Betasciencepress.com | STM Publishing Corporation                                                                                                                                             

Expert Opinion

In vivo characterization of therapeutic monoclonal antibodies

Xiaobin Xu

Center for Biomedical Mass Spectrometry, Boston University School of Medicine, Boston, MA 02118, USA.
Vol.2, No.1, Pages 10-15, doi: 10.17145/jab.16.003. (ISSN 2405-710X).   Download PDF

 

Correspondence

Present address: Regeneron Pharmaceuticals, Inc., 777 Old Saw Mill River Rd, Tarrytown, NY 10591, USA. Phone: + 1 914 847 7076, E-mail:This email address is being protected from spambots. You need JavaScript enabled to view it.

Funding/Manuscript writing assistance

The author has no financial support or funding to report and also declared that no writing assistance was utilized in the production of this article.

Competing interest

The author has declared that no competing interest exist.

Article history

Received: 20 December 2015, Revised 29 December 2015, Accepted 29 December 2015, available online 15 January 2016.

 

Keywords: monoclonal antibody, product quality attribute, critical quality attribute, post-translational modification, mass spectrometry, in vivo characterization.

Introduction
Therapeutic monoclonal antibodies (mAbs) are complex drug molecules with a high degree of heterogeneity, including charge variants, aggregates, fragments, and post-translational modifications (PTMs). PTMs can be introduced by chemical or enzymatic modifications over the lifespan of mAbs during production, storage, and in vivo circulation. Common PTMs in mAbs include glycosylation, deamidation, oxidation, glycation, N-terminal glutamine cyclization, and C-terminal lysine processing. These product-related modifications are termed as product quality attributes (PQAs). A given PQA has the potential to impact the safety and/or efficacy of a therapeutic mAb. A PQA may enhance immunogenicity or off-target bindings, resulting in changes of the safety profile of a therapeutic mAb. A PQA may change the interaction of a therapeutic mAb to its target antigen, and therefore alter the efficacy. The PQAs that impact on drug safety and/or efficacy are considered as critical quality attributes (CQAs). Control of PQAs, especially CQAs, within predefined acceptance criteria is required by regulatory agencies to ensure product quality, consistency, and stability. During drug development, manufacturing processes aim to maximize the production of a desired product in high purity with minimal and consistent levels of variant forms. Formulation development seeks to stabilize mAb products and minimize additional formation of variants during storage. The modifications occurred during mAb production and storage can be reliably monitored and controlled. However, additional modifications can occur after a mAb is administered to patients. mAbs are typically administered intravenously or subcutaneously, and quickly get into the circulation of patients. The circulating half-life of a typical mAb in human blood system is 2-3 weeks. The blood environment is remarkably different from formulation buffers and storage temperatures. Typical mAb drug products are formulated at a pH of 5-6 with multiple excipients, such as sugars, salts, amino acids, and surfactants. The formulation buffer, pH, and excipients are optimized to enhance mAb solubility/stability and to reduce viscosity as well as the formation of aggregates/particulates. In contrast, blood is a dynamic environment with thousands of proteins including many protein-modifying enzymes, and is tightly regulated at a pH of 7.4 and a temperature of 37 °C. The clearance and modification of a mAb in blood circulation may result in changes of the PQA profile, leading to increased attribute exposure and decreased effective drug exposure to patients, and therefore causing the concerns of immune responses and loss of efficacy. To this end, a recent FDA guidance for industry recommends sponsors to evaluate susceptibilities of therapeutic protein products to modifications in the in vivo milieu as early as in product design and development, in order to facilitate product engineering for enhancing the product stability under in vivo conditions [1]. Therefore, in vitro and in vivo characterization of PQAs can monitor the attribute stability before and after drug administration and determine the attribute exposure to patients, which can help to establish attribute criticality and control strategy during the design, engineering, selection and development of therapeutic mAbs.

