Multi-Attribute Method (MAM) Evaluation and

Regulatory Considerations for Implementation

CASSS MS 2018Sarah Rogstad

FDA/CDER/OPQ/OTRSeptember 12, 2018

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This presentation reflects the views of the author and should not be construed to represent FDA’s

views or policies

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Seminar Overview

• Mass Spectrometry (MS) in BLAs• FDA’s Emerging Technology Program• Multi-attribute method (MAM)• MAM research at FDA• Future of MAM research

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MS Usage in Protein Therapeutic BLAs

Rogstad, S. et al., JASMS. 2016.

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MS as a Release AssayMS is commonly used for lot release of small molecules and peptide therapeutics.

• Molecular mass measurements only for peptide NDAs

n=7n=4

n=6

n=3

0102030405060708090

100

2003-2006 2007-2010 2011-2014 2015-2017%

of P

eptid

e N

DAs

MS Usage for Control

MS Usage Protein BLAs

Peptide NDAs

Characterization 100% 100%

Control 0 65%

Rogstad, S. et al., JASMS. 2016. and unpublished data

Emerging Technology Program

• Features Emerging Technology Team (ETT)• To encourage novel techniques and applications• To promote the adoption of innovative approaches to pharmaceutical product design

and manufacturing• Work directly with sponsor prior to submission to help develop new

technologies• Recent improvements in instrumentation have led to a push toward MS for

protein therapeutic control • ETT is reviewing use of MAM for control purposes• In-house assessment of MAM methodology focusing on reproducibility,

robustness, and applicability (vs traditional methods)

6https://www.fda.gov/AboutFDA/CentersOffices/OfficeofMedicalProductsandTobacco/CDER/ucm523228.htm

Multi-Attribute Method (MAM)

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MAM vs Traditional Methods

• MAM measures specific targets, while traditional methods measure broader targets

• How do you compare?

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Traditional Method Traditional Target MAM Target

HILIC Glycan Profiling Released Glycans Glycopeptides

CEX All Acidic Species Specific Acidic Modifications

rCE-SDS All Clipped Species Specific Clipped Species

General Benefits of MAM

• More detailed information at the molecular level• Analysis of site-specific modifications can allow for tighter control

• Can differentiate between species that may overlap using chromatographic approaches

• Testing multiple attributes at once Fewer instruments and assays

• New peak detection allows for control of unexpected new modifications

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Factors for MAM Implementation

• Method validation• Reproducibility• Robustness• Cost• Expertise• Speed• Instrumentation• System suitability• Comparisons with traditional

methods

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• ICH Q2 (R1) – Validation of Analytical Procedures

• ICH Q6B – Specifications: Test Procedures and Acceptance Criteria for Biotechnological/Biological Products

• FDA Guidance on Analytical Procedures and Methods Validation for Drugs and Biologics

Relevant Guidance Documents:

Additional Considerations

• May lose information at the protein level• Can’t generally tell distribution of modifications based on bottom up approaches• Would a difference in distribution of a modification affect safety or efficacy?

• Likely case by case based on risk assessment

• Fit for purpose• Demonstrate that new QC method is monitoring all relevant CQAs• Which PQAs are CQAs and need to be monitored is product specific

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vs vsOR

MAM at FDA

• Working to establish in-house MAM capabilities in order to explore and better evaluate usage of the approach.

• Four major points to consider for MAM implementation:1. Risk assessment2. Method validation3. Performance comparisons to traditional methods4. Capabilities and specificities of new peak detection

• Initial testing compared US approved and unapproved rituximab through MAM and orthogonal methods

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FDA Research Overview

• Method Development• US Approved vs Unapproved Comparison• System Suitability Assessment• Lot-to-lot Comparisons• Method Validation• Forced Degradation

• Comparative Analysis• New Peak Detection

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Method validation Reproducibility Robustness Cost Expertise Speed Instrumentation System suitability Comparisons with traditional

methods

Factors for Implementation:

