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Etravirine (formerly TMC125) is a non-nucleoside reverse transcriptase inhibitor (NNRTI) with activity against wild-type and NNRTI-resistant strains of HIV-1. Etra virine has been approved in several countries for use as part of highly active antiretroviral therapy in treatment-experienced patients. In vivo , etravirine is a substrate for, and weak inducer of, the hepatic cytochrome P450 (CYP) isoenzyme 3A4 and a substrate and weak inhibitor of CYP2C9 and CYP2C19. Etravirine is also a weak inhibitor of P-glycoprotein. An extensive drug-drug interaction programme in HIV-negative subjects has been carried out to assess the potential for pharmacokinetic interactions between etravirine and a variety of non-antiretroviral drugs. Effects of atorvastatin, clarithromycin, methadone, omeprazole, oral contraceptives, paroxetine, ranitidine and sildenafil on the pharmacokinetic disposition of etravirine were of no clinical relevance. Likewise, etravirine had no clinically significant effect on the pharmacokinetics of fluconazole, methadone, oral contraceptives, paroxetine or voriconazole. No clinically relevant interactions are expected between etravirine and azithromycin or ribavirin, therefore, etravirine can be combined with these agents without dose adjustment. Fluconazole and voriconazole increased etravirine exposure 1.9- and 1.4-fold, respectively, in healthy subjects, however, no increase in the incidence of adverse effects was observed in patients receiving etravirine and fluconazole during clinical trials, therefore, etravirine can be combined with these antifungals although caution is advised. Digoxin plasma exposure was slightly increased when co-administered with etravirine. No dose adjustments of digoxin are needed when used in combination with etravirine, however, it is recommended that digoxin levels should be monitored. Caution should be exercised in combining rifabutin with etravirine in the presence of certain boosted HIV protease inhibitors due to the risk of decreased exposure to etravirine. Although adjustments to the dose of clarithromycin are unnecessary for the treatment of most infections, the use of an alternative macrolide (e.g. azithromycin) is recommended for the treatment of Mycobacterium avium complex infection since the overall activity of clarithromycin against this pathogen may be altered when co-administered with etravirine. Dosage adjustments based on clinical response are recommended for clopidogrel, HMG-CoA reductase inhibitors (e.g. atorvastatin) and for phosphodiesterase type-5 inhibitors (e.g. sildenafil) because changes in the exposure of these medications in the presence of co-administered etravirine may occur. When co-administered with etravirine, a dose reduction or alternative to diazepam is recommended. When combining etravirine with warfarin, the international normalized ratio (INR) should be monitored. Systemic dexamethasone should be co-administered with caution, or an alternative to dexamethasone be found as dexamethasone induces CYP3A4. Caution is also warranted when co-administering etravirine with some antiarrhythmics, calcineurin inhibitors (e.g. ciclosporin) and antidepressants (e.g. citalopram). Coadministration of etravirine with some antiepileptics (e.g. carbamazepine and phenytoin), rifampicin (rifampin), rifapentine or preparations containing St John’s wort ( Hypericum perforatum ) is currently not recommended as these are potent inducers of CYP3A and/or CYP2C and may potentially decrease etravirine exposure. Antiepileptics that are less likely to interact based on their known pharmacological properties include gabapentin, lamotrigine, levetiracetam and pregabalin. Overall, pharmacokinetic and clinical data show etravirine to be well tolerated and generally safe when given in combination with non-antiretroviral agents, with minimal clinically significant drug interactions and no need for dosage adjustments of etravirine in any of the cases, or of the non-antiretroviral agent in the majority of cases studied.

Currently, most guidelines on drug-drug interaction (DDI) neither consider the potential effect of genetic polymorphism in the strength of the interaction nor do they account for the complex interaction caused by the combination of DDI and drug-gene interaction (DGI) where there are multiple biotransformation pathways, which is referred to as drug-drug-gene interaction (DDGI). In this systematic review, we report the impact of pharmacogenetics on DDI and DDGI in which three major drug-metabolizing enzymes - CYP2C9, CYP2C19 and CYP2D6 - are central. We observed that several DDI and DDGI are highly gene-dependent, leading to a different magnitude of interaction. Precision drug therapy should take pharmacogenetics into account when drug interactions in clinical practice are expected.

