Improving Bioassay Performance by Optimizing Plate Layout and Data Analysis

Transcription

1 CASSS Bioassays 2011 Improving Bioassay Performance by Optimizing Plate Layout and Data Analysis Wei Zhang, Ph.D. Biogen Idec 01NOV11

2 Presentation Outline Introduction Plate layout Data analysis Case studies. Non-cell-based binding assay Cell-based assay with short treatment time Cell-based assay with long treatment time 2

3 Plate Layouts Assay plate layout design Number of plates, blocks and replicates Oi Orientation ti of curves By row More points per curve Better full curve fit and more points in linear portion Fewer blocks or replicates By column More blocks or replicates Fewer points per curve May be more proper for assays with large variations 3

4 Data Analyses: Curve Fitting Models Data analysis Curve fitting models Curve parallelism test models Data transformation Curve fitting models linear vs. full curve fitting Linear regression 4-PL full curve fit 4

5 Curve Parallelism Test: F-test vs. Equivalence Test H 0 (null hypothesis): typically corresponds to a general or default position. H A (alternative or research hypothesis): the hypothesis one tries to prove. Hypotheses F-test (Difference Test) H 0 : Reference and test are parallel H A : Reference and test are not parallel Equivalence Test H 0 : Reference and test are not parallel H A : Reference and test are parallel Rule/Test 5 Issues Reject H 0 if F value (calculated from the sum of squared errors) is higher than a critical value (usually at α = Confidence interval for parallelism parameters (e.g., difference or ratio of slopes) must fall in the equivalence 0.05) interval ( goalposts ) - Not proving not parallel proving parallel - F 1/variance F assay precision Fail more with good precision or pass more with poor precision - Historical data or sound knowledge of the assay system is needed to develop a good equivalence interval may need adjustment when more data become available. - Confidence interval in non-linear regression is very complex and currently no statistics software can perform full curve equivalence test.

6 Data Transformation Reason Normal data distribution with constant variance across the range of the data is required for analysis of bioassay data. (USP Chapter 1032) Increased variance with increased signal (variance heterogeneity). Purpose To stabilize the data variance. Without data transformation With square root transformation 6

7 Case Study 1: Antibody-Antigen Binding Assay Product: monoclonal antibody against virus-like particle (VLP) Antibody-antigen competitive binding MSD assay Emission: 620 nm SULFO-TAG SULFO-TAG-Ab Ab Electricity VLP MSD plate Unlabeled Ab in sample 7

8 Assay Variability Observation 1: Deviation of the top and bottom rows. All 8 rows Middle 6 rows Observation 2: Larger variance near the upper asymptote in some assay runs. 8

9 Plate Layouts Standard Sample Control X 1 X 2 Plate Layout 1 Plate Layout 2 3 blocks (8 rows)/plate 2 blocks (6 rows)/plate 1 plate/sample/assay 4 blocks/2 plates/sample/assay 9

10 Square Root Transformation of Raw Data on Curve Parallelism No data transformation Square root transformation 100% control. Failed Passed 50% sample. Failed Passed 10

11 Assay Qualification Square root transformation Notebook reference 1 plate, 1 plate, 3 blocks 8 row s 2 plates, 2 plates, 4 blocks 6 row s per plate Without data With square root Without data Expected transformation No transformation Yes transformation No Yes Assay sample RP Qualification Assay date (%) Nov Nov Nov Nov With square root transformation Sample RP (%) Sample RP (%) Sample RP (%) Mean Sample Mean Sample RP sample Control RP recovery sample RP % RSD (%) recovery (%) (%) (%) (%) Failed recovery Nov Nov Failed parallelism Nov Nov Nov Nov Nov Nov Nov Nov

12 Comparison of Plate Layouts and Effect of Data Transformation Plate layout / data transformation comparison Linear range tested: % Plate Layout 1 2 Assay format One plate three blocks (all eight rows) Two plates four blocks (six middle rows/plate) Square root transformation Blocks passed % blocks meeting acceptance criteria Assays passed % assays meeting acceptance criteria No 30/ / Yes 37/ / No 23/ / Yes 27/ / Assay qualification using the two-plate, four-block plate layout with square root transformation Accuracy: 102.8% Intermediate precision: 3.9% Linearity: slope = 1.07, R 2 =

