Identification of potential drug targets and drug candidates.

To identify potential druggable targets and compounds.

In WP1 the results from compound screening efforts for cyst swelling as well as ‘omics’ analyses of models for polycystic kidney disease (PKD), will be thoroughly integrated and explored in order to identify druggable pathways, potential druggable targets and compounds. These data-sets will be used for computational modelling and will be combined/extended with data from new drug libraries screened in WP2. Complementary approaches will be applied:

  1. Previously identified compounds will be used to apply machine learning approaches using molecular descriptors, e.g. SMILES or fingerprints, for the structures of the compounds and a classification outcome for cyst growth inhibition.
  2. Ligand-based experimental input data from drug screenings will be combined with 3D structural information of protein targets, by applying cheminformatics and bioinformatics approaches.
  3. In parallel, an image based strategy will be applied, involving a machine learning approach and QSAR analysis, applied on phenotypic image analysis, using features obtained from high-content images, combined with chemical finger-prints of compounds and target gene expression data.


Drug testing and drug screening in vitro/in vivo and insight into drug effects on cellular signaling

To test, validate drugs and identify compounds in ‘in vitro’ and ‘in vivo’ assays;
To get insight into drug-related cellular mechanisms.

In WP2 selected drugs, targets and related pathways identified in WP1 will be validated and further characterised using in vitro assays and in mice in early and advanced disease progression. For the druggable targets miniaturised assays will be developed. These assays will be used to test the selected targets and compounds from WP1 and will be used in high-throughput screening campaigns to identify new compounds for these targets. In parallel, effects of new compounds will be tested in a phenotypic assay for cyst swelling. Compound synergies will be explored in the different assays in order to identify multi-target strategies. Selected compounds will be tested in vivo and molecular assays to test bioavailability and effects on the target/pathway) will be conducted.
Both, wet-lab and in silico approaches, will be applied and integrated. These methods are not only important for elucidating mechanisms of action but also for identifying effective pathways involved in disease as a preliminary step to design therapeutic strategies.


Pharmacokinetics and Identification, and reduction, of adverse events

To develop and apply complementary approaches to get insight into pharmacokinetics and identify and reduce adverse events at different phases of drug development. 

In WP3 a variety of approaches based on translational analysis and deconvolution will be optimised and applied to get insight into potential off-target effects and PharmacoKinetics and PharmacoDynamics (PK/PD).
The list of candidate compounds from WP1,2 will be further explored to obtain a comprehensive overview of on-targets and off-targets through the use of literature and computational modelling. Moreover, model output will be coupled to resources containing phenotypic compound effects on an organism level (e.g. the SIDER database) to link the bioactivity spectrum to known off-target effects, as done previously on a cellular level. Relevant parts of the predicted bioactivity spectra will be validated in vitro, in an a transwell testing system, to measure directional transport of drugs, proper drug metabolism and testing of specific toxicological effects. A variety of in vitro assays will be used to determine intracellular free drug concentrations and subcellular target identification. Physiologically-based pharmacokinetic (PBPK) models of drug metabolism and transport in both rodent and human kidney will be developed. For selected drugs, optimal delivery systems will be developed and tested in model systems.