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NVS123 is a poorly water-soluble protease 56 inhibitor in clinical development. Data from in vitro hepatocyte studies suggested that NVS123 is mainly metabolized by CYP3A4. As a consequence of limited solubility, NVS123 therapeutic plasma exposures could not be achieved even with high doses and optimized formulations. One approach to overcome NVS123 developability issues was to increase plasma exposure by coadministrating it with an inhibitor of CYP3A4 such as ritonavir. A clinical boost effect was predicted by using physiologically based pharmacokinetic (PBPK) modeling. However, initial boost predictions lacked sufficient confidence because a key parameter, fraction of drug metabolized by CYP3A4 (ƒₘCYP3A4), could not be estimated with accuracy on account of disconnects between in vitro and in vivo preclinical data. To accurately estimate ƒₘCYP3A4 in human, an in vivo boost effect study was conducted using CYP3A4-humanized mouse model which showed a 33- to 56-fold exposure boost effect. Using a top-down approach, human ƒₘCYP3A4 for NVS123 was estimated to be very high and included in the human PBPK modeling to support subsequent clinical study design. The combined use of the in vivo boost study in CYP3A4-humanized mouse model mice along with PBPK modeling accurately predicted the clinical outcome and identified a significant NVS123 exposure boost (∼42-fold increase) with ritonavir.
The Monte Carlo code FLUKA is used to simulate the production of a number of positron emitting radionuclides, ¹⁸F, ¹³N, ⁹⁴Tc, ⁴⁴Sc, ⁶⁸Ga, ⁸⁶Y, ⁸⁹Zr, ⁵²Mn, ⁶¹Cu and ⁵⁵Co, on a small medical cyclotron with a proton beam energy of 13 MeV. Experimental data collected at the TR13 cyclotron at TRIUMF agree within a factor of 0.6 ± 0.4 with the directly simulated data, except for the production of ⁵⁵Co, where the simulation underestimates the experiment by a factor of 3.4 ± 0.4. The experimental data also agree within a factor of 0.8 ± 0.6 with the convolution of simulated proton fluence and cross sections from literature. Overall, this confirms the applicability of FLUKA to simulate radionuclide production at 13 MeV proton beam energy.
[⁶⁸Ga(DOTATATE)] has demonstrated its clinical usefulness. Both Fe³⁺ and Cu²⁺, potential contaminants in Gallium-68 generator eluent, substantially reduce the radiochemical (RC) yield of [⁶⁸Ga(DOTATATE)] if the metal/ligand ratio of 1:1 is exceeded. A variety of compounds were examined for their potential ability to reduce this effect. Most had no effect on RC yield. However, addition of phosphate diminished the influence of Fe³⁺ by likely forming an insoluble iron salt. Addition of ascorbic acid reduced Cu²⁺ and Fe³⁺ to Cu⁺ and Fe²⁺ respectively, both of which have limited impact on RC yields. At low ligand amounts (5 nmol DOTATATE), the addition of 30 nmol phosphate (0.19 mM) increased the tolerance of Fe3⁺ from 4 nmol to 10 nmol (0.06 mM), while the addition of ascorbic acid allowed high RC yields (>95%) in the presence of 40 nmol Fe³⁺ (0.25 mM) and 100 nmol Cu²⁺ (0.63 mM). The effect of ascorbic acid was highly pH-dependant, and gave optimal results at pH 3.
Characterization and evaluation of lignocellulosic biomass 130 hydrolysates for ABE fermentation
(2016)
The composition of plant biomass varies depending on the feedstock and pre-treatment conditions and influences its processing in biorefineries. In order to ensure optimal process conditions, the quantitative proportion of the main polymeric components of the pre-treated biomass has to be determined. Current standard procedures for biomass compositional analysis are complex, the measurements are afflicted with errors and therefore often not comparable. Hence, new powerful analytical methods are urgently required to characterize biomass. In this contribution, Differential Scanning Calorimetry (DSC) was applied in combination with multivariate data analysis (MVA) to detect the cellulose content of the plant biomass pretreated by Liquid Hot Water (LHW) and Organosolv processes under various conditions. Unlike conventional techniques, the developed analytic method enables the accurate quantification of monosaccharide content of the plant biomass without any previous sample preparation. It is easy to handle and avoids errors in sample preparation.