Sanguk Kim

Keywords Drug safety Translational medicine Cross-species translation Machine learning Artificial intelligence Evidence before this study Predicting human drug toxicity remains a persistent challenge in pharmaceutical development. A significant proportion of drug candidates that demonstrate safety in preclinical models subsequently fail in clinical trials or are withdrawn post-marketing due to unforeseen severe adverse events (SAEs).

Abstract Innate immune cells generate a multifaceted antitumor immune response, including the conservation of essential nutrients such as iron. These cells can be modulated by commensal bacteria; however, identifying and understanding how this occurs is a challenge. Here we show that the food commensal Lactiplantibacillus plantarum IMB19 augments antitumor immunity in syngeneic and xenograft mouse tumor models.

Abstract Immune checkpoint inhibitors (ICIs) have substantially improved the survival of cancer patients over the past several years. However, only a minority of patients respond to ICI treatment (~30% in solid tumors), and current ICI-response-associated biomarkers often fail to predict the ICI treatment response. Here, we present a machine learning (ML) framework that leverages network-based analyses to identify ICI treatment biomarkers (NetBio) that can make robust predictions.