High-Throughput Screening – News and Features

Phenotypic screening has re-emerged as a powerful strategy for identifying drugs based on their observable effects. Discover how phenotypic screening accelerates drug discovery, supports drug repurposing and enables novel therapeutic breakthroughs.

Scientists have designed new drug frameworks that selectively target CYP3A4, one of the most critical CYP proteins. Structural insights from this work offer a roadmap for future drug developers to better evaluate drug interactions.

Drug discovery efforts have expanded beyond the traditional approaches of small molecules and antibodies to include cells. This article explores the use of microfluidic methods for high-throughput screening of cell-based drug products.

Researchers discovered that ATP-sensitive potassium (KATP) channels in the brain play a pivotal role in sleep regulation. These energy sensors help maintain sleep-wake cycles and smooth transitions between them.

The complexity of the TME in vivo limits the conclusions that can be drawn from in vivo models. Optimizing cell culture models to mimic the TME is vital to understanding cancer progression and the development of optimized therapies.

New research shows that glucose regulates tissue differentiation by binding to hundreds of proteins throughout the cell to modulate their function and promote differentiation.

This article explores methods to enhance efficiency in the drug discovery and development pipeline, focusing on optimizing processes and exploring innovations that could streamline the path from research to approval.

This article explores how broadly neutralizing antibodies are being researched, not only for their potential in the prevention of HIV, but also for other rapidly mutating viruses such as influenza and SARS-CoV-2.

This article provides examples of how AI is helping to shape drug discovery, from drug design through to clinical trials.

The use of human liver microtissues (hLiMTs) cultured in a 384-well microplate system was explored as a predictive model for assessing drug-induced liver injury (DILI) with promising results.