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This presentation describes a set of computational methods to identify better analogs for evaluating alerting chemicals during mutagenicity prediction. A novel distributed representation of chemicals fragments was learned from millions of unlabeled chemicals structures and subsequently used for computing chemical fingerprints from structural environments of mutagenicity alerts. The methodology demonstrates the value of transfer machine learning in chemistry. The fingerprints also facilitate automatic mining of mitigating and activating features of mutagenic alerts. Excellent prediction performance was achieved using the distributed fingerprints when compared to traditional binary fingerprints. Additional factors that are relevant in analog based mutagenicity evaluation will also be discussed e.g. how to determine optimal similarity thresholds, number of analogs etc.
As the pharmaceutical industry attempts to reduce the development timeline to deliver new medicines, the quality and safety of these medicines continue to meet the highest standards. Genotoxic risk assessments incorporated into key positions of the development timeline assist with work plan creation and strategic decisions. Many advantages have been realized using the purge factor methodology to apply appropriate risk on work plans for early and late phase candidates.
Discussion of examples of Purge Tool usage within GSK, sharing learnings and focussing on the following:
Elemental impurities in drug products may arise from several sources: they may be intentionally added in synthesis, or may be present as contaminants and consequently detectable in the drug product. ICH Q3D guideline on elemental impurities provides an appropriate process to assess and control elemental impurities in pharmaceuticals. The guideline established oral, parenteral and inhalation Permitted Daily Exposures for each element of toxicological concern. In the present talk it will be presented the pragmatic approach employed in Angelini to set specific safety limits of such potential impurities in the final pharmaceutical formulation.