Skip to main page content
U.S. flag

An official website of the United States government

Dot gov

The .gov means it’s official.
Federal government websites often end in .gov or .mil. Before sharing sensitive information, make sure you’re on a federal government site.

Https

The site is secure.
The https:// ensures that you are connecting to the official website and that any information you provide is encrypted and transmitted securely.

Access keys NCBI Homepage MyNCBI Homepage Main Content Main Navigation
. 2019 Oct 16;14(10):e0223899.
doi: 10.1371/journal.pone.0223899. eCollection 2019.

Quantitation and modeling of post-translational modifications in a therapeutic monoclonal antibody from single- and multiple-dose monkey pharmacokinetic studies using mass spectrometry

Affiliations

Quantitation and modeling of post-translational modifications in a therapeutic monoclonal antibody from single- and multiple-dose monkey pharmacokinetic studies using mass spectrometry

Xiaobin Xu et al. PLoS One. .

Abstract

Post-translational modifications (PTMs) of therapeutic monoclonal antibodies (mAbs) are important product quality attributes (PQAs) that can potentially impact drug stability, safety, and efficacy. The PTMs of a mAb may change remarkably in the bloodstream after drug administration compared to in vitro conditions. Thus, monitoring in vivo PTM changes of mAbs helps evaluate the criticality of PQAs during the product risk assessment. In addition, quantitation of the subject exposures to PTM variants helps assess the impact of PTMs on the safety and efficacy of therapeutic mAbs. Here, we developed an immunocapture-liquid chromatography/mass spectrometry (LC/MS) method to quantify in vivo PTM changes a therapeutic mAb overtime in single- and multiple-dose monkey pharmacokinetic (PK) studies. We also built mathematical models to predict the in vivo serum concentrations of PQAs, the subject exposures to PQAs, and the relative abundance of PQAs in single- and multiple-dose regimens. The model predictions are in good agreement with the experimental results. The immunocapture-LC/MS method and mathematical models enable bioanalytical chemists to quantitatively assess the criticality of PQAs during drug development.

PubMed Disclaimer

Conflict of interest statement

The affiliation Regeneron Pharmaceuticals Inc. does not alter our adherence to PLOS ONE policies on sharing data and materials.