The majority of the antipsychotic drugs are also known to interact with other co-administered drugs. Drug-drug interaction (DDI) reports among patients receiving antipsychotic medications are common. The study aims to identify the potential drug-drug, drug-tobacco, and drug-ethanol interactions associated with antipsychotics and significant predictors of potential DDIs (pDDIs). A prospective observational study was conducted among psychiatric inpatients receiving antipsychotic therapy and met the inclusion criteria that were reviewed for the presence of pDDIs using DRUGDEX-Micromedex database 2.0. The identified pDDIs were graded according to the severity and type of documentation. A total of 110 patients had a minimum of a single interaction, and the overall frequency of pDDIs reported was 64.7%. Of 158 pDDIs, 92 interactions (58.2%) were of major severity, while 66 interactions were of moderate severity (41.8%). Olanzapine with valproate (40 [25.3%]) was the most commonly documented pDDIs, followed by risperidone with valproate (20 [12.6%]). Olanzapine with tobacco (20 [69%]) was the most common drug-tobacco interaction. Simultaneously, olanzapine with ethanol was the most common potential drug and ethanol interaction (9 [50%]). Variables such as the number of drugs and polypharmacy statistically significantly predicted pDDIs (F[7, 162] = 8.155, P < 0.05, R2 = 0.26). Knowing the severity of different pDDIs will help clinicians and prescribers monitor patient safety through regular monitoring for interactions and adverse drug effects in future. The number of medications and polypharmacy was found to be the most significant predictor of pDDIs.

Purpose Several electronic databases which report the prevalence of drug-drug interactions (DDIs) are used as a tool for evaluation of potentially harmful DDIs. The aim of our review was to evaluate the usability and appropriateness of commercially available electronic databases which assess the prevalence of potential DDIs. Methods The systematic electronic literature search was conducted with the following search terms: “database” AND “software,” and “drug-drug interactions” AND “database,” and the inclusion and exclusion criteria were applied in order to identify the publications of interest. Results A total of 3766 papers were identified by systematic search. After applying inclusion and exclusion criteria, 38 publications were included in the analysis. The most commonly used software in the included studies was Micromedex® Drug-Reax, for which some authors argue to be the most reliable due to highest sensitivity. It gives information about clinical consequences of DDIs, classifies underlying mechanism and onset of the adverse outcome (either rapid, or delayed) as well as severity (such as minor, moderate, or major), and provides the level of evidence which supports this information. This data is also provided by Drug Interaction Facts®, Lexi-Interact®, and Pharmavista®. A small number of studies which compared assessment of DDIs with electronic database and the clinician’s assessment showed large discrepancy in number and relevance of detected DDIs. The overlap was in some cases as low as 11 %. Conclusion The deficiency of clinical relevance of detected DDIs should be addressed in the upcoming research as it would provide more relevant information to the prescribers’ in clinical practice.

Introduction: The landscape of drug-drug interactions (DDIs) has evolved significantly over the past 60 years, necessitating a retrospective analysis to identify research trends and under-explored areas. While methodologies like bibliometric analysis provide valuable quantitative perspectives on DDI research, they have not successfully delineated the complex interrelations between drugs. Understanding these intricate relationships is essential for deciphering the evolving architecture and progressive transformation of DDI research structures over time. We utilize network analysis to unearth the multifaceted relationships between drugs, offering a richer, more nuanced comprehension of shifts in research focus within the DDI landscape. Methods: This groundbreaking investigation employs natural language processing, techniques, specifically Named Entity Recognition (NER) via ScispaCy, and the information extraction model, SciFive, to extract pharmacokinetic (PK) and pharmacodynamic (PD) DDI evidence from PubMed articles spanning January 1962 to July 2023. It reveals key trends and patterns through an innovative network analysis approach. Static network analysis is deployed to discern structural patterns in DDI research, while evolving network analysis is employed to monitor changes in the DDI research trend structures over time. Results: Our compelling results shed light on the scale-free characteristics of pharmacokinetic, pharmacodynamic, and their combined networks, exhibiting power law exponent values of 2.5, 2.82, and 2.46, respectively. In these networks, a select few drugs serve as central hubs, engaging in extensive interactions with a multitude of other drugs. Interestingly, the networks conform to a densification power law, illustrating that the number of DDIs grows exponentially as new drugs are added to the DDI network. Notably, we discovered that drugs connected in PK and PD networks predominantly belong to the same categories defined by the Anatomical Therapeutic Chemical (ATC) classification system, with fewer interactions observed between drugs from different categories. Discussion: The finding suggests that PK and PD DDIs between drugs from different ATC categories have not been studied as extensively as those between drugs within the same categories. By unearthing these hidden patterns, our study paves the way for a deeper understanding of the DDI landscape, providing valuable information for future DDI research, clinical practice, and drug development focus areas.

