Preclinical pharmacology of RA15127343: In vitro and in vivo activity of a novel ultralong-acting basal insulin.

AIM: To report the in vitro and in vivo preclinical pharmacokinetic (PK) and pharmacodynamic (PD) properties of RA15127343, a novel ultralong-acting insulin analogue targeting once-weekly administration, in female Göttingen minipigs. METHODS: In vitro binding and activation of human insulin receptor isoforms (IR-A/IR-B), glucose uptake in rat myocytes, as well as mitogenic activity of RA15127343 were evaluated. In vivo, the PK and PD activities of RA15127343 were assessed in female, normoglycaemic Göttingen minipigs. The half-life (t1/2 ) and time to maximum plasma concentration (Tmax ) of subcutaneously (SC) administered RA15127343 (10/30/45/60 nmol/kg) were estimated. In vivo blood glucose and endogenous plasma C-peptide concentrations after single SC administration (10/30/45/60 nmol/kg) or repeated dosing (15 nmol/kg) were analysed. RESULTS: In comparison to human insulin, RA15127343 showed lower in vitro binding affinity (19.9/6.31 µM vs. 1.10/1.14 nM) and activation (2.054 µM/669.6 nM vs. 26.04/18.24 nM) of IR-A/IR-B, lower potency to activate glucose uptake (855.2 vs. 3.37 nM) and lower mitogenic activity (17.92 µM vs. 10.78 nM; proliferation in MCF7 cells). In vivo, the mean t1/2 and Tmax of RA15127343 after SC administration ranged from 48 to 59 and 30 to 39 hours, respectively. Blood glucose and plasma C-peptide concentrations were significantly lower with RA15127343 (single/repeated doses) versus vehicle. CONCLUSIONS: RA15127343 showed an ultra-long t1/2 with a slow onset of action. The preclinical pharmacological outcomes suggest RA15127343 could be a potential ultralong-acting insulin analogue with low risk of hypoglycaemia in humans.

Ultracold Bose Gases in Dynamic Disorder with Tunable Correlation Time.

We study experimentally the dissipative dynamics of ultracold bosonic gases in a dynamic disorder potential with tunable correlation time. First, we measure the heating rate of thermal clouds exposed to the dynamic potential and present a model of the heating process, revealing the microscopic origin of dissipation from a thermal, trapped cloud of bosons. Second, for Bose-Einstein condensates, we measure the particle loss rate induced by the dynamic environment. Depending on the correlation time, the losses are either dominated by heating of residual thermal particles or the creation of excitations in the superfluid, a notion we substantiate with a rate model. Our results illuminate the interplay between superfluidity and time-dependent disorder and on more general grounds establish ultracold atoms as a platform for studying spatiotemporal noise and time-dependent disorder.

Using neural networks for (13)c NMR chemical shift prediction-comparison with traditional methods.

Interpretation of (13)C chemical shifts is essential for structure elucidation of organic molecules by NMR. In this article, we present an improved neural network approach and compare its performance to that of commonly used approaches. Specifically, our recently proposed neural network (J. Chem. Inf. Comput. Sci. 2000, 40, 1169-1176) is improved by introducing an extended hybrid numerical description of the carbon atom environment, resulting in a standard deviation (std. dev.) of 2.4 ppm for an independent test data set of approximately 42,500 carbons. Thus, this neural network allows fast and accurate (13)C NMR chemical shift prediction without the necessity of access to molecule or fragment databases. For an unbiased test dataset containing 100 organic structures the accuracy of the improved neural network was compared to that of a prediction method based on the HOSE code (hierarchically ordered spherical description of environment) using SPECINFO. The results show the neural network predictions to be of quality (std. dev. = 2.7 ppm) comparable to that of the HOSE code prediction (std. dev. = 2.6 ppm). Further we compare the neural network predictions to those of a wide variety of other (13)C chemical shift prediction tools including incremental methods (CHEMDRAW, SPECTOOL), quantum chemical calculation (GAUSSIAN, COSMOS), and HOSE code fragment-based prediction (SPECINFO, ACD/CNMR, PREDICTIT NMR) for the 47 (13)C-NMR shifts of Taxol, a natural product including many structural features of organic substances. The smallest standard deviations were achieved here with the neural network (1.3 ppm) and SPECINFO (1.0 ppm).

Validation of structural proposals by substructure analysis and 13C NMR chemical shift prediction.

The 2D NMR-guided computer program COCON can be extremely valuable for the constitutional analysis of unknown compounds, if its results are evaluated by neural network-assisted 13C NMR chemical shift and substructure analyses. As instructive examples, data sets of four differently complex marine natural products were thoroughly investigated. As a significant step towards a true automated structure elucidation, it is shown that the primary COCON output can be safely diminished to less than 1% of its original size without losing the correct structural proposal.

Genius: a genetic algorithm for automated structure elucidation from 13C NMR spectra.

The automated structure elucidation of organic molecules from experimentally obtained properties is extended by an entirely new approach. A genetic algorithm is implemented that uses molecular constitution structures as individuals. With this approach, the structure of organic molecules can be optimized to meet experimental criteria, if in addition a fast and accurate method for the prediction of the used physical or chemical features is available. This is demonstrated using 13C NMR spectrum as readily obtainable information. By means of artificial neural networks a fast and accurate method for calculating the 13C NMR spectrum of the generated structures exists. The method is implemented and tested successfully for organic molecules with up to 18 non-hydrogen atoms.