Open-Source Bayesian Hierarchical PBPK Modeling in Julia

Physiologically based pharmacokinetic (PBPK) models characterize a drug’s distribution in the body using prior knowledge. Bayesian tools are well suited to infer PBPK model parameters using the informative prior knowledge available while quantifying the parameter uncertainty. The presentation will review a full Bayesian hierarchical PBPK modeling framework in Julia, using the SciML ecosystem and Turing.jl, to accurately infer the posterior distributions of the parameters of interest.

Physiologically based pharmacokinetic (PBPK) models are mechanistic models that characterize how a drug is distributed in the body. These models are built based on an investigator’s prior knowledge of the in vivo system of interest. Bayesian inference incorporates an investigator’s prior knowledge of parameters while using the data to update this knowledge. As such, Bayesian tools are well suited to infer PBPK model parameters using the strong prior knowledge available while quantifying the uncertainty on these parameters. The presentation will review a full Bayesian hierarchical PBPK modeling framework in Julia, using the open-source SciML ecosystem and Turing.jl, that can accurately infer the posterior distributions of the parameters of interest. Additionally, diagnostics will be reviewed to evaluate general goodness-of-fit and the model predictive performance. The framework displays the composability of Julia packages that can be synced together using a single model definition to run various analyses, which include Bayesian analysis, sensitivity analysis, as well as population simulations that can explore alternative dosing scenarios. The general applicability of the proposed framework makes it a valuable tool for investigators interested in building Bayesian hierarchical PBPK models in an efficient, flexible, and convenient way.