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Revolutionizing drug discovery

jing-tang-fimm

Jing Tang from the Institite of Molecular Medicine Finland (FIMM) has been awarded one of the prestigious ERC Starting Grants by the European Research Council.

The 5-year grant totals 1.5 million euros. Tang’s project utilises computational systems medicine approaches, and aims to develop new ways to identify personalised drug combinations that are more effective against cancer. Tang is developing mathematical and computational tools, and is applying them to the rational selection of cancer drug combinations that can be used to provide personalised and more effective therapeutic strategies.

“Recent biotechnical advances combined with computing and network modelling of pathological processes can fundamentally impact the way we approach drug discovery. We want to be able to provide personalised treatment options for cancer patients based on both genomic and drug sensitivity profiling data,” Jing Tang explains.

Many of the questions Tang wants to answer are the same that puzzle clinicians every day: Why did these two patients respond so differently to the same treatment? Why did this patient develop resistance to a drug that was so effective earlier? How can we determine the best treatment for this particular patient?

Individualised Systems Medicine in cancer

He is the first researcher at FIMM to receive an ERC grant.

“I believe that my strengths in this very tough competition were my multidisciplinary background and the strong translational aspect of my proposal, but also, and importantly, the fact that my proposal builds on the well-established personalised medicine research programme and state-of-the-art infrastructure available at FIMM.”

Instead of the more common PI-centric approach, FIMM has decided to focus on a few of the ‘grand challenges’ in today’s world, and almost the entire institute is working towards meeting these challenges. Notably, since the establishment of the institute, researchers and clinical collaborators have worked together on a programme called ‘Individualised Systems Medicine in cancer’, aiming to develop strategies to accelerate translational cancer research.

“It has become very clear that we are able to produce diverse, high-quality molecular profiling data. The bottleneck is now the interpretation of the data, not generating it. Without introducing a data integration and mathematical modelling framework, however, we can easily get lost in translating the ‘big data’ into valid treatment options.”

Personalised drug-disease network

With the ERC funding, Tang will recruit both post-doctoral researchers and doctoral students to his group. He wants to build a multidisciplinary team having open-minded people with different academic backgrounds.

“We have started the drug combination prediction work with haematological cancers but our methods are widely applicable, and next we’ll test them with ovarian cancers. Any cancer types and later also other complex diseases can be considered. I am always looking for new clinical collaborators and am open to all data-intensive biomedical questions,” Tang points out.

Tang is committed to making all the informatics tools and software applications developed by his group autonomously usable by translational scientists and clinical practitioners.

“I envision that in five years we’ll be able to provide relevant ‘personalised drug-disease network’ information to all patients. Newly identified drug and disease associations should provide significant added value to clinicians when monitoring the progress of the disease or making treatment decisions for their patients.”