Dataset Information

In Silico HCT116 Human Colon Cancer Cell-Based Models En Route to the Discovery of Lead-Like Anticancer Drugs.

ABSTRACT: To discover new inhibitors against the human colon carcinoma HCT116 cell line, two quantitative structure?activity relationship (QSAR) studies using molecular and nuclear magnetic resonance (NMR) descriptors were developed through exploration of machine learning techniques and using the value of half maximal inhibitory concentration (IC₅₀). In the first approach, A, regression models were developed using a total of 7339 molecules that were extracted from the ChEMBL and ZINC databases and recent literature. The performance of the regression models was successfully evaluated by internal and external validations, the best model achieved R² of 0.75 and 0.73 and root mean square error (RMSE) of 0.66 and 0.69 for the training and test sets, respectively. With the inherent time-consuming efforts of working with natural products (NPs), we conceived a new NP drug hit discovery strategy that consists in frontloading samples with 1D NMR descriptors to predict compounds with anticancer activity prior to bioactivity screening for NPs discovery, approach B. The NMR QSAR classification models were built using 1D NMR data (¹H and ¹³C) as descriptors, from 50 crude extracts, 55 fractions and five pure compounds obtained from actinobacteria isolated from marine sediments collected off the Madeira Archipelago. The overall predictability accuracies of the best model exceeded 63% for both training and test sets.

SUBMITTER: Cruz S

PROVIDER: S-EPMC6164384 | biostudies-literature | 2018 Jul

REPOSITORIES: biostudies-literature

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In Silico HCT116 Human Colon Cancer Cell-Based Models En Route to the Discovery of Lead-Like Anticancer Drugs.

Cruz Sara S Gomes Sofia E SE Borralho Pedro M PM Rodrigues Cecília M P CMP Gaudêncio Susana P SP Pereira Florbela F

Biomolecules 20180717 3

To discover new inhibitors against the human colon carcinoma HCT116 cell line, two quantitative structure⁻activity relationship (QSAR) studies using molecular and nuclear magnetic resonance (NMR) descriptors were developed through exploration of machine learning techniques and using the value of half maximal inhibitory concentration (IC<sub>50</sub>). In the first approach, A, regression models were developed using a total of 7339 molecules that were extracted from the ChEMBL and ZINC databases ...[more]

PMID: 30018273

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Project description:IntroductionCancer is a big challenge of the 21 century, whose defeat requires efficient antitumor drugs.ObjectivesThe paper aims to investigate the synergistic effect of two structural building blocks, phenothiazine and poly(ethylene glycol), towards efficient antitumor drugs.MethodsTwo PEGylated phenothiazine derivatives were synthetized by attaching poly(ethylene glycol) of 550 Da to the nitrogen atom of phenothiazine by ether or ester linkage. Their antitumor activity has been investigated on five human tumour lines and a mouse tumor line as well, by determination of IC50. The in vivo toxicity was determined by measuring the LD50 in BALB/c mice by the sequential method and the in vivo antitumor potential was measured by the tumours growth test. The antitumor mechanism was investigated by complexation studies of zinc and magnesium ions characteristic to the farnesyltransferase enzyme, by studies of self-aggregation in the cells proximity and by investigation of the antitumor properties of the acid species resulted by enzymatic cleavage of the PEGylated derivatives.ResultsThe two compounds showed antitumor activity, with IC50 against mouse colon carcinoma cell line comparable with that of the traditional antitumor drugs 5-Fluorouracil and doxorubicin. The phenothiazine PEGylation resulted in a significant toxicity diminishing, the LD50 in BALB/c mice increasing from 952.38 up to 1450 mg/kg, in phenothiazine equivalents. Both compounds inflicted a 92% inhibition of the tumour growth for doses much smaller than LD50. The investigation of the possible tumour inhibition mechanism suggested the nanoaggregate formation and the cleavage of ester bonds as key factors for the inhibition of cancer cell proliferation and biocompatibility improvement.ConclusionPhenothiazine and PEG building blocks have a synergetic effect working for both tumour growth inhibition and biocompatibility improvement. All these findings recommend the PEGylated phenothiazine derivatives as a valuable workbench for a next generation of antitumor drugs.

Dataset Information

In Silico HCT116 Human Colon Cancer Cell-Based Models En Route to the Discovery of Lead-Like Anticancer Drugs.

Publications

In Silico HCT116 Human Colon Cancer Cell-Based Models En Route to the Discovery of Lead-Like Anticancer Drugs.

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