3D-QSAR and docking studies on ursolic acid derivatives for anticancer activity based on bladder cell line T24...

Area of Science:

• Medicinal Chemistry
• Computational Chemistry
• Oncology

Background:

• Bladder cancer poses a significant global health challenge, demanding novel therapeutic strategies due to existing drug limitations and emerging resistance.
• Current treatments for bladder cancer face challenges including side effects, toxicity, and the development of drug resistance in cancer cells.
• There is a critical need for the development of new, effective, affordable, and safer anticancer drugs.

Purpose of the Study:

• To develop and apply a three-dimensional quantitative structure-activity relationship (3D-QSAR) model for predicting novel anticancer agents against human bladder cancer cell line T24.
• To elucidate the structure-activity relationships of terpenes and their derivatives at a molecular level for anticancer activity.
• To identify potential lead compounds for bladder cancer drug discovery through computational modeling.

Main Methods:

• Development of a 3D-QSAR model, specifically Comparative Molecular Field Analysis (CoMFA), using T24 bladder cancer cell line in vitro data.
• Prediction of natural compounds and analogs based on the derived QSAR model, assessing structural similarity to active compounds.
• Evaluation of predicted compounds using Lipinski's rule of five, absorption, distribution, metabolism, and excretion (ADME), and toxicity filters.
• Prioritization of candidate compounds through molecular docking studies against identified anticancer targets.

Main Results:

• A CoMFA model was successfully developed with a cross-validation coefficient (q²) of 0.54 and a regression coefficient (r²) of 0.86, indicating good predictive power.
• The model effectively described structure-activity relationships for terpenes and their derivatives, aiding in the design of new anticancer agents.
• Two compounds, T9 and B42, were identified as top candidates, meeting pharmacokinetic and toxicity criteria and exhibiting favorable binding affinities.
• Molecular docking studies confirmed the binding potential of T9 and B42 to identified anticancer targets.

Conclusions:

• The developed 3D-QSAR model provides a reliable framework for predicting the anticancer activity of novel compounds against bladder cancer.
• Compounds T9 and B42 emerged as promising lead candidates for further preclinical development in bladder cancer therapy.
• The computational strategy employed is valuable for accelerating the identification and optimization of drug leads in early-stage drug discovery.