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Analytical Ultracentrifugation for Protein Aggregate Analysis John P Gabrielson 2010 Workshop on Protein Aggregation and Immunogenicity July 22, 2010 Aggregation is a Common Protein Degradation Pathway • Aggregation (soluble and insoluble) is one of the most prevalent, complex and problematic degradation pathways • Challenge during protein drug development, storage and delivery • Potential for immunogenic responses Aggregation Heat Freeze – Thaw Agitation Shear Formulation Conditions Protein aggregates are critical product quality attributes due to their immunogenicity potential and typically low bioactivity The Particle Landscape 1016 ≥ 100% protein by mass SEC Able to quantify mass concentration 1014 # / mL trillion 1012 AUC 1010 Large enough to measure #/mL 108 million 106 ≤ pg/mL 104 Light obscuration 102 0 001 0.001 0 01 0.01 oligomer monomer 01 0.1 1 Diameter (µm) 10 100 sub-visible visible With Distinct Advantages Compared to SEC, AUC is a Regulatory Expectation for Aggregate Characterization • Sample analysis in product formulation • Expanded detection size range • Essentiallyy free from sample-matrix p interactions resulting g in reduced disturbance of self-associated proteins1 • Enhanced resolution of size variants AUC is a regulatory expectation for product characterization and comparability 1Philo, J.S., (2006). The AAPS Journal. 8(3). p. E564-E571. C Capability bili off AUC Method Capability Should Define Appropriate Applications of AUC in Biopharmaceutical Development • Method development and optimization • Identify and control key sources of method variability • Examples: channel alignment, centerpiece differences, and solution conditions1,2 • Method characterization and qualification • Characterize method operating space and demonstrate method fitness for purpose • ICH Q2(R1) recommends establishing performance of product purity methods based on demonstration of specificity, linearity, range accuracy, range, accuracy precision, precision and detection limit Development 1 Gabrielson, 2 Optimization Characterization J.P., et al., (2009). Journal of Pharmaceutical Sciences. 98(1). p. 50-62. Schuck, P., (2004). Biophysical Chemistry. 108(1-3). p. 187-200. Qualification / validation Channel Misalignment Leads to Artifact Aggregates that Contribute to AUC Variability Channel aligned: All forces act in radial direction Channel misaligned: Convective transport induces artifact peaks Aggregate gg g (larger ( g than dimer)) sedimentation coefficient (S) • At 0° offset aggregate quantitation matches expectation based on known blend • Slopes indicate sensitivity to misalignment • g is a significant g source of Misalignment aggregate measurement variability Arthur, K.K., et al., (2009). Journal of Pharmaceutical Sciences. 98(10). p. 3522-39. Small Differences Between Centerpieces Can Significantly Affect Measured Aggregate Levels • Slight differences between centerpieces can arise from aging, surface defects, or production imprecision Tighter precision with improved centerpiece manufacturing Exc cess dimer (% dim mer) Lower quality centerpieces 0.5 -0 0.5 5 0.0 0.5 1.0 1.5 Source of Variance Std Deviation (% aggregate) Rotor < 0.1 Cell housing < 0.1 Centerpiece From ~ 0.2 to < 0.1 R id l Residual ~ 0.2 02 Std deviation of dimer levels (% dimer) Pekar, A., and M. Sukumar, (2007). Analytical Biochemistry. 367(2). p. 225-237. Gabrielson, J.P., et al., (2009). Analytical Biochemistry. 396(2). p. 231-241. AUC Method Performance is Improved by Applying Best Practices to Control Variability Method Precision (% aggregate) Detection Limit (% aggregate) SEC 0.1 0.3 AUC (prior to improvements1) 0.5 1.7 AUC (with improvements2) 0.3 1.0 Prior to improvements With improvements 50x magnified c(s) c(s) 50x magnified 2 6 10 14 Sedimentation Coefficient (Sved) 2 6 10 Sedimentation Coefficient (Sved) 1 Obtained from multi-laboratory, multi-product intermediate precision study (Gabrielson, J.P., et al., (2009). Analytical Biochemistry. 396(2). p. 231-241.) 2 Improved 14 method includes using more precisely manufactured centerpieces, more consistent alignment control, and more accurate data fitting approaches (Gabrielson, J.P., et al., (2009). Journal of Pharmaceutical Sciences. 