Drug safety indices for heart and liver derived from network modelling

SafetyNet proposes an in silico approach to replace animal testing in drug development. The approach is focused on computing safety indices (SIs) for drugs that induce liver and heart toxicity in humans. SI prediction is based on network modelling using different kinds of omics data that are available for the drug under study (methylome, proteome, transcriptome) . Applicants have previously developed drug response network (DRN) models for heart and liver using iPSC-derived 3D human cardiac and liver microtissues that were exposed to a panel of different drugs known to induce heart or liver toxicity. The goal of the SafetyNet project is to further refine, extend and test the DRNs by using a large body of publicly available drug response data (~ 500 drugs). For each of the drugs we will map the available timeand dose-sensitive data onto the DRN and perform network propagation modelling to derive a final prioritization of the proteins. Functional testing of the most relevant proteins will be done to associate these proteins with heart and liver toxicities and to phenotypically anchor the DRNs. Additionally, literature mining and disease associations will be used to propose novel protein candidates to extend the DRNs. In an iterative process of computational network modelling, functional testing and text mining the two DRN models will be refined and ultimately used to compute a safety index (SI) for each drug. Use cases will be carried out to compare and validate SI predictions. A prototype will be implemented that contains the different elements of the SafetyNet approach for further exploitation in regulatory and pre-clinical testing.

SafetyNet will be compatible with rodent data in order to be able to compare adverse outcome predictions using human in vitro data and rodent in vivo data. The final SafetyNet predictions for liver and heart toxicity should reduce uncertainty in adverse outcome prediction and should contribute to gradual replacement of animal testing.