Method Development and Validation

• Monitored the relative abundance levels of 21 product quality attributes (PQAs) across 11 sites

• Previously, showed that method was capable of distinguishing between products for 10 of those PQAs

• Conducted lot-to-lot comparison using MAM• Partially validated method

• Still need to complete LOD/LOQ studies

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System Suitability Assessment

SFANQPLEVVYSK GILFVGSGVSGGEEGAR

Peptide Sequence Mass (Uncharged)

Precursor m/z (+2 charge)

Transitions Used

1 2 3

SSAAPPPPPR 985.5220 493.7683 493.7683 494.2697 494.7710

GISNEGQNASIK 12224.6189 613.3168 613.3168 613.8182 614.3195

HVLTSIGEK 990.6189 496.2867 496.2867 496.7882 497.2895

DIPVPKPK 900.5524 451.2834 451.2835 451.7849 452.2863

IGDYAGIK 843.4582 422.7363 422.7364 423.2378 423.7391

TASEFDSAIAQDK 1389.6503 695.8324 695.8325 696.3339 696.8352

SAAGAFGPELSR 1171.5861 586.8003 586.8004 587.3018 587.8030

ELGQSGVDTYLQTK 1545.7766 773.8955 773.8956 774.3970 774.8984

GLILVGGYGTR 1114.6374 558.3259 558.3260 558.8274 559.3287

GILFVGSGVSGGEEGAR 1600.8084 801.4115 801.4115 801.9129 802.4143

SFANQPLEVVYSK 1488.7704 745.3924 745.3925 745.8939 746.3953

LTILEELR 995.5890 498.8018 498.8018 499.3033 499.8046

NGFILDGFPR 1144.5905 573.3025 573.3025 573.8040 574.3053

ELASGLSFPVGFK 1358.7326 680.3735 680.3735 680.8750 681.3764

LSSEAPALFQFDLK 1572.8279 787.4212 787.4212 787.9227 788.4241

• Injected Pierce RT Peptide Standard at the start and end of every set of MAM runs.

• For preliminary assessment n=17

• Goal is to establish expected values over time.

System Suitability Assessment

Peptide SequencePeak area Peak area

%CV % Relative abundance

Relative abundance

%CV

Relative abundance

accuracy

Retention time (min)

Retention time %CV

Mass accuracy

(ppm)

Signal-to-noise ratio

Mass resolution

N/A <15% N/A <15% ±20% of mean N/A <15% <3 ppm >3 140,000 at

m/z 200

SSAAPPPPPR 6417647 13.3 3.19% 5.29 Pass 10.14 1.52 -1.83 454 94351

GISNEGQNASIK 8454118 12.1 4.21% 2.92 Pass 10.74 1.86 -2.18 422 80700

HVLTSIGEK 9832353 13.5 4.89% 3.35 Pass 12.14 2.04 -1.39 412 95440

DIPVPKPK 5653529 12.5 2.82% 4.52 Pass 14.28 2.06 -2.54 326 98105

IGDYAGIK 11570588 12.1 5.76% 5.96 Pass 14.33 2.08 -2.53 400 105684

TASEFDSAIAQDK 9018235 12.5 4.48% 1.58 Pass 17.21 1.94 -2.84 426 79072

SAAGAFGPELSR 14482353 12.2 7.20% 2.06 Pass 19.54 1.65 -1.63 529 88909

ELGQSGVDTYLQTK 16770588 12.7 8.34% 4.64 Pass 22.58 1.39 -3.64 390 75872

GLILVGGYGTR 15000000 11.9 7.46% 1.65 Pass 29.40 1.10 -0.49 411 90053

GILFVGSGVSGGEEGAR 20311765 15.5 10.1% 6.37 Pass 30.60 1.06 -3.81 547 75605

SFANQPLEVVYSK 17582353 11.9 8.75% 2.05 Pass 30.63 1.06 -3.18 261 76060

LTILEELR 14305882 12.9 7.12% 4.31 Pass 36.22 0.87 -0.14 371 92371

NGFILDGFPR 18135294 11.1 9.03% 2.37 Pass 39.89 0.81 -0.82 456 86864

ELASGLSFPVGFK 19676471 11.0 9.80% 3.27 Pass 42.77 0.74 -2.27 387 79113

LSSEAPALFQFDLK 13868235 18.4 6.87% 9.85 Pass 46.79 0.69 -3.57 1400 74546

Cutoffs based on Zhou et al. mAbs, 2015.

