OBJECTIVE: Trimethoprim-sulfamethoxazole (TMP-SMX) may increase digoxin concentration, a medication with a narrow therapeutic index. Small changes in digoxin concentration could predispose individuals to the risk of toxicity.
OBJECTIVE: To characterize the risk of digoxin toxicity in older adults taking digoxin following co-prescription of TMP-SMX compared with co-prescription of amoxicillin.
METHODS: Retrospective population-based cohort study in Ontario, Canada (2002-2020) using linked health care data. Participants comprised 47,961 older adults taking digoxin (58% women; median age 80 years [interquartile range 74-86]) who were newly treated with TMP-SMX (n = 10,273) compared with those newly treated with amoxicillin (n = 37,688).
METHODS: Co-prescription of TMP-SMX versus amoxicillin in older adults concurrently taking digoxin.
METHODS: The primary outcome was a hospital encounter (i.e., hospital admission or emergency department visit) with digoxin toxicity within 30 days of the antibiotic prescription. Inverse probability of treatment weighting on the propensity score was used to balance comparison groups on indicators of baseline health. Weighted risk ratios (RR) were obtained using modified Poisson regression and weighted risk differences (RD) using binomial regression. The number needed to harm (NNH) was calculated as 1/RD.
RESULTS: A hospital encounter with digoxin toxicity occurred in 49/10,273 (0.48%) patients treated with TMP-SMX versus 32/37,688 (0.08%) in those treated with amoxicillin (weighted RR, 5.71 [95% confidence interval (CI), 3.19 to 10.24]; weighted RD, 0.39% [95% CI, 0.25% to 0.53%]; NNH 256 [95% CI, 233 to 400]).
CONCLUSIONS: In older adults taking digoxin, the 30-day risk of a hospital encounter with digoxin toxicity was nearly 6 times higher in those co-prescribed TMP-SMX versus amoxicillin, although the absolute risk difference was low (0.4%). Physicians should prescribe an alternative antibiotic when clinically appropriate. If TMP-SMX must be co-prescribed with digoxin (if the benefit is believed to outweigh the risk), digoxin should be dose-reduced on an individual basis.

Taking several medications at the same time is an increasingly common phenomenon in our society. The combination of drugs is certainly not without risk of potentially dangerous interactions. Taking into account all possible interactions is a very complex task as it is not yet known what all possible interactions between drugs and their types are. Machine learning based models have been developed to help with this task. However, the output of these models is not structured enough to be integrated in a clinical reasoning process on interactions. In this work, we propose a clinically relevant and technically feasible model and strategy for drug interactions.

BACKGROUND: Nirmatrelvir/ritonavir (Paxlovid®) is currently one of the few therapeutic options for coronavirus disease 2019 (COVID-19) curative treatment in non-oxygen-requiring adult patients at-high risk of progressing to severe disease. This recently approved boosted antiviral therapy presents a significant risk of drug-drug interactions (DDI). As part of the enhanced surveillance program in France for COVID-19 drugs and vaccines, the French national pharmacovigilance database (BNPV [base nationale de pharmacovigilance]) was queried in order to better characterize the drug safety profile, with a special focus on DDI. The aim of the study was to describe the adverse drug reactions reported through the BNPV.
METHODS: All nirmatrelvir/ritonavir reports validated in the BNPV from the first authorization in France (January, 20th 2022) to December, 3rd 2022 (date of the query) were considered. An analysis of the scientific literature (PubMed®) and from the WHO pharmacovigilance database (Vigibase) was also performed.
RESULTS: Over this period (11 months), 228 reports (40% of serious reports) were registered with a sex ratio of 1.9 female/1 male and a mean age of 66 years old. DDI reports account for more than 13% of reports (n=30) and were mainly related to immunosuppressive drugs overexposure (n=16). A total of 10/228 reports with fatal outcomes were reported in complex clinical settings. The main reported unexpected adverse drug reaction (ADRs) were high blood pressure (n=7), confusion (n=5), acute kidney injuries (AKI, n=7) and various skin reactions (n=22). Apart from situations of disease recurrence (not found in this analysis), data from Pubmed® and Vigibase also reported the above-mentioned events of interest.
CONCLUSIONS: Overall, this analysis shows that nirmatrelvir/ritonavir safety profile was conform to current summary of product characteristics (SmPC). The main concern was the risk of DDI. Therefore, SmPC and expert recommendations should be systematically consulted before initiation of this antiviral, which is particularly indicated in polypharmacy patients. A case-by-case multidisciplinary approach including a clinical pharmacologist is required in these complex situations. Blood pressure elevation, confusion, cutaneous reactions and AKIs were the main unexpected ADRs of interest to follow, but need to be confirmed with a qualitative approach over time and new reports.

