Evaluating manufacturing process profile comparability with multivariate equivalence testing: Case study of cell‐culture small scale model transfer

Abstract

This article studies the Generalized Mahalanobis Distance (GMD) approach proposed by Hoffelder which measures the dissimilarity of two multivariate Gaussian distributions with arbitrary covariance matrices and unequal sample sizes. This investigation demonstrated that, with appropriate adjustment, the GMD approach can achieve the targeted nominal Type I error and provide sufficient power for testing equivalence between two profile populations. The adjusted GMD approach was applied to examine the equivalence of harvest profiles between a 12L small scale model and 2000L manufacturing scale in a transfer study performed at Sanofi Specialty Care Framingham Biologics. The harvest profiles were evaluated based on three critical parameters (Productivity, Lactate Production, and Total Cell Density) and deemed practically equivalent using a pre-defined equivalence margin per the adjusted GMD approach. © 2017 American Institute of Chemical Engineers Biotechnol. Prog., 34:187-195, 2018.

Development of bioreactor scale‐down model using orthogonal projections to latent structures method and CO2 supplementation

“…Typically, the focus is on data collected at harvest to demonstrate consistency between small-scale and large-scale bioreactor performances. 4,5 Mining the totality of the historical data helps gain insights into fluctuations in process performance, uncovering hidden characteristics of important process parameters with pivotal contributions to the overall process performance.…”

“…In addition, product‐quality attributes (such as purity, charge heterogeneity, and glycan profiles) are measured at or around harvest time to link bioreactor performance to mAb characteristics. Typically, the focus is on data collected at harvest to demonstrate consistency between small‐scale and large‐scale bioreactor performances 4,5 . Mining the totality of the historical data helps gain insights into fluctuations in process performance, uncovering hidden characteristics of important process parameters with pivotal contributions to the overall process performance.…”