title: Utility of a Bayesian Mathematical Model to Predict the Impact of Immunogenicity on Pharmacokinetics of Therapeutic Proteins.
abstract: The impact of an anti-drug antibody (ADA) response on pharmacokinetic (PK) of a therapeutic protein (TP) requires an in-depth understanding of both PK parameters and ADA characteristics. The ADA and PK bioanalytical assays have technical limitations due to high circulating levels of TP and ADA, respectively, hence, significantly hindering the interpretation of this assessment. The goal of this study was to develop a population-based modeling and simulation approach that can identify a more relevant PK parameter associated with ADA-mediated clearance. The concentration-time data from a single dose PK study using five monoclonal antibodies were modeled using a non-compartmental analysis (NCA), one-compartmental, and two-compartmental Michaelis-Menten kinetic model (MMK). A novel PK parameter termed change in clearance time of the TP (α) derived from the MMK model could predict variations in α much earlier than the time points when ADA could be bioanalytically detectable. The model could also identify subjects that might have been potentially identified as false negative due to interference of TP with ADA detection. While NCA and one-compartment models can estimate loss of exposures, and changes in clearance, the two-compartment model provides this additional ability to predict that loss of exposure by means of α. Modeling data from this study showed that the two-compartment model along with the conventional modeling approaches can help predict the impact of ADA response in the absence of relevant ADA data.
authors.affiliation: Global Biostatistical Science, Amgen Inc., One Amgen Center Drive, Thousand Oaks, California, 91320, USA.
authors.forename: Steven
authors.initials: S
authors.lastname: Kathman
authors.affiliation: Pharmacokinetic and Drug Metabolism Department, Amgen Inc., One Amgen Center Drive, Thousand Oaks, California, 91320, USA.
authors.forename: Theingi M
authors.initials: TM
authors.lastname: Thway
authors.affiliation: Global Biostatistical Science, Amgen Inc., One Amgen Center Drive, Thousand Oaks, California, 91320, USA.
authors.forename: Lei
authors.initials: L
authors.lastname: Zhou
authors.affiliation: Clinical Immunology, Medical Sciences, Amgen Inc., One Amgen Center Drive, Thousand Oaks, California, 91320, USA.
authors.forename: Stephanie
authors.initials: S
authors.lastname: Lee
authors.affiliation: Pharmacokinetic and Drug Metabolism Department, Amgen Inc., One Amgen Center Drive, Thousand Oaks, California, 91320, USA.
authors.forename: Steven
authors.initials: S
authors.lastname: Yu
authors.affiliation: Pharmacokinetic and Drug Metabolism Department, Amgen Inc., One Amgen Center Drive, Thousand Oaks, California, 91320, USA.
authors.forename: Mark
authors.initials: M
authors.lastname: Ma
authors.affiliation: Clinical Immunology, Medical Sciences, Amgen Inc., One Amgen Center Drive, Thousand Oaks, California, 91320, USA.
authors.forename: Naren
authors.initials: N
authors.lastname: Chirmule
authors.affiliation: Clinical Immunology, Medical Sciences, Amgen Inc., One Amgen Center Drive, Thousand Oaks, California, 91320, USA. vibha.jawa@amgen.com.
authors.forename: Vibha
authors.initials: V
authors.lastname: Jawa
mesh_terms: D000818:Animals; D000911:Antibodies, Monoclonal / Q000493:pharmacokinetics*; D001499:Bayes Theorem; D001685:Biological Factors / Q000493:pharmacokinetics*; D005544:Forecasting; D006801:Humans; D055261:Immunogenetic Phenomena / Q000187:drug effects*; D008252:Macaca fascicularis; D008962:Models, Theoretical
chemical_list: D000911:Antibodies, Monoclonal; D001685:Biological Factors
keywords: PK/PD; anti-drug antibodies (ADA); biologics; immunogenicity; impact assessment; modeling; population-based modeling; simulation; statistics; therapeutic proteins