Predicting the Mitochondrial Toxicity of Small Molecules: Insights from Mechanistic Assays and Cell Painting Data

Abstract

Mitochondrial toxicity is a significant concern in the drug discovery process, as compounds that disrupt the function of these organelles can lead to serious side effects, including liver injury and cardiotoxicity. Different in vitro assays exist to detect mitochondrial toxicity at varying mechanistic levels: disruption of the respiratory chain, disruption of the membrane potential, or general mitochondrial dysfunction. In parallel, whole cell imaging assays like Cell Painting provide a phenotypic overview of the cellular system upon treatment and enable the assessment of mitochondrial health from cell profiling features. In this study, we aim to establish machine learning models for the prediction of mitochondrial toxicity, making the best use of the available data. For this purpose, we first derived highly curated datasets of mitochondrial toxicity, including subsets for different mechanisms of action. Due to the limited amount of labeled data often associated with toxicological endpoints, we investigated the potential of using morphological features from a large Cell Painting screen to label additional compounds and enrich our dataset. Our results suggest that models incorporating morphological profiles perform better in predicting mitochondrial toxicity than those trained on chemical structures alone (up to +0.08 and +0.09 mean MCC in random and cluster cross-validation, respectively). Toxicity labels derived from Cell Painting images improved the predictions on an external test set up to +0.08 MCC. However, we also found that further research is needed to improve the reliability of Cell Painting image labeling. Overall, our study provides insights into the importance of considering different mechanisms of action when predicting a complex endpoint like mitochondrial disruption as well as into the challenges and opportunities of using Cell Painting data for toxicity prediction.

Self-supervision advances morphological profiling by unlocking powerful image representations

“…Specifically, clustering compounds or genes by morphological similarity provides mechanistic insights from annotated cluster members. Alternatively, classifiers trained on extracted features can predict downstream tasks, such as drug toxicity 24 or cell health phenotypes 25 . However, label scarcity generally precludes end-to-end supervised learning from images, with only few exceptions 26 .…”

Introduction to the Special Issue: AI Meets Toxicology

“…The methods described in this special issue cover a wide range of AI methods ranging from expert systems, ,, over similarity measures including read-across methods, − to classical machine learning such as random forests (RF), support vector machines (SVM), and artificial neural networks (ANN) − ,, to deep learning (DL) methods ,,− , including equivariant neural networks, deep generative models, and even large language models . In addition to models relying purely on the chemical structure, there is a notable trend of bringing in additional modalities to improve or inform predictive models. , In the following, we provide an overview of the AI approaches used in the publications contained in the SI. The methodological overview presented in this section highlights the variety of AI approaches employed in toxicology, showcasing the potential for combining different methods to enhance predictive performance and expand the scope of toxicity modeling.…”

“…Most of the presented methods perform prediction tasks on molecules (for exceptions, see paragraphs below) and, thus, have to use representations of molecules. While for data handling, the simplified molecular-input line-entry system (SMILES) representation is often used, the predominant representations of molecules for modeling are still extended connectivity fingerprint (ECFP) or Morgan fingerprints. − ,− Several publications use graph neural networks that operate on the molecular graph. , Aside from the chemical structure, there is a growing tendency to incorporate biological characterizations and read-outs, for example, via cell morphology , or transcriptomics . The utilization of diverse representations, ranging from molecular structures to biological features, enhances the predictive models showcased in this section and could improve the comprehensive understanding of toxicological properties.…”

“…Several methods of this SI explore these multimodal modeling approaches further. Garcia de Lomana et al developed machine learning models for predicting mitochondrial toxicity by incorporating curated data sets and leveraging morphological features from a cell painting screen, which improved the quality of predictions, while exploring the challenges and opportunities of using this approach. Herman et al used an active learning approach combining ligand-based quantitative structure–activity relationship models and a phenotypic cell painting screen to improve the model performance for a mitochondrial toxicity assay, enhancing chemical diversity coverage and expanding the recognition of compounds from a broader chemical space.…”

FSL-CP: a benchmark for small molecule activity few-shot prediction using cell microscopy images

“…Cell painting is a morphological profiling method in which cells are perturbed with a compound, have their different compartments stained using six dyes, and have their images of the five fluorescence channels captured. 4 Cell painting data have been used for a range of applications, from predicting mitochondrial toxicity, 3,11 in vitro toxicity, 12 hit identification, 13 and more. In addition, cell painting can be used in combination with other modalities to enhance prediction such as chemical structure 14 and gene expression data.…”