Benevolent AI and AstraZeneca expand collaboration on heart failure and systemic lupus discovery
pharmafile | January 13, 2022 | News story | Business Services |
Leading clinical stage AI drug discovery company, Benevolent AI, has expanded its AI-driven drug discovery collaboration with AstraZeneca. This means that the number of disease areas being explored through the partnership will be doubled.
The expansion builds on the partnership initiated in 2019, and adds systemic lupus erythematosus (SLE) and heart failure (HF) to research on identifying multiple novel targets in chronic kidney disease (CKD) and idiopathic pulmonary fibrosis (IPF).
SLE is a complex autoimmune condition that can affect multiple organ systems, and people often experience inadequate disease control, long-term organ damage and poor health-related quality of life. It can cause a range of symptoms, including pain, rashes, fatigue, swelling in joints and fevers. At least five million worldwide have a form of lupus.
The Benevolent Platform is disease agnostic, meaning it can be applied to any disease, and is capable of generating novel targets at scale. Scientists and technologists from both companies will use the Benevolent Platform and biomedical Knowledge Graph – which includes AstraZeneca proprietary data with public and licensed data from scientific literature, patents, genetics, chemistry, clinical trials, and more.
Anne Phelan, Chief Science Officer at BenevolentAI, said, “Our collaboration with AstraZeneca represents the future of drug discovery. It brings together traditional biology with innovative AI-driven technologies to integrate and analyse vast amounts of scientific data from diverse sources. In doing so, we can uncover new ways to understand complex disease biology and identify novel and biologically plausible drug targets.
“Our progress so far has created the best possible foundation to further catalyse productivity at this critical stage in drug R&D, and ensure new therapies reach patients as efficiently as possible.”
Lina Adams
