CSS Working Groups

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Instructions

This wiki is available to document the progress of the PhUSE Computational Science (CS) working groups and projects. The wiki enables quick,easy and transparent online collaboration. Working groups each have a main page which is the starting page for all Working Group activity.

If you are new to wikis you might want to check out the help section. To get started click on your working group below.

For instructions on how to subscribe to the working group/project email list or contact a working goup leadership team click here

The Working Groups are governed by the Computational Science Steering Committee which provides oversight to the Working Groups.

Useful Information for Projects

Brand New: PhUSE has been working on producing a Standard Operating Procedure for Version Control of PhUSE Deliverables. We are pleased to announce this has now been agreed by the Steering Committee and PhUSE Board of Directors. Moving forward, we ask all members that are working towards a deliverable for review and approval, to start using this agreed process. If you have any queries, please reach out to your Working Group Lead.



Have a Project idea?
Complete the New Project Request template for submission to the Steering Committee through your Working Group Lead

Giving a PhUSE Presentation?
Here is the link for PhUSE CS Power Point Template

Writing a White Paper / PhUSE Deliverable?
Use this PhUSE Deliverable Template to get you going

Need to create a Wiki project area?
Use this link to start promoting the work your project is doing

Call for Volunteers
Need additional support? People to join the project? Contact wendy(at)phuse.eu who can advertise through the PhUSE media outlets

Overview Of PhUSE Structure And Projects
PhUSE Structure

PhUSE CS Working Groups

CS Catalog of Deliverables on www.phuse.eu

CS Catalog of Deliverables Under Review on www.phuse.eu

Optimizing the Use of Data Standards The development and adoption of data standards over the last decade has shown significant promise in improving efficiencies in the product submission and review process. However, there have also been gaps, issues and challenges in the interpretation and use of the standards. This group will identify specific gaps preventing FDA and industry from optimizing the use of standards and collaborate to close those gaps. Here is a list of our current projects.

Best Practices for Data Collection Instructions
Best Practices for Metadata Documentation (define.xml versus reviewer's guide)
Data Reviewer's Guide in XML
Define-XML V2.0 Completion Guidelines and Stylesheet Recommendations
Clinical Legacy Data Conversion Plan & Report
SDTM ADaM Implementation FAQ
(Relaunched) Standardizing Data within the Inspection Site Selection Process
Study Data Standardization Plan (SDSP)
Pooling WHO Drug B3 Format
GDPR Impact on Data Collection Practices
Industry Experiences Submitting Standardised Study Data to Regulatory Authorities

Standard Analyses and Code Sharing Leverage crowd-sourcing to improve the content and implementation of analyses for medical research, leading to better data interpretations and increased efficiency in the clinical drug development and review processes. Here is a list of our current projects.

Script discovery and acquisition
Repository content and delivery
Repository governance and infrastructure

Communication, Promotion and Education
Analysis and Display White papers
Test Dataset Factory


Standard Analyses Deliverables & Key Links

Nonclinical Topics There is a need to improve nonclinical assessment and regulatory science by identifying key needs and challenges in the field and then establish an innovative framework for addressing them in a collaborative manner. The group created a framework for moving certain projects forward to support nonclinical informatics efforts and to develop specific implementation solutions and SEND. Here is a list of our current projects.

Nonclinical Study Data Reviewers Guide
SEND Implementation User Group
Visualisation of Group Related Differences in Histopathology Data
Industry SEND Progress Survey
Nonclinical Script Assessment Project
Data Consistency: SEND Datasets and the Study Report
Demystifying Define-XML Codelist Handling for Nonclinical Studies
Modeling Endpoints: How to Model Anti-Drug Antibody Data in Nonclinical Studies

Emerging Trends and Technologies This emerging technologies working group will be an open, transparent forum for sharing pre-competitive means of applying new technologies and is being challenged with creation of well-defined collaborative projects that will describe, prioritize, assess, and assist advancement of these opportunities. Possible topics include (but are not limited to) semantic web applications, analysis metadata, modeling, simulation, and “The Cloud”. Here is a list of our current projects.

Cloud Adoption in the Life Sciences Industry
Data Visualizations for Clinical Data
Investigating the use of FHIR in Clinical Research
Clinical Trials Data as RDF
Introduction to Clinical Development Design (CDD) Framework
Blockchain Technology
ODM4 Submissions

Educating For The Future
Methodologies and technologies utilized in the clinical development process have been largely unchanged since 2005. Externally, there have been immense advancements in technology and mindsets related to working with data that have had little impact in our industry. This Working Group sees this as a risk. The risks breakdown into 3 categories:

1) There is a risk of advancement happening outside of our domain that may expose us to threats that we are unable to protect ourselves against.

2) There is a risk that our skill sets and capabilities become outdated and external forces strongly influence the Industry without us having a say

3) There is a risk that we are missing opportunities to become more efficient and to use these advancements to deliver better value for patients

The goal of this Working Group is to develop frameworks by which to educate the PhUSE community at large. The frameworks will be designed to educate the community on the importance of topics where we feel we have gaps, the details of the topics themselves, and how they can be used to drive innovation in the industry.

Data Science
Machine Learning / Artificial Intelligence
Design Thinking
Data Engineering

PhUSE Archived Working Groups & Projects

Archived Working Groups & Projects

Disclaimer

FDA comments are an informal communication and represent the individual's best judgment. These comments do not bind or obligate FDA. The contents of this wiki are from the individual contributors and do not necessarily reflect the view and/or policies of the Food and Drug Administration, the employers of the individuals involved or any of their staff.