Boosting productivity and performance in drug discovery with SEQUOIA - Semantic Query of Information Assets

Informations projet

SEQUOIA-DD

N° de convention de subvention: 736397

DOI

10.3030/736397

Projet clôturé

Date de signature de la CE 25 Juillet 2016

Date de début 1 Septembre 2016

Date de fin 28 Février 2017

Financé au titre de

• INDUSTRIAL LEADERSHIP - Leadership in enabling and industrial technologies - Information and Communication Technologies (ICT)

Coût total

€ 71 429,00

Contribution de l’UE

€ 50 000,00

Coordonné par

SYNAPSE INFORMATION LIMITED
United Kingdom

Periodic Reporting for period 1 - SEQUOIA-DD (Boosting productivity and performance in drug discovery with SEQUOIA - Semantic Query of Information Assets)

Période du rapport: 2016-09-01 au 2017-02-28

Résumé du contexte et des objectifs généraux du projet

Synapse currently builds software for complex data integration so forensics experts can ask sophisticated queries that span multiple online financial repositories to discover the next Bernie Madoff, hidden amongst millions of online filed financial statements worldwide. This is now technically possible because regulators mandated that the millions of annual accounts filings must be carried out in XBRL – a precursor to semantic query. Unlike a Google search that returns thousands of records, semantic search returns just the handful that precisely match the query. Drug R&D data is now published in semantic format and so Synapse could potentially answer similarly complex, but high impact questions such as ‘will this candidate drug fail toxicity tests’? This report shows how Synapse will build a powerful software query platform for Drug Researchers worldwide.

Travail effectué depuis le début du projet jusqu’à la fin de la période considérée dans le rapport et principaux résultats atteints jusqu’à présent

This Phase 1 (work package 2) required finding classes of high impact questions in the Drug R&D domain which we concluded had to be at the start of the Drug R&D process rather than towards the end, because for the latter, the confidentiality of data and regulatory hurdles are insurmountable for our short term goals. We needed to find an unmet (work packages 3, 4 & 5) need that was common across all Researchers in the domain whether they worked in Pharma, Biotech or Academic R&D. We needed a common process, which researchers know is incompletely thorough and where the consequences for lack of thoroughness are highly expensive. We needed to find a process which is all too frequently heavily manual but which would lend itself to new inexpensive technology – in this case 3-D printing of macrofluidics discovered with a local partner (work packages 6, 7 & 8) – such that the most budget pressed academic could use it. Assuming that we then bundled this inexpensive open source macrofluidics hardware platform with the semantic query system we will build, we had one final hurdle to overcome. For decades, data query companies have tried to sell business and scientific users graphical interfaces that sought to automate the work that programmers do but have failed to have great success. Researchers instead frequently choose to do all of their work ‘inside’ spreadsheets. In this Phase 1 we have discovered a method to overcome this market adoption problem.

Progrès au-delà de l’état des connaissances et impact potentiel prévu (y compris l’impact socio-économique et les conséquences sociétales plus larges du projet jusqu’à présent)

By giving researchers a powerful interrogation tool to access and investigate datasets from around the world to help focus their research, we will be improving the efficiency of drug discovery and potentially increase the number of successful drug candidates reaching patients and consequently lead to better prognosis for millions of patients worldwide. Synapse has created a system today where the ordinary spreadsheet ‘sits on top of’ financial data, and lets the user query millions of records as if they are locally present. This is well adopted in the financial world and we believe we can succeed in Drug R&D. We have found collaborators specialising in the ADMET (early stage R&D) with an important un-met need to act as a starting point for commercialisation and have a clear plan of action defined showing a path to wider adoption.

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