Turbine secures €3 million EUR seed fund to expand the potential of simulation-first drug discovery

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Turbine, a simulation-based drug discovery company today announced the closing of an institutional financing round led by Delin Ventures. The seed fund will be used to redesign the failure-prone oncology drug discovery process into a series of rational steps facilitated by Turbine’s proprietary human cell model and simulation platform.

The company’s first institutional financing round of €3m will be used to expand the rational, simulation-based drug discovery workflow into every phase of drug discovery from research to lifecycle management. Leading the round was computational biology venture fund, Delin Ventures with participation from follow-on angel investors such as health-tech veterans Esther Dyson, Vishal Gulati and Atlantic Labs. Newly joining investors include o2h Ventures, who have launched the UK’s first seed and enterprise investment scheme fund backing early stage biotech. Alan Barge, former VP & Head of Oncology Therapy Area at AstraZeneca and Partner at Delin Ventures, now joins the Board of Directors of Turbine.

Turbine was founded on the premise that a computational model of human cell biology would rationalize drug discovery the same way that computer-aided design revolutionized architecture.

Based on a decade of research, Turbine’s biologists, bioinformaticians, data scientists, and AI engineers built the Simulated Cell. This platform is comprised of a dynamic computational model of the human cell and the underlying simulation technology to find the smartest route to novel targets, biomarkers, and combination therapies. Unlike other solutions in drug discovery, the Simulated Cell explains the response of cancer cells to drugs on a mechanistic level. The 50-strong team has also recently kicked-off its initial simulation-based drug discovery program centred around DNA Damage Repair (DDR).

Designing life-saving therapies for cancer patients demands a vast amount of financial investment, time and brainpower from pharmaceutical companies, yet 96.6% of new anticancer drugs still fail during clinical trials¹.

Many of these failures can be attributed to the incredible complexity of biology. Current lab methodologies provide only a limited understanding of how and why cancer cells respond to drugs. Turbine believes that narrowing down true novelty will be a success rate booster in every step drug discovery can fail today.

[/vc_column_text][vc_row_inner][vc_column_inner width=”1/4″][vc_single_image image=”210″ img_size=”medium”][/vc_column_inner][vc_column_inner width=”3/4″][vc_column_text el_class=”vertical-center”]Szabolcs Nagy, CEO of Turbine, said: “Over the last couple of years, we have guided the anti-cancer drug discovery process of leading pharmaceutical companies, allowing us to strengthen our simulation-first capabilities. We believe our methods have the potential to transform the current trial-and-error approach to drug discovery into an iterative process, marked by rational decisions, and leading to better drugs faster.”Many of the Budapest-based team’s in silico predictions for pharmaceutical partners are now in the clinical validation phase.[/vc_column_text][/vc_column_inner][/vc_row_inner][vc_row_inner][vc_column_inner width=”3/4″][vc_column_text el_class=”vertical-center”]Alan Barge, former VP & Head of Oncology Therapy Area at AstraZeneca, Partner at Delin Ventures, and Non-Executive Director at Turbine,, commented: “We have been very impressed by Turbine’s capability to model and simulate complex biological problems first, and then decide on the fate of drugs based on a deep understanding of the problem. This resourcefulness is deeply rooted in the DNA of the team. They have come a long way in developing and validating the platform leaning on revenue from industry collaborations, while also arriving at promising in vitro validation of their simulation-first DDR drug discovery program within just a couple of months. We look forward to supporting this talented team during the next stage of its development.”[/vc_column_text][/vc_column_inner][vc_column_inner width=”1/4″][vc_single_image image=”215″ img_size=”medium”][/vc_column_inner][/vc_row_inner][/vc_column][/vc_row][vc_row][vc_column][vc_column_text]¹ Source: Wong, C., Siah, K. and Lo, A. (2018). Estimation of clinical trial success rates and related parameters. Biostatistics, 20(2), pp.273-286. https://doi.org/10.1093/biostatistics/kxx069.[/vc_column_text][/vc_column][/vc_row]