Figures

Fig 1
Fig 1. The recovery of the affinity purification of MAB1.
The recovery rate of the affinity purification of MAB1 was >99.5% while <0.5% total MAB1 was detected in the flow-through. The calculation of percent recovery rate was described in the Materials and Methods section. A known amount of high molecular-weight standard (shown as “Upper Marker”) was run with each sample as an internal normalization control. The ratio of the MAB1 peak area to the Upper Marker peak area in each sample was calculated to correct for run-to-run variability.
Fig 2
Fig 2
Example MS/MS spectra of peptide identification (A) and example extracted ion chromatograms for peptide peak integration and PTM quantitation (B). (A) The MS/MS spectrum of an example Met oxidized (Met site 1) peptide, DTLMISR (top panel) and the MS/MS spectrum of the example wild-type peptide, DTLMISR (bottom panel). (B) Extracted ion chromatograph of the Met oxidized peptide, DTLMISR (top panel) and the wild-type peptide, DTLMISR (bottom panel).
Fig 3
Fig 3
The relative abundance of deamidation at each of the three Asn sites in the Fc region of MAB1 from the single-dose PK study (A) and the multiple-dose PK study (B). (A) In the single-dose study, the relative abundance of deamidation at each of the three Asn sites increased over time at different rates following the first-order kinetic equation: Pdeam(t)=1(1P0)ekdeamt. Using non-linear regression, the in vivo deamidation rate constants at Asn site 1, 2, and 3 were determined to be 0.003523% day-1, 0.5394% day-1, and 0.1546% day-1, respectively. (B) In the multiple-dose study, the relative abundance of deamidation at each of the three Asn sites increased over time during each dosing interval following the first-order kinetic equation, but decreased sharply following each subsequent dose due to dilution with newly administrated unmodified MAB1, exhibiting an upward trending saw-tooth pattern. Each dosing time is indicated with an arrow “↑”.
Fig 4
Fig 4
The subject exposure to total MAB1 from the single-dose PK study (A) and the subject exposure to MAB1 with a PQA from the single-dose study (B). (A) The MAB1 serum concentration-time curve (black line) described by the two-compartment pharmacokinetic model equation as C(t) = Aeαt+Beβt was fitted to the ELISA measured MAB1 serum concentrations (black dots). The subject exposure to total MAB1 over the course of 56 days, represented by the AUC of C(t) was determined as 4302.0 μg/mL∙day. (B) The serum concentration-time curve of MAB1 with deamidation is described as CPQA(t) = C(t)∙PPQA(t), The AUC of the CPQA(t) curve corresponds to the subject exposure to MAB1 with the PQA. The subject exposure to MAB1 with deamidation at Asn site 2 over 56 days was determined as 426.7 μg/mL∙day (solid line). The subject exposure to MAB1 with deamidation at Asn site 3 over 56 days was determined as 152.5 μg/mL∙day (hyphenated line). The subject exposure to MAB1 with N-terminal pyroglutamate over 56 days was determined as 191.5 μg/mL∙day (dot line).
Fig 5
Fig 5
Model predictions and experiment measurements of the serum concentration of total MAB1 and MAB1 with deamidation at Asn site 2 or Asn site 3 from the multiple-dose PK study (A) and model predictions and experimental measurements of the relative abundances of deamidation at Asn site 2 or Asn site 3 (B). (A) The predicted pre-dose and post-dose serum concentrations of total MAB1 and MAB1 with deamidation at Asn site 2 or Asn site 3 are in good agreement with the experimental measurements. The pre-dose and post-dose concentrations of total MAB1 and MAB1 with deamidation at Asn site 2 or 3 approach the steady-state levels following an extended period of dosing. (B) The predicted levels are in good agreement with the experimental values. The pre-dose and post-dose relative abundances of deamidation at Asn site 2 or Asn site 3 approach the steady-state levels following an extended period of dosing. Each dosing time is indicated with an arrow “↑”.
Fig 6
Fig 6
Model predictions and experiment measurements of the serum concentration of MAB1 with N-terminal pyroglutamate from the multiple-dose PK study (A) and model predictions and experimental measurements of the relative abundances of N-terminal pyroglutamate (B). (A) The predicted pre-dose and post-dose serum concentrations of MAB1 with N-terminal pyroglutamate are in good agreement with the experimental measurements. The pre-dose and post-dose concentrations of MAB1 with N-terminal pyroglutamate approach the steady-state levels following an extended period of dosing. (B) The predicted levels are in good agreement with the experimental values. The pre-dose and post-dose relative abundances of N-terminal pyroglutamate approach the steady-state levels following an extended period of dosing. Each dosing time is indicated with an arrow “↑”.
Fig 7
Fig 7
Modeling the subject exposures to a hypothetical CDR deamidation with an in vivo deamidation rate of 2.5% per day-1 and initial deamidation levels at 0%, 10%, and 20% in the single-dose study (A) and the multiple-dose study (B). (A) The subject exposure to the hypothetical CDR deamidated variants with 0%, 10%, and 20% initial deamidation over 56 days in the single-dose study are 1385 μg/mL∙day, 1653 μg/mL∙day, and 1921 μg/mL∙day, respectively, consisting of 32.5%, 38.8%, and 45.1% subject exposure to the total mAb, respectively. (B) The subject exposure to the hypothetical CDR deamidated variants with 0%, 10%, and 20% initial deamidation over 5 doses (56 days) in the multiple-dose study are 1086 μg/mL∙day, 1478 μg/mL∙day, and 1871 μg/mL∙day, respectively, consisting of 21.7%, 29.5%, and 37.3% subject exposure to the total mAb, respectively Each dosing time is indicated with an arrow “↑”.

References

    1. Kozlowski S, Swann P. Current and future issues in the manufacturing and development of monoclonal antibodies. Advanced drug delivery reviews. 2006;58(5–6):707–22. 10.1016/j.addr.2006.05.002 - DOI - PubMed
    1. Liu H, Gaza-Bulseco G, Faldu D, Chumsae C, Sun J. Heterogeneity of monoclonal antibodies. Journal of pharmaceutical sciences. 2008;97(7):2426–47. 10.1002/jps.21180 - DOI - PubMed
    1. Goetze AM, Schenauer MR, Flynn GC. Assessing monoclonal antibody product quality attribute criticality through clinical studies. mAbs. 2010;2(5):500–7. 10.4161/mabs.2.5.12897 - DOI - PMC - PubMed
    1. Wang W, Singh S, Zeng DL, King K, Nema S. Antibody structure, instability, and formulation. Journal of pharmaceutical sciences. 2007;96(1):1–26. 10.1002/jps.20727 - DOI - PubMed
    1. Xu X, Qiu H, Li N. LC-MS multi-attribute method for characterization of biologics. Journal of Applied Bioanalysis. 2017;3(2):21–5. 10.17145/jab.17.003 - DOI

Substances