Translational Bayesian Pharmacokinetic Framework for Uncertainty-Aware First-in-Human Dose Selection of Therapeutic Monoclonal Antibodies

First-in-human (FIH) dose selection for monoclonal antibodies (mAbs) typically relies on deterministic allometric scaling but lacks formal uncertainty quantification. While Bayesian methods have been widely applied in population PK modeling and dose individualization, their use for propagating uncertainty through allometric scaling in mAb FIH dose selection has not been systematically explored. This is a critical limitation for molecules with narrow therapeutic windows, such as CNS-targeted mAbs, where the blood-brain barrier restricts IgG penetration to [~]0.1-0.3% of plasma concentrations, requiring high systemic doses that must be balanced against dose-limiting toxicities. To provide uncertainty-aware FIH dose recommendations, we developed and systematically evaluated a Bayesian hierarchical PK framework tested on CNS mAbs. By simultaneously learning population-level PK distributions and allometric scaling relationships from 9 well-characterized mAbs, the model propagates inter-antibody variability and scaling imprecision into full posterior predictive distributions. For validation, the framework was applied to 3 Alzheimers disease mAbs, donanemab, lecanemab, and aducanumab, using only cynomolgus monkey PK data to predict human outcomes. Leave-one-out cross-validation yielded a mean absolute prediction error of 11.6% for human clearance. Predicted FIH doses were 10 mg/kg for donanemab and lecanemab, and 30 mg/kg for aducanumab. Retrospective comparison with clinical data showed prediction errors of -36.1%, -36.1%, and -15.7%, respectively, all within two-fold of observed values. The systematic under-prediction of clearance is attributable to target-mediated drug disposition not captured by the linear model. However, this bias is pharmacologically conservative, as it over-predicts systemic exposure to ensure wider safety margins. This framework enables risk-informed FIH dose selection by providing full probability distributions of predicted exposures rather than point estimates.