Project Charter The goal of the Pharmacovigilance Systems and Processes Standards (PS2) program is to establish a set of standard solution requirements for pharmacovigilance systems, starting with Case Intake in 2025 via the PS2 – Case intake project, to avoid replication of effort during solution selection and implementation and foster...
We propose to develop harmonized standards for describing animal alternative methods, their characterization, and develop best practices for management and analysis of data.
The goal of the system is to make it easier for regulators and other stakeholders to exchange information about substances in medicines, supporting scientific research on the use and safety of the ingredients in medicinal products.
The goal of the In Vitro Pharmacology project is to develop a shared data template that standardizes description vitro methodologies.
Project CharterThis collaborative project was launched to create a bottom-up qualitative Natural Language Processing (NLP) Use Case Database, to allow NLP practitioners in pharma companies to share successes and failures with their peers.
The use of Large Language Models such as GPT-4, presents a transformative opportunity for pharmaceutical R&D, particularly in target discovery and validation.
This project aims to convert unstructured assay protocol descriptions into a high-quality FAIR data set, and create standards for this information.
Project CharterOur project aims to develop a pharmaceutical (CMC) process ontology, based on the ISA88/95 framework. This ontology will serve to standardize laboratory and plant production process recipes, and in turn, establish standardized definitions.
The project goal is to build an IDMP Ontology that enables deep, semantic interoperability based on FAIR principles to enhance and augment the existing IDMP standards.
This project will develop and publish key regulatory guidelines associated with the use of social media as real-world data to produce real-world evidence.
This project will provide a core framework to create interoperability between FAIR data sets across the pharmaceutical industry.
The project wishes to develop a dynamic model that compares the carbon footprint of a clinical trial in traditional, hybrid and decentralised settings.
