Turbine, a biotechnology company specializing in AI-based virtual disease modeling, has announced a collaboration with AstraZeneca to enhance the discovery of antibody-drug conjugates (ADCs). The partnership will use Turbine’s simulation platform to predict treatment responses, guide ADC development strategies, and reduce the need for large-scale laboratory screening.
ADCs are a class of targeted cancer therapies that deliver potent drugs directly to tumor cells. However, identifying effective ADC candidates typically requires testing across hundreds of cell lines and patient-derived models, a process that is both time-consuming and resource-intensive. To address this, Turbine and AstraZeneca are introducing a lab-in-the-loop approach. Turbine’s platform will recommend a smaller, strategically selected subset of cell lines for lab testing and simulate responses across thousands of in silico models using AstraZeneca’s datasets, including both monotherapies and combination treatments.
This method is designed to not only accelerate discovery but also generate mechanistic insights that traditional screening methods often miss. Turbine’s simulations go beyond predicting cell survival, offering data on gene expression changes that help explain why cells respond or resist certain treatments. The collaboration aims to eventually expand this approach to patient-derived models and clinical applications.
“By implementing a lab-in-the-loop approach, we can move beyond broad experimental screening toward a more efficient, targeted strategy that selects the ADC combinations most likely to succeed in patients,” said Dr. Daniel Veres, Chief Scientific Officer and Co-Founder of Turbine. “This also lays the groundwork for deeper integration of our Virtual Lab into discovery workflows, helping ensure that the right experiments are run to generate the greatest impact for patients.”
This is not the first time the two companies have worked together. In a prior collaboration, Turbine’s Simulated Cell™ platform was used to study resistance mechanisms in hematological cancers and predict drug synergy involving DNA damage repair pathways.