Dataset Information

Predicting the bioactivity of 2-alkoxycarbonylallyl esters as potential antiproliferative agents against pancreatic cancer (MiaPaCa-2) cell lines: GFA-based QSAR and ELM-based models with molecular docking.

ABSTRACT:

Background

The number of cancer-related deaths is on the increase, combating this deadly disease has proved difficult owing to resistance and some serious side effects associated with drugs used to combat it. Therefore, scientists continue to probe into the mechanism of action of cancer cells and designing novel drugs that could combat this disease more safely and effectively. Here, we developed a genetic function approximation model to predict the bioactivity of some 2-alkoxyecarbonyl esters and probed into the mode of interaction of these molecules with an epidermal growth factor receptor (3POZ) using the three-dimensional quantitative structure activity relationship (QSAR), extreme learning machine (ELM), and molecular docking techniques.

Results

The developed QSAR model with predicted (R²_pred) of 0.756 showed that the model was fit to be validated parameter for a built model and also proved that the developed model could be used in practical situation, R² for training set (0.9929) and test set (0.8397) confirmed that the model could successfully predict the activity of new compounds due to its correlation with the experimental activity, the models generated with ELM models showed improved prediction of the activity of the molecules. The lead compounds (22 and 23) had binding energies of -6.327 and -7.232 kcalmol^-1 for 22 and 23 respectively and displayed better inhibition at the binding sites of 3POZ when compared with that of the standard drug, chlorambucil (-6.0 kcalmol^-1). This could be attributed to the presence of double bonds and the α-ester groups.

Conclusion

The QSAR and ELM models had good prognostic ability and could be used to predict the bioactivity of novel anti-proliferative drugs.

SUBMITTER: Oyeneyin OE

PROVIDER: S-EPMC7947105 | biostudies-literature | 2021 Mar

REPOSITORIES: biostudies-literature

ACCESS DATA

Publications

Predicting the bioactivity of 2-alkoxycarbonylallyl esters as potential antiproliferative agents against pancreatic cancer (MiaPaCa-2) cell lines: GFA-based QSAR and ELM-based models with molecular docking.

Oyeneyin Oluwatoba Emmanuel OE Obadawo Babatunde Samuel BS Olanrewaju Adesoji Alani AA Owolabi Taoreed Olakunle TO Gbadamosi Fahidat Adedamola FA Ipinloju Nureni N Modamori Helen Omonipo HO

Journal, genetic engineering & biotechnology 20210310 1

<h4>Background</h4>The number of cancer-related deaths is on the increase, combating this deadly disease has proved difficult owing to resistance and some serious side effects associated with drugs used to combat it. Therefore, scientists continue to probe into the mechanism of action of cancer cells and designing novel drugs that could combat this disease more safely and effectively. Here, we developed a genetic function approximation model to predict the bioactivity of some 2-alkoxyecarbonyl e ...[more]

PMID: 33689046

Similar Datasets

Project description:Malaria which is mainly caused by Plasmodium falciparum parasite remains a devastating public health concern, necessitating the need to develop new antimalarial agents. P. falciparum heat shock protein 90 (Hsp90), is indispensable for parasite survival and a promising drug target. Inhibitors targeting the ATP-binding pocket of the N-terminal domain have anti-Plasmodium effects. We proposed a de novo active learning (AL) driven method in tandem with docking to predict inhibitors with unique scaffolds and preferential selectivity towards PfHsp90. Reference compounds, predicted to bind PfHsp90 at the ATP-binding pocket and possessing anti-Plasmodium activities, were used to generate 10,000 unique derivatives and to build the Auto-quantitative structures activity relationships (QSAR) models. Glide docking was performed to predict the docking scores of the derivatives and > 15,000 compounds obtained from the ChEMBL database. Re-iterative training and testing of the models was performed until the optimum Kennel-based Partial Least Square (KPLS) regression model with a regression coefficient R2 = 0.75 for the training set and squared correlation prediction Q2 = 0.62 for the test set reached convergence. Rescoring using induced fit docking and molecular dynamics simulations enabled us to prioritize 15 ATP/ADP-like design ideas for purchase. The compounds exerted moderate activity towards P. falciparum NF54 strain with IC50 values of ≤ 6μM and displayed moderate to weak affinity towards PfHsp90 (KD range: 13.5-19.9μM) comparable to the reported affinity of ADP. The most potent compound was FTN-T5 (PfN54 IC50:1.44μM; HepG2/CHO cells SI≥ 29) which bound to PfHsp90 with moderate affinity (KD:7.7μM), providing a starting point for optimization efforts. Our work demonstrates the great utility of AL for the rapid identification of novel molecules for drug discovery (i.e., hit identification). The potency of FTN-T5 will be critical for designing species-selective inhibitors towards developing more efficient agents against malaria.

Project description: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 analysis revealed efficient binding of compounds 4f, 4e, 3e, 4g, and 4h against all target proteins. The in vitro cytotoxic assay revealed significant anticancer activity of compound 4f having IC50 of 2.15 μM. The compounds 4e, 3e, 4g, and 4h also showed anticancer activities with IC50 of 2.401, 2.41, 2.47 and 2.33 μM, respectively. The standard tamoxifen showed IC50 1.88 μM. The 2D qualitative structure-activity relationship (QSAR) analysis was also carried out to identify potential breast cancer targets through QSARINS. The final QSAR equation revealed good predictivity and statistical validation R 2 and Q 2 values for the model obtained from QSARINS was 0.98 and 0.97, respectively. The active compounds showed very good anticancer activities, and the binding analysis has revealed stable hydrogen bonding of these compounds with the target proteins. Moreover, the QSAR analysis has predicted useful information on the structural requirement of these compounds as anticancer agents with the importance of topological and autocorrelated descriptors in effecting the cancer activities.

Dataset Information

Predicting the bioactivity of 2-alkoxycarbonylallyl esters as potential antiproliferative agents against pancreatic cancer (MiaPaCa-2) cell lines: GFA-based QSAR and ELM-based models with molecular docking.

Background

Results

Conclusion

Publications

Predicting the bioactivity of 2-alkoxycarbonylallyl esters as potential antiproliferative agents against pancreatic cancer (MiaPaCa-2) cell lines: GFA-based QSAR and ELM-based models with molecular docking.

Similar Datasets

OmicsDI is part of the ELIXIR infrastructure

Tweets