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In silico analysis of potential inhibitors for breast cancer targeting 17beta-hydroxysteroid dehydrogenase type 1
Md Rezaul Islam1, Jehad Zuhair Tayyeb2, Hridoy Kumar Paul3
1Department of Pharmacy, Faculty of Allied Health Sciences, Daffodil International University, Dhaka, Bangladesh.
Abstract:
Breast cancer (BC) is still one of the major issues in world health, especially for women, which necessitates innovative therapeutic strategies. In this study, we investigated the efficacy of retinoic acid derivatives as inhibitors of 17beta-hydroxysteroid dehydrogenase type 1 (17beta-HSD1), which plays a crucial role in the biosynthesis and metabolism of oestrogen and thereby influences the progression of BC and, the main objective of this investigation is to identify the possible drug candidate against BC through computational drug design approach including PASS prediction, molecular docking, ADMET profiling, molecular dynamics simulations (MD) and density functional theory (DFT) calculations. The result has reported that total eight derivatives with high binding affinity and promising pharmacokinetic properties among 115 derivatives. In particular, ligands 04 and 07 exhibited a higher binding affinity with values of -9.9 kcal/mol and -9.1 kcal/mol, respectively, than the standard drug epirubicin hydrochloride, which had a binding affinity of -8.2 kcal/mol. The stability of the ligand-protein complexes was further confirmed by MD simulations over a 100-ns trajectory, which included assessments of hydrogen bonds, root mean square deviation (RMSD), root mean square Fluctuation (RMSF), dynamic cross-correlation matric (DCCM) and principal component analysis. The study emphasizes the need for experimental validation to confirm the therapeutic utility of these compounds. This study enhances the computational search for new BC drugs and establishes a solid foundation for subsequent experimental and clinical research.
Insights
Computational drug design identified novel retinoic acid derivatives as potent inhibitors of 17beta-hydroxysteroid dehydrogenase type 1 (17beta-HSD1), offering new therapeutic strategies for breast cancer (BC). Ligands 04 and 07 showed superior binding affinity, paving the way for further experimental validation.
Area of Science:
- Computational chemistry and drug discovery
- Oncology and pharmaceutical sciences
- Molecular biology and biochemistry
Background:
- Breast cancer (BC) remains a significant global health challenge, necessitating novel therapeutic approaches.
- 17beta-hydroxysteroid dehydrogenase type 1 (17beta-HSD1) is a key enzyme in estrogen metabolism, influencing BC progression.
- Retinoic acid derivatives are explored for their potential as 17beta-HSD1 inhibitors.
Purpose of the Study:
- To identify potential drug candidates against breast cancer using a computational drug design approach.
- To evaluate the efficacy of retinoic acid derivatives as inhibitors of 17beta-HSD1.
- To assess the binding affinity and pharmacokinetic properties of novel compounds.
Main Methods:
- Computational drug design including PASS prediction, molecular docking, ADMET profiling, molecular dynamics (MD) simulations, and density functional theory (DFT) calculations.
- Screening of 115 retinoic acid derivatives against 17beta-HSD1.
- Analysis of ligand-protein complex stability using MD simulations (100-ns trajectory).
Main Results:
- Eight retinoic acid derivatives demonstrated high binding affinity and favorable pharmacokinetic properties.
- Ligands 04 and 07 exhibited superior binding affinities (-9.9 and -9.1 kcal/mol) compared to epirubicin hydrochloride (-8.2 kcal/mol).
- MD simulations confirmed the stability of ligand-protein complexes, with detailed analyses of hydrogen bonds, RMSD, RMSF, DCCM, and PCA.
Conclusions:
- The study identified promising retinoic acid derivatives as potential drug candidates for breast cancer treatment.
- These compounds show significant potential as 17beta-HSD1 inhibitors, warranting further investigation.
- The findings provide a strong computational foundation for subsequent experimental validation and clinical research.
