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PHUSE collaborations are organised into a number of specialist Working Groups, each with a broad topic area. The Working Groups have specific projects designed to achieve a set of particular objectives. This page will highlight the latest news and information from our projects. Participation is open to anyone who wants to contribute and if you would like to get involved, please email workinggroups@phuse.global.

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Community Forum: Regulatory Landscape of AI/ML in DHTs (Current Landscape, Knowledge Gaps, Best Practices for Regulatory Submissions, Challenges of Regulating AI)

The Emerging Trends & Technologies Working Group is hosting its second Community Forum of the year, Regulatory Landscape of AI/ML in DHTs (Current Landscape, Knowledge Gaps, Best Practices for Regulatory Submissions, Challenges of Regulating AI).

We will be hearing from both FDA and Pfizer speakers, who will be looking at how Digital Healthcare Technologies (DHTs) show significant potential for enhancing data acquisition in clinical trials by enabling objective and accurate data collection. Examples of DHTs include wearable devices – sensors, smartwatches, fitness trackers – that continuously monitor vital signs, physical activity, sleep patterns, and medication adherence. However, the emergence of DHTs also raises regulatory-related questions. In this PHUSE Community Forum, we will discuss the development of digitally derived measures (clinical outcome assessments and biomarkers) and germane regulatory policies from the perspectives of government and industry experts.

This Community Forum will be taking place over Zoom on 25 September at 13:30-14:45 (BST) / 08:30-09:45 (EDT).

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PHUSE collaborations are organised into a number of specialist Working Groups, each with a broad topic area. The Working Groups have specific projects designed to achieve a set of particular objectives. This page will highlight the latest news and information from our projects. Participation is open to anyone who wants to contribute and if you would like to get involved, please email workinggroups@phuse.global.


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Open Source Forum: Open Source Technologies in Clinical Data Analysis

The Open Source Technology in Clinical Data Analysis (OSTCDA) project was set up with the aim to create a manuscript on the integration of open-source software solutions for clinical data management, analysis and reporting.

A significant amount of time and energy has been invested in recent years exploring the desirability (do we want it?), feasibility (can we do it?), and viability (is it worth it?) of integrating open source solutions into our clinical data pipelines which transform source data into clinical study reports and submission data packages. In this October edition of the Open Source Open Forums, we will provide an update on the status of this initiative and continue to hear from you on what we’ve missed so far.  

When this manuscript is complete, we hope to put to rest some of the burning questions that we believe we now know the answers to. This will allow industry, and all the passionate people in it, to look ahead and start tackling the next horizon of challenges related to using open source solutions for clinical data pipelines. We hope you will contribute your expertise to this effort. The OSTCDA Project will be hosting their next forum to discuss the above, titled Open Source Technologies in Clinical Data Analysis.

This Forum will be taking place over Zoom on 11 October at 15:00-16:00 (BST) / 10:00-11:00 (EDT).

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Community Forum: RWD Sources – How the Genesis of Your Data Determines What Questions You Can Answer

RWD Sources – How the Genesis of Your Data Determines What Questions You Can Answer.

When considering sources of RWD, it is important to consider more than just the number of available patients. Recently released guidance from the FDA encourages researchers to build accurate, complete and traceable real-world datasets. Combining data from structured and unstructured electronic health record data, closed claims, and other sources is essential to building the patient journey.

In our October Community Forum, we will explore two different angles of RWD sourcing from the provider’s point of view: 

  • Tim (Verantos) will share with us novel insights into unstructured data and examples of the value that can be leveraged from such data.
  • Matt (Azimuth) will share with us details and example analyses of a unique research data source derived from linking claims and EHR data hosted by the Department of Defense network.

Come and join us for what promises to be another very interesting Community Forum!

This Forum will be taking place over Zoom on 24 October at 14:00-15:00 (BST) / 9:00-10:00 (EDT).

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Data Transparency Autumn Event 2024 

Active since 2014, PHUSE’s Data Transparency Working Group has provided subject matter expertise for the review of draft deliverables and guidance documents from regulatory bodies (such as the EMA and Health Canada), as well as other industry organisations (such as TransCelerate) and academia. Since their inception in 2020, the free-to-attend Data Transparency Events have gone from strength to strength. These virtual events have created an unrestricted space where questions can be asked and challenges addressed. Individuals passionate about the area can come together to share vital knowledge, develop new ideas and spark innovation through presentations, panel discussions and Q&A sessions alongside experts in the data-sharing field.

The PHUSE Data Transparency Autumn Event took place from 17–19 September 2024.  During this virtual event, presentations were delivered across the three days in bitesize chunks. Each day also hosted a panel discussion and Q&A session focused on the day's themes.

Visit the PHUSE archive for all previous event recordings and presentations. 



The Nonclinical Topics Working Group has just formed a new project titled Developing Predictive Models to Facilitate Interpretation of Toxicology Study Results which is now calling for volunteers.

