Synthesis, in vitro evaluation and QSAR modelling of potential antitumoral 3,4-dihydropyrimidin-2-(1H)-thiones (original) (raw)
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RSC Advances, 2016
The search for novel anticancer agents with higher selectivity and lower toxicity remains a priority. This work aimed to design more potent and selective anticancer molecules among the class of 3,4-dihydropyrimidin-2-(1H)-ones. Thus, a series of molecules was synthesized through the Biginelli reaction and their in vitro antiproliferative activity was evaluated in different human cell lines. Then, a quantitative structure-activity relationship (QSAR) analysis was performed using Bayesian regularized artificial neural networks to model the relationships between in silico molecular descriptors and the observed antiproliferative activity of molecules across the tested cell lines. Interestingly, among the compounds prepared, the molecules containing chloro atoms in their structure demonstrated a relevant potency and a selective antiproliferative activity against a novel hepatic cancer cell line (HepaRG) without exhibiting noticeable cytotoxicity in normal dermal cells (NHDF). However, in prostatic (LNCaP), colon (Caco-2) and breast (T47D and MCF-7) cancer cell lines generally the compounds did not exhibit relevant cytoxicity. A statistically valid QSAR model was obtained (internal validation Q(2) - 0.663, RMSECV - 0.071, 10-fold cross-validation procedure, and external validation R-pred(2) = 0.740, RMSE = 0.077), which allowed the analysis of the involved relationships between molecular descriptors and the reliable prediction of the antiproliferative activity for hypothetical related compounds in the studied cell lines. Moreover, flow cytometry analysis showed that in HepaRG and MCF-7 cell lines, compound 16 did not decrease cell viability but, interestingly, led to an accumulation of cells in the G(0)/G(1) phase of the cell cycle. Therefore, chlorinated 3,4-dihydropyrimidin-2-(1H)-ones may be considered promising compounds for further optimization as new antitumor agents.
DARU Journal of Pharmaceutical Sciences, 2013
Background and the purpose of the study A common approach in cancer chemotherapy is development of drugs that interrupt the mitosis phase of cell division. Dimethylenastron is a known kinesin inhibitor. In this study, six novel dimethylenastron analogues (4a-f), in which 3-hydroxyphenyl substituent has been replaced with substituted benzylimidazolyl, were synthesized through Biginelli reaction. Methods Six novel Biginelli compounds (4a-f) were synthesized through one step Biginelli reaction of imidazole aldehydes (3a-c), dimedone and urea or thioura. In vitro cytotoxicities of prepared compounds were investigated using MTT assay. Furthermore the ELIPA kit was implemented to study inhibitory effects of synthesized compounds on ATPase activity of kinesin by measuring of organic phosphate. Results Our results indicated that analogue 4c is the most toxic and analogues 4f, 4b and dimethylenasteron were less cytotoxic in compare with other analogues. On the other hand, analogue 4a, 4b, 4c...
Bioorganic …, 2006
The synthesis and differential antiproliferative activity of monastrol (1a), oxo-monastrol (1b) and eight oxygenated derivatives 3a,b–6a,b on seven human cancer cell lines are described. For all evaluated cell lines, monastrol (1a) was shown to be more active than its oxo-analogue, except for HT-29 cell line, suggesting the importance of the sulfur atom for the antiproliferative activity. Monastrol (1a) and the thio-derivatives 3a, 4a and 6a displayed relevant antiproliferative properties with 3,4-methylenedioxy derivative 6a being approximately more than 30 times more potent than monastrol (1a) against colon cancer (HT-29) cell line.
BMC cancer, 2015
In past, numerous quantitative structure-activity relationship (QSAR) based models have been developed for predicting anticancer activity for a specific class of molecules against different cancer drug targets. In contrast, limited attempt have been made to predict the anticancer activity of a diverse class of chemicals against a wide variety of cancer cell lines. In this study, we described a hybrid method developed on thousands of anticancer and non-anticancer molecules tested against National Cancer Institute (NCI) 60 cancer cell lines. Our analysis of anticancer molecules revealed that majority of anticancer molecules contains 18-24 carbon atoms and are dominated by functional groups like R2NH, R3N, ROH, RCOR, and ROR. It was also observed that certain substructures (e.g., 1-methoxy-4-methylbenzene, 1-methoxy benzene, Nitrobenzene, Indole, Propenyl benzene) are more abundant in anticancer molecules. Next, we developed anticancer molecule prediction models using various machine-l...
International Journal of Molecular Sciences
A series of novel 2-[(4-amino-6-R2-1,3,5-triazin-2-yl)methylthio]-4-chloro-5-methyl-N-(5-R1-1H-benzo[d]imidazol-2(3H)-ylidene)benzenesulfonamides 6–49 was synthesized by the reaction of 5-substituted ethyl 2-{5-R1-2-[N-(5-chloro-1H-benzo[d]imidazol-2(3H)-ylidene)sulfamoyl]-4-methylphenylthio}acetate with appropriate biguanide hydrochlorides. The most active compounds, 22 and 46, showed significant cytotoxic activity and selectivity against colon (HCT-116), breast (MCF-7) and cervical cancer (HeLa) cell lines (IC50: 7–11 µM; 15–24 µM and 11–18 µM), respectively. Further QSAR (Quantitative Structure–Activity Relationships) studies on the cytotoxic activity of investigated compounds toward HCT-116, MCF-7 and HeLa were performed by using different topological (2D) and conformational (3D) molecular descriptors based on the stepwise multiple linear regression technique (MLR). The QSAR studies allowed us to make three statistically significant and predictive models for them. Moreover, the ...
