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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.

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We are delighted to announce our annual fall conference as a virtual event!

On October 19-23, Pistoia Alliance Virtual Conference Week will address the need to “Collaborate to Innovate” in life science research and development.

News

Almost One Third of Life Science Companies Set to Begin Quantum Computing Evaluation This Year

Boston, 14 July, 2020: New survey results from...

Pistoia Alliance and Mission: Cure win Bio-IT World 2020 Innovative Practices Award

Bio-IT World has announced the winners of the 2020...

AI CoE 2020 Drug repurposing datathon postponed

Our second drug repurposing datathon is dedicated to finding...

CAS and Pistoia Alliance partnering for Chemical Safety Library

CAS Makes Commitment to Enhance Laboratory Safety by Partnering...

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Latest Projects

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.

Project one: Informed Consent Blockchain

The Pistoia Alliance Informed Consent blockchain project will demonstrate the value of blockchain technology in underpinning the clinical trial Informed Consent process.

Why is this important?
What will the project achieve?
How will the project do this?
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Obtaining and revoking consent in clinical trials is a complex process involving multiple stakeholders, documents and transactions in which the creation and maintenance of a secure audit is imperative for regulatory purposes. Blockchain is an emerging technology which enables secure transactions between parties by creating an immutable record of these transactions and storing them as part of a decentralised system.

Demonstrate the value of blockchain technology in the Informed Consent process by creating an end-to-end audit trail in a secure and consistent manner, underpinning a higher level of trust between stakeholders participating in a clinical trial.

Implement common (multi-pharma) Decentralized ID (DID) management across multiple stakeholders which is agnostic of front-end applications.
Present a tamper-proof audit trail for a defined clinical trial Informed Consent process.

Get in touch

If you would like to get involved or have further questions or feedback email the project manager, Richard Norman.

Project two: Digital health data in clinical studies

Bringing the industry together to define quality in study design and reporting for digital medicine by establishing consistency in the evidentiary base used to support clinical applications of digital medicine tools.

Why is this important?
What will the project achieve?
How will the project do this?
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To achieve broad adoption and positively impact health, digital medicine products require evidence to demonstrate that they are trustworthy. The quality and quantity of evidence required to deem a product fit-for-purpose are not agreed upon by industry, patient, or government stakeholders. Currently, there is no consensus on what constitutes “evidence” or what metrics should be used to distinguish high-quality evidence from low-quality evidence.

Defining quality in study design and reporting will drive the field of digital medicine towards maturity by establishing consistency in the evidentiary base used to support clinical applications of digital medicine tools. This will in turn allow trials to be run in a distributed fashion, collecting more critical data which will help produce better outcomes and more precision medicines.

The project will combine inputs from the key areas of digital medicine – patients, CROs, sponsors and technology companies to ensure that each stakeholders requirements are considered.

Project three: SEED Project

SEED will enable a FAIR aligned comprehensive semantic capture and translation of data across eLN providers at the point of entry. Output will be computer readable standard data, increasing capacity for provenance and attribute connection for insight and analysis

Why is this important?
What will the project achieve?
How will the project do this?
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Volumes of unstructured eLN content are exponentially growing
Most eLN content is free text, unstructured information and thus challenging to identify common concepts, to effectively search and extract deeper Insights
Retrospective data analysis and searching of generally unstructured content is a huge challenge
Data Workflows to/from eLNs can be relatively restricted in approach. Connectivity to Study Registration systems to enable Scientists to reuse such metadata through API to eLN are a must
Availability of persistent identifiers challenges the capability of making data to be digitally discoverable and hinders aggregation of data inter-applications
Valuable insights are hampered by unstructured content which prevents deeper data analysis thereby losing competitive advantage

Through collaboration and alignment across the pharma industry, the SEED project will create a standardized computer readable FAIR aligned data foundation.

A working prototype solution will be created to enable data discoverability/insights inter and intra eLN system.