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Predicting the Anticancer Activity of 2-alkoxycarbonylallyl Esters against MDA-MB-231 Breast Cancer - QSAR, Machine
Babatunde Samuel Obadawo1, Oluwatoba Emmanuel Oyeneyin2, Adesoji Alani Olanrewaju3
1Department of Chemistry, University of Toledo, Ohio, OH, USA.
Background:
The continuous increase in mortality of breast cancer and other forms of cancer due to the failure of current drugs, resistance, and associated side effects calls for the development of novel and potent drug candidates.
Methods:
In this study, we used the QSAR and extreme learning machine models in predicting the bioactivities of some 2-alkoxycarbonylallyl esters as potential drug candidates against MDA-MB-231 breast cancer. The lead candidates were docked at the active site of a carbonic anhydrase target.
Results:
The QSAR model of choice satisfied the recommended values and was statistically significant. The R2pred (0.6572) was credence to the predictability of the model. The extreme learning machine ELM-Sig model showed excellent performance superiority over other models against MDAMB- 231 breast cancer. Compound 22 with a docking score of 4.67 kcal mol-1 displayed better inhibition of the carbonic anhydrase protein, interacting through its carbonyl bonds.
Conclusion:
The extreme learning machine's ELM-Sig model showed excellent performance superiority over other models and should be exploited in the search for novel anticancer drugs.
Insights
Novel drug candidates were identified using quantitative structure-activity relationship (QSAR) and extreme learning machine (ELM) models for breast cancer treatment. Compound 22 showed promising inhibition against carbonic anhydrase, a key cancer target.
Area of Science:
- Medicinal Chemistry
- Computational Drug Discovery
- Oncology
Background:
- Rising cancer mortality necessitates new therapeutic agents.
- Current cancer drugs face challenges with resistance and side effects.
- Development of novel, potent drug candidates is crucial.
Purpose of the Study:
- To predict the bioactivity of 2-alkoxycarbonylallyl esters against MDA-MB-231 breast cancer cells.
- To identify potential drug candidates using computational modeling.
- To evaluate lead compounds via molecular docking against a carbonic anhydrase target.
Main Methods:
- Quantitative Structure-Activity Relationship (QSAR) modeling was employed.
- Extreme Learning Machine (ELM) models, specifically ELM-Sig, were utilized for prediction.
- Molecular docking simulations were performed on the carbonic anhydrase target.
Main Results:
- The QSAR model demonstrated statistical significance and good predictability (R 2 pred = 0.6572).
- The ELM-Sig model exhibited superior performance in predicting activity against MDA-MB-231 breast cancer.
- Compound 22 exhibited significant inhibition of carbonic anhydrase, with a docking score of 4.67 kcal mol -1 , interacting via carbonyl bonds.
Conclusions:
- The ELM-Sig model is a powerful tool for discovering novel anticancer drugs.
- Compound 22 shows potential as a lead candidate for breast cancer therapy.
- Further investigation into ELM-Sig models is recommended for drug discovery efforts.
