The rat acute oral toxicity of trifluoromethyl compounds (TFMs): a computational toxicology study combining the 2D-QSTR, read-across and consensus modeling methods.

All areas of the modern society are affected by fluorine chemistry. In particular, fluorine plays an important role in medical, pharmaceutical and agrochemical sciences. Amongst various fluoro-organic compounds, trifluoromethyl (CF3) group is valuable in applications such as pharmaceuticals, agrochemicals and industrial chemicals. In the present study, following the strict OECD modelling principles, a quantitative structure-toxicity relationship (QSTR) modelling for the rat acute oral toxicity of trifluoromethyl compounds (TFMs) was established by genetic algorithm-multiple linear regression (GA-MLR) approach. All developed models were evaluated by various state-of-the-art validation metrics and the OECD principles. The best QSTR model included nine easily interpretable 2D molecular descriptors with clear physical and chemical significance. The mechanistic interpretation showed that the atom-type electro-topological state indices, molecular connectivity, ionization potential, lipophilicity and some autocorrelation coefficients are the main factors contributing to the acute oral toxicity of TFMs against rats. To validate that the selected 2D descriptors can effectively characterize the toxicity, we performed the chemical read-across analysis. We also compared the best QSTR model with public OPERA tool to demonstrate the reliability of the predictions. To further improve the prediction range of the QSTR model, we performed the consensus modelling. Finally, the optimum QSTR model was utilized to predict a true external set containing many untested/unknown TFMs for the first time. Overall, the developed model contributes to a more comprehensive safety assessment approach for novel CF3-containing pharmaceuticals or chemicals, reducing unnecessary chemical synthesis whilst saving the development cost of new drugs.

Identification of Pharmacophoric Fragments of DYRK1A Inhibitors Using Machine Learning Classification Models.

Dual-specific tyrosine phosphorylation regulated kinase 1 (DYRK1A) has been regarded as a potential therapeutic target of neurodegenerative diseases, and considerable progress has been made in the discovery of DYRK1A inhibitors. Identification of pharmacophoric fragments provides valuable information for structure- and fragment-based design of potent and selective DYRK1A inhibitors. In this study, seven machine learning methods along with five molecular fingerprints were employed to develop qualitative classification models of DYRK1A inhibitors, which were evaluated by cross-validation, test set, and external validation set with four performance indicators of predictive classification accuracy (CA), the area under receiver operating characteristic (AUC), Matthews correlation coefficient (MCC), and balanced accuracy (BA). The PubChem fingerprint-support vector machine model (CA = 0.909, AUC = 0.933, MCC = 0.717, BA = 0.855) and PubChem fingerprint along with the artificial neural model (CA = 0.862, AUC = 0.911, MCC = 0.705, BA = 0.870) were considered as the optimal modes for training set and test set, respectively. A hybrid data balancing method SMOTETL, a combination of synthetic minority over-sampling technique (SMOTE) and Tomek link (TL) algorithms, was applied to explore the impact of balanced learning on the performance of models. Based on the frequency analysis and information gain, pharmacophoric fragments related to DYRK1A inhibition were also identified. All the results will provide theoretical supports and clues for the screening and design of novel DYRK1A inhibitors.

Prediction on the mutagenicity of nitroaromatic compounds using quantum chemistry descriptors based QSAR and machine learning derived classification methods.

Nitroaromatic compounds (NACs) are an important type of environmental organic pollutants. However, it is lack of sufficient information relating to their potential adverse effects on human health and the environment due to the limited resources. Thus, using in silico technologies to assess their potential hazardous effects is urgent and promising. In this study, quantitative structure activity relationship (QSAR) and classification models were constructed using a set of NACs based on their mutagenicity against Salmonella typhimurium TA100 strain. For QSAR studies, DRAGON descriptors together with quantum chemistry descriptors were calculated for characterizing the detailed molecular information. Based on genetic algorithm (GA) and multiple linear regression (MLR) analyses, we screened descriptors and developed QSAR models. For classification studies, seven machine learning methods along with six molecular fingerprints were applied to develop qualitative classification models. The goodness of fitting, reliability, robustness and predictive performance of all developed models were measured by rigorous statistical validation criteria, then the best QSAR and classification models were chosen. Moreover, the QSAR models with quantum chemistry descriptors were compared to that without quantum chemistry descriptors and previously reported models. Notably, we also obtained some specific molecular properties or privileged substructures responsible for the high mutagenicity of NACs. Overall, the developed QSAR and classification models can be utilized as potential tools for rapidly predicting the mutagenicity of new or untested NACs for environmental hazard assessment and regulatory purposes, and may provide insights into the in vivo toxicity mechanisms of NACs and related compounds.

