Improving the Adverse Outcome Pathways Framework via the Application of Deep Learning

Despite a significant investment in in vitro and in vivo screening, clinical safety concerns are still a major cause of compounds being withdrawn from the development process. A lack of mechanistic understanding of safety findings, combined with sparse data sets, restricts the development of approaches capable of predicting earlier potential safety concerns for new chemical entities. It is often unclear whether these are truly surprise events or, with hindsight, could actually have been predicted based on the available data at the time.

In this work we propose to employ novel mathematical approaches to data representation and modelling, such as deep learning, of the processes that lead to an adverse outcome in order to reduce or eliminate events of this type. The current project will involve GSK and AstraZeneca as industrial partners, with the charity Lhasa Ltd. as an 'honest broker', to exchange and pool compound profiling data for model generation. This framework is currently already being established in the context of the Cambridge Alliance on Medicines Safety (CAMS), and it hence improves significantly upon previous analyses which were based either on public data, or data from only a single source.

A pilot study on Structural Cardiotoxicity has already been completed, proving the viability and benefit of exchanging data for safety prediction in this framework. We now would like to broaden the approach to other adverse events where data is available. Increasing our understanding of the molecular mechanisms by which compounds can cause adverse events will lead to the implementation of much improved in silico and in vitro screens to detect safety risks will avoid unnecessary investments in preclinical and clinical studies.