In silico tools for the development of biotherapeutics

Abstract

The production of a new drug is an inherently risky process. While there are many causes for failure, a number of these are due to the many potential degradation pathways a protein can undergo. Often these reactions are slow and negligible. However, there are times where any one of these reactions can be significant enough to delay or prevent a drug's development. Testing for all of these degradation routes can be difficult in the early stages of drug development due to small amount of available protein and the long times needed to sample these reactions. In order to reduce the risk to the drug development process, in silico methods have been developed to predict the likelihood of these reactions, without the need for any material. This work focuses on two degradation routes; the aggregation pathway and the acidcatalyzed, non-enzymatic hydrolysis of peptide bonds. Aggregation of antibodies can be a limiting factor for liquid formulations; however, two major factors control this reaction: the surface hydrophobicity and the protein charge. These two factors were combined into a new tool, called the Developability Index, to predict protein aggregation rates. This tool was successfully applied to both antibody and individual antibody domains. The non-enzymatic, acid-catalyzed hydrolysis of an amide bond following an aspartic or glutamic acid residue is controlled by a wider range of factors. These include: the secondary structure, the surface exposure of the amide bond, relative orientation of the sidechain, and the availability of the sidechain. These four factors impact the first two steps of the hydrolysis mechanism: the addition of the proton to the peptide bond and the addition of the sidechain to the peptide backbone. The secondary structure and surface exposure of the peptide bond impact the ability of the proton to add to the peptide bond, and thus start the reaction, while the orientation and availability of the sidechain impact the ability of the sidechain to cyclize and form a ring. These factors can be combined to produce a method to predict the reactivity of peptide bonds with high accuracy.