Reductive Activity and Mechanism of Hypoxia- Targeted AGT Inhibitors: An Experimental and Theoretical Investigation.

O6-alkylguanine-DNA alkyltransferase (AGT) is the main cause of tumor cell resistance to DNA-alkylating agents, so it is valuable to design tumor-targeted AGT inhibitors with hypoxia activation. Based on the existing benchmark inhibitor O6-benzylguanine (O6-BG), four derivatives with hypoxia-reduced potential and their corresponding reduction products were synthesized. A reductase system consisting of glucose/glucose oxidase, xanthine/xanthine oxidase, and catalase were constructed, and the reduction products of the hypoxia-activated prodrugs under normoxic and hypoxic conditions were determined by high-performance liquid chromatography electrospray ionization tandem mass spectrometry (HPLC-ESI-MS/MS). The results showed that the reduction products produced under hypoxic conditions were significantly higher than that under normoxic condition. The amount of the reduction product yielded from ANBP (2-nitro-6-(3-amino) benzyloxypurine) under hypoxic conditions was the highest, followed by AMNBP (2-nitro-6-(3-aminomethyl)benzyloxypurine), 2-NBP (2-nitro-6-benzyloxypurine), and 3-NBG (O6-(3-nitro)benzylguanine). It should be noted that although the levels of the reduction products of 2-NBP and 3-NBG were lower than those of ANBP and AMNBP, their maximal hypoxic/normoxic ratios were higher than those of the other two prodrugs. Meanwhile, we also investigated the single electron reduction mechanism of the hypoxia-activated prodrugs using density functional theory (DFT) calculations. As a result, the reduction of the nitro group to the nitroso was proven to be a rate-limiting step. Moreover, the 2-nitro group of purine ring was more ready to be reduced than the 3-nitro group of benzyl. The energy barriers of the rate-limiting steps were 34-37 kcal/mol. The interactions between these prodrugs and nitroreductase were explored via molecular docking study, and ANBP was observed to have the highest affinity to nitroreductase, followed by AMNBP, 2-NBP, and 3-NBG. Interestingly, the theoretical results were generally in a good agreement with the experimental results. Finally, molecular docking and molecular dynamics simulations were performed to predict the AGT-inhibitory activity of the four prodrugs and their reduction products. In summary, simultaneous consideration of reduction potential and hypoxic selectivity is necessary to ensure that such prodrugs have good hypoxic tumor targeting. This study provides insights into the hypoxia-activated mechanism of nitro-substituted prodrugs as AGT inhibitors, which may contribute to reasonable design and development of novel tumor-targeted AGT inhibitors.

QSAR and Classification Study on Prediction of Acute Oral Toxicity of N-Nitroso Compounds.

To better understand the mechanism of in vivo toxicity of N-nitroso compounds (NNCs), the toxicity data of 80 NNCs related to their rat acute oral toxicity data (50% lethal dose concentration, LD50) were used to establish quantitative structure-activity relationship (QSAR) and classification models. Quantum chemistry methods calculated descriptors and Dragon descriptors were combined to describe the molecular information of all compounds. Genetic algorithm (GA) and multiple linear regression (MLR) analyses were combined to develop QSAR models. Fingerprints and machine learning methods were used to establish classification models. The quality and predictive performance of all established models were evaluated by internal and external validation techniques. The best GA-MLR-based QSAR model containing eight molecular descriptors was obtained with Q²loo = 0.7533, R² = 0.8071, Q²ext = 0.7041 and R²ext = 0.7195. The results derived from QSAR studies showed that the acute oral toxicity of NNCs mainly depends on three factors, namely, the polarizability, the ionization potential (IP) and the presence/absence and frequency of C⁻O bond. For classification studies, the best model was obtained using the MACCS keys fingerprint combined with artificial neural network (ANN) algorithm. The classification models suggested that several representative substructures, including nitrile, hetero N nonbasic, alkylchloride and amine-containing fragments are main contributors for the high toxicity of NNCs. Overall, the developed QSAR and classification models of the rat acute oral toxicity of NNCs showed satisfying predictive abilities. The results provide an insight into the understanding of the toxicity mechanism of NNCs in vivo, which might be used for a preliminary assessment of NNCs toxicity to mammals.

