The characterization of protein stability in relation to formulation is an important and developing analytical field. The field of protein formulation science aims at finding the solvent conditions that optimize protein colloidal stability, conformational stability, and solubility. The studied proteins are often biotherapeutics, and direct intravenous admission of the formulated protein into patients necessitates a fully characterized and flawless drug product. The analytical toolbox for protein stability studies is large, and consists of high-throughput screening methods, low-throughput methods, and long-term storage or stress studies.
The PIPPI consortium has built a uniquely comprehensive data set in the field of protein formulation, as a combination of varying proteins, formulation aspects, and analytical techniques. These data are built up as two formulation screenings. Within this comprehensive set, protein conformational stability is measured by various fluorescence spectroscopy techniques, where data dimensionality can be used and expanded to gain advantages over current methods.
Protein fluorescence spectroscopy is an important tool to monitor protein conformational stability, as changes to the fluorescent amino acid electrostatic environment are observable as fluorescence peak deformations. For two of these techniques, innovative multivariate data analysis approaches have been developed as improvements over the traditionally solely univariate analysis methods. First, it is shown how PARAFAC2 can be applied in the analysis of fluorescence spectroscopy in isothermal chemical denaturation experiments (P1). Secondly, a study finds how non-linear curve fitting of nDSF data can improve and automate analysis, as particularly beneficial in the case of overlapping protein unfolding transitions (P2). An example data set is used to show protein unfolding as three-way fluorescence data. This set is used to show how extending the dimensionality of protein fluorescence data, and by using its multi-linear properties, the specificity can be enhanced (Chapter 7).
The characterizations of protein stability over a range of proteins and protein formulations are part of the large effort to create an all-inclusive data set for protein formulation scientists. The protein formulations included in this dataset have been chosen based on an experimental design that mimics industrial formulation development. The culmination of the screening effort, the PIPPI-data database and its web-interface (P3), thus encompasses a comprehensive data set that is unique in the field of protein formulation. The PIPPI-data database is discussed with regards to challenges of protein formulation science as a whole.