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A new fibre-optic method lets researchers monitor amyloid plaque buildup in living, freely moving mice – offering a minimally invasive way to track Alzheimer’s disease progression and test potential therapies.
Multiomics, AI and liquid biopsies are giving researchers real-time insight into tumour biology and enabling more personalised cancer therapies. Find out how these technologies are advancing biomarker discovery, improving patient stratification, and guiding the design of new treatments.
Scientists have mapped the diversity of fibroblasts and discovered how ‘rogue’ fibroblasts drive multiple diseases, revealing drug targets that could transform treatments across the body.
AI is increasingly used in drug discovery, but hidden bias and ‘black box’ models threaten trust and transparency. This article explores how explainable AI can turn opaque predictions into clear, accountable insights.
By combining human tissue models with explainable AI, researchers can analyse complex patient data to identify which treatments work best for which patients. First applied to inflammatory bowel disease, this approach could improve clinical trial success rates across many diseases.
Quantitative Systems Pharmacology (QSP) is fast becoming a standard tool in drug development, offering a human-relevant way to predict drug effects before the clinic. Dr Josh Apgar of Certara explains how it is helping to cut reliance on animal testing and speed discovery.
AI is starting to transform drug discovery, but progress is still slow and big challenges remain. Thibault Géoui explores the gaps, hurdles and breakthroughs needed before it can truly change pharma R&D.
Thibault Géoui explains why AI could finally help pharma overcome its productivity crisis and why the payoff won’t come as quickly as the optimists claim.
Drug discovery is slow, costly and often unsuccessful. DTR hears how GATC Health is applying AI and multiomics to make the process faster, more precise and less reliant on trial and error.
AI is moving beyond drug design to answer a critical question: can a promising compound actually be manufactured at scale? By predicting synthetic feasibility early, machine learning tools are helping drug developers avoid costly failures, streamline R&D and design molecules that are both effective and practical to produce.
Biomarkers are redefining how precision therapies are discovered, validated and delivered. This exclusive expert-led report reveals how leading teams are using biomarker science to drive faster insights, cleaner data and more targeted treatments – from discovery to diagnostics.
Effective financial management is vital for clinical trial success, yet many preclinical and clinical companies face inefficiencies due to outdated systems. Jennifer Kyle, CEO of Condor Software, explains how advanced financial platforms can streamline processes, improve forecasting and ensure better resource allocation throughout drug development.
What role could large language models and AI agents play in drug safety? In Part 3, Layla Hosseini-Gerami of Ignota Labs discusses how emerging technologies might make toxicity analysis faster, more accessible and part of the drug discovery workflow from day one.
Researchers in Brazil and Poland have developed an AI-powered tool that predicts cancer aggressiveness by analysing protein expression - offering new insights into tumour behaviour.
Why do so many drug candidates fail before reaching patients – and can AI help stop the losses? In Part 2, Layla Hosseini-Gerami of Ignota Labs outlines the scope of the toxicity problem and explains why failures often come too late to fix.
