Lonza, Onava Strike AI Biologics Pact to Speed Drug Discovery

Editor’s Take: AI can design drug candidates in days, but validating those designs in the lab remains a costly and time-consuming hurdle. Lonza and Onava are betting that tighter integration between AI models and experimental data generation will help produce biologic therapies that are not only effective but also easier to manufacture at commercial scale.

Lonza has entered a collaboration with biotechnology startup Onava aimed at accelerating the development of AI-designed biologic drugs, underscoring a broader industry push to bridge the gap between artificial intelligence and real-world laboratory validation.

Under a research evaluation, Onava will use Lonza’s GS Gene Expression System and GS Ori-Go vectors to test and refine biologic drug candidates generated by its AI-native discovery platform. The companies said the arrangement is designed to shorten the path from computational predictions to experimental evidence, a key bottleneck in AI-driven drug development.

Rather than relying solely on computer-generated predictions, Onava plans to feed experimental results from Lonza’s expression system back into its platform, creating a continuous feedback loop intended to improve future drug designs.

For Onava, the agreement provides access to industry-standard expression technology that can generate large-scale datasets on how AI-designed molecules perform in biological systems. The company said those insights will help it optimize not only for therapeutic activity but also for manufacturability and development readiness, factors that often determine whether a drug candidate can successfully advance toward commercialization.

The companies indicated the collaboration could extend beyond an initial program. Onava expects to use Lonza’s technology across multiple external partnerships as well as selected internal research efforts.

The partnership also positions Lonza to capture downstream development and manufacturing work if candidates emerging from the collaboration advance into later-stage development.

The announcement comes as pharmaceutical and biotechnology companies increasingly combine AI-driven discovery tools with established laboratory platforms in an effort to reduce development timelines, improve candidate selection and lower the risk of failure in later stages of drug development.