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During therapeutic candidate selection, diverse panels of monoclonal antibodies (mAbs) are routinely subjected to various stress conditions, and assayed for biophysical and biochemical stability. A novel high throughput method has been developed to differentiate candidate molecules in a mixture based on their propensity for forming aggregates when subjected to agitation (vortexing) stress. Protein monomers are separated from soluble and insoluble aggregates using size exclusion chromatography, under nondenaturing conditions, and the individual components in the mixture are identified by mass spectrometry and quantitated relative to an unstressed control. An internal standard was added to the mixture after stress, and used to correct for differences in ionization between samples. Treatment of the samples with the enzyme IdeS (FabRICATOR) significantly reduces sample complexity, and allows for a large number of candidate molecules to be assessed in a single analysis. Simple and robust, the method is well suited for measuring relative aggregation propensity (RAP) in conjunction with molecule selection and coformulation development.

Understanding the underlying mechanisms of Fc aggregation is an important prerequisite for developing stable and efficacious antibody-based therapeutics. In our study, high resolution two-dimensional nuclear magnetic resonance (NMR) was employed to probe structural changes in the IgG1 Fc. A series of (1)H-(15)N heteronuclear single-quantum correlation NMR spectra were collected between pH 2.5 and 4.7 to assess whether unfolding of C(H)2 domains precedes that of C(H)3 domains. The same pH range was subsequently screened in Fc aggregation experiments that utilized molecules of IgG1 and IgG2 subclasses with varying levels of C(H)2 glycosylation. In addition, differential scanning calorimetry data were collected over a pH range of 3-7 to assess changes in C(H)2 and C(H)3 thermostability. As a result, compelling evidence was gathered that emphasizes the importance of C(H)2 stability in determining the rate and extent of Fc aggregation. In particular, we found that Fc domains of the IgG1 subclass have a lower propensity to aggregate compared with those of the IgG2 subclass. Our data for glycosylated, partially deglycosylated, and fully deglycosylated molecules further revealed the criticality of C(H)2 glycans in modulating Fc aggregation. These findings provide important insights into the stability of Fc-based therapeutics and promote better understanding of their acid-induced aggregation process.

The prevention of aggregation in therapeutic antibodies is of great importance to the biopharmaceutical industry. In our investigation, acid-induced aggregation of monoclonal IgG1 and IgG2 antibodies was studied at pH 3.5 as a function of salt concentration and buffer type. The extent of aggregation was estimated using a native cation-exchange chromatography (CEX) method based on the loss of soluble monomer. This approach allowed quantitative analysis of antibody aggregation kinetics for individual and mixed protein solutions. Information regarding the aggregation mechanism was gained by assessing stabilities of intact antibodies relative to their Fc and Fab fragments. The role of protein thermodynamic stability in aggregation was deduced from differential scanning calorimetry (DSC). The rate of aggregation under conditions mimicking the viral inactivation step during monoclonal antibody (mAb) processing was found to be strongly dependent on the antibody subclass (IgG1 vs IgG2). At 25 °C, IgG1s were resistant to low pH aggregation, but IgG2s aggregated readily in the presence of salt. The observed distinction between IgG1 and IgG2 aggregation resulted from differential stability of the corresponding C(H)2 domains. This was further confirmed by experimenting with an IgG1 molecule containing an aglycosylated C(H)2 domain. Interestingly, comparative analysis of two buffer systems (based on acetic acid vs citric acid) revealed differences in mAb aggregation under identical pH conditions. Evidence is provided for the importance of the total acid concentration for antibody aggregation at low pH. The effects of C(H)2 instability and solution composition on aggregation are significant and deserve careful consideration during the development of mAb- or Fc-based therapeutics.