MAM Results: Lot-to-Lot Comparison

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100

Gln1 (HC) pyro-Glu Gln1 (LC) pyro-Glu Lys451 clipping

% R

elat

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abun

danc

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Pyroglutamination and lysine clipping

0

1

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5

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% R

elat

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abun

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Asparagine deamidation and methionine oxidation

*

*

*

MAM Results: Glycan Profiling

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G0F-N G0 G0F M5 G1F G2F G2FSa

% R

elat

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Abun

danc

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HILIC - 3128252 HILIC - 3166652 HILIC - 3185087 HILIC - 3191021 HILIC - 3196990

MAM - 3128252 MAM - 3166652 MAM - 3185087 MAM - 3191021 MAM - 3196990

R² = 0.9754

05

101520253035404550

0 10 20 30 40 50 60

% R

elat

vie

Abun

danc

e -M

AM

% Relative Abundance - HILIC

MAM Results: Overall Lot-to-Lot Variation

0

5

10

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0 20 40 60 80 100

% C

V

% Relative abundance

Lot-to-Lot Variation

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Relative AbundanceIntra-day

(n = 3)Inter-day

(n = 9)Analyst-to-analyst

(n = 9)Analyst-to-analyst, inter-day

(n = 27)Average (%) % CV Average (%) % CV Average (%) % CV Average (%) % CV

Gln1(HC) pyro-Glu 99.8 0.0 99.8 0.0 99.8 0.0 99.8 0.0

Gln1(LC) pyro-Glu 97.6 0.1 97.7 0.1 97.6 0.0 97.7 0.1

Lys451 clipping 98.0 0.1 98.1 0.1 98.1 0.1 98.1 0.1

Met20(HC) oxidation 1.0 23.4 1.4 33.3 1.2 26.7 1.2 40.4

Met34 (HC) oxidation 2.2 17.2 2.6 27.3 2.4 23.4 2.3 33.6

Met81 (HC) oxidation 2.4 10.1 2.8 20.7 2.7 14.3 2.5 28.3

Met256 (HC) oxidation 3.3 8.2 3.5 14.1 3.3 10.9 3.3 16.7

Met432 (HC) oxidation 0.9 14.5 1.0 22.4 1.0 20.5 0.9 27.8

Met21 (LC) oxidation 0.9 16.7 1.1 29.0 1.0 27.0 1.0 37.7Asn388 (HC) deamidation 0.5 5.2 0.6 13.8 0.6 10.1 0.6 14.5

Asn301 none 0.7 3.4 0.7 7.6 0.8 7.5 0.7 9.3

Asn301 G0F-N 1.0 3.2 1.0 4.0 1.0 3.4 1.0 8.5

Asn301 G1F-N 0.7 9.1 0.7 6.2 0.7 8.2 0.7 9.9

Asn301 G0 1.3 5.3 1.2 5.1 1.2 6.3 1.2 5.6

Asn301 G0F 38.9 0.4 38.7 0.7 38.7 0.6 38.6 0.6

Asn301 G1F 43.3 0.5 43.5 0.7 43.6 0.7 43.6 0.9

Asn301 G1FSa 0.4 1.7 0.4 5.5 0.4 4.7 0.4 10.7

Asn301 G2F 10.0 1.7 9.9 1.3 10.0 1.6 9.9 1.4

Asn301 G2FSa 1.1 2.1 1.1 2.7 1.1 2.7 1.1 5.2

Asn301 G2FSa2 1.2 3.7 1.3 8.1 1.2 5.9 1.3 9.9

Asn301 M5 1.4 1.4 1.4 2.8 1.4 4.0 1.4 5.0

MAM Results: Reproducibility and Precision

MAM Results: Reproducibility

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0 50 100

% C

V

Relative abundance (%)

