Protillion Biosciences has developed a high-throughput protein display platform that quantitatively characterizes protein candidates to discover therapeutic antibodies with precise specificity and affinity. This technology addresses the inefficiencies in traditional therapeutic antibody discovery, enabling faster identification of optimal drug candidates.
Funding
$19.3M raised to dateRaised to date based on public sources. This may differ from the amount the company actually raised and is based only on what is publicly available on the internet.
Founders
Product
Problem
Traditional therapeutic antibody discovery and optimization methods are inefficient, often requiring extensive screening and characterization to identify candidates with the desired specificity, affinity, and manufacturability. The low throughput and limited quantitative data in conventional approaches can lead to suboptimal drug candidates and prolonged development timelines.
Solution
Protillion Biosciences offers a high-throughput protein display platform that enables quantitative characterization of protein candidates, accelerating the discovery of therapeutic antibodies. The platform simultaneously analyzes protein candidates at a massive scale, providing comprehensive quantitative data on specificity, affinity, and manufacturability. By integrating advanced computation with high-throughput experimentation, Protillion's technology identifies optimal drug candidates with exquisite specificity and precisely tuned affinity, including ultra-high affinity. This approach streamlines the discovery process, bringing the best therapeutic candidates to light faster and more efficiently than traditional methods.
Target Audience
The primary target audience includes pharmaceutical and biotechnology companies involved in therapeutic antibody discovery and development.
Features
- High-throughput protein analysis chip for comprehensive protein characterization
- Library encoding protein-based drug candidates
- Quantitative data generation for entire library of candidates
- Next-generation sequencing (NGS) integration for DNA data analysis
- Machine learning algorithms for drug discovery and optimization