2D- and 3D-QSAR modelling, molecular docking and <i>in vitro</i> evaluation studies on 18β-glycyrrhetinic acid derivatives against triple-negative breast cancer cell line

Shukla, Aparna; Tyagi, Rekha; Meena, Sanjeev; Datta, Dipak; Srivastava, Santosh K.; Khan, Feroz

doi:10.1080/07391102.2019.1570868

Cited by 30 publications

(12 citation statements)

References 42 publications

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“…According to the dimensions of molecular descriptors used for model generation, QSAR methods can be classified into several classes of modeling, such as 1D, 2D, 3D, 4D, 5D, and so forth [85]. In most cases, 2D-and 3D-QSAR studies are commonly used to evaluate the series of chemical compounds [86][87][88]. The 1D-QSAR approach allows for the determination of correlations for 1D descriptors (pKa, log P, structural fragments, and fingerprints) with biological activity [89].…”

Section: Qsar Assaymentioning

confidence: 99%

Current Strategies in Assessment of Nanotoxicity: Alternatives to In Vivo Animal Testing

Huang

Lee

Hsu

et al. 2021

IJMS

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Millions of experimental animals are widely used in the assessment of toxicological or biological effects of manufactured nanomaterials in medical technology. However, the animal consciousness has increased and become an issue for debate in recent years. Currently, the principle of the 3Rs (i.e., reduction, refinement, and replacement) is applied to ensure the more ethical application of humane animal research. In order to avoid unethical procedures, the strategy of alternatives to animal testing has been employed to overcome the drawbacks of animal experiments. This article provides current alternative strategies to replace or reduce the use of experimental animals in the assessment of nanotoxicity. The currently available alternative methods include in vitro and in silico approaches, which can be used as cost-effective approaches to meet the principle of the 3Rs. These methods are regarded as non-animal approaches and have been implemented in many countries for scientific purposes. The in vitro experiments related to nanotoxicity assays involve cell culture testing and tissue engineering, while the in silico methods refer to prediction using molecular docking, molecular dynamics simulations, and quantitative structure–activity relationship (QSAR) modeling. The commonly used novel cell-based methods and computational approaches have the potential to help minimize the use of experimental animals for nanomaterial toxicity assessments.

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Section: Qsar Assaymentioning

confidence: 99%

Current Strategies in Assessment of Nanotoxicity: Alternatives to In Vivo Animal Testing

Huang

Lee

Hsu

et al. 2021

IJMS

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“…BC receives energy from glycolysis based on the Warburg effect, and GLO-1 is able to inhibit and inactivate methyl glyoxalases, a compound formed during glycolysis, making GLO-I inhibitors potential anticancer agents. It has been shown that GA-1 increases cytotoxic action, and a study using molecular docking confirmed the binding of GA derivatives with GLO-I [52].…”

Section: New Synthesized Moleculesmentioning

confidence: 88%

“…Shukla et al [52] designed five glycyrrhetinic acid (GA) derivatives and analyzed their in vitro action in a metastatic breast cancer cell line (i.e., MDA-MB-231). Molecular docking studies have been carried out to investigate the action of compounds on BC targets such as glyoxalase-I (GLO-I).…”

Section: New Synthesized Moleculesmentioning

confidence: 99%

Integration of Molecular Docking and In Vitro Studies: A Powerful Approach for Drug Discovery in Breast Cancer

Cava

Castiglioni

2020

Applied Sciences

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Molecular docking in the pharmaceutical industry is a powerful in silico approach for discovering novel therapies for unmet medical needs predicting drug–target interactions. It not only provides binding affinity between drugs and targets at the atomic level, but also elucidates the fundamental pharmacological properties of specific drugs. The purpose of this review was to illustrate newer and emergent uses of docking when combined with in vitro techniques for drug discovery in metastatic breast cancer. We grouped the selected articles into five main categories; namely, systematic repositioning of drugs, natural drugs, new synthesized molecules, combinations of drugs, and drug latentiation. We focused on new promising drugs that have a good affinity with their targets, thus inducing a favorable biological response. This review suggests that the integration of molecular docking and in vitro studies can accelerate cancer drug discovery showing a good consistency of the results between the two approaches.

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“…Among the reported QSAR studies of MDA-MB-231 TNBC cells, the particularly recent work has been done using the dataset of 61 parthenolide derivatives ( R 2 = 0.67, Q 2 = 0.55 and R 2 pred = 0.53) [ 36 ], and yet another using the dataset of 18β –glycyrrhetinic acid derivatives ( R 2 = 0.84, Q 2 LOO = 0.83 and R 2 pred = 0.75) [ 37 ]. These QSAR models are developed on the dataset of compounds with a single scaffold, e.g., parthenolide scaffold, 18β–glycyrrhetinic acid etc., and limited pharmacophoric features.…”

Section: Discussionmentioning

confidence: 99%

Quantitative Structure–Activity Relationship Evaluation of MDA-MB-231 Cell Anti-Proliferative Leads

Gandhi

Masand²,

Zaki

et al. 2021

Molecules

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In the present endeavor, for the dataset of 219 in vitro MDA-MB-231 TNBC cell antagonists, a (QSAR) quantitative structure–activity relationships model has been carried out. The quantitative and explicative assessments were performed to identify inconspicuous yet pre-eminent structural features that govern the anti-tumor activity of these compounds. GA-MLR (genetic algorithm multi-linear regression) methodology was employed to build statistically robust and highly predictive multiple QSAR models, abiding by the OECD guidelines. Thoroughly validated QSAR models attained values for various statistical parameters well above the threshold values (i.e., R2 = 0.79, Q2LOO = 0.77, Q2LMO = 0.76–0.77, Q2-Fn = 0.72–0.76). Both de novo QSAR models have a sound balance of descriptive and statistical approaches. Decidedly, these QSAR models are serviceable in the development of MDA-MB-231 TNBC cell antagonists.

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2D- and 3D-QSAR modelling, molecular docking and in vitro evaluation studies on 18β-glycyrrhetinic acid derivatives against triple-negative breast cancer cell line

Cited by 30 publications

References 42 publications

Current Strategies in Assessment of Nanotoxicity: Alternatives to In Vivo Animal Testing

Current Strategies in Assessment of Nanotoxicity: Alternatives to In Vivo Animal Testing

Integration of Molecular Docking and In Vitro Studies: A Powerful Approach for Drug Discovery in Breast Cancer

Quantitative Structure–Activity Relationship Evaluation of MDA-MB-231 Cell Anti-Proliferative Leads

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