News Overview
- BigHat Biosciences and Eli Lilly and Company are collaborating to discover and develop novel antibody therapeutics using BigHat’s AI/ML-driven antibody discovery platform.
- The partnership aims to leverage BigHat’s Milliner™ platform to design antibodies with specific functional properties and improved developability profiles.
- Lilly will pay BigHat an upfront payment and may provide additional payments based on achieving specific development, regulatory, and commercial milestones, along with tiered royalties on future sales.
🔗 Original article link: BigHat Biosciences and Lilly Collaborate to Advance AI-Driven Antibody Therapeutics
In-Depth Analysis
The core of this collaboration lies in BigHat Biosciences’ Milliner™ platform. This is an AI/ML-powered system designed to improve the antibody discovery process. The Milliner platform integrates:
- High-throughput experimentation: Allows for the rapid generation of vast amounts of experimental data.
- Machine Learning: Employs algorithms to analyze the data, identify patterns, and predict antibody properties like binding affinity, specificity, and developability.
- Protein Engineering: Facilitates the design and optimization of antibody sequences based on the AI’s predictions.
The collaboration focuses on designing antibodies with:
- Specific functional properties: This could include enhanced target binding, improved neutralization of disease-causing agents, or optimized delivery to target tissues.
- Improved developability profiles: Addressing crucial factors for therapeutic success, such as stability, solubility, and manufacturability. Developability problems are a common cause of drug development failure; BigHat’s platform directly attempts to mitigate these risks.
The financial terms of the agreement include:
- Upfront payment: A payment to BigHat upon initiation of the collaboration. The specific amount was not disclosed.
- Milestone payments: Contingent on achieving predetermined milestones in the development, regulatory approval, and commercialization phases. The total potential value of these payments was not specified.
- Tiered royalties: BigHat will receive a percentage of future sales if the developed antibodies reach the market. The royalty rates are likely structured to increase with sales volume.
Commentary
This collaboration represents a significant validation of BigHat Biosciences’ AI-driven antibody discovery platform. For Lilly, it offers access to a potentially faster and more efficient way to identify and optimize antibody therapeutics. The partnership illustrates the growing adoption of AI and ML in drug discovery, particularly in antibody engineering where large datasets are readily available.
Potential implications include:
- Accelerated drug development: The AI-driven approach could significantly shorten the time required to identify and optimize antibody candidates, potentially bringing therapies to patients faster.
- Reduced development costs: By improving developability early in the process, BigHat’s platform may reduce the risk of late-stage failures, leading to lower overall development costs.
- Improved antibody properties: The platform’s focus on both functional properties and developability could result in more effective and safer antibody therapeutics.
However, key considerations remain. The success of the collaboration depends on the ability of the Milliner platform to accurately predict antibody properties and translate those predictions into tangible therapeutic benefits. The specifics of the targets and therapeutic areas involved were not disclosed, which would provide greater context on the potential impact. Finally, the undisclosed deal terms make it hard to evaluate the partnership in comparison to other AI-driven pharmaceutical collaborations.