Roche-owned Genentech has penned a blockbuster deal with Earendil Labs to discover and develop bispecific antibodies for oncology.
In exchange for $55m upfront and up to $1.445bn in development, regulatory and sales milestone payments, Earendil will help Genentech identify and develop a range of bispecific antibody programmes based on pre-agreed target combinations.
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Under the agreement, the US and China-based biotech will also take charge of each programme’s early development up until the early clinical stage – after which Genentech will take the helm for any subsequent development or commercialisation activities. If any of these drugs make it to market, Earendil is also eligible to receive tiered royalties on their sales.
At the core of this deal is Earendil’s artificial intelligence (AI)-powered high-throughput biology platform, which incorporates predictive protein modelling and generative protein design for the rapid identification of therapeutics. By using this platform, Earendil hopes to create differentiated bispecifics to address unmet needs such as treatment resistance or relapse.
Roche is not the first big pharma company to ink a deal with Earendil. Sanofi forged a $2.56bn autoimmune-focused agreement with the biotech back in January.
Sanofi’s dealings with Earendil did not stop there, with the French pharma giant having contributed to the company’s $787m funding round. Dimension Capital and Pfizer & Hillhouse’s Biotech Development Fund also played a role in this financing, allowing the biotech to boost the use of its AI-powered technology while progressing its selection of more than 40 programmes spanning disease areas like immunology, inflammation, and oncology.
AI becomes a mainstay tool in drug R&D, but governance key
In recent years, there has been significant buzz around the use of AI in drug discovery, as it promises to expedite the screening process and identify molecules that could offer significant therapeutic benefit.
This has led several pharma giants like Novo, Eli Lilly, GSK and Bristol Myers Squibb (BMS) – among others – to invest in AI capabilities to restock pipelines and secure future revenue streams amid key patent expiries.
However, a recent hacking event involving an OpenAI model, which gained unintended access to private healthcare files held by the Australian government, raises questions around the safety of using such a technology in a highly regulated context such as drug development.
With this in mind, experts are increasingly stressing the importance of effective governance procedures when incorporating AI into the workflow, with suitable guardrails in place to prevent an agent from deviating from its expected activity.
