Towards Heat-stable Oxytocin Formulations: Analysis of Degradation Kinetics and Identification of Degradation Products

Purpose. To investigate degradation kinetics of oxytocin as a function of temperature and pH, and identify the degradation products. Materials and Methods. Accelerated degradation of oxytocin formulated at pH 2.0, 4.5, 7.0 and 9.0 was performed at 40, 55, 70 and 80°C. Degradation rate constants were determined from RP-HPLC data. Formulations were characterized by HP-SEC, UV absorption and fluorescence spectroscopy. Degradation products were identified by ESI-MS/MS. Results. The loss of intact oxytocin in RP-HPLC was pH- and temperature-dependent and followed (pseudo) first order kinetics. Degradation was fastest at pH 9.0, followed by pH 7.0, pH 2.0 and pH 4.5. The Arrhenius equation proved suitable to describe the kinetics, with the highest activation energy (116.3 kJ/mol) being found for pH 4.5 formulations. At pH 2.0 deamidation of Gln 4, Asn 5, and Gly 9-NH2, as well as combinations thereof were found. At pH 4.5, 7.0 and 9.0, the formation of tri- and tetrasulfidecontaining oxytocin as well as different types of disulfide and dityrosine-linked dimers were found to occur. Beta-elimination and larger aggregates were also observed. At pH 9.0, mono-deamidation of Gln 4, Asn 5, and Gly 9-NH2 additionally occurred. Conclusions. Multiple degradation products of oxytocin have been identified unequivocally, including various deamidated species, intramolecular oligosulfides and covalent aggregates. The strongly pH dependent degradation can be described by the Arrhenius equation. KEY WORDS: aggregation; Arrhenius kinetics; degradation; mass spectrometry; oxytocin

Bone sarcomas: ESMO–EURACAN–GENTURIS–ERN PaedCan Clinical Practice Guideline for diagnosis, treatment and follow-up

Production costs have been covered by ESMO from central funds

An international effort towards developing standards for best practices in analysis, interpretation and reporting of clinical genome sequencing results in the CLARITY Challenge

There is tremendous potential for genome sequencing to improve clinical diagnosis and care once it becomes routinely accessible, but this will require formalizing research methods into clinical best practices in the areas of sequence data generation, analysis, interpretation and reporting. The CLARITY Challenge was designed to spur convergence in methods for diagnosing genetic disease starting from clinical case history and genome sequencing data. DNA samples were obtained from three families with heritable genetic disorders and genomic sequence data were donated by sequencing platform vendors. The challenge was to analyze and interpret these data with the goals of identifying disease-causing variants and reporting the findings in a clinically useful format. Participating contestant groups were solicited broadly, and an independent panel of judges evaluated their performance. RESULTS: A total of 30 international groups were engaged. The entries reveal a general convergence of practices on most elements of the analysis and interpretation process. However, even given this commonality of approach, only two groups identified the consensus candidate variants in all disease cases, demonstrating a need for consistent fine-tuning of the generally accepted methods. There was greater diversity of the final clinical report content and in the patient consenting process, demonstrating that these areas require additional exploration and standardization. CONCLUSIONS: The CLARITY Challenge provides a comprehensive assessment of current practices for using genome sequencing to diagnose and report genetic diseases. There is remarkable convergence in bioinformatic techniques, but medical interpretation and reporting are areas that require further development by many groups

Quantitation of newly synthesized proteins by pulse labeling with azidohomoalanine

Measuring protein synthesis and degradation rates on a proteomic scale is an important step toward modeling the kinetics in complicated cellular response networks. A gel-free method, able to quantify changes in the formation of new proteins on a 15 min timescale, compatible with mass spectrometry is described. The methionine analogue, azidohomoalanine (azhal), is used to label newly formed proteins during a short pulse-labeling period following an environmental switch in Escherichia coli. Following digestion a selective reaction against azhal-containing peptides is applied to enrich these peptides by diagonal chromatography. This technique enables quantitation of hundreds of newly synthesized proteins and provides insight into immediate changes in newly synthesized proteins on a proteomic scale after an environmental perturbatio

Delayed Antibody and T-Cell Response to BNT162b2 Vaccination in the Elderly, Germany

We detected delayed and reduced antibody and T-cell responses after BNT162b2 vaccination in 71 elderly persons (median age 81 years) compared with 123 healthcare workers (median age 34 years) in Germany. These data emphasize that nonpharmaceutical interventions for coronavirus disease remain crucial and that additional immunizations for the elderly might become necessary.Peer Reviewe

Metabolite Identification Using Automated Comparison of High-Resolution Multistage Mass Spectral Trees

Multistage mass spectrometry (MS^<i>n</i>) generating so-called spectral trees is a powerful tool in the annotation and structural elucidation of metabolites and is increasingly used in the area of accurate mass LC/MS-based metabolomics to identify unknown, but biologically relevant, compounds. As a consequence, there is a growing need for computational tools specifically designed for the processing and interpretation of MS^<i>n</i> data. Here, we present a novel approach to represent and calculate the similarity between high-resolution mass spectral fragmentation trees. This approach can be used to query multiple-stage mass spectra in MS spectral libraries. Additionally the method can be used to calculate structure–spectrum correlations and potentially deduce substructures from spectra of unknown compounds. The approach was tested using two different spectral libraries composed of either human or plant metabolites which currently contain 872 MS^<i>n</i> spectra acquired from 549 metabolites using Orbitrap FTMS^<i>n</i>. For validation purposes, for 282 of these 549 metabolites, 765 additional replicate MS^<i>n</i> spectra acquired with the same instrument were used. Both the dereplication and de novo identification functionalities of the comparison approach are discussed. This novel MS^<i>n</i> spectral processing and comparison approach increases the probability to assign the correct identity to an experimentally obtained fragmentation tree. Ultimately, this tool may pave the way for constructing and populating large MS^<i>n</i> spectral libraries that can be used for searching and matching experimental MS^<i>n</i> spectra for annotation and structural elucidation of unknown metabolites detected in untargeted metabolomics studies