A computational pipeline to build models to predict target organs of toxicity from SEND datasets has been developed and published on GitHub under PHUSE. Project team members will evaluate the feasibility and performance of this pipeline when run on data from within their organisations. The pipeline will be updated to improve compatibility with different database systems, and efforts will be made to improve its performance across disparate data sources. Additional study interpretations – e.g. adversity of findings, NOAEL determination, clinical translatability, structure activity relationship – will be explored for development of predictive models. Successful modeling approaches will be published in peer-reviewed scientific journal articles.

If you would like to volunteer or learn more about this project, email workinggroups@phuse.global. Closing date: 24 October



The SDTM ADaM Implementation FAQ project within the Optimizing the Use of Data Standards Working Group has published a new FAQ! The question they have answered is 'When to Submit an eCTD or Standardised Data Sample to the FDA'.You can view this question and the team's response via the Data Submission page. You can see all the FAQ's this project has answered so far via here

Do you have a SDTM ADaM Implementation FAQ question? You can send your questions to the team by emailing workinggroups@phuse.global


TheTreatment Emergent Definitions Recommendations project within the Safety Analytics Working Grouphas published a new White Paper, 'Recommended Definition of Treatment-Emergent Adverse Events in Clinical Trials'. This White Paper defines treatment-emergent events in Phase I to IV clinical trials and integrates summary documents across therapeutic areas. The recommendations described herein were based on the authors' collective experiences and a survey conducted by the PHUSE Treatment Emergent Definitions Recommendations project team to solicit input from respondents on various TEAE scenarios for a simple clinical study design. 


The Comparing Analysis Method Implementations in Software (CAMIS) project under the Data Visualisation & Open Source Technology Working Group have just published a blog titled "A Story from the Novartis Hackathon: Industry Acceptance of the CAMIS Project". This blog talks about the CAMIS project and their involvement atthe Novartis Hackathon.




The Risk Based Quality Management Working Group has a new Working Group Lead. Mireille Lovejoy currently serves as Director, Clinical Risk Management and Process Excellence at GE HealthCare, a role dedicated to the design and implementation of Risk Based Quality Management (RBQM) and quality at the system level within diagnostic clinical trials. Mireille has extensive experience of RBQM having previously led the development of processes, resources, and technology underpinning the approach within 2 bio-pharmaceutical companies.


The CDISC Standard for Exchange of Nonclinical Data (SEND) data standard has created new opportunities for collaborative development of open-source software solutions to facilitate cross-study analyses of toxicology study data.

A sub team of the BioCelerate SEND Implementation for Cross Study Analysis Initiative was formed and through collaborative efforts via a public–private partnership between BioCelerate and the FDA/Center for Drug Evaluation and Research (CDER), they worked to develop and publicise novel methods to facilitate cross-study analysis of SEND datasets.  

As part of this work in collaboration with the Pharmaceutical Users Software Exchange (PHUSE), an R package called sendigR has been developed to enable users to construct a relational database from a collection of SEND datasets and then query that database to perform cross-study analyses. The sendigR package also includes an integrated Python package, xptcleaner, which can be used to harmonise the terminology used in SEND datasets by mapping to CDISC controlled terminologies. This package, sendigR, will provide data scientists and toxicologists with a free, open-source tool that can be utilised to query large repositories of electronic standardised toxicology study data. sendigR was published to the Comprehensive R Archive Network (CRAN) and GitHub - phuse-org/sendigR: Enable Cross-Study Analysis of 'CDISC' 'SEND' Datasets. An R Shiny web application was included in the R package to enable toxicologists with no coding experience to perform historical control analyses. Experienced R programmers will be able to integrate the package functions into their own custom scripts/packages and potentially contribute improvements to the functionality of sendigR.

To learn more, read the sendigR accompanying manuscript here: Frontiers | sendigR: an R package to leverage the value of CDSIC SEND datasets for cross-study analysis (frontiersin.org)

sendigR reference manual: Enable Cross-Study Analysis of CDISC SEND Datasets • sendigR (phuse-org.github.io)

sendigR R Shiny demo app: phuse-org.shinyapps.io/sendigR/-------------------------------------------                                                                                                                                                                                                                                                                                        



Working Groups Report: Includes project updates, recent and upcoming deliverables and future plans for each Working Group.

Monthly Mailings: The monthly newsletter. Here you will find a full update from each month of the year, easily accessible and divided into key areas of PHUSE.

PHUSE Blogs: Fancy a quick read? A blog is a perfect way to catch up on all things Working Groups. Get the lowdown on the latest events from across the globe and stay updated on industry topics brought to you by industry professionals. 



Initiate and lead a new project under the PHUSE Working Groups umbrella. The new project must address problems of significant relevance to computational science related to drug, biological and device development and must meet all of the guidelines for projects within the collaboration, including the following mandatory requirements: 

  • The projects must address significant research issues relevant to Computational Science
  • The project must not attempt to address FDA policy issues
  • There must be at least one Project Lead personally involved in planning and carrying out the project

New projects can be submitted anytime during the year, click here to submit.