In Silico Prediction of O6-Methylguanine-DNA Methyltransferase Inhibitory Potency of Base Analogs with QSAR and Machine Learning Methods.

O6-methylguanine-DNA methyltransferase (MGMT), a unique DNA repair enzyme, can confer resistance to DNA anticancer alkylating agents that modify the O6-position of guanine. Thus, inhibition of MGMT activity in tumors has a great interest for cancer researchers because it can significantly improve the anticancer efficacy of such alkylating agents. In this study, we performed a quantitative structure activity relationship (QSAR) and classification study based on a total of 134 base analogs related to their ED50 values (50% inhibitory concentration) against MGMT. Molecular information of all compounds were described by quantum chemical descriptors and Dragon descriptors. Genetic algorithm (GA) and multiple linear regression (MLR) analysis were combined to develop QSAR models. Classification models were generated by seven machine-learning methods based on six types of molecular fingerprints. Performances of all developed models were assessed by internal and external validation techniques. The best QSAR model was obtained with Q²Loo = 0.83, R² = 0.87, Q²ext = 0.67, and R²ext = 0.69 based on 84 compounds. The results from QSAR studies indicated topological charge indices, polarizability, ionization potential (IP), and number of primary aromatic amines are main contributors for MGMT inhibition of base analogs. For classification studies, the accuracies of 10-fold cross-validation ranged from 0.750 to 0.885 for top ten models. The range of accuracy for the external test set ranged from 0.800 to 0.880 except for PubChem-Tree model, suggesting a satisfactory predictive ability. Three models (Ext-SVM, Ext-Tree and Graph-RF) showed high and reliable predictive accuracy for both training and external test sets. In addition, several representative substructures for characterizing MGMT inhibitors were identified by information gain and substructure frequency analysis method. Our studies might be useful for further study to design and rapidly identify potential MGMT inhibitors.

Synthesis and antitumor activity evaluation of a novel combi-nitrosourea prodrug: Designed to release a DNA cross-linking agent and an inhibitor of O(6)-alkylguanine-DNA alkyltransferase.

The drug resistance of CENUs induced by O(6)-alkylguanine-DNA alkyltransferase (AGT), which repairs the O(6)-alkylated guanine and subsequently inhibits the formation of dG-dC cross-links, hinders the application of CENU chemotherapies. Therefore, the discovery of CENU analogs with AGT inhibiting activity is a promising approach leading to novel CENU chemotherapies with high therapeutic index. In this study, a new combi-nitrosourea prodrug 3-(3-(((2-amino-9H-purin-6-yl)oxy)methyl)benzyl)-1-(2-chloroethyl)-1-nitrosourea (6), designed to release a DNA cross-linking agent and an inhibitor of AGT, was synthesized and evaluated for its antitumor activity and ability to induce DNA interstrand cross-links (ICLs). The results indicated that 6 exhibited higher cytotoxicity against mer(+) glioma cells compared with ACNU, BCNU, and their respective combinations with O(6)-benzylguanine (O(6)-BG). Quantifications of dG-dC cross-links induced by 6 were performed using HPLC-ESI-MS/MS. Higher levels of dG-dC cross-link were observed in 6-treated human glioma SF763 cells (mer(+)), whereas lower levels of dG-dC cross-link were observed in 6-treated calf thymus DNA, when compared with the groups treated with BCNU and ACNU. The results suggested that the superiority of 6 might result from the AGT inhibitory moiety, which specifically functions in cells with AGT activity. Molecular docking studies indicated that five hydrogen bonds were formed between the O(6)-BG analogs released from 6 and the five residues in the active pocket of AGT, which provided a reasonable explanation for the higher AGT-inhibitory activity of 6 than O(6)-BG.

Identification of the Structural Features of Guanine Derivatives as MGMT Inhibitors Using 3D-QSAR Modeling Combined with Molecular Docking.

