METHODS: The MEDLINE and EMBASE databases, as well as the reference lists of all articles, were searched to identify population PK models of tacrolimus developed from adult transplant recipients published from the inception of the databases to 29 February 2020.
RESULTS: Of the 69 studies identified, 55% were developed from kidney transplant recipients and 30% from liver transplant recipients. Most studies (91%) investigated the oral immediate-release formulation of tacrolimus. Few studies (17%) explained the effect of drug-drug interactions on tacrolimus PK. Only 35% of the studies performed an external evaluation to assess the generalizability of the models. Studies related variability in tacrolimus whole blood clearance among transplant recipients to either cytochrome P450 (CYP) 3A5 genotype (41%), days post-transplant (30%), or hematocrit (29%). Variability in the central volume of distribution was mainly explained by body weight (20% of studies).
CONCLUSION: The effect of clinically significant drug-drug interactions and different formulations and brands of tacrolimus should be considered for any future tacrolimus population PK model development. Further work is required to assess the generalizability of existing models and identify key factors that influence both initial and maintenance doses of tacrolimus, particularly in heart and lung transplant recipients.
METHODS: Population pharmacokinetic models of tacrolimus were selected (n=17) for evaluation from a recent systematic review. The models were transcribed and implemented in NONMEM version 7.4.3. Data from 85 heart transplant recipients (2387 tacrolimus concentrations) administered the oral immediate-release formulation of tacrolimus (Prograf®) were obtained up to 391 days post-transplant. The performance of each model was evaluated using (1) prediction-based assessment (bias and imprecision) of the individual predicted tacrolimus concentration of the fourth dosing occasion (MAXEVAL=0, FOCE-I) from 1-3 prior dosing occasions and (2) simulation-based assessment (prediction-corrected visual predictive check, pcVPC). Both assessments were stratified based on concomitant azole antifungal use.
RESULTS: Regardless of the number of prior dosing occasions (1-3) and concomitant azole antifungal use, all models demonstrated unacceptable individual predicted tacrolimus concentration of the fourth dosing occasion (n=152). The pcVPC graphics indicated these models inadequately predicted observed tacrolimus concentrations.
CONCLUSIONS: All models evaluated were unable to adequately describe tacrolimus pharmacokinetics in adult heart transplant recipients included in this study. Further work is required to describe tacrolimus pharmacokinetics for our heart transplant recipient cohort.
METHODS: A retrospective audit of heart transplant recipients (n = 87) treated with tacrolimus was performed. Relevant data were collected from the time of transplant to discharge. The concordance of tacrolimus dosing and monitoring according to hospital guidelines was assessed. The observed and software-predicted tacrolimus concentrations (n = 931) were compared for the first 3 weeks of oral immediate-release tacrolimus (Prograf) therapy, and the predictive performance (bias and imprecision) of the software was evaluated.
RESULTS: The majority (96%) of initial oral tacrolimus doses were guideline concordant. Most initial intravenous doses (93%) were lower than the guideline recommendations. Overall, 36% of initial tacrolimus doses were administered to transplant recipients with an estimated glomerular filtration rate of <60 mL/min/1.73 m despite recommendations to delay the commencement of therapy. Of the tacrolimus concentrations collected during oral therapy (n = 1498), 25% were trough concentrations obtained at steady-state. The software displayed acceptable predictions of tacrolimus concentration from day 12 (bias: -6%; 95%confidence interval, -11.8 to 2.5; imprecision: 16%; 95% confidence interval, 8.7-24.3) of therapy.
CONCLUSIONS: Tacrolimus dosing and monitoring were discordant with the guidelines. The Bayesian forecasting software was suitable for guiding tacrolimus dosing after 11 days of therapy in heart transplant recipients. Understanding the factors contributing to the variability in tacrolimus pharmacokinetics immediately after transplant may help improve software predictions.
METHODS: Data from heart transplant recipients (n = 87) administered the oral immediate-release formulation of tacrolimus (Prograf®) were collected. Routine drug monitoring data, principally trough concentrations, were used for model building (n = 1099). A published tacrolimus model was used to inform the estimation of Ka , V2 /F, Q/F and V3 /F. The effect of concomitant azole antifungal use on tacrolimus CL/F was quantified. Fat-free mass was implemented as a covariate on CL/F, V2 /F, V3 /F and Q/F on an allometry scale. Subsequently, stepwise covariate modelling was performed. Significant covariates influencing tacrolimus CL/F were included in the final model. Robustness of the final model was confirmed using prediction-corrected visual predictive check (pcVPC). The final model was externally evaluated for prediction of tacrolimus concentrations of the fourth dosing occasion (n = 87) from one to three prior dosing occasions.
RESULTS: Concomitant azole antifungal therapy reduced tacrolimus CL/F by 80%. Haematocrit (∆OFV = -44, P
METHODS: Data from 43 lung transplant recipients (1021 tacrolimus concentrations) administered an immediate-release oral formulation of tacrolimus were used to evaluate the predictive performance of 17 published population pharmacokinetic models for tacrolimus. Data were collected from immediately after transplantation up to 90 days after transplantation. Model performance was evaluated using (1) prediction-based assessments (bias and imprecision) of individual predicted tacrolimus concentrations at the fourth dosing based on 1 to 3 previous dosings and (2) simulation-based assessment (prediction-corrected visual predictive check; pcVPC). Both assessments were stratified based on concomitant azole antifungal use. Model performance was clinically acceptable if the bias was within ±20%, imprecision was ≤20%, and the 95% confidence interval of bias crossed zero.
RESULTS: In the presence of concomitant antifungal therapy, no model showed acceptable performance in predicting tacrolimus concentrations at the fourth dosing (n = 33), and pcVPC plots displayed poor model fit to the data set. However, this fit slightly improved in the absence of azole antifungal use, where 4 models showed acceptable performance in predicting tacrolimus concentrations at the fourth dosing (n = 33).
