Under the three-year deal, the partnership will use insitro’s platform to create disease models for NASH and discover targets that can impact clinical progression and regression of the disease.
The insitro Human (ISH) platform is said to apply machine learning, human genetics and functional genomics for the generation and optimization of unique in vitro models, as well enhancement of the therapeutic discovery and development.
The ISH platform will offer insights into disease progression, recommend candidate targets and predict patient responses to potential therapeutic interventions.
Gilead intends to advance up to five targets identified via collaboration, and will take responsibility for chemistry and development against these targets.
Gilead Sciences research and development head and chief scientific officer Dr John McHutchison said: “Gilead is committed to researching and developing treatments for patients living with NASH, particularly those with advanced fibrosis who have the greatest unmet need.
“We are excited about the opportunity to partner with insitro to tackle the scientific challenges associated with this complex disease.”
As per terms of the deal, insitro will secure an upfront payment of $15m and additional near-term payments up to $35m based on the achievement of operational milestones.
insitro is also eligible to secure up to $200m for the achievement of preclinical, development, regulatory and commercial milestones for each of the five Gilead targets. It will also receive low double-digit tiered royalties on net sales.
The company will also be responsible for co-develop and co-detail of the selected programs in the US. It will secure a profit share in China, as well as milestone payments and royalties on other ex-US sales.
Insitro CEO and founder Dr Daphne Koller said: “NASH is a progressive liver disease that can lead to fibrosis, cirrhosis, and liver cancer and will soon be the predominant cause of liver transplantation in the U.S.”
insitro is a data-driven drug discovery and development company, which uses machine learning for the discovery of drugs.