Where life science
Collaborates
We are a global, not-for-profit members’ organization working to lower barriers to innovation in life science and healthcare R&D through pre-competitive collaboration.
Our second drug repurposing datathon is dedicated to finding...
Our projects transform R&D innovation through pre-competitive collaboration. We bring together the key constituents to identify the root causes that lead to R&D inefficiencies. We develop best practices and technology pilots to overcome common obstacles.
A collaborative project aiming to make assay information available in a FAIR format to increase the efficiency of bench scientists engaged in experiment planning and enable research that currently requires tedious expert literature review.
There are over one million biological assay protocols published to-date. Biological assay is a popular data type for post-hoc data mining for research program planning, but most assay descriptions are not in a suitable form. It is expected that making assay information available in a FAIR format would increase the efficiency of bench scientists engaged in experiment planning and enable research that currently requires tedious expert literature review. The common data model can simplify data-sharing in collaborative research and publishing of research results. At this time most of the pharmaceutical businesses already have internal programs for conversion of assay protocols into a FAIR format. The effort for annotation of public assay protocols in these internal programs is therefore duplicated. Shifting the annotation of these public assays to a collaborative project would result in immediate cost savings to the member companies.
We will create a data product that would provide bioassay protocol information in a machine-readable FAIR form ready for data mining. Since the largest global pharmaceutical companies support this effort, the resulting data model, data ingestion business processes, and software would become de-facto industry standards that would further facilitate data sharing and collaboration.
We will use Natural Language Processing in combination with manual review of automatically produced assay annotations. Specific technologies will be selected in the pilot phase in the coming months.
To support the implementation of the FAIR guiding principles for the data management cycle by Life Science industry, through sharing best practices and provision of a FAIR Toolkit.
As the life sciences industry continues to transform digitally, the sector needs clear and practical guidance on how data and relevant metadata is captured and managed to foster greater collaboration and more effective partnerships. The FAIR guiding principles help to meet this need, but many companies are struggling to implement the guiding principles as discussed by Wise and co-authors (DOI:10.1016/j.drudis.2019.01.008). This practical need will be addressed by the development of the FAIR Toolkit which will consist of relevant methods and case studies.
The Pistoia FAIR Toolkit will include methods and case studies to support the implementation of the FAIR data management life cycle by life science industry. The methods will include selected tools, tips, FAQs, training materials and change management which will be illustrated by relevant case studies. The FAIR Toolkit will be assembled together on a user-friendly and freely accessible web site.
The project team will collect candidate content in a simple wiki and select the most relevant content to support implementation of FAIR by scientists in life science industry. This will lead to the specification, design and implementation of a FAIR Toolkit web site serving as a “one-stop shop”. This is expected to work much like the Pistoia UXLS Toolkit (uxls.org), which will be a useful reference for building the FAIR Toolkit website.
Method recapitulation is difficult and time consuming, within a company and even more across collaborating partners. A solution to store, search and retrieve a digital version of a method will improve scientist’s ability to retain institutional knowledge, reduce time for method development and improve method execution
Reproducibility of methods is expected to be significantly improved while time and resources to re-establish a method in a different lab will be drastically reduced.
From a cyber security perspective, a centralized storage location will create a much more resilient environment.
The project will achieve:
The transformation of text-based method descriptions into digital instruction sets for different analytical methods and instrument types, including a common digital format and an ontology for analytical chemistry methods.
The HPLC experts have identified a common set of HPLC parameters across different CDS and HPLC’s.
Semantic experts have built the common parameters into a semantic Data Model. The Allotrope Data Format is used to bring semantic description, result data and files together in one universal HDF5 container.
