title: Application of multi-factorial design of experiments to successfully optimize immunoassays for robust measurements of therapeutic proteins.
abstract: 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.
authors.affiliation: Pharmacokinetics and Drug Metabolism, Amgen Inc., Thousand Oaks, CA, USA. caray@amgen.com
authors.forename: Chad A
authors.initials: CA
authors.lastname: Ray
authors.forename: Vimal
authors.initials: V
authors.lastname: Patel
authors.forename: Judy
authors.initials: J
authors.lastname: Shih
authors.forename: Chris
authors.initials: C
authors.lastname: Macaraeg
authors.forename: Yuling
authors.initials: Y
authors.lastname: Wu
authors.forename: Theingi
authors.initials: T
authors.lastname: Thway
authors.forename: Mark
authors.initials: M
authors.lastname: Ma
authors.forename: Jean W
authors.initials: JW
authors.lastname: Lee
authors.forename: Binodh
authors.initials: B
authors.lastname: Desilva
mesh_terms: 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
chemical_list: D007202:Indicators and Reagents; D008024:Ligands; D011506:Proteins; D011994:Recombinant Proteins; C093483:neutravidin; D001360:Avidin; D019809:Streptavidin; D006735:Horseradish Peroxidase