Many drugs still fail after promising preclinical results, raising difficult questions about how disease is modelled in the lab. Researchers are now turning to organoids and iPSC-derived systems to build more predictive models for drug discovery and reduce costly late-stage failures.
Traditional preclinical models are struggling to keep pace with a new generation of targeted therapies. As regulators embrace new approach methodologies (NAMs), vascularised tissue platforms are offering a more human-relevant approach to predicting drug efficacy and safety.
Dr Raminderpal Singh speaks with Dr Srijit Seal about why specialised AI agents are outperforming general-purpose models in drug discovery and what a new consortium paper shows about their use in practice.
Researchers at Phenomix Sciences are using machine learning and genetic risk scoring to investigate emotional hunger, an obesity phenotype linked to emotional and reward-driven eating behaviours. Dr Timothy O’Connor discusses how the approach could improve patient stratification, obesity research and treatment selection.
Designing gene control from scratch is becoming possible. SynGenSys is using computational design to create synthetic promoters for advanced therapies.