PAVING THE WAY FOR ASSESSING IN VIVO DYNAMICS OF MULTIPLE QUALITY ATTRIBUTES FOR PROTEIN THERAPEUTICS

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1 PAVING THE WAY FOR ASSESSING IN VIVO DYNAMICS OF MULTIPLE QUALITY ATTRIBUTES FOR PROTEIN THERAPEUTICS CASSS Mass Spec September 21, 2017 Haihong Zhou Principal Scientist, Biologics, Vaccines & Bioanalytics PPDM Merck & Co., Inc., Kenilworth, NJ, USA 1

2 Outline Workflow for measuring multiple quality attributes in vivo Key drivers Challenges Method development considerations Proof of concept studies Differential Analysis 2

3 In Vivo Dynamics of Quality Attributes: Bioanalytical Multi-Attribute Method In Vivo Dosing Drug (antibody, fusion protein, peptide, nanobody, etc) Background protein Blood Collection Attribute Time (min) Affinity Purification Elution/Digestion High-resolution LC/MS Characterization Relative Abundance Impact of PQAs on product safety and efficacy mab Concentration mab PK Profile X Proportion with Attribute = Proportion of mab with Attribute X = Time In Vivo Attribute Exposure Profile Attribute Concentration Differential Analysis Peptide Identification Attribute Identification and Quantification Adapted from Goetze AM, et al. MAbs. 2010;Sep-Oct;2(5): ; Li Y, et al. MAbs. 2016;Aug-Sep;8(6):

4 Key Drivers Help evaluate criticality of the quality attributes and facilitate manufacturing process development Assess the effect of product quality attributes on safety and efficacy Establish better understanding of PK/PD relationship 4

5 Challenges in Quantifying Multiple Quality Attributes In Vivo Bioanalytical MAM Bioanalytical MAM Determine drug concentration (PK) In Vivo Limited volume Low concentration Complex matrix May need purification LC/MS QQQ Simple processing steps (Monitor one or two peptides) - Absolute quantitation - High throughput - Known Detect changes in multiattributes in vitro (CMC) In Vitro Volume not limited High concentration Buffer No need for purification LC/MS High Resolution Complex processing steps (Monitor all peptides) - Relative quantitation - Low throughput - Known & unknown Detect changes in multiattributes in vivo (metabolism) In Vivo Limited volume Low concentration Complex matrix Require purification LC/MS High Resolution Complex processing steps (Monitor all peptides) - Relative quantitation - Low throughput - Known & unknown 5

6 Proof of Concept Using an IgG1 mab Starting point: CMC MAM Drug in Buffer Direct analysis Drug in Buffer Immuno-enrichment Control for sample processing induced modifications Drug Spiked in Serum Immuno-enrichment Drug in Serum ex vivo Immuno-enrichment Drug in Serum in vivo Immuno-enrichment 6

7 Method Development Considerations Sensitivity requirement Concentrations in PK profile Available sample volume Affinity purification Choice of the capture antibody Affinity purification media (binding capacity, nonspecific binding, ease of automation, etc.) Optimization on yield and purity Digestion conditions Choice of protease LC/MS instrumentation Low flow vs high flow chromatography Low resolution vs high resolution mass spectrometer Overall method reproducibility 7

8 Method Performance Reproducibility Sequence coverage Control for sample processing induced modifications Relative Intensity Heavy Chain 3 Leader Sequence 2 Light Chain 4 Unidentified RT (min)

9 Controls HC D222 IsoD HC M429 Oxidation 40 8 % Attribute % Attribute Ref. Material #1 Ref. Material #2 12M25C #1 12M25C #2 0 0 % Attribute Direct Digestion (100 µg) Direct Digestion (15 µg) IP from Serum (15 µg) HC N385 & N390 Deamidation Direct Digestion (100 µg) Direct Digestion (15 µg) IP from Serum (15 µg) % Attribute Direct Digestion (100 µg) Direct Digestion (100 µg) Direct Digestion (15 µg) LC pe Direct Digestion (15 µg) IP from Serum (15 µg) IP from Serum (15 µg) Consistent recovery of all the quality attributes with and without affinity purification and with different digestion methods indicates that 1. There was no bias in the affinity purification step toward any specific quality attribute 2. Overall sample processing procedure did not alter quality attributes 9

10 Bioanalytical MAM Study Design In Vivo IV infusion at 200 mg/kg Predose D0 (0.5hr) D2 D4 D7 Ex Vivo 0.5 mg/ml spiked in cynomolgus monkey serum; incubate at 37 C D0 D2 D4 D7 10

