Naresh Chennamsetty

Design of therapeutic proteins with enhanced stability

Therapeutic proteins such as antibodies constitute the most rapidly growing class of pharmaceuticals for use in diverse clinical settings including cancer, chronic inflammatory diseases, kidney transplantation, cardiovascular medicine, and infectious diseases. Unfortunately, they tend to aggregate when stored under the concentrated conditions required in their usage. Aggregation leads to a decrease in antibody activity and could elicit an immunological response. Using full antibody atomistic molecular dynamics simulations, we identify the antibody regions prone to aggregation by using a technology that we developed called spatial aggregation propensity (SAP). SAP identifies the location and size of these aggregation prone regions, and allows us to perform target mutations of those regions to engineer antibodies for stability. We apply this method to therapeutic antibodies and demonstrate the significantly enhanced stability of our mutants compared with the wild type. The technology described here could be used to incorporate developability in a rational way during the screening of antibodies in the discovery phase for several diseases.

Aggregation-prone motifs in human immunoglobulin G.

Therapeutic antibodies of many different IgG subclasses (IgG1, IgG2 and IgG4) are used in the treatment of various cancers, rheumatoid arthritis and other inflammatory and infectious diseases. These antibodies are stored for long durations under high concentrations as required in the disease treatment. Unfortunately, these antibodies aggregate under these storage conditions, leading to a decrease in antibody activity and raising concerns about causing an immunological response. Thus, there is a tremendous need to identify the aggregation-prone regions in different classes of antibodies. We use the SAP (spatial-aggregation-propensity) technology based on molecular simulations to determine the aggregation-prone motifs in the constant regions of IgG1 classes of antibodies. Mutations engineered on these aggregation-prone motif regions led to antibodies of enhanced stability. Fourteen aggregation-prone motifs are identified, with each motif containing one to seven residues. While some of these motifs contain residues that are neighbors in primary sequence, others contain residues that are far apart in primary sequence but are close together in the tertiary structure. Comparison of the IgG1 sequence with those of other subclasses (IgG2, IgG3 and IgG4) showed that these aggregation-prone motifs are largely preserved among all IgG subclasses. Other broader classes of antibodies (IgA1, IgD, IgE and IgM), however, differed in these motif regions. The aggregation-prone motifs identified were therefore common to all IgG subclasses, but differ from those of non-IgG classes. Moreover, since the motifs identified are in the constant regions, they are applicable for all antibodies within the IgG class irrespective of the variable region. Thus, the motif regions identified could be modified on all IgGs to yield antibodies of enhanced stability.

Glycosylation influences on the aggregation propensity of therapeutic monoclonal antibodies

Monoclonal antibodies are the fastest growing class of biologics in the pharmaceutical industry. The correlation between mAb glycosylation and aggregation has not been elucidated in detail, yet understanding the structure-stability relationship involving glycosylation is critical for developing successful drug formulations. We conducted studies of temperature-induced aggregation and compared the stability of both glycosylated and aglycosylated forms of a human IgG1. In parallel, we also performed molecular dynamics simulations of the glycosylated full antibody to gain an understanding of the polysaccharide surroundings at the molecular level. Aglycosylated mAbs are somewhat less stable and therefore aggregate more easily than the glycosylated form at the temperatures studied. Glycosylation seems to enhance solubility and stability of these therapeutics and thus might be important for long-term storage.

Developability Index: A Rapid In Silico Tool for the Screening of Antibody Aggregation Propensity

Determining the aggregation propensity of protein-based biotherapeutics is an important step in the drug development process. Typically, a great deal of data collected over a large period of time is needed to estimate the aggregation propensity of biotherapeutics. Thus, candidates cannot be screened early on for aggregation propensity, but early screening is desirable to help streamline drug development. Here, we present a simple molecular computational method to predict the aggregation propensity via hydrophobic interactions, thought to be the most common mechanism of aggregation, and electrostatic interactions. This method uses a new quantity termed Developability Index. It is a function of an antibodys net charge, calculated on the full-length antibody structure, and the spatial aggregation propensity, calculated on the complementarity-determining region structure. Its accuracy is due to the molecular level details and the incorporation of the tertiary structure of the antibody. It is particularly applicable to antibodies or other proteins for which structures are available or could be determined accurately using homology modeling. Applications include the selection of molecules in the discovery or early development process, selection of mutants for stability, and estimation of resources needed for development of a given biomolecule.

Evaluation of a Non-Arrhenius Model for Therapeutic Monoclonal Antibody Aggregation

Understanding antibody aggregation is of great significance for the pharmaceutical industry. We studied the aggregation of five different therapeutic monoclonal antibodies (mAbs) with size-exclusion chromatography-high-performance liquid chromatography (SEC-HPLC), fluorescence spectroscopy, electron microscopy, and light scattering methods at various temperatures with the aim of gaining insight into the aggregation process and developing models of it. In particular, we find that the kinetics can be described by a second-order model and are non-Arrhenius. Thus, we develop a non-Arrhenius model to connect accelerated aggregation experiments at high temperature to long-term storage experiments at low temperature. We evaluate our model by predicting mAb aggregation and comparing it with long-term behavior. Our results suggest that the number of monomers and mAb conformations within aggregates vary with the size and age of the aggregates, and that only certain sizes of aggregates are populated in the solution. We also propose a kinetic model based on conformational changes of proteins and monomer peak loss kinetics from SEC-HPLC. This model could be employed for a detail analysis of mAb aggregation kinetics.

