Quantitative Prediction of Human Pharmacokinetics for mAbs Exhibiting Target-Mediated Disposition

Abstract

Abstract. Prediction of human pharmacokinetics (PK) can be challenging for monoclonal antibodies (mAbs) exhibiting target-mediated drug disposition (TMDD). In this study, we performed a quantitative analysis of a diverse set of six mAbs exhibiting TMDD to explore translational rules that can be utilized to predict human PK. A TMDD model with rapid-binding approximation was utilized to fit PK and PD (i.e., free and/or total target levels) data, and average absolute fold error (AAFE) was calculated for each model parameter. Based on the comparative analysis, translational rules were developed and applied to a test antibody not included in the original analysis. AAFE of less than two-fold was observed between monkey and human for baseline target levels (R 0 ), body-weight (BW) normalized central elimination rate (K el /BW −0.25 ) and central volume (V c /BW 1.0 ). AAFE of less than three-fold was estimated for the binding affinity constant (K D ). The other four parameters, i.e., complex turnover rate (K int ), target turnover rate (K deg ), central to peripheral distribution rate constant (K pt ) and peripheral to central rate constant (K tp ) were poorly correlated between monkey and human. The projected human PK of test antibody based on the translation rules was in good agreement with the observed nonlinear PK. In conclusion, we recommend a TMDD model-based prediction approach that integrates in vitro human biomeasures and in vivo preclinical data using translation rules developed in this study.

Linear pharmacokinetic parameters for monoclonal antibodies are similar within a species and across different pharmacological targets: A comparison between human, cynomolgus monkey and hFcRn Tg32 transgenic mouse using a population-modeling approach

“…Alternatively, a more mechanistic TMDD model that incorporates target properties can be used. Previous work has shown that the TMDD approach is more reliable for human projections because it can capture differences in target properties between species and disease populations 5 , 15 , 17 . Since both the M-M and TMDD models require parameterization of the linear CL pathway, the parameters presented herein are a useful guide.…”

“…11-14 For mAbs that exhibit non-linear CL due to TMDD, scaling of PK is more challenging. In order to take into account the kinetics of mAb binding to its target, a mechanistic TMDD model is required, with proper exploration of species differences in target expression and binding 5 , 15-17. …”

Mathematical modeling of receptor occupancy data: A valuable technology for biotherapeutic drug development

“…Supplementing the mAb concentration data with in vivo RO measurements can further improve the estimation of the model parameters by integrating information from different sources, thereby increasing confidence in the results and related extrapolations (8). This is especially critical when the model results obtained in preclinical species are extrapolated to humans for early clinical predictions (2).…”

Prediction of non-linear pharmacokinetics in humans of an antibody-drug conjugate (ADC) when evaluation of higher doses in animals is limited by tolerability: Case study with an anti-CD33 ADC

“…To predict human PK of antibodies that undergo TMDD, species differences of target expression levels and turnover as well as antibody interaction with target need to be considered. Scale up of the target-dependent component of antibody PK has been tackled with varying degrees of success using TMDD 7 , 8 and Michaelis-Menten (MM) 9 non-linear PK models. In both cases, appropriate scale up of the parameters that describe the PK non-linearity is critical to capture the differences across species.…”