Tuesday 25 June 2019
07:00 [PDT] 10:00 [EDT] 15:00 [BST]
Alternative time zones
Mechanistic models that mathematically describe the pharmacokinetic and pharmacodynamic (PK/PD) relationship of biotherapeutics and their target proteins are used to project the dose levels and rationalize the dosing regimen. Accurate experimental data can reduce assumptions and fill the gap in modeling; however, experimentally verified target turnover kinetics has not been routinely available. LC–MS-based Stable Isotope Labeling by/with Amino acids in Cell culture (SILAC) measurement is capable of delivering reliable, economical and straightforward protein turnover rate in vitro. This webinar describes how serial immunoaffinity (IA) coupled with microflow LC–MS/MS can be used to analyze turnover of cytokine receptor α/β/γ in human T-cells, with treatment by different antibody-fused ligand drugs A, B and C. Divergent half-life of the receptors in different treatment groups indicates the drug treatment impact, the receptor degradation and may have effect on model prediction.
What will you learn?
- How IA enrichment can be employed in this workflow
- Why targeted MS offers the possibility to measure multiple protein targets with great sensitivity from a single sample
- The sensitivity gain microflow LC–MS/MS gives conventional analytical flow LC
Who may this interest?
- CROs, Pharma and Biotech companies focused on enhancing selectivity for LC–MS quantitative bioanalysis
- Mass spectrometrists and bioanalytical chemists
- Academics and students interested in microflow LC–MS/MS workflows
Xiaomeng “Christina” Shen
Christina holds a PhD in Biochemistry from the University at Buffalo, SUNY, School of Medicine and Biomedical Sciences where she studied under Dr Jun Qu. She is currently a scientist at Amgen, where she studies large-scale quantitative proteomics, post-translational modification analysis for biomarker discovery using LC/MS, and develops bioanalytical strategies and assays for the quantification of protein, peptide and small molecule therapeutic drug candidates.
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