Integrating Experimental And Computational Pharmacology For Intelligent Drug Design

Sensor technology

Drugs targeting GPCRs are classically selected and optimized by focusing on the affinity, efficacy, potency and specificity of a compound to its target receptor. However, because of the complex nature of GPCR physiological and pathophysiological mechanisms, selecting a drug candidate with the desired therapeutic outcome is very challenging.

InterAx has built a proprietary technology platform that opens new possibilities for the design and selection of GPCR-targeting compounds. We look at the interplay of a drug candidate with its target receptor over time, and develop rich data sets that characterize time-dependent signaling profiles which must be understood to effectively leverage predictive analytics. The data we generate is rich and is derived from in vitro analyses, which allows for the selection of drug candidates with high efficacy and reduced adverse effects in vivo. Our key differentiator is the application of mathematical models and simulations to drug discovery with the aim to close the current gap between laboratory experiments and in vivo studies.

Systems Biology

An analogy to our approach is the design of airplanes: The combination of computational fluid dynamics simulations with wind tunnel experiments allows to reduce experimental trial and error and reduces the costs and time required to engineer a new airplane. We apply the same principles by using systems biology simulations in combination with cell-based time-resolved assays to assist the design and selection of premium drug candidates.

Systems Biology



Systems Biology is a technology that allows for a holistic mathematical model describing all potential signaling pathways activated by a given GPCR independent of the cellular background it is expressed in. The signaling patterns elicited by an activated GPCR consist of a very complex system of multifactorial parameters, such as differences in time scales, enzyme kinetics and/or sub-cellular localization changes of effector proteins. Moreover, the composition and expression levels of proteins in cell lines (e.g. those used for in vitro screening assays) often differ dramatically from the target tissues in humans. This complexity makes it extremely difficult to predict the in vivo efficacies and potencies of a new compound from the parameters derived in the in vitro assays.

Systems biology addresses this complexity and closes the current gap between in vitro and in vivo correlation. It allows to determine the most important nodes in the signaling network and thereby helps to identify the most critical properties a novel drug candidate needs to possess in order to elicit the desired cellular response.
Using our proprietary network model, we will be able to mimic the composition of target tissues (e.g. by mathematically varying protein expression levels, enzyme kinetic parameters) and thereby predict responses of a novel drug candidate in vivo.

Systems Biology

Selection of Key Publications



  1. Roed, S.N., Wismann P., Underwood C.R., Kulahin N., Iversen H., Cappelen K.A., Schäffer L., Lehtonen J., Hecksher-Soerensen J., Secher A., Mathiesen J.M., Bräuner-Osborne H., Whistler J.L., Knudsen S.M., Waldhoer M. (2014). Real-time trafficking and signaling of the glucagon-like peptide-1 receptor. Mol Cell Endocrinol., 8.


  2. Heitzler, D., Durand, G., Gallay, N., Rizk, A., Ahn, S., Kim, J., et al. (2012). Competing G protein-coupled receptor kinases balance G protein and beta-arrestin signaling. Molecular Systems Biology, 8.


  3. Ostermaier, M. K., Peterhans, C., Jaussi, R., Deupi, X., & Standfuss, J. (2014). Functional map of arrestin-1 at single amino acid resolution. Proceedings of the National Academy of Sciences of the United States of America, 111(5), 1825–1830.


  4. Ostermaier, M. K., Schertler, G. F. X., & Standfuss, J. (2014). Molecular mechanism of phosphorylation-dependent arrestin activation. Current Opinion in Structural Biology, 29, 143–151.


  5. Ostermaier, M. K., Schertler, G. F. X., & Standfuss, J. Method for determining mutateable ligand-gpcr binding at single amino acid resolution and pairs of mutated ligand and GPCR. EP13171505.4, Priority date: June 2013, published Dec. 2014


  6. Rizk, A., Mansouri, M., Ballmer-Hofer, K., & Berger, P. (2015). Subcellular object quantification with Squassh3C and SquasshAnalyst. Biotechniques, 59(5), 309–312.


  7. Rizk, A., Paul, G., Incardona, P., Bugarski, M., Mansouri, M., Niemann, A., et al. (2014). Segmentation and quantification of subcellular structures in fluorescence microscopy images using Squassh. Nature Protocols, 9(3), 586–596.


  8. Singhal, A., Ostermaier, M. K., Vishnivetskiy, S. A., Panneels, V., Homan, K. T., Tesmer, J. J. G., et al. (2013). Insights into congenital stationary night blindness based on the structure of G90D rhodopsin. Embo Reports, 14(6), 520–526.


  9. Vishnivetskiy, S. A., Ostermaier, M. K., Singhal, A., Panneels, V., Homan, K. T., Glukhova, A., et al. (2013). Constitutively active rhodopsin mutants causing night blindness are effectively phosphorylated by GRKs but differ in arrestin-1 binding. Cellular Signalling, 25(11), 2155–2162.