Leveraging large language models to compare perspectives on integrating QSP and AI/ML

Abstract

Two recent papers offer contrasting perspectives on integrating Quantitative Systems Pharmacology (QSP) and Artificial Intelligence/Machine Learning (AI/ML): one views QSP as the primary driver using AI/ML to enhance computational tasks, while the other argues that AI/ML should provide an alternative mechanistic framework. Rather than perpetuate this tension, we used Large Language Models (LLMs) to examine both papers in two tests-one comparing their core arguments and another probing which methodology LLM should take precedence. Repeating each test multiple times with an identical and neutral prompt, the LLM revealed that each perspective suits specific stages of the drug development pipeline. QSP offers mechanistic rigor and regulatory clarity, and AI/ML excels in high-dimensional data analysis and exploratory modeling. A hybrid approach might best serve researchers and decision-makers, especially when harmonizing data-driven insights with mechanistic integrity. This exercise also highlights the potential of LLMs as promising tools for synthesizing complex information, offering an arguably less biased viewpoint that can trigger deeper discussion from the broader community seeking to align QSP and AI/ML in model-informed drug development (MIDD). By combining our human expertise with AI-driven analyses, we hope to further discuss with the scientific community how QSP and AI/ML-and the synergy between them-can drive innovation in therapeutic discovery and optimization.