Purpose Drug–drug interaction (DDI) screening systems report potential DDIs. This study aimed to find the prevalence of probable DDI-related adverse drug reactions (ADRs) and compare the clinical usefulness of different DDI screening systems to prevent or warn against these ADRs. Methods A prospective cohort study was conducted in patients urgently admitted to medical departments. Potential DDIs were checked using Complete Drug Interaction®, Lexicomp® Online™, and Drug Interaction Checker®. The study team identified the patients with probable clinically relevant DDI-related ADRs on admission, the causality of which was assessed using the Drug Interaction Probability Scale (DIPS). Sensitivity, specificity, and positive and negative predictive values of screening systems to prevent or warn against probable DDI-related ADRs were evaluated. Results Overall, 50 probable clinically relevant DDI-related ADRs were found in 37 out of 795 included patients taking at least two drugs, most common of them were bleeding, hyperkalemia, digitalis toxicity, and hypotension. Complete Drug Interaction showed the best sensitivity (0.76) for actual DDI-related ADRs, followed by Lexicomp Online (0.50), and Drug Interaction Checker (0.40). Complete Drug Interaction and Drug Interaction Checker had positive predictive values of 0.07; Lexicomp Online had 0.04. We found no difference in specificity and negative predictive values among these systems. Conclusion DDI screening systems differ significantly in their ability to detect probable clinically relevant DDI-related ADRs in terms of sensitivity and positive predictive value.

Background The novel coronavirus pandemic calls for a rapid adaptation of conventional medical practices to meet the evolving needs of such vulnerable patients. People with coronavirus disease (COVID-19) may frequently require treatment with psychotropic medications, but are at the same time at higher risk for safety issues because of the complex underlying medical condition and the potential interaction with medical treatments. Methods In order to produce evidence-based practical recommendations on the optimal management of psychotropic medications in people with COVID-19, an international, multi-disciplinary working group was established. The methodology of the WHO Rapid Advice Guidelines in the context of a public health emergency and the principles of the AGREE statement were followed. Available evidence informing on the risk of respiratory, cardiovascular, infective, hemostatic, and consciousness alterations related to the use of psychotropic medications, and drug–drug interactions between psychotropic and medical treatments used in people with COVID-19, was reviewed and discussed by the working group. Results All classes of psychotropic medications showed potentially relevant safety risks for people with COVID-19. A set of practical recommendations was drawn in order to inform frontline clinicians on the assessment of the anticipated risk of psychotropic-related unfavorable events, and the possible actions to take in order to effectively manage this risk, such as when it is appropriate to avoid, withdraw, switch, or adjust the dose of the medication. Conclusions The present evidence-based recommendations will improve the quality of psychiatric care in people with COVID-19, allowing an appropriate management of the medical condition without worsening the psychiatric condition and vice versa.

PurposeAdverse events (AEs) caused by polypharmacy have recently become a clinical problem, and it is important to monitor the safety profile of drug-drug interactions (DDIs). Mining signals using the spontaneous reporting systems is a very effective method for single drug-induced AE monitoring as well as early detection of DDIs. The objective of this study was to compare signal detection algorithms for DDIs based on frequency statistical models.MethodsFive frequency statistical models: the Ω shrinkage measure, additive (risk difference), multiplicative (risk ratio), combination risk ratio, and chi-square statistics models were compared using the Japanese Adverse Drug Event Report (JADER) database as the spontaneous reporting system in Japan. The drugs targeted for the survey are all registered and classified as “suspect drugs” in JADER, and the AEs targeted for this study were the same as those in a previous study on Stevens-Johnson syndrome (SJS).ResultsOf 3924 pairs that reported SJS, the number of signals detected by the Ω shrinkage measure, additive, multiplicative, combination risk ratio, and chi-square statistics models was 712, 3298, 2252, 739, and 1289 pairs, respectively. Among the five models, the Ω shrinkage measure model showed the most conservative signal detection tendency.ConclusionSpecifically, caution should be exercised when the number of reports is low because results differ depending on the statistical models. This study will contribute to the selection of appropriate statistical models to detect signals of potential DDIs.

