Dataset Information

Two different approaches for pharmacokinetic modeling of exhaled drug concentrations.

ABSTRACT: Online measurement of drug concentrations in patient's breath is a promising approach for individualized dosage. A direct transfer from breath- to blood-concentrations is not possible. Measured exhaled concentrations are following the blood-concentration with a delay in non-steady-state situations. Therefore, it is necessary to integrate the breath-concentration into a pharmacological model. Two different approaches for pharmacokinetic modelling are presented. Usually a 3-compartment model is used for pharmacokinetic calculations of blood concentrations. This 3-compartment model is extended with a 2-compartment model based on the first compartment of the 3-compartment model and a new lung compartment. The second approach is to calculate a time delay of changes in the concentration of the first compartment to describe the lung-concentration. Exemplarily both approaches are used for modelling of exhaled propofol. Based on time series of exhaled propofol measurements using an ion-mobility-spectrometer every minute for 346 min a correlation of calculated plasma and the breath concentration was used for modelling to deliver R(2) = 0.99 interdependencies. Including the time delay modelling approach the new compartment coefficient k(e0lung) was calculated to k(e0lung) = 0.27 min(-1) with R(2) = 0.96. The described models are not limited to propofol. They could be used for any kind of drugs, which are measurable in patient's breath.

SUBMITTER: Kreuer S

PROVIDER: S-EPMC4067807 | biostudies-other | 2014

REPOSITORIES: biostudies-other

ACCESS DATA

Similar Datasets

Project description:In the emerging market of nano-sized products, silver nanoparticles (Ag NPs) are widely used due to their antimicrobial properties. Human interaction with Ag NPs can occur through the lung, skin, gastrointestinal tract, and bloodstream. However, the inhalation of Ag NP aerosols is a primary concern. To study the possible effects of inhaled Ag NPs, an in vitro triple cell co-culture model of the human alveolar/airway barrier (A549 epithelial cells, human peripheral blood monocyte derived dendritic and macrophage cells) together with an air-liquid interface cell exposure (ALICE) system was used in order to reflect a real-life exposure scenario. Cells were exposed at the air-liquid interface (ALI) to 0.03, 0.3, and 3 µg Ag/cm(2) of Ag NPs (diameter 100 nm; coated with polyvinylpyrrolidone: PVP). Ag NPs were found to be highly aggregated within ALI exposed cells with no impairment of cell morphology. Furthermore, a significant increase in release of cytotoxic (LDH), oxidative stress (SOD-1, HMOX-1) or pro-inflammatory markers (TNF-α, IL-8) was absent. As a comparison, cells were exposed to Ag NPs in submerged conditions to 10, 20, and 30 µg Ag/mL. The deposited dose per surface area was estimated by using a dosimetry model (ISDD) to directly compare submerged vs ALI exposure concentrations after 4 and 24 h. Unlike ALI exposures, the two highest concentrations under submerged conditions promoted a cytotoxic and pro-inflammatory response after 24 h. Interestingly, when cell cultures were co-incubated with lipopolysaccharide (LPS), no synergistic inflammatory effects were observed. By using two different exposure scenarios it has been shown that the ALI as well as the suspension conditions for the lower concentrations after 4 h, reflecting real-life concentrations of an acute 24 h exposure, did not induce any adverse effects in a complex 3D model mimicking the human alveolar/airway barrier. However, the highest concentrations used in the ALI setup, as well as all concentrations under submerged conditions after 24 h, reflecting more of a chronic lifetime exposure concentration, showed cytotoxic as well as pro-inflammatory effects. In conclusion, more studies need to address long-term and chronic Ag NP exposure effects.

Dataset Information

Two different approaches for pharmacokinetic modeling of exhaled drug concentrations.

Similar Datasets

OmicsDI is part of the ELIXIR infrastructure

Tweets