DNA repair enzyme O6-methylguanine-DNA methyltransferase (MGMT), which plays an important role in inducing drug resistance against alkylating agents that modify the O6 position of guanine in DNA, is an attractive target for anti-tumor chemotherapy. A series of MGMT inhibitors have been synthesized over the past decades to improve the chemotherapeutic effects of O6-alkylating agents. In the present study, we performed a three-dimensional quantitative structure activity relationship (3D-QSAR) study on 97 guanine derivatives as MGMT inhibitors using comparative molecular field analysis (CoMFA) and comparative molecular similarity indices analysis (CoMSIA) methods. Three different alignment methods (ligand-based, DFT optimization-based and docking-based alignment) were employed to develop reliable 3D-QSAR models. Statistical parameters derived from the models using the above three alignment methods showed that the ligand-based CoMFA (Qcv² = 0.672 and Rncv² = 0.997) and CoMSIA (Qcv² = 0.703 and Rncv² = 0.946) models were better than the other two alignment methods-based CoMFA and CoMSIA models. The two ligand-based models were further confirmed by an external test-set validation and a Y-randomization examination. The ligand-based CoMFA model (Qext² = 0.691, Rpred² = 0.738 and slope k = 0.91) was observed with acceptable external test-set validation values rather than the CoMSIA model (Qext² = 0.307, Rpred² = 0.4 and slope k = 0.719). Docking studies were carried out to predict the binding modes of the inhibitors with MGMT. The results indicated that the obtained binding interactions were consistent with the 3D contour maps. Overall, the combined results of the 3D-QSAR and the docking obtained in this study provide an insight into the understanding of the interactions between guanine derivatives and MGMT protein, which will assist in designing novel MGMT inhibitors with desired activity.

Investigations on the effect of O(6)-benzylguanine on the formation of dG-dC interstrand cross-links induced by chloroethylnitrosoureas in human glioma cells using stable isotope dilution high-performance liquid chromatography electrospray ionization tandem mass spectrometry.

Chloroethylnitrosoureas (CENUs) are bifunctional alkylating agents widely used for the clinical treatment of cancer. They exert anticancer activity by inducing DNA interstrand cross-links (ICLs) within GC base pairs to form dG-dC cross-links. This lesion inhibits DNA double strand separation during replication and transcription and results in the apoptosis of cancer cells. However, O(6)-alkylguanine DNA alkyltransferase (AGT) repairs the DNA ICLs by removing the alkyl group at the O(6) position of either O(6)-(2-chloroethyl)deoxyguanosine (O(6)-ClEtdGuo) or N1,O(6)-ethanodeoxyguanosine (N1,O(6)-EtdGuo), which are intermediates in the formation of dG-dC cross-links. The action of AGT leads to drug resistance against CENUs. O(6)-Benzylguanine (O(6)-BG) was identified as an effective AGT inhibitor that enhances the antitumor effects of CENUs. In this study, the effect of O(6)-BG on the formation of dG-dC cross-links was investigated by treating human brain glioma SF767 cells with 1-[(4-amino-2-methyl-5-pyrimidinyl)methyl]-3-(2-chloroethyl)-3-nitrosourea (ACNU). The levels of dG-dC cross-link were determined using stable isotope dilution high-performance liquid chromatography electrospray ionization tandem mass spectrometry (HPLC-ESI-MS/MS). The results indicated that ACNU induced higher levels of dG-dC cross-link in SF767 cells pretreated with O(6)-BG compared to cells without O(6)-BG pretreatment. The highest dG-dC cross-linking levels were generally observed at 12 h for all drug concentration groups, a result which was consistent with cytotoxicity assay. These results provided direct evidence for the enhancement of dG-dC cross-linking levels caused by the inhibition of AGT by O(6)-BG. These data indicate that dG-dC cross-links may be developed as a biomarker for evaluating the activity of novel O(6)-BG analogues as AGT inhibitors for combination therapy with CENUs.

From molecular descriptors to the developmental toxicity prediction of pesticides/veterinary drugs/bio-pesticides against zebrafish embryo: Dual computational toxicological approaches for prioritization.

