Reduction of monoclonal antibody viscosity using interpretable machine learning.

Early identification of antibody candidates with drug-like properties is essential for simplifying the development of safe and effective antibody therapeutics. For subcutaneous administration, it is important to identify candidates with low self-association to enable their formulation at high concentration while maintaining low viscosity, opalescence, and aggregation. Here, we report an interpretable machine learning model for predicting antibody (IgG1) variants with low viscosity using only the sequences of their variable (Fv) regions.

Effects of nonintubated thoracoscopic surgery on postoperative neurocognitive function: a randomized controlled trial.

Objectives: Postoperative neurocognitive disorder following thoracoscopic surgery with general anaesthesia may be linked to reduced intraoperative cerebral oxygenation and perioperative inflammation, which can potentially be exacerbated by mechanical ventilation. However, nonintubated thoracoscopic surgery, which utilizes regional anaesthesia and maintains spontaneous breathing, provides a unique model for studying the potential benefits of avoiding mechanical ventilation. This approach allows investigation into the impact on perioperative neurocognitive profiles, inflammatory responses and intraoperative cerebral oxygen levels.

Simplifying complex antibody engineering using machine learning.

Machine learning is transforming antibody engineering by enabling the generation of drug-like monoclonal antibodies with unprecedented efficiency. Unsupervised algorithms trained on massive and diverse protein sequence datasets facilitate the prediction of panels of antibody variants with native-like intrinsic properties (e.g.