Group- based quantitative structural activity relationship analysis of B-cell Lymphoma Extra Large (BCL-XL) inhibitors
B-cell Lymphoma Extra Large (Bcl-xL) belongs to B-cell Lymphoma two (Bcl-2) family and owing to its anti-apoptotic role in many cancers, is proven to be an attractive target for anti-cancer therapy. Different classes of potent anti-Bcl-xL small molecules inhibitors have been discovered, and both t...
Saved in:
| Main Authors: | , , , |
|---|---|
| Format: | Article |
| Language: | English English |
| Published: |
International Journal of Pharmacy and Pharmaceutical Science
2014
|
| Subjects: | |
| Online Access: | http://irep.iium.edu.my/41552/1/ijpps_nadia_gp_based_qsar_bcl.pdf http://irep.iium.edu.my/41552/4/41552_Group-%20based%20quantitative_scopus.pdf http://irep.iium.edu.my/41552/ http://www.ijppsjournal.com/ |
| Tags: |
Add Tag
No Tags, Be the first to tag this record!
|
| Institution: | Universiti Islam Antarabangsa Malaysia |
| Language: | English English |
| Summary: | B-cell Lymphoma Extra Large (Bcl-xL) belongs to B-cell Lymphoma two (Bcl-2) family and owing to its anti-apoptotic role in many cancers, is
proven to be an attractive target for anti-cancer therapy. Different classes of potent anti-Bcl-xL small molecules inhibitors have been discovered,
and both three-dimensional (3D) and two-dimensional (2D) Quantitative Structural Activity Relationship (QSAR) approaches have been used to
study and predict the biological activities of new inhibitors prior to their synthesis.
Objectives: This study was aimed to generate new candidate small inhibitory molecules against Bcl-xL by using G-QSAR analysis of known Bcl-xL inhibitors.
Methods: In the present study, we used group-based QSAR (G-QSAR)—a novel fragment-based method—to develop QSAR models from known BclxL
inhibitors. A set of Bcl-xL inhibitors adopted from extant literature was fragmented into three common fragments, and a pool of 214 descriptors
was calculated for each one.
Results: Two models were obtained by using different combination of variable selection and model building method; stepwise-multiple linear
regression (STP-MLR) and simulated annealing-multiple linear regression (SA-MLR). STP-MLR was found to be the best mode, with r2 = 0.80, q2 =
0.70 and predictive r2 = 0.87.
Conclusion: The G-QSAR results indicate that the generated models are statistically significant and can be used for design and generation of new
potent inhibitors. |
|---|