among patients with metastatic prostate cancer in routine care.

111 Background: Prostate cancer often occurs in an elderly population, at higher risk of drug-drug interactions (DDIs). There is a medical need to increase the knowledge of DDI prevalence and their severity among patients coming from routine care. This study used a prospective approach with the aim to quantify and describe DDIs in medical prescription of prostate cancer treatments. Methods: DDIs were assessed from two perspectives: 1) “real DDI” between the patient’s comedications and the actual anti-cancer agent prescribed; 2) “modelized DDI” between the patient’s comedications and drugs that have proven activity in metastatic endocrine sensitive prostate cancer (MESPC) or castrate resistant prostate cancer (CRPC) settings with positive phase 3 trial and gained EMA approval. French national thesaurus, Micromedex software and complementary manual analysis by pharmacist were used to identified DDIs. Clinical relevance of DDIs was scored using the validated scale of Hatoum. Results: A total of 91 patients were included, 15% of whom were participating in a clinical trial. Some imbalances were observed between patients from clinical trial and routine care (age, ECOG, MESPC/MCRPC). The median number of comedications used per patient was 6 [1-16]. Among real DDI, Enzalutamide was the anticancer agent with the higher risk of interaction (18-54% of patients). In modelized DDI, Enzalutamide and Apalutamide were the two most risky drugs (35-76% and 22-73% depending on Micromedex or Thesaurus analyze). Not all DDIs discovered were clinically significant according to Hatoum scale. Conclusions: DDIs are frequents, according to Micromedex and National Thesaurus tools but pharmacist and physician advices are essentials to determine their potential impacts.

Drug-drug Interactions between Hypoglycemic and Non-hypoglycemic Medication in Diabetic Patients with Comorbidities in a Tertiary Care Center: A Descriptive Cross-sectional Study.

Drug-drug interaction is one of the causes of adverse drug reactions. Generally, drug-drug interaction is common in multidrug therapy. Diabetic patients, particularly due to associated comorbidities tend to have various drug-drug interactions due to the effect of multiple drugs. The objective of this study was to find out the prevalence of drug-drug interactions in diabetic patients. It was a descriptive cross-sectional study that was conducted among previously diagnosed diabetic patients visiting the outpatient department of medicine at a tertiary care hospital between March 2021 and August 2021. Ethical approval was taken from the institutional review committee (Ref no: 030-076/077). Data was collected from diabetic patients presenting to the outpatient department of medicine using a preformed self-constructed questionnaire. Convenient sampling was done. Statistical Package for Social Sciences version 21 and Microsoft Excel were used for data analysis. Point estimate at 95% confidence interval was calculated along with frequency and proportion for binary data. The prevalence of drug-drug interaction between hypoglycemic and non-hypoglycemic medication was 56 (44.1%) (35.5-52.7 at 95% Confidence Interval) of the patients out of which at least one drug-drug interaction was seen in 48 (37.8%) of the patients. Our study showed the prevalence of drug-drug interactions in diabetic patients to be higher than other studies done in similar settings. Based on the severity, we observed two types of drug-drug interactions; close monitoring drug-drug interactions and minor drug-drug interactions.

Prevalence of drug-drug interactions in older people before and after hospital admission: analysis from the OPERAM trial

(N = 351)BackgroundDrug-drug interactions (DDIs) are highly prevalent in older patients but little is known about prevalence of DDIs over time. Our main objective was to assess changes in the prevalence and characteristics of drug-drug interactions (DDIs) during a one-year period after hospital admission in older people, and associated risk factors.MethodsWe conducted a sub-study of the European OPERAM trial (OPtimising thERapy to prevent Avoidable hospital admissions in Multimorbid older people), which assessed the effects of a structured medication review (experimental arm) compared to usual care (control arm) on reducing drug-related hospital readmissions. All OPERAM patients (≥70 years, with multimorbidity and polypharmacy, hospitalized in four centers in Bern, Brussels, Cork and Utrecht between December 2016 and October 2018, followed over 1 year) who were alive at hospital discharge and had full medication data during the index hospitalization (at baseline i.e., enrolment at admission, and at discharge) were included. DDIs were assessed using an international consensus list of potentially clinically significant DDIs in older people. The point-prevalence of DDIs was evaluated at baseline, discharge, and at 2, 6 and 12 months after hospitalization. Logistic regression models were performed to assess independent variables associated with changes in DDIs 2 months after baseline.ResultsOf the 1950 patients (median age 79 years) included, 1045 (54%) had at least one potentially clinically significant DDI at baseline; point-prevalence rates were 58, 57, 56 and 57% at discharge, and 2, 6 and 12 months, respectively. The prevalence increased significantly from baseline to discharge (P < .001 [significant only in the control group]), then remained stable over time (P for trend .31). The five most common DDIs –all pharmacodynamic in nature– accounted for 80% of all DDIs and involved drugs that affect potassium concentrations, centrally-acting drugs and antithrombotics. At 2 months, DDIs had increased in 459 (27%) patients and decreased in 331 (19%). The main factor predictive of a change in the prevalence of DDIs was hyperpolypharmacy (≥10 medications).ConclusionsDDIs were very common; their prevalence increased during hospitalization and tended to remain stable thereafter. Medication review may help control this increase and minimize the risk of adverse drug events.