The escalating introduction of pesticides/veterinary drugs into the environment has necessitated a rapid evaluation of their potential risks to ecosystems and human health. The developmental toxicity of pesticides/veterinary drugs was less explored, and much less the large-scale predictions for untested pesticides, veterinary drugs and bio-pesticides. Alternative methods like quantitative structure-activity relationship (QSAR) are promising because their potential to ensure the sustainable and safe use of these chemicals. We collected 133 pesticides and veterinary drugs with half-maximal active concentration (AC50) as the zebrafish embryo developmental toxicity endpoint. The QSAR model development adhered to rigorous OECD principles, ensuring that the model possessed good internal robustness (R2 > 0.6 and QLOO2 > 0.6) and external predictivity (Rtest2 > 0.7, QFn2 >0.7, and CCCtest > 0.85). To further enhance the predictive performance of the model, a quantitative read-across structure-activity relationship (q-RASAR) model was established using the combined set of RASAR and 2D descriptors. Mechanistic interpretation revealed that dipole moment, the presence of C-O fragment at 10 topological distance, molecular size, lipophilicity, and Euclidean distance (ED)-based RA function were main factors influencing toxicity. For the first time, the established QSAR and q-RASAR models were combined to prioritize the developmental toxicity of a vast array of true external compounds (pesticides/veterinary drugs/bio-pesticides) lacking experimental values. The prediction reliability of each query molecule was evaluated by leverage approach and prediction reliability indicator. Overall, the dual computational toxicology models can inform decision-making and guide the design of new pesticides/veterinary drugs with improved safety profiles.

Prioritization of the ecotoxicological hazard of PAHs towards aquatic species spanning three trophic levels using 2D-QSTR, read-across and machine learning-driven modelling approaches.

Polycyclic aromatic hydrocarbons (PAHs) represent a common group of environmental pollutants that endanger various aquatic organisms via various pathways. To better prioritize the ecotoxicological hazard of PAHs to aquatic environment, we used 2D descriptors-based quantitative structure-toxicity relationship (QSTR) to assess the toxicity of PAHs toward six aquatic model organisms spanning three trophic levels. According to strict OECD guideline, six easily interpretable, transferable and reproducible 2D-QSTR models were constructed with high robustness and reliability. A mechanistic interpretation unveiled the key structural factors primarily responsible for controlling the aquatic ecotoxicity of PAHs. Furthermore, quantitative read-across and different machine learning approaches were employed to validate and optimize the modelling approach. Importantly, the optimum QSTR models were further applied for predicting the ecotoxicity of hundreds of untested/unknown PAHs gathered from Pesticide Properties Database (PPDB). Especially, we provided a priority list in terms of the toxicity of unknown PAHs to six aquatic species, along with the corresponding mechanistic interpretation. In summary, the models can serve as valuable tools for aquatic risk assessment and prioritization of untested or completely new PAHs chemicals, providing essential guidance for formulating regulatory policies.

Energy Blocker Lonidamine Reverses Nimustine Resistance in Human Glioblastoma Cells through Energy Blockade, Redox Homeostasis Disruption, and O6-Methylguanine-DNA Methyltransferase Downregulation: In Vitro and In Vivo Validation.

Tumor resistance seriously hinders the clinical application of chloroethylnitrosoureas (CENUs), such as O6-methylguanine-DNA methylguanine (MGMT), which can repair O6-alkyl lesions, thereby inhibiting the formation of cytotoxic DNA interstrand cross-links (ICLs). Metabolic differences between tumor and normal cells provide a biochemical basis for novel therapeutic strategies aimed at selectively inhibiting tumor energy metabolism. In this study, the energy blocker lonidamine (LND) was selected as a chemo-sensitizer of nimustine (ACNU) to explore its potential effects and underlying mechanisms in human glioblastoma in vitro and in vivo. A series of cell-level studies showed that LND significantly increased the cytotoxic effects of ACNU on glioblastoma cells. Furthermore, LND plus ACNU enhanced the energy deficiency by inhibiting glycolysis and mitochondrial function. Notably, LND almost completely downregulated MGMT expression by inducing intracellular acidification. The number of lethal DNA ICLs produced by ACNU increased after the LND pretreatment. The combination of LND and ACNU aggravated cellular oxidative stress. In resistant SF763 mouse tumor xenografts, LND plus ACNU significantly inhibited tumor growth with fewer side effects than ACNU alone. Finally, we proposed a new "HMAGOMR" chemo-sensitizing mechanism through which LND may act as a potential chemo-sensitizer to reverse ACNU resistance in glioblastoma: moderate inhibition of hexokinase (HK) activity (H); mitochondrial dysfunction (M); suppressing adenosine triphosphate (ATP)-dependent drug efflux (A); changing redox homeostasis to inhibit GSH-mediated drug inactivation (G) and increasing intracellular oxidative stress (O); downregulating MGMT expression through intracellular acidification (M); and partial inhibition of energy-dependent DNA repair (R).