Mass spectrometry based mAb PTM characterization methods
The most common type of therapeutic mAbs is γ-immunoglobulin (IgG). The IgG is a glycoprotein comprised of two identical heavy chains (HC) and two identical light chains (LC). Each heavy chain contains one variable (VH) and three constant domains (CH1, CH2, and CH3), whereas each light chain contains one variable (VL) and one constant domain (CL). Each heavy chain is connected to a light chain by a disulfide bond, and the two heavy chains are linked in the hinge region by multiple disulfide bonds. Antigen binding regions are located at three loops in both VH and VL domains, which are termed as complementarity determining regions (CDRs). The VH and CH1 domains together with the disulfide linked light chain are called the fragment antigen-binding (Fab). The hinge region disulfide bond linked CH2 and CH3 domains together are called the fragment crystallizable (Fc) region. A single N-linked antennary oligosaccharide is located on a conserved asparagine residue in the CH2 domain. Based on heavy chain sequences and inter-chain disulfide bond patterns, IgGs are classified into four group, IgG1, IgG2, IgG3, and IgG4. IgG1, IgG2, and IgG4 are widely used as therapeutics, whereas IgG3 is rarely used due to the short half-life in serum.
Mass spectrometry (MS) based mAb PTM characterization methods can be classified into three approaches: the top-down approach (i.e. intact mass measurement), the middle-up approach (i.e. mAb fragment analysis), and the bottom-up approach (i.e. peptide mapping). The top-down and middle-up approaches require less sample preparation and consumption, and therefore have higher throughput as compared to the bottom-up approach. However, these two approaches only provide modification information at the global structure level. In contrast, the bottom-up approach could locate the modification site to a specific amino acid residue.
Intact mass measurement often uses high end quadrupole-time-of-flight (Q-TOF) mass spectrometers and advanced mass deconvolution algorithms to measure molecular masses of intact mAbs with mass accuracy within a few Daltons. Intact mass measurement reveals the mAb molecular weight, the glycosylation profile, and a few of modifications (e.g. glycation) at the protein level.
To locate the modification into a specific chain or domain, mAb can be cleaved into a few large fragments prior to mass analysis. One mAb fragment method is to reduce disulfide bonds to generate light chains and heavy chains. Another mAb fragment method is the limited enzyme digestion under native condition. For example, papain digestion can generate two Fab and one Fc fragments, whereas Ides can generate one F(ab’)2 and two Fc/2 fragments.
Although the middle-up approach can locate a modification to a specific region of a mAb, it could not locate the modification at the residue level. The bottom-up approach not only confirms the mAb sequence but also identifies and quantifies site-specific modifications. In the bottom-up approach, mAb is subjected to denaturation, reduction, alkylation, and protease digestion. The resulted peptides are separated by reversed-phase C18 chromatography coupled to a mass spectrometer for MS and MS/MS analyses. MS/MS can be used to identify and locate modifications. The extracted ion peak areas of the modified peptide and the native peptide were integrated to calculate the relative abundance of the modified peptide. Peptide mapping can also be performed under the non-reduced condition, which preserves disulfide bonds and therefore can be used to confirm the disulfide bond linkages.
To analyze the structures and the relative abundance of N-linked glycans, mAbs are treated with endoglycosidase PNGaseF to release the N-linked glycans, followed by glycan derivatization with fluorescent reagents, such as 2-anthranilic acid (2-AA) or 2-aminobenzamide (2-AB). The labeled glycans are then separated by hydrophilic interaction chromatography and detected by a fluorescence detector and a mass spectrometer (HILIC-FLR-MS). The MS spectra provide the identity of the glycans, and the fluorescent absorption of each HILIC separated glycan peak represents the relative abundance.
For in vivo characterization, a therapeutic mAb needs to be purified from the sera of subjects using immunopurification strategies prior to the mass spectrometry based characterization. For animal toxicology and pharmacokinetics studies, anti-human IgG antibodies can be used to pull-down the therapeutic mAb. For human clinical studies, the anti-idiotypic antibody and a target antigen can be used to purify a corresponding therapeutic mAb.

