TIRESIA and TISBE: Explainable Artificial Intelligence Based Web Platforms for the Transparent Assessment of the Developmental Toxicity of Chemicals and Drugs.

Developmental toxicity is key human health endpoint, especially relevant for safeguarding maternal and child well-being. It is an object of increasing attention from international regulatory bodies such as the US EPA (US Environmental Protection Agency) and ECHA (European CHemicals Agency). In this challenging scenario, non-test methods employing explainable artificial intelligence based techniques can provide a significant help to derive transparent predictive models whose results can be easily interpreted to assess the developmental toxicity of new chemicals at very early stages. To accomplish this task, we have developed web platforms such as TIRESIA and TISBE.Based on a benchmark dataset, TIRESIA employs an explainable artificial intelligence approach combined with SHAP analysis to unveil the molecular features responsible for calculating the developmental toxicity. Descending from TIRESIA, TISBE employs a larger dataset, an explainable artificial intelligence framework based on a fragment-based fingerprint encoding, a consensus classifier, and a new double top-down applicability domain. We report here some practical examples for getting started with TIRESIA and TISBE.

Research trends of computational toxicology: a bibliometric analysis.

BACKGROUND: Computational toxicology utilizes computer models and simulations to predict the toxicity of chemicals. Bibliometric studies evaluate the impact of scientific research in a specific field. METHODS: A bibliometric analysis of the computational methods used in toxicity assessment was conducted on the Web of Science between 1977 and 2024 February 12. RESULTS: Findings of this study showed that computational toxicology has evolved considerably over the years, moving towards more advanced computational methods, including machine learning, molecular docking, and deep learning. Artificial intelligence significantly enhances computational toxicology research by improving the accuracy and efficiency of toxicity predictions. CONCLUSION: Generally, the study highlighted a significant rise in research output in computational toxicology, with a growing interest in advanced methods and a notable focus on refining predictive models to optimize drug properties using tools like pkCSM for more precise predictions.

Molecular similarity in chemical informatics and predictive toxicity modeling: from quantitative read-across (q-RA) to quantitative read-across structure-activity relationship (q-RASAR) with the application of machine learning.

This article aims to provide a comprehensive critical, yet readable, review of general interest to the chemistry community on molecular similarity as applied to chemical informatics and predictive modeling with a special focus on read-across (RA) and read-across structure-activity relationships (RASAR). Molecular similarity-based computational tools, such as quantitative structure-activity relationships (QSARs) and RA, are routinely used to fill the data gaps for a wide range of properties including toxicity endpoints for regulatory purposes. This review will explore the background of RA starting from how structural information has been used through to how other similarity contexts such as physicochemical, absorption, distribution, metabolism, and elimination (ADME) properties, and biological aspects are being characterized. More recent developments of RA's integration with QSAR have resulted in the emergence of novel models such as ToxRead, generalized read-across (GenRA), and quantitative RASAR (q-RASAR). Conventional QSAR techniques have been excluded from this review except where necessary for context.

Predictive toxicology of chemical mixtures using proteome-wide thermal profiling and protein target properties.

Our capability to predict the impact of exposure to chemical mixtures on environmental and human health is limited in comparison to the advances on the chemical characterization of the exposome. Current approaches, such as new approach methodologies, rely on the characterization of the chemicals and the available toxicological knowledge of individual compounds. In this study, we show a new methodological approach for the assessment of chemical mixtures based on a proteome-wide identification of the protein targets and revealing the relevance of new targets based on their role in the cellular function. We applied a proteome integral solubility alteration assay to identify 24 protein targets from a chemical mixture of 2,3,7,8-tetrachlorodibenzo-p-dioxin, alpha-endosulfan, and bisphenol A among the HepG2 soluble proteome, and validated the chemical mixture-target interaction orthogonally. To define the range of interactive capability of the new targets, the data from intrinsic properties of the targets were retrieved. Introducing the target properties as criteria for a multi-criteria decision-making analysis called the analytical hierarchy process, the prioritization of targets was based on their involvement in multiple pathways. This methodological approach that we present here opens a more realistic and achievable scenario to address the impact of complex and uncharacterized chemical mixtures in biological systems.

