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.

How It Works

1. G Protein Coupled Receptors & Ligands

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.

Our platform is applicable to all GPCR targets, indication or type of compound, e.g. biologics or small molecules, full/partial agonists, antagonists, orthosteric or allosteric ligands. We perform all our assays using unlabeled ligands, thereby excluding labeling artefacts.

We express your target GPCR in cell lines containing signaling biosensors. We use a small set of reference ligands to calibrate the signaling responses of your target GPCR.

Assays

2. Time resolved cellular signaling assays

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 perform time-resolved cellular signaling assays to obtain kinetic profiles of your test compounds. This approach captures cellular signaling cascades to a much higher extent than static end-point assays.

3. Proprietary systems biology models

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.

We generate mathematical models using ordinary differential equations (ODEs) to describe the specific signaling pathways of your target GPCR. We then analyze the kinetic profiles of your test compounds using this unique systems biology model.

Compounds to Clustered

4. Early selection of high quality leads

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.

Our systems biology analysis provides you with a multitude of novel ligand parameters. We then cluster your test compounds based on this analysis. We can easily spot outliers (off-target effects) and help you to identify compounds with the most promising profiles for in vivo testing

In Vivo

5. Better prediction of in vivo therapeutic effects

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.

We generate mathematical models using ordinary differential equations (ODEs) to describe the specific signaling pathways of your target GPCR. We then analyze the kinetic profiles of your test compounds using this unique systems biology model.We generate mathematical models using ordinary differential equations (ODEs) to describe the specific signaling pathways of your target GPCR. We then analyze the kinetic profiles of your test compounds using this unique systems biology model.

AI signaling

6. AI-based generation of drug candidates with specific cellular response

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. 

The InterAx Edge

ligand-triad-20200120 1 (2)

Full ligand range

No limitations on ligand types

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No ligand labeling

Ensuring better predictions

single-assay-20200120 1 (1)

Time-resolved assays

More accurately characterize in-cell
behaviour

Asthma Case Study

We exemplified the power of our platform by predicting asthma drug action using our proprietary beta2-adrenergic receptor ODE model.

Selected Supporting Literature

Systems Biology

The European Research Network on Signal Transduction (ERNEST): Toward a Multidimensional Holistic Understanding of G Protein-Coupled Receptor Signaling

Sommer ME, Selent J, Carlsson J, De Graaf C, Gloriam DE, Keseru GM, Kosloff M, Mordalski S, Rizk A, Rosenkilde MM, Sotelo E, Tiemann JKS, Tobin A, Vardjan N, Waldhoer M, Kolb P. (2020) ACS Pharmacol Transl Sci. 3:361-370.

G Protein-Coupled Receptor Signaling Networks from a Systems Perspective

Roth S, Kholodenko BN, Smit MJ, Bruggeman FJ (2015). Mol Pharmacol., 88: 604-16.

A conformation-equilibrium model captures ligand-ligand interactions and ligand-biased signalling by G-protein coupled receptors

Roth S, Bruggeman FJ (2014). FEBS J, 281: 4659-71.

Competing G protein-coupled receptor kinases balance G protein and β-arrestin signaling

Heitzler D, Durand G, Gallay N, Rizk A, Ahn S, Kim J, Violin JD, Dupuy L, Gauthier C, Piketty V, Crépieux P, Poupon A, Clément F, Fages F, Lefkowitz RJ,Reit er E. (2012). Mol Syst Biol. 8:590.

A general computational method for robustness analysis with applications to synthetic gene networks

Rizk A, Batt G, Fages F, Soliman S (2009). Bioinformatics, 25:i169-78

On the Analysis of Numerical Data Time Series in Temporal Logic

Fages F, Rizk A. (2007). Computational Methods in Systems Biology, CMSB’07 Edinburgh. Springer-Verlag LNBI, 4695 :48-63.

Drug design and Computational chemistry

Mechanistic insights into dopaminergic and serotonergic neurotransmission – concerted interactions with helices 5 and 6 drive the functional outcome

Tomasz Maciej Stepniewski, Arturo Mancini, Richard Ågren, Mariona Torrens-Fontanals, Meriem Semache, Michel Bouvier, Kristoffer Sahlholm, Billy Breton and Jana Selent (2021) Chemical Science, Issue 33, The Royal Society of Chemistry.

Ligand with Two Modes of Interaction with the Dopamine D2 Receptor–An Induced-Fit Mechanism of Insurmountable Antagonism

Richard Ågren, Hugo Zeberg, Tomasz Maciej Stępniewski, R. Benjamin Free, Sean W. Reilly, Robert R. Luedtke, Peter Århem, Francisco Ciruela, David R. Sibley, Robert H. Mach, Jana Selent, Johanna Nilsson, and Kristoffer Sahlholm (2020) ACS Chemical Neuroscience 3130–3143.

How Do Molecular Dynamics Data Complement Static Structural Data of GPCRs

Mariona Torrens-Fontanals, Tomasz Maciej Stepniewski, David Aranda-García, Adrián Morales-Pastor, Brian Medel-Lacruz and Jana Selent (2020) Int. J. Mol. Sci. 21(16), 5933.

A Focus on Unusual ECL2 Interactions Yields β2 -Adrenergic Receptor Antagonists with Unprecedented Scaffolds

Scharf MM, Zimmermann M, Wilhelm F, Stroe R, Waldhoer M, Kolb P. (2020) ChemMedChem. 15:882-890.

Technology - Biosensors & Assays

New Insights into Arrestin Recruitment to GPCRs

Spillmann M, Thurner L, Romantini N, Zimmermann M, Meger B, Behe M, Waldhoer M, Schertler GFX, Berger P. (2020) Int J Mol Sci. 21:4949.

Systems NMR: single-sample quantification of RNA, proteins and metabolites for biomolecular network analysis

Yaroslav Nikolaev, Nina Ripin, Martin Soste, Paola Picotti, Dagmar Iber & Frédéric H.-T. Allain (2019) Nature Methods volume 16, pages743–749

Quantification of Molecular Interactions in Living Cells in Real Time using a Membrane Protein Nanopattern

Reichmuth AM, Zimmermann M, Wilhelm F, Frutiger A, Blickenstorfer Y, Fattinger C, Waldhoer M, Vörös J. (2020) Anal Chem. 92:8983-8991.

Real-time trafficking and signaling of the glucagon-like peptide-1 receptor

Roed SN, Wismann P, Underwood CR, Kulahin N, Iversen H, Cappelen KA, Schäffer L, Lehtonen J, Hecksher-Soerensen J, Secher A, Mathiesen JM, Bräuner-Osborne H, Whistler JL, Knudsen SM, Waldhoer M. (2014) Mol Cell Endocrinol. 382:938-49.

Segmentation and quantification of subcellular structures in fluorescence microscopy images using Squassh

Rizk A, Paul G, Incardona P, Bugarski M, Mansouri M, Niemann A, Ziegler U, Berger P, Sbalzarini IF (2014). Nat Protoc., 9:586-96.

Arrestin-1 engineering facilitates complex stabilization with native rhodopsin.

Haider RS, Wilhelm F, Rizk A, Mutt E, Deupi X, Peterhans C, Mühle J, Berger P, Schertler GFX, Standfuss J, Ostermaier MK (2019). Sci Rep, 9:439.

Molecular mechanism of phosphorylation-dependent arrestin activation

Ostermaier MK, Schertler GF, Standfuss J. (2014) Curr Opin Struct Biol. 29:143-51.

We are open to a range of collaborations,
from pilot projects to long-term partnerships