Utility of a Bayesian Mathematical Model to Predict the Impact of Immunogenicity on Pharmacokinetics of Therapeutic Proteins

Abstract

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 concentrationtime data from a single dose PK study using five monoclonal antibodies were modeled using a noncompartmental 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 onecompartment 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.

Understanding Inter-Individual Variability in Monoclonal Antibody Disposition

“…Immune response varies from patient to patient and also within patients, as the concentration and nature (isotype, affinity) of the ADA distribution change with time and with continued drug dosing [269]. Mathematical models have shown utility in their ability to characterize ADA responses following different doses, and to develop relationships between drug exposure and the impact of ADA on drug clearance [287,288]. In clinic, increasing the mAb dose, co-administration of immunosupressants [289][290][291] and switching to a different mAb [292] are considered as strategies to overcome immunogenicity and have shown moderate benefit.…”

Mathematical Models to Characterize the Absorption, Distribution, Metabolism, and Excretion of Protein Therapeutics

“…Empirical models with time-varying PK parameters have been used to study the impact of immunogenicity on PK of TPs. For example, a parameter termed change in clearance time (𝛼) was included in a two-compartment TMDD model, which allows the linear clearance to suddenly change at time 𝛼, presumably due to ADA formation (Kathman et al, 2016). It was found that the derived 𝛼 can be much earlier than the time when ADA is detected in bioassays, and thus is helpful in identifying falsenegative subjects.…”

A Model‐Based Approach to Quantify the Time‐Course of Anti‐Drug Antibodies for Therapeutic Proteins

“…ADA PRED,T,ijk = ADA OBS,BL,ik + ADA PRED,M,ijk + ADA PRED,G,ijk (4) log 2 (ADA T,ijk ) = log 2 (ADA PRED,T,ijk ) ⋅ (1 + prop,ijk ) + add,ijk (5) P i = TVP i ⋅ exp( P,i ) (6) η P,i_Box−Cox = ((e η P,i ) γ − 1) γ (7) TVP…”

“…Modeling-based approaches have been reported to assess the impact of ADAs on pharmacokinetics/pharmacodynamics (PKs/ PDs) and efficacy. [4][5][6] Chen et al 4 reported a mathematical PK/ ADA model to quantify the impact of immunogenicity on protein drug clearance based on concentration data from animal studies. This work described scenarios on the potential time course and dynamics of the development of ADA.…”