Respirometric Screening and Characterization of Mitochondrial Toxicants Within the ToxCast Phase I and II Chemical Libraries

Cumulative dietary risk assessment for French consumers exposed to succinate dehydrogenase inhibitor pesticides

Consumers are exposed to succinate dehydrogenase inhibitor (SDHI) pesticides through their diet. A cumulative dietary risk assessment for the French population has been performed with French monitoring data (2017-2021) and consumption data from INCA3. The calculation followed a two-tiered approach, using deterministic then probabilistic methods. It was carried out, using European health based guidance values (HBGV) derived for each active substance to characterise their toxicity. In Tier I, the calculated hazard index of 0.12 was below the threshold of 1 and in Tier II, the total margin of exposure at percentile 99.9 remains above the trigger value of 100 (1798 [1631-2311]). In Tier II, the three main risk drivers identified at the upper tail of the distribution were strawberries-fluopyram (19.1%), peaches-fluopyram (14.1%) and table grapes-boscalid (10.5%). Finally, the impact of the major sources of uncertainties was qualitatively evaluated. All together, they were considered of low impact on the outcomes. This work demonstrates the absence of unacceptable chronic risk related to the cumulative exposure of SDHI for French consumers during the 2017-2021 period.

Identification of neural-relevant toxcast high-throughput assay intended gene targets: Applicability to neurotoxicity and neurotoxicant putative molecular initiating events

The US EPA's Toxicity Forecaster (ToxCast) is a suite of high-throughput in vitro assays to screen environmental toxicants and predict potential toxicity of uncharacterized chemicals. This work examines the relevance of ToxCast assay intended gene targets to putative molecular initiating events (MIEs) of neurotoxicants. This effort is needed as there is growing interest in the regulatory and scientific communities about developing new approach methodologies (NAMs) to screen large numbers of chemicals for neurotoxicity and developmental neurotoxicity. Assay gene function (GeneCards, NCBI-PUBMED) was used to categorize gene target neural relevance (1 = neural, 2 = neural development, 3 = general cellular process, 3 A = cellular process critical during neural development, 4 = unlikely significance). Of 481 unique gene targets, 80 = category 1 (16.6 %); 16 = category 2 (3.3 %); 303 = category 3 (63.0 %); 97 = category 3 A (20.2 %); 82 = category 4 (17.0 %). A representative list of neurotoxicants (548) was researched (ex. PUBMED, PubChem) for neurotoxicity associated MIEs/Key Events (KEs). MIEs were identified for 375 compounds, whereas only KEs for 173. ToxCast gene targets associated with MIEs were primarily neurotransmitter (ex. dopaminergic, GABA)receptors and ion channels (calcium, sodium, potassium). Conversely, numerous MIEs associated with neurotoxicity were absent. Oxidative stress (OS) mechanisms were 79.1 % of KEs. In summary, 40 % of ToxCast assay gene targets are relevant to neurotoxicity mechanisms. Additional receptor and ion channel subtypes and increased OS pathway coverage are identified for potential future assay inclusion to provide more complete coverage of neural and developmental neural targets in assessing neurotoxicity.

An integrated in vitro approach to identifying chemically induced oxidative stress and toxicity in mitochondria

Growing concerns exist about increasing chemical usage and the potential health risks. Developing an efficient strategy to evaluate or predict the toxicity of chemicals is necessary. The mitochondria are essential organelles for cell maintenance and survival but also serve as one of the main targets of toxic chemicals. Mitochondria play an important role in the pathology of respiratory disease, and many environmental chemicals may induce impairment of the respiratory system through mitochondrial damage. This study aimed to develop integrated in vitro approaches to identify chemicals that could induce adverse health effects by increasing mitochondria-mediated oxidative stress using the H441 cells, which have a club-cell-like phenotype. Twenty-six environmental toxicants (biocides, phthalates, bisphenols, and particles) were tested, and each parameter was compared with eleven reference compounds. The inhibitory concentrations (IC20 and IC50) and benchmark doses (BMD) of the tested compounds were estimated from three in vitro assays, and the toxic concentration was determined. At the lowest IC20, the effects of compounds on mitochondrial reactive oxygen species (ROS) production and mitochondrial membrane potential (MMP) were compared. Principal component analysis and k-mean clustering were performed to cluster the chemicals that had comparable effects on the cells. Chemicals that induce mitochondrial damage at different concentrations were used for an in-depth high-tier assessment and classification as electron transport system (ETS) uncoupling or inhibiting agents. Additionally, using in vitro to in vivo extrapolation (IVIVE) tools, equivalent administration doses and maximum plasma concentrations of tested compounds in human were estimated. This study suggests an in vitro approach to identifying mitochondrial damage by integrating several in vitro toxicity tests and calculation modeling.

