Sensitive CE-MS method for monitoring of riociguat and desmethylriociguat levels in human serum

Riociguat is novel antihypertensive drug for treatment of pulmonary hypertension. As such, it is still being tested in many clinical and pharmacokinetic trials. Existing methods that determine serum riociguat and desmethylriociguat (DMR) are based solely on liquid chromatography with mass spectrometry. Therefore, we present a novel capillary electrophoresis with mass spectrometry method (CE-MS) for their determination in human serum as alternative method for ongoing trials. Complete resolution of both analytes was achieved by means of pH optimization of ammonium formate background electrolytes that are fully compatible with ESI/MS detection. Simple liquid-liquid extraction was used as sample pretreatment. The calibration dependence of the method was linear (in the range of 10–1000 ng/mL), with adequate accuracy (90.1–114.9%) and precision (13.4%). LOD and LOQ were arbitrarily set at 10 ng/mL for both analytes. Clinical applicability was validated using serum samples from patients treated with riociguat in pharmacokinetic study and the results corresponded with reference HPLC-MS/MS values. Capillary electrophoresis proved to be sensitive and selective tool for the analysis of riociguat and DMR.     

A review on machine learning algorithms for the ionic liquid chemical space

There are thousands of papers published every year investigating the properties and possible applications of ionic liquids. Industrial use of these exceptional fluids requires adequate understanding of their physical properties, in order to create the ionic liquid that will optimally suit the application. Computational property prediction arose from the urgent need to minimise the time and cost that would be required to experimentally test different combinations of ions. This review discusses the use of machine learning algorithms as property prediction tools for ionic liquids (either as standalone methods or in conjunction with molecular dynamics simulations), presents common problems of training datasets and proposes ways that could lead to more accurate and efficient models.

In this review article, the authors discuss the use of machine learning algorithms as tools for the prediction of physical and chemical properties of ionic liquids.     

Determination of 5-nitroindazole using silver solid amalgam electrode

Monatshefte für Chemie - Chemical Monthly - The voltammetric behavior of 5-nitroindazole was investigated at polished (p-AgSAE) and at mercury meniscus-modified (m-AgSAE) silver solid amalgam...     

Carbon precipitation and diffusion in SiGeC alloys under silicon self-interstitial injection

In this work we investigate the diffusion and precipitation of supersaturated substitutional carbon in 200-nm-thick SiGeC layers buried under a silicon cap layer of 40 nm. The samples were annealed in either inert (N₂) or oxidizing (O₂) ambient at 850 °C for times ranging from 2 to 10 h. The silicon self-interstitial (I) flux coming from the surface under oxidation enhances the C diffusion with respect to the N₂-annealed samples. In the early stages of the oxidation process, the loss of C from the SiGeC layer by diffusion across the layer/cap interface dominates. This phenomenon saturates after an initial period (2–4 h), which depends on the C concentration. This saturation is due to the formation and growth of C-containing precipitates that are promoted by the I injection and act as a sink for mobile C atoms. The influence of carbon concentration on the competition between precipitation and diffusion is discussed. Received: 19 October 2001 / Accepted: 19 December 2001 / Published online: 20 March 2002 / Published online: 20 March 2002