The dual role of DNA repair protein MGMT in cancer prevention and treatment.

Alkylating agents are genotoxic chemicals that can induce and treat various types of cancer. This occurs through covalent bonding with cellular macromolecules, in particular DNA, leading to the loss of functional integrity under the persistence of modifications upon replication. O6-alkylguanine (O6-AlkylG) adducts are proposed to be the most potent DNA lesions induced by alkylating agents. If not repaired correctly, these adducts can result, at the molecular level, in DNA point mutations, chromosome aberrations, recombination, crosslinking, and single- and double-strand breaks (SSB/DSBs). At the cellular level, these lesions can result in malignant transformation, senescence, or cell death. O6-methylguanine-DNA methyltransferase (MGMT) is a DNA repair protein capable of removing the alkyl groups from O6-AlkylG adducts in a damage reversal process that can prevent the adverse biological effects of DNA damage caused by guanine O6-alkylation. MGMT can thereby defend normal cells against tumor initiation, however it can also protect tumor cells against the beneficial effects of chemotherapy. Hence, MGMT can play an important role in both the prevention and treatment of cancer; thus, it can be considered as a double-edged sword. From a clinical perspective, MGMT is a therapeutic target, and it is important to explore the rational development of its clinical exploitation.

Modulating MGMT expression through interfering with cell signaling pathways.

Guanine O6-alkylating agents are widely used as first-line chemotherapeutic drugs due to their ability to induce cytotoxic DNA damage. However, a major hurdle in their effectiveness is the emergence of chemoresistance, largely attributed to the DNA repair pathway mediated by O6-methylguanine-DNA methyltransferase (MGMT). MGMT plays an important role in removing the alkyl groups from lethal O6-alkylguanine (O6-AlkylG) adducts formed by chemotherapeutic alkylating agents. By doing so, MGMT enables tumor cells to evade apoptosis and develop drug resistance toward DNA alkylating agents. Although covalent inhibitors of MGMT, such as O6-benzylguanine (O6-BG) and O6-(4-bromothenyl)guanine (O6-4-BTG or lomeguatrib), have been explored in clinical settings, their utility is limited due to severe delayed hematological toxicity observed in most patients when combined with alkylating agents. Therefore, there is an urgent need to identify new targets and unravel the underlying molecular mechanisms and to develop alternative therapeutic strategies that can overcome MGMT-mediated tumor resistance. In this context, the regulation of MGMT expression via interfering the specific cell signaling pathways (e.g., Wnt/ß-catenin, NF-κB, Hedgehog, PI3K/AKT/mTOR, JAK/STAT) emerges as a promising strategy for overcoming tumor resistance, and ultimately enhancing the efficacy of DNA alkylating agents in chemotherapy.

Ecotoxicological QSAR modelling of the acute toxicity of fused and non-fused polycyclic aromatic hydrocarbons (FNFPAHs) against two aquatic organisms: Consensus modelling and comparison with ECOSAR.

Fused and non-fused polycyclic aromatic hydrocarbons (FNFPAHs) are a type of organic compounds widely occurring in the environment that pose a potential hazard to ecosystem and public health, and thus receive extensive attention from various regulatory agencies. Here, quantitative structure-activity relationship (QSAR) models were constructed to model the ecotoxicity of FNFPAHs against two aquatic species, Daphnia magna and Oncorhynchus mykiss. According to the stringent OECD guidelines, we used genetic algorithm (GA) plus multiple linear regression (MLR) approach to establish QSAR models of the two aquatic toxicity endpoints: D. magna (48 h LC50) and O. mykiss (96 h LC50). The models were established using simple 2D descriptors with explicit physicochemical significance and evaluated using various internal/external validation metrics. The results clearly show that both models are statistically robust (QLOO2 = 0.7834 for D. magna and QLOO2 = 0.8162 for O. mykiss), have good internal fitness (R2 = 0.8159 for D. magna and R2 = 0.8626 for O. mykiss and external predictive ability (D. magna: Rtest2 = 0.8259, QFn2 = 0.7640∼0.8140, CCCtest = 0.8972; O. mykiss:Rtest2 = 0.8077, QFn2 = 0.7615∼0.7722, CCCtest = 0.8910). To prove the predictive performance of the developed models, an additional comparison with the standard ECOSAR tool obviously shows that our models have lower RMSE values. Subsequently, we utilized the best models to predict the true external set compounds collected from the PPDB database to further fill the toxicity data gap. In addition, consensus models (CMs) that integrate all validated individual models (IMs) were more externally predictive than IMs, of which CM2 has the best prediction performance towards the two aquatic species. Overall, the models presented here could be used to evaluate unknown FNFPAHs inside the domain of applicability (AD), thus being very important for environmental risk assessment under current regulatory frameworks.

