In Vitro Noval Alternative Methods (NAMs) are human cell-based assays and systems used in biomedical research which represent human physiology, predict clinical outcomes and and reduce, replace or refine the requirements for animal testing in drug discovery.
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
This project built a solution for Informed Consent in clinical trials and implemented key components of decentralized digital identities
This project established best practices and tool recommendations for the application, management and mapping of ontologies in life sciences
This project developed and published best practice guidelines for the storage of collected sensor-generated data from remote modelling technologies
This project generated novel Ab structures and addressed the need for improved Ab structural data sharing to accelerate drug discovery
This project created an open, extendable, and freely available data format for the exchange of experimental information about compound synthesis and testing
This project enabled the digitization of analytical methods for seamless exchange between different High-Performance Liquid Chromatography (HPLC) systems and Chromatography Data Systems (CDS).
This project created and established Hierarchical Editing Language for Macromolecules (HELM) as the industry’s complex biomolecule notation of choice
This project helped the life sciences industry unlock the value of data in Electronic Lab Notebooks (ELN) using data standards and semantic enrichment
In vitro Pharmacology screening is performed extensively in the drug discovery process before IND (Investigational New Drug) applications for the best drug candidates are submitted to the FDA and regulatory bodies who assess their safety.
This 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