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

Genome-scale modeling of the protein secretory machinery in yeast

The protein secretory machinery in Eukarya is involved in post-translational modification (PTMs) and sorting of the secretory and many transmembrane proteins. While the secretory machinery has been well-studied using classic reductionist approaches, a holistic view of its complex nature is lacking. Here, we present the first genome-scale model for the yeast secretory machinery which captures the knowledge generated through more than 50 years of research. The model is based on the concept of a Protein Specific Information Matrix (PSIM: characterized by seven PTMs features). An algorithm was developed which mimics secretory machinery and assigns each secretory protein to a particular secretory class that determines the set of PTMs and transport steps specific to each protein. Protein abundances were integrated with the model in order to gain system level estimation of the metabolic demands associated with the processing of each specific protein as well as a quantitative estimation of the activity of each component of the secretory machinery

Productdossiers : Een beschrijvend onderzoek

Inspectie Gezondheidszor

Productdossiers : Een beschrijvend onderzoek

Inspectie Gezondheidszor