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Abdollahi Z, Nejabat M, Abnous K, Hadizadeh F. The therapeutic value of thiazole and thiazolidine derivatives in Alzheimer's disease: a systematic literature review. Res Pharm Sci 2024; 19:1-12. [PMID: 39006977 PMCID: PMC11244712 DOI: 10.4103/1735-5362.394816] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/30/2022] [Revised: 04/19/2023] [Accepted: 12/23/2023] [Indexed: 07/16/2024] Open
Abstract
Background and purpose Alzheimer's disease (AD) is a common neurodegenerative disease and the fifth leading cause of death among the elderly. The development of drugs for AD treatment is based on inhibiting cholinesterase (ChE) activity and inhibiting amyloid-beta peptide and tau protein aggregations. Many in vitro findings have demonstrated that thiazole-and thiazolidine-based compounds have a good inhibitory effect on ChE and other elements involved in the AD pathogenicity cascade. Experimental approach In the present review, we collected available documents to verify whether these synthetic compounds can be a step forward in developing new medications for AD. A systematic literature search was performed in major electronic databases in April 2021. Twenty-eight relevant in vitro and in vivo studies were found and used for data extraction. Findings/Results Findings demonstrated that thiazole-and thiazolidine-based compounds could ameliorate AD's pathologic condition by affecting various targets, including inhibition of ChE activity, amyloid-beta, and tau aggregation in addition to cyclin-dependent kinase 5/p25, beta-secretase-1, cyclooxygenase, and glycogen synthase kinase-3β. Conclusion and implications Due to multitarget effects at micromolar concentration, this review demonstrated that these synthetic compounds could be considered promising candidates for developing anti-Alzheimer drugs.
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Affiliation(s)
- Zahra Abdollahi
- Department of Medicinal Chemistry, School of Pharmacy, Mashhad University of Medical Sciences, Mashhad, Iran
| | - Mojgan Nejabat
- Department of Medicinal Chemistry, School of Pharmacy, Mashhad University of Medical Sciences, Mashhad, Iran
| | - Khalil Abnous
- Department of Medicinal Chemistry, School of Pharmacy, Mashhad University of Medical Sciences, Mashhad, Iran
| | - Farzin Hadizadeh
- Department of Medicinal Chemistry, School of Pharmacy, Mashhad University of Medical Sciences, Mashhad, Iran
- Biotechnology Research Center, Pharmaceutical Technology Institute, Mashhad University of Medical Sciences, Mashhad, Iran
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2
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Ostapiuk YV, Barabash OV, Ostapiuk MY, Goreshnik E, Obushak MD, Schmidt A. Thiocyanatoarylation of Methyl Vinyl Ketone under Meerwein Conditions for the Synthesis of 2-Aminothiazole-Based Heterocyclic Systems. Org Lett 2022; 24:4575-4579. [PMID: 35735270 DOI: 10.1021/acs.orglett.2c01677] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/29/2022]
Abstract
4-Aryl-3-thiocyanatobutan-2-ones were prepared by Meerwein reactions from methyl vinyl ketone and aryldiazonium salts under copper(II) catalysis in 35-75% yields. α-Thiocyanato ketones regioselectively react with 1-methyl-3-aminopyrazole forming N-(3-pyrazolyl)-substituted 2-aminothiazoles in 80-91% yields. An ester group in position 3 of the pyrazole induced a regioselective ring-closure reaction followed by an intramolecular cyclization, which gave first representatives of a new heterocyclic system, pyrazolo[4,3-e]thiazolo[3,2-a]pyrimidine, in 74-93% yields. In addition, the preparations of 5-benzyl-4-methylthiazol-2-ones in 84-93% yields are described.
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Affiliation(s)
- Yurii V Ostapiuk
- Department of Organic Chemistry, Ivan Franko National University of Lviv, Kyryla i Mefodiya Str. 6, 79005 Lviv, Ukraine
| | - Oksana V Barabash
- Department of Organic Chemistry, Ivan Franko National University of Lviv, Kyryla i Mefodiya Str. 6, 79005 Lviv, Ukraine
| | - Mary Y Ostapiuk
- Department of Organic Chemistry, Ivan Franko National University of Lviv, Kyryla i Mefodiya Str. 6, 79005 Lviv, Ukraine
| | - Evgeny Goreshnik
- Department of Inorganic Chemistry and Technology, Jozef Stefan Institute, Jamova 39, SI-1000 Ljubljana, Slovenia
| | - Mykola D Obushak
- Department of Organic Chemistry, Ivan Franko National University of Lviv, Kyryla i Mefodiya Str. 6, 79005 Lviv, Ukraine
| | - Andreas Schmidt
- Institute of Organic Chemistry, Clausthal University of Technology, Leibnizstrasse 6, D-38678 Clausthal-Zellerfeld, Germany
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3
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Deng C, Liang L, Xing G, Hua Y, Lu T, Zhang Y, Chen Y, Liu H. Multi-channel GCN ensembled machine learning model for molecular aqueous solubility prediction on a clean dataset. Mol Divers 2022:10.1007/s11030-022-10465-x. [PMID: 35739374 DOI: 10.1007/s11030-022-10465-x] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/05/2022] [Accepted: 05/19/2022] [Indexed: 10/17/2022]
Abstract
This study constructed a new aqueous solubility dataset and a solubility regression model which was ensembled by GCN and machine learning models. Aqueous solubility is a key physiochemical property of small molecules in drug discovery. In the past few decades, there have been many studies about solubility prediction. However, many of these studies have high root mean squared error (RMSE). Meanwhile, their dataset always contains salt compounds and solubility data obtained from different experimental conditions. In this paper, we constructed a clean dataset with 2609 compounds, which was small but contains only solubility records without salts at the same temperatures (25 °C). Here, we applied graph convolutional neural network (GCN) to construct an aqueous solubility prediction model. To enhance the performance of the model, the molecular MACCS key fingerprints and physiochemical descriptors were also combined with the GCN model to build a multi-channel model. Additionally, the authors also built two machine learning models (support vector regression and gradient boost decision tree) and assembled them to the GCN model to improve the root mean squared error (RMSE = 0.665). Finally, comparative experiments have shown that our framework achieved the best performance on ESOL dataset (RMSEval = 0.56, RMSEtest = 0.44) and surpassed four established software on aqueous solubility prediction of new compounds.
