BackgroundThe mechanisms underlying the non-antimicrobial immunomodulatory properties of macrolides are not well understood.ObjectivesTo systematically review the evidence for the immunomodulatory properties of macrolides in humans and to describe the underlying mechanism and extent of their influence on the innate and adaptive immune system.MethodsA systematic literature search was done in MEDLINE using the OVID interface from 1946 to December 2016 according to the preferred reporting items for systematic reviews and meta-analysis (PRISMA). Original articles investigating the influence of four macrolides (azithromycin, clarithromycin, erythromycin, and roxithromycin) on immunological markers in humans were included.ResultsWe identified 22 randomized, controlled trials, 16 prospective cohort studies, and 8 case–control studies investigating 47 different immunological markers (186 measurements) in 1,834 participants. The most frequently reported outcomes were a decrease in the number of neutrophils, and the concentrations of neutrophil elastase, interleukin (IL)-8, IL-6, IL-1beta, tumor necrosis factor (TNF)-alpha, eosinophilic cationic protein, and matrix metalloproteinase 9. Inhibition of neutrophil function was reported more frequently than eosinophil function. A decrease in T helper (Th) 2 cells cytokines (IL-4, IL-5, IL-6) was reported more frequently than a decrease in Th1 cytokines (IL-2, INF-gamma).ConclusionMacrolides influence a broad range of immunological mechanisms resulting in immunomodulatory effects. To optimize the treatment of chronic inflammatory diseases by macrolides, further studies are necessary, particularly comparing different macrolides and dose effect relationships.
Fentanyl is primarily metabolized by CYP3A, but has also been suggested to act as a weak inhibitor of CYP3A. We investigated the influence of CYP3A inhibition by ketoconazole on the pharmacokinetics of intravenously administered fentanyl and the effect of fentanyl on CYP3A activity. A prospective, open-label, randomized, monocentre, crossover study was conducted in 16 healthy volunteers. They received fentanyl alone (5 microgram per kilogram) or fentanyl plus ketoconazole (200 milligram orally B.I.D. over 2 days). Naloxone (2 × 0.2 milligram i.v.) was given simultaneously with fentanyl to mitigate any opioid effect. Midazolam was administered as a CYP3A probe drug. Fentanyl and its metabolites were quantified by LC/MS/MS in blood and urine samples obtained over 24 hour. Exposure of fentanyl (AUC0- ∞ ) was significantly increased to 133% and systemic clearance was reduced to 78% by ketoconazole, norfentanyl formation was significantly delayed and partial metabolic clearance decreased to 18%. Fentanyl had no influence on midazolam exposure and CYP3A activity whereas ketoconazole decreased CYP3A activity to 13%. Although fentanyl N-dealkylation is substantially inhibited by ketoconazole, exposure of fentanyl itself increased by one third only. Clinically fentanyl dosage adjustments may become necessary when ketoconazole or other strong CYP3A inhibitors are given simultaneously. Fentanyl itself does not influence CYP3A activity.
Fentanyl and its derivatives sufentanil, alfentanil, and remifentanil are potent opioids. A comprehensive review of the use of fentanyl and its derivatives in the pediatric population was performed using the National Library of Medicine PubMed. Studies were included if they contained original pharmacokinetic parameters or models using established routes of administration in patients younger than 18 years of age. Of 372 retrieved articles, 44 eligible pharmacokinetic studies contained data of 821 patients younger than 18 years of age, including more than 46 preterm infants, 64 full-term neonates, 115 infants/toddlers, 188 children, and 28 adolescents. Underlying diagnoses included congenital heart and pulmonary disease and abdominal disorders. Routes of drug administration were intravenous, epidural, oral-transmucosal, intranasal, and transdermal. Despite extensive use in daily clinical practice, few studies have been performed. Preterm and term infants have lower clearance and protein binding. Pharmacokinetics was not altered by chronic renal or hepatic disease. Analyses of the pooled individual patients' data revealed that clearance maturation relating to body weight could be best described by the Hill function for sufentanil (R = 0.71, B 876 mL/min, K 16.3 kg) and alfentanil (R = 0.70, B 420 mL/min, K 28 kg). The allometric exponent for estimation of clearance of sufentanil was 0.99 and 0.75 for alfentanil clearance. Maturation of remifentanil clearance was described by linear regression to bodyweight (R = 0.69). The allometric exponent for estimation of remifentanil clearance was 0.76. For fentanyl, linear regression showed only a weak correlation between clearance and bodyweight in preterm and term neonates (R = 0.22) owing to a lack of data in older age groups. A large heterogeneity regarding study design, clinical setting, drug administration, laboratory assays, and pharmacokinetic estimation was observed between studies introducing bias into the analyses performed in this review. A limitation of this review is that pharmacokinetic data, based on different modes of administration, dosing schemes, and parameter estimation methods, were combined.
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