98(1). p. 50-62.) Method Precision is Largely ProductIndependent We analyzed AUC measurement variance of 7 protein products over a 3 year period • • Multiple instruments, analysts, centerpieces / cell assemblies, products, and product lots Partition total variance into contributions from product, lot, run, and residual variance 5 Aggregate ((%) 4 Product 1 (n = 149) Product 2 (n = 75) Histograms 3 2 1 0 Date of Analysis Jan 2006 Jan 2009 Std deviation (% a aggregate) • This protein exhibits reversible selfassociation on an AUC relevant time-scale 1.0 0.8 52 0.6 0.4 149 77 Std Deviation (% aggregate) Inter-run 0.27 Intra-run 0.35 Total 0.44 0 44 a 9 75 74 0.2 Source of Variance 11 0.0 Variability of 0.57% aggregate prior to method improvements a Protein Product Universal AUC qualification is a possibility for proteins with sufficiently stable aggregate species The Detection Limit of AUC Remains Unclear There are two common approaches to determine the detection limit Fi d – based Fixed b d on std td deviation d i ti D Dynamic i – based b d on signal-to-noise i lt i 20 DL = 15 Absorbance e (AU) Meas sured Aggregatte (%) 25 3.3σ S 10 signal RMSE 6.0 6.5 Radius (cm) 5 0 0 7.0 5 10 15 20 100 25 Expected Aggregate (%) 0.7 to 1.3% aggregate DL (%) DL fixed during method qualification DL varies for each sample 40 krpm 60 krpm 10 1 0.5 to 3% aggregate 0.1 0.01 1 10 s20,w / s20,w monomer 100 A li i Applications off AUC Applications of AUC in the Biopharmaceutical Industry AUC is NOT useful for product lot release or stability testing due to: • Precision = ~ 0.3% aggregate • DL = ~ 1.0% aggregate • Low throughput • Extensive analyst training BUT the advantages of AUC make it an attractive method for: • Product characterization (case study #1) • SEC method development (case study #2) • Binding interactions Case Study #1 AUC Can Detect Weakly Associated Aggregates Undetectable by SEC Protein Sample Dimer measured by AUC (%) Dimer measured by SEC (%) Drug substance lot 1 3.9 1.0 Drug substance lot 2 4.1 1.2 Drug substance lot 3 4.3 1.4 Drug product < 0.1 < 0.1 monomer AUC allows direct analysis in various solutions 3.9% dimer 100x magnified c(s) Drug substance Drug substance diluted into SEC mobile phase (agrees with SEC result) Drug substance diluted into drug product formulation Drug product 2 4 6 8 Sedimentation Coefficient (Sved) Case Study #1 AUC Allows Characterization of Monomer-Dimer Monomer Dimer Equilibrium Reaction: Α+Α k on ⎯⎯→ k ←⎯⎯ dA = −kon A2 + koff A2 dt A2 off Kd = koff kon A2 = A2 SEC: Dimer (%)) 1.5 • Dimer dissociates to monomer within 12 hrs at 37°C or upon addition of PS80 • Kinetics: koff ~ 10-4 M-1s-1 • Equilibrium: Kd ~ 1 M 4°C 4 C, - PS80 1.0 4°C, + PS80 37°C, - PS80 0.5 0.0 0 2 4 6 8 Time (hr) 12 10 15 10 Dimer (%) D AUC: Dimer (%) D 20 Kd ~ 1 mM 5 8 6 4 2 0 0 2 4 6 8 Protein Concentration (g/L) 10 0 3 4 5 pH 6 7 8 10 12 Case Study #2 SEC Method Validated for Quantitation of Aggregates Generated by Multiple Pathways • AUC and SEC results are well correlated • AUC confirms SEC adequately quantifies aggregates generated from multiple pathways AUC (% aggregate) AUC is a valuable tool in SEC method development and qualification Conclusions • AUC offers several advantages compared to other size separation techniques • Sample analyzed in product formulation • Reduced disturbance of self-associated proteins • Enhanced resolution • Recent advances have helped to define and improve AUC method capability • Identified key contributors to method variability • Universal precision is approximately 0.3% aggregate • “Best practices” DL is approximately 1.0% aggregate • AUC is an important orthogonal complement to SEC and other size separation techniques • Product and process development / characterization • SEC method development / evaluation • Binding interactions Acknowledgments Amgen Beckman • K ll Arthur Kelly A h • Paul Voelker • Michael Stoner (formerly Amgen) • Cledwyn Fernandes • Brent Kendrick • Ron Ridgeway • Brad Winn • Ed Towers • Rick Burdick • George Svitel • Vladimir Razinkov • Yijia Jiang • Linda Narhi