Ecotoxicological QSAR study of fused/non-fused polycyclic aromatic hydrocarbons (FNFPAHs): Assessment and priority ranking of the acute toxicity to Pimephales promelas by QSAR and consensus modeling methods.

Fused/non-fused polycyclic aromatic hydrocarbons (FNFPAHs) have a variety of toxic effects on ecosystems and human body, but the acquisition of their toxicity data is greatly limited by the limited resources available. Here, we followed the EU REACH regulation and used Pimephales promelas as a model organism to investigate the quantitative structure-activity relationship (QSAR) between the FNFPAHs and their toxicity for the aquatic environment for the first time. We developed a single QSAR model (SM1) containing five simple and interpretable 2D molecular descriptors, which met the validation of OECD QSAR-related principles, and analyzed their mechanistic relationships with toxicity in detail. The model had good degree of fitting and robustness, and had better external prediction performance (MAEtest = 0.4219) than ECOSAR model (MAEtest = 0.5614). To further enhance its prediction accuracy, the three qualified single models (SMs) were used for constructing consensus models (CMs), the best one CM2 (MAEtest = 0.3954) had a significantly higher prediction accuracy for test compounds than SM1, and also outperformed the T.E.S.T. consensus model (MAEtest = 0.4233). Subsequently, the toxicity of 252 true external FNFPAHs from Pesticide Properties Database (PPDB) was predicted by SM1, the prediction results showed that 94.84 % compounds were reliably predicted within the model's application domain (AD). We also applied the best CM2 to predict the untested 252 FNFPAHs. Furthermore, we provided a mechanistic analysis and explanation for pesticides ranked as top 10 most toxic FNFPAHs. In summary, all developed QSAR and consensus models can be used as efficient tools for predicting the acute toxicity of unknown FNFPAHs to Pimephales promelas, thus being important for the risk assessment and regulation of FNFPAHs contamination in aquatic environment.

QSAR and Chemical Read-Across Analysis of 370 Potential MGMT Inactivators to Identify the Structural Features Influencing Inactivation Potency.

O6-methylguanine-DNA methyltransferase (MGMT) constitutes an important cellular mechanism for repairing potentially cytotoxic DNA damage induced by guanine O6-alkylating agents and can render cells highly resistant to certain cancer chemotherapeutic drugs. A wide variety of potential MGMT inactivators have been designed and synthesized for the purpose of overcoming MGMT-mediated tumor resistance. We determined the inactivation potency of these compounds against human recombinant MGMT using [3H]-methylated-DNA-based MGMT inactivation assays and calculated the IC50 values. Using the results of 370 compounds, we performed quantitative structure-activity relationship (QSAR) modeling to identify the correlation between the chemical structure and MGMT-inactivating ability. Modeling was based on subdividing the sorted pIC50 values or on chemical structures or was random. A total of nine molecular descriptors were presented in the model equation, in which the mechanistic interpretation indicated that the status of nitrogen atoms, aliphatic primary amino groups, the presence of O-S at topological distance 3, the presence of Al-O-Ar/Ar-O-Ar/R..O..R/R-O-C=X, the ionization potential and hydrogen bond donors are the main factors responsible for inactivation ability. The final model was of high internal robustness, goodness of fit and prediction ability (R2pr = 0.7474, Q2Fn = 0.7375-0.7437, CCCpr = 0.8530). After the best splitting model was decided, we established the full model based on the entire set of compounds using the same descriptor combination. We also used a similarity-based read-across technique to further improve the external predictive ability of the model (R2pr = 0.7528, Q2Fn = 0.7387-0.7449, CCCpr = 0.8560). The prediction quality of 66 true external compounds was checked using the "Prediction Reliability Indicator" tool. In summary, we defined key structural features associated with MGMT inactivation, thus allowing for the design of MGMT inactivators that might improve clinical outcomes in cancer treatment.

Environmental toxicity risk evaluation of nitroaromatic compounds: Machine learning driven binary/multiple classification and design of safe alternatives.

