In Silico Evaluation of Approved Drug Library to Discover Tyrosine Threonine Kinase Inhibitors for Colon Cancer

Abstract One of the most common cancers that kill cancer patients globally is colon cancer (CC), a malignant tumor of the gastrointestinal system. Most anti‐cancer medications on the market today have serious adverse effects, and prolonged use of these medications can result in issues including multidrug resistance and reduced drug absorption, which raises the risk of cancer recurrence. Therefore, the main goal of this research was to discover anti‐CC against threonine tyrosine kinase (TTK) kinase from the authorized drug library utilizing in silico methods, including molecular docking, molecular dynamic (MD) simulation, MM/GBSA, and principal component analysis (PCA) techniques. Luvixasertib is a TTK inhibitor, and the therapeutic agents of Luvixasertib analogues from the approved drug library were tested for their ability to inhibit TTK. According to the molecular docking results, two drugs (Elbasvir and Venetoclax) were shown to be potent TTK inhibitors when compared to the binding energy of Luvixasertib (−8.8 kcal/mol). Elbasvir and Venetoclax showed binding energies of −8.9 and −9.4 kcal/mol, as well as binding free energies of −76.51 ± 5.95 and −59.39 ± 6.07 kcal/mol, respectively. Furthermore, MD simulation trajectories were analyzed using several parameters, including RMSD, RMSF, RoG, SASA, and HB, and based on the results, we determined that Elbasvir and Venetoclax appear to be interesting potential TTK inhibitors. In vitro, in vivo, and clinical studies of Elbasvir and Venetoclax may open the door to their potential use in the prevention of CC.

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Publication Details

Journal
ChemistrySelect
Published
2025-05-01
DOI
https://doi.org/10.1002/slct.202500093
Citations
2
Primary Topic
Computational Drug Discovery Methods
Type
article
Field-Weighted Citation Impact
3.01
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article

In Silico Evaluation of Approved Drug Library to Discover Tyrosine Threonine Kinase Inhibitors for Colon Cancer

Anke Wilhelm, Sabiha Fatima, Abdul Rashid Issahaku, Mohd Fadhlizil Fasihi Mohd Aluwi et al.
2 citations
ChemistrySelect
Computational Drug Discovery Methods
3.01
article

In Silico Evaluation of Approved Drug Library to Discover Tyrosine Threonine Kinase Inhibitors for Colon Cancer

Anke Wilhelm, Sabiha Fatima, Abdul Rashid Issahaku, Mohd Fadhlizil Fasihi Mohd Aluwi, Asrar Ahmad, Miah Roney, Azmat Ali Khan, S. M. Istiaque Hamim
article en
2 citations

Abstract

Abstract One of the most common cancers that kill cancer patients globally is colon cancer (CC), a malignant tumor of the gastrointestinal system. Most anti‐cancer medications on the market today have serious adverse effects, and prolonged use of these medications can result in issues including multidrug resistance and reduced drug absorption, which raises the risk of cancer recurrence. Therefore, the main goal of this research was to discover anti‐CC against threonine tyrosine kinase (TTK) kinase from the authorized drug library utilizing in silico methods, including molecular docking, molecular dynamic (MD) simulation, MM/GBSA, and principal component analysis (PCA) techniques. Luvixasertib is a TTK inhibitor, and the therapeutic agents of Luvixasertib analogues from the approved drug library were tested for their ability to inhibit TTK. According to the molecular docking results, two drugs (Elbasvir and Venetoclax) were shown to be potent TTK inhibitors when compared to the binding energy of Luvixasertib (−8.8 kcal/mol). Elbasvir and Venetoclax showed binding energies of −8.9 and −9.4 kcal/mol, as well as binding free energies of −76.51 ± 5.95 and −59.39 ± 6.07 kcal/mol, respectively. Furthermore, MD simulation trajectories were analyzed using several parameters, including RMSD, RMSF, RoG, SASA, and HB, and based on the results, we determined that Elbasvir and Venetoclax appear to be interesting potential TTK inhibitors. In vitro, in vivo, and clinical studies of Elbasvir and Venetoclax may open the door to their potential use in the prevention of CC.

ChemistrySelectVol. 10(17)
Universiti Malaysia Pahang Al-Sultan Abdullah (MY), Howard University (US), University of the Free State (ZA), King Saud University (SA)
Openalex Percentile: Top 9%
Computational Drug Discovery Methods
3.01
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