Tumor Energy Metabolism and Potential of 3-Bromopyruvate as an Inhibitor of Aerobic Glycolysis: Implications in Tumor Treatment

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.

The Potential of Lonidamine in Combination with Chemotherapy and Physical Therapy in Cancer Treatment

Lonidamine (LND) has the ability to resist spermatogenesis and was first used as an anti-spermatogenic agent. Later, it was found that LND has a degree of anticancer activity. Currently, LND is known to target energy metabolism, mainly involving the inhibition of monocarboxylate transporter (MCT), mitochondrial pyruvate carrier (MPC), respiratory chain complex I/II, mitochondrial permeability transition (PT) pore, and hexokinase II (HK-II). However, phase II clinical studies showed that LND alone had a weak therapeutic effect, and the effect was short and reversible. Interestingly, LND does not have the common side effects of traditional chemotherapeutic drugs, such as alopecia and myelosuppression. In addition, LND has selective activity toward various tumors, and its toxic and side effects do not overlap when combined with other chemotherapeutic drugs. Therefore, LND is commonly used as a chemosensitizer to enhance the antitumor effects of chemotherapeutic drugs based on its disruption of energy metabolism relating to chemo- or radioresistance. In this review, we summarized the combination treatments of LND with several typical chemotherapeutic drugs and several common physical therapies, such as radiotherapy (RT), hyperthermia (HT), and photodynamic therapy (PDT), and discussed the underlying mechanisms of action. Meanwhile, the development of novel formulations of LND in recent years and the research progress of LND derivative adjudin (ADD) as an anticancer drug were also discussed.

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.

Introduction of multilayered magnetic core-dual shell SERS tags into lateral flow immunoassay: A highly stable and sensitive method for the simultaneous detection of multiple veterinary drugs in complex samples

Direct, convenient, and sensitive monitoring of the residues of multiple drugs in complex environments is important but remains a challenge. Here, we report a surface-enhanced Raman scattering (SERS)-based multiplexed lateral flow immunoassay (LFA) that supports the simultaneous and sensitive detection of commonly used drugs kanamycin, ractopamine, clenbuterol, and chloramphenicol in unprocessed complex samples through the dual signal amplification strategy of numerous efficient hotspots and magnetic enrichment. Multilayered magnetic-core dual-shell nanoparticles (MDAu@Ag) with controllable subtle nanogaps were fabricated via the polyethyleneimine-mediated layer-by-layer (LBL) assembly of two layers of Au@Ag satellites onto superparamagnetic Fe3O4 cores and conjugated with specific antibodies as multifunctional tags in the LFA system for rapid capture, separation, and quantitative analysis. Two Raman reporters were embedded in internal nanogaps and modified on the surface of MDAu@Ag for the simultaneous and ultrasensitive detection of four targets on two test lines, which greatly simplified the fabrication and signal reading of SERS-LFA. The proposed assay can rapidly detect multiple drug residues in 35 min with detection limits down to pg/mL level. Moreover, the MDAu@Ag-based SERS-LFA demonstrated better stability, higher throughput, and superior sensitivity (at least 400 times) than traditional colloidal gold immunochromatography, showing its great potential in the field of point-of-care testing.

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.

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.

The specific role of O6-methylguanine-DNA methyltransferase inhibitors in cancer chemotherapy

The DNA repair protein, O6-methylguanine DNA methyltransferase (MGMT), can confer resistance to guanine O6-alkylating agents. Therefore, inhibition of resistant MGMT protein is a practical approach to increase the anticancer effects of such alkylating agents. Numerous small molecule inhibitors were synthesized and exhibited potential MGMT inhibitory activities. Although they were nontoxic alone, they also inhibited MGMT in normal tissues, thereby enhancing the side effects of chemotherapy. Therefore, strategies for tumor-specific MGMT inhibition have been proposed, including local drug delivery and tumor-activated prodrugs. Over-expression of MGMT in hematopoietic stem cells to protect bone marrow from the toxic effects of chemotherapy is also a feasible selection. The future prospects and challenges of MGMT inhibitors in cancer chemotherapy were also discussed.

