Ahmed Elmokadem
Ahmed earned his PhD in Biomedical Sciences from the University of Connecticut developing Bayesian statistical algorithms to solve issues with super-resolution imaging. He joined the Translational and Systems Pharmacology group at Metrum Research Group in 2017 and has been conducting a variety of modeling and simulation analyses including quantitative systems pharmacology (QSP), Physiologically Based Pharmacokinetic (PBPK), population pharmacokinetic/pharmacodynamic (PKPD) modeling, and Bayesian PKPD modeling.
Sessions
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.
As Julia usage continues to grow within regulated biomedical environments, it is vital to ensure analyses are traceable and reproducible. Conducting analyses in an open-science manner is also critical to expand the adoption of Julia and to facilitate the infrastructure growth of Julia as an accessible ecosystem. A step-by-step model-building example of a classic monoclonal antibody-drug conjugate PBPK/tumor dynamics system illustrates how to develop such a reproducible open-science framework.