Technology
Our breakthrough platform combines systems biology, pharmacology, and artificial intelligence to accelerate the hit-to-lead process in GPCR drug discovery. InterAx is setting an industry example of how to better understand the biology of a disease and de-risk drug candidates. Instead of only leveraging large quantities of data with machine learning algorithms, we simultaneously incorporate crucial biological knowledge of the underlying system into the intelligent drug discovery algorithms. Rigorously building such a bottom-up understanding of the target systems is a crucial milestone on the path to the rational design of effective and safe drug candidates.
Our platform is applicable to all GPCR targets, indications, and ligand modalities, e.g. full/partial agonists, antagonists, orthosteric or allosteric ligands. We perform all our assays using unlabeled ligands, thereby excluding labeling artifacts.
We express the target GPCR in cell lines containing signaling biosensors. We use a small set of reference ligands to calibrate the signaling responses of the target GPCR.
We perform time-resolved cellular signaling assays to obtain kinetic profiles of compounds. This approach captures cellular signaling cascades to a much higher extent than static end-point assays.
We generate mathematical models using ordinary differential equations (ODEs) to describe the specific signaling pathways of the target GPCR. These models incorporate crucial biological knowledge, including the signaling pathways modulated by a given target, cross-talks between signaling pathways, signaling amplification mechanisms, and influence of protein expression levels.
Validation, calibration, and numerical simulations of these systems biology models generate more and better information than initially contained in the experimental data. Our models indeed derive mechanistic signaling parameters (i) quantifying the activation of proteins not directly measured (more information) and (ii) which are independent of the experimental conditions and cellular background where the measurements were made (better information).
Additional information provided by the InterAx Systems Biology platform enables early selection of compounds, prediction of therapeutic effects from in vitro data, and AI-based generation of new drug candidates with high efficacy and potency. Examples are available upon request.
Our systems biology analysis provides a multitude of novel ligand parameters. We then cluster test compounds based on this analysis. We can easily spot outliers (off-target effects) and identify compounds with the most promising profiles for in vivo testing.
We correlate the new compound parameter profiles to in vivo data – thereby honing in on promising compounds at an early stage. This accelerates the hit-to-lead identification process.
InterAx AI models learn from drug-target molecular dynamics and systems biology parameters – to predict which drug chemistries will show the desired efficacy and biological response.
The InterAx machine learning algorithms are trained on proprietary systems biology datasets to understand both the biological effects of GPCR drugs on cell signaling and the chemical-structural rules of drug-target interactions. This union of systems biology and machine learning enables InterAx technology to guide the design of new chemical entities with desired cellular biology signaling parameters.