FUTURELAB – The Digital Lab of the Future

FUTURELAB – The Digital Lab of the Future

FutureLab is a project aiming at using modern software tools to create a cloud environment connecting all R&D elements of the chemical, pharmaceutical and biotech manufacturing industries to increase their productivity and efficiency, transcending geographical barriers. Analytical chemistry and chemometric technology will connect chemists, biochemists and other support positions in these traditional industries with their counterparts in academic institutions and Custom Research Organizations (CROs), as well as analytical equipment manufacturers. This will create a global community and marketplace fostering collaboration and service exchange, with individuals, entities and instrumentation directly interacting with each other, regardless of their location. The data in the system can be exploited by Artificial Intelligence technologies, including Machine and Deep Learning, while preserving confidentiality. This will facilitate automation in the value chain and increase predictive science.

The project is funded by the Galician Innovation Agency (GAIN), through the Industrias do Futuro programme and the European Regional Development Fund (ERDF). It also has the support of the Consellería de Economía, Emprego e Industria.

Objective 1: Development of the cloud architecture and infrastructure needed to ensure data integrity, fluid communication between teams, users’ transactions, including offer and hiring of services.

Objective 2: Module connecting instruments to the environment, resulting in an inventory of instruments and experiments available. It will allow services and maintenance management, as well as the creation of a marketplace for the analytical instruments time.

Objective 3: Chemical reaction module, designed to collect, store and manage information for all type of chemical reactions and to show information about the different components of a reaction (reagents, solvents, products, reaction conditions, yield, analytical data, suppliers, etc).

Objective 4: Transform Mnova in the market reference for processing and analysis of data derived from several analytical techniques by including new ones and integrating third parties’ contribution to the software.

Objective 5: Implementation of workflows adapted to industry, with scalable modules, easy to configure and customize. This will allow the storage of data, as well as the extraction of information using algorithms developed as part of Objective 4.

Objetivo 6: Development of a module that will allow the environment to use data, ensuring its confidentiality, and to develop tools based in Artificial Intelligence and Automatic Learning for decision-making.

Objective 7: Integration of a chemometric system in the cloud for multivariate analysis of spectral data. Integración de un sistema quimiométrico basado en la nube para el análisis multivariable de datos espectroscópico.

Advances during 2019

All milestones set for the first months of the project have been achieved, including the definition of the main functionalities of all modules described in the working plan and draft verification and validation plans. New algorithmia has been added to Mnova and work to include new analytical techniques to our suite is progressing, as well as the connection to instruments via a cloud system. The first tests of automation of processing and analysis, as well as the databasing of results, have been succesful.

Advances during 2020

  • The trading platform linked to the project has been established.
  • Improvements have been developed in the integration components with instruments to support compound experiments or data integrity checks, as well as new functionalities.
  • A management console application was developed to verify GxP installations.
  • In Mbook, and its evolution Mdrive, automatic integration with analysis instruments has been completed, with the possibility of defining standard work procedures.
  • The functionalities that will allow the deployment of tools in regulated markets have been included: GxP.
  • New algorithms for analyzing digital signals and artificial intelligence methods have been developed for the analysis of both small molecules and biomolecules (such as monoclonal antibodies) using NMR techniques.
  • Substantial improvements have also been made to mass spectrometry, optical spectroscopy and chromatography modules.
  • A new module has been developed for the automatic analysis of drugs using low-field NMR relaxometry techniques and new fully automated workflows have been implemented.
  • New versions of the automation platform and automated workflows have been launched.
  • Systems that provide new automated workflows and analytics have continued to be developed.
  • The automation platform continues to improve thanks to internal and customer feedback.