Optimization of loading conditions on Capto adhere

Transcription

1 Optimization of loading conditions on Capto adhere Design of Experiments 1/

2 Capto adhere Designed for the polishing step in MAb purification Provides key contaminants clearance in one post Protein A step Allows the design of a two step process with one of the MabSelect family members Cell culture Cell removal MabSelect SuRe Virus Inactivation & Filtration Capto adhere Pool for Final Filtration UF/DF General overview of Capto adhere with application examples. Hans Johansson Wed, February 28 at /

3 Capto adhere Multimodal anion exchanger Ionic interactions, hydrogen bonding, hydrophobic interactions Capto High flow agarose OH OH O O N + OH Capto High flow agarose N-Benzyl-N-methylethanolamine 3/

4 Capto adhere Different selectivity than traditional ion exchangers gradient elution of 5 Mabs on Capto adhere and Capto Q Same elution order, but different interval Capto adhere MAb 4 MAb 3 MAb 5 MAb 2 MAb ml Capto Q MAb 4 MAb 3 MAb 5 MAb 2 MAb ml 4/

5 Capto adhere mau Flowthrough mode Start Wash start ml Yield Purity Partial binding Non-binding OPTIMIZATION 5/

6 Yield Amount bound = f (, cond, ) Antibody yield = + mau Bound Ab Flowthrough Nonbound Ab Low load Yield FT-pool ml Bound Ab mau Flowthrough Nonbound Ab High load Yield FT-pool ml 6/

7 Contaminant clearance Amount bound = f (, cond, ) mau Contaminant conc = + Bound contaminant Good clearance mau ml load clearance Bound contaminant Contaminant in FT Poor clearance ml 7/

8 Design of Experiments for Optimization Factors (inputs) Load (x 1 ) (x 2 ) Conductivity (x 3 ) Responses (outputs) Yield (y 1 ) Dimer/aggregates (y 2 ) HCP (y 3 ) Protein A (y 4 ) 8/

9 The DoE approach A designed set of experiments where the selected factors are varied (and evaluated) simultaneously! Lo,Hi Hi,Hi Two factors at two levels Factor B Lo,Lo Centerpoint Factor A Hi,Lo A B Result Lo Lo? Lo Hi? Hi Lo? Hi Hi? Mid* Mid? * Centerpoint: detection of curvature * Replicated centerpoint: estimation of noise 9/

10 Why DoE? 1. Detection and quantification of interactions A two-factor interaction effect The influence of one factor depends on the setting of the other factor Two-factor interactions are common in chemistry One could imagine three-factor interactions but they are rarely (never) significant Example Effect of Temp and on Yield of a reaction No interaction Mild interaction Strong interaction T (Hi) T (Lo) T (Hi) T (Lo) T (Hi) T (Lo) Yield Yield Yield /

11 Why DoE? 2. Separating true effects from noise The coefficients in the model estimate the true effects Confidence intervals show uncertainty of the estimates Noise (experimental error or model error (too simplistic?)) 10 Not significant p > Flow BH FC -5 Temp -10 Coefficient plot Output from MODDE software, Umetrics ( 11 /

12 Model Examples linear, interaction, quadratic Coefficient plot Response surface plot Regression equation Linear: y= β 0 + β 1 x 1 + β 2 x 2 + ε Temp *Temp Interaction: Temp *Temp y= β 0 + β 1 x 1 + β 2 x 2 + β 12 x 1 x 2 + ε Quadratic: Temp * y= β 0 + β 1 x 1 + β 2 x 2 + β 22 x 2 x 2 + ε 12 /

13 Design of Experiments for Optimization Sample: Ab elution pool from protein A chromatography (pi Ab: 9) Sample conditioning prior to Capto adhere: Desired and conductivity of sample was obtained through buffer exchange Sample composition: Ab concentration: 5 mg/ml Dimer/aggregate (D/A) concentration: 3.2% HCP concentration: 206 ppm Protein A concentration: 36 ppm 13 /

14 Factor setting Load Set according to goal: mg/ml Conductivity Normal conductivity range chosen: 2-15 ms/cm Goal: With a load of 100 mg/ml Yield 90% D/A 1% PrA 10 ppm HCP 50 ppm 14 /

15 Lower limit 5.95 lower in DoE: 6.0 Sample loaded at 7.8 Load 1 mg/ml Elution in gradient from 7.8 to /

16 Upper limit Sample load 75 mg/ml 6.0, 2 ms/cm (green) Lower limit in DoE, , 2 ms/cm (blue) Upper limit in DoE, /

17 The Design A full factorial design in 3 factors at 2 levels: No exp = centerpoints = = 10 Four additional point at 7 were added to resolve curvature effects Load (mg/ml) Cond (ms/cm) /

18 Yield Coefficient plot (MODDE software) Load*Cond Goal: High Yield ( 90%) in flowthrough pool % Load Cond Load* Load Cond Confidence interval Conductivity 2 ms/cm Conductivity 8.5 ms/cm Conductivity 15 ms/cm Response surface plots 18 /

19 Dimer/aggregate clearance Goal: Low dimer/aggregate conc ( 1%) in flowthrough pool Cond Load 1 % 0 Load * -1 D/A conc in start material: 3.2% Quadratic model: y = β 0 + β 1 x 1 + β 2 x 2 + β 11 x 12 + β 22 x 22 + β 12 x 1 x ε 19 /

20 HCP clearance 5 ppm Load Cond Goal: Low HCP conc ( 50 ppm) in flowthrough pool Cond Load Load 75 mg/ml Load mg/ml Load 300 mg/ml HCP conc in start material: 206 ppm 20 /

21 Protein A clearance Goal: Low Protein A conc ( 10ppm) in flowthrough pool Cond Load ppm *Cond Cond -4-5 Protein A conc in start material: 36 ppm 21 /

22 Summary YIELD Conductivity 15 ms/cm Dimer/Aggregates Conductivity 15 ms/cm HCP Load 300 mg/ml Protein A Load 300 mg/ml 7, Cond 15, Load 250 Yield: 92% D/A: 0.6% Protein A: 0.3 ppm HCP: 17 ppm Sweet spot analysis Gives the area (red) where the the goal is fulfilled. At a load of at least 100 mg Ab/ml resin: Yield in FT 90% D/A in FT 1% Protein A in FT 10 ppm HCP in FT 50 ppm

23 General trends Yield D/A PrA HCP Conductivity Yield D/A PrA HCP 23 /

24 Summary Ab Cell line pi Loading conditions Cond Load HCP <50 ppm PrA <10 ppm D/A <1% Yield 90% Ab1 CHO (12) <2 (560) 0.4 (1.4) 91 Ab2 CHO ~ (206) 0 (36) 0.54 (3.2) 90 Ab3 SP2/0 ~ ( ) 0 (<1) 0.14 (0.7) 90 Ab4 NS (85) 0 (0) 0.77 (1.5) 95 Ab5 CHO 8.8 (9-10) 7.5 ~ (38) 0 (0) <0.1 (0.7) 92 Values within parenthesis correspond to the initial levels (start values) Ab6: HCP reduced from to 8 ppm Yield 90%, load 50 mg/ml 24 /

25 Thank you! Acknowledgements Annika Ericson Martin Antti Hans J Johansson DoE software used: MODDE, Umetrics 25 /