Emerging Trends & Technologies

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Cloud Adoption in the Life Sciences Industry: Cloud Technology and it's use of multi-tennant app solutions are increasing the capabilities of Life Sciences solutions and reducing IT infrastructure costs through the sharing of infrastructure and investment cross-industry. In some areas multi-tennant cloud solutions have become ubiquitous. For example, Salesforce.com claim >150K customers utilise their platform for Customer Relationship Management. Furthermore, many routinely rely on cloud services associated with the computer backups and data services associated with cell phones. Nonetheless, the perceptions and interpretations of the regulations by which the Life Sciences industry must conduct it's business, still leave many uncertain about whether or not they can or should pursue the use of cloud solutions for GxP applications. The goal of this work stream has been to provide a practical, usable framework to overcome those 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.

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. This group will meet as part of the CSS to define how the project will proceed for the remainder of 2017. We invite anyone with an interest in EHR, Clinical Trials and eSource to join us.

Development of a Clinical Development Design (CDD) Ontology: Facilitating information sharing requires a standardised information model. Information modelling today focuses on individual clinical trials, and the representation of clinical trial data. Although work is ongoing to expand standards to cover the protocol, these are insufficient to capture the objectives, rationale, and design thinking behind clinical programs. The Clinical Development Design (CDD) Framework aims to provide structure and rigour to the design of clinical research, more specifically clinical development programs consisting of clinical trials and the interventions before clinical trials that are needed in implementation of the clinical research project. The CDD Framework explores the gaps and challenges in design practices supported by templates, documents and unstructured tools.

Clinical Trials Data as RDF: SDTM domain data as Resource Description Framework (RDF) has the potential to create highly compliant, high-quality submissions. Graph data can be mapped to versions of the standard, reducing costly recoding. Creation of Define documentation becomes a graph-based query when the proper metadata is integrated into the model. Costs for data review, validation, and rework will be greatly reduced.

Blockchain Technology: (New Project) Introduce BlockChain and describe how it works. Pre-requisites to adopt BlockChain. Understand the qualities of BlockChain relevant to the Pharma setting and the example of use cases and applications. Provide high level analysis of Pros/Cons of BlockChain in Pharma and Healthcare

ODM4 Submissions (New Project): 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. 

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