A Quantitative Adverse Outcome Pathway for Embryonic Activation of the Aryl Hydrocarbon Receptor of Fishes by Polycyclic Aromatic Hydrocarbons Leading to Decreased Fecundity at Adulthood.

Quantitative adverse outcome pathways (qAOPs) describe the response-response relationships that link the magnitude and/or duration of chemical interaction with a specific molecular target to the probability and/or severity of the resulting apical-level toxicity of regulatory relevance. The present study developed the first qAOP for latent toxicities showing that early life exposure adversely affects health at adulthood. Specifically, a qAOP for embryonic activation of the aryl hydrocarbon receptor 2 (AHR2) of fishes by polycyclic aromatic hydrocarbons (PAHs) leading to decreased fecundity of females at adulthood was developed by building on existing qAOPs for (1) activation of the AHR leading to early life mortality in birds and fishes, and (2) inhibition of cytochrome P450 aromatase activity leading to decreased fecundity in fishes. Using zebrafish (Danio rerio) as a model species and benzo[a]pyrene as a model PAH, three linked quantitative relationships were developed: (1) plasma estrogen in adult females as a function of embryonic exposure, (2) plasma vitellogenin in adult females as a function of plasma estrogen, and (3) fecundity of adult females as a function of plasma vitellogenin. A fourth quantitative relationship was developed for early life mortality as a function of sensitivity to activation of the AHR2 in a standardized in vitro AHR transactivation assay to integrate toxic equivalence calculations that would allow prediction of effects of exposure to untested PAHs. The accuracy of the predictions from the resulting qAOP were evaluated using experimental data from zebrafish exposed as embryos to another PAH, benzo[k]fluoranthene. The qAOP developed in the present study demonstrates the potential of the AOP framework in enabling consideration of latent toxicities in quantitative ecological risk assessments and regulatory decision-making. Environ Toxicol Chem 2024;43:2145-2156. © 2024 SETAC.

Unlocking Biological Activity and Metabolomics Insights: Primary Screening of Cyanobacterial Biomass from a Tropical Reservoir.

Cyanobacterial harmful algal blooms can pose risks to ecosystems and human health worldwide due to their capacity to produce natural toxins. The potential dangers associated with numerous metabolites produced by cyanobacteria remain unknown. Only select classes of cyanopeptides have been extensively studied with the aim of yielding substantial evidence regarding their toxicity, resulting in their inclusion in risk management and water quality regulations. Information about exposure concentrations, co-occurrence, and toxic impacts of several cyanopeptides remains largely unexplored. We used liquid chromatography-mass spectrometry (LC-MS)-based metabolomic methods associated with chemometric tools (NP Analyst and Data Fusion-based Discovery), as well as an acute toxicity essay, in an innovative approach to evaluate the association of spectral signatures and biological activity from natural cyanobacterial biomass collected in a eutrophic reservoir in southeastern Brazil. Four classes of cyanopeptides were revealed through metabolomics: microcystins, microginins, aeruginosins, and cyanopeptolins. The bioinformatics tools showed high bioactivity correlation scores for compounds of the cyanopeptolin class (0.54), in addition to microcystins (0.54-0.58). These results emphasize the pressing need for a comprehensive evaluation of the (eco)toxicological risks associated with different cyanopeptides, considering their potential for exposure. Our study also demonstrated that the combined use of LC-MS/MS-based metabolomics and chemometric techniques for ecotoxicological research can offer a time-efficient strategy for mapping compounds with potential toxicological risk. Environ Toxicol Chem 2024;43:2222-2231. © 2024 SETAC.

Predicting non-chemotherapy drug-induced agranulocytosis toxicity through ensemble machine learning approaches.

