
Alan Barge
ex VP and Head of Oncology and Infection at AstraZeneca
“We believe Turbine can fundamentally change the way in which novel targets in precision oncology are identified.”
Conventional in vitro and in vivo models cannot capture disease behavior in real patients, and tools – like CRISPR – don’t act like actual drugs. This makes it incredibly hard to translate preclinical hypotheses to the clinic and create targeted drugs that truly help.
Before running any wet lab experiments, Turbine computationally simulates tumor cell behavior in patients to understand the complex mechanisms driving the disease. Simulations can reveal the right modality and combination approach to treat even the most resistant cancers. Observing these in silico experiments our biologists and translational experts gain insight into the molecular context by which mono- and combination therapies can potentially lead to patient benefit. Simulations are used to select the right preclinical models, as well as the right patients to confirm the predicted mechanisms, targets, biomarkers & combinations.
https://pubmed.ncbi.nlm.nih.gov/14518029/
https://www.nature.com/articles/s41467-021-25175-5
globaldata.com
Pioneering an approach that combines simulation with machine learning, we map and model how thousands of signaling proteins interact characterizing cancer behavior at a cellular level and response or resistance to treatment.
Our platform enables us to simulate drug-like effects from compoundsthat may not exist yet, on cells potentially unavailable for lab-based testing, like those of cancer patients.
This approach will potentially allow us to predict not only what works in cells, mice and people but more importantly, why and how. Continuous iterations of simulations and proprietary in vitro and in vivo experiments confirm predictions and progress our pipeline while simultaneously improving the underlying Simulated Cell™. As all programs and partnerships run on the latest version of the in silicocell, model training benefits accumulate, leading to a constantly improving platform. Using results to both generate the initial idea and to guide its iterations, as the models improve, this leads to a more rational process to undestand the underlying disease biology. Our benchmarks show that simulations prevent 2 out of 3 failed experiments in vitro and every 2nd failure in vivo as well.
Simulated Cells™ can be used to run the equivalent of any preclinical or clinical protocol, at computational speed and scale. Running millions of simulations before conducting the most promising wet experiments, Turbine’s platform guides every step from target ID to clinical Proof of Concept.
The Simulated Cell™ has already been validated from target discovery to patient stratification and life cycle management in collaboration with multiple big pharma companies as well as in our proprietary pipeline.
Simulating cancer behavior to understand drivers of disease & mechanisms of resistance observed in clinical settings
Uncovering ideal patient population and combination strategy for therapies already in development or on the market
Identifying truly novel targets to manage unmet need in patients who don’t benefit from existing therapies
Building an engineer’s toolkit to understand biology requires software and data scientists who wish to enable biological translational science instead of replacing it. Our team combines the amazing molecular biology, network- and data science expertise in Hungary with seasoned drug developers from the UK and the US. We’re here to change the status quo and bring truly impactful, simulation-guided therapies to patients.
Translational Scientist & Junior Data Analyst
Engineering Lead
Head of HR
Investor Director and Board Chair, Partner at Delin Ventures
Investor Director, Investment Manager of VCT Funds at Mercia Asset Management
ex SVP, Head of R&D at Merck KGaA
ex VP of Oncology at AstraZeneca
Director of Precision Oncology at OHSU Knight Cancer Institute
ex SVP of Biology at Blueprint Medicines
ex VP of Discovery at F-star Therapeutics
ex Director of Cancer Data Science at Broad Institute
Head of Data Science at Adyen