Senior Principal Scientist Genentech Inc. South San Francisco, California
Tg32 huFcRn transgenic mice, which are designed to express huFcRn in place of muFcRn, have been developed as a promising tool to replace cynomolgus monkey (Cyno) in predicting nonspecific clearance (CL) of therapeutic antibodies (mAb) in human. However, it is still unclear how well Tg32 mice perform compared to Cyno in predicting human pharmacokinetics (PK) of therapeutic mAbs. A detailed guideline on how to use PK parameters obtained from Tg32 mice to predict the human PK parameter is also lacking. In this presentation, using a series of statistical tests, I will compare the performance of Tg32 and Cyno in predicting the human CL of 21 commercially available mAbs. I will also describe a statistically determined optimal Tg32 clearance (CL) cutoff that identify mAbs with fast human CL. I will also demonstrate a method to accurately translate Tg32 PK to human PK using allometric scaling equations.
Learning Objectives:
Utilize a series of statistical analyses to compare the predictability of Tg32 CL and Cyno CL for human CL.
Statistically determine the optimal Tg32 CL cutoffs for predicting molecules with fast vs slow human CL. This optimal cutoff could be used to screen for molecules with good PK characteristics in human.
Statistically determine the allometric scaling equation to translate Tg32 CL to human CL, and the guideline on using this equation for human PK prediction.
