Emerging Trends & Technologies

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Blockchain Technology: This project introduces blockchain, describes how it works and discusses the pre-requisites for adopting blockchain. Members explore the qualities of blockchain relevant to the pharma setting, looking at examples of use cases and applications. Collectively, project members provide high-level analyses of the pros and cons of blockchain in pharma and healthcare.

Cloud Adoption in the Life Sciences Industry: Cloud Technology and it's use of multi-tenant app solutions are increasing the capabilities of Life Sciences solutions and reducing IT infrastructure costs through the sharing of infrastructure and investment cross-industry. The goal of this work stream has been to provide a practical, usable framework to overcome the barriers. Through the use of this framework, it is envisaged that the barriers to adoption by pharma of cloud-based technology will be addressed.

Data Visualisations for Clinical Data: The FDA Guidance on a Risk-Based approach to Monitoring (August 2013) opened the door to using scientifically founded monitoring solutions as alternatives to 100% source verification of clinical data. Individual companies have proposed a range of opportunities to look at the applicability of data visualisation within the Pharmaceutical environment that addresses cross-domain questions and insight associated with RMB.

Going Translational with Linked Data: This project builds on the successful completion of the "Clinical Trials Data as RDF" project where four SDTM domains (DM, VS, EX, TS) were modeled in RDF, and the ontologies used to create RDF instance data. Existing domains will be broadened to include non-clinical concepts, thus extending the impact of the project further along the data lifecycle. A minimum of two additional domains will be added, starting with AE (and non-clinical AE equivalent observations).

ODM4 Submissions : Widespread support exists for modernising the data transport format for the standardised submissions of clinical research data as part of an application to a regulatory authority. Despite the known limitations of the outdated SAS® Version 5 Transport (SAS V5 XPORT) format, it remains the current standard transport format for regulatory submission datasets. Its limitations are impacting the CDISC standards data representations as well as the technologies available to support data exchange. The Operational Data Model (ODM) standard has been the CDISC standard format for data exchange since 2000. Define-XML and Dataset-XML are ODM extensions supporting the transport of CDISC dataset metadata and data, respectively. Define-XML is now a required part of a regulatory submission. However, despite using Define-XML to submit dataset metadata, other data and metadata required for submissions are submitted in different file formats that adversely restrict data representation, machine-readability options, and the ability to validate submissions. This paper describes how the use of the existing ODM standards could simplify and modernise data exchange in support of regulatory submissions, as well as improving data exchange practices in other areas of clinical research. 

Investigating the USE of FHIR in Clinical Research: Increasing interest in eSource keeps the issue of data integration between Research Systems (EDC, CTMS, CDMS, etc) and healthcare systems (EHR, etc) as a consistent want for Sponsors clinical investigators and Regulators. The new PhUSE project 'Evaluating the Use of FHIR in Clinical Research' will look at how the HL7 FHIR standard could be used as a fundamental part of the clinical trial process in the future.

Key Performance Indicators & Metrics : Collecting, tracking and evaluating data on an ongoing basis can provide organisations with credible, compelling information when communicating with key decision-makers and stake-holders.The PhUSE Data Science & KPI Metric Reporting Group has been working to establish a set of common Data Reporting Metrics which are more detailed than industry wide metrics, therefore, allowing a greater level of granularity in our project reporting, and business process management.

Machine Learning/Artificial Intelligence 

The most popular buzz word nowadays in the technology world is 'Machine Learning' (ML) and Artificial Intelligence (AI). Most economists and business experts foresee ML & AI changing every aspect of our lives in the next 10 years through automating and optimizing processes. This project will explore its application to the pharmaceutical industry. Our goal is first to introduce ML & AI to the pharmaceutical industry. ML & AI are foreign to most programmers and statisticians in the pharmaceutical industry. This project will help them to start compiling ML & AI educations materials. Secondly we will explore how ML & AI can foster innovative approaches in data-driven research and drug development, personalised medicines, faster drug discovery and many more.

 

 

 

 

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