Design and Application of Antibody Cysteine Variants

Antibodies are multidomain proteins that are extensively used as a research tool in molecular biology and as therapeutics in medicine. In many cases, antibodies are engineered to contain surface cysteines for the site-specific conjugation of payloads. These antibodies can serve as payload vehicles in targeting a diseased cell to which the conjugated molecules exercise their activity. Here, we design and analyze a set of fourteen new IgG1 cysteine variants, with at least one variant per immunoglobulin fold domain. The cross-linking propensity of these mutants correlates very well with a tool we have developed for measuring aggregation propensity in silico, called spatial aggregation propensity (SAP). Our results indicate the utility of the SAP technology in selecting antibody cysteine variants with desired properties. Moreover, the different oligomerization propensity of the variants suggests a variety of applications in molecular biology and medicine, such as payload delivery, structural analysis, electrophoresis, and chromatography.

Predictive tools for stabilization of therapeutic proteins

Monoclonal antibodies represent the fastest growing class of pharmaceuticals. A major problem, however, is that the proteins are susceptible to aggregation at the high concentration commonly used during manufacturing and storage. Our recent publication describes a technology based on molecular simulations to identify aggregation-prone regions of proteins in silico. The technology, called spatial aggregation propensity (SAP), identifies hot-spots for aggregation based on the dynamic exposure of spatially-adjacent hydrophobic amino acids. Monoclonal antibodies (mAbs) in which patches with high-SAP scores are changed to patches with significantly reduced SAP scores via a single mutation are more stable than wild type, thus validating the SAP method for mapping aggregation-prone regions on proteins. We propose that the SAP technology will be useful for protein stabilization, and as a screening tool to bridge discovery and development of protein-based therapeutics by a rational assessment of the developability of candidate protein drugs.

Dynamic Fluctuations of Protein-Carbohydrate Interactions Promote Protein Aggregation

Protein-carbohydrate interactions are important for glycoprotein structure and function. Antibodies of the IgG class, with increasing significance as therapeutics, are glycosylated at a conserved site in the constant Fc region. We hypothesized that disruption of protein-carbohydrate interactions in the glycosylated domain of antibodies leads to the exposure of aggregation-prone motifs. Aggregation is one of the main problems in protein-based therapeutics because of immunogenicity concerns and decreased efficacy. To explore the significance of intramolecular interactions between aromatic amino acids and carbohydrates in the IgG glycosylated domain, we utilized computer simulations, fluorescence analysis, and site-directed mutagenesis. We find that the surface exposure of one aromatic amino acid increases due to dynamic fluctuations. Moreover, protein-carbohydrate interactions decrease upon stress, while protein-protein and carbohydrate-carbohydrate interactions increase. Substitution of the carbohydrate-interacting aromatic amino acids with non-aromatic residues leads to a significantly lower stability than wild type, and to compromised binding to Fc receptors. Our results support a mechanism for antibody aggregation via decreased protein-carbohydrate interactions, leading to the exposure of aggregation-prone regions, and to aggregation.

Conformational stability and aggregation of therapeutic monoclonal antibodies studied with ANS and Thioflavin T binding

Characterization of aggregation profiles of monoclonal antibodies (mAb) is gaining importance because an increasing number of mAb-based therapeutics are entering clinical studies and gaining marketing approval. To develop a successful formulation, it is imperative to identify the critical biochemical properties of each potential mAb drug candidate. We investigated the conformational change and aggregation of a human IgG1 using external dye-binding experiments with fluorescence spectroscopy and compared the aggregation profiles obtained to the results of size-exclusion chromatography. We show that using an appropriate dye at selected mAb concentration, unfolding or aggregation can be studied. In addition, dye-binding experiments may be used as conventional assays to study therapeutic mAb stability.

Prediction of protein binding regions

Identifying protein binding sites provides important clues to the function of a protein. Experimental methods to identify the binding sites such as determining the crystal structures of protein complexes are extremely laborious and expensive. Here, we present a computational technique called spatial aggregation propensity (SAP) based on molecular simulations to predict protein binding sites. We apply this technique to two model proteins, an IgG1 antibody and epidermal growth factor receptor (EGFR) and demonstrate that SAP predicts protein binding regions with very good accuracy. In the case of the IgG1 antibody, SAP accurately predicts binding regions with the Fc‐receptor, protein‐A, and protein‐G. For EGFR, SAP accurately predicts binding regions with EGF, TGFα, and with another EGFR. The resolution of SAP is varied to obtain a detailed picture of these binding sites. We also show that some of these binding sites overlap with protein self‐aggregation prone regions. We demonstrate how SAP analysis can be used to engineer the protein to remove unfavorable aggregation prone regions without disturbing protein binding regions. The SAP technique could be also used to predict the yet unknown binding sites of numerous proteins, thereby providing clues to their function. Proteins 2011.