Preprint: Addressing Toxicity Risk when Designing and Selecting Compounds in Early Drug Discovery
We’ve just submitted this article, co-authored with Chris Barber, CSO of Lhasa Limited. In it, we discuss how application of expert knowledge-based predictions of toxicity can be used with multi-parameter optimisation, to guide the design and selection of high quality compounds with a reduced risk of toxicity, early in the drug discovery process.
Toxicity accounts for approximately 30% of expensive, late stage failures in development. Therefore, identifying and prioritising chemistries with a lower risk of toxicity, as early as possible in the drug discovery process, would help to address the high attrition rate in pharmaceutical R&D. We will describe how expert knowledge-based prediction of toxicity can alert chemists if their proposed compounds are likely to have an increased risk of causing toxicity. However, an alert for potential toxicity should be given appropriate weight in the selection of compounds to balance potential opportunities against downstream toxicity risk. If a series achieves good outcomes for other requirements, it may be appropriate to progress selected compounds and generate experimental data to confirm or refute a prediction of potential toxicity. We will discuss how multi-parameter optimisation approaches can be used to balance the potential for toxicity with other properties required in a high quality candidate drug, such as potency and appropriate absorption, distribution, metabolism and elimination (ADME). Furthermore, it may be possible to modify a compound to reduce its likelihood of toxicity and we will describe how information on the region of a compound that triggers a toxicity alert can be interactively visualised to guide this redesign.