In vivo mAb modifications
Many in vivo modifications of mAbs have been reported, including disulfide bond rearrangement, deamidation, formation of N-terminal pyroglutamate, removal of C-terminal lysine, glycation, and oxidation. Some of these modifications behave differently between in vitro and in vivo, leading to the reassessment of attribute criticality.
The IgG antibody subclasses contain a total of 12 intra-chain disulfide bonds within each anti-parallel β barrel domain, an inter-chain disulfide bond between the light chain (LC) and the heavy chain (HC), and multiple inter-chain disulfide bonds between the hinge region of the HCs [2]. Disulfide bonds are static to stabilize the structure of a mAb. However, disulfide bonds can undergo dynamic rearrangements in vivo while circulating in human blood [3], which may alter the structure and function of a mAb. Thus, in vivo rearrangement of disulfide bonds needs to be monitored.
Asparagine (Asn) deamidation is a common non-enzymatic modification. The side-chain carbonyl group of Asn is vulnerable to the nucleophilic attack by the nitrogen of the n+1 peptide bond, forming an unstable five-member ring intermediate, succinimide (Asu), which rapidly hydrolyzes to form aspartate (Asp) and isoaspartate (isoAsp) under basic pH. Therapeutic mAbs are usually formulated at slightly acidic pH, which preserves the succinimide form. After administration, the accumulated succinimide hydrolyzes to Asp and/or isoAsp under the physiologic pH. In mAbs, deamidation has been reported in the Fc regions [4, 5] and the CDRs [6-12]. Deamidation in the CDR regions could affect drug efficacy [10, 12]. Deamidation can accumulate more rapidly in in vitro and in vivo serum samples than that in PBS or formulation buffers[7,13,14], which raises concerns about loss of potency and the potential immunogenicity of the therapeutics after administration.
Both glutamine and glutamate at the N-termini of proteins can cyclize to pyroglutamate chemically or enzymatically. The conversion of glutamine occurs much faster than that of glutamate. The N-termini cyclization is resistant to amino peptidases digestion, preventing the degradation. Although glutaminyl cyclase, an enzyme that catalyzes this conversion, is found in human blood [15], the formation of pyroglutamate from glutamate of mAbs in vivo is a pH dependent non-enzymatic reaction [14,16], as it replicated the rate constant in PBS in vitro.
The C-terminal lysine residue of a mAb heavy chain is susceptible to be removed by unidentified basic carboxypeptidase during protein expression [17]. Partial removal of the C-terminal lysine leads to charge heterogeneity. The remaining lysine residue of mAbs was found to be rapidly removed within an hour through an enzymatic process [18]. As a result, control of the initial lot-to-lot level of C-terminal lysine removal may not be critical.
Glycation is a non-enzymatic modification of primary amines (e.g. lyinse side-chains or N-termini) resulting from incubation with reducing sugars (e.g. glucose, fructose) in vitro or during circulation in vivo. The degree of glycation of a specific residue depends on protein sequence, structure, and sugar concentration. Glycation of a mAb in vitro is reversible in the absence of glucose [19]. In contrast, in vivo glycation in human blood increases at a slow rate [20]. The evaluated level of glycation in the Fc region was found to have no impact on binding to Fc receptors [20]. Since glycation of lysine residues will impair the cleavages of lysine residues by trypsin, other enzymes, such as Asp-N or Glu-C, can be used to generate proper peptides for glycation quantitation.
The N-linked Fc glycan is the biantennary complex type composed of a core pentasaccharide with various sugars attached to the core, resulting in mass heterogeneity. The glycan profile of mAbs is an important PQA that needs to be well controlled. The two major N-linked glycan classes are the fucosylated biantennary oligosaccharide and the high-mannose oligosaccharide. The high-mannose species are cleared much faster in human blood than the fucosylated biantennary oligosaccharides [21,22].
Oxidation of methionine is another common post-translational modification. Oxidation of Met252 and Met428 in the Fc region can impact on the binding to Fc receptors [23]. However, no significant accumulation of oxidation in vivo was observed [14].
Potential differential clearance of the native and the modified mAb forms in blood circulation needs to be taken into account when conducting in vivo characterization of PQAs, because the increased modified form in relation to the native form could be a result of faster clearance of the native form. In vitro experiments and knowledge of chemical modification mechanisms can help to distinguish differential clearance from chemical conversion. If the in vivo chemical conversion can be reproduced in vitro either in physiological buffers or serum incubations, the increase of the modified form is likely due to the chemical conversion. On the other hand, if the in vivo chemical conversion cannot be duplicated and the mechanism of the modification is implausible, the increase of modified form may be a result of differential clearance.

