A framework for multi-scale intervention modeling: virtual cohorts, virtual clinical trials, and model-to-model...

Area of Science:

• Computational biology
• Systems biology
• Informatics

Background:

• Computational models for disease progression are common, but in-silico intervention studies often lack standardized design.
• Existing approaches frequently mimic experimental study designs, limiting their scope for multi-scale analysis.
• Tracking disease dynamics and intervention effects across different scales (e.g., cellular, patient, population) remains a challenge.

Purpose of the Study:

• To introduce a novel Multi-scale Interventional Design (MID) framework for computational disease modeling.
• To enable tracking of disease dynamics and intervention impacts across within-body, patient, and population scales.
• To prioritize the investigation of intervention impacts on individual patients within virtual pre-clinical trials.

Main Methods:

• Applied the MID framework to develop and analyze a cohort of virtual patients using the HostSim computational model.
• HostSim models Mycobacterium tuberculosis infection, including lung granulomas, blood, and lymph node compartments.
• Integrated a drug intervention into HostSim and utilized the MID framework to quantify treatment impact across cellular, tissue, patient, and population scales.

Main Results:

• The MID framework successfully quantified the impact of a drug intervention on tuberculosis at multiple scales.
• Sensitivity analyses identified key virtual patient features predicting intervention efficacy across scales.
• Identified patient-heterogeneous mechanisms driving outcomes across different scales.

Conclusions:

• The MID framework provides a robust approach for in-silico intervention studies, offering insights into multi-scale disease dynamics.
• This framework facilitates the identification of patient-specific factors influencing treatment response in silico.
• The study demonstrates the utility of MID and HostSim for understanding tuberculosis progression and intervention effects.