EMAIL THIS PAGE TO A FRIEND

European journal of medicinal chemistry

Exploring QSAR and QAAR for inhibitors of cytochrome P450 2A6 and 2A5 enzymes using GFA and G/PLS techniques.


PMID 19110342

Abstract

A series of naphthalene and non-naphthalene derivatives (n=42) having cytochrome P450 2A6 and 2A5 inhibitory activities, reported by Rahnasto et al., were subjected to QSAR and QAAR studies. The analyses were performed using electronic, spatial, shape and thermodynamic descriptors to develop quantitative models for prediction of the inhibitory activities and to explore importance of different descriptors for the responses. The data set was divided into training and test sets (with test set size being approximately 25% of the full data set size) based on K-means clustering applied on the standardized descriptor matrix. Genetic function approximation (GFA) and genetic partial least-squares (G/PLS) were used as chemometric tools for modeling, and the derived equations were of acceptable statistical and prediction (both internal and external) qualities although different equations varied in quality in a wide range (R(2): 0.561-0.898, R(a)(2): 0.508-0.870, Q(2): 0.495-0.814, R(pred)(2): 0.615-0.914, r(2): 0.679-0.905, r(0)(2): 0.639-0.904, r(m)(2): 0.494-0.876). In the case of CYP2A5 inhibition, the GFA derived QSAR model is better than the G/PLS derived model considering both internal and external validations. In the case of CYP2A6 inhibitory potency data, the GFA derived QSAR model is better than the G/PLS model considering internal validation whereas the latter is better in external validation (which is more important) than the former. The model development process was subjected to randomization test at 90% confidence level by taking into account the whole pool of descriptors, while the developed models were also subjected to randomization test (99% confidence level) for validation. Based on the randomization test results, GFA models are found to be superior to the G/PLS models. Among the parameters, which were found important in modeling both the responses, were different Jurs descriptors, electronic descriptors (like Sr, Apol), steric descriptors (like shadow indices, Molref), shape descriptors (like COSV, Fo) and lipophilicity descriptors. This indicates that the CYP2A5 and CYP2A6 inhibition of these compounds is related to charge distribution, surface area, electronic, hydrophobic and spatial properties of the molecules.

Related Materials

Product #

Image

Description

Molecular Formula

Add to Cart

360740
1,2-Dimethylnaphthalene, 95%
C12H12
D170208
1,3-Dimethylnaphthalene, 96%
C12H12
33990
1,4-Dibromobenzene, purum, ≥97.0% (GC)
C6H4Br2
D39029
1,4-Dibromobenzene, 98%
C6H4Br2
35775
1,4-Dichlorobenzene, PESTANAL®, analytical standard
C6H4Cl2
D56829
1,4-Dichlorobenzene, ≥99%
C6H4Cl2
250937
1,6-Dimethylnaphthalene, 99%
C12H12
185752
1-Chloronaphthalene, technical grade
C10H7Cl
264938
1-Methylisoquinoline, 97%
C10H9N
W319309
1-Methylnaphthalene, ≥95%
C11H10
M56808
1-Methylnaphthalene, 95%
C11H10
45795
1-Methylnaphthalene, analytical standard
C11H10
70440
1-Naphthol, puriss., for fluorescence, ≥99.0% (GC)
C10H8O
126535
2,6-Dimethylnaphthalene, 99%
C12H12
279919
2,7-Dimethylnaphthalene, 99%
C12H12
183644
2-Bromonaphthalene, 97%
C10H7Br
E40005
2-Ethylnaphthalene, ≥99%
C12H12
148245
2-Methoxynaphthalene, 99%
C11H10O
45796
2-Methylnaphthalene, analytical standard
C11H10
442359
2-Methylnaphthalene, analytical standard
C11H10
M57006
2-Methylnaphthalene (β), 97%
C11H10
70448
2-Naphthol, BioXtra, ≥99.0% (GC)
C10H8O
70450
2-Naphthol, fluorescence indicator, ≥99.0%
C10H8O
70452
2-Naphthol, purum, ≥98.0% (GC)
C10H8O
185507
2-Naphthol, 99%
C10H8O
130109
2-Naphthol, 98%
C10H8O
129895
3-Methylisoquinoline, 98%
C10H9N
T19003
α-Tetralone, 97%
C10H10O
184500
Naphthalene, suitable for scintillation, ≥99%
C10H8
147141
Naphthalene, 99%
C10H8