AI Assisted Mechanistic and Translational Evaluation of Turmeric in Breast Cancer: A Preclinical-to-Clinical Study

Abstract

Breast cancer continues to be a biologically diverse and therapeutically complex malignancy, marked by intricate signaling networks, variability among subtypes, and mechanisms of adaptive resistance. There is growing evidence that underscores the necessity for multi-target therapeutic approaches that can modulate interconnected oncogenic pathways. Turmeric (Curcuma longa), a medicinal plant that has been extensively researched, contains bioactive phytoconstituents such as curcumin, demethoxycurcumin, and turmerone, which are known for their anti-inflammatory, antioxidant, and anticancer effects. Nevertheless, there is a scarcity of systematic translational frameworks for the structured assessment of these compounds in the context of breast cancer. This study introduces an AI-assisted translational drug discovery framework that integrates large language models (LLMs) and scientific prompting to assess turmeric-derived compounds in breast cancer. The framework is divided into six modular domains: target identification and mechanistic mapping, lead optimization, design of in vitro validation, in vivo translational modeling, development of clinical strategies, and alignment with regulatory and commercial standards. The structured prompt-driven interrogation facilitates hypothesis generation, prioritization of pathways, and mapping of compound-target interactions within a systems biology framework. AI-driven analysis has pinpointed crucial oncogenic pathways, including PI3K/AKT/mTOR, MAPK/ERK, NF-κB, and apoptotic regulators, as primary targets of interest. Phytoconstituents derived from turmeric have shown compatibility with multiple targets, thereby endorsing a polypharmacological approach. The downstream components included ADMET profiling, docking simulations, frameworks for experimental validation, pharmacovigilance strategies, and positioning of intellectual property, which collectively ensure a seamless transition from molecular inference to translational viability. This research establishes a scalable and integrative framework, supported by AI, for the discovery of phytochemical drugs aimed at breast cancer. By merging computational intelligence with structured translational modeling, this methodology propels turmeric-derived compounds from the generation of mechanistic hypotheses towards the potential development of therapeutics, while underscoring the importance of both experimental and clinical validation.