A Novel Method for Qualification of a Potency Assay through Partial Computer Simulation.

For biotherapeutics and vaccines, potency is measured in a bioassay that compares the concentration-response curves of a new batch to that of a reference standard. Acceptable accuracy and precision of potency measurement is critical to the manufacturing of these products. These characteristics of a bioassay are typically assessed in a procedure that is carried out with samples spanning the acceptable range for the product. During early development, however, a full validation study such as that which is carried out in late development can be costly as it relates to the likelihood of eventual program success. For these reasons, the laboratory may look for alternative ways to ensure the validity of the bioassay across a range that will support product development. One such alternative combines information from a reduced procedure using only reference standard and 100% relative potency concentration-response data sets, together with computer simulation, to estimate missing relative potency values across the desired range. Fits to the reduced dataset provide estimates of bioassay model parameters such as those for an S-shaped potency assay that follows a four-parameter logistic relationship, along with estimates of their variance-covariance structure and independent experimental unit (e.g., well-to-well or animal-to-animal) errors. Using Bayesian Markov Chain Monte Carlo modeling, the predictive distribution of the concentration-response data for the desired levels of relative potency is generated. Results from use of the reduced procedure are compared to results calculated from a full dataset in Monte Carlo simulation and in a motivating example. For biotherapeutics and vaccines, potency is measured in a bioassay that compares the concentration-response curves of a new batch to that of a reference standard. Acceptable accuracy and precision of potency measurement is critical to the manufacturing of these products. These characteristics of a bioassay are typically assessed in a procedure that is carried out with samples spanning the acceptable range for the product. During early development, however, a full validation study such as that which is carried out in late development can be costly as it relates to the likelihood of eventual program success. For these reasons, the laboratory may look for alternative ways to ensure the validity of the bioassay across a range that will support product development. One such alternative combines information from a reduced procedure using only reference standard and 100% relative potency concentration-response data sets, together with computer simulation, to estimate missing relative potency values across the desired range. Bayesian Markov Chain Monte Carlo modeling is used to generate the distributions of the missing potency levels. Results from use of the reduced procedure are compared to results calculated from a full dataset in Monte Carlo simulation and in a motivating example.