Agranulocytosis, induced by non-chemotherapy drugs, is a serious medical condition that presents a formidable challenge in predictive toxicology due to its idiosyncratic nature and complex mechanisms. In this study, we assembled a dataset of 759 compounds and applied a rigorous feature selection process prior to employing ensemble machine learning classifiers to forecast non-chemotherapy drug-induced agranulocytosis (NCDIA) toxicity. The balanced bagging classifier combined with a gradient boosting decision tree (BBC + GBDT), utilizing the combined descriptor set of DS and RDKit comprising 237 features, emerged as the top-performing model, with an external validation AUC of 0.9164, ACC of 83.55%, and MCC of 0.6095. The model's predictive reliability was further substantiated by an applicability domain analysis. Feature importance, assessed through permutation importance within the BBC + GBDT model, highlighted key molecular properties that significantly influence NCDIA toxicity. Additionally, 16 structural alerts identified by SARpy software further revealed potential molecular signatures associated with toxicity, enriching our understanding of the underlying mechanisms. We also applied the constructed models to assess the NCDIA toxicity of novel drugs approved by FDA. This study advances predictive toxicology by providing a framework to assess and mitigate agranulocytosis risks, ensuring the safety of pharmaceutical development and facilitating post-market surveillance of new drugs.

CarcSeq detection of lorcaserin-induced clonal expansion of Pik3ca H1047R mutants in rat mammary tissue.

Lorcaserin is a 5-hydroxytryptamine 2C (serotonin) receptor agonist and a nongenotoxic rat carcinogen, which induced mammary tumors in male and female rats in a 2-yr bioassay. Female Sprague Dawley rats were treated by gavage daily with 0, 30, or 100 mg/kg lorcaserin, replicating bioassay dosing but for shorter duration, 12 or 24 wk. To characterize exposure and eliminate possible confounding by a potentially genotoxic degradation product, lorcaserin and N-nitroso-lorcaserin were quantified in dosing solutions, terminal plasma, mammary, and liver samples using ultra-high-performance liquid chromatography-electrospray tandem mass spectrometry. N-nitroso-lorcaserin was not detected, supporting lorcaserin classification as nongenotoxic carcinogen. Mammary DNA samples (n = 6/dose/timepoint) were used to synthesize PCR products from gene segments encompassing hotspot cancer driver mutations, namely regions of Apc, Braf, Egfr, Hras, Kras, Nfe2l2, Pik3ca, Setbp1, Stk11, and Tp53. Mutant fractions (MFs) in the amplicons were quantified by CarcSeq, an error-corrected next-generation sequencing approach. Considering all recovered mutants, no significant differences between lorcaserin dose groups were observed. However, significant dose-responsive increases in Pik3ca H1047R mutation were observed at both timepoints (ANOVA, P < 0.05), with greater numbers of mutants and mutants with greater MFs observed at 24 wk as compared with 12 wk. These observations suggest lorcaserin promotes outgrowth of spontaneously occurring Pik3ca H1047R mutant clones leading to mammary carcinogenesis. Importantly, this work reports approaches to analyze clonal expansion and demonstrates CarcSeq detection of the carcinogenic impact (selective Pik3ca H0147R mutant expansion) of a nongenotoxic carcinogen using a treatment duration as short as 3 months.

Characterizing Freshwater Ecotoxicity of More Than 9000 Chemicals by Combining Different Levels of Available Measured Test Data with In Silico Predictions.

Ecotoxicological impacts of chemicals released into the environment are characterized by combining fate, exposure, and effects. For characterizing effects, species sensitivity distributions (SSDs) estimate toxic pressures of chemicals as the potentially affected fraction of species. Life cycle assessment (LCA) uses SSDs to identify products with lowest ecotoxicological impacts. To reflect ambient concentrations, the Global Life Cycle Impact Assessment Method (GLAM) ecotoxicity task force recently recommended deriving SSDs for LCA based on chronic EC10s (10% effect concentration, for a life-history trait) and using the 20th percentile of an EC10-based SSD as a working point. However, because we lacked measured effect concentrations, impacts of only few chemicals were assessed, underlining data limitations for decision support. The aims of this paper were therefore to derive and validate freshwater SSDs by combining measured effect concentrations with in silico methods. Freshwater effect factors (EFs) and uncertainty estimates for use in GLAM-consistent life cycle impact assessment were then derived by combining three elements: (1) using intraspecies extrapolating effect data to estimate EC10s, (2) using interspecies quantitative structure-activity relationships, or (3) assuming a constant slope of 0.7 to derive SSDs. Species sensitivity distributions, associated EFs, and EF confidence intervals for 9862 chemicals, including data-poor ones, were estimated based on these elements. Intraspecies extrapolations and the fixed slope approach were most often applied. The resulting EFs were consistent with EFs derived from SSD-EC50 models, implying a similar chemical ecotoxicity rank order and method robustness. Our approach is an important step toward considering the potential ecotoxic impacts of chemicals currently neglected in assessment frameworks due to limited test data. Environ Toxicol Chem 2024;43:1914-1927. © 2024 The Author(s). Environmental Toxicology and Chemistry published by Wiley Periodicals LLC on behalf of SETAC.

