Computational methods to predict therapeutic protein aggregation.
Protein based biotherapeutics have emerged as a successful class of pharmaceuticals. However, these macromolecules endure a variety of physicochemical degradations during manufacturing, shipping, and storage, which may adversely impact the drug product quality. Of these degradations, the irreversible self-association of therapeutic proteins to form aggregates is a major challenge in the formulation of these molecules. Tools to predict and mitigate protein aggregation are, therefore, of great interest to biopharmaceutical research and development. In this chapter, a number of such computational tools developed to understand and predict the various steps involved in protein aggregation are described. These tools can be grouped into three general classes: unfolding kinetics and native state thermal stability, colloidal stability, and sequence/structure based aggregation liabilities. Chapter sections introduce each class by discussing how these predictive tools provide insight into the molecular events leading to protein aggregation. The computational methods are then explained in detail along with their advantages and limitations.
Biotherapeutics Pharmaceutical Research and Development, Pfizer, Inc, St. Louis, MO, USA.
Patrick M
PM
Buck
Sandeep
S
Kumar
Xiaoling
X
Wang
Neeraj J
NJ
Agrawal
Bernhardt L
BL
Trout
Satish K
SK
Singh
D000911:Antibodies, Monoclonal / Q000737:chemistry / Q000302:isolation & purification / Q000627:therapeutic use; D019295:Computational Biology; D003198:Computer Simulation; D006801:Humans; D007700:Kinetics; D008958:Models, Molecular; D008962:Models, Theoretical; D011487:Protein Conformation; D011506:Proteins / Q000737:chemistry* / Q000302:isolation & purification / Q000627:therapeutic use
D000911:Antibodies, Monoclonal; D011506:Proteins