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3-D QSAR studies on maslinic acid analogs
This document presents research on developing a 3D-QSAR model to identify potential anticancer compounds. It describes collecting data on maslinic acid analogs, generating their conformations and pharmacophores, developing a 3D-QSAR model with r2=0.92 and q2=0.75, and validating the model. The model identified compound P-902 as a best fit maslinic acid analog predicted to be cytotoxic against breast cancer cells through ligand-based virtual screening, ADMET analysis, and docking studies targeting the glucocorticoid receptor NR3C1.
3-D QSAR studies on maslinic acid analogs
- 1. 1 Alam S. &Khan F., Scientific Reports , 2017, 7, xx Presented By Neha Dhiman M.S. (Pharm.) 2nd sem PI/336 NIPER , HAJIPUR
- 2. 2 INTRODUCTION MATERIALS AND METHODSUSED FOR 3D-STRUCTURE ANALOGS RESULTS CONCLUSION FLOW OF PRESENTATION
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- 5. 5 Breast Cancer Anticancer Drugs Natural Anticancer Drugs Indoles,Isoflavones, & Resveratrol etc Drug Resistance RATIONALE BEHIND THIS RESEARCH
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- 7. 7 Data Collection AndStructure Preparation Conformation Hunt And Pharmacophore Generation Compound Alignment And 3D QSAR Model Development Validation of the QSAR Model Parameters for QSAR model development
- 8. 8 Steps Involved Visualization ofSAR Activity-Atlas models Field pattern contribution to the predicted activity Lipinski’s Rule of five and ADMET Target identification, Docking In silico PK/PD evaluation
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- 10. 10 Prior Reviews and Literatures Trainingdatasets of compound ChemBio 3D 2-D 3-D
- 11. 11 Negative electrostatic potential Hydrophobicity Positiveelectrostatic potential Vander Waals Descriptors
- 12. 12 High active Lowactive
- 13. 13 r² = 0.92q²= 0.75 Regression method Cross validation method
- 14. 14 (A) Field pointsin red color region of a strong effect on higher activity (B) High electrostatic variance and high steric variance field points represent the region of high changes and points with low variance indicates the fields in that region with less or no changes.
- 15. 15 The red colorcircle : Electrostatics field points, controlling the decrease in predicted activity The purple color triangle : Steric field points, controlling the increase in predicted activity Blue color square : Electrostatics field points, controlling the increase in predicted activity
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- 17. Ligand-based virtual screeningfor hits prediction Lipinski’s rule of five of oral bioavailability Virtually screened through ADMET parameters The lower ADMET risk score is a mostly preferable step in drug discovery process. 17
- 18. 18 Control co- crystallised inhibitorCV-7 docked with NR3C1. Predicted best hit maslinic acid analogs well docked with NR3C1.
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- 20. Glucocorticoid receptor. Foundin Cytoplasm, but transported to nucleus after ligand binding. 20 NR3C1 Binding Target Reported to have a role in promoting cancer cell survival & induce chemoresistance in breast cancer patients.
- 21. 21 P-902 best Fitcompound
- 22. 22 The compoundP-902 was slightly lipophilic in nature. Thus its renal clearance will decrease while metabolic clearance may increase. Absorption P-450 Oxidation Mutage nicity Toxicity ADME T risk Risk parameters Risk Range 0-8 0-6 0-4 0-7 0-24 P-902 3.26 0.0 0.0 0.96 4.22 Size, charge, water solubility, lipophilicity Topotecan 0.0 0.0 2.0 2.0 2.0 Hepatotoxicity
- 23. 23 Properties P-902 Topotecan Max.recommended therapeutic dose orally Below 3.16 mg Above 3.16 mg Estrogen receptor Non-toxic Toxic Measure of estrogen receptor toxicity 0 4.3531 Triggering mutagenic chromosomal abbreviations Non-toxic Toxic
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- 25. P-902 top mosthit compound Compounds P-902 and P-701 best fit against NR3C1 Docking 39 top hits Screening 74 Have similar pharmacophore features 593 prediction set compounds 25
- 26. 26 Hence Compound P-902 wasfound best fit maslinic acid analog clearing all filters and predicted to be cytotoxic against human breast cancer cell line MCF7.
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