TSAssistant: A Human-in-the-Loop Agentic Framework for Automated Target Safety Assessment
For toxicologists and pharmaceutical researchers, TSAssistant addresses the scalability and reproducibility challenges of expert-driven TSA, but the paper presents a framework design without quantitative evaluation, making it an incremental contribution.
TSAssistant is a multi-agent framework for automated Target Safety Assessment (TSA) report drafting that decomposes the process into specialized subagents, integrates heterogeneous biomedical data, and includes a human-in-the-loop refinement loop. It aims to reduce the mechanical burden of evidence synthesis while keeping toxicologists in control.
Target Safety Assessment (TSA) requires systematic integration of heterogeneous evidence, including genetic, transcriptomic, target homology, pharmacological, and clinical data, to evaluate potential safety liabilities of therapeutic targets. This process is inherently iterative and expert-driven, posing challenges in scalability and reproducibility. We present TSAssistant, a multi-agent framework designed to support TSA report drafting through a modular, section-based, and human-in-the-loop paradigm. The framework decomposes report generation into a coordinated pipeline of specialised subagents, each targeting a single TSA section. Specialised subagents retrieve structured and unstructured data as well as literature evidence from curated biomedical sources through standardised tool interfaces, producing individually citable, evidence-grounded sections. Agent behaviour is governed by a hierarchical instruction architecture comprising system prompts, domain-specific skill modules, and runtime user instructions. A key feature is an interactive refinement loop in which users may manually edit sections, append new information, upload additional sources, or re-invoke agents to revise specific sections, with the system maintaining conversational memory across iterations. TSAssistant is designed to reduce the mechanical burden of evidence synthesis and report drafting, supporting a hybrid model in which agentic AI augments evidence synthesis while toxicologists retain final decision authority.