CONCLUSIONS: Although none of the evaluated models were appropriate in guiding tacrolimus dosing in lung transplant recipients receiving concomitant azole antifungal therapy, 4 of these models displayed potential applicability in guiding dosing in recipients not receiving concomitant azole antifungal therapy. However, further model refinement is required before the widespread implementation of such models in clinical practice.
OBJECTIVES: To assess the effects of favipiravir compared to no treatment, supportive treatment, or other experimental antiviral treatment in people with acute COVID-19.
SEARCH METHODS: We searched the Cochrane COVID-19 Study Register, MEDLINE, Embase, the World Health Organization (WHO) COVID-19 Global literature on coronavirus disease, and three other databases, up to 18 July 2023.
SELECTION CRITERIA: We searched for RCTs evaluating the efficacy of favipiravir in treating people with COVID-19.
DATA COLLECTION AND ANALYSIS: We used standard Cochrane methodological procedures for data collection and analysis. We used the GRADE approach to assess the certainty of evidence for each outcome.
MAIN RESULTS: We included 25 trials that randomized 5750 adults (most under 60 years of age). The trials were conducted in Bahrain, Brazil, China, India, Iran, Kuwait, Malaysia, Mexico, Russia, Saudi Arabia, Thailand, the UK, and the USA. Most participants were hospitalized with mild to moderate disease (89%). Twenty-two of the 25 trials investigated the role of favipiravir compared to placebo or standard of care, whilst lopinavir/ritonavir was the comparator in two trials, and umifenovir in one trial. Most trials (24 of 25) initiated favipiravir at 1600 mg or 1800 mg twice daily for the first day, followed by 600 mg to 800 mg twice a day. The duration of treatment varied from five to 14 days. We do not know whether favipiravir reduces all-cause mortality at 28 to 30 days, or in-hospital (risk ratio (RR) 0.84, 95% confidence interval (CI) 0.49 to 1.46; 11 trials, 3459 participants; very low-certainty evidence). We do not know if favipiravir reduces the progression to invasive mechanical ventilation (RR 0.86, 95% CI 0.68 to 1.09; 8 trials, 1383 participants; very low-certainty evidence). Favipiravir may make little to no difference in the need for admission to hospital (if ambulatory) (RR 1.04, 95% CI 0.44 to 2.46; 4 trials, 670 participants; low-certainty evidence). We do not know if favipiravir reduces the time to clinical improvement (defined as time to a 2-point reduction in patients' admission status on the WHO's ordinal scale) (hazard ratio (HR) 1.13, 95% CI 0.69 to 1.83; 4 trials, 721 participants; very low-certainty evidence). Favipiravir may make little to no difference to the progression to oxygen therapy (RR 1.20, 95% CI 0.83 to 1.75; 2 trials, 543 participants; low-certainty evidence). Favipiravir may lead to an overall increased incidence of adverse events (RR 1.27, 95% CI 1.05 to 1.54; 18 trials, 4699 participants; low-certainty evidence), but may result in little to no difference inserious adverse eventsattributable to the drug (RR 1.04, 95% CI 0.76 to 1.42; 12 trials, 3317 participants; low-certainty evidence).
AUTHORS' CONCLUSIONS: The low- to very low-certainty evidence means that we do not know whether favipiravir is efficacious in people with COVID-19 illness, irrespective of severity or admission status. Treatment with favipiravir may result in an overall increase in the incidence of adverse events but may not result in serious adverse events.
METHODS: Published population pharmacokinetic models and the Australasian Neonatal Medicines Formulary were used to simulate antimicrobial concentration-time profiles in a virtual neonate population. Laboratory quality assurance data were used to quantify analytical variation in antimicrobial measurement methods used in clinical practice. Guideline-informed dosing recommendations based on drug concentrations were applied to compare the impact of analytical variation and nonanalytical factors on antimicrobial dosing.
RESULTS: Analytical variation caused differences in subsequent guideline-informed dosing recommendations in 9.3-12.1% (amikacin), 16.2-19.0% (tobramycin), 12.2-45.8% (gentamicin), and 9.6-19.5% (vancomycin) of neonates. For vancomycin, inaccuracies in drug administration time (45.6%), use of non-trough concentrations (44.7%), within-subject biological variation (38.2%), and dosing errors (27.5%) were predicted to result in more dosing discrepancies than analytical variation (12.5%). Using current analytical performance specifications, tolerated dosing discrepancies would be up to 14.8% (aminoglycosides) and 23.7% (vancomycin).
CONCLUSIONS: Although analytical variation can influence neonatal antimicrobial dosing recommendations, nonanalytical factors are more influential. These result in substantial variation in subsequent dosing of antimicrobials, risking inadvertent under- or overexposure. Harmonization of measurement methods and improved patient management systems may reduce the impact of analytical and nonanalytical factors on neonatal antimicrobial dosing.
METHODS: The manuscript search was carried out in six electronic databases-PubMed/MEDLINE, LILACS, SciELO, Cochrane, Web of Science and DOAJ, without publication year limits. Only manuscripts in English (including the translated articles) were selected, and the last search was performed in December 2020. The Preferred Reporting Items for Systematic Reviews and Meta-Analyses (PRISMA) statement was followed.
RESULTS: From the 128 potentially eligible studies, 48 full-text articles were assessed for eligibility. After eligibility assessment and exclusions, 14 studies were considered for systematic review and seven studies for meta-analysis. Amongst the selected studies for meta-analysis, three had a medium and four had a low risk of bias.
CONCLUSIONS: It was evidenced by the available data that Proanthocyanidin is the most efficient natural cross-linker to date, in preserving the bond strength even after ageing.