Aims: Insulin degludec (IDeg) is a new-generation basal insulin with an ultra-long duration of action. We compared the pharmacodynamic (PD) variability of IDeg and insulin glargine (IGlar) under steady-state conditions.Methods: Day-to-day variability in glucose-lowering effect was investigated in 54 subjects with type 1 diabetes who underwent a 24-h euglycaemic glucose clamp on the 6th, 9th and 12th day of treatment with 0.4 U/kg of IDeg or IGlar once daily. Within-subject variability was estimated using a linear mixed model on log-transformed PD endpoints derived from the glucose infusion rate (GIR) profiles during the clamps. Results:For IDeg the day-to-day variability in glucose-lowering effect was four-times lower than for IGlar for total metabolic effect (AUC GIR,0-24h,SS , CV 20% vs. 82%) and for the last 22 h [AUC GIR,2-24h,SS (not influenced by intravenous insulin during the clamp), CV 22% vs. 92%]. Furthermore, lower variability in the maximum effect was observed for IDeg vs. IGlar (GIR max,SS , CV 18% vs. 60%). The lower within-subject variability of IDeg was consistent over time (CVs of 33% for AUC GIR,0-2h,SS , 32% for AUC GIR,10-12h,SS and 33% for AUC GIR,22-24h,SS ), whereas the variability of IGlar was higher and increased substantially 8 h post-dosing (CVs of 60% for AUC GIR,0-2h,SS , 135% for AUC GIR,10-12h,SS and 115% for AUC GIR,22-24h,SS ). Conclusions:These results show that IDeg has a significantly more predictable glucose-lowering effect from day to day than IGlar.

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We apply MALDI-TOF/TOF mass spectrometry for the rapid and high-confidence identification of intact Bacillus spore species. In this method, fragment ion spectra of whole (undigested) protein biomarkers are obtained without the need for biomarker prefractionation, digestion, separation, and cleanup. Laser-induced dissociation (unimolecular decay) of higher mass (>5 kDa) precursor ions in the first TOF analyzer is followed by reacceleration and subsequent high-resolution mass analysis of the resulting sequence-specific fragments in a reflectron TOF analyzer. In-house-developed software compares an experimental MS/MS spectrum with in silico-generated tandem mass spectra from all protein sequences, contained in a proteome database, with masses within a preset range around the precursor ion mass. A p-value, the probability that the observed matches between experimental and in silico-generated fragments occur by chance, is computed and used to rank the database proteins to identify the most plausible precursor protein. By inference, the source microorganism is then identified on the basis of the identification of individual, unique protein biomarker(s). As an example, intact Bacillus atrophaeus and Bacillus cereus spores, either pure or in mixtures, were unambiguously identified by this method after fragmenting and identifying individual small, acid-soluble spore proteins that are specific for each species. Factors such as experimental mass accuracy and number of detected fragment ions, precursor ion charge state, and sequence-specific fragmentation have been evaluated with the objective of extending the approach to other microorganisms. MALDI-TOF/TOF-MS in a lab setting is an efficient tool for in situ confirmation/verification of initial microorganism identification.

An improved data analysis method is described for rapid identification of intact microorganisms from MALDI-TOF-MS data. The method makes no use of mass spectral fingerprints. Instead, a microorganism database is automatically generated that contains biomarker masses derived from ribosomal protein sequences and a model of N-terminal Met loss. We quantitatively validate the method via a blind study that seeks to identify microorganisms with known ribosomal protein sequences. We also include in the database microorganisms with incompletely known sets of ribosomal proteins to test the specificity of the method. With an optimal MALDI protocol, and at the 95% confidence level, microorganisms represented in the database with 20 or more biomarkers (i.e., those with complete or nearly completely sequenced genomes) are correctly identified from their spectra 100% of the time, with no incorrect identifications. Microorganisms with seven or less biomarkers (i.e., incompletely sequenced genomes) are either not identified or misidentified. Robustness with respect to variations in sample preparation protocol and mass analysis protocol is demonstrated by collecting data with two different matrixes and under two different ion-mode configurations. Statistical analysis suggests that, even without further improvement, the method described here would successfully scale up to microorganism databases with roughly 1000 microorganisms. The results demonstrate that microorganism identification based on proteome data and modeling can perform as well as methods based on mass spectral fingerprinting.