Prediction of the liver safety profile of a first-in-class myeloperoxidase inhibitor using quantitative systems toxicology modeling.

The novel myeloperoxidase inhibitor verdiperstat was developed as a treatment for neuroinflammatory and neurodegenerative diseases. During development, a computational prediction of verdiperstat liver safety was performed using DILIsym v8A, a quantitative systems toxicology (QST) model of liver safety.A physiologically-based pharmacokinetic (PBPK) model of verdiperstat was constructed in GastroPlus 9.8, and outputs for liver and plasma time courses of verdiperstat were input into DILIsym. In vitro experiments measured the likelihood that verdiperstat would inhibit mitochondrial function, inhibit bile acid transporters, and generate reactive oxygen species (ROS); these results were used as inputs into DILIsym, with two alternate sets of parameters used in order to fully explore the sensitivity of model predictions. Verdiperstat dosing protocols up to 600 mg BID were simulated for up to 48 weeks using a simulated population (SimPops) in DILIsym.Verdiperstat was predicted to be safe, with only very rare, mild liver enzyme increases as a potential possibility in highly sensitive individuals. Subsequent Phase 3 clinical trials found that ALT elevations in the verdiperstat treatment group were generally similar to those in the placebo group. This validates the DILIsym simulation results and demonstrates the power of QST modelling to predict the liver safety profile of novel therapeutics.

In-silico green toxicology approach toward discovering safer ligands for development of safe-by-design metal-organic frameworks.

A vast variety of chemical compounds have been fabricated and commercialized, they not only result in industrial exposure during manufacturing and usage, but also have environmental impacts throughout their whole life cycle. Consequently, attempts to assess the risk of chemicals in terms of toxicology have never ceased. In-silico toxicology, also known as predictive toxicology, has advanced significantly over the last decade as a result of the drawbacks of experimental investigations. In this study, ProTox-III was applied to predict the toxicity of the ligands used for metal-organic framework (MOF) design and synthesis. Initially, 35 ligands, that have been frequently utilized for MOF synthesis and fabrication, were selected. Subsequently, canonical simplified molecular-input line-entry system (SMILES) of ligands were extracted from the PUBCHEM database and inserted into the ProTox-III online server. Ultimately, webserver outputs including LD50 and the probability of toxicological endpoints (cytotoxicity, carcinogenicity, mutagenicity, immunotoxicity, and ecotoxicity) were obtained and organized. According to retrieved LD50 data, the safest ligand was 5-hydroxyisophthalic. In contrast, the most hazardous ligand was 5-chlorobenzimidazole, with an LD50 of 8 mg/kg. Among evaluated endpoints, ecotoxicity was the most active and was detected in several imidazolate ligands. This data can open new horizons in design and development of green MOFs.

A QSAR study for predicting malformation in zebrafish embryo.

BACKGROUND: Developmental toxicity tests are extremely expensive, require a large number of animals, and are time-consuming. It is necessary to develop a new approach to simplify the analysis of developmental endpoints. One of these endpoints is malformation, and one group of ongoing methods for simplifying is in silico models. In this study, we aim to develop a quantitative structure-activity relationship (QSAR) model and identify the best algorithm for predicting malformations, as well as the most important and effective physicochemical properties associated with malformation. METHODS: The dataset was extracted from a reliable database called COMPTOX. Physicochemical properties (descriptors) were calculated using Mordred and RDKit chemoinformatics software. The data were cleaned, preprocessed, and then split into training and testing sets. Machine learning algorithms, such as gradient boosting model (GBM) and logistic regression (LR), as well as deep learning models, including multilayer perceptron (MLP) and neural networks (NNs) trained with train set data and different sets of descriptors. The models were then validated with test set and various statistical parameters, such as Matthew's correlation coefficient (MCC) and balanced accuracy (BAC) score, were used to compare the models. RESULTS: A set of descriptors containing with 78% AUC was identified as the best set of descriptors. Gradient boosting was determined to be the best algorithm with 78% predictive power. CONCLUSIONS: The descriptors that were the most effective for developing models directly impact the mechanism of malformation, and GBM is the best model due to its MCC and BAC.

