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Yurttaş L, Çavuşoğlu BK, Temel HE, Çiftçi GA. Novel N-(2-Methoxydibenzofuran-3-yl)-2-aryloxyacetamide Derivatives: Synthesis and Biological Investigation. LETT DRUG DES DISCOV 2021. [DOI: 10.2174/1570180817999201110114107] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/22/2022]
Abstract
Background:
Dibenzofuran ring is a typical heterocyle that is found in many natural
sources and its derivatives exhibit a wide scale of biological applications similar to its analog ring
systems; furan and benzofuran.
Materials and Methods:
Novel N-(2-methoxydibenzofuran-3-yl)-2-aryloxyacetamide derivatives
(2a-l) were synthesized and evaluated for their cytotoxic activity against A549 lung cancer and
NIH/3T3 mouse embryofibroblast cell lines. The inhibition percentages of cathepsin D, L, acetylcholinesterase
(AChE) and butrylcholinesterase (BuChE) enzymes provoked by the compounds
were also determined.
Results and Discussion:
Most of the compounds exhibited significant cytotoxicity whose IC50 values
were identified lower than the tested lowest concentration (<3.90 μg/mL). Compound 2i against
cathepsin D and compound 2k against cathepsin L displayed the highest inhibitory activity. Regrettably,
the compounds demonstrated very weak AChE and BuChE inhibition.
Conclusion:
Compounds 2b, 2c, 2e, 2i and 2k exhibited the highest antiproliferative activity
against A549 cell lines with selective profile. However, they did not display satisfying results on
tested enzymes.
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Affiliation(s)
- Leyla Yurttaş
- Department of Pharmaceutical Chemistry, Faculty of Pharmacy, Anadolu University, Eskisehir 26470,Turkey
| | - Betül Kaya Çavuşoğlu
- Department of Pharmaceutical Chemistry, Faculty of Pharmacy, Bulent Ecevit University, Zonguldak 67600,Turkey
| | - Halide Edip Temel
- Department of Biohemistry, Faculty of Pharmacy, Anadolu University, Eskisehir 26470,Turkey
| | - Gülşen Akalın Çiftçi
- Department of Biohemistry, Faculty of Pharmacy, Anadolu University, Eskisehir 26470,Turkey
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Wu F, Zhou Y, Li L, Shen X, Chen G, Wang X, Liang X, Tan M, Huang Z. Computational Approaches in Preclinical Studies on Drug Discovery and Development. Front Chem 2020; 8:726. [PMID: 33062633 PMCID: PMC7517894 DOI: 10.3389/fchem.2020.00726] [Citation(s) in RCA: 106] [Impact Index Per Article: 26.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/29/2020] [Accepted: 07/14/2020] [Indexed: 12/11/2022] Open
Abstract
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.
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Affiliation(s)
- Fengxu Wu
- Key Laboratory of Big Data Mining and Precision Drug Design of Guangdong Medical University, Research Platform Service Management Center, Dongguan, China
- Key Laboratory of Pesticide & Chemical Biology, Ministry of Education, College of Chemistry, Central China Normal University, Wuhan, China
| | - Yuquan Zhou
- Key Laboratory of Big Data Mining and Precision Drug Design of Guangdong Medical University, Research Platform Service Management Center, Dongguan, China
- The Second School of Clinical Medicine, Guangdong Medical University, Dongguan, China
| | - Langhui Li
- Key Laboratory of Big Data Mining and Precision Drug Design of Guangdong Medical University, Research Platform Service Management Center, Dongguan, China
- Key Laboratory for Research and Development of Natural Drugs of Guangdong Province, School of Pharmacy, Guangdong Medical University, Dongguan, China
| | - Xianhuan Shen
- Key Laboratory of Big Data Mining and Precision Drug Design of Guangdong Medical University, Research Platform Service Management Center, Dongguan, China
- Key Laboratory for Research and Development of Natural Drugs of Guangdong Province, School of Pharmacy, Guangdong Medical University, Dongguan, China
| | - Ganying Chen
- Key Laboratory of Big Data Mining and Precision Drug Design of Guangdong Medical University, Research Platform Service Management Center, Dongguan, China
- The Second School of Clinical Medicine, Guangdong Medical University, Dongguan, China
| | - Xiaoqing Wang
- Key Laboratory of Big Data Mining and Precision Drug Design of Guangdong Medical University, Research Platform Service Management Center, Dongguan, China
- Key Laboratory for Research and Development of Natural Drugs of Guangdong Province, School of Pharmacy, Guangdong Medical University, Dongguan, China
| | - Xianyang Liang
- Key Laboratory of Big Data Mining and Precision Drug Design of Guangdong Medical University, Research Platform Service Management Center, Dongguan, China
