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 Q²_Loo = 0.83, R² = 0.87, Q²_ext = 0.67, and R²_ext = 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.

Carmustine (BCNU), a vital type of chloroethylnitrosourea (CENU), inhibits tumor cells growth by inducing DNA damage at O⁶ position of guanine and eventually forming dG-dC interstrand cross-links (ICLs). However, the clinical application of BCNU is hindered to some extent by the absence of tumor selectivity, poor stability and O⁶-alkylguanine-DNA alkyltransferase (AGT) mediated drug resistance. In recent years, tumor microenvironment has been widely utilized for advanced drug delivery. In the light of the features of tumor microenvironment, we constructed a multifunctional hypoxia/esterase-degradable nanomicelle with AGT inhibitory activity named HACB NPs for tumor-targeting BCNU delivery and tumor sensitization. HACB NPs was self-assembled from hyaluronic acid azobenzene AGT inhibitor conjugates, in which O⁶-BG analog acted as an AGT inhibitor, azobenzene acted as a hypoxia-responsive linker and carboxylate ester bond acted as both an esterase-sensitive switch and a connector with hyaluronic acid (HA). The obtained HACB NPs possessed good stability, favorable biosafety and hypoxia/esterase-responsive drug-releasing ability. BCNU-loaded HACB/BCNU NPs exhibited superior cytotoxicity and apoptosis-inducing ability toward the human uterine cervix carcinoma HeLa cells compared with traditional combined medication of BCNU plus O⁶-BG. In vivo studies further demonstrated that after a selective accumulation in the tumor site, the micelles could respond to hypoxic tumor tissue for rapid drug release to an effective therapeutic dosage. Thus, this multifunctional stimulus-responsive nanocarrier could be a new promising strategy to enhance the anticancer efficacy and reduce the side effects of BCNU and other CENUs.

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 (Q_cv² = 0.672 and R_ncv² = 0.997) and CoMSIA (Q_cv² = 0.703 and R_ncv² = 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 (Q_ext² = 0.691, R_pred² = 0.738 and slope k = 0.91) was observed with acceptable external test-set validation values rather than the CoMSIA model (Q_ext² = 0.307, R_pred² = 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.

Engineered nanoparticles (ENPs), defined as nanoscale materials with at least one dimension between 1 and 100 nm, exhibit multifunctional and tunable physicochemical properties, that are at the center of several innovative fields. However, ENPs may induce a variety of biochemical reactions upon entry into organisms that could be a threat to human health. Therefore, a systematic evaluation of the toxicity of ENPs is essential. Quantitative structure–activity relationship (QSAR) is a practical in vitro modeling approach used to evaluate the toxicity of nanoparticles. In this study, we established the nanometric QSAR (Nano-QSAR) modelling based on cell membrane damage of ENPs to HepaRG cells. The toxicity data of ENPs and related 2D descriptor information were collected from the NanoCommons Knowledge Base. Periodic table descriptors of the elements were calculated using the Elemental Descriptor Calculator software. A multiple linear regression (MLR) model was constructed, and subsequently combined with read-across (RA) descriptors to establish the Nano-quantitative read-across structure–activity relationship (Nano-q-RASAR) model. Furthermore, machine learning (ML) algorithms were applied to optimize the predictive performance of the models. All models were validated according to the stringent OECD QSAR validation guidelines. Finally, a series of true external ENPs without experimental values were autonomously designed, and predicted using the best GB-Nano-QSAR model. Overall, this study can provide efficient and reliable predictions for the cell membrane damage of ENPs and a detailed theoretical explanation of their toxicity mechanism, which is of practical value for the toxicity assessment of ENPs.