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Affiliation(s)
- Chenglong Deng
- Laboratory of Molecular Design and Drug Discovery, School of Science, China Pharmaceutical University, 639 Longmian Avenue, Nanjing, 211198, China
| | - Li Liang
- Laboratory of Molecular Design and Drug Discovery, School of Science, China Pharmaceutical University, 639 Longmian Avenue, Nanjing, 211198, China
| | - Guomeng Xing
- Laboratory of Molecular Design and Drug Discovery, School of Science, China Pharmaceutical University, 639 Longmian Avenue, Nanjing, 211198, China
| | - Yi Hua
- Laboratory of Molecular Design and Drug Discovery, School of Science, China Pharmaceutical University, 639 Longmian Avenue, Nanjing, 211198, China
| | - Tao Lu
- Laboratory of Molecular Design and Drug Discovery, School of Science, China Pharmaceutical University, 639 Longmian Avenue, Nanjing, 211198, China.,State Key Laboratory of Natural Medicines, China Pharmaceutical University, 24 Tongjiaxiang, Nanjing, 210009, China
| | - Yanmin Zhang
- Laboratory of Molecular Design and Drug Discovery, School of Science, China Pharmaceutical University, 639 Longmian Avenue, Nanjing, 211198, China
| | - Yadong Chen
- Laboratory of Molecular Design and Drug Discovery, School of Science, China Pharmaceutical University, 639 Longmian Avenue, Nanjing, 211198, China.
| | - Haichun Liu
- Laboratory of Molecular Design and Drug Discovery, School of Science, China Pharmaceutical University, 639 Longmian Avenue, Nanjing, 211198, China.
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4
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Small molecules targeting γ-secretase and their potential biological applications. Eur J Med Chem 2022; 232:114169. [DOI: 10.1016/j.ejmech.2022.114169] [Citation(s) in RCA: 4] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/10/2022] [Revised: 01/30/2022] [Accepted: 01/30/2022] [Indexed: 12/14/2022]
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5
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Rynearson KD, Ponnusamy M, Prikhodko O, Xie Y, Zhang C, Nguyen P, Hug B, Sawa M, Becker A, Spencer B, Florio J, Mante M, Salehi B, Arias C, Galasko D, Head BP, Johnson G, Lin JH, Duddy SK, Rissman RA, Mobley WC, Thinakaran G, Tanzi RE, Wagner SL. Preclinical validation of a potent γ-secretase modulator for Alzheimer's disease prevention. J Exp Med 2021; 218:211838. [PMID: 33651103 PMCID: PMC7931646 DOI: 10.1084/jem.20202560] [Citation(s) in RCA: 27] [Impact Index Per Article: 9.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/30/2020] [Revised: 01/13/2021] [Accepted: 01/20/2021] [Indexed: 12/20/2022] Open
Abstract
A potent γ-secretase modulator (GSM) has been developed to circumvent problems associated with γ-secretase inhibitors (GSIs) and to potentially enable use in primary prevention of early-onset familial Alzheimer's disease (EOFAD). Unlike GSIs, GSMs do not inhibit γ-secretase activity but rather allosterically modulate γ-secretase, reducing the net production of Aβ42 and to a lesser extent Aβ40, while concomitantly augmenting production of Aβ38 and Aβ37. This GSM demonstrated robust time- and dose-dependent efficacy in acute, subchronic, and chronic studies across multiple species, including primary and secondary prevention studies in a transgenic mouse model. The GSM displayed a >40-fold safety margin in rats based on a comparison of the systemic exposure (AUC) at the no observed adverse effect level (NOAEL) to the 50% effective AUC or AUCeffective, the systemic exposure required for reducing levels of Aβ42 in rat brain by 50%.