- The Second School of Clinical Medicine, Guangdong Medical University, Dongguan, China
| | - Mengyuan Tan
- Key Laboratory of Big Data Mining and Precision Drug Design of Guangdong Medical University, Research Platform Service Management Center, Dongguan, China
- Key Laboratory for Research and Development of Natural Drugs of Guangdong Province, School of Pharmacy, Guangdong Medical University, Dongguan, China
| | - Zunnan Huang
- Key Laboratory of Big Data Mining and Precision Drug Design of Guangdong Medical University, Research Platform Service Management Center, Dongguan, China
- Key Laboratory for Research and Development of Natural Drugs of Guangdong Province, School of Pharmacy, Guangdong Medical University, Dongguan, China
- Marine Biomedical Research Institute of Guangdong Zhanjiang, Zhanjiang, China
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Vladimir A, Mikhail F, Amsharov K. Alumina-promoted oxodefluorination. RSC Adv 2020; 10:10879-10882. [PMID: 35492952 PMCID: PMC9050430 DOI: 10.1039/d0ra01369b] [Citation(s) in RCA: 6] [Impact Index Per Article: 1.5] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/12/2020] [Accepted: 03/06/2020] [Indexed: 12/28/2022] Open
Abstract
A simple protocol for the clean preparation of heterocyclic compounds containing dibenzofuran's core via oxodefluorination of fluoroarenes on activated γ-Al2O3 is reported. Alumina can be considered as a reliable oxygen source enabling one-pot substitution of fluorine atoms and yielding benzoannulated furan derivatives. The corresponding C–F bond activation is selective towards less stable C–Br/C–I and occurs under metal- and solvent-free conditions. A simple protocol for the clean preparation of heterocyclic compounds containing dibenzofuran's core via oxodefluorination of fluoroarenes on activated γ-Al2O3 is reported.![]()
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Affiliation(s)
- Akhmetov Vladimir
- Friedrich-Alexander University Erlangen-Nuernberg
- Department of Chemistry and Pharmacy
- Organic Chemistry II
- 91058 Erlangen
- Germany
| | - Feofanov Mikhail
- Friedrich-Alexander University Erlangen-Nuernberg
- Department of Chemistry and Pharmacy
- Organic Chemistry II
- 91058 Erlangen
- Germany
| | - Konstantin Amsharov
- Friedrich-Alexander University Erlangen-Nuernberg
- Department of Chemistry and Pharmacy
- Organic Chemistry II
- 91058 Erlangen
- Germany
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Discovery of Non-Peptidic Compounds against Chagas Disease Applying Pharmacophore Guided Molecular Modelling Approaches. Molecules 2018; 23:molecules23123054. [PMID: 30469538 PMCID: PMC6321154 DOI: 10.3390/molecules23123054] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/21/2018] [Revised: 11/12/2018] [Accepted: 11/14/2018] [Indexed: 01/07/2023] Open
Abstract
Chagas disease is one of the primary causes of heart diseases accounting to 50,000 lives annually and is listed as the neglected tropical disease. Because the currently available therapies have greater toxic effects with higher resistance, there is a dire need to develop new drugs to combat the disease. In this pursuit, the 3D QSAR ligand-pharmacophore (pharm 1) and receptor-based pharmacophore (pharm 2) search was initiated to retrieve the candidate compounds from universal natural compounds database. The validated models were allowed to map the universal natural compounds database. The obtained lead candidates were subjected to molecular docking against cysteine protease (PDB code: 1ME3) employing -Cdocker available on the discovery studio. Subsequently, two Hits have satisfied the selection criteria and were escalated to molecular dynamics simulation and binding free energy calculations. These Hits have demonstrated higher dock scores, displayed interactions with the key residues portraying an ideal binding mode complemented by mapping to all the features of pharm 1 and pharm 2. Additionally, they have rendered stable root mean square deviation (RMSD) and potential energy profiles illuminating their potentiality as the prospective antichagastic agents. The study further demonstrates the mechanism of inhibition by tetrad residues compromising of Gly23 and Asn70 holding the ligand at each ends and the residues Gly65 and Gly160 clamping the Hits at the center. The notable feature is that the Hits lie in close proximity with the residues Glu66 and Leu67, accommodating within the S1, S2 and S3 subsites. Considering these findings, the study suggests that the Hits may be regarded as effective therapeutics against Chagas disease.
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