Comparability Analysis of Protein Therapeutics by Bottom-Up LC-MS with Stable Isotope-Tagged Reference Standards

Transcription

1 Comparability Analysis of Protein Therapeutics by Bottom-Up LC-MS with Stable Isotope-Tagged Reference Standards 16 September 2011 Abbott Bioresearch Center, Worcester, MA USA Manuilov, A. V., C. H. Radziejewski and D. H. Lee (2011). "Comparability analysis of protein therapeutics by bottom-up LC-MS with stable isotope-tagged reference standards." MAbs 3(4):

2 Comparability studies in drug development Consistency of manufacturing process Effect of manufacturing process changes Biosimilarity of follow-on biologics Selection of PhII clones Stability studies 2

3 Analytical methods for comparability studies Low resolution chromatographic and electrophoretic methods facilitate quantitation but do not always yield detailed insight into the effect of the manufacturing change or environmental stress Mass spectrometry-based methods can provide high resolution information on the molecule but conventional methods are not very quantitative This gap can be reconciled by use of a Stable Isotope-Tagged Reference Standard (SITRS) 3

4 What is SITRS? SITRS Stable Isotope-Tagged Reference Standard internal reference standard that allows quantitation of differences by MS has the same amino acid sequence as the analyte protein every Arginine and Lysine is isotopically labeled (Arg-6 and Lys-6) must be prepared in a chemically-defined media 4

5 How is SITRS used? Sample mab (unlabeled) (1) mix 1:1 + SITRS mab (Arg-6, Lys-6 labeled) (2) denature, reduce, alkylate, desalt and digest with trypsin (3) RP-HPLC UV RT (min) MS of a given peak (no mutation / modification) m/z (4) MS detection 6 Da { 100 Reduced intensity is directly dependent on quantity of mutant or modification in the sample (5) Analyze the intensity ratios of peptide pairs 0 m/z { 6 Da { 5

6 SITRS: Schematic Overview 6

7 SITRS: Qualitative and Quantitative Analysis Comparison of MS spectra of various peptides from mutant-spiked and control (wt) samples sample sample 7

8 Comparison of mab-1 spiked with 20% or 2.5% of a double mutant 20% spike variability in G0F mutation mutation Each bar represents a % ratio of peptide present in spiked mutant sample (B) vs. wt sample (A), i.e. B/A (%) If a bar is 100% - no modification present 2.5% spike If a bar is not 100%, then there is something different about that peptide There is more G0F in spiked mutant than in wt (this method can also show PTMs!) 8

9 SITRS: Method Linearity and Sensitivity The response in mass detector is linear the more mutant is spiked, the larger the difference is observed SITRS method identified differences down to 2% Instrument noise was a limiting factor in many cases (hence larger error bars for less ionizable or abundant peptides) 9

10 SITRS: Method Linearity Use of a SITRS gives a linear response on the mass spectrometer for every peptide used in the analysis Various ratios of SITRS to unlabeled sample were mixed and analyzed The more labeled sample was spiked, the lower the UNLABELED / LABELED ratios were obtained Average R 2 for 52 peptides comprising the entire sequence of mab-1 was ±0.003 Low abundance peptides however, have lower R 2 and greater standard deviations 10

11 SITRS analysis of the same mab-1 batch analyzed on different days Six separate digestions and analyses were performed each time on day 1 and day 2 The average standard deviation for the predominant peptides covering the entire mab-1 sequence was ~0.7%, and was always below 1.1% Comparison of the same batch of mab-1: day 1 vs. day 2 (n=6) - the analysis was shown to be robust and reproducible 11

12 SITRS analysis of different mab-1 batches Comparison of different batches of mab-1 produced in CHO (same cell line and identical production process) HEK-produced mab-1 vs. CHO-produced mab-1 (different cell lines and production processes) CHO-expressed (1 and 2); HEK (3). 12

13 SITRS vs. conventional analysis of modifications The results of the SITRS analysis of HEK- vs. CHO-produced mab-1 were compared to the results derived from conventional methods Measured Characteristic SITRS Conventional Method (G0F) HEK / (G0F) CHO (%) ± ± AB labeling (G1F) HEK / (G1F) CHO (%) ± ± AB labeling (M5) HEK / (M5) CHO (%) ± ± AB labeling (Q1) HEK / (Q1) CHO (%) ± ± 2.80 Label-free MS (pe1) HEK / (pe1) CHO (%) ± ± 0.60 Label-free MS (K446) HEK / (K446) CHO (%) ± ± 1.22 Label-free MS The results of the SITRS analysis are in agreement with the conventional methods 13

14 SITRS analysis of the mab-1: stressed vs. non-stressed sample One of the biggest advantages of this method is the identification of modifications without a prior knowledge of the modification type or location mab-1 stressed for 12 vs. 6 months at 4C in different buffers Several known modifications were identified as well as some previously uncharacterized modifications were also detected 14

15 SITRS a practical method for drug development SITRS method was developed for use in a fast-paced environment, common to the most protein analytics groups supporting the laterstage drug development Once the SITRS method is implemented - requires about the same time as peptide mapping with UV detection software processing allows for the data analysis to become fully automated (once the parameters are in place) uses as little as 50 ug of protein (for a mab sample) per analysis the labeled protein standard needs to be generated only once for each drug project, and 500 mg of it could last for >5000 runs the unlabeled reference standards that are already generated for other methods can be used (i.e. the new labeled standards don t have to be characterized again to the same extent) 15

16 Conclusions A sensitive, quantitative and fully-automated SITRS method was developed for identification of protein modifications that does not require a prior knowledge of the modification type or location Method linearity, sensitivity and robustness were demonstrated for a monoclonal antibody The results obtained by SITRS analysis are in full agreement with other conventional methods The SITRS method is useful for - comparability studies of biologics - selection of mutant-free cell lines - optimization of growth media and other production-related processes - identification of potential stress hot-spots in protein samples - optimization of drug formulations 16

17 Acknowledgements Protein Analytics David Lee Czeslaw Radziejewski Cell Culture Keith Cochran Statistical Analysis David Resendes Management Gary Welch George Avgerinos Purification Yumiko Kobayashi Keith Hickman Thank You! 17