Ahmed Elmokadem, Ph.D.

Senior Scientist II

Ahmed earned his PhD in Biomedical Sciences from the University of Connecticut. His thesis work revolved around building statistical models to solve problems with super-resolution imaging. Other fields of experience include systems biology and pharmacokinetics. More recently, he focused on developing physiologically-based pharmacokinetic (PBPK) models to get a better mechanistic understanding of drugs’ pharmacokinetics.

Recent publications by this scientist

Symbolic PBPK-PDE Modeling using Open-Source Julia Tools.

December 6, 2024

Presented at ACoP 2024. The poster introduces a framework for developing physiologically based pharmacokinetic (PBPK) models that incorporate partial differential equations (PDEs) to account for spatial drug distribution, using open-source Julia tools. This approach simplifies the integration of spatial components into PBPK models, demonstrated through a case study on naphthalene diffusion, and is applicable to various pharmacometric models requiring spatial considerations, such as topical, inhaled, and antitumor therapies.

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How to Make a Salad? Rethinking Pharmacometric/QSP Model Composition using Open-Source Julia Tools.

December 6, 2024

Presented at ACoP 2024. The poster presents a framework for pharmacometric and quantitative systems pharmacology (QSP) model composition using open-source Julia tools. This framework allows for seamless integration and reuse of independent model components, facilitating the creation of complex models from simpler ones, and demonstrating applications in drug interactions, viral dynamics, and bispecific antibody modeling.

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UDE Know It If UDE Saw It: Leveraging Deep Machine Learning for QSP Model Development and Evaluation

November 14, 2023

Roller Coaster Talk presented at ACoP14. A workflow was introduced that integrates QSP modeling and machine learning in the form of UDEs. The workflow utilizes machine learning to learn missing parts in our knowledge of a QSP system while quantifying the uncertainty around the learned dynamics using Bayesian analysis.

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