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Affiliation(s)
- Kevin D Rynearson
- Department of Neurosciences, University of California, San Diego, La Jolla, CA
| | - Moorthi Ponnusamy
- Department of Molecular Medicine and Byrd Alzheimer's Institute, University of South Florida, Morsani College of Medicine, Tampa, FL
| | - Olga Prikhodko
- Department of Neurosciences, University of California, San Diego, La Jolla, CA
| | - Yuhuan Xie
- Department of Neurosciences, University of California, San Diego, La Jolla, CA
| | - Can Zhang
- Genetics and Aging Research Unit, Department of Neurology, Massachusetts General Hospital, Charlestown, MA
| | - Phuong Nguyen
- Department of Neurosciences, University of California, San Diego, La Jolla, CA
| | - Brenda Hug
- Veterans Administration San Diego Healthcare System, La Jolla, CA
| | - Mariko Sawa
- Department of Neurosciences, University of California, San Diego, La Jolla, CA
| | - Ann Becker
- Department of Neurosciences, University of California, San Diego, La Jolla, CA
| | - Brian Spencer
- Department of Neurosciences, University of California, San Diego, La Jolla, CA
| | - Jazmin Florio
- Department of Neurosciences, University of California, San Diego, La Jolla, CA
| | - Michael Mante
- Department of Neurosciences, University of California, San Diego, La Jolla, CA
| | - Bahar Salehi
- Department of Neurosciences, University of California, San Diego, La Jolla, CA
| | - Carlos Arias
- Department of Neurosciences, University of California, San Diego, La Jolla, CA
| | - Douglas Galasko
- Department of Neurosciences, University of California, San Diego, La Jolla, CA
| | - Brian P Head
- Veterans Administration San Diego Healthcare System, La Jolla, CA.,Department of Anesthesiology, University of California, San Diego, La Jolla, CA
| | | | | | - Steven K Duddy
- Integrated Nonclinical Development Solutions, Ann Arbor, MI
| | - Robert A Rissman
- Department of Neurosciences, University of California, San Diego, La Jolla, CA.,Veterans Administration San Diego Healthcare System, La Jolla, CA
| | - William C Mobley
- Department of Neurosciences, University of California, San Diego, La Jolla, CA
| | - Gopal Thinakaran
- Department of Molecular Medicine and Byrd Alzheimer's Institute, University of South Florida, Morsani College of Medicine, Tampa, FL
| | - Rudolph E Tanzi
- Genetics and Aging Research Unit, Department of Neurology, Massachusetts General Hospital, Charlestown, MA
| | - Steven L Wagner
- Department of Neurosciences, University of California, San Diego, La Jolla, CA.,Veterans Administration San Diego Healthcare System, La Jolla, CA
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Jia H, Wang Z, Zhang J, Feng F. γ-Secretase inhibitors for breast cancer and hepatocellular carcinoma: From mechanism to treatment. Life Sci 2021; 268:119007. [PMID: 33428878 DOI: 10.1016/j.lfs.2020.119007] [Citation(s) in RCA: 20] [Impact Index Per Article: 6.7] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/27/2020] [Revised: 12/22/2020] [Accepted: 12/28/2020] [Indexed: 12/21/2022]
Abstract
The γ-secretase complex is a key hydrolase for many type 1 transmembrane proteins. It is very important for activation of the Notch receptor and regulation of target-gene transcription. Abnormal activation and expression of the Notch pathway are closely related to the occurrence and development of many tumor types, including breast cancer and liver cancer. In this review, we elaborated on the basic situation of γ-secretase complex and the biological function and role of γ-secretase in APP and Notch signal pathway are described in detail. Subsequently, all currently known γ-secretase inhibitors and γ-secretase modulators are listed and their mechanism of action, value of IC50, chemical structure and current research stage are summarized. Next, the selection presented the treatment progress of γ-secretase inhibitors in breast cancer and hepatocellular carcinoma in the past five years. Finally, the mechanism of action of γ-secretase-mediated breast cancer and hepatocellular carcinoma and the advantages and disadvantages of γ-secretase inhibitors are discussed, and the concept of further research is proposed.
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Affiliation(s)
- Hui Jia
- Department of Pharmacy, General Hospital of Northern Theater Command, No. 83 Wenhua Road, Shenhe District, Shenyang City 110840, Liaoning Province, PR China; School of Traditional Chinese Materia Medica, Shenyang Pharmaceutical University, Shenyang 110006, PR China
| | - Zuojun Wang
- Department of Pharmacy, General Hospital of Northern Theater Command, No. 83 Wenhua Road, Shenhe District, Shenyang City 110840, Liaoning Province, PR China
| | - Jingyi Zhang
- Department of Pharmacy, General Hospital of Northern Theater Command, No. 83 Wenhua Road, Shenhe District, Shenyang City 110840, Liaoning Province, PR China.
| | - Fan Feng
- Center for Clinical Laboratory, The Fifth Medical Center, General Hospital of Chinese PLA, Beijing 100039, PR China.
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