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      Marine-derived sea urchin compounds as potential anti-cancer drug candidate against colorectal cancer: In silico and in vitro studies

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          Abstract

          Sea urchin-derived compounds are potential candidates for the development of effective drugs for the treatment of cancer diseases. In this study, 19 compounds derived from sea urchin ( Diadema savignyi) were used to treat colorectal cancer using the HCT116 cell line. However, molecular docking, ADME (absorption, distribution, metabolism, and excretion), toxicity, molecular dynamic (MD) simulation, and molecular mechanics generalized Born surface area (MM-GBSA) were used to confirm the ligand–protein interaction. Interactions of Importin-11 receptor with sea urchin compounds reveal that four compounds have higher binding affinities (ranging from -8.6 to -7.1 kcal/mol). In vitro testing revealed that the CID 6432458 compound was effective (docking score of −8.6 kcal/mol) against the HCT116 cell line. The cytotoxicity of HCT116 has been documented, with an IC50 value of 6.885 ± 4. MTT assay, apoptosis analysis, and cell cycle assay were utilized to examine cell death in colorectal cancer. In the MTT experiment, 15 µM and 20 µM dosages were associated with 77% cell death; however, flow cytometry analysis using the IC50 value revealed that the selected chemical induced greater apoptosis in the HCT116 cell line (58.5%). The gene expression data revealed that the apoptotic gene BAX is expressed at a higher level than the BCL-2 gene. The IPO11 gene was downregulated during treatment. In the experiment involving the cell cycle, the S phase for the 30  µM dose showed 75.1% apoptosis, which was greater than the other concentrations used alone. These in silico and in vitro analysis will not only provide new information about Importin-11 receptor and insight into colorectal cancer but will also facilitate the development of natural compounds in a significant and worthwhile manner.

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          Global colorectal cancer burden in 2020 and projections to 2040

          • There are estimated 1.93 million new CRC cases diagnosed, and 0.94 million CRC caused deaths in 2020 worldwide. • The global new CRC cases is predicted to reach 3.2 million in 2040. • China and the United States have the highest estimated number of new CRC cases in the next 20 years. • The number of new CRC cases is increased from 0.56 million (2020) to 0.91 million (2040) in China. • The number of new CRC cases is increased from 0.16 million (2020) to 0.21 million (2040) in the United States. As the third most common malignancy and the second most deadly cancer, colorectal cancer (CRC) induces estimated 1.9 million incidence cases and 0.9 million deaths worldwide in 2020. The incidence of CRC is higher in highly developed countries, and it is increasing in middle- and low-income countries due to westernization. Moreover, a rising incidence of early-onset CRC is also emerging. The large number of CRC cases poses a growing global public health challenge. Raising awareness of CRC is important to promote healthy lifestyle choices, novel strategies for CRC management, and implementation of global screening programs, which are critical to reducing CRC morbidity and mortality in the future. CRC is a heterogeneous disease, and its subtype affiliation influences prognosis and therapeutic response. An accurate CRC subtype classification system is of great significance for basic research and clinical outcome. Here, we present the global epidemiology of CRC in 2020 and projections for 2040, review the major CRC subtypes to better understand CRC molecular basis, and summarize current risk factors, prevention, and screening strategies for CRC.
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            ProTox-II: a webserver for the prediction of toxicity of chemicals

            Abstract Advancement in the field of computational research has made it possible for the in silico methods to offer significant benefits to both regulatory needs and requirements for risk assessments, and pharmaceutical industry to assess the safety profile of a chemical. Here, we present ProTox-II that incorporates molecular similarity, pharmacophores, fragment propensities and machine-learning models for the prediction of various toxicity endpoints; such as acute toxicity, hepatotoxicity, cytotoxicity, carcinogenicity, mutagenicity, immunotoxicity, adverse outcomes pathways (Tox21) and toxicity targets. The predictive models are built on data from both in vitro assays (e.g. Tox21 assays, Ames bacterial mutation assays, hepG2 cytotoxicity assays, Immunotoxicity assays) and in vivo cases (e.g. carcinogenicity, hepatotoxicity). The models have been validated on independent external sets and have shown strong performance. ProTox-II provides a freely available webserver for in silico toxicity prediction for toxicologists, regulatory agencies, computational and medicinal chemists, and all users without login at http://tox.charite.de/protox_II. The webserver takes a two-dimensional chemical structure as an input and reports the possible toxicity profile of the chemical for 33 models with confidence scores, and an overall toxicity radar chart along with three most similar compounds with known acute toxicity.
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              Computational methods in drug discovery.

              Computer-aided drug discovery/design methods have played a major role in the development of therapeutically important small molecules for over three decades. These methods are broadly classified as either structure-based or ligand-based methods. Structure-based methods are in principle analogous to high-throughput screening in that both target and ligand structure information is imperative. Structure-based approaches include ligand docking, pharmacophore, and ligand design methods. The article discusses theory behind the most important methods and recent successful applications. Ligand-based methods use only ligand information for predicting activity depending on its similarity/dissimilarity to previously known active ligands. We review widely used ligand-based methods such as ligand-based pharmacophores, molecular descriptors, and quantitative structure-activity relationships. In addition, important tools such as target/ligand data bases, homology modeling, ligand fingerprint methods, etc., necessary for successful implementation of various computer-aided drug discovery/design methods in a drug discovery campaign are discussed. Finally, computational methods for toxicity prediction and optimization for favorable physiologic properties are discussed with successful examples from literature.
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                Author and article information

                Contributors
                Journal
                Saudi J Biol Sci
                Saudi J Biol Sci
                Saudi Journal of Biological Sciences
                Elsevier
                1319-562X
                2213-7106
                28 July 2023
                September 2023
                28 July 2023
                : 30
                : 9
                : 103748
                Affiliations
                Department of Biological Sciences, Faculty of Science, King Abdulaziz University, P.O. Box 80203, Jeddah 21598, Saudi Arabia
                Author notes
                [* ]Corresponding author at: King Abdulaziz University, Faculty of Science Biological Sciences Department, P.O. Box 80203, Jeddah 21589, Saudi Arabia. moaljahdali@ 123456kau.edu.sa
                Article
                S1319-562X(23)00193-6 103748
                10.1016/j.sjbs.2023.103748
                10407901
                37560480
                7df3d64d-d84a-4e3e-9205-0c6d62ae4ac6
                © 2023 The Author(s)

                This is an open access article under the CC BY-NC-ND license (http://creativecommons.org/licenses/by-nc-nd/4.0/).

                History
                : 3 June 2023
                : 16 July 2023
                : 22 July 2023
                Categories
                Original Article

                sea urchin,gc–ms,molecular docking,md simulation,mm/gbsa,ipo11,colorectal cancer

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