Association of microbial dynamics with urinary estrogens and estrogen metabolites in patients with endometriosis.

Endometriosis is an estrogen dependent gynecological disease associated with altered microbial phenotypes. The association among endogenous estrogen, estrogen metabolites, and microbial dynamics on disease pathogenesis has not been fully investigated. Here, we identified estrogen metabolites as well as microbial phenotypes in non-diseased patients (n = 9) and those with pathologically confirmed endometriosis (P-EOSIS, n = 20), on day of surgery (DOS) and ~1-3 weeks post-surgical intervention (PSI). Then, we examined the effects of surgical intervention with or without hormonal therapy (OCPs) on estrogen and microbial profiles of both study groups. For estrogen metabolism analysis, liquid chromatography/tandem mass spectrometry was used to quantify urinary estrogens. The microbiome data assessment was performed with Next generation sequencing to V4 region of 16S rRNA. Surgical intervention and hormonal therapy altered gastrointestinal (GI), urogenital (UG) microbiomes, urinary estrogen and estrogen metabolite levels in P-EOSIS. At DOS, 17ß-estradiol was enhanced in P-EOSIS treated with OCPs. At PSI, 16-keto-17ß-estradiol was increased in P-EOSIS not receiving OCPs while 2-hydroxyestradiol and 2-hydroxyestrone were decreased in P-EOSIS receiving OCPs. GI bacterial α-diversity was greater for controls and P-EOSIS that did not receive OCPs. P-EOSIS not utilizing OCPs exhibited a decrease in UG bacterial α-diversity and differences in dominant taxa, while P-EOSIS utilizing OCPs had an increase in UG bacterial α-diversity. P-EOSIS had a strong positive correlation between the GI/UG bacteria species and the concentrations of urinary estrogen and its metabolites. These results indicate an association between microbial dysbiosis and altered urinary estrogens in P-EOSIS, which may impact disease progression.

Vancomycin monitoring in children using bayesian estimation.

BACKGROUND: Optimal monitoring of vancomycin in children needs evaluation using the exposure target with area under the curve (AUC) of the serum concentrations versus time over 24 hours. Our study objectives were to: (1) compare the accuracy and precision of vancomycin AUC estimations using 2 sampling strategies-1 serum concentration sample (1S, near trough) versus 2 samples (2S, near peak and trough) against the rich sample (RS) method; and (2) determine the performance of these strategies in predicting future AUC against an internal validation sample (VS). METHODS: This was a retrospective cohort study using population-based pharmacokinetic modeling with Bayesian post hoc individual estimations in nonlinear mixed effects modeling (version 7.2). Pediatric subjects 3 months-21 years of age who received vancomycin ≥48 hours and had more than 3 drug samples within the first ≤96 hours of therapy were enrolled. Outcome measures were the accuracy, precision, and internal predictive performance of AUC estimations using 2 monitoring strategies (ie, 1S versus 2S) against the RS (which was derived from modeling all serum vancomycin concentrations obtained anytime during therapy) and VS (from serum concentrations obtained after 96 hours of therapy). RESULTS: Analysis included 138 subjects with 712 vancomycin serum concentrations. Median age was 6.1 (interquartile range, 2.2-12.2) years, weight 22 (13-38) kg, and baseline serum creatinine 0.37 (0.30-0.50) mg/dL. Both accuracy and precision were improved with the 2S, compared with 1S, for AUC estimations (-2.0% versus -7.6% and 10.3% versus 12.8%, respectively) against the RS. Improved accuracy and precision were also observed for 2S when evaluated against VS in predicting future AUC. CONCLUSIONS: Compared with 1S, the 2S sampling strategy for vancomycin monitoring improved accuracy and precision in estimating and predicting future AUC. Evaluating 2 drug concentrations in children may be prudent to ensure adequate drug exposure.