In Patients With Severe Alcoholic Hepatitis, Prednisolone Increases Susceptibility to Infection and Infection-Related Mortality, and Is Associated With High Circulating Levels of Bacterial DNA

Background & Aims Infections are common in patients with severe alcoholic hepatitis (SAH), but little information is available on how to predict their development or their effects on patients. Prednisolone is advocated for treatment of SAH, but can increase susceptibility to infection. We compared the effects of infection on clinical outcomes of patients treated with and without prednisolone, and identified risk factors for development of infection in SAH. Methods We analyzed data from 1092 patients enrolled in a double-blind placebo-controlled trial to evaluate the efficacy of treatment with prednisolone (40 mg daily) or pentoxifylline (400 mg 3 times each day) in patients with SAH. The 2 × 2 factorial design led to 547 patients receiving prednisolone; 546 were treated with pentoxifylline. The trial was conducted in the United Kingdom from January 2011 through February 2014. Data on development of infection were collected at evaluations performed at screening, baseline, weekly during admission, on discharge, and after 90 days. Patients were diagnosed with infection based on published clinical and microbiologic criteria. Risk factors for development of infection and effects on 90-day mortality were evaluated separately in patients treated with prednisolone (n = 547) and patients not treated with prednisolone (n = 545) using logistic regression. Pretreatment blood levels of bacterial DNA (bDNA) were measured in 731 patients. Results Of the 1092 patients in the study, 135 had an infection at baseline, 251 developed infections during treatment, and 89 patients developed an infection after treatment. There was no association between pentoxifylline therapy and the risk of serious infection (P = .084), infection during treatment (P = .20), or infection after treatment (P = .27). Infections classified as serious were more frequent in patients treated with prednisolone (odds ratio [OR], 1.27; 95% confidence interval [CI], 1.27−2.92; P = .002). There was no association between prednisolone therapy and infection during treatment (OR, 1.04; 95% CI, 0.78−1.37; P = .80). However, a higher proportion (10%) of patients receiving prednisolone developed an infection after treatment than of patients not given prednisolone (6%) (OR, 1.70; 95% CI, 1.07−2.69; P = .024). Development of infection was associated with increased 90-day mortality in patients with SAH treated with prednisolone, independent of model for end-stage liver disease or Lille score (OR, 2.46; 95% CI, 1.41−4.30; P = .002). High circulating bDNA predicted infection that developed within 7 days of prednisolone therapy, independent of Model for End-Stage Liver Disease and white blood cell count (OR, 4.68; 95% CI, 1.80−12.17; P = .001). In patients who did not receive prednisolone, infection was not independently associated with 90-day mortality (OR, 0.94; 95% CI, 0.54−1.62; P = .82) or levels of bDNA (OR, 0.83; 95% CI, 0.39−1.75; P = .62). Conclusions Patients with SAH given prednisolone are at greater risk for developing serious infections and infections after treatment than patients not given prednisolone, which may offset its therapeutic benefit. Level of circulating bDNA before treatment could identify patients at high risk of infection if given prednisolone; these data could be used to select therapies for patients with SAH. EudraCT no: 2009-013897-42; Current Controlled Trials no: ISRCTN88782125

Can We Predict Individual Concentrations of Tacrolimus After Liver Transplantation? Application and Tweaking of a Published Population Pharmacokinetic Model in Clinical Practice

International audienceBackground: Various population pharmacokinetic models have been developed to describe the pharmacokinetics of tacrolimus in adult liver transplantation. However, their extrapolated predictive performance remains unclear in clinical practice. The purpose of this study was to predict concentrations using a selected literature model and to improve these predictions by tweaking the model with a subset of the target population.Methods: A literature review was conducted to select an adequate population pharmacokinetic model (L). Pharmacokinetic data from therapeutic drug monitoring of tacrolimus in liver-transplanted adults were retrospectively collected. A subset of these data (70%) was exploited to tweak the L-model using the $PRIOR subroutine of the NONMEM software, with 2 strategies to weight the prior information: full informative (F) and optimized (O). An external evaluation was performed on the remaining data; bias and imprecision were evaluated for predictions a priori and Bayesian forecasting.Results: Seventy-nine patients (851 concentrations) were enrolled in the study. The predictive performance of L-model was insufficient for a priori predictions, whereas it was acceptable with Bayesian forecasting, from the third prediction (ie, with ≥2 previously observed concentrations), corresponding to 1 week after transplantation. Overall, the tweaked models showed a better predictive ability than the L-model. The bias of a priori predictions was -41$PRIOR approach allows to obtain better predictions

