InSilify DrugTox

In the project InSilify DrugTox, a large-scale retrospective computational toxicology (in silico) analysis will be performed with an extensive and pristine pool of toxicity data that originate from regulatory drug development and authorisation sources which have never been used for this purpose. The overarching aim of these computational toxicology investigations is to guide and support the regulatory transition towards drug development programs with a reduced demand of pre- and non-clinical animal studies. In short, the aim of the project is to InSilify Drug development through computational Toxicology. This endeavour will be achieved by a collaboration of scientific and regulatory experts of three renowned Austrian institutions (the Austrian Medicines and Medical Devices Agency, the Pharmacoinformatics Research Group of the University of Vienna, and the Regulatory Toxicology Group of the Medical University of Innsbruck). Specifically relevant for InSilify is the fact that toxicology in silico modelling bears a high potential to replace animal studies during drug development. However, in order to receive regulatory acceptance, these models need to deliver safe and accurate prediction outputs. The retrospective modelling analysis of InSilify will inform whether the replacement and reduction of animal studies with potent computational in silico models is safe and fit-for-use. An important advantage of InSilify is that the data used for modelling originate from regulatory drug development and authorisation sources. It is therefore expected that the results gathered in this project will receive high regulatory attention and acceptance, which may directly translate into a rapid implementation of in silico models into regulatory drug frameworks for toxicity endpoints for which a sufficient extent of prediction accuracy has been obtained in the project. A rapid regulatory implementation of computational toxicology modelling will directly correlate with a replacement of pre- and nonclinical animal studies in drug development. If the retrospective analyses of InSilify demonstrate that particular toxicity endpoints cannot yet be predicted with sufficient accuracy, this outcome will point out weaknesses of the currently available toxicology models. Reporting these weaknesses is anticipated to stimulate developers to refine their models, which on the longer run will also directly support the implementation of computational toxicology in drug development. Finally, apart from the high potential to replace animal studies by in silico toxicology, an increased implementation of accurate and fit-for-use in silico models in drug development will directly lead to cost reductions and faster development durations of drug candidates for patients which suffer from an unmet medical need. The results of InSilify will support this process.