Preclinical Development – Chemical
2019 PharmSci 360
The increasing pace of technological developments of computational methods in toxicology allows to call them “disruptive technologies”. The development of alternatives to traditional approaches for product development and safety assessment benefits from this. The creation of large toxicological databases (“big data”) and data-mining technologies (“artificial intelligence”) allow predictive computational approaches on a new scale. As an example, our new automated read-across (RASAR, i.e. read-across-based structure activity relationships) is given. Continues optimization to include deep learning, metabolite prediction, biological deep phenotyping, chemicophysical descriptors etc. expand the use cases including potency prediction and Green Toxicology finding alternative chemistries.
Such technological advances promise to be real “game-changers”.