ALL (N = 27)

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0 5 0 1 00

% C

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Relative abundance (%)

INTRA-DAY (N = 3)

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% C

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Relative abundance (%)

INTER-DAY (N = 9)

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% C

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Relative abundance (%)

ANALYST-TO-ANALYST (N = 9)

Forced Degradation Study

Tested capabilities of MAM to detect degradants through forced degradation study• US approved rituximab, Exp. Feb 2019• Forced degradation conditions – 40 °C/75% RH• Frozen (reference), 0, 1, 2, 7, 14, 28 days

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Forced Degradation: PQA Trends

M256 Oxidation

y = 0.135x + 2.4096R² = 0.9406

0

2

4

6

8

10

0 5 10 15 20 25 30

% R

elat

ive

Abun

danc

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Days

N388 Deamidation

y = 0.1699x + 1.9166R² = 0.9866

0

2

4

6

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0 5 10 15 20 25 30

% R

elat

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abun

danc

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Days

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MAM detected linear increases in oxidation and deamidation over time.

Forced Degradation: Comparative Analysis

R² = 0.9881

R² = 0.9815

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% R

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MAM-deamidation (N388) Acidic

Linear (MAM-deamidation (N388)) Linear (Acidic)

y = 4.0527x + 10.939R² = 0.9422

15

20

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30

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40

1 2 3 4 5 6 7 8

% A

cidi

c (Ch

arge

Var

iant

)% Deamidated (N388 - MAM)

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Comparative analysis of deamidation by MAM and acidic peaks by charge variant analysis indicated a correlation between the two measurements.

Forced Degradation: New Peak Detection

• Filters: • 0.5 min RT window• 10 ppm m/z window• 0.1% TIC peak intensity

threshold• One new peak was

detected• Aspartic Acid

Isoaspartic Acid• > 12.5-fold increase

over 28 days

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FNWYVDGVEVHNAKm/z 559.9378

FNWYVD(iso-D)GVEVHNAKm/z 559.9378increase 12.5x

ReferenceDay28

One new peak was detected over the forced degradation study.

Isoaspartic Acid Formation

y = 0.0196x + 0.0795R² = 0.9997

0.00

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Multivariate AnalysisCan we take the large amount of data present in MAM results and devise a

multivariate approach for release decisions?

PCA of Initial Dataset PCA of Lot-to-Lot Data

MAM Research Summary

• Developed and implemented MAM platform• MAM was able to distinguish between products• Used standards for system suitability assessment• Good lot-to-lot reproducibility• Good inter-user reproducibility (most PQAs had CV<15%), but higher

variability across users for low abundance (<5%) PQAs, particularly oxidation

• Forced degradation study found trends in oxidation and deamidation and one new peak was identified

• A similar trend was observed with MAM (deamidation) and charge variant analysis (acidic peaks)

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Method validation Reproducibility Robustness Cost Expertise Speed Instrumentation System suitability Comparisons with traditional

methods

Next Steps for FDA MAM Research

• Continue system suitability testing• Continue method validation• Additional comparisons between

MAM and traditional methods• Accelerated stability studies• Long term stability study (in

progress)• Test additional products• More detailed statistical analyses

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Factors for Implementation:

Acknowledgements

• OTR (Past and Present)• Xiaoshi Wang• Xiangkun (Shawn) Yang• Mercy Oyugi• Brandon Kim• Hongping Ye• Hongbin Zhu• Jinhui Zhang• Sau (Larry) Lee• David Keire• Jason Rodriguez

• OBP• Haoheng Yan• Phillip Angart• David Powers• Kurt Brorson• Cyrus Agarabi

• Emerging Technology Team

• MAM Consortium

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