Developing a GNN-based AI model to predict mitochondrial toxicity using the bagging method

Mitochondrial toxicity has been implicated in the development of various toxicities, including hepatotoxicity. Therefore, mitochondrial toxicity has become a major screening factor in the early discovery phase of drug development. Several models have been developed to predict mitochondrial toxicity based on chemical structures. However, they only provide a binary classification of positive or negative results and do not provide the substructures that contribute to a positive decision. Therefore, we developed an artificial intelligence (AI) model to predict mitochondrial toxicity and visualize structural alerts. To construct the model, we used the open-source software library kMoL, which employs a graph neural network approach that allows learning from chemical structure data. We also utilized the integrated gradient method, which enables the visualization of substructures that contribute to positive results. The dataset used to construct the AI model exhibited a significant imbalance, with significantly more negative than positive data. To address this, we employed the bagging method, which resulted in a model with high predictive performance, as evidenced by an F1 score of 0.839. This model can also be used to visualize substructures that contribute to mitochondrial toxicity using the integrated gradient method. Our AI model predicts mitochondrial toxicity based on chemical structures and may contribute to screening mitochondrial toxicity in the early stages of drug discovery.

Definition of the Neurotoxicity-Associated Metabolic Signature Triggered by Berberine and Other Respiratory Chain Inhibitors

To characterize the hits from a phenotypic neurotoxicity screen, we obtained transcriptomics data for valinomycin, diethylstilbestrol, colchicine, rotenone, 1-methyl-4-phenylpyridinium (MPP), carbaryl and berberine (Ber). For all compounds, the concentration triggering neurite degeneration correlated with the onset of gene expression changes. The mechanistically diverse toxicants caused similar patterns of gene regulation: the responses were dominated by cell de-differentiation and a triggering of canonical stress response pathways driven by ATF4 and NRF2. To obtain more detailed and specific information on the modes-of-action, the effects on energy metabolism (respiration and glycolysis) were measured. Ber, rotenone and MPP inhibited the mitochondrial respiratory chain and they shared complex I as the target. This group of toxicants was further evaluated by metabolomics under experimental conditions that did not deplete ATP. Ber (204 changed metabolites) showed similar effects as MPP and rotenone. The overall metabolic situation was characterized by oxidative stress, an over-abundance of NADH (>1000% increase) and a re-routing of metabolism in order to dispose of the nitrogen resulting from increased amino acid turnover. This unique overall pattern led to the accumulation of metabolites known as biomarkers of neurodegeneration (saccharopine, aminoadipate and branched-chain ketoacids). These findings suggest that neurotoxicity of mitochondrial inhibitors may result from an ensemble of metabolic changes rather than from a simple ATP depletion. The combi-omics approach used here provided richer and more specific MoA data than the more common transcriptomics analysis alone. As Ber, a human drug and food supplement, mimicked closely the mode-of-action of known neurotoxicants, its potential hazard requires further investigation.

Integrating cell morphology with gene expression and chemical structure to aid mitochondrial toxicity detection

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.

Cell Painting, gene expression, and chemical structural data are used to examine the differences between mitochondrial toxicants and non-toxicants and enhance the detection of mitotoxic compounds for future drug discovery.

Mild pentachlorophenol-mediated uncoupling of mitochondria depletes ATP but does not cause an oxidized redox state or dopaminergic neurodegeneration in Caenorhabditis elegans

Aims

Mitochondrial dysfunction is implicated in several diseases, including neurological disorders such as Parkinson's disease. However, there is uncertainty about which of the many mechanisms by which mitochondrial function can be disrupted may lead to neurodegeneration. Pentachlorophenol (PCP) is an organic pollutant reported to cause mitochondrial dysfunction including oxidative stress and mitochondrial uncoupling. We investigated the effects of PCP exposure in Caenorhabditis elegans, including effects on mitochondria and dopaminergic neurons. We hypothesized that mild mitochondrial uncoupling by PCP would impair bioenergetics while decreasing oxidative stress, and therefore would not cause dopaminergic neurodegeneration.

Results

A 48-hour developmental exposure to PCP causing mild growth delay (∼10 % decrease in growth during 48 h, covering all larval stages) reduced whole-organism ATP content > 50 %, and spare respiratory capacity ∼ 30 %. Proton leak was also markedly increased. These findings suggest a main toxic mechanism of mitochondrial uncoupling rather than oxidative stress, which was further supported by a concomitant shift toward a more reduced cellular redox state measured at the whole organism level. However, exposure to PCP did not cause dopaminergic neurodegeneration, nor did it sensitize animals to a neurotoxic challenge with 6-hydroxydopamine. Whole-organism uptake and PCP metabolism measurements revealed low overall uptake of PCP in our experimental conditions (50 μM PCP in the liquid exposure medium resulted in organismal concentrations of < 0.25 μM), and no measurable production of the oxidative metabolites tetra-1,4-benzoquinone and tetrachloro-p-hydroquinone.

Innovation

This study provides new insights into the mechanistic interplay between mitochondrial uncoupling, oxidative stress, and neurodegeneration in C. elegans. These findings support the premise of mild uncoupling-mediated neuroprotection, but are inconsistent with proposed broad "mitochondrial dysfunction"-mediated neurodegeneration models, and highlight the utility of the C. elegans model for studying mitochondrial and neurotoxicity.