Prevalence of drug interactions in hospitalised elderly patients: a systematic review

Background: The prevalence of drug–drug interactions (DDIs) in hospital settings is variable, and elderly patients are considered a high risk population for DDIs. There are no systematic reviews describing the prevalence of DDIs in hospitalised elderly patients. Objectives: To assess and summarise the available data on the prevalence of DDIs in hospitalised elderly patients and to describe which drugs, drug classes and drug combinations are most commonly involved in DDIs. Data source: A systematic electronic literature search was conducted on Medline/PubMed, Embase, Lilacs, SciElo, Web of Science, Cinahl, Scopus, Cochrane, OpenGrey, Capes Thesis Bank, OasisBR, OpenAire and abstracts from scientific events, without limitation on language or period of publication. Study selection was completed on 21 September 2018. Study eligibility criteria, participants and interventions: Original observational studies that reported the prevalence of actual or potential DDIs during hospitalisation in patients aged 60 years or older were included. The main outcome measure was prevalence of DDIs and number of DDIs per patient. Subgroup analysis was performed in studies that reported the prevalence of DDIs in geriatric units. Study appraisal and synthesis methods: Study quality was assessed using the Agency for Healthcare Research and Quality methodological checklist for cross sectional and prevalence studies. Results: 34 studies were included, involving 9577 patients. The prevalence of DDIs ranged from 8.34% to 100%. In studies conducted in geriatric units, the prevalence ranged from 80.5% to 90.5%. The number of DDIs per patient ranged from 1.2 to 30.6. Single drugs most commonly involved in DDIs were furosemide, captopril, warfarin and dipyrone. Drug classes mostly involved were potassium sparing diuretics and angiotensin converting enzyme inhibitors. Limitations: The main limitation is the heterogeneity between the included studies that precluded a metaanalysis. Several different methods were used to identify DDIs, majorly, and potential DDIs. Few studies have reported measures to control the quality of the collected data. Conclusions and implications of key findings: The prevalence of DDIs ranged widely, and the variation may reflect differences in the conditions of the elderly patients and level of attention (or complexity of care), as well as methodological differences, especially the methods and/ or software used to identify DDIs.

Prevalence and characteristics of drug-drug interactions among hospitalised patients in the Capital Region of Denmark

Summary Previous studies on the prevalence of drug-drug interactions (DDIs) have largely been restricted to specific hospital departments. We identified common drug combinations and potential drug-drug interactions (pDDIs) in an entire hospital population throughout 1 year. Frequently administered drug combinations involved analgesics, cardiovascular medicines and gastrointestinal medicines. The prevalence of pDDIs among newly hospitalised patients was 2.6%. The most common pDDIs were ibuprofen – paracetamol, pantoprazole – paracetamol and morphine – paracetamol. Critical pDDIs were rare and only accounted for 0.4% of pDDIs.

Potential Drug-Drug Interactions in Hospitalized Medical Patients: Data From Low Resource Settings.

