An aiHumanoid Simulation of Gram-Negative Sepsis: A Comprehensive Multi-Organoid Platform for Advanced Disease Modeling, Drug Discovery, and Personalized Medicine

Abstract

AbstractOrganoids are three-dimensional cellular structures resembling human organs, which have emerged as valuable tools for studying organ development, disease modeling, and drug discovery. Integrating multiple organoid systems allows for the examination of complex interactions between different organs. In this study, we present the development and initial validation of the aiHumanoid simulation, an advanced AI-based computational framework that integrates 18 individual organoid simulations through a common cardiovascular system. Our aim is to investigate the systemic effects of gram-negative sepsis, a life-threatening condition that affects multiple organ systems.In this study, we evaluated the impact of gram-negative sepsis on the organoid systems that make up the aiHumanoid simulation. Our findings indicate significant alterations in cardiovascular, nervous system, respiratory, renal, hepatic, hematologic, gastrointestinal, musculoskeletal, immune, and endocrine parameters in both male and female sepsis-affected organoids. Notably, markers of inflammation, coagulation, renal dysfunction, liver damage, immune response, and endocrine regulation were significantly affected by sepsis. While some parameters showed gender-specific differences in response, such as hormonal changes, the overall impact of gram-negative sepsis was observed in both sexes.This study demonstrates the potential of the aiHumanoid to accurately simulate the systemic effects of diseases on various organ systems. The integration of computational simulations with organoid systems offers a powerful approach for understanding disease mechanisms and evaluating potential therapies. By providing a more efficient and physiologically relevant platform for drug testing, the aiHumanoid simulation has the potential to accelerate the drug development process, reduce costs, and minimize the need for animal testing. Further research and ongoing validation will be crucial to fully exploit the capabilities of this revolutionary computational framework for advancing disease modeling and therapeutic interventions.