Two InnoSysTox-moving projects selected in the latest call for animal-free innovations for systems biology and toxicology
InnoSysTox is a collaboration of the German Ministry of Education and Science (BMBF), Projektträger Jülich (PtJ) and ZonMw. The projects, one in which computer calculations are used for the development of medicines, and the other in which computer models and a model based on human physiology, predict liver toxicity, have a maximum duration of four years.
The aim of this call is to bring about a mind shift towards human biology in the field of toxicology and computational modelling. In addition, to stimulate innovations without the use of animal testing. The projects are based on an international public-private partnership in a broad-based strategic consortium. The consortia represent strong partnerships with complementary expertise, in which all stakeholders are represented. Through international and interdisciplinary cooperation, the development and application of innovative systems-biology-based replacement methods in toxicology will be strengthened.
SafetyNet: Drug safety indices for heart and liver derived from network modelling
Projectleader: Prof. Jos Kleinjans, Maastricht University
SafetyNet proposes an in silico approach to replace animal testing in drug development. The approach is focused on computing safetyvindices (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 time and 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.
SysBioToP-Moving: Systems Biology of Liver Toxicity Predictions - Moving on
Projectleader: Prof. Bob van de Water, Leiden University
There is an urgent need for next generation chemical safety assessment strategies that reflect human biology and do not employ animal testing. For appropriate quantitative adverse outcome predictions it is essential to integrate quantitative information on chemical-biological interactions using computational systems biology. Our vision is that quantitative dynamic measurements on xenobiotic induced cellular perturbations and adverse outcomes from human-based in vitro test systems followed by translation to human in vivo target tissue through quantitative in-vitro-in-vivo-extrapolation (qIVIVE) will provide an innovative strategy for future safety testing approaches. Systems biology and physiologically-based pharmacokinetic (PBPK) modelling are intrinsic cornerstones for such a strategy. Since the liver is a critical target organ of toxicity and highly difficult to predict, we will focus on the prediction of hepatotoxicity. Cellular stress responses in association with biochemical perturbation at the metabolic and cellular energy- and redox-status level are critical determinants for the onset of such hepatotoxicity. In SysBioToP-Moving we will integrate computational models of cellular stress response pathways and cellular adversity with PBPK models for in vivo translation. We will parameterize these models based on existing experimental data sets in which gaps will be filled with respect to concentration-time response to a panel of drugs having liability for drug-induced liver injury. These datasets involve live cell microscopy of cellular stress response reporters as well as metabolomics and transcriptomics studies. We will assess target organ variability based on different iPSC reporter cell line-derived cell lineage information. Ultimately, the models will be tested with substances that are relevant for the chemical and cosmetics industry sector, to ensure future implementation in integrated approaches to testing and assessment of chemical safety.