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Rapid Screening, Binding Characterization or Developability Profiling: Which RushData Package Fits Your Program?

Biointron 2026-08-21 Read time: 4 mins

High-throughput antibody discovery technologies can generate and screen increasingly large candidate pools, but downstream experimental validation remains an important part of the workflow.

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The amount of experimental characterization required varies across a discovery program. Some datasets may focus on rapid expression and binding readouts, while others include purified-antibody characterization or additional measurements related to developability. 

Biointron's RushData platform is designed around this range of data needs. Built on 1-day CHO expression, RushData combines high-throughput antibody production with binding and developability assays to generate structured experimental datasets in days. The platform can support more than 20,000 molecules per week, with Basic, Standard and Premium packages providing broad assay coverage.  

Basic: Rapid Screening

The Basic package provides a supernatant-level readout for early candidate triage. Antibodies are expressed using the high-throughput CHO workflow and analyzed directly from supernatant, without the purification step included in the higher-tier packages.  

The package includes: 

  • High-throughput expression in supernatant  

  • Titer measurement by bio-layer interferometry (BLI)  

  • Two-point affinity assessment by BLI  

This keeps the experimental scope focused on expression and initial binding measurements. Unlike other RushData packages, Basic does not include purified-antibody analytical or developability measurements.  

Standard: Binding Characterization

The Standard package adds antibody purification and expands the analytical and binding dataset. It is a purified-antibody dataset containing expression, purity, and affinity readouts.  

Standard includes: 

  • High-throughput expression and purification  

  • Concentration measurement by A280  

  • Non-reducing capillary gel electrophoresis (cGE)  

  • Multi-point affinity analysis by BLI or surface plasmon resonance (SPR)  

The difference from Basic is that Standard moves from a supernatant-based screen to characterization of purified antibodies and adds concentration and cGE measurements alongside more detailed affinity analysis.  

Premium: Adding Developability Profiling

The Premium package includes all assays included in Standard and adds three measurements for early developability profiling. Biointron positions this package as a broader discovery dataset for developability risk ranking and AI-ready feedback.  

In addition to the Standard dataset, Premium includes: 

  • Differential scanning fluorimetry (DSF): Tm and Tonset measurements  

  • PSR-BVP: polyreactivity assessment  

  • AC-SINS: self-interaction assessment  

These assays extend candidate characterization beyond expression, analytical properties and antigen binding. The RushData workflow uses DSF to assess thermostability-related parameters, PSR-BVP to evaluate polyreactivity and AC-SINS to examine self-interaction.  

Choosing the Level of Data

The main difference among the three packages is the level of experimental characterization. However, assays may be customized according to the client.

Package 

RushData scope 

Basic 

Supernatant expression, titer and two-point BLI affinity assessment 

Standard 

Purified antibody, concentration, non-reducing cGE and multi-point BLI or SPR affinity 

Premium 

All Standard measurements plus DSF, PSR-BVP and AC-SINS developability profiling 

These packages allow the experimental dataset to be selected according to the measurements required for a particular project. Project timelines can vary with candidate number, package selection, and selected assays.  

Across all three packages, RushData connects high-throughput CHO expression with experimental characterization and structured data output. Depending on the package selected, those data can include expression-related measurements, binding characterization, thermostability, polyreactivity and self-interaction. The resulting datasets can be used for candidate comparison and can also be returned in structured formats for integration into computational antibody discovery workflows.

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