In vivo PQA exposure modeling
The next step, also the most important step, is to use in vivo characterization data to access the impacts of PQAs on drug safety and efficacy. The two-compartment pharmacokinetic model is the most common model used to describe the pharmacokinetic behavior of a therapeutic mAb[24]. After intravenous administration, the drug is disturbed rapidly from the central compartment (e.g. plasma) to the peripheral compartment (e.g. tissue) by a first-order process. After the distribution process is completed and the equilibrium is reached, the drug concentration in the plasma declines at a rate dependent on drug elimination. The plasma drug concentration-time profile can be described by the two-compartment model equation as Cp(t)=Ae-αt+Be-βt, where A and B are the hybrid coefficients when α and β are the hybrid first order constants for the rapid distribution phase and the slow elimination phase, respectively. The equation can be solved by nonlinear-fitting to the drug plasma concentration determined by ELISA or LC-MS/MS based quantification.
To quantitatively estimate the impact of PQAs on drug safety, the patient exposure to a PQA over the lifespan of a therapeutic mAb in the patient’s circulation, from the point of the drug administration to the point of complete clearance of the drug, needs to be determined. The relative percentage of a PQA, such as deamidation, is often described by the first-order reaction equation as P(t)=1-(1-P0)∙e-kt, where P0 is the initial percentage of modification before drug administration and k is the rate constant. The equation can be solved by nonlinear-fitting to the percentage of the attribute in relation to the total mAb determined by in vivo characterization discussed above. The attribute plasma concentration-time profile can be described as the product of the drug plasma concentration-time profile and the attribute percentage-time profile, Ca(t)= Cp(t)×P(t) [25]. The patient exposure to the drug is represented by the integrated area under the curve of the attribute plasma concentration-time profile. Thus, a higher attribute formation rate in vivo will result in a higher patient exposure to the attribute. This model can be used to simulate the quantitative impacts of the initial lot-to-lot attribute difference on the attribute exposure. If an attribute formation is much faster relative to the drug clearance, the exposure to the attribute will be relatively high regardless the initial attribute levels, resulting small differences in the patient exposure. In this case, process and storage controls of the initial attribute level have little influence on the patient exposure. On the other hand, if an attribute formation is slow and the drug clearance is relatively fast, the difference in the initial levels of attributes become more significant due to large difference in the patient exposure. In this case, process and storage controls of the initial attribute level become more critical.
The mAb with a given attribute may have reduced potency as compared to the native antibody. A pure fraction of a mAb with a given attribute can be obtained by chromatography enrichment or mutation, and then the relative potency of the mAb with the given attribute can be determined by binding assay or bioassay. The subject exposure to the effective drug over time can be used to quantitatively estimate the impact of a PQA on drug efficacy. The effective drug concentration-time profile can be described as the product of the drug plasma concentration-time profile and the sum of potency-time profile, Ce(t)=∑Potency(t)∙Cp(t), where ∑Potency(t) is the sum of the potency of the native mAb and the modified mAb. The patient exposure to the effective drug is represented by the integrated area under the curve of the effective drug concentration-time profile. This model can be used to simulate the quantitative impacts of the initial lot-to-lot attribute difference on the effective drug exposure, which can help to establish the attribute criticality.

Conclusion and perspective
In vitro characterization of PQAs is important to ensure product quality and consistency at the time of a lot release. However, attributes are not necessarily static after administration, but may change in vivo and impact on drug safety and efficacy, resulting in altering attribute criticality. Thus, the biopharmaceutical industry and the regulatory agencies have paid great attentions to the in vivo behaviors of PQAs. In vivo characterization of PQAs can determine the attribute stability, and help to model the patient exposure to attributes and to the remaining effective drug. Both in vitro and in vivo characterization help evaluate the impacts of PQAs on drug safety and efficacy and establish the attribute criticality and control strategy, leading to development of therapeutic mAbs with better safety and efficacy.