IntroductionA proof-of-concept demonstration of the use of label-free quantitative glycoproteomics for biomarker discovery workflow is presented in this paper, using a mouse model for skin cancer as an example.Materials and MethodsBlood plasma was collected from ten control mice and ten mice having a mutation in the p19ARFgene, conferring them high propensity to develop skin cancer after carcinogen exposure. We enriched for N-glycosylated plasma proteins, ultimately generating deglycosylated forms of the tryptic peptides for liquid chromatography mass spectrometry (LC-MS) analyses. LC-MS runs for each sample were then performed with a view to identifying proteins that were differentially abundant between the two mouse populations. We then used a recently developed computational framework, Corra, to perform peak picking and alignment, and to compute the statistical significance of any observed changes in individual peptide abundances. Once determined, the most discriminating peptide features were then fragmented and identified by tandem mass spectrometry with the use of inclusion lists.Results and DiscussionsWe assessed the identified proteins to see if there were sets of proteins indicative of specific biological processes that correlate with the presence of disease, and specifically cancer, according to their functional annotations. As expected for such sick animals, many of the proteins identified were related to host immune response. However, a significant number of proteins are also directly associated with processes linked to cancer development, including proteins related to the cell cycle, localization, transport, and cell death. Additional analysis of the same samples in profiling mode, and in triplicate, confirmed that replicate MS analysis of the same plasma sample generated less variation than that observed between plasma samples from different individuals, demonstrating that the reproducibility of the LC-MS platform was sufficient for this application.ConclusionThese results thus show that an LC-MS-based workflow can be a useful tool for the generation of candidate proteins of interest as part of a disease biomarker discovery effort.

Polysorbate 20 (PS20) is a nonionic surfactant frequently used to stabilize protein biopharmaceuticals. During the development of mAb formulations containing PS20, small clouds of particles were observed in solutions stored in vials. The degree of particle formation was dependent on PS20 concentration. The particles were characterized by reversed-phase HPLC after dissolution and labeling with the fluorescent dye 1-pyrenyldiazomethane. The analysis showed that the particles consisted of free fatty acids (FFAs), with the distribution of types consistent with those found in the PS20 raw material. Protein solutions formulated with polysorbate 80, a chemically similar nonionic surfactant, showed a substantial delay in particle formation over time compared with PS20. Multiple lots of polysorbates were evaluated for FFA levels, each exhibiting differences based on polysorbate type and lot. Polysorbates purchased in more recent years show a greater distribution and quantity of FFA and also a greater propensity to form particles. This work shows that the quality control of polysorbate raw materials could play an important role in biopharmaceutical product quality.

Competitive inhibitors of dihydrofolate reductase (DHFR) are used in chemotherapy or prophylaxis of many microbial pathogens, including the eukaryotic parasites Plasmodium falciparum and Toxoplasma gondii . Unfortunately, point mutations in the DHFR gene can confer resistance to inhibitors specific to these pathogens. We have developed a rapid system for testing inhibitors of DHFRs from a variety of parasites. We replaced the DHFR gene from the budding yeast Saccharomyces cerevisiae with the DHFR-coding region from humans, P. falciparum , T. gondii , Pneumocystis carinii , and bovine or human-derived Cryptosporidium parvum . We studied 84 dicyclic and tricyclic 2,4-diaminopyrimidine derivatives in this heterologous system and identified those most effective against the DHFR enzymes from each of the pathogens. Among these compounds, six tetrahydroquinazolines were effective inhibitors of every strain tested, but they also inhibited the human DHFR and were not selective for the parasites. However, two quinazolines and four tetrahydroquinazolines were both potent and selective inhibitors of the P. falciparum DHFR. These compounds show promise for development as antimalarial drugs.

This study introduces a novel analytical approach for studying aggregation and phase separation of monoclonal antibodies (mAbs). The approach is based on using analytical scale cation-exchange chromatography (CEX) for measuring the loss of soluble monomer in the case of individual and mixed protein solutions. Native CEX outperforms traditional size-exclusion chromatography in separating complex protein mixtures, offering an easy way to assess mAb aggregation propensity. Different IgG1 and IgG2 molecules were tested individually and in mixtures consisting of up to four protein molecules. Antibody aggregation was induced by four different stress factors: high temperature, low pH, addition of fatty acids, and rigorous agitation. The extent of aggregation was determined from the amount of monomeric protein remaining in solution after stress. Consequently, it was possible to address the role of specific mAb regions in antibody aggregation by co-incubating Fab and Fc fragments with their respective full-length molecules. Our results revealed that the relative contribution of Fab and Fc regions in mAb aggregation is strongly dependent on pH and the stress factor applied. In addition, the CEX-based approach was used to study reversible protein precipitation due to phase separation, which demonstrated its use for a broader range of protein-protein association phenomena. In all cases, the role of Fab and Fc was clearly dissected, providing important information for engineering more stable mAb-based therapeutics.

