January 27, 2014

Predicting Progress in Protein Aggregation

Techniques to enable the design and formulation of stable, protein-based therapeutics.

In silico analysis and the evaluation of formulability, aided by new analytical tools such as hydrogen deuterium exchange mass spectrometry (HDX MS), are enabling the improved design, production, and formulation of protein-based drugs. As the development of biologic drugs based on antibodies and other protein/peptide molecules continues apace, the control and prevention of aggregation during manufacturing, downstream processing, formulation, storage, shipping, and administration remains an important issue. As a result, significant efforts have been invested in understanding the mechanisms behind aggregation and the establishment of predictive models and more advanced analytical techniques, particularly high-throughput methods, to enable the design and formulation of stable, protein-based therapeutics.

The importance of aggregation
Because aggregation can occur at any point during manufacturing, processing, formulation, and use of protein-based drugs, the consequences of this phenomenon can be significant. “Cells may stop secreting a protein if it takes on a new, aggregated form, leading to low titers and reduced yields and productivity. In addition, the formation of larger precipitates can cause the physical clogging of equipment and handling of the product during downstream processing,” says Jesus Zurdo, head of innovation with Lonza Custom Manufacturing’s development services team.

If aggregation occurs during formulation, which is of particular concern for protein-based products that require high concentration, the performance and safety of the product can be affected. “There is evidence to suggest that the presence of aggregates increases the immunogenicity of biologic drugs. How aggregates trigger immune responses, and which aggregates are responsible, isn’t known at this point, but it is thought that aggregates are more easily recognized than the parent protein,” Zurdo explains. As a result, there is increasingly regulatory pressure for biopharmaceutical manufacturers to reduce the risk of immunogenicity by not only controlling, but also preventing, aggregate formation.

Understanding aggregation
Aggregation involves the undesired unfolding or misfolding of proteins, which then enables protein-protein interactions and the formation of a possible range of larger molecules, from oligomers to very high molecular weight species. The most well understood aggregates are amyloid fibrils, which are composed of beta sheets stabilized by hydrogen bonds.

Typically, short polypeptide segments of 5-15 amino acids in length within folded protein structures that are affected by changes in the protein environment are responsible for nucleating the aggregation of the entire protein. Aggregates are classified as soluble or insoluble, as well as covalent (involves the formation of a covalent bond, often a disulfide linkage) or hydrogen-bonded (weaker interactions). Self-association of therapeutic proteins via covalent bonds is typically irreversible while aggregates formed via weaker interactions may be reversible upon changes in protein concentration, temperature, and pH, for example. As a result, aggregates can range in size from minute, invisible, non-filterable particles to large precipitates that are visible with the naked eye. In addition, some aggregates may be static while others may be dynamic.

External factors do not alone determine when protein aggregation will occur. The physicochemical characteristics of the short polypeptide segments that comprise protein therapeutics also determine the propensity of each segment to undergo aggregation. While much remains unknown about the actual mechanism(s) of aggregation, a great deal has been learned about the influence of these physicochemical factors on the likelihood of an amino-acid sequence to serve as an aggregation nucleating site, according to Zurdo.

Predicting protein aggregation
This knowledge about the relationship between the structural characteristics of peptide segments and the likelihood of aggregate formation has been used to develop predictive models. Other factors that have been used to develop these models include the unfolding kinetics and the thermal and colloidal stability of native proteins. The properties of the peptide segments of interest generally include the polarity, charge, dipole moment, hydrophilicity and hydrophobicity patterns, accessible surface area, charge, and number and location of aromatic residues.

“The idea is to match specific amino acid sequences (and structural features) within proteins to the probability of aggregation by comparing their physicochemical and structural attributes to those of sequences with known aggregation behavior using predictive algorithms generated based on actual experimental data,” notes Zurdo. “It is very difficult to predict aggregation de novo,” he adds. Thus, efforts have been invested in synthesizing analogs and studying their behavior to expand the database of information on known proteins and antibodies. Lonza’s database currently includes information on the aggregation behavior and properties of more than 1000 individual antibody molecules.

Potential protein-based drug candidates are screened against the database to identify the potential for aggregation. If the results indicate that any amino acid sequences within the molecule might have the propensity for aggregation, then the protein can be modified, or if necessary, redesigned. “We have been successful to date in using such in silico analyses to address the issue of protein aggregation much earlier in the drug development process, which helps avoid potential problems throughout the scale-up, manufacturing, and formulation stages,” asserts Zurdo.

New analytical tools for aggregate prediction
Because the success of these predictive models depends in large part on the accuracy of the physicochemical property data of the peptide segments, effort has also been directed at developing more advanced analytical methods for obtaining more detailed structural protein information. One such method is hydrogen deuterium exchange mass spectrometry (HDX MS), which enables the detection of potential sites of aggregation because they are systematically protected from solvent exposure (HD exchange) when in the aggregate state, but not protected when in the free protein (monomer) state. “By knowing the specific site or sites prone to aggregation, targeted formulations or mutagenesis studies can be devised,” says Asish Chakraborty, business development manager with Waters Corporation.


Tags: monoclonal anitbodies, Mab, high thorughput analysis, analysis, protein aggregation, aggregates, high throughput process development, HTPD