IntroductionAdverse events related to Drug-Drug Interactions (DDIs) are among the few common reasons for hospitalization worldwide; however, they can be prevented with an efficient patient-centered system. Different mechanisms have successfully limited the prevalence of DDIs in developed countries. There are limited data regarding DDIs from limited-resource settings. Furthermore, there is no cost-effective system that has shown promising results in preventing them in this setting. This study aims to assess the frequency of potential DDIs in a low-resource setting and to check its association with different factors such as poly-pharmacy and demographics.MethodsThrough this cross-sectional study, drug charts of patients admitted to a medical unit in November 2019 were analyzed using a structured questionnaire. A list of drugs co-prescribed to each patient was entered into the Medscape Drug Interaction checker to calculate the frequency and severity of potential DDIs.ResultsThe mean age of patients was 49 years, and on average, seven drugs were prescribed to each patient. Among 100 analyzed prescriptions, 400 potential DDIs were identified with a mean of 4±5.42 per patient. According to Medscape interaction checker classification, 2 DDIs were contraindicated, 28 were serious, 246 required close monitoring, and 124 were minor. The most frequently encountered drug interaction was "spironolactone with furosemide." There was a significant correlation of the occurrence of potential DDIs with increased numbers of prescribed drugs.ConclusionOur patient population was prescribed more drugs per patient than calculated in other settings. Poly-pharmacy is an independent risk factor for DDIs. Lastly, advancing age exposes patients to poly-pharmacy, and therefore, they are at a higher risk of developing DDIs.

Prevalence of drug-drug interactions with pangenotypic direct-acting antivirals for hepatitis C and real-world care management in the United States: a retrospective observational study.

BACKGROUND: Direct-acting antiviral (DAA) regimens for hepatitis C virus (HCV) have varying potentials for drug-drug interactions (DDIs). OBJECTIVES: To (1) identify prevalence of potential DDI with glecaprevir-pibrentasvir (GLE-PIB) and sofosbuvir-velpatasvir (SOF-VEL) and (2) describe health care provider actions in response to pharmacist recommendations based on potential interactions with GLE-PIB or SOF-VEL, using 2 complementary data sources. METHODS: Possible interacting drugs were identified among adult patients in a United States electronic medical record database covering 21 health care organizations and 26 million patients in 2018. DDIs were categorized as potential weak interaction (Level 1), potential interaction (Level 2), or contraindicated (Level 3). Real-world recommendations and resultant actions regarding DDIs with GLE-PIB and SOF-VEL were obtained from a specialty pharmacy database. Categorical variable comparisons were done via chi-square analysis with subsequent z-tests of column proportions. RESULTS: DDI prevalence was higher for patients prescribed GLE-PIB (317/769 [41%]) compared with those prescribed SOF-VEL (170/633 [27%]), and the prevalence of a Level 3 (contraindicated) interaction was higher with GLE-PIB than SOF-VEL (61/769 [8%] vs 2/633 [< 1%]). Across all DDI levels, analgesics (139/317 [44%]), proton-pump inhibitors (129/317 [41%]), and lipid-lowering agents (59/317 [19%]) were the top drug classes for the GLE-PIB group with potential for DDI. For SOF-VEL prescribed patients, the top drug classes were proton-pump inhibitors (83/170 [49%]), histamine-2 blockers (42/170[25%]), and lipid-lowering agents (42/170 [25%]). In real-world care management, the overall prevalence of pharmacist recommendations regarding DDIs was significantly lower for SOF-VEL (28/419 [7%]) relative to GLE-PIB (151/1,216 [12%]). Recommended guidance from pharmacists was not followed for 39% (69/179) of patients, 36% (54/151) for GLE-PIB, and 54% (15/28) for SOF-VEL. CONCLUSIONS: The potential for DDIs with pangenotypic HCV DAAs is frequent and may be more frequent with GLE-PIB than SOF-VEL. Physician responses to pharmacist alerts regarding potential interaction can be highly variable, even in cases of contraindication. DISCLOSURES: Funding for this study was provided by Gilead Sciences, Inc. Trio Health Analytics received funding from Gilead Sciences, Inc., to conduct research for this study. Tsai consults for and has received grants and honoraria from Gilead, AbbVie, Merck, and Bristol Myers Squibb; he has also received lecture fees from Gilead and AbbVie. Curry consults for Trio Health Analytics and Gilead and has also consulted for and received grants from Mallinckrodt. Flamm consults for and has received lecture fees from Gilead and AbbVie; he has also received grants from Gilead and AbbVie. Milligan and Wick are employed by Trio Health Analytics and have received research support from Gilead, Merck, and AbbVie. Younossi has received grants from Gilead, Intercept, Bristol Myers Squibb, GlaxoSmithKline, and Novo Nordisk. Afdhal is a paid consultant/advisory board member for Gilead, Echosens, Ligand, Shionogi, and Trio Health; owns stock in Spring-Bank and Allurion and has stock options in SpringBank; receives royalty income from UpToDate; and is on the board of directors for the nonprofit Liver Institute for Education and Research. Data from this study were presented at the American Association for the Study of Liver Disease (AASLD) Liver Meeting 2019; November 8-12, 2019; Boston, MA, and the European Association for Study of the Liver (EASL) International Liver Congress 2020; August 27-29, 2020; virtual.

