Literature DB >> 33389522

Improving priors for human monoclonal antibody linear pharmacokinetic parameters by using half-lives from non-human primates.

Vittal Shivva1, Martin Fink2, Philip J Lowe3.   

Abstract

Obtaining a good prior for the linear pharmacokinetics of new monoclonal antibodies (mAbs) would be an advantage not only for designing first-in-human (FIH) studies but also for stabilizing fitting of data with non-linear target-mediated disposition models. We estimated the pharmacokinetics from FIH studies for five mAbs using a two-compartment model, both separately and together, using a simple pool, a third hierarchical level of random effects for between mAb differences and non-human-primate half-lives as a predictor covariate for said differences. There was good agreement between compounds for the rapidly accessible central volume of 2.9 L (70 kg human), but clearances and peripheral volumes differed with terminal half-lives ranging from 15 to 28 days. The simple pool of human studies gave inter-individual variability estimates of 32% coefficient of variation (CV) for clearance and 33% CV for peripheral volume, larger than for separate fits (13-26% CV and 15-35% CV for clearance and volume respectively). Using third level hierarchical random effects gave inter-individual variability estimates close to those of separate fits (24% and 16% CV respectively). The between-mAb differences became predictable if non-human primate body weight scaled terminal half-life estimates were included as covariates on clearance and peripheral volume. In conclusion, ignoring inter-mAb variation leads to inflated estimates of inter-individual variability and unrealistic simulations for FIH studies. However, by using 70 kg body weight scaled terminal half-life estimates from non-human primates one can account for between-mAb differences and provide non-inflated priors for the linear pharmacokinetic parameters of new mAbs.

Entities:  

Keywords:  Allometry; Bayesian priors; Modelling; Monoclonal antibody; Pharmacokinetics; Simulation

Mesh:

Substances:

Year:  2021        PMID: 33389522     DOI: 10.1007/s10928-020-09731-y

Source DB:  PubMed          Journal:  J Pharmacokinet Pharmacodyn        ISSN: 1567-567X            Impact factor:   2.745


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