Background: An international consensus list of potentially clinically significant drug-drug interactions (DDIs) in older people has been recently validated. Our objective was to describe the prevalence and characteristics of drug combinations potentially causing clinically significant DDIs identified in the medication history of older patients admitted to the hospital and the prevalence and characteristics of manifest DDIs-DDIs involved in adverse drug events present at hospital admission, DDIs that contributed to ADE-related hospital admissions, and DDIs involved in drug-related laboratory deviations. Methods: The data were obtained from our previous study that examined the drug-relatedness of hospital admissions to University Hospital Hradec Králové via the department of emergency medicine in the Czech Republic. Patients ≥ 65 years old were included. Drug combinations potentially causing clinically significant DDIs were identified using the international consensus list of potentially clinically significant DDIs in older people. Results: Of the 812 older patients admitted to the hospital, 46% were exposed to drug combinations potentially causing clinically significant DDIs. A combination of medications that affect potassium concentrations accounted for 47% of all drug combinations potentially causing clinically significant DDIs. In 27 cases, potentially clinically significant DDIs were associated with drug-related hospital admissions. In 4 cases, potentially clinically significant DDIs were associated with ADEs that were present at admissions. In 4 cases, the potentially clinically significant DDIs were associated with laboratory deviations. Manifest DDIs that contributed to drug-related hospital admissions most frequently involved antithrombotic agents and central nervous system depressants. Conclusion: The results confirm the findings from the European OPERAM trial, which found that drug combinations potentially causing clinically significant DDIs are very common in older patients. Manifest DDIs were present in 4.3% of older patients admitted to the hospital. In 3.3%, manifest DDIs contributed to drug-related hospital admissions. The difference in the rates of potential and manifest DDIs suggests that if a computerized decision support system is used for alerting potentially clinically significant DDIs in older patients, it needs to be contextualized (e.g., take concomitant medications, doses of medications, laboratory values, and patients\' comorbidities into account).

The ReMIAMes project proposes a methodological framework to provide a reliable and reproducible measurement of the frequency of drug-drug interactions (DDI) when performed on real-world data. This framework relies on (i) a fine-grained and contextualized definition of DDIs, (ii) a shared minimum information model to select the appropriate data for the correct interpretation of potential DDIs, (iii) an ontology-based inference module able to handle missing data to classify prescription lines with potential DDIs, (iv) a report generator giving the value of the measurement and explanations when potential false positive are detected due to a lack of available data. All the tools developed are intended to be publicly shared under open license.

Rivaroxaban is commonly used for the prophylaxis of venous thromboembolism (VTE) for patients undergoing major orthopedic surgery. Rivaroxaban is primarily eliminated by hepatic CYP450 metabolism and renal excretion. Rifampin is a commonly used antibiotic for prosthetic joint infections (PJI) and a potent inducer of CYP450 enzymes. Clinical data about drug-drug interactions of rivaroxaban and rifampin are limited. The present study is to describe DDI of rivaroxaban and rifampin in several prosthetic joint infections patients undergoing major orthopedic surgery. We retrospectively identified six patients concomitantly administered with rivaroxaban and rifampin between 2019 and 2020. Plasma samples of these patients with accurate sampling time were chosen from the biobank and plasma levels of rivaroxaban were measured at each time point. A physiologically based pharmacokinetic model for the rivaroxaban-rifampin interaction was developed to predict the optimal dosing regimen of rivaroxaban in the case of co-medication with rifampin. The model was validated by the observed plasma concentration of rivaroxaban from the above patients. From this model, it could be simulated that when rifampin starts or stops, gradually changing rivaroxaban dose during the first few days would elevate the efficacy and safety of rivaroxaban.

Ivacaftor is currently the only CFTR potentiator approved and is increasingly used since the development of CFTR correctors. Ivacaftor is metabolized by CYP3A4 and therefore dose reduction is required when treating patients on ivacaftor with CYP3A4 inhibiting drugs. As this advice is based on studies in healthy volunteers and not in cystic fibrosis (CF) patients, we need to investigate this in both groups to be able to extrapolate these data to CF.
A cohort of CF patients and healthy subjects were exposed to a single dose of ivacaftor in combination with a strong (ritonavir), moderate (clarithromycin) and mild (azithromycin) CYP3A4 inhibitor. Ivacaftor concentrations were measured in all blood samples in order to calculate the pharmacokinetic parameters for ivacaftor.
We found that exposure to ivacaftor was higher in healthy volunteers than in subjects with CF. However this difference was not statistically significant. No differences were observed in the interaction potential of CYP3A4 inhibitors between both study groups. The strong CYP3A4 inhibitor ritonavir, increased exposure to ivacaftor 7 times.
Our data support current recommendations for dose adjustment of ivacaftor in case of co-treatment with CYP3A4 inhibitors in people with CF. However, exposure to ivacaftor was higher in healthy subjects than in CF patients. Further study is needed to investigate the cause and implication of this difference.