EVALUATION OF DRUG-DRUG INTERACTIONS IN HOSPITALISED GERIATRIC PATIENTS AT TERTIARY CARE HOSPITAL

A study was planned to evaluate the prevalence, nature, and severity of drug-drug interactions (DDI) in geriatrics. A prospective observational study was carried out in hospitalized patients. DDIs were evaluated using a medscape drug interaction checker. Data of 300 patients were collected who were followed up for 3 days and analyzed over a period of 7 months. The prevalence rate of DDI was 90.3 %, as 271 out of 300 prescriptions showed drug-drug interaction. Out of 300 patients, 4797 possible DDIs were identified, of which 39 (0.81 %) were contraindicated, 290 (6.04 %) serious drug interactions, 3312 (68.85 %) significant interactions and 1156 (24.09 %) minor interactions. Out of 4797 possible DDIs, 2962 (62.91 %) were pharmacodynamics. 1835 (37.08 %) were pharmacokinetic type. Antihypertensive class of drug showed potential for DDIs. Total 244 (81.33 %) cases were found with potentially inappropriate medicines(PIM). The prevalence of DDIs in geriatric patients was 90.3 %. Poly pharmacy and multiple associated conditions in the elderly were the major contributing factors for DDIs.

Evaluation of Drug-Drug Interactions in EGFR-Mutated Non-Small-Cell Lung Cancer Patients during Treatment with Tyrosine-Kinase Inhibitors.

(1) Background. The onset of a drug–drug interaction (DDI) may affect treatment efficacy and toxicity of advanced non-small-cell lung cancer (aNSCLC) patients during epidermal growth factor receptor (EGFR) tyrosine-kinase inhibitor (TKI) use. Here we present the use of Drug-PIN® (Personalized Interactions Network) software to detect DDIs in aNSCLC patients undergoing EGFR-TKIs. (2) Methods. We enrolled patients with Stage IV aNSCLC already treated with or candidates to receive EGFR-TKIs, in any line; ECOG PS 0–2; taking at least one concomitant drug. Cancer treatments, concomitant drugs, and clinical and laboratory data were collected and inserted in Drug-PIN®. (3) Results. Ninety-two patients, median age of 68.5 years (range 43–89), were included. In total, 20 clinically relevant DDIs needing medical intervention in a total of 14 patients were identified; the 14 major DDIs were related to a high-grade interaction between TKIs and SSRIs, antipsychotics, antiepileptics, H2-receptor antagonist and calcium antagonists. A negative association between statin intake and PFS was identified (p = 0.02; HR 0.281, 95% CI 0.096–0.825). (4) Conclusions. This is the first retrospective study assessing the prevalence of DDIs, the clinical need for medical intervention and the impact of concomitant drugs on EGFR-TKIs survival in aNSCLC.

High frequency of potential phosphodiesterase type 5 inhibitor drug interactions in males with HIV infection and erectile dysfunction.

We sought to determine the prevalence of phosphodiesterase type 5 inhibitor (PDE-5) mediated drug-drug interactions (DDIs) in males with HIV infection receiving antiretroviral therapy (ART) and identify factors associated with PDE-5-mediated DDIs. Male US Military HIV Natural History Study participants diagnosed with erectile dysfunction (ED) and having a PDE-5 inhibitor and potentially-interacting ART co-dispensed within 30 days were included. DDIs were defined according to criteria found in published guidelines and drug information resources. The primary outcome of interest was overall PDE-5 inhibitor-mediated DDI prevalence and episode duration. A secondary logistic regression analysis was performed on those with and without DDIs to identify factors associated with initial DDI episode. A total of 235 male participants with ED met inclusion criteria. The majority were White (50.6%) or African American (40.4%). Median age at medication co-dispensing (45 years), duration of HIV infection (14 years), and duration of ED (1 year) did not differ between the two groups (p>0.05 for all). PDE-5 inhibitors included sildenafil (n = 124), vardenafil (n = 99), and tadalafil (n = 14). ART regimens included RTV-boosted protease inhibitors (PIs) atazanavir (n = 83) or darunavir (n = 34), and COBI-boosted elvitegravir (n = 43). Potential DDIs occurred in 181 (77.0%) participants, of whom 122 (67.4%) had multiple DDI episodes. The median DDI duration was 8 (IQR 1-12) months. In multivariate analyses, non-statistically significant higher odds of DDIs were observed with RTV-boosted PIs or PI-based ART (OR 2.13, 95% CI 0.85-5.37) and in those with a diagnosis of major depressive disorder (OR 1.74, 95% CI 0.83-3.64). PDE-5-mediated DDIs were observed in the majority of males with HIV infection on RTV- or COBI-boosted ART in our cohort. This study highlights the importance of assessing for DDIs among individuals on ART, especially those on boosted regimens.

