Multiple machine learning, molecular docking, and ADMET screening approach for identification of selective inhibitors...

Area of Science:

• Biochemistry
• Pharmacology
• Computational Chemistry

Background:

• Cytochrome P4501B1 (CYP1B1) is overexpressed in tumors and inactivates anti-cancer drugs, leading to chemotherapy resistance.
• Identifying selective CYP1B1 inhibitors is crucial for overcoming this drug resistance.

Purpose of the Study:

• To identify selective inhibitors of Cytochrome P4501B1 (CYP1B1) using integrated in-silico approaches.
• To find novel compounds that can overcome drug resistance in cancer treatment.

Main Methods:

• Development of machine learning models for CYP1A1 and CYP1B1 isoforms.
• Screening of small molecule and natural compound databases.
• Molecular docking, ADMET analysis, and molecular dynamics simulations for selectivity and stability assessment.

Main Results:

• Two novel compounds, CYP-D9 and CYP-14, were identified as highly selective CYP1B1 inhibitors.
• These compounds demonstrated stability and key interactions within the CYP1B1 active site.
• The identified inhibitors show potential for overcoming chemotherapy resistance and are deemed safe for further preclinical studies.

Conclusions:

• Novel selective CYP1B1 inhibitors (CYP-D9 and CYP-14) were identified using in-silico methods.
• These compounds offer a promising strategy to address drug resistance in cancer therapy.
• The identified inhibitors are suitable for subsequent cell-based and animal model investigations.