Computational Investigation of Drug Phototoxicity: Photosafety Assessment, Photo-Toxophore Identification, and Machine Learning
| Autor: | Laure Delafoy, Friedemann Schmidt, Nis Halland, Andreas Czich, Stefan Güssregen, Jan Wenzel |
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| Rok vydání: | 2019 |
| Předmět: |
Hazard (logic)
Computer science 010501 environmental sciences Toxicology Machine learning computer.software_genre 01 natural sciences Domain (software engineering) Machine Learning 03 medical and health sciences Mice Animals Humans Sensitivity (control systems) 030304 developmental biology 0105 earth and related environmental sciences Interpretability 0303 health sciences Photosensitizing Agents business.industry General Medicine 3T3 Cells Models Theoretical Bayesian statistics Identification (information) Neutral Red Data quality Biological Assay Artificial intelligence business computer Test data Dermatitis Phototoxic |
| Zdroj: | Chemical research in toxicology. 32(11) |
| ISSN: | 1520-5010 |
| Popis: | One of the most appreciated capabilities of computational toxicology is to support the design of pharmaceuticals with reduced toxicological hazard. To this end, we have strengthened our drug photosafety assessments by applying novel computer models for the anticipation of in vitro phototoxicity and human photosensitization. These models are typically used in pharmaceutical discovery projects as part of the compound toxicity assessments and compound optimization methods. To ensure good data quality and aiming at models with global applicability we separately compiled and curated highly chemically diverse data sets from 3T3 NRU phototoxicity reports (450 compounds) and clinical photosensitization alerts (1419 compounds) which are provided as supplements. The latter data gives rise to a comprehensive list of explanatory fragments for visual guidance, termed phototoxophores, by application of a Bayesian statistics approach. To extend beyond the domain of well sampled fragments we applied machine learning techniques based on explanatory descriptors such as pharmacophoric fingerprints or, more important, accurate electronic energy descriptors. Electronic descriptors were extracted from quantum chemical computations at the density functional theory (DFT) level. Accurate UV/vis spectral absorption descriptors and pharmacophoric fingerprints turned out to be necessary for predictive computer models, which were both derived from Deep Neural Networks but also the simpler Random Decision Forests approach. Model accuracies of 83-85% could typically be reached for diverse test data sets and other company in-house data, while model sensitivity (the capability of correctly detecting toxicants) was even better, reaching 86%-90%. Importantly, a computer model-triggered response-map allowed for graphical/chemical interpretability also in the case of previously unknown phototoxophores. The photosafety models described here are currently applied in a prospective manner for the hazard identification, prioritization, and optimization of newly designed molecules. |
| Databáze: | OpenAIRE |
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