Prediction of drug-induced mitochondrial dysfunction using succinate-cytochrome c reductase activity, QSAR and molecular docking

Rosell-Hidalgo, Alicia; Moore, Anthony L.; Ghafourian, Taravat

doi:10.1016/j.tox.2022.153412

Cited by 15 publications

(7 citation statements)

References 76 publications

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“…The total number of molecular descriptors was reduced based on specific criteria. These criteria included: 1) Removing descriptors with a standard deviation less than 0.001, 2) Eliminating descriptors with at least one missing value, 3) Removing descriptors with constant values, and 4) Removing descriptors that were constant across all molecules or highly correlated (R ≥ 0.9) [33][34][35] . As a result, a total of 321 descriptors were obtained for the calculation of mixture descriptors according to Eq.…”

Section: Data Set and Calculation Of Mixture Molecular Descriptorsmentioning

confidence: 99%

Combined interaction of fungicides binary mixtures: experimental study and machine learning-driven QSAR modeling

Abbod,

Mohammad

2024

Sci Rep

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Fungicide mixtures are an effective strategy in delaying the development of fungicide resistance. In this research, a fixed ratio ray design method was used to generate fifty binary mixtures of five fungicides with diverse modes of action. The interaction of these mixtures was then analyzed using CA and IA models. QSAR modeling was conducted to assess their fungicidal activity through multiple linear regression (MLR), support vector machine (SVM), and artificial neural network (ANN). Most mixtures exhibited additive interaction, with the CA model proving more accurate than the IA model in predicting fungicidal activity. The MLR model showed a good linear correlation between selected theoretical descriptors by the genetic algorithm and fungicidal activity. However, both ML-based models demonstrated better predictive performance than the MLR model. The ANN model showed slightly better predictability than the SVM model, with R2 and R2cv at 0.91 and 0.81, respectively. For external validation, the R2test value was 0.845. In contrast, the SVM model had values of 0.91, 0.78, and 0.77 for the same metrics. In conclusion, the proposed ML-based model can be a valuable tool for developing potent fungicidal mixtures to delay fungicidal resistance emergence.

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Section: Data Set and Calculation Of Mixture Molecular Descriptorsmentioning

confidence: 99%

Combined interaction of fungicides binary mixtures: experimental study and machine learning-driven QSAR modeling

Abbod,

Mohammad

2024

Sci Rep

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“…For this reason, developing drugs (antibiotics, anti-fungal agents, or anti-parasite agents) acting as specific bc 1 inhibitors is of commercial interest due to their use in medicine or agriculture to control diseases or cure severe infections. According to the state-of-the-art literature, specific bc 1 inhibitors are divided into three classes based on their points of action [45], such as (i) complex III Q i inhibitors (antimycin A, amisulbrom, cyazofamid); (ii) complex III Q 0 inhibitors (stigmatellin, ametoctradin, azoxystrobin); and (iii) dual site inhibitors (ascochlorin). Moreover, based on in vivo studies, Aureliano et al [46] pointed out that mitochondria might be a target for DV units that could affect its bioenergetics through different toxicity mechanisms by blocking the respiratory electron transport chain and acting as an antimycin A-like inhibitor.…”

Section: Molecular Docking Studiesmentioning

confidence: 99%

Decavanadate-Bearing Guanidine Derivatives Developed as Antimicrobial and Antitumor Species