The unique cation exchange chromatography (CEX) charge variant profile of mAb1 is characterized by a combination of mass spectrometry, limited Lys-C digestion followed by CEX separation and structural analysis. During CEX method development, mAb1 showed several unexpected phenomena, including a unique profile containing two main species (acidic 2 and main) and significant instability during stability studies of the main species. Reduced Lys-C peptide mapping identified a small difference in one of the heavy chain peptides (H4) in acidic 2 and further mass analysis identified this difference as Asn55 deamidation. However, the amount of Asn55 deamidation in acidic 2 could account for only half of the species present in this peak. Lys-C limited digest followed by CEX separated several unique peaks in the acidic peak 2 including two pre Fab peaks (LCC1 and LCC2). Whole protein mass analysis suggested that both LCC1 and LCC2 were potentially deamidated species. Subsequent peptide mapping with MS/MS determined that LCC1 contained isoAsp55 and LCC2 contained Asp55. Combining LCC1 and LCC2 CEX peak areas could account for nearly all of the species present in acidic peak 2. Subsequent detailed sequence analysis combined with molecular modeling identified Asn55 and its surrounding residues are responsible for the different CEX behavior and instability of mAb1 following forced degradation at high pH. Overall, the combinatorial approach used in this study proved to be a powerful tool to understand the unique charge variant and stability profile of a monoclonal antibody.

A new cation-exchange high-performance liquid chromatography (HPLC) method that separates fragment antigen-binding (Fab) and fragment crystallizable (Fc) domains generated by the limited proteolysis of monoclonal antibodies (mAbs) was developed. This assay has proven to be suitable for studying complex degradation processes involving various immunoglobulin G1 (IgG1) molecules. Assignment of covalent degradations to specific regions of mAbs was facilitated by using Lys-C and papain to generate Fab and Fc fragments with unique, protease-dependent elution times. In particular, this method was useful for characterizing protein variants formed in the presence of salt under accelerated storage conditions. Two isoforms that accumulated during storage were readily identified as Fab-related species prior to mass-spectrometric analysis. Both showed reduced biological activity likely resulting from modifications within or in proximity of the complementarity-determining regions (CDRs). Utility of this assay was further illustrated in the work to characterize light-induced degradations in mAb formulations. In this case, a previously unknown Fab-related species which populated upon light exposure was observed. This species was well resolved from unmodified Fab, allowing for direct and high-purity fractionation. Mass-spectrometric analysis subsequently identified a histidine-related degradation product associated with the CDR2 of the heavy chain. In addition, the method was applied to assess the structural organization of a noncovalent IgG1 dimer. A new species corresponding to a Fab-Fab complex was found, implying that interactions between Fab domains were responsible for dimerization. Overall, the data presented demonstrate the suitability of this cation-exchange HPLC method for studying a wide range of covalent and noncovalent degradations in IgG1 mAbs.

Background Quantitative proteomics holds great promise for identifying proteins that are differentially abundant between populations representing different physiological or disease states. A range of computational tools is now available for both isotopically labeled and label-free liquid chromatography mass spectrometry (LC-MS) based quantitative proteomics. However, they are generally not comparable to each other in terms of functionality, user interfaces, information input/output, and do not readily facilitate appropriate statistical data analysis. These limitations, along with the array of choices, present a daunting prospect for biologists, and other researchers not trained in bioinformatics, who wish to use LC-MS-based quantitative proteomics. Results We have developed Corra, a computational framework and tools for discovery-based LC-MS proteomics. Corra extends and adapts existing algorithms used for LC-MS-based proteomics, and statistical algorithms, originally developed for microarray data analyses, appropriate for LC-MS data analysis. Corra also adapts software engineering technologies (e.g. Google Web Toolkit, distributed processing) so that computationally intense data processing and statistical analyses can run on a remote server, while the user controls and manages the process from their own computer via a simple web interface. Corra also allows the user to output significantly differentially abundant LC-MS-detected peptide features in a form compatible with subsequent sequence identification via tandem mass spectrometry (MS/MS). We present two case studies to illustrate the application of Corra to commonly performed LC-MS-based biological workflows: a pilot biomarker discovery study of glycoproteins isolated from human plasma samples relevant to type 2 diabetes, and a study in yeast to identify in vivo targets of the protein kinase Ark1 via phosphopeptide profiling. Conclusion The Corra computational framework leverages computational innovation to enable biologists or other researchers to process, analyze and visualize LC-MS data with what would otherwise be a complex and not user-friendly suite of tools. Corra enables appropriate statistical analyses, with controlled false-discovery rates, ultimately to inform subsequent targeted identification of differentially abundant peptides by MS/MS. For the user not trained in bioinformatics, Corra represents a complete, customizable, free and open source computational platform enabling LC-MS-based proteomic workflows, and as such, addresses an unmet need in the LC-MS proteomics field.