NovAlix conference, Strasbourg, France
June 9, 2017
Since the introduction of the drug-target residence time model 10 years ago (1,2), binding kinetics have become an important enhancement to “classical” drug discovery metrics (such as potency) when it comes to making compound progression and candidate selection decisions. This paradigm shift has been backed by retrospective analysis of recently approved drugs showing that nearly one third act via non-equilibrium mechanisms (3), and by studies suggesting that the incorporation of in-vitro kinetic information in drug discovery programs could support better prediction and modeling of in vivo efficacy and PK/PD relationships (4,5,6). Thus, there is an increasing demand for assay technologies that enable measurements of drug–target association (kon) and dissociation (koff) rates.
The challenge in leveraging kinetic information in early drug discovery stages lies in how to cost - effectively obtain kon and koff parameters at sufficient throughput for systematic assessment of compounds. Recently, the Kinetic Probe Competition Assay (kPCA) - a universal and scalable assay format relying on TR-FRET technology and the Motulsky-Mahan competitive binding kinetics theory (7,8) - has been reported as possible solution to this challenge. Here we introduce a significant improvement to the kPCA compound screening workflow: an automated analysis procedure based on the Motulsky-Mahan model implemented in the Genedata Screener® software.
In this poster, we first illustrate the principle of the kPCA assay and its application in a high-throughput setting. We further describe a fast, efficient, and controlled data analysis workflow covering all kPCA assay steps from raw data analytics to result evaluation and direct reporting of scientist-reviewed residence times to a corporate data warehouse. We describe a variant of the analysis to cope with fluorescence photobleaching. Finally, we demonstrate the power of this novel analysis method with the example of a binding kinetics selectivity screen covering 273 drugs profiled against 30 targets, a typical scenario for Bayer’s drug discovery projects.