Application of multi-factorial design of experiments to successfully optimize immunoassays for robust measurements of therapeutic proteins.
Developing a process that generates robust immunoassays that can be used to support studies with tight timelines is a common challenge for bioanalytical laboratories. Design of experiments (DOEs) is a tool that has been used by many industries for the purpose of optimizing processes. The approach is capable of identifying critical factors and their interactions with a minimal number of experiments. The challenge for implementing this tool in the bioanalytical laboratory is to develop a user-friendly approach that scientists can understand and apply. We have successfully addressed these challenges by eliminating the screening design, introducing automation, and applying a simple mathematical approach for the output parameter. A modified central composite design (CCD) was applied to three ligand binding assays. The intra-plate factors selected were coating, detection antibody concentration, and streptavidin-HRP concentrations. The inter-plate factors included incubation times for each step. The objective was to maximize the logS/B (S/B) of the low standard to the blank. The maximum desirable conditions were determined using JMP 7.0. To verify the validity of the predictions, the logS/B prediction was compared against the observed logS/B during pre-study validation experiments. The three assays were optimized using the multi-factorial DOE. The total error for all three methods was less than 20% which indicated method robustness. DOE identified interactions in one of the methods. The model predictions for logS/B were within 25% of the observed pre-study validation values for all methods tested. The comparison between the CCD and hybrid screening design yielded comparable parameter estimates. The user-friendly design enables effective application of multi-factorial DOE to optimize ligand binding assays for therapeutic proteins. The approach allows for identification of interactions between factors, consistency in optimal parameter determination, and reduced method development time.
Ray
Chad A
CA
Pharmacokinetics and Drug Metabolism, Amgen Inc., Thousand Oaks, CA, USA. caray@amgen.com
Patel
Vimal
V
Shih
Judy
J
Macaraeg
Chris
C
Wu
Yuling
Y
Thway
Theingi
T
Ma
Mark
M
Lee
Jean W
JW
Desilva
Binodh
B
D001331:Automation / Q000706:statistics & numerical data; D001360:Avidin / Q000737:chemistry; D003627:Data Interpretation, Statistical; D004797:Enzyme-Linked Immunosorbent Assay / Q000592:standards / Q000706:statistics & numerical data; D006735:Horseradish Peroxidase / Q000737:chemistry; D007118:Immunoassay; D007202:Indicators and Reagents / Q000737:chemistry; D008024:Ligands; D049449:Luminescence; D008163:Luminescent Measurements; D015233:Models, Statistical; D011485:Protein Binding; D011506:Proteins / Q000737:chemistry / Q000592:standards / Q000627:therapeutic use*; D011994:Recombinant Proteins / Q000378:metabolism; D015203:Reproducibility of Results; D012107:Research Design; D019809:Streptavidin / Q000737:chemistry; D013997:Time Factors
D007202:Indicators and Reagents; D008024:Ligands; D011506:Proteins; D011994:Recombinant Proteins; C093483:neutravidin; D001360:Avidin; D019809:Streptavidin; D006735:Horseradish Peroxidase