Cairo University

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Frontiers in Pharmacology Sep 25, 2026
Review proposes a microbiome-aware oral formulation framework: excipient risk classification, nanocarrier design rules, and a tiered testing roadmap
This review integrates evidence on microbial drug metabolism, excipient–microbiome interactions, nanocarrier–microbiome interfaces, microbiota-responsive release mechanisms, and experimental models to propose an authors' evidence-informed framework for oral formulation design, comprising an excipient–microbiome risk classification, nanocarrier design rules, microbiota-responsive delivery decision logic, and a tiered testing roadmap, illustrated by clinically relevant examples such as digoxin inactivation by Eggerthella lenta, bacterial levodopa metabolism, and microbial beta-glucuronidase-mediated irinotecan toxicity.
This review integrates evidence on microbial drug metabolism, excipient–microbiome interactions, nanocarrier–microbiome interfaces, microbiota-responsive release mechanisms, and experimental models to propose an authors' evidence-informed framework for oral formulation design, comprising an excipient–microbiome risk classification, nanocarrier design rules, microbiota-responsive delivery decision logic, and a tiered testing roadmap, illustrated by clinically relevant examples such as digoxin inactivation by Eggerthella lenta, bacterial levodopa metabolism, and microbial beta-glucuronidase-mediated irinotecan toxicity.
Karolinska University Hospital Egypt-Japan University of Science and Technology Cairo University Karolinska Institutet
mohamed sedeek（First author）Sadek Ahmed（Corresponding author）Osama Saher（Corresponding author）
Microbiome-Aware Oral Drug Delivery Excipient–Microbiome Interactions Microbial Drug Metabolism Controlled drug release
Journal of Gastroenterology and Hepatology Sep 18, 2026
Artificial Intelligence in Endohepatology: A Roadmap Toward an Intelligent One-Stop Shop for Liver-Directed Endoscopy
This narrative review is the first to synthesize the convergence of artificial intelligence and endohepatology into four functional pillars—intelligent hemodynamic assessment, virtual histology, precision tissue acquisition, and integrated risk stratification with therapeutic and decision support—and proposes a three-tier readiness framework to separate demonstrated capabilities from extrapolated and conceptual applications, concluding that the field is still very early-stage but that AI has strong potential to turn endohepatology into a single machine-driven diagnostic and therapeutic platform provided standardized datasets, prospective validation, and clear regulatory and governance standards are in place.
This narrative review is the first to synthesize the convergence of artificial intelligence and endohepatology into four functional pillars—intelligent hemodynamic assessment, virtual histology, precision tissue acquisition, and integrated risk stratification with therapeutic and decision support—and proposes a three-tier readiness framework to separate demonstrated capabilities from extrapolated and conceptual applications, concluding that the field is still very early-stage but that AI has strong potential to turn endohepatology into a single machine-driven diagnostic and therapeutic platform provided standardized datasets, prospective validation, and clear regulatory and governance standards are in place.
Cairo University
Ahmed Salman（First author, Corresponding author）
Narrative Review AI in Endohepatology Endohepatology

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Review proposes a microbiome-aware oral formulation framework: excipient risk classification, nanocarrier design rules, and a tiered testing roadmap

Artificial Intelligence in Endohepatology: A Roadmap Toward an Intelligent One-Stop Shop for Liver-Directed Endoscopy