Selective serotonin reuptake inhibitors (SSRIs) are widely prescribed for depression and anxiety, but their potential for drug-drug interactions (DDIs) poses significant risks, particularly given their influence on cytochrome P450 enzymes. Variability in identifying and classifying these interactions among drug interaction checkers (ICs) can complicate clinical decision-making and compromise patient safety. This study aims to compare five widely used ICs in identifying DDIs related to SSRIs, highlighting discrepancies in DDI identification and severity classification to inform best practices. A comparative study was conducted using five popular ICs (Micromedex, Lexi-Interact, Epocrates, Medscape, and Drugs.com) to evaluate their performance in identifying SSRIs-related DDIs. Data on drug-SSRIs interaction pairs were extracted over 2 weeks in 2025. Descriptive analysis was used to quantify potential interactions and their severity. Gwet's AC1 coefficient was employed to assess agreement among all five ICs and to compare groups of four- and two-pair sets of ICs. A total of 1,190 potentially interacting drugs with fluoxetine (FXT) were reported, 1,129 for fluvoxamine (FVM), 1,131 for citalopram (CIT), 1,084 for paroxetine (PAR), 1,206 for sertraline (SER) and 1,146 for escitalopram (ESC). The agreement among all five ICs was notably low, with Gwet's AC1 values ranging from 0.16 to 0.24 across different SSRIs. Similarly, it was poor in 4 and 2 sets analyses. The level of agreement among the ICs in classifying the severity of potential DDIs or restricting DDIs identified as severe was poor, also in 4 and 2 sets analysis. The findings reveal substantial discrepancies in the identification and severity categorization of SSRIs-related DDIs among ICs, underscoring the challenges faced by healthcare providers in ensuring safe prescribing practices. The study advocates for the standardization of IC databases and severity criteria to enhance consistency and reliability.

Inferring drug–drug interactions (DDIs) is an essential step in drug development and drug administration. Most computational inference methods focus on modeling drug pharmacokinetics, aiming at interactions that result from a common metabolizing enzyme (CYP). Here, we introduce a novel prediction method, INDI (INferring Drug Interactions), allowing the inference of both pharmacokinetic, CYP‐related DDIs (along with their associated CYPs) and pharmacodynamic, non‐CYP associated ones. On cross validation, it obtains high specificity and sensitivity levels (AUC (area under the receiver‐operating characteristic curve)⩾0.93). In application to the FDA adverse event reporting system, 53% of the drug events could potentially be connected to known (41%) or predicted (12%) DDIs. Additionally, INDI predicts the severity level of each DDI upon co‐administration of the involved drugs, suggesting that severe interactions are abundant in the clinical practice. Examining regularly taken medications by hospitalized patients, 18% of the patients receive known or predicted severely interacting drugs and are hospitalized more frequently. Access to INDI and its predictions is provided via a web tool at http://www.cs.tau.ac.il/∼bnet/software/INDI , facilitating the inference and exploration of drug interactions and providing important leads for physicians and pharmaceutical companies alike. INDI is a similarity‐based drug–drug interaction prediction method that can infer both pharmacokinetic and pharmacodynamic interactions, as well as their severity levels. Both known and predicted drug interactions are found to be prevalent in clinical practice. Synopsis INDI is a similarity‐based drug–drug interaction prediction method that can infer both pharmacokinetic and pharmacodynamic interactions, as well as their severity levels. Both known and predicted drug interactions are found to be prevalent in clinical practice. INDI is a similarity‐based drug–drug interaction prediction method, capable of handling both pharmacokinetic and pharmacodynamic interactions. INDI predicts the severity of the interaction and the Cytochrome P450 isozyme involved in pharmacokinetic interactions. We show the prevalence of known and predicted drug interactions in drug adverse reports and in chronic medications taken by hospitalized patients.