Characterizing the Pharmacokinetics and Biodistribution of Therapeutic Proteins: An Industry White Paper.

Characterization of the pharmacokinetics and biodistribution of therapeutic proteins (TPs) is a hot topic within the pharmaceutical industry, particularly with an ever-increasing catalog of novel modality TPs. Here, we review the current practices, and provide a summary of extensive cross-company discussions as well as a survey completed by International Consortium for Innovation and Quality members on this theme. A wide variety of in vitro, in vivo and in silico techniques are currently used to assess pharmacokinetics and biodistribution of TPs, and we discuss the relevance of these from an industry perspective, focusing on pharmacokinetic/pharmacodynamic understanding at the preclinical stage of development, and translation to human. We consider that the 'traditional in vivo biodistribution study' is becoming insufficient as a standalone tool, and thorough characterization of the interaction of the TP with its target(s), target biology, and off-target interactions at a microscopic scale are key to understand the overall biodistribution on a full-body scale. Our summary of the current challenges and our recommendations to address these issues could provide insight into the implementation of best practices in this area of drug development, and continued cross-company collaboration will be of tremendous value. SIGNIFICANCE STATEMENT: The Innovation and Quality Consortium Translational and ADME Sciences Leadership Group working group for the absorption, distribution, metabolism, and excretion of therapeutic proteins evaluates the current practices and challenges in characterizing the pharmacokinetics and biodistribution of therapeutic proteins during drug development, and proposes recommendations to address these issues. Incorporating the in vitro, in vivo and in silico approaches discussed herein may provide a pragmatic framework to increase early understanding of pharmacokinetic/pharmacodynamic relationships, and aid translational modeling for first-in-human dose predictions.