High Throughput Prediction of the Long-Term Stability of Pharmaceutical Macromolecules from Short-Term Multi-Instrument Spectroscopic Data

Abstract

The field of pharmaceutical chemistry is currently struggling with the question of how to relate changes in the physical form of a macromolecular biopharmaceutical, such as a therapeutic protein, to changes in the drug's efficacy, safety, and long term stability (ESS). A great number of experimental methods are typically utilized to investigate the differences between forms of a macromolecule, yet conclusions regarding changes in ESS are frequently tentative.An opportunity exists, however, to relate changes in form to changes in ESS.At least once during the development of a new drug, a study is undertaken (at great expense) of the ESS of the drug upon perturbation by multiple manufacturing, formulation, storage and transportation variables. The data acquired is then used to build a model that relates changes in ESS to manufacturing, formulation, storage and transportation variables. It is not common in the pharmaceutical industry, however, to relate changes in comprehensive ESS data sets to comprehensive measurements of changes in macromolecular form.We bridge the gap between physical measurements of a macromolecule's form and measurements of its long term stability, utilizing two data sets collected in a collaboration between our group at the University of Kansas and a group at the Ludwig Maximilians University in Munich, Germany. The long term stability data, collected by the team in Germany, contains measurements of the chemical and conformation stability of Granulocyte Colony Stimulating Factor (GCSF) over a period of two years in 16 different liquid formulations. The short term iii physical data, collected in our lab, is comprised of spectroscopic characterization of the response of GCSF to thermal unfolding.The same 16 liquid formulations of GCSF were used in each study, allowing us to fit models predicting the long term stability of GCSF from short term measurements. We first apply a novel data reduction method to the short term data.This method selects data in the neighborhood of thermal unfolding transitions, and automates traditional comparative analyses. We then model the long term stability measurements using a linear technique, least squares fits, and a nonlinear one, radial basis function networks (RBFN). Using a Pearson correlation coefficient permutation test, we find that many of the fitted results have less than a 1% probability of occurring by chance.

Boosting antibody developability through rational sequence optimization

“…8 The observed degradation rate constant or endpoint read-out in longterm stability experiments can be different depending on the actual assay chosen. 43 However, as shown in Figure 5, there is an obvious trend in the current data. For the series of antibodies where only one chain is engineered, we see that long-term stability, as judged by SEC-monomer content after 126 d, increases with the number of mutations introduced.…”

The effect of charge mutations on the stability and aggregation of a human single chain Fv fragment

“…The long term loss of monomer is generally predicted through quantitative monitoring of aggregation under accelerated and stress conditions over weeks to months. Recent progress has been made in: (i) the development of detailed kinetic models [2,4,5,34,42,49,60,96]; (ii) correlating aggregation kinetics with protein structure and folding [11,15,16,32,35,52,53,54,67,88]; (iii) and with native-state protein-protein interaction measurements [36,46,57,74,75,79,82].…”

Formulations That Suppress Aggregation During Long-Term Storage of a Bispecific Antibody are Characterized by High Refoldability and Colloidal Stability

“…The application and usefulness of short-term biophysical characterization to predict aggregation during storage were investigated for various proteins, e.g. cytokines [8][9][10] , IgGs [11][12][13][14][15][16][17][18][19][20][21] , dualviable domain IgG 22 and others 13,[23][24][25] . These case studies often show contradicting conclusions whether biophysical parameters describing protein conformational and colloidal stability can or cannot provide reasonable predictions for protein storage stability.…”