Development of a Complex Parent-Metabolite Joint Population Pharmacokinetic Model

This study aimed to develop a joint population pharmacokinetic model for an antipsychotic agent in development (S33138) and its active metabolite (S35424) produced by reversible metabolism. Because such a model leads to identifiability problems and numerical difficulties, the model building was performed using the FOCE-I and the Stochastic Approximation Expectation Maximization (SAEM) estimation algorithms in NONMEM and MONOLIX, respectively. Four different structural models were compared based on Bayesian information criteria. Models were first written as ordinary differential equations systems and then in closed form (CF) to facilitate further analyses. The impact of polymorphisms on genes coding for the CYP2C19 and CYP2D6 enzymes, respectively involved in the parent drug and the metabolite elimination were investigated using permutation Wald test. The parent drug and metabolite plasma concentrations of 101 patients were analyzed on two occasions after 4 and 8 weeks of treatment at 1, 3, 6, and 24 h following daily oral administration. All configurations led to a two compartment model with back-transformation of the metabolite into the parent drug and a first-pass effect. The elimination clearance of the metabolite through other processes than back-transformation was decreased by 35% [9–53%] in CYP2D6 poor metabolizer. Permutation tests were performed to ensure the robustness of the analysis, using SAEM and CF. In conclusion, we developed a complex joint pharmacokinetic model adequately predicting the impact of CYP2D6 polymorphisms on the parent drug and its metabolite concentrations through the back-transformation mechanism

Host Factors and Portal of Entry Outweigh Bacterial Determinants To Predict the Severity of Escherichia coli Bacteremia▿

Escherichia coli ranks among the organisms most frequently isolated from cases of bacteremia. The relative contribution of the host and bacteria to E. coli bacteremia severity remains unknown. We conducted a prospective multicenter cohort study to identify host and bacterial factors associated with E. coli bacteremia severity. The primary endpoint was in-hospital death, up to 28 days after the first positive blood culture. Among 1,051 patients included, 136 (12.9%) died. Overall, 604 (57.5%) patients were female. The median age was 70 years, and 202 (19.2%) episodes were nosocomial. The most frequent comorbidities were immunocompromised status (37.9%), tobacco addiction (21.5%), and diabetes mellitus (20.1%). The most common portal of entry was the urinary tract (56.9%). Most E. coli isolates belonged to phylogenetic group B2 (52.0%). The multivariate analysis retained the following factors as predictive of death: older age (odds ratio [OR] = 1.25 [95% confidence interval {CI}, 1.09 to 1.43] for each 10-year increment), cirrhosis (OR = 4.85 [95% CI, 2.49 to 9.45]), hospitalization before bacteremia (OR = 4.13 [95% CI, 2.49 to 6.82]), being an immunocompromised patient not hospitalized before bacteremia (OR = 3.73 [95% CI, 2.25 to 6.18]), and a cutaneous portal of entry (OR = 6.45 [95% CI, 1.68 to 24.79]); a urinary tract portal of entry and the presence of the ireA virulence gene were negatively correlated with death (OR = 0.46 [95% CI, 0.30 to 0.70] and OR = 0.53 [95% CI, 0.30 to 0.91], respectively). In summary, host factors and the portal of entry outweigh bacterial determinants for predicting E. coli bacteremia severity