3-Bromopyruvate regulates the status of glycolysis and BCNU sensitivity in human hepatocellular carcinoma cells

Chloroethylnitrosoureas (CENUs) are bifunctional antitumor alkylating agents, which exert their antitumor activity through inducing the formation of dG-dC interstrand crosslinks (ICLs) within DNA double strand. However, the complex process of tumor biology enables tumor cells to escape the killing triggered by CENUs, as for instance with the detoxifying activity of O6-methylguanine DNA methyltransferase (MGMT) to accomplish DNA damage repair. Considering the fact that most tumor cells highly depend on aerobic glycolysis to provide energy for survival even in the presence of oxygen (Warburg effect), inhibition of aerobic glycolysis may be an attractive strategy to overcome the resistance and improve the chemotherapeutic effects of CENUs. Especially, 3-bromopyruvate (3-BrPA), a small molecule alkylating agent, has been emerged as an effective glycolytic inhibitor (energy blocker) in cancer treatment. In view of its tumor specificity and inhibition on cellular multiple targets, it is likely to reduce the chemoresistance when chemotherapeutic drugs are combined with 3-BrPA. In this study, we investigated the effects of 3-BrPA on the chemosensitivity of two human hepatocellular carcinoma (HCC) cell lines to the cytotoxic effects of l,3-bis(2-chloroethyl)-1-nitrosourea (BCNU) and the underlying molecular mechanism. The sensitivity of SMMC-7721 and HepG2 cells to BCNU was significantly increased by 2 h pretreatment with micromolar dosage of 3-BrPA. Moreover, 3-BrPA decreased the cellular ATP and GSH levels, and extracellular lactate excreted by tumor cells, and the effects were more effective when 3-BrPA was combined with BCNU. Cellular hexokinase-II (HK-II) activity was also reduced after exposure to the treatment of 3-BrPA plus BCNU. Based on the above results, the effects of 3-BrPA on the formation of dG-dC ICLs induced by BCNU was investigated by stable isotope dilution high-performance liquid chromatography electrospray ionization tandem mass spectrometry (HPLC-ESI-MS/MS). The results indicated that BCNU produced higher levels of dG-dC ICLs in SMMC-7721 and HepG2 cells pretreated with 3-BrPA compared to that without 3-BrPA pretreatment. Notably, in MGMT-deficient HepG2 cells, the levels of dG-dC ICLs were significantly higher than MGMT-proficient SMMC-7721 cells. In general, these findings revealed that 3-BrPA, as an effective glycolytic inhibitor, may be considered as a potential clinical chemosensitizer to optimize the therapeutic index of CENUs.

Chemometric QSAR modeling of acute oral toxicity of Polycyclic Aromatic Hydrocarbons (PAHs) to rat using simple 2D descriptors and interspecies toxicity modeling with mouse

The information of the acute oral toxicity for most polycyclic aromatic hydrocarbons (PAHs) in mammals are lacking due to limited experimental resources, leading to a need to develop reliable in silico methods to evaluate the toxicity endpoint. In this study, we developed the quantitative structure-activity relationship (QSAR) models by genetic algorithm (GA) and multiple linear regression (MLR) for the rat acute oral toxicity (LD50) of PAHs following the strict validation principles of QSAR modeling recommended by OECD. The best QSAR model comprised eight simple 2D descriptors with definite physicochemical meaning, which showed that maximum atom-type electrotopological state, van der Waals surface area, mean atomic van der Waals volume, and total number of bonds are main influencing factors for the toxicity endpoint. A true external set (554 compounds) without rat acute oral toxicity values, and 22 limit test compounds, were firstly predicted along with reliability assessment. We also compared our proposed model with the OPERA predictions and recently published literature to prove the prediction reliability. Furthermore, the interspecies toxicity (iST) models of PAHs between rat and mouse were also established, validated and employed for filling data gap. Overall, our developed models should be applicable to new or untested or not yet synthesized PAHs falling within the applicability domain (AD) of the models for rapid acute oral toxicity prediction, thus being important for environmental or personal exposure risk assessment under regulatory frameworks.

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.

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.

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.

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.

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.

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.

The potential of combi-molecules with DNA-damaging function as anticancer agents

DNA-damaging agents, such as methylating agents, chloroethylating agents and platinum-based agents, have been extensively used as anticancer drugs. However, the side effects, high toxicity, lack of selectivity and resistance severely limit their clinical applications. In recent years, a strategy combining a DNA-damaging agent with a bioactive molecule (e.g., enzyme inhibitors) or carrier (e.g., steroid hormone and DNA intercalators) to produce a new 'combi-molecule' with improved efficacy or selectivity has been attempted to overcome these drawbacks. The combi-molecule simultaneously acts on two targets and is expected to possess better potency than the parent compounds. Many studies have shown DNA-damaging combi-molecules exhibiting excellent anticancer activity in vitro and in vivo. This review focuses on the development of combi-molecules, which possess increased DNA-damaging potency, anticancer efficacy and tumor selectivity and reduced side reactions than the parent compounds. The future opportunities and challenges in the discovery of combi-molecules were also discussed.