Dumitrescu,

Maxim,

Badea

et al. 2023

IJMS

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To obtain biologically active species, a series of decavanadates (Hpbg)4[H2V10O28]·6H2O (1) (Htbg)4[H2V10O28]·6H2O; (2) (Hgnd)2(Hgnu)4[V10O28]; (3) (Hgnu)6[V10O28]·2H2O; and (4) (pbg = 1-phenyl biguanide, tbg = 1-(o-tolyl)biguanide, gnd = guanidine, and gnu = guanylurea) were synthesized and characterized by several spectroscopic techniques (IR, UV-Vis, and EPR) as well as by single crystal X-ray diffraction. Compound (1) crystallizes in space group P-1 while (3) and (4) adopt the same centrosymmetric space group P21/n. The unusual signal identified by EPR spectroscopy was assigned to a charge-transfer π(O)→d(V) process. Both stability in solution and reactivity towards reactive oxygen species (O2− and OH·) were screened through EPR signal modification. All compounds inhibited the development of Escherichia coli, Pseudomonas aeruginosa, Staphylococcus aureus, and Enterococcus faecalis bacterial strains in a planktonic state at a micromolar level, the most active being compound (3). However, the experiments conducted at a minimal inhibitory concentration (MIC) indicated that the compounds do not disrupt the biofilm produced by these bacterial strains. The cytotoxicity assayed against A375 human melanoma cells and BJ human fibroblasts by testing the viability, lactate dehydrogenase, and nitric oxide levels indicated compound (1) as the most active in tumor cells.

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“…While this approach has proven that it can accurately predict numerous kinds of drug-induced toxicity such as general cytotoxicity (3), mitotoxicity (4) or even rhabdomyolysis (a fatal myopathy characterized by the breakdown of muscle tissue) (5,6) , it is still subject to some limitations. For instance, a QSPR model trained on a chemical family or specific scaffold can still be challenged by an intensively studied phenomenon called the activity cliff, where the observed property (such as toxicity) can sharply decrease/increase with only a slight displacement in the chemical space, and therefore adding complexity to a predictive model (7).…”

Section: Introductionmentioning

confidence: 99%

Drug-induced cytotoxicity prediction in muscle cells, an application of the Cell Painting assay

Lambert,

Aparicio,

Candelas

et al. 2024

Preprint

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In silicotoxicity prediction offers the chance of reducing or replacing most animal testing through the integration of large experimental assay datasets with the appropriate computational approaches. The use of Cell Painting to detect various phenotypic changes induced by chemicals is emerging as a powerful technique in toxicity prediction. However, most Cell Painting approaches use cancer cells that are less relevant for many toxicological endpoints, which may limit the usefulness of this data. In this study, a myoblast cell line is used to characterize cellular responses to a panel of 30 known myotoxicants. In place of traditional structural descriptors, here each perturbation is described by a fingerprint of calculated properties, deducted from the intensity, shape, or texture of individual cells. We show that these kinds of descriptors convey information to allow the prediction of the cellular viability and fate of cells in myoblasts and differentiated myotubes of the C2C12 cell line, and the clustering of drugs by their cytotoxicity responses.Author SummaryStudying the toxicity of chemical compounds and drugs is crucial to avoid potentially lethal adverse effects of commercialized products, but also to detect the unsuspected toxicity of existing drugs. While these assays traditionally rely on animal models raising important ethical concerns, a need forin vitroandin silicomodels is present and increasing in recent years. We here propose a predictive model capable of predicting the values of a cell viability assay using cell morphology profiles captured with a microscopy experiment. This model predicts the healthiness of muscle cells treated with 30 compounds suspected to induce muscular damage or even myopathies in humans. We also use these profiles to find an interesting morphological similarity between two different classes of drugs: statins (used for cholesterol treatments) and tyrosine kinase inhibitors (anti-cancer drugs). This analysis opens a new perspective for understanding the mechanisms responsible for drug-induced muscular toxicity, an area of toxicology that is currently under-researched.

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Prediction of drug-induced mitochondrial dysfunction using succinate-cytochrome c reductase activity, QSAR and molecular docking

Cited by 15 publications

References 76 publications

Combined interaction of fungicides binary mixtures: experimental study and machine learning-driven QSAR modeling

Combined interaction of fungicides binary mixtures: experimental study and machine learning-driven QSAR modeling

Decavanadate-Bearing Guanidine Derivatives Developed as Antimicrobial and Antitumor Species

Drug-induced cytotoxicity prediction in muscle cells, an application of the Cell Painting assay

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