Probing Liver Injuries Induced by Thioacetamide in Human In Vitro Pooled Hepatocyte Experiments.

Animal studies are typically utilized to understand the complex mechanisms associated with toxicant-induced hepatotoxicity. Among the alternative approaches to animal studies, in vitro pooled human hepatocytes have the potential to capture population variability. Here, we examined the effect of the hepatotoxicant thioacetamide on pooled human hepatocytes, divided into five lots, obtained from forty diverse donors. For 24 h, pooled human hepatocytes were exposed to vehicle, 1.33 mM (low dose), and 12 mM (high dose) thioacetamide, followed by RNA-seq analysis. We assessed gene expression variability using heat maps, correlation plots, and statistical variance. We used KEGG pathways and co-expression modules to identify underlying physiological processes/pathways. The co-expression module analysis showed that the majority of the lots exhibited activation for the bile duct proliferation module. Despite lot-to-lot variability, we identified a set of common differentially expressed genes across the lots with similarities in their response to amino acid, lipid, and carbohydrate metabolism. We also examined efflux transporters and found larger lot-to-lot variability in their expression patterns, indicating a potential for alteration in toxicant bioavailability within the cells, which could in turn affect the gene expression patterns between the lots. Overall, our analysis highlights the challenges in using pooled hepatocytes to understand mechanisms of toxicity.

Beyond the hype and toward application: liver complex in vitro models in preclinical drug safety.

INTRODUCTION: Drug induced Liver-Injury (DILI) is a leading cause of drug attrition and complex in vitro models (CIVMs), including three dimensional (3D) spheroids, 3D bio printed tissues and flow-based systems, could improve preclinical prediction. Although CIVMs have demonstrated good sensitivity and specificity in DILI detection their adoption remains limited. AREAS COVERED: This article describes DILI, the challenges with its prediction and the current strategies and models that are being used. It reviews data from industry-FDA collaborations and strategic partnerships and finishes with an outlook of CIVMs in preclinical toxicity testing. Literature searches were performed using PubMed and Google Scholar while product information was collected from manufacturer websites. EXPERT OPINION: Liver CIVMs are promising models for predicting DILI although, a decade after their introduction, routine use by the pharmaceutical industry is limited. To accelerate their adoption, several industry-regulator-developer partnerships or consortia have been established to guide the development and qualification. Beyond this, liver CIVMs should continue evolving to capture greater immunological mimicry while partnering with computational approaches to deliver systems that change the paradigm of predicting DILI.

Prediction of HC5s for phthalate esters by use of the QSAR-ICE model and ecological risk assessment in Chinese surface waters.

Due to their endocrine-disrupting effects and the risks posed in surface waters, in particular by chronic low-dose exposure to aquatic organisms, phthalate esters (PAEs) have received significant attention. However, most assessments of risks posed by PAEs were performed at a selection level, and thus limited by empirical data on toxic effects and potencies. A quantitative structure activity relationship (QSAR) and interspecies correlation estimation (ICE) model was constructed to estimate hazardous concentrations (HCs) of selected PAEs to aquatic organisms, then they were used to conduct a multiple-level environmental risk assessment for PAEs in surface waters of China. Values of hazardous concentration for 5% of species (HC5s), based on acute lethality, estimated by use of the QSAR-ICE model were within 1.25-fold of HC5 values derived from empirical data on toxic potency, indicating that the QSAR-ICE model predicts the toxicity of these three PAEs with sufficient accuracy. The five selected PAEs may be commonly measured in China surface waters at concentrations between ng/L and µg/L. Risk quotients according to median concentrations of the five PAEs ranged from 3.24 for di(2-ethylhexhyl) phthalate (DEHP) to 4.10 × 10-3 for dimethyl phthalate (DMP). DEHP and dibutyl phthalate (DBP) had risks to the most vulnerable aquatic biota, with the frequency of exceedances of the predicted no-effect concentration (PNECs) of 75.5% and 38.0%, respectively. DEHP and DBP were identified as having "high" or "moderate" risks. Results of the joint probability curves (JPC) method indicated DEHP posed "intermediate" risk to freshwater species with a maximum risk product of 5.98%. The multiple level system introduced in this study can be used to prioritize chemicals and other new pollutant in the aquatic ecological.