Conclusions

Developmental exposure to pentachlorophenol causes gross toxicological effects (growth delay and arrest) at high levels. At a lower level of exposure, still causing mild growth delay, we observed mitochondrial dysfunction including uncoupling and decreased ATP levels. However, this was associated with a more-reduced cellular redox tone and did not exacerbate dopaminergic neurotoxicity of 6-hydroxydopamine, instead trending toward protection. These findings may be informative of efforts to define nuanced mitochondrial dysfunction-related adverse outcome pathways that will differ depending on the form of initial mitochondrial toxicity.

Assessing Drug-Induced Mitochondrial Toxicity in Cardiomyocytes: Implications for Preclinical Cardiac Safety Evaluation

Drug-induced cardiotoxicity not only leads to the attrition of drugs during development, but also contributes to the high morbidity and mortality rates of cardiovascular diseases. Comprehensive testing for proarrhythmic risks of drugs has been applied in preclinical cardiac safety assessment for over 15 years. However, other mechanisms of cardiac toxicity have not received such attention. Of them, mitochondrial impairment is a common form of cardiotoxicity and is known to account for over half of cardiovascular adverse-event-related black box warnings imposed by the U.S. Food and Drug Administration. Although it has been studied in great depth, mitochondrial toxicity assessment has not yet been incorporated into routine safety tests for cardiotoxicity at the preclinical stage. This review discusses the main characteristics of mitochondria in cardiomyocytes, drug-induced mitochondrial toxicities, and high-throughput screening strategies for cardiomyocytes, as well as their proposed integration into preclinical safety pharmacology. We emphasize the advantages of using adult human primary cardiomyocytes for the evaluation of mitochondrial morphology and function, and the need for a novel cardiac safety testing platform integrating mitochondrial toxicity and proarrhythmic risk assessments in cardiac safety evaluation.

Machine learning models on chemical inhibitors of mitochondrial electron transport chain

Chemicals can induce adverse effects in humans by inhibiting mitochondrial electron transport chain (ETC) such as disrupting mitochondrial membrane potential, enhancing oxidative stress and causing some diseases. Thus, identifying ETC inhibitors (ETCi) is important to chemical risk assessment and protecting the public health. However, it is not feasible to identify all ETCi with experimental methods. Quantitative structure-activity relationship (QSAR) modeling is a promising method to rapidly and effectively identify ETCi. In this study, QSAR models for predicting ETCi were developed using machine learning methods. A clustering-based under-sampling (CBUS) method was developed to handle the imbalance issue in training sets. Structure-activity landscapes were generated and analyzed for training sets generated by the CBUS method. The consensus QSAR models constructed with CBUS achieved satisfactory performances (balanced accuracy = 0.852) in 100 iterations of five-fold cross validations, indicating the models can effectively classify ETCi. The classification model was further employed to screen chemicals in the Inventory of Existing Chemical Substances of China and 13 chemicals were identified as ETCi. Fifteen structural alerts for ETCi were identified in this study. These results demonstrated that the model and structural alerts are useful to screen ETCi.

Mapping the cellular response to electron transport chain inhibitors reveals selective signaling networks triggered by mitochondrial perturbation

Mitochondrial perturbation is a key event in chemical-induced organ toxicities that is incompletely understood. Here, we studied how electron transport chain (ETC) complex I, II, or III (CI, CII and CIII) inhibitors affect mitochondrial functionality, stress response activation, and cell viability using a combination of high-content imaging and TempO-Seq in HepG2 hepatocyte cells. CI and CIII inhibitors perturbed mitochondrial membrane potential (MMP) and mitochondrial and cellular ATP levels in a concentration- and time-dependent fashion and, under conditions preventing a switch to glycolysis attenuated cell viability, whereas CII inhibitors had no effect. TempO-Seq analysis of changes in mRNA expression pointed to a shared cellular response to CI and CIII inhibition. First, to define specific ETC inhibition responses, a gene set responsive toward ETC inhibition (and not to genotoxic, oxidative, or endoplasmic reticulum stress) was identified using targeted TempO-Seq in HepG2. Silencing of one of these genes, NOS3, exacerbated the impact of CI and CIII inhibitors on cell viability, indicating its functional implication in cellular responses to mitochondrial stress. Then by monitoring dynamic responses to ETC inhibition using a HepG2 GFP reporter panel for different classes of stress response pathways and applying pathway and gene network analysis to TempO-Seq data, we looked for downstream cellular events of ETC inhibition and identified the amino acid response (AAR) as being triggered in HepG2 by ETC inhibition. Through in silico approaches we provide evidence indicating that a similar AAR is associated with exposure to mitochondrial toxicants in primary human hepatocytes. Altogether, we (i) unravel quantitative, time- and concentration-resolved cellular responses to mitochondrial perturbation, (ii) identify a gene set associated with adaptation to exposure to active ETC inhibitors, and (iii) show that ER stress and an AAR accompany ETC inhibition in HepG2 and primary hepatocytes.