Quantitative Structure-Activity Relationship (QSAR) Studies on the Toxic Effects of Nitroaromatic Compounds (NACs): A Systematic Review

Nitroaromatic compounds (NACs) are ubiquitous in the environment due to their extensive industrial applications. The recalcitrance of NACs causes their arduous degradation, subsequently bringing about potential threats to human health and environmental safety. The problem of how to effectively predict the toxicity of NACs has drawn public concern over time. Quantitative structure-activity relationship (QSAR) is introduced as a cost-effective tool to quantitatively predict the toxicity of toxicants. Both OECD (Organization for Economic Co-operation and Development) and REACH (Registration, Evaluation and Authorization of Chemicals) legislation have promoted the use of QSAR as it can significantly reduce living animal testing. Although numerous QSAR studies have been conducted to evaluate the toxicity of NACs, systematic reviews related to the QSAR modeling of NACs toxicity are less reported. The purpose of this review is to provide a thorough summary of recent QSAR studies on the toxic effects of NACs according to the corresponding classes of toxic response endpoints.

Ecotoxicological risk assessment of pesticides against different aquatic and terrestrial species: using mechanistic QSTR and iQSTTR modelling approaches to fill the toxicity data gap

The toxicity prediction for newly designed or untested pesticides will reduce unnecessary chemical synthesis and animal testing, and contribute to the design of “greener and safer” pesticide chemicals.

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.

The Induction and Repair of DNA Interstrand Crosslinks and Implications in Cancer Chemotherapy

DNA interstrand crosslinks (ICLs) can be induced by numerous endogenous and exogenous chemical agents with the capacity of covalently binding to two base sites in the two strands of the DNA duplex. A series of normal DNA metabolism processes are affected by ICLs. For example, DNA replication and transcription are interfered during cell division, which is fatal to cell survival. In cancer cells, the induction of ICLs is a significant target for cancer chemotherapies. However, the formation of ICLs in cancer cells can be weakened by the repair mechanisms of DNA damage, which results in resistance to chemotherapies. Therefore, it is necessary to develop highly effective ICL agents for the purpose of achieving good chemotherapeutic effects. Furthermore, the combination of ICL agents with inhibitors of ICL repair is a promising strategy for the clinical treatment of cancer. This review summarizes the development of several types of ICL agents as chemotherapies over the past decades and the mechanisms underlying the repair of DNA ICLs. The potential of ICL repair inhibitors for combination therapy with ICL agents in cancer treatment is also discussed.

Glycolytic inhibition by 3-bromopyruvate increases the cytotoxic effects of chloroethylnitrosoureas to human glioma cells and the DNA interstrand cross-links formation

DNA interstrand cross-links (ICLs) are essential for the antitumor activity of chloroethylnitrosoureas (CENUs). Commonly, CENUs resistance is mainly considered to be associated with O6-methylguanine-DNA methyltransferase (MGMT) within tumors. Bypassing the MGMT-mediated resistance, to our knowledge, herein, we first utilized a novel glycolytic inhibitor, 3-bromopyruvate (3-BrPA), to increase the cytotoxic effects of l,3-bis(2-chloroethyl)-1-nitrosourea (BCNU) to human glioma cells based on the hypothesis that blocking energy metabolism renders tumor cells more sensitive to chemotherapy. We found 3-BrPA significantly increased the cell killing by BCNU in human glioma SF763 and SF126 cell lines. Significantly decreased levels of extracellular lactate, cellular ATP and glutathione (GSH) were observed after 3-BrPA treatment, and the effects were more remarkable with 3-BrPA in combination with BCNU. Considering that the role of ATP and GSH in drug efflux, DNA damage repair and drug inactivation, we determined the effect of 3-BrPA on the formation of dG-dC ICLs induced by BCNU using stable isotope dilution high-performance liquid chromatography electrospray ionization tandem mass spectrometry (HPLC-ESI-MS/MS). As expected, the levels of lethal dG-dC ICLs induced by BCNU were obviously enhanced after 3-BrPA pretreatment. Based on these results, 3-BrPA and related glycolytic inhibitors may be promising to enhance the cell killing effect and reverse the clinical chemoresistance of CENUs and related antitumor agents.

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.

Systematic QSAR and iQCCR modelling of fused/non-fused aromatic hydrocarbons (FNFAHs) carcinogenicity to rodents: reducing unnecessary chemical synthesis and animal testing

The prediction of new or untested FNFAHs will reduce unnecessary chemical synthesis and animal testing, and contribute to the design of safer chemicals for production activities.

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.