THE PROBLEM WITH BLACK BOXES

 

How do you know if your results are correct if you don’t know how they were analyzed?

Kinetic binding data can help you understand more about your drug’s potential residence time, toxicity, and selectivity… but how do you know you can trust the data if you don’t know how they were calculated? Many label-free platforms like SPR and BLI provide results for your experiments, but the inner workings are black boxes. When the experiment isn’t successful, figuring out what went wrong can be frustrating when the analysis is a mystery.

That black box might have a lump of coal in it

How do you know that your black box is running the correct analysis for your specific experimental parameters? You might dig through a multitude of publications, search through forums, and maybe even pick up the convoluted instruction manual to glean some insight into manipulations being performed. However, it’s difficult to follow calculations of higher-order binding effects when the back-end software is opaque and the equations performed are hidden. When your data is on the line, knowing how the black box works and how to modify it can help you have confidence that your results are accurate.

Welcome to transparency

Introducing Agile R100, the first label-free assay that is a glass house. Calculations and methods used in the Agile Plus software for Agile R100 are described in full detail here: Kinetic Binding Analysis on Agile R100. The Langmuir model, binding rates and binding constants, association and dissociation, sensor responses, Hill’s Equation, and kinetics plots are all described in this technical note so you can understand the math behind the kinetics analysis performed and ensure you have accurate results. Black boxes are finally stepping into the light.