New thiobarbituric acid scaffold-based small molecules: Synthesis, cytotoxicity, 2D-QSAR, pharmacophore modelling and in-silico ADME screening

Elzahabi, Heba S. A.; Khalifa, Maha M.; Gado, Yomna M.H.; Farrag, Ayman A.; Elaasser, Mahmoud M.; Safwat, Nesreen A.; AbdelRaouf, Reham R.; Arafa, Reem K.

doi:10.1016/j.ejps.2019.01.023

Cited by 17 publications

(6 citation statements)

References 24 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…Therefore, the availability of ligand data is essential to the construction of pharmacophore models. In recent years, there have been many cases of using pharmacophore models to screen promising compounds with outstanding ADMET properties (Nandekar et al, 2016 ; El-Zahabi et al, 2019 ; Mohan et al, 2020 ; Patel D. B. et al, 2020 ; Rawat and Verma, 2020 ). For example, Nandekar et al ( 2016 ) generated and validated a pharmacophore model to screen anticancer compounds acting via cytochrome P450 1A1 (CYP1A1).…”

Section: In Silico Approachesmentioning

confidence: 99%

Computational Approaches in Preclinical Studies on Drug Discovery and Development

Zhou

et al. 2020

Front. Chem.

184

View full text Add to dashboard Cite

Because undesirable pharmacokinetics and toxicity are significant reasons for the failure of drug development in the costly late stage, it has been widely recognized that drug ADMET properties should be considered as early as possible to reduce failure rates in the clinical phase of drug discovery. Concurrently, drug recalls have become increasingly common in recent years, prompting pharmaceutical companies to increase attention toward the safety evaluation of preclinical drugs. In vitro and in vivo drug evaluation techniques are currently more mature in preclinical applications, but these technologies are costly. In recent years, with the rapid development of computer science, in silico technology has been widely used to evaluate the relevant properties of drugs in the preclinical stage and has produced many software programs and in silico models, further promoting the study of ADMET in vitro. In this review, we first introduce the two ADMET prediction categories (molecular modeling and data modeling). Then, we perform a systematic classification and description of the databases and software commonly used for ADMET prediction. We focus on some widely studied ADMT properties as well as PBPK simulation, and we list some applications that are related to the prediction categories and web tools. Finally, we discuss challenges and limitations in the preclinical area and propose some suggestions and prospects for the future.

show abstract

Section: In Silico Approachesmentioning

confidence: 99%

Computational Approaches in Preclinical Studies on Drug Discovery and Development

Zhou

et al. 2020

Front. Chem.

184

View full text Add to dashboard Cite

show abstract

“…On the other J o u r n a l P r e -p r o o f hand, there are studies where MCF-7 cells have been more sensitive to e.g. doxorubicin (El-Zahabi et al, 2019) or epigallocatechin gallate (Silva et al, 2019) compared to Caco-2 cells.…”

Section: Discussionmentioning

confidence: 99%

“…The robustness of the cell lines differs depending on the compound studied and on the test system applied (Coleman et al, 2007;Xu et al, 2016;El-Zahabi et al, 2019;Silva et al, 2019).…”

Section: Introductionmentioning

confidence: 99%

Toxicity of diuron metabolites in human cells

Mohammed

Huovinen

Vähäkangas

2020

Environmental Toxicology and Pharmacology

View full text Add to dashboard Cite

This is a PDF file of an article that has undergone enhancements after acceptance, such as the addition of a cover page and metadata, and formatting for readability, but it is not yet the definitive version of record. This version will undergo additional copyediting, typesetting and review before it is published in its final form, but we are providing this version to give early visibility of the article. Please note that, during the production process, errors may be discovered which could affect the content, and all legal disclaimers that apply to the journal pertain.

show abstract

“…The structure of eleven reported compounds of diverse classes with the reported biological activity against CXCL12 were retrieved from literature as indicated in Figure 7 . By using MOE 2019.01, [ 20 ] 3D structures of all inhibitors were sketched and prepared using MMFF94 force field for energy minimization [ 21 ]. The same protocol for energy minimization and preparation were also used for ChEMBL [ 22 ] and in-house databases.…”

Section: Methodsmentioning

confidence: 99%

Discovery of Potential Chemical Probe as Inhibitors of CXCL12 Using Ligand-Based Virtual Screening and Molecular Dynamic Simulation

2020

View full text Add to dashboard Cite

CXCL12 are small pro-inflammatory chemo-attractant cytokines that bind to a specific receptor CXCR4 with a role in angiogenesis, tumor progression, metastasis, and cell survival. Globally, cancer metastasis is a major cause of morbidity and mortality. In this study, we targeted CXCL12 rather than the chemokine receptor (CXCR4) because most of the drugs failed in clinical trials due to unmanageable toxicities. Until now, no FDA approved medication has been available against CXCL12. Therefore, we aimed to find new inhibitors for CXCL12 through virtual screening followed by molecular dynamics simulation. For virtual screening, active compounds against CXCL12 were taken as potent inhibitors and utilized in the generation of a pharmacophore model, followed by validation against different datasets. Ligand based virtual screening was performed on the ChEMBL and in-house databases, which resulted in successive elimination through the steps of pharmacophore-based and score-based screenings, and finally, sixteen compounds of various interactions with significant crucial amino acid residues were selected as virtual hits. Furthermore, the binding mode of these compounds were refined through molecular dynamic simulations. Moreover, the stability of protein complexes, Root Mean Square Deviation (RMSD), Root Mean Square Fluctuation (RMSF), and radius of gyration were analyzed, which led to the identification of three potent inhibitors of CXCL12 that may be pursued in the drug discovery process against cancer metastasis.

show abstract

New thiobarbituric acid scaffold-based small molecules: Synthesis, cytotoxicity, 2D-QSAR, pharmacophore modelling and in-silico ADME screening

Cited by 17 publications

References 24 publications

Computational Approaches in Preclinical Studies on Drug Discovery and Development

Computational Approaches in Preclinical Studies on Drug Discovery and Development

Toxicity of diuron metabolites in human cells

Discovery of Potential Chemical Probe as Inhibitors of CXCL12 Using Ligand-Based Virtual Screening and Molecular Dynamic Simulation

Contact Info

Product

Resources

About