Valinor and Renew Unite to strengthen Neuro Research

Partnership integration of high-resolution multi-omics data generation with predictive multimodal machine learning to support biopharma decision-making in neurology, which avoids treatment delay.

Strategic collaboration between Valinor Discovery and Renew Biotechnologies aimed to generate the largest clinical multi-omics dataset to date for neurological disorders. It is designed to improve the prediction of disease and therapeutic responses.

Intuitively, Valinor will provide access to multimodal datasets derived from thousands of patients having Alzheimer's disease, Parkinson's disease, and other neurological disorders to support large foundation model development.

The collaboration combines Valinor's expertise in machine learning with clinical laboratory expertise in generating high-resolution multi-omics data from patient samples.

Advancements in technology and neurological drug development are exceptionally challenging in the biological complexity of the central nervous system and the limited availability of clinically relevant human data. Companies are trying to integrate large longitudinal molecular datasets with advanced predictive modelling to fill this gap.

Joshua Pacini, Co-Founder and CEO of Valinor, said Predictive models are crucially dependent on the quality and relevance of the underlying training data.

A combination of neurological domain expertise and access to high-quality, longitudinal molecular and clinical data that are relevant to drug development.

Chad Pollard, Co-founder & CEO of Renew Biotechnologies, said Neurology programs often slow or fail when the underlying disease biology is not fully understood. This partnership aims to increase decision-making confidence, accelerate programs toward clinical milestones, and reduce R&D costs.

Renew Biotechnologies is a translational diagnostics company focused on resolving disease-specific molecular signals to advance research, diagnostic, and therapeutic development.

Valinor is revolutionising therapeutic R&D by using machine learning combined with proprietary clinical datasets. By reducing trial-and-error and de-risking development, it aims to accelerate breakthroughs across therapeutic areas and drug modalities.