Mitochondrial toxicity is an important safety endpoint in drug discovery. Models based solely on chemical structure for predicting mitochondrial toxicity are currently limited in accuracy and applicability domain to the chemical space of the training compounds. In this work, we aimed to utilize both -omics and chemical data to push beyond the state-of-the-art. We combined Cell Painting and Gene Expression data with chemical structural information from Morgan fingerprints for 382 chemical perturbants tested in the Tox21 mitochondrial membrane depolarization assay. We observed that mitochondrial toxicants differ from non-toxic compounds in morphological space and identified compound clusters having similar mechanisms of mitochondrial toxicity, thereby indicating that morphological space provides biological insights related to mechanisms of action of this endpoint. We further showed that models combining Cell Painting, Gene Expression features and Morgan fingerprints improved model performance on an external test set of 244 compounds by 60% (in terms of F1 score) and improved extrapolation to new chemical space. The performance of our combined models was comparable with dedicated in vitro assays for mitochondrial toxicity. Our results suggest that combining chemical descriptors with biological readouts enhances the detection of mitochondrial toxicants, with practical implications in drug discovery.
This review summarises different data, data resources and methods for computational mechanism of action (MoA) analysis, and highlights some case studies where integration of data types and methods enabled MoA elucidation on the systems-level.
PROteolysis TArgeting Chimeras (PROTACs)
use the ubiquitin–proteasome
system to degrade a protein of interest for therapeutic benefit. Advances
made in targeted protein degradation technology have been remarkable,
with several molecules having moved into clinical studies. However,
robust routes to assess and better understand the safety risks of
PROTACs need to be identified, which is an essential step toward delivering
efficacious and safe compounds to patients. In this work, we used
Cell Painting, an unbiased high-content imaging method, to identify
phenotypic signatures of PROTACs. Chemical clustering and model prediction
allowed the identification of a mitotoxicity signature that could
not be expected by screening the individual PROTAC components. The
data highlighted the benefit of unbiased phenotypic methods for identifying
toxic signatures and the potential to impact drug design.
The understanding of the
mechanism-of-action (MoA) of compounds and the prediction of potential
drug targets play an important role in small-molecule drug discovery.
The aim of this work was to compare chemical and cell morphology information
for bioactivity prediction. The comparison was performed using bioactivity
data from the ExCAPE database, image data (in the form of CellProfiler
features) from the Cell Painting data set (the largest publicly available
data set of cell images with ∼30,000 compound perturbations),
and extended connectivity fingerprints (ECFPs) using the multitask
Bayesian matrix factorization (BMF) approach Macau. We found that
the BMF Macau and random forest (RF) performance were overall similar
when ECFPs were used as compound descriptors. However, BMF Macau outperformed
RF in 159 out of 224 targets (71%) when image data were used as compound
information. Using BMF Macau, 100 (corresponding to about 45%) and
90 (about 40%) of the 224 targets were predicted with high predictive
performance (AUC > 0.8) with ECFP data and image data as side information,
respectively. There were targets better predicted by image data as
side information, such as β-catenin, and others better predicted
by fingerprint-based side information, such as proteins belonging
to the G-protein-Coupled Receptor 1 family, which could be rationalized
from the underlying data distributions in each descriptor domain.
In conclusion, both cell morphology changes and chemical structure
information contain information about compound bioactivity, which
is also partially complementary, and can hence contribute to in silico MoA analysis.
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