Population pharmacokinetics of fluconazole in critically ill patients receiving continuous venovenous hemodiafiltration - using Monte Carlo Simulations to predict doses for specified pharmacodynamic targets

Fluconazole is a widely used antifungal agent that is extensively reabsorbed in patients with normal renal function. However, its reabsorption can be compromised in patients with acute kidney injury, thereby leading to altered fluconazole clearance and total systemic exposure. Here, we explore the pharmacokinetics of fluconazole in 10 critically ill anuric patients receiving continuous venovenous hemodiafiltration (CVVHDF). We performed Monte Carlo simulations to optimize dosing to appropriate pharmacodynamic endpoints for this population. Pharmacokinetic profiles of initial and steady-state doses of 200 mg intravenous fluconazole twice daily were obtained from plasma and CVVHDF effluent. Nonlinear mixed-effects modeling (NONMEM) was used for data analysis and to perform Monte Carlo simulations. For each dosing regimen, the free drug area under the concentration-time curve (fAUC)/MIC ratio was calculated. The percentage of patients achieving an AUC/MIC ratio greater than 25 was then compared for a range of MIC values. A two-compartment model adequately described the disposition of fluconazole in plasma. The estimate for total fluconazole clearance was 2.67 liters/h and was notably 2.3 times faster than previously reported in healthy volunteers. Of this, fluconazole clearance by the CVVHDF route (CL(CVVHDF)) represented 62% of its total systemic clearance. Furthermore, the predicted efficiency of CL(CVVHDF) decreased to 36.8% when filters were in use >48 h. Monte Carlo simulations demonstrated that a dose of 400 mg twice daily maximizes empirical treatment against fungal organisms with MIC up to 16 mg/liter. This is the first study we are aware of that uses Monte Carlo simulations to inform dosing requirements in patients where tubular reabsorption of fluconazole is probably nonexistent

Genome screen in familial intracranial aneurysm

Research articl

Sequencing three crocodilian genomes to illuminate the evolution of archosaurs and amniotes

The International Crocodilian Genomes Working Group (ICGWG) will sequence and assemble the American alligator (Alligator mississippiensis), saltwater crocodile (Crocodylus porosus) and Indian gharial (Gavialis gangeticus) genomes. The status of these projects and our planned analyses are described

Learning Poisson Binomial Distributions

We consider a basic problem in unsupervised learning: learning an unknown \emph{Poisson Binomial Distribution}. A Poisson Binomial Distribution (PBD) over $\{0,1,\dots,n\}$ is the distribution of a sum of $n$ independent Bernoulli random variables which may have arbitrary, potentially non-equal, expectations. These distributions were first studied by S. Poisson in 1837 \cite{Poisson:37} and are a natural $n$-parameter generalization of the familiar Binomial Distribution. Surprisingly, prior to our work this basic learning problem was poorly understood, and known results for it were far from optimal. We essentially settle the complexity of the learning problem for this basic class of distributions. As our first main result we give a highly efficient algorithm which learns to \eps-accuracy (with respect to the total variation distance) using \tilde{O}(1/\eps^3) samples \emph{independent of $n$}. The running time of the algorithm is \emph{quasilinear} in the size of its input data, i.e., \tilde{O}(\log(n)/\eps^3) bit-operations. (Observe that each draw from the distribution is a $\log(n)$-bit string.) Our second main result is a {\em proper} learning algorithm that learns to \eps-accuracy using \tilde{O}(1/\eps^2) samples, and runs in time (1/\eps)^{\poly (\log (1/\eps))} \cdot \log n. This is nearly optimal, since any algorithm {for this problem} must use \Omega(1/\eps^2) samples. We also give positive and negative results for some extensions of this learning problem to weighted sums of independent Bernoulli random variables.Comment: Revised full version. Improved sample complexity bound of O~(1/eps^2