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. 2022 Sep 7;33(9):1659-1677.
doi: 10.1021/jasms.2c00129. Epub 2022 Aug 26.

Attribute Analytics Performance Metrics from the MAM Consortium Interlaboratory Study

Trina Mouchahoir  1   2 John E Schiel  1   2 Rich Rogers  3 Alan Heckert  1 Benjamin J Place  1 Aaron Ammerman  4 Xiaoxiao Li  4 Tom Robinson  4 Brian Schmidt  4 Chris M Chumsae  5 Xinbi Li  5 Anton V Manuilov  5 Bo Yan  5 Gregory O Staples  6 Da Ren  7 Alexander J Veach  7 Dongdong Wang  8 Wael Yared  8 Zoran Sosic  9 Yan Wang  9 Li Zang  9 Anthony M Leone  10 Peiran Liu  10 Richard Ludwig  10 Li Tao  10 Wei Wu  10 Ahmet Cansizoglu  11 Andrew Hanneman  11 Greg W Adams  12 Irina Perdivara  12 Hunter Walker  12 Margo Wilson  12 Arnd Brandenburg  13 Nick DeGraan-Weber  14 Stefano Gotta  13 Joe Shambaugh  14 Melissa Alvarez  15 X Christopher Yu  15 Li Cao  16 Chun Shao  16 Andrew Mahan  17 Hirsh Nanda  17 Kristen Nields  17 Nancy Nightlinger  3 Ben Niu  18 Jihong Wang  18 Wei Xu  18 Gabriella Leo  19 Nunzio Sepe  19 Yan-Hui Liu  20 Bhumit A Patel  20 Douglas Richardson  20 Yi Wang  20 Daniela Tizabi  1   2 Oleg V Borisov  21 Yali Lu  21 Ernest L Maynard  21 Albrecht Gruhler  22 Kim F Haselmann  22 Thomas N Krogh  22 Carsten P Sönksen  22 Simon Letarte  23 Sean Shen  23 Kristin Boggio  24 Keith Johnson  24 Wenqin Ni  24 Himakshi Patel  24 David Ripley  24 Jason C Rouse  24 Ying Zhang  24 Carly Daniels  25 Andrew Dawdy  25 Olga Friese  25 Thomas W Powers  25 Justin B Sperry  25 Josh Woods  25 Eric Carlson  26 K Ilker Sen  26 St John Skilton  26 Michelle Busch  27 Anders Lund  27 Martha Stapels  27 Xu Guo  28 Sibylle Heidelberger  28 Harini Kaluarachchi  28 Sean McCarthy  29 John Kim  30 Jing Zhen  30 Ying Zhou  30 Sarah Rogstad  31 Xiaoshi Wang  31 Jing Fang  32 Weibin Chen  32 Ying Qing Yu  32 John G Hoogerheide  33 Rebecca Scott  33 Hua Yuan  33
Affiliations

Attribute Analytics Performance Metrics from the MAM Consortium Interlaboratory Study

Trina Mouchahoir et al. J Am Soc Mass Spectrom. .

Abstract

The multi-attribute method (MAM) was conceived as a single assay to potentially replace multiple single-attribute assays that have long been used in process development and quality control (QC) for protein therapeutics. MAM is rooted in traditional peptide mapping methods; it leverages mass spectrometry (MS) detection for confident identification and quantitation of many types of protein attributes that may be targeted for monitoring. While MAM has been widely explored across the industry, it has yet to gain a strong foothold within QC laboratories as a replacement method for established orthogonal platforms. Members of the MAM consortium recently undertook an interlaboratory study to evaluate the industry-wide status of MAM. Here we present the results of this study as they pertain to the targeted attribute analytics component of MAM, including investigation into the sources of variability between laboratories and comparison of MAM data to orthogonal methods. These results are made available with an eye toward aiding the community in further optimizing the method to enable its more frequent use in the QC environment.

Keywords: MAM Consortium; NISTmAb; attribute analytics; multi-attribute method; targeted analytics.

PubMed Disclaimer

Conflict of interest statement

The authors declare no competing financial interest.