Drug-drug interactions in subjects enrolled in SWOG trials of oral chemotherapy

BackgroundPatients with cancer are at increased risk of drug-drug interactions (DDI), which can increase treatment toxicity or decrease efficacy. It is especially important to thoroughly screen DDI in oncology clinical trial subjects to ensure trial subject safety and data accuracy. This study determined the prevalence of potential DDI involving oral anti-cancer trial agents in subjects enrolled in two SWOG clinical trials.MethodsCompleted SWOG clinical trials of commercially available agents with possible DDI that had complete concomitant medication information available at enrollment were included. Screening for DDI was conducted through three methods: protocol-guided screening, Lexicomp® screening, and pharmacist determination of clinical relevance. Descriptive statistics were calculated.ResultsSWOG trials S0711 (dasatinib, n = 83) and S0528 (everolimus/lapatinib, n = 84) were included. Subjects received an average of 6.6 medications (standard deviation = 4.9, range 0–29) at enrollment. Based on the clinical trial protocols, at enrollment 18.6% (31/167) of subjects had a DDI and 12.0% (20/167) had a DDI that violated a protocol exclusion criterion. According to Lexicomp®, 28.7% of subjects (48/167) had a DDI classified as moderate or worse, whereas pharmacist review indicated that 7.2% of subjects (12/167) had a clinically relevant interaction. The majority of clinically relevant DDI identified were due to the coadministration of acid suppression therapies with dasatinib (83.3%, 10/12).ConclusionsThe high DDI prevalence in subjects enrolled on SWOG clinical trials, including a high prevalence that violate trial exclusion criteria, support the need for improved processes for DDI screening to ensure trial subject safety and trial data accuracy.

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.

Prevalence of drug-drug interactions for oral anticoagulant drugs in patients with atrial fibrillation and relationship with outcomes: a population-based cohort study

Abstract Introduction The introduction of non-vitamin K antagonist oral anticoagulants (NOACs) changed the treatment of atrial fibrillation (AF) patients, promising a better safety profile and a lower chance of interaction with drugs than vitamin K antagoniste (VKA). Aim To evaluate the prevalence of possible drug-drug interactions (DDIs) in a cohort of newly anticoagulated AF patients, their impact on outcomes and possible differences between VKA and NOACs users. Methods We performed an analysis derived from administrative databases in Lombardy Italian region. All patients ≥40 years admitted from 01/06/2013 to 30/06/2018 with an AF diagnosis that were VKA or NOACs new users were included in this analysis. Possible DDIs were evaluated according to the prescription of OAC therapy, on the basis of current available evidence. Stroke, intracerebral hemorrhage (ICH), any bleeding and all-cause death were the study outcomes. Results Among the 122816 patients included in the analysis, mean (SD) age 76.3 (9.6) with 47.3% females, a mean (SD) CHA2DS2-VASc of 3.5 (1.4) was found. A total of 70180 (57.1%) patients were prescribed with VKA and 52636 (42.9%) with NOACs. A possible DDI was recorded in 63273 (51.5%). Patients exposed to DDIs were older and less likely female (both p&amp;lt;0.0001) and with a higher mean (SD) CHA2DS2-VASc (p&amp;lt;0.0001). Rate of stroke, ICH, any bleeding and all-cause death were higher in those patients exposed to DDIs (all p&amp;lt;0.001). After full adjustment, exposure to possible DDIs was associated with an increased risk for any bleeding (HR: 1.08, 95% CI: 1.05–1.12) and all-cause death (HR: 1.10, 95% CI: 1.07–1.13), with no differences for stroke and ICH. Comparing VKA and NOACs patients exposed to possible DDIs, we found that VKA users exposed to possible DDIs, after adjustments, were at higher risk for all the outcomes (Table). Conclusions In a large cohort of AF patients newly prescribed with OAC, possible DDIs were largely prevalent, in particular in VKA users. Presence of a possible DDI is associated with an increased risk of any bleeding and all-cause death. VKA users exposed to a possible DDI were at higher risk for any outcome than NOACs users exposed to a possible DDI. Funding Acknowledgement Type of funding source: None