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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.

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.

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.

Introduction The availability of potent antiretroviral therapy has transformed HIV infection into a chronic disease such that people living with HIV (PLWH) have a near normal life expectancy. However, there are continuing challenges in managing HIV infection, particularly in older patients, who often experience age‐related comorbidities resulting in complex polypharmacy and an increased risk for drug‐drug interactions. Furthermore, age‐related physiological changes may affect the pharmacokinetics and pharmacodynamics of both antiretrovirals and comedications thereby predisposing elderly to adverse drug reactions. This review provides an overview of the therapeutic challenges when treating elderly PLWH (i.e. >65 years). Particular emphasis is placed on drug‐drug interactions and other common prescribing issues (i.e. inappropriate drug use, prescribing cascade, drug‐disease interaction) encountered in elderly PLWH. Discussion Prescribing issues are common in elderly PLWH due to the presence of age‐related comorbidities, organ dysfunction and physiological changes leading to a higher risk for drug‐drug interactions, drugs dosage errors and inappropriate drug use. Conclusions The high prevalence of prescribing issues in elderly PLWH highlights the need for ongoing education on prescribing principles and the optimal management of individual patients. The knowledge of adverse health outcomes associated with polypharmacy and inappropriate prescribing should ensure that there are interventions to prevent harm including medication reconciliation, medication review and medication prioritization according to the risks/benefits for each patient.

PurposeDue to polypharmacy and the rising popularity of complementary and alternative medicines (CAM), oncology patients are particularly at risk of drug–drug interactions (DDI) or herb–drug interactions (HDI). The aims of this study were to assess DDI and HDI in outpatients taking oral anticancer drug.MethodAll prescribed and non-prescribed medications, including CAM, were prospectively collected by hospital pharmacists during a structured interview with the patient. DDI and HDI were analyzed using four interaction software programs: Thériaque®, Drugs.com®, Hédrine, and Memorial Sloan Kettering Cancer Center (MSKCC) database. All detected interactions were characterized by severity, risk and action mechanism. The need for pharmaceutical intervention to modify drug use was determined on a case-by-case basis.Results294 patients were included, with a mean age of 67 years [55–79]. The median number of chronic drugs per patient was 8 [1–29] and 55% of patients used at least one CAM. At least 1 interaction was found for 267 patients (90.8%): 263 (89.4%) with DDI, 68 (23.1%) with HDI, and 64 (21.7%) with both DDI and HDI. Only 13% of the DDI were found in Thériaque® and Drugs.com® databases, and 125 (2.5%) were reported with similar level of risk on both databases. 104 HDI were identified with only 9.5% of the interactions found in both databases. 103 pharmaceutical interventions were performed, involving 61 patients (20.7%).ConclusionPotentially clinically relevant drug interaction were frequently identified in this study, showing that several databases and structured screening are required to detect more interactions and optimize medication safety.

The rising of global geriatric population has contributed to increased prevalence of dementia. Dementia is a neurodegenerative disease, which is characterized by progressive deterioration of cognitive functions, such as judgment, language, memory, attention and visuospatial ability. Dementia not only has profoundly devastating physical and psychological health outcomes, but it also poses a considerable healthcare expenditure and burdens. Acetylcholinesterase inhibitors (AChEIs), or so-called anti-dementia medications, have been developed to delay the progression of neurocognitive disorders and to decrease healthcare needs. AChEIs have been widely prescribed in clinical practice for the treatment of Alzheimer's disease, which account for 70% of dementia. The rising use of AChEIs results in increased adverse drug reactions (ADRs) such as cardiovascular and gastrointestinal adverse effects, resulting from overstimulation of peripheral cholinergic activity and muscarinic receptor activation. Changes in pharmacokinetics (PK), pharmacodynamics (PD) and pharmacogenetics (PGx), and occurrence of drug interactions are said to be major risk factors of ADRs of AChEIs in this population. To date, comprehensive reviews in ADRs of AChEIs have so far been scarcely studied. Therefore, we aimed to recapitulate and update the diverse aspects of AChEIs, including the mechanisms of action, characteristics and risk factors of ADRs, and preventive strategies of their ADRs. The collation of this knowledge is essential to facilitate efforts to reduce ADRs of AChEIs.

