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

Physiologically-based pharmacokinetic model for predicting drug-drug interactions perpetrated by posaconazole in healthy subjects with normal weight and obesity: Concomitant use and washout

July 22, 2025

This study presents a validated whole-body PBPK model characterizing drug-drug interactions (DDIs) involving posaconazole, a broad-spectrum antifungal, in individuals with normal weight and obesity. Findings highlight a prolonged half-life and irreversible CYP3A4 inhibition in patients with BMI ≥35 kg/m², underscoring the increased risk of DDIs even after discontinuation. The model supports optimized dosing and risk assessment for co-administered CYP3A4-sensitive drugs.

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Pharmacometric Machine Learning: Integrating Neural Networks for Flexible, Advanced Covariate Analysis

June 13, 2025

Presented at ASCPT 2025 Annual Meeting. Neural networks can be integrated with traditional pharmacometric models using several free open-source programming languages. Both Julia and R environments are suitable platforms, but there are tradeoffs regarding development speed, built-in capabilities, and documentation. DCM simplifies the covariate modeling process and uncovers complex, non-linear relationships in computationally efficient workflows.

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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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