Utility of physiologically based pharmacokinetic modeling to predict inter-antibody variability in monoclonal antibody pharmacokinetics in mice

ABSTRACT

In this investigation, we tested the hypothesis that a physiologically based pharmacokinetic (PBPK) model incorporating measured in vitro metrics of off-target binding can largely explain the inter-antibody variability in monoclonal antibody (mAb) pharmacokinetics (PK). A diverse panel of 83 mAbs was evaluated for PK in wild-type mice and subjected to 10 in vitro assays to measure major physiochemical attributes. After excluding for target-mediated elimination and immunogenicity, 56 of the remaining mAbs with an eight-fold variability in the area under the curve ($AU{C}_{\left(0-672h\right)}$: 1.74 × 10⁶ −1.38 × 10⁷ ng∙h/mL) and 10-fold difference in clearance (2.55–26.4 mL/day/kg) formed the training set for this investigation. Using a PBPK framework, mAb-dependent coefficients F1 and F2 modulating pinocytosis rate and convective transport, respectively, were estimated for each mAb with mostly good precision (coefficient of variation (CV%) <30%). F1 was estimated to be the mean and standard deviation of 0.961 ± 0.593, and F2 was estimated to be 2.13 ± 2.62. Using principal component analysis to correlate the regressed values of F1/F2 versus the multidimensional dataset composed of our panel of in vitro assays, we found that heparin chromatography retention time emerged as the predictive covariate to the mAb-specific F1, whereas F2 variability cannot be well explained by these assays. A sigmoidal relationship between F1 and the identified covariate was incorporated within the PBPK framework. A sensitivity analysis suggested plasma concentrations to be most sensitive to F1 when F1 > 1. The predictive utility of the developed PBPK model was evaluated against a separate panel of 14 mAbs biased toward high clearance, among which area under the curve of PK data of 12 mAbs was predicted within 2.5-fold error, and the positive and negative predictive values for clearance prediction were 85% and 100%, respectively. MAb heparin chromatography assay output allowed a priori identification of mAb candidates with unfavorable PK.

KEYWORDS:

• Monoclonal antibodies
• pharmacokinetics
• physiochemical
• physiologically based pharmacokinetic modeling
• variability

Acknowledgments

The authors acknowledge the Biotechnology Institute of Singapore for the production of antibodies, and the Syngene Amgen Research and Development Center (SARC), Benjamin Alba, Fuyi Chen, Michelle Hortter, and Ling Liu for the production of Fabs. The authors recognize Dhritiman Jana, Philip An, Evelyn Yang, and Heidi Jones for their support in registering protein lots, and Kevin Kalenian for running the FcRn AlphaScreen assay. The authors thank Isabel Figueroa for her technical review of the manuscript. The authors are grateful to John M. Harrold for his scientific input during discussions.

Disclosure statement

The authors declare the following competing financial interest(s): S.C.H., K.D.C., K.P.C., M.Y., R.P^.e, M.S., R.P^.c, M.L., M.M., and V.A.T. are full-time employees and shareholders of Amgen Inc. A.R.C., A.W.J., and R.S. are shareholders of Amgen Inc.

Supplementary material

Supplemental data for this article can be accessed online at https://doi.org/10.1080/19420862.2023.2263926

Additional information

Funding

The author(s) reported there is no funding associated with the work featured in this article.