PurposeThe aim of our study was to analyse the frequency and severity of different types of potential interactions in oncological outpatients’ therapy. Therefore, medications, food and substances in terms of complementary and alternative medicine (CAM) like dietary supplements, herbs and other processed ingredients were considered.MethodsWe obtained data from questionnaires and from analysing the patient records of 115 cancer outpatients treated at a German university hospital. Drug–drug interactions were identified using a drug interaction checking software. Potential CAM-drug interactions and food–drug interactions were identified based on literature research.Results92.2% of all patients were at risk of one or more interaction of any kind and 61.7% of at least one major drug–drug interaction. On average, physicians prescribed 10.4 drugs to each patient and 6.9 interactions were found, 2.5 of which were classified as major. The most prevalent types of drug–drug interactions were a combination of QT prolonging drugs (32.3%) and drugs with a potential for myelotoxicity (13.4%) or hepatotoxicity (10.1%). In 37.2% of all patients using CAM supplements the likelihood of interactions with medications was rated as likely. Food-drug interactions were likely in 28.7% of all patients.ConclusionThe high amount of interactions could not be found in literature so far. We recommend running interaction checks when prescribing any new drug and capturing CAM supplements in medication lists too. If not advised explicitly in another way drugs should be taken separately from meals and by using nonmineralized water to minimize the risk for food–drug interactions.

Caffeine is by far the most ubiquitous psychostimulant worldwide found in tea, coffee, cocoa, energy drinks, and many other beverages and food. Caffeine is almost exclusively metabolized in the liver by the cytochrome P-450 enzyme system to the main product paraxanthine and the additional products theobromine and theophylline. Besides its stimulating properties, two important applications of caffeine are metabolic phenotyping of cytochrome P450 1A2 (CYP1A2) and liver function testing. An open challenge in this context is to identify underlying causes of the large inter-individual variability in caffeine pharmacokinetics. Data is urgently needed to understand and quantify confounding factors such as lifestyle (e.g., smoking), the effects of drug-caffeine interactions (e.g., medication metabolized via CYP1A2), and the effect of disease. Here we report the first integrative and systematic analysis of data on caffeine pharmacokinetics from 141 publications and provide a comprehensive high-quality data set on the pharmacokinetics of caffeine, caffeine metabolites, and their metabolic ratios in human adults. The data set is enriched by meta-data on the characteristics of studied patient cohorts and subjects (e.g., age, body weight, smoking status, health status), the applied interventions (e.g., dosing, substance, route of application), measured pharmacokinetic time-courses, and pharmacokinetic parameters (e.g., clearance, half-life, area under the curve). We demonstrate via multiple applications how the data set can be used to solidify existing knowledge and gain new insights relevant for metabolic phenotyping and liver function testing based on caffeine. Specifically, we analyzed 1) the alteration of caffeine pharmacokinetics with smoking and use of oral contraceptives; 2) drug-drug interactions with caffeine as possible confounding factors of caffeine pharmacokinetics or source of adverse effects; 3) alteration of caffeine pharmacokinetics in disease; and 4) the applicability of caffeine as a salivary test substance by comparison of plasma and saliva data. In conclusion, our data set and analyses provide important resources which could enable more accurate caffeine-based metabolic phenotyping and liver function testing.

Drug–drug interactions play a vital role in drug research. However, they may also cause adverse reactions in patients, with serious consequences. Manual detection of drug–drug interactions is time-consuming and expensive, so it is urgent to use computer methods to solve the problem. There are two ways for computers to identify drug interactions: one is to identify known drug interactions, and the other is to predict unknown drug interactions. In this paper, we review the research progress of machine learning in predicting unknown drug interactions. Among these methods, the literature-based method is special because it combines the extraction method of DDI and the prediction method of DDI. We first introduce the common databases, then briefly describe each method, and summarize the advantages and disadvantages of some prediction models. Finally, we discuss the challenges and prospects of machine learning methods in predicting drug interactions. This review aims to provide useful guidance for interested researchers to further promote bioinformatics algorithms to predict DDI.

Drug interactions are a well-known cause of adverse drug events, and drug interaction databases can help the clinician to recognize and avoid such interactions and their adverse events. However, not every interaction leads to an adverse drug event. This is because the clinical relevance of drug–drug interactions also depends on the genetic profile of the patient. If inhibitors or inducers of drug metabolising enzymes (e.g., CYP and UGT) are added to the drug therapy, phenoconcversion can occur. This leads to a genetic phenotype that mismatches the observable phenotype. Drug–drug–gene and drug–gene–gene interactions influence the toxicity and/or ineffectivness of the drug therapy. To date, there have been limited published studies on the impact of genetic variations on drug–drug interactions. This review discusses the current evidence of drug–drug–gene interactions, as well as drug–gene–gene interactions. Phenoconversion is explained, the and methods to calculate the phenotypes are described. Clinical recommendations are given regarding the integratation of the PGx results in the assessment of the relevance of drug interactions in the future.