21 research outputs found

    Simulating Intestinal Transporter and Enzyme Activity in a Physiologically Based Pharmacokinetic Model for Tenofovir Disoproxil Fumarate

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    Tenofovir disoproxil fumarate (TDF), a prodrug of tenofovir, has oral bioavailability (25%) limited by intestinal transport (P-glycoprotein), and intestinal degradation (carboxylesterase). However, the influence of luminal pancreatic enzymes is not fully understood. Physiologically based pharmacokinetic (PBPK) modeling has utility for estimating drug exposure from in vitro data. This study aimed to develop a PBPK model that included luminal enzyme activity to inform dose reduction strategies. TDF and tenofovir stability in porcine pancrelipase concentrations was assessed (0, 0.48, 4.8, 48, and 480 U/ml of lipase; 1 mM TDF; 37°C; 0 to 30 min). Samples were analyzed using mass spectrometry. TDF stability and permeation data allowed calculation of absorption rates within a human PBPK model to predict plasma exposure following 6 days of once-daily dosing with 300 mg of TDF. Regional absorption of drug was simulated across gut segments. TDF was degraded by pancrelipase (half-lives of 0.07 and 0.62 h using 480 and 48 U/ml, respectively). Previously reported maximum concentration (Cmax; 335 ng/ml), time to Cmax (Tmax; 2.4 h), area under the concentration-time curve from 0 to 24 h (AUC0–24; 3,045 ng · h/ml), and concentration at 24 h (C24; 48.3 ng/ml) were all within a 0.5-fold difference from the simulated Cmax (238 ng/ml), Tmax (3 h), AUC0–24 (3,036 ng · h/ml), and C24 (42.7 ng/ml). Simulated TDF absorption was higher in duodenum and jejunum than in ileum (p<0.05). These data support that TDF absorption is limited by the action of intestinal lipases. Our results suggest that bioavailability may be improved by protection of drug from intestinal transporters and enzymes, for example, by coadministration of enzyme-inhibiting agents or nanoformulation strategies

    Who Shares? Who Doesn't? Factors Associated with Openly Archiving Raw Research Data

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    Many initiatives encourage investigators to share their raw datasets in hopes of increasing research efficiency and quality. Despite these investments of time and money, we do not have a firm grasp of who openly shares raw research data, who doesn't, and which initiatives are correlated with high rates of data sharing. In this analysis I use bibliometric methods to identify patterns in the frequency with which investigators openly archive their raw gene expression microarray datasets after study publication
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