GTx-024 (also known as enobosarm) is a first in class selective androgen receptor modulator being developed for diverse indications in oncology. Preclinical studies of GTx-024 supported the evaluation of several potential drug-drug interactions in a clinical setting. A series of open-label Phase I GTx-024 drug-drug interaction studies were designed to interrogate potential interactions with CYP3A4 inhibitor (itraconazole), a CYP3A4 inducer (rifampin), a pan-UGT inhibitor (probenecid), a CYP2C9 substrate (celecoxib) and a BCRP substrate (rosuvastatin). The plasma pharmacokinetics of GTx-024, its major metabolite (GTx-024 glucuronide), and each substrate were characterized in detail. Itraconazole administration had no effect on GTx-024 pharmacokinetics. Likewise, GTx-024 administration did not significantly change the pharmacokinetics of celecoxib or rosuvastatin. Rifampin administration had the largest impact on GTx-024 pharmacokinetics of any co-administered agent and reduced the maximal plasma concentration (Cmax) by 23 % and the area under the curve (AUC∞) by 43 %. Probenecid had a complex interaction with GTx-024 whereby both GTx-024 plasma levels and GTx-024 glucuronide plasma levels (AUC∞) were increased by co-administration of the UGT inhibitor (50 and 112 %, respectively). Overall, GTx-024 was well tolerated and poses very little risk of generating clinically relevant drug-drug interactions.

Because of health benefits that are manifested across various disease areas, the consumption of herbal products and/or health supplements containing different kinds of flavonoids has been on the rise. While the drug-drug interaction potential between flavonoids and co-ingested drugs still remain an issue, opportunities exist for the combination of flavonoids with suitable anti-cancer drugs to enhance the bioavailability of anti-cancer drugs and thereby reduce the dose size of the anti-cancer drugs and improve its therapeutic index. In recent years, scores of flavonoids have undergone preclinical investigation with variety of drugs encompassing therapeutic areas such as oncology (etoposide, doxorubicin, paclitaxel, tamoxifen etc.), immunosuppression (cyclosporine) and hypertension (losartan, felodipine, nitrendipine etc.). The review provides examples of the recent trends in the preclinical investigation of 14 flavonoids (morin, quercetin, silibinin, kaempferol etc.) with various co-administered drugs. The relevance of combination of flavonoids with anti-cancer drugs and a framework to help design the in vitro and in vivo preclinical studies to gain better mechanistic insights are discussed. Also, concise discussions on the various physiological factors that contribute for the reduced bioavailability of flavonoids along with the significant challenges in the data interpretation are provided.

BACKGROUND: Increasing the adoption of electronic health records (EHRs) with integrated clinical decision support (CDS) is a key initiative of the current US healthcare administration. High over-ride rates of CDS alerts strongly limit these potential benefits. As a result, EHR designers aspire to improve alert design to achieve better acceptance rates. In this study, we evaluated drug-drug interaction (DDI) alerts generated in EHRs and compared them for compliance with human factors principles.
METHODS: We utilized a previously validated questionnaire, the I-MeDeSA, to assess compliance with nine human factors principles of DDI alerts generated in 14 EHRs. Two reviewers independently assigned scores evaluating the human factors characteristics of each EHR. Rankings were assigned based on these scores and recommendations for appropriate alert design were derived.
RESULTS: The 14 EHRs evaluated in this study received scores ranging from 8 to 18.33, with a maximum possible score of 26. Cohen\'s κ (κ=0.86) reflected excellent agreement among reviewers. The six vendor products tied for second and third place rankings, while the top system and bottom five systems were home-grown products. The most common weaknesses included the absence of characteristics such as alert prioritization, clear and concise alert messages indicating interacting drugs, actions for clinical management, and a statement indicating the consequences of over-riding the alert.
CONCLUSIONS: We provided detailed analyses of the human factors principles which were assessed and described our recommendations for effective alert design. Future studies should assess whether adherence to these recommendations can improve alert acceptance.