Pharmaceutical Business review

Turbine and MSD aim to advance cancer treatment through AI simulations

Turbine noted that laboratory models often fail to capture the molecular diversity and biological complexity of various solid tumours found in patients. Credit: National Cancer Institute on Unsplash.

Turbine noted that laboratory models often fail to capture the molecular diversity and biological complexity of many solid tumours found in patients.

This limitation hampers the development of new treatments as these cancers rely on pathways and conditions that “cannot be replicated” in standard laboratory cultures.

Through this collaboration, MSD will leverage Turbine’s Turbine’s Simulated Cells technology to simulate disease subtypes that are resistant to current pre-clinical study methods.

The alliance is structured to run for one year, with the potential for MSD to extend access to Turbine’s virtual lab and tumour models.

This initiative could lead to the identification of new drug targets, biomarkers, and combination therapies for resistant tumour populations.

Turbine chief scientific officer Daniel Veres said: “Millions of cancer patients are fighting forms of the disease for which no lab-based model exists today.

“Unconstrained by the physical limitations of lab-based tumour models, the promise of Simulated Cells is to mimic the biology of disease in patients and open new paths to treat them. It’s great to get a chance to make this possible with an organisation committed to rethinking the research and development (R&D) process with AI.”

Turbine’s approach claims to have been validated through partnerships with pharmaceutical firms such as AstraZeneca and Bayer.

By employing AI to virtualise experiments, Turbine notes it can expedite the discovery and clinical decision-making process.

The company also said that by simulating the behaviour of tissues and cells under treatment, it aims to assist pharmaceutical companies in pinpointing therapeutic concepts, thereby potentially shortening the duration of research that does not yield results.

This could also decrease the incidence of Phase II trial failures due to poor efficacy.