11 N-terminal Modification: HC Glutamine to Pyro-glutamate A complete conversion of N-terminal glutamine residue into N-terminal pyroglutamate was observed in vivo In Vivo Ex Vivo Relative Percentage (%) Cyno-1 Cyno-2 Cyno-3 Relative Percentage (%) Replicate 1 Replicate 2 Replicate Relative Percentage (%) = PPPP AAAA (PPPPPPP mmmm) PPPP AAAA PPPPPPP mmmm +PPPP AAAA PPPPPPP mmmm +PPPP AAAA (PPPPPPP uuuuu) 11

12 Deamidation: HC N385/N390 A conserved site in the Fc domain commonly found in humanized monoclonal antibodies exhibited a rapid increase in deamidation In Vivo Ex Vivo Relative Percentage (%) Cyno-1 Cyno-2 Cyno-3 Relative Percentage (%) Replicate 1 Replicate 2 Replicate Deamidation Rate: In vivo: 0.9%/day (1.1% 1 ) Ex vivo: 1.5%/day (1.7% 1 ) 1 Yin S, et al. Pharm Res. 2013;30:

13 C-terminal Modification: HC Lysine Processing C-terminal lysine processing was likely due to carboxypeptidase activity In Vivo Ex Vivo Relative Percentage (%) Cyno-1 Cyno-2 Cyno-3 Relative Percentage (%) Replicate 1 Replicate 2 Replicate

14 High-mannose Glycans High-mannose glycans on the Fc region of therapeutic IgG antibodies increase serum clearance Relative Percentage (%) Man5 Relative Percentage (%) Man6 Man7 Man8 Relative Percentage (%) A2G0F A2G1F

15 Example of Attribute Exposure Profile HC N385 & N390 Deamidation 8000 PK Profile 8 % Attribute 160 Attribute Exposure Profile Serum Concentration (µg/ml) Relative Percentage (%) Serum Concentration (µg/ml) Cyno-1 Cyno-2 Cyno

16 Looking for Unknown Changes in an Unbiased Manner Ex Vivo Day 0 Ex Vivo Day 7 Monkey Serum Affinity Purification LC/MS/MS Differential Analysis Identification SIEVE (ThermoFisher) BioPharma Finder (ThermoFisher) Progenesis QI (Waters-Nonlinear Dynamics) Quantification 16

17 SIEVE Ex Vivo Day 0 vs Ex Vivo Day 7 SIEVE was able to pick up most of the known changes in the sample 12.0 Ex Vivo Relative Percentage Replicate 1 Replicate 2 Replicate Time (Day) Deamidation HC N385/N390 17

18 SIEVE (continued) Relative Abundance HC76-86 Deamidation NL: 5.74E4 Base Peak m/z= F: FTMS + p ESI Full ms [ ] MS _cyno_D0_1 NL: 2.10E5 Base Peak m/z= F: FTMS + p ESI Full ms [ ] MS _cyno_d7_1 Low abundant deamidation was missed by SIEVE with threshold of 1e6 Lower threshold (eg, 1e4) allowed SIEVE to detect HC76-86 deamidation. However, more false positives were observed when using lower threshold Time (min) 18

19 BioPharma Finder Low abundant attribute changes were detected, and the percent attribute was calculated automatically 19

20 Progenesis QI Low abundant HC76-86 deamidation was also detected using Progenesis D0 D7 Deamidated Intensity Unmodified Intensity 20

21 Differential Analysis for Bioanalytical MAM All three algorithms can pick up known attribute changes BioPharma Finder has better identification capability and calculates attribute percentage automatically when peptide identity is known Challenges for all three algorithms include: Setting up the appropriate threshold to minimize the number of false positives Statistical analysis tools to help reduce false positives Filtering strategies and visualization tools to help distinguish false positives from true positives The ability to normalize overall intensity, align retention time, and perform peak picking when molecule concentration differs several folds across multiple time points 21

22 Summary We demonstrate that it is feasible to track changes in multiple quality attributes for a IgG1 monoclonal antibody in cynomologus monkey serum We present a general strategy on how to identify and quantify changes in multiple quality attributes of protein therapeutics in vivo Further developmental efforts are needed to improve Overall sample processing sensitivity and reproducibility Algorithms for differential analysis can be modified to streamline the data analysis workflow (eg, comparison with multiple time points and better visualization tools) 22

23 Acknowledgments Merck & Co., Inc. Yi Wang, Douglas Richardson, Bhumit Patel, Daniela Tomazela, Vibha Jawa, Richard Wong, Dong Hun Lee, Sejal Patel, Maribel Beaumont, Yan-hui Liu, Ayesha Sitlani, David Pollard, Shuangping Shi, Hetal Sarvaiya, Amy Beebe, Yaoli Song, Mohammad Tabrizifard, David McLaren, Stephen Previs, Maria Webb, Xiang Yu Just Biotherapeutics Richard Rogers ThermoFisher Michael Blank, Jennifer Sutton Rockefeller University Yinyin Li 23