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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.

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.

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.

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 aim of this review was to provide all the pharmacokinetic data for semaglutide in humans concerning its pharmacokinetics after subcutaneously and oral applications in healthy and diseased populations, to provide recommendations for clinical use. The PubMed and Embase databases were searched to screen studies associated with the pharmacokinetics of semaglutide. The pharmacokinetic parameters included area under the curve plasma concentrations (AUC), maximal plasma concentration (C ), time to C , half-life (t ), and clearance. The systematic literature search retrieved 17 articles including data on pharmacokinetic profiles after subcutaneously and oral applications of semaglutide, and at least one of the above pharmacokinetic parameter was reported in all included studies. Semaglutide has a predictable pharmacokinetic profile with a long t that allows for once-weekly subcutaneous administration. The AUC and C of both oral and subcutaneous semaglutide increased with dose. Food and various dosing conditions including water volume and dosing schedules can affect the oral semaglutide exposure. There are limited drug-drug interactions and no dosing adjustments in patients with upper gastrointestinal disease, renal impairment or hepatic impairment. Body weight may affect semaglutide exposure, but further studies are needed to confirm this. This review encompasses all the pharmacokinetic data for subcutaneous and oral semaglutide in both healthy and diseased participants. The existing pharmacokinetic data can assist in developing and evaluating pharmacokinetic models of semaglutide and will help clinicians predict semaglutide dosages. In addition, it can also help optimize future clinical trials.

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.

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.

To study the main comedications associated with major bleeding during anticoagulant therapy with coumarins in a non-selected population under everyday circumstances. The study population for this retrospective cohort study included all new users of phenprocoumon or acenocoumarol aged 40-80 years, during the period 1992-2000 in the PHARMO Record Linkage System. All patients were followed until the last dispensing of phenprocoumon or acenocoumarol, the first bleeding complication requiring hospitalization, death, or the end of the study period. The number of days on coumarins alone and the number of days on coumarins in combination with several potentially interactive drugs during follow-up were determined for each patient. The inclusion criteria of this study were met by 19,935 new users of phenprocoumon or acenocoumarol. During follow-up, 552 patients were hospitalized for bleeding. Of all potentially interactive drugs started during anticoagulant therapy by at least 50 patients and with at least five bleedings, antibacterial drugs were associated with a four to seven times increased risk of bleeding. Among non-steroidal anti-inflammatory drugs, naproxen had the highest relative risk. Antithrombotic salicylates and tramadol were associated with a three times increased risk of bleeding. Antibacterial drugs, non-steroidal anti-inflammatory drugs, antithrombotic salicylates and tramadol were the main potentially interactive drugs associated with major bleeding during anticoagulant therapy with coumarins under everyday circumstances.