Figures

Figure 1
Figure 1
Total relative abundance of Calibration Sample peptides. Total relative abundances (RA) were calculated by each participant for each of three injections, and then the average was taken for each peptide. These average relative abundances were used to generate the box plot (see Figure S15). The dashed line at 6.67% represents the theoretical total relative abundance of the 15 peptides which were provided at equimolar concentration. Symbols noting outlier data points are unique for each participant.
Figure 2
Figure 2
Total relative abundance variability of Calibration Sample peptides. The total relative abundance values of each calibration sample peptide were reported by participants for three injections. (a) Repeatability (sr) and reproducibility (sR) standard deviations were calculated for each peptide; (b) coefficient of variation (CV) values (expressed as percentages) were calculated based on repeatability (CVr) and reproducibility (CVR) standard deviations. Note that because sr and sR are not sample standard deviations, the statistical properties and inferences associated with the standard definition of CV do not apply to CVr and CVR. Data points are summarized in Supplemental Table S1. Equations for sr, sR, CVr, and CVR are provided in Supplemental Appendix S1 (Section A).
Figure 3
Figure 3
Interlaboratory reproducibility of NISTmAb Reference Peptide retention times. The observed retention times of 15 NISTmAb Reference Peptides were reported by participants. The interlaboratory standard deviation (s) in retention time was calculated for each peptide. Data points are summarized in Supplemental Table S2; the equation for s is provided in Supplemental Appendix S1 (Section B).
Figure 4
Figure 4
Interlaboratory evaluation of NISTmAb Reference Peptide mass accuracy. The observed mass of each NISTmAb Reference Peptide was reported by participants for one injection. Absolute ppm values were calculated from the observed and theoretical masses of each peptide. The interlaboratory average |ppm| value (x̿) for each peptide is noted by the “X”, with error bars indicating the interlaboratory standard deviation (s). Data points are summarized in Supplemental Table S2; equations for x̿ and s are provided in Supplemental Appendix S1 (Section B).
Figure 5
Figure 5
Total relative abundance variability of NISTmAb Reference Peptides. The observed peak areas of NISTmAb reference peptides were reported by participants and used to calculate the total relative abundance of 15 peptides. (a) Interlaboratory standard deviation (s) in total relative abundance and (b) interlaboratory coefficient of variation (CV) values were calculated for each peptide. Data points are summarized in Supplemental Table S2; equations for s and CV values are provided in Supplemental Appendix S1 (Section B).
Figure 6
Figure 6
Interlaboratory evaluation of NISTmAb Reference Peptide relative abundance. The relative abundance (RA) of each monitored attribute in the NISTmAb Reference digest was reported by each participant for one injection. The interlaboratory average relative abundance value (x̿) for each attribute is noted by an “X”, with error bars indicating the interlaboratory standard deviation (s). Data points are summarized in Supplemental Table S3; equations for x̿ and s are provided in Supplemental Appendix S1 (Section B). Note that error bar ranges for EEQYNSTYR+A2G2F, DTLMISR and GFYPSDIAVEWESNGQPENNYK are smaller than the boundaries of the “X” symbol marking the average.
Figure 7
Figure 7
Quantitation of NISTmAb attributes. For (a–d) the relative abundance (RA) of each modification was calculated as the ratio of the peak intensity of the modified peptide to the sum of peak intensities of modified and unmodified peptides (see Supplemental Figures S9–S12, respectively, for peptides included by each participant). For (e) the RA of glycopeptides was calculated as the ratio of the individual glycopeptide species to the sum of the three most abundant glycan species found on heavy chain N300 (see Supplemental Table S4 and Figure S13 for glycopeptides included in the calculation).
Figure 8
Figure 8
Comparison of orthogonal methods for measuring relative abundance. (a) Glycopeptide relative abundance (RA) values derived from MAM are compared to glycan and glycopeptide RA values reported by Prien et al. (ref (32))* and De Leoz et al. (ref (33)). MAM = interlaboratory average RA of the top three glycopeptides as reported by participants or with outliers recalculated from raw data. 2-AB = intralaboratory average RA of glycans released by peptide N-glycosidase F (PNGase F), labeled with 2-aminobenzamide, and analyzed by HILIC-FLD (as calculated from Prien et al.). 2-AA = RA of a single analysis of glycans released by peptide N-glycosidase F (PNGase F), labeled with 2-aminobenzoic acid, and analyzed by HILIC-FLD-MS (as calculated from Prien et al.). Multimethod = interlaboratory median RA values as measured from various glycan forms (i.e., released glycans, glycopeptides, intact molecule, etc.) using multiple analytical methods (as calculated from De Leoz et al.). (b) Lys-loss relative abundance (RA) values derived from MAM are compared to those calculated from various methods reported by Michels et al. (ref (34)). MAM = interlaboratory average RA of Lys-loss as reported by participants or with outliers recalculated from raw data. CEX-HPLC = cation exchange-high performance liquid chromatography, CZE = capillary zone electrophoresis, cIEF = capillary isoelectric focusing, ICIEF = imaged capillary isoelectric focusing. See Table S3 for summarized values. See Supplemental Appendix S1 (Section B) and Supplemental Appendix S2 for quantitative and statistical equations. *Adapted with permission from ref (32). Copyright 2015 American Chemical Society. Adapted with permission from ref (33). under the Creative Commons Attribution License CC BY (https://creativecommons.org/licenses/by/4.0/). Copyright 2020 NIST. Adapted with permission from ref (34). Copyright 2015 American Chemical Society.

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