Casein kinase II (CK2) is an intensively studied enzyme, involved in different diseases, cancer in particular. Different scaffolds were used to develop inhibitors of this enzyme. Here, we report on the synthesis and biological evaluation of twenty phenolic, ketonic, and -quinonic indeno[1,2-]indole derivatives as CK2 inhibitors. The most active compounds were 5-isopropyl-1-methyl-5,6,7,8-tetrahydroindeno[1,2-]indole-9,10-dione and 1,3-dibromo-5-isopropyl-5,6,7,8-tetrahydroindeno[1,2-]indole-9,10-dione with identical IC values of 0.11 µM. Furthermore, the development of a QSAR model based on the structure of indeno[1,2-]indoles was performed. This model was used to predict the activity of 25 compounds with naphtho[2,3-]furan-4,9-dione derivatives, which were previously predicted as CK2 inhibitors via a molecular modeling approach. The activities of four naphtho[2,3-]furan-4,9-dione derivatives were determined in vitro and one of them (-isopentyl-2-methyl-4,9-dioxo-4,9-dihydronaphtho[2,3-]furan-3-carboxamide) turned out to inhibit CK2 with an IC value of 2.33 µM. All four candidates were able to reduce the cell viability by more than 60% after 24 h of incubation using 10 µM.

Download full-text PDF

Source
http://www.ncbi.nlm.nih.gov/pmc/articles/PMC6982966PMC
http://dx.doi.org/10.3390/molecules25010097DOI Listing

Publication Analysis

Top Keywords

qsar model
8
indeno[12-]indole derivatives
8
ck2 inhibitors
8
naphtho[23-]furan-49-dione derivatives
8
ck2
5
model indeno[12-]indole
4
derivatives
4
derivatives identification
4
identification -isopentyl-2-methyl-49-dioxo-49-dihydronaphtho[23-]furan-3-carboxamide
4
-isopentyl-2-methyl-49-dioxo-49-dihydronaphtho[23-]furan-3-carboxamide potent
4

Similar Publications

Comprehensive benchmarking of computational tools for predicting toxicokinetic and physicochemical properties of chemicals.

J Cheminform

December 2024

Laboratory of Environmental Chemistry and Toxicology, Department of Environmental Health Sciences, Istituto di Ricerche Farmacologiche Mario Negri IRCCS, Milan, Italy.

Ensuring the safety of chemicals for environmental and human health involves assessing physicochemical (PC) and toxicokinetic (TK) properties, which are crucial for absorption, distribution, metabolism, excretion, and toxicity (ADMET). Computational methods play a vital role in predicting these properties, given the current trends in reducing experimental approaches, especially those that involve animal experimentation. In the present manuscript, twelve software tools implementing Quantitative Structure-Activity Relationship (QSAR) models were selected for the prediction of 17 relevant PC and TK properties.

View Article and Find Full Text PDF

Intelligent consensus-based predictions of early life stage toxicity in fish tested in compliance with OECD Test Guideline 210.

Aquat Toxicol

December 2024

Drug Theoretics and Cheminformatics Laboratory, Department of Pharmaceutical Technology, Jadavpur University, 188 Raja S C Mullick Road, 700032, Kolkata, India. Electronic address:

Early life stage (ELS) toxicity testing in fish is a crucial test procedure used to evaluate the long-term effects of a wide range of chemicals, including pesticides, industrial chemicals, pharmaceuticals, and food additives. This test is particularly important for screening and prioritizing thousands of chemicals under the Registration, Evaluation, Authorization, and Restriction of Chemicals (REACH) regulation. In silico methods can be used to estimate the toxicity of a chemical when no experimental data is available and to reduce the cost, time, and resources involved in the experimentation process.

View Article and Find Full Text PDF

Evaluation of Machine Learning Based QSAR Models for the Classification of Lung Surfactant Inhibitors.

Environ Health (Wash)

December 2024

Department of Environmental Science, Baylor University, Waco, Texas 76798-7266, United States.

Inhaled chemicals can cause dysfunction in the lung surfactant, a protein-lipid complex with critical biophysical and biochemical functions. This inhibition has many structure-related and dose-dependent mechanisms, making hazard identification challenging. We developed quantitative structure-activity relationships for predicting lung surfactant inhibition using machine learning.

View Article and Find Full Text PDF

QSAR-guided strategy for accurate annotation of FAHFA regioisomers.

Talanta

December 2024

Department of Chemistry, Wuhan University, Wuhan, 430072, China; School of Bioengineering and Health, Wuhan Textile University, Wuhan, 430200, China; Frontier Science Center for Immunology and Metabolism, Wuhan University, Wuhan, 430071, China. Electronic address:

Branched fatty acid esters of hydroxy fatty acids (FAHFAs) represent a novel class of bioactive lipids with significant physiological roles. However, their identification, particularly of low-abundance FAHFA regioisomers, remains challenging due to their high structural similarity, low natural abundance, and the limited availability of reliable FAHFA standards. In this study, we present a QSAR-based FAHFA annotation strategy that integrates a QSAR model with an ester bond position (EP) rule to determine the EPs of FAHFA regioisomers.

View Article and Find Full Text PDF

Selective inhibition of histone deacetylase 8 (HDAC8) has emerged as a promising approach for treating various diseases, including cancer. However, finding key structural features for HDAC8 inhibition and developing effective and selective HDAC8 inhibitors (HDAC8s) pose significant challenges. In the past few years, the development of various scaffolds for inhibiting HDAC8 has significantly risen and the quest continues.

View Article and Find Full Text PDF

Want AI Summaries of new PubMed Abstracts delivered to your In-box?

Enter search terms and have AI summaries delivered each week - change queries or unsubscribe any time!