ANN-QSAR model for selection of anticancer leads from structurally heterogeneous series of compounds
European Journal of Medicinal Chemistry, 2007
Developing a model for predicting anticancer activity of any classes of organic compounds based on molecular structure is very important goal for medicinal chemist. Different molecular descriptors can be used to solve this problem. Stochastic molecular descriptors so-called the MARCH-INSIDE approach, shown to be very successful in drug design. Nevertheless, the structural diversity of compounds is so vast that we may need non-linear models such as artificial neural networks (ANN) instead of linear ones. SmartMLP-ANN analysis used to model the anticancer activity of organic compounds has shown high average accuracy of 93.79% (train performance) and predictability of 90.88% (validation performance) for the 8:3-MLP topology with different training and predicting series. This ANN model favourably compares with respect to a previous linear discriminant analysis (LDA) model [H. González-Díaz et al., J. Mol. Model 9 (2003) 395] that showed only 80.49% of accuracy and 79.34% of predictability. The present SmartMLP approach employed shorter training times of only 10 h while previous models give accuracies of 70–89% only after 25–46 h of training. In order to illustrate the practical use of the model in bioorganic medicinal chemistry, we report the in silico prediction, and in vitro evaluation of six new synthetic tegafur analogues having IC50 values in a broad range between 37.1 and 138 μg mL−1 for leukemia (L1210/0) and human T-lymphocyte (Molt4/C8, CEM/0) cells. Theoretical predictions coincide very well with experimental results.
Synthesis and antitumoral activity of novel analogues monastrol–fatty acids against glioma cells
MedChemComm, 2018
Monastrol is a small cell-permeable heterocyclic molecule that is recognized as an inhibitor of mitotic kinesin Eg5. Heterocyclic-fatty acid derivatives are a new class of compounds with a broad range of biological activities. This work describes a comparative study of the in vitro antitumoral activity of a series of new long-chain monastrol analogues against rat glioblastoma cells. The novel analogues C6-substituted monastrol and oxo-monastrol were synthesized via Biginelli multicomponent condensation of fatty β-ketoester in good yields using a simple approach catalyzed by nontoxic and free-metal sulfamic acid. Following synthesis, their in vitro antitumoral activities were investigated. Notably, all analogues tested were active against rat glioblastoma cells. Superior activity was observed by analogues derived from palmitic and stearic fatty acid chains; these compounds were the most potent molecules, showing 13-fold higher potency than monastrol with IC 50 values of 5.11 and 6.85 μM, respectively. These compounds could provide promising new lead derivatives for more potent antitumor drugs.
Journal of Oncology
The diverse pharmacological role of dihydropyrimidinone scaffold has made it to be an interesting drug target. Because of the high incidence and mortality rate of breast cancer, there is a dire need of discovering new pharmacotherapeutic agents in managing this disease. A series of twenty-two derivatives of 6-(chloromethyl)-4-(4-hydroxyphenyl)-2-oxo-1,2,3,4-tetrahydropyrimidine-5-carboxylate (3a-3k) and ethyl 6-(chloromethyl)-4-(2-hydroxyphenyl)-2-oxo-1,2,3,4-tetrahydropyrimidine-5-carboxylate (4a-4k) synthesized in a previous study were evaluated for their anticancer potential against breast cancer cell line. Molecular docking studies were performed to analyze the binding mode and interaction pattern of these compounds against nine breast cancer target proteins. The in vitro cell proliferation assay was performed against the breast cancer cell line MCF-7. The structure activity relationship of these compounds was further studied using QSARINS. Among nine proteins, the docking analy...
Journal of Molecular Graphics & Modelling, 2018
This paper deals with in silico evaluation of newly proposed heterocyclic derivatives in search of potential anticancer activity. Best possible drug candidates have been proposed using a rational approach employing a pipeline of computational techniques namely MetaPrint2D prediction, molinspiration, cheminformatics, Osiris Data warrior, AutoDock and iGEMDOCK. Lazar toxicity prediction, AdmetSAR predictions, and targeted docking studies were also performed. 27 heterocyclic derivatives were selected for bioactivity prediction and drug likeness score on the basis of Lipinski's rule, Viber rule, Ghose filter, leadlikeness and Pan Assay Interference Compounds (PAINS) rule. Bufuralol, Sunitinib, and Doxorubicin were selected as reference standard drug for the comparison of molecular descriptors and docking. Bufuralol is a known non-selective adreno-receptor blocking agent. Studies showed that beta blockers are also used against different types of cancers. Sunitinib is well known Food and Drug administration (FDA) approved pyrrole containing tyrosine kinase inhibitor and our proposed molecules possess similarities with both drug and doxorubicin is another moiety having anticancer activity. All heterocyclic derivatives were found to obey the drug filters except standard drug Doxorubicin. Bioactivity score of the compounds was predicted for drug targets including enzymes, nuclear receptors, kinase inhibitors, G protein-coupled receptor (GPCR) ligands and ion channel modulators. Absorption, distribution, metabolism and toxicity (ADMET) prediction of all proposed compound showed good Blood-brain barrier (BBB) penetration, Human intestinal absorption (HIA), Caco-2 cell permeability except compound-11 and was found to have no AdmetSAR toxicity as well as carcinogenic effect. Compounds 1-9 were slightly mutagenic while compound 2, 11, 20 and 21 showed carcinogenic effect according to Lazar toxicity prediction. Rests of the compounds were predicted to have no side effect. Molecular docking was performed with vascular endothelial growth factor receptor-2(VEGFR2) and glutathione Stransferase-1(GSTP1) because both are common cancer causing proteins. Sunitinib and Doxorubicin possess great affinity to inhibit these cancers causing protein. Self-organizing map (SOM) was used to depict data in a simple 2D presentation. Our studies justify that good oral bioavailability and therapeutic efficacy of 10, 12-19 and 22-27 compounds can be considered as potential anticancer agents.