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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.
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