Search

Owing to the continuous growth of farming activities, pesticides, a class of toxic and harmful chemicals, are widely present in the environment and thus pose a potential risk to the ecosystem and human health via biomagnification. Therefore, it is significant to conduct a more comprehensive environmental risk assessment towards pesticides in the context of green sustainable chemistry. This study collected the ecotoxicity data of hundreds of pesticides to develop four easily understandable, transferable and reproducible 2D descriptor-based quantitative structure-toxicity relationship (QSTR) models for evaluating the acute toxicity towards Colinus virginianus, Oncorhynchus mykiss, Daphnia magna, and Rat, respectively. According to the strict OECD guidelines, four individual QSTR models were obtained with excellent goodness-of-fit, robustness and predictivity. Moreover, four acceptable interspecies quantitative structure toxicity–toxicity relationship (iQSTTR) models were also developed with good statistical parameters. Applicability domain (AD) analysis indicates that the developed models have a wide application range and reliable predictive ability. Mechanistic explanation reveals that the electronegativity, lipophilicity and molecular bulk can obviously enhance the toxicity of pesticides, whereas the hydrophilic and polar features are shown to reduce the toxicity. Importantly, we predicted the toxicity of many untested pesticides in the comprehensive database for the first time using the developed models and provided the priority ranking list for the toxicity of untested pesticides. In conclusion, the established QSTR and iQSTTR models can be used for the ecotoxicological risk assessment, toxicity data gap filling and regulatory decision of pesticides, as well as aiding the design of new "green" pesticide products. [ABSTRACT FROM AUTHOR]

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. [ABSTRACT FROM AUTHOR]

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. [ABSTRACT FROM AUTHOR]

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. [ABSTRACT FROM AUTHOR]

Tumor formation and growth depend on various biological metabolism processes that are distinctly different with normal tissues. Abnormal energy metabolism is one of the typical characteristics of tumors. It has been proven that most tumor cells highly rely on aerobic glycolysis to obtain energy rather than mitochondrial oxidative phosphorylation (OXPHOS) even in the presence of oxygen, a phenomenon called "Warburg effect". Thus, inhibition of aerobic glycolysis becomes an attractive strategy to specifically kill tumor cells, while normal cells remain unaffected. In recent years, a small molecule alkylating agent, 3-bromopyruvate (3-BrPA), being an effective glycolytic inhibitor, has shown great potential as a promising antitumor drug. Not only it targets glycolysis process, but also inhibits mitochondrial OXPHOS in tumor cells. Excellent antitumor effects of 3-BrPA were observed in cultured cells and tumor-bearing animal models. In this review, we described the energy metabolic pathways of tumor cells, mechanism of action and cellular targets of 3-BrPA, antitumor effects, and the underlying mechanism of 3-BrPA alone or in combination with other antitumor drugs (e.g., cisplatin, doxorubicin, daunorubicin, 5-fluorouracil, etc.) in vitro and in vivo. In addition, few human case studies of 3-BrPA were also involved. Finally, the novel chemotherapeutic strategies of 3-BrPA, including wafer, liposomal nanoparticle, aerosol, and conjugate formulations, were also discussed for future clinical application. [ABSTRACT FROM AUTHOR]

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. [ABSTRACT FROM AUTHOR]

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. [ABSTRACT FROM AUTHOR]

Lung cancer ranks as the second most diagnosed cancer and the leading cause of cancer-related deaths worldwide. Novel chemotherapeutic strategies are crucial to efficiently target tumor cells while minimizing toxicity to normal cells. In this study, we proposed a combination strategy using energy blocker lonidamine (LND) and cytotoxic drug nimustine (ACNU, 1-[(4-amino-2-methyl-5-pyrimidinyl)methyl]-3-(2-chloroethyl)-3-nitrosourea) to enhance the killing of a human lung cancer cell line and investigated the potential chemo-sensitizing mechanism of LND. LND was found to remarkably increase the cytotoxicity of ACNU to A549 and H1299 cells without significantly affecting normal lung BEAS2B cells. The combination of LND and ACNU also produced significant effects on cell apoptosis, colony formation, cell migration, and invasion assays compared to single drug treatment. Mechanistically, LND decreased intracellular ATP levels by inhibiting glycolysis and inducing mitochondrial dysfunction. Furthermore, the combination of LND and ACNU could intensify cellular oxidative stress, decrease cellular GSH contents, and increase reactive oxygen species (ROS) production. Notably, LND alone dramatically downregulated the expression of DNA repair protein MGMT (O 6 -methylguanine-DNA methyltransferase), enhancing DNA interstrand cross-link formation induced by ACNU. Overall, LND represents a potential chemo-sensitizer to enhance ACNU therapy through energy inhibition, disrupting redox homeostasis and downregulating MGMT expression in human lung cancer cell line under preclinical and clinical background., Competing Interests: The authors declare no competing financial interest., (© 2024 The Authors. Published by American Chemical Society.)

Tumor resistance seriously hinders the clinical application of chloroethylnitrosoureas (CENUs), such as O 6 -methylguanine-DNA methylguanine (MGMT), which can repair O 6 -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)., Competing Interests: The authors declare no competing financial interest., (© 2024 American Chemical Society.)

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.

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.