13 Case Study 2: Receptor Phosphorylation Assay Product: anti-cancer cell, monoclonal antibody against a transmembrane receptor Receptor phosphorylation induced by ligand-receptor binding Inhibition of receptor phosphorylation by receptor Ab Receptor Ab R P Receptor tyrosine kinase Intermediate signaling molecules Survival Proliferation 13

14 Receptor Phosphorylation Assay - Assay Principle and Procedure Assay Procedure Receptor Ab serial dilution Cells (Dilution plate) (Assay plate) 37 C, 60 minutes MSD assay for phosphorylated receptor Anti-phosphotyrosine Emission at 620 nm Ligand 37 C, 30 minutes Cell lysis SULFO-TAG Receptor Ab Electricity MSD plate (Receptor Ab-coated MSD plate) 14

15 Plate Layouts 12-point curves (by row) 8-point curves (by column) Standard Sample 1 Sample 2 Control Standard Sample 1 Sample 2 Control Standard Sample Control Plate Layout 1 Plate Layout 2 Plate Layout 3 1 data replicate/curve 2 data replicates/curve 4 data replicates/curve 3 plates (6 blocks) 3 plates 3 plates 15 2 samples/3 plates 2 samples/3 plates 1 sample/3 plates

16 Comparison of Plate Layouts Linear range tested: % Plate Layout Points per curve Data replicates Plates per assay Samples per assay Linear regression Regression line R 2 Accuracy Precision (RSD) y = 0.810x % 14.8% y = 0.836x % 10.2% y = 0.863x % 8.9% y = 0.907x % 6.1% Closer to 1 Closer to 1 Closer to 100% Lower 16

17 Case Study 3: Caspase 3/7 Assay Product: monoclonal antibody against a transmembrane protein Caspase activation induced by aggregation of the transmembrane protein by antibody binding Crosslinking Ab (anti-igg Fc) Ab against transmembrane protein Transmembrane protein Ab serial dilution (Dilution plate) Crosslinking Ab Cells (Assay plate) Caspase -3/7 37 C, 72 hours Cell lysis 17 Apoptosis Caspase 3/7 substrate Luciferase substrate Luciferase Luminescence

18 Plate Layout Based on Assay Variability Observations There was significant edge effect - wells along the edges had lower signals than wells in the center There would be only 6 points per curve if by column Assay variability was high Solutions - Only the center wells (6 x 10) should be used - Run the assay curves by row (10 points per curve) - Run the assay using multiple plates - Use multiple replicates to plot curves There was a slight row effect - Alternate the locations of standard, control and sample curves Plate 1 Plate 2 Plate 3 18

19 Data Processing Option 1: Use individual plates (2 replicates per curve, 3 curves) Option 2: Calculate the averages from the 3 plates (2 replicates per curve, 1 curve) Option 3: Combine data from the 3 plates into a single large plate (6 replicates per curve, 1 curve) 19

20 Data Analysis Plot curves using 2 replicates on each plate (2 replicates/curve, 3 curves) Plot curves using averages from the 3 plates (2 replicate/curve, 1 curve) Plot curves using all 6 replicates from the 3 plates (6 replicates/curve, 1 curve) Expected RP (%) Measured RP (%) % Recovery 95% CI (%) Measured RP (%) % Recovery 95% CI (%) Measured RP (%) % Recovery 95% CI (%) Average , , , 124 Linearity R Assay qualification using averages from 3 plates Accuracy: 99% Intermediate precision: 7.0% Linearity: slope = 0.928, R 2 =

21 Conclusions Plate layout and data analysis method have significant impact on assay performance. Assay plate layout design should be based on: o Assay variability o Plate uniformity and column/row effect o Convenience & simplicity Proper data transformation is a useful tool for data analysis, and its use should be based on assay variability. 21

22 Acknowledgments Deepthi Kanuparthi Courtney Keizer Aeona Wasserman Yucai Peng Svetlana Bergelson Helena Madden 22