Examining animal testing for risk assessment: A WC-12 workshop report.

In toxicology and regulatory testing, the use of animal methods has been both a cornerstone and a subject of intense debate. To continue this discourse a panel and audience representing scientists from various sectors and countries convened at a workshop held during the 12th World Congress on Alternatives and Animal Use in the Life Sciences (WC-12). The ensuing discussion focused on the scientific and ethical considerations surrounding the necessity and responsibility of defending the creation of new animal data in regulatory testing. The primary aim was to foster an open dialogue between the panel members and the audience while encouraging diverse perspectives on the responsibilities and obligations of various stakeholders (including industry, regulatory bodies, technology developers, research scientists, and animal welfare NGOs) in defending the development and subsequent utilization of new animal data. This workshop summary report captures the key elements from this critical dialogue and collective introspection. It describes the intersection of scientific progress and ethical responsibility as all sectors seek to accelerate the pace of 21st century predictive toxicology and new approach methodologies (NAMs) for the protection of human health and the environment.

The First Steps on AOPs' Concepts, Development, and Applications in Teratology.

An Adverse Outcome Pathway (AOP) is an analytical model that describes, through a graphical representation, a linear sequence of biologically connected events at different levels of biological organization, causally leading to an adverse effect on human health or the environment. In general, AOPs are constructed based on five central principles: systematic development and review, chemical-agnostic, modular, networks, and living documents. Furthermore, AOPs have the potential to be used, for example, to investigate certain molecular targets; relate the regulation of specific genes or proteins among AOPs; extrapolate biological processes, pathways, or diseases from one species to another; and even predict adverse effects in particular populations. AOPs also emerge as an alternative to animal experimentation in studies of developmental malformations. It's even possible now to develop a quantitative AOP to predict teratogenic effects for some substances. However, the construction of high-quality AOPs requires standardization in the way these models are developed and reviewed, ensuring an adequate degree of flexibility and guaranteeing efficiency. The development of AOPs should strictly be based on the guidance documents developed by the OECD. Nevertheless, an important step for those developing AOPs is the choice of an apical endpoint or an initiating molecular event in order to initiate the construction of the pathway. Another crucial step is a systematic literature review based on the random combination of the blocks of information. With these two fundamental steps completed, it only remains to follow the guidance documents on Developing and Assessing Adverse Outcome Pathways and AOP Developers' Handbook supplement provided by the OECD to organize and construct an AOP. This modern approach will bring radical changes in the field of toxicity testing, regarding the prediction of apical toxic effects using molecular-level effects.

Extrapolating Metal (Cu, Ni, Zn) Toxicity from Individuals to Populations Across Daphnia Species Using Mechanistic Models: The Roles of Uncertainty Propagation and Combined Physiological Modes of Action.