Nitroaromatic compounds (NACs) represent a significant source of organic pollutants in the environment. In this study, a well-rounded dataset containing 371 NACs with rat oral median lethal doses (LD50s) was developed. Based on the dataset, binary and multiple classification models were established. Seven machine learning algorithms were used to establish the prediction models in combination with six fingerprints. In the binary classification models, the overall predictive accuracy of 10-fold cross-validation for training set in the top ten models ranged from 0.823 to 0.874. In the multiple classification models, the combination of graph fingerprint and random forest (Graph-RF) yielded the best predictive effects with AUC values of 0.929 and 0.956 for the training set and the test set, respectively. Model prediction performance was further evaluated using the true external set comprising 1366 NACs, including 96.6% belonging to the applicability domain. Further, we determined the structural features influencing the acute oral toxicity based on information gain and substructure frequency analysis. Finally, we identified highly toxic compounds based on the structural alerts and successfully transformed a representative highly toxic compound into low-toxic alternatives via structural modification. Overall, the models constructed facilitate environmental risk assessment and the design of green and safe chemicals.

2-Deoxy-D-glucose increases the sensitivity of glioblastoma cells to BCNU through the regulation of glycolysis, ROS and ERS pathways: In vitro and in vivo validation.

Chloroethylnitrosoureas (CENUs) exert antitumor activity via producing dG-dC interstrand crosslinks (ICLs). However, tumor resistance make it necessary to find novel strategies to improve the therapeutic effect of CENUs. 2-Deoxy-D-glucose (2-DG) is a well-known glycolytic inhibitor, which can reprogram tumor energy metabolism closely related to tumor resistance. Here, we investigated the chemosensitization effect of 2-DG on l,3-bis(2-chloroethyl)-1-nitrosourea (BCNU) against glioblastoma cells and the underlying mechanisms. We found that 2-DG significantly increased the inhibitory effects of BCNU on tumor cells compared with BCNU alone, while 2-DG showed no obvious enhancing effect on the BCNU-induced cytotoxicity for normal HaCaT and HA1800 cells. Proliferation, migration and invasion determinations presented the same trend as survival on tumor cells. 2-DG plus BCNU increased the energy deficiency through a more effective inhibition of glycolytic pathway. Notably, the combination of 2-DG and BCNU aggravated oxidative stress in glioblastoma cells, along with a significant decrease in glutathione (GSH) levels, and an increase in intracellular reactive oxygen species (ROS). Subsequently, we demonstrated that the combination treatment led to increased apoptosis via activating mitochondria and endoplasmic reticulum stress (ERS) related apoptosis pathways. Finally, we found that the dG-dC level was significantly increased after 2-DG pretreatment compared to BCNU alone by HPLC-ESI-MS/MS analysis. Finally, in vivo, 2-DG plus BCNU significantly suppressed tumor growth with lower side effects compared with BCNU alone in tumor-bearing mice. In summary, we proposed that 2-DG may have potential to increase the sensitivity of glioblastoma cells to BCNU by regulating glycolysis, ROS and ERS pathways in clinical setting.

In vivo toxicity of nitroaromatic compounds to rats: QSTR modelling and interspecies toxicity relationship with mouse.

Nitroaromatic compounds (NACs) in the environment can cause serious public health and environmental problems due to their potential toxicity. This study established quantitative structure-toxicity relationship (QSTR) models for the acute oral toxicity of NACs towards rats following the stringent OECD principles for QSTR modelling. All models were assessed by various internationally accepted validation metrics and the OECD criteria. The best QSTR model contains seven simple and interpretable 2D descriptors with defined physicochemical meaning. Mechanistic interpretation indicated that van der Waals surface area, presence of C-F at topological distance 6, heteroatom content and frequency of C-N at topological distance 9 are main factors responsible for the toxicity of NACs. This proposed model was successfully applied to a true external set (295 compounds), and prediction reliability was analysed and discussed. Moreover, the rat-mouse and mouse-rat interspecies quantitative toxicity-toxicity relationship (iQTTR) models were also constructed, validated and employed in toxicity prediction for true external sets consisting of 67 and 265 compounds, respectively. These models showed good external predictivity that can be used to rapidly predict the rat oral acute toxicity of new or untested NACs falling within the applicability domain of the models, thus being beneficial in environmental risk assessment and regulatory purposes.