Pharmacokinetics-Informed Agentic Architecture for Drug Dynamics with LLM-Driven In Silico Patients

This research presents the architecture, implementation, and validation of a novel Agentic Large Model (ALM) framework for in silico patient simulation, synergistically combining pharmacokinetic modeling with autonomous clinical reasoning. The multi-agent architecture enables emerging collaboration between specialized components for physiological simulation, medication management, and safety monitoring, coordinated through a central orchestrator to ensure behavioral alignment. Leveraging LLaMA3 via Ollama, the system demonstrates clinically plausible strategic planning across 12 diverse patient scenarios while maintaining trustworthiness through structured JSON-constrained outputs. Key innovations include a modular agent design with clear separation of concerns, integrated drug interaction checking with hierarchical severity assessment, and multi-layer safety assurance. Rigorous evaluation demonstrates effective pain management (39–58% reduction) with appropriate safety flagging in complex cases, particularly for geriatric and polypharmacy patients. The system achieves an average decision latency of 2.1 s while maintaining 98.7% structured output compliance, advancing the foundations of reliable agentic systems in critical healthcare domains.