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Nonclinical Advance Event | ||
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The Nonclinical Topics Working Group is excited to announce a multi-day virtual event coming 10–12 February 2026 at 09:00–11:30 (EST) / 14:00–16:30 (GMT) / 15:00–17:30 (CET). We have an exciting agenda of expert presentations, interactive breakout sessions and project updates, spread across three days. The event will focus on four core themes – new approach methodologies (NAMs), nonclinical predictive modelling, machine learning and PHUSE project contributions and developments – and enable attendees to explore each topic in depth with leaders from industry, regulatory agencies and the wider PHUSE Community.
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Real World Evidence | ||
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Webinar: Implementing Estimands & Target Trial Emulation (TTE) in Real-World Evidence: Case Studies & Perspectives 24 February 2026 at 11:00-12:00 (EST) / 16:00-17:00 (GMT) / 17:00-18:00 (CET).
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Risk Based Quality Management | ||
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The End to End RBQM Education project is hosting a new webinar: Decoding AI in Clinical Trials: Practical Applications, Regulatory Reality, and the Road Ahead. This webinar offers a practical, reality-check view of how AI and large language models are being applied in clinical trial conduct today. It explores real-world use of AI in RBQM, regulatory and compliance considerations, and what generative AI can (and can’t) deliver. Helping organisations prepare for what’s coming over the next 3–5 years. This webinar will be held virtually on 26 February 2026 at 10:00-11:00 (EST) / 15:00-16:00 (GMT) / 16:00-17:00 (CET).
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Imaging Data Anonymization Guideline |
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The Anonymization of Imaging Data project within the Data Transparency Working Group have published a new white paper: Imaging Data Anonymization Guideline which is now open for public review. The purpose of this guideline is to define the minimum anonymization standards for DICOM imaging data shared externally via a secure, governed research environment (SGRE). This document aims to ensure that all shared clinical trial imaging data complies with privacy regulations and ethical standards, thereby protecting the identities of clinical trial participants. It is intended for people involved in data processing, anonymization, and external collaborations, as well as for external researchers who will access these anonymized data. The team welcome feedback on the paper from 6 January – 3 February. Please send responses to workinggroups@phuse.global. |
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PharmaForest: A collaborative repository of SAS packages for pharmaceutical industry (PLSs) project team within the Data Visualisation & Open Source Technology Working Group, is calling for volunteers! PharmaForest is a collaborative initiative focused on improving how SAS programming is developed and shared across the pharmaceutical industry. Built on the SAS Packages Framework (SPF), it promotes reusable, standardised SAS packages to reduce duplication, improve efficiency, and support compliance. By combining an open package repository with community collaboration and practical guidance, PharmaForest helps move SAS programming from siloed approaches to a more scalable, industry-wide model. If you’d like to become a valued project member, please get in touch with workinggroups@phuse.global. |
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