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Updated: May 22, 2026

A Three-Dimensional Spheroid Model to Investigate the Tumor-Stromal Interaction in Hepatocellular Carcinoma
Published on: September 30, 2021
Rational drug design for identifying novel multi-target inhibitors for hepatocellular carcinoma
Ahmed Temirak1, Mona Abdulla, Mahmoud Elhefnawi
1Biomedical Informatics and Chemoinformatics Group, Centre of Excellence for Advanced Sciences, National Research Centre, Cairo, Egypt.
Abstract:
Hepatocellular carcinoma (HCC) is one of the hard-treating and high mortality cancers for which novel therapies are very much in need. Sorafenib is the first medication that is now approved for the treatment of patients with advanced HCC [1]. Sorafenib is a multikinase inhibitor targeting the Raf serine/ threonine kinases and the VEGFR1-3, PDGFR-b, c-Kit, Flt3 and p38 tyrosine kinases [1]. Here, an in silico approach was directed to identify novel multi-kinase inhibitors as potential candidate therapies for HCC. The Molecular Operating Environment (MOE) was used for docking studies, pharmacophore building and virtual screening of chemical molecules databases. The docking/scoring methods of MOE were validated by reproducing the docking interactions and poses of Sorafenib with smallest root mean square deviations. The three receptors for which multi-targeting compounds were screened for were: B-Raf, p38 and VEGFR-2 tyrosine kinases. After identifying the main binding sites of the target receptors, we started our studies by the docking of Sorafenib in comparison to tyrosine kinase inhibitors collected from the literature. A pharmacophore based on the SAR of Sorafenib was built using flexible alignment methods. Next, pharmacophore based virtual screening on four chemical molecules databases; Open NCI Database [2], Zinc [3], Maybridge [4] and drug bank [5] was done resulting in 2928 hit compounds that were subsequently subjected to filtration according to their binding free energies, interactions exhibited with the receptors, in silico ADMET properties and Lipinski's rule of five for molecule drugability [6]. Finally 7 compounds were selected as they exhibited excellent binding interactions with the receptors in addition to their high safety profile that are recommended for further development.
Insights
This study used computational methods to discover new multi-kinase inhibitors for hepatocellular carcinoma (HCC) treatment. Seven promising drug candidates were identified with strong binding and safety profiles for further development.
Area of Science:
- Medicinal Chemistry
- Computational Biology
- Oncology
Background:
- Hepatocellular carcinoma (HCC) presents a significant challenge due to high mortality and limited treatment options.
- Sorafenib, a multikinase inhibitor, is an approved therapy for advanced HCC, highlighting the potential of kinase inhibition.
Purpose of the Study:
- To identify novel multi-kinase inhibitors for hepatocellular carcinoma (HCC) using an in silico approach.
- To screen chemical databases for compounds targeting B-Raf, p38, and VEGFR-2 tyrosine kinases.
Main Methods:
- Utilized Molecular Operating Environment (MOE) for docking studies, pharmacophore building, and virtual screening.
- Validated docking methods by reproducing Sorafenib interactions.
- Employed pharmacophore-based virtual screening across multiple chemical databases.
Main Results:
- Screened 2928 hit compounds, filtering based on binding energy, receptor interactions, ADMET properties, and Lipinski's rule.
- Identified 7 compounds demonstrating excellent binding interactions with target kinases.
- Selected compounds exhibited a high safety profile, indicating potential drugability.
Conclusions:
- The in silico approach successfully identified potential novel multi-kinase inhibitors for HCC.
- The 7 selected compounds are recommended for further preclinical development as HCC therapeutics.
- This study provides a foundation for developing new targeted therapies against hepatocellular carcinoma.
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