The Protein Journal · 2020 · 39 citations · 24 references
PharmacyBiochemistryMolecular BiologyMonoclonal AntibodiesProtein FoldingThermal AnalysisAntibody CharacterizationAntibody FormulationAntibody FunctionAntibody StabilityAntibody StructureAntibody-antigen RecognitionAntibody Binding ThermodynamicsBeta SheetsFourier Transform Infrared SpectroscopyInsulin-like Growth Factor-1Protein Aggregation AnalysisProtein Conformational ChangesSecondary Structure PropensityThermal Stability Of Antibodies
Abstract The temperature-dependent secondary structure of two monoclonal IgG antibodies, anti-IGF1R and anti-TSLP, were examined by transmission mode Fourier Transform Infrared (FTIR) spectroscopy. Anti-IGF1R and anti-TSLP are IgG monoclonal antibodies (mAbs) directed against human Insulin-like Growth Factor 1 Receptor for anti-tumor activity and Thymic Stromal Lymphopoietin cytokine for anti-asthma activity, respectively. Differential scanning calorimetry (DSC) clearly indicates both antibodies in their base formulations have a lower temperature protein conformational change near 70 °C (T m1 ) and a higher temperature protein conformational change near 85 °C (T m2 ). Thermal scanning dynamic light scatting (TS-DLS) indicates a significant particle size increase for both antibodies near T m2 suggesting a high level of protein aggregation. The nature of these protein conformational changes associated with increasing the formulation temperature and decreasing sucrose concentration were identified by transmission mode FTIR and second derivative FTIR spectroscopy of temperature controlled aqueous solutions of both monoclonal antibodies. The transition from intra -molecular β sheets to inter -molecular β sheets was clearly captured for both monoclonal antibodies using FTIR spectroscopy. Finally, FTIR Spectroscopy was able to show the impact of a common excipient such as sucrose on the stability of each monoclonal antibody, further demonstrating the usefulness of FTIR spectroscopy for studying protein aggregation and formulation effects.
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Nearest neighbor pattern classification
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