Can We Predict Individual Concentrations of Tacrolimus After Liver Transplantation? Application and Tweaking of a Published Population Pharmacokinetic Model in Clinical Practice

International audienceBackground: Various population pharmacokinetic models have been developed to describe the pharmacokinetics of tacrolimus in adult liver transplantation. However, their extrapolated predictive performance remains unclear in clinical practice. The purpose of this study was to predict concentrations using a selected literature model and to improve these predictions by tweaking the model with a subset of the target population.Methods: A literature review was conducted to select an adequate population pharmacokinetic model (L). Pharmacokinetic data from therapeutic drug monitoring of tacrolimus in liver-transplanted adults were retrospectively collected. A subset of these data (70%) was exploited to tweak the L-model using the $PRIOR subroutine of the NONMEM software, with 2 strategies to weight the prior information: full informative (F) and optimized (O). An external evaluation was performed on the remaining data; bias and imprecision were evaluated for predictions a priori and Bayesian forecasting.Results: Seventy-nine patients (851 concentrations) were enrolled in the study. The predictive performance of L-model was insufficient for a priori predictions, whereas it was acceptable with Bayesian forecasting, from the third prediction (ie, with ≥2 previously observed concentrations), corresponding to 1 week after transplantation. Overall, the tweaked models showed a better predictive ability than the L-model. The bias of a priori predictions was -41$PRIOR approach allows to obtain better predictions

Lack of a clinically significant drug-drug interaction in healthy volunteers between the HCV protease inhibitor boceprevir and the proton pump inhibitor omeprazole

Contains fulltext : 118714.pdf (publisher's version ) (Open Access)OBJECTIVES: Proton pump inhibitors (PPIs) can limit the solubility of concomitant drugs, which can lead to decreased absorption and exposure. Reduced efficacy can be a consequence and in the case of an antimicrobial agent this may contribute to development of resistance. Patients chronically infected with the hepatitis C virus can be treated with a boceprevir-containing regimen and it is relevant to know if interactions between PPIs and boceprevir exist. This study was designed to investigate the influence of a frequently used PPI, omeprazole, on the pharmacokinetics of boceprevir and vice versa. METHODS: In this open-label, three-period, randomized, cross-over, Phase I study, healthy subjects were randomly assigned to 40 mg of omeprazole once daily for 5 days, 800 mg of boceprevir three times daily for 5 days and 40 mg of omeprazole once daily + 800 mg of boceprevir three times daily for 5 days, or the same treatment in a different order. Every treatment was followed by a wash-out period. At day 5 of every treatment pharmacokinetic blood sampling was performed for 8 h after medication intake. ClinicalTrials.gov: NCT01470690. RESULTS: All 24 subjects (15 males) completed the study and no serious adverse events were reported. Geometric mean ratios (90% CI) of the area under the plasma concentration-time curve up to 8 h (AUC0-8) and maximum plasma concentration (Cmax) of boceprevir with omeprazole versus boceprevir alone were 0.92 (0.87-0.97) and 0.94 (0.86-1.02), respectively. For omeprazole these values were 1.06 (0.90-1.25) for AUC0-8 and 1.03 (0.85-1.26) for Cmax for the combination versus omeprazole alone. CONCLUSIONS: Omeprazole did not have a clinically significant effect on boceprevir exposure, and boceprevir did not affect omeprazole exposure

Total synthesis of (+)-5, 14-bis-epi-Spirovibsanin A

The total synthesis of (+/-)-5,14-bis-epi-spirovibsanin A was achieved in 18 steps. Physical data obtained from (+/-)-5,14-bis-epi-spirovibsanin A lends strong support to the proposed connectivity and relative stereochemistry of spirovibsanin A