References
1. U.S. Department of Health and Human Services & Food and Drug Adminstration. Guidance for Industry, Immunogenicity Assessment for Therapeutic Protein Products. (2014) at http://www.fda.gov/downloads/drugs/guidancecomplianceregulatoryinformation/guidances/ucm338856.pdf
2. Liu H, May K. Disulfide bond structures of IgG molecules: structural variations, chemical modifications and possible impacts to stability and biological function. mAbs 4(1), 17-23 (2012). [CrossRef]
3. Liu YD, Chen X, Enk JZ, Plant M, Dillon TM, Flynn GC. Human IgG2 antibody disulfide rearrangement in vivo. J Biol Chem 283(43), 29266-29272 (2008). [CrossRef]
4. Sinha S, Zhang L, Duan S et al. Effect of protein structure on deamidation rate in the Fc fragment of an IgG1 monoclonal antibody. Protein Sci 18(8), 1573-1584 (2009). [CrossRef]
5. Chelius D, Rehder DS, Bondarenko PV. Identification and characterization of deamidation sites in the conserved regions of human immunoglobulin gamma antibodies. Anal Chem 77(18), 6004-6011 (2005). [CrossRef]
6. Harris RJ, Kabakoff B, Macchi FD et al. Identification of multiple sources of charge heterogeneity in a recombinant antibody. J Chromatogr B Biomed Appl 752(2), 233-245 (2001). [CrossRef]
7. Huang L, Lu J, Wroblewski VJ, Beals JM, Riggin RM. In vivo deamidation characterization of monoclonal antibody by LC/MS/MS. Anal Chem 77(5), 1432-1439 (2005). [CrossRef]
8. Chu GC, Chelius D, Xiao G, Khor HK, Coulibaly S, Bondarenko PV. Accumulation of succinimide in a recombinant monoclonal antibody in mildly acidic buffers under elevated temperatures. Pharm Res 24(6), 1145-1156 (2007). [CrossRef]
9. Vlasak J, Bussat MC, Wang S et al. Identification and characterization of asparagine deamidation in the light chain CDR1 of a humanized IgG1 antibody. Anal Biochem 392(2), 145-154 (2009). [CrossRef]
10. Yan B, Steen S, Hambly D et al. Succinimide formation at Asn 55 in the complementarity determining region of a recombinant monoclonal antibody IgG1 heavy chain. J Pharm Sci 98(10), 3509-3521 (2009). [CrossRef]
11. Diepold K, Bomans K, Wiedmann M et al. Simultaneous assessment of Asp isomerization and Asn deamidation in recombinant antibodies by LC-MS following incubation at elevated temperatures. PloS one 7(1), e30295 (2012). [CrossRef]
12. Haberger M, Bomans K, Diepold K et al. Assessment of chemical modifications of sites in the CDRs of recombinant antibodies: Susceptibility vs. functionality of critical quality attributes. mAbs 6(2), 327-339 (2014). [CrossRef]
13. Ouellette D, Chumsae C, Clabbers A, Radziejewski C, Correia I. Comparison of the in vitro and in vivo stability of a succinimide intermediate observed on a therapeutic IgG1 molecule. mAbs 5(3), 432-444 (2013). [CrossRef]
14. Yin S, Pastuskovas CV, Khawli LA, Stults JT. Characterization of therapeutic monoclonal antibodies reveals differences between in vitro and in vivo time-course studies. Pharm Res 30(1), 167-178 (2013). [CrossRef]
15. Schilling S, Wasternack C, Demuth HU. Glutaminyl cyclases from animals and plants: a case of functionally convergent protein evolution. Biol Chem 389(8), 983-991 (2008). [CrossRef]
16. Liu YD, Goetze AM, Bass RB, Flynn GC. N-terminal glutamate to pyroglutamate conversion in vivo for human IgG2 antibodies. J Biol Chem 286(13), 11211-11217 (2011). [CrossRef]
17. Dick LW, Jr., Qiu D, Mahon D, Adamo M, Cheng KC. C-terminal lysine variants in fully human monoclonal antibodies: investigation of test methods and possible causes. Biotechnol Bioeng 100(6), 1132-1143 (2008). [CrossRef]
18. Cai B, Pan H, Flynn GC. C-terminal lysine processing of human immunoglobulin G2 heavy chain in vivo. Biotechnol Bioeng 108(2), 404-412 (2011). [CrossRef]
19. Miller AK, Hambly DM, Kerwin BA, Treuheit MJ, Gadgil HS. Characterization of site-specific glycation during process development of a human therapeutic monoclonal antibody. J Pharm Sci 100(7), 2543-2550 (2011). [CrossRef]
20. Goetze AM, Liu YD, Arroll T, Chu L, Flynn GC. Rates and impact of human antibody glycation in vivo. Glycobiology 22(2), 221-234 (2012). [CrossRef]
21. Goetze AM, Liu YD, Zhang Z et al. High-mannose glycans on the Fc region of therapeutic IgG antibodies increase serum clearance in humans. Glycobiology 21(7), 949-959 (2011). [CrossRef]
22. Alessandri L, Ouellette D, Acquah A et al. Increased serum clearance of oligomannose species present on a human IgG1 molecule. mAbs 4(4), 509-520 (2012). [CrossRef]
23. Pan H, Chen K, Chu L, Kinderman F, Apostol I, Huang G. Methionine oxidation in human IgG2 Fc decreases binding affinities to protein A and FcRn. Protein Sci 18(2), 424-433 (2009). [CrosRef]
24. Mould DR, Sweeney KR. The pharmacokinetics and pharmacodynamics of monoclonal antibodies--mechanistic modeling applied to drug development. Curr Opin Drug Discov Devel 10(1), 84-96 (2007). [CrossRef]
25. Goetze AM, Schenauer MR, Flynn GC. Assessing monoclonal antibody product quality attribute criticality through clinical studies. mAbs 2(5), 500-507 (2010). [CrossRef]

All site content, except where otherwise noted, is licensed under a Creative Commons Attribution 4.0 License.

  CC by

http://dx.doi.org/10.1021/ac504300n

Click on the badge to see more information about this article.

Article Tools

 Open Access Article

 Peer-reviewed Article

cloud download 2 Free download (PDF)

 Creative Commons Attribution 4.0 License 

Submit to Journal of Applied Bioanalysis
Go to top

Betasciencepress Publishing © 2014-2017