Mechanistic effect modeling is a promising tool to improve the ecological realism of environmental risk assessment. An open question for the mechanistic modeling of metal toxicity is whether the same physiological mode of action (PMoA) could be assumed for closely related species. The implications of various modeling choices, such as the use of parameter point estimates and assumption of simplistic toxicodynamic models, are largely unexplored. We conducted life-table experiments with Daphnia longispina, Daphnia magna, and Daphnia pulex exposed to the single metals Cu, Ni, and Zn, and calibrated toxicokinetic-toxicodynamic (TKTD) models based on dynamic energy budget theory. We developed TKTD models with single and combined PMoAs to compare their goodness-of-fit and predicted population-level sensitivity. We identified the PMoA reproduction efficiency as most probable in all species for Ni and Zn, but not for Cu, and found that combined-PMoA models predicted higher population-level sensitivity than single-PMoA models, which was related to the predicted individual-level sensitivity, rather than to mechanistic differences between models. Using point estimates of parameters, instead of sampling from the probability distributions of parameters, could also lead to differences in the predicted population-level sensitivity. According to model predictions, apical chronic endpoints (cumulative reproduction, survival) are conservative for single-metal population effects across metals and species. We conclude that the assumption of an identical PMoA for different species of Daphnia could be justified for Ni and Zn, but not for Cu. Single-PMoA models are more appropriate than combined-PMoA models from a model selection perspective, but propagation of the associated uncertainty should be considered. More accurate predictions of effects at low concentrations may nevertheless motivate the use of combined-PMoA models. Environ Toxicol Chem 2024;43:338-358. © 2023 The Authors. Environmental Toxicology and Chemistry published by Wiley Periodicals LLC on behalf of SETAC.

Using Multiple Metal Mixture Models to Predict Toxicity of Riverine Sediment Porewater to the Benthic Life Stage of Juvenile White Sturgeon (Acipenser transmontanus).

Five metal mixture dose-response models were used to predict the toxicity of porewater to young sturgeon at areas of interest in the Upper Columbia River (WA, USA/BC, Canada) and to evaluate these models as tools for risk assessments. Dose components of metal mixture models included exposure to free metal ion activities or metal accumulation by biotic ligands or humic acid, and links of dose to response used logistic equations, independent joint action equations, or additive toxicity functions. Laboratory bioassay studies of single metal exposures to juvenile sturgeon, porewater collected in situ in the fast-flowing Upper Columbia River, and metal mixture models were used to evaluate toxicity. The five metal mixture models were very similar in their predictions of adverse response of juvenile sturgeon and in identifying copper (Cu) as the metal responsible for the most toxic conditions. Although the modes of toxic action and the 20% effective concentration values were different among the dose models, predictions of adverse response were consistent among models because all doses were tied to the same biological responses. All models indicated that 56% ± 5% of 122 porewater samples were predicted to have <20% adverse response, 25% ± 5% of samples were predicted to have 20% to 80% adverse response, and 20% ± 4% were predicted to have >80% adverse response in juvenile sturgeon. The approach of combining bioassay toxicity data, compositions of field porewater, and metal mixture models to predict lack of growth and survival of aquatic organisms due to metal toxicity is an important tool that can be integrated with other information (e.g., survey studies of organism populations, life cycle and behavior characteristics, sediment geochemistry, and food sources) to assess risks to aquatic organisms in metal-enriched ecosystems. Environ Toxicol Chem 2024;43:62-73. Published 2023. This article is a U.S. Government work and is in the public domain in the USA.

Evaluation of in vitro effects of ifosfamide drug on mitochondrial functions using isolated mitochondria obtained from vital organs.

Mitochondrial toxicity has been shown to contribute to a variety of organ toxicities such as, brain, heart, kidney, and liver. Ifosfamide (IFO) as an anticancer drug, is associated with increased risk of neurotoxicity, cardiotoxicity nephrotoxicity, hepatotoxicity, and hemorrhagic cystitis. The aim of this study was to evaluate the direct effect of IFO on isolated mitochondria obtained from the rat brain, heart, kidney, and liver. Mitochondria were isolated with mechanical lysis and differential centrifugation from different organs and treated with various concentrations of IFO. Using biochemical and flowcytometry assays, we evaluated mitochondrial succinate dehydrogenase (SDH) activity, mitochondrial swelling, lipid peroxidation, reactive oxygen species (ROS) production, and mitochondrial membrane potential (MMP). Our data showed that IFO did not cause deleterious alterations in mitochondrial functions, mitochondrial swelling, lipid peroxidation ROS formation, and MMP collapse in mitochondria isolated from brain, heart, kidney, and liver. Altogether, the data showed that IFO is not directly toxic in mitochondria isolated from brain, heart, kidney, and liver. This study proved that mitochondria alone does not play the main role in the toxicity of IFO, and suggests to reduce the toxicity of this drug, other pathways resulting in the production of toxic metabolites should be considered.