Despite advances in computational biology and regulatory moves to reduce animal use, a coherent framework for AI-driven polypharmacology as a bridge between animal models and human drug development is still missing. The shift from "cage" to "code" brings opportunities but also unresolved issues in biological interpretation, regulation, and education. This review critically examines this transition within the 3R framework, focusing on: (i) regulatory and ethical drivers for non-animal methods, (ii) gaps left by reduced animal use, and (iii) AI's potential to reconstruct biological complexity via multi-omics and predictive modelling. The review suggests that AI-driven polypharmacology offers a scalable, ethical, and precision-oriented path forward.

An Osaka Metropolitan University-led research team evaluated the efficacy of a structured rehabilitation program for maintaining ADLs among terminally ill cancer patients. 

Work intensity is significantly higher under poor line managers, according to new research led by Bayes Business School (City St George’s, University of London) that could have strong implications for global occupational health and productivity.

Observational studies have shown that educational attainment is associated with the risk of myopia, but the causality of this relationship is unclear. The aim of the present study was to investigate the causal association between educational attainment and myopia.Observational studies have shown that educational attainment is associated with the risk of myopia, but the causality of this relationship is unclear. The aim of the present study was to investigate the causal association between educational attainment and myopia.

A practitioner article published in the SMS Explorer, “AI Will Help Formulate Strategy, But Should Never Finish It,” by Michael Olenick, challenges a growing assumption in the business world: that generative AI can take strategy off executives’ plates. As AI tools grow more capable, the debate has shifted from whether AI can contribute to strategy to whether it should lead it. Olenick, drawing on more than two decades of AI-aided strategy research, argues it should not—and explains precisely where the line must be drawn.

The article examines the role generative AI can—and cannot—play in the strategy process, drawing on Olenick’s development of VSTRAT, an AI-aided strategy system he first built in 2002 and later advanced as a research fellow at INSEAD. The piece references published findings that large language models can generate and evaluate strategies at levels comparable to entrepreneurs and investors, while insisting that the ability to produce strategic outputs is not the same as the authority to commit to them.