Biointron Biological USA Inc. has introduced RushData, a new integrated service platform designed to accelerate antibody discovery by providing rapid experimental validation for artificial intelligence (AI) and machine learning (ML)-generated antibody candidates.
The launch comes as pharmaceutical and biotechnology companies increasingly adopt AI-driven approaches to design therapeutic antibodies. While computational models can now generate hundreds or even thousands of antibody sequences in a single design cycle, validating those candidates in the laboratory remains a major challenge, often slowing drug discovery.
Traditional workflows typically require antibody expression, binding analysis, and developability testing to be carried out separately, frequently across multiple providers. According to Biointron, this process can take three to four weeks or longer, creating a bottleneck in the design-build-test-learn cycle that underpins modern AI-based drug discovery.
RushData aims to streamline that process by integrating these critical experimental steps into a single workflow.
The platform is built around Biointron’s one-day transient Chinese hamster ovary (CHO) cell expression system, allowing researchers to move from antibody sequence submission to high-quality experimental data within days. The company said the platform combines antibody expression, binding characterization, and optional early developability testing while generating standardized datasets suitable for AI model training, validation, and optimization.
By using CHO cells—the industry standard for therapeutic antibody production—RushData is designed to generate biologically relevant data that better predicts how antibody candidates may perform during later stages of development. CHO-based expression provides human-like post-translational modifications and proper protein folding, both of which are important for evaluating the therapeutic potential of antibody candidates.
Biointron said the platform can process more than 3,000 antibody molecules in parallel within a single batch, enabling researchers to rapidly screen large libraries of AI-generated candidates. The structured output is designed to be easily interpreted by both scientists and machine learning systems.
RushData also integrates advanced binding analysis using bio-layer interferometry (BLI) and surface plasmon resonance (SPR), alongside antibody expression and purity data. Higher-tier service packages include early developability assessments such as differential scanning fluorimetry (DSF) to evaluate thermal stability, affinity-capture self-interaction nanoparticle spectroscopy (AC-SINS) to assess self-interaction, and polyspecificity reagent binding (PSR-BVP) assays to measure polyreactivity.
To meet different research needs, Biointron is offering RushData through three service packages. The Basic package focuses on rapid screening with antibody expression, titer measurement, and affinity testing. The Standard package expands on this by including concentration analysis, capillary gel electrophoresis (cGE), and additional binding characterization. The Premium package adds comprehensive developability profiling to help researchers identify the most promising therapeutic candidates early in development.
Biointron said the launch of RushData reflects the growing need for integrated experimental platforms that can keep pace with advances in AI-driven antibody design. With more than 14 years of experience supporting over 3,000 biopharmaceutical organizations worldwide, the company aims to provide researchers with faster, more efficient tools to accelerate biologics discovery and reduce development timelines.