Computational Investigation of Drug Phototoxicity: Photosafety Assessment, Photo-Toxophore Identification, and Machine Learning

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.