- Original
- Open Access
2D and 3D-QSAR study on 4-anilinoquinozaline derivatives as potent apoptosis inducer and efficacious anticancer agent
- Vivek Kumar Vyas^{1}Email author,
- Manjunath Ghate^{1} and
- Hitesh Katariya^{1}
https://doi.org/10.1186/2191-2858-1-13
© Vyas et al; licensee Springer. 2011
- Received: 26 May 2011
- Accepted: 4 October 2011
- Published: 4 October 2011
Abstract
Background
Apoptosis is known as programmed cell death that plays an important role in tumor biology.
Methods
In this study, apoptosis-inducing activity is predicted by using a QSAR modeling approach for a series of 4-anilinoquinozaline derivatives. 2D-QSAR model for the prediction of apoptosis-inducing activity was obtained by applying multiple linear regression giving r^{2} = 0.8225 and q^{2} = 0.7626, principal component regression giving r^{2} = 0.7539 and q^{2} = 0.6669 and partial least squares giving r^{2} = 0.8237 and q^{2} = 0.6224.
Results
QSAR study revealed that alignment-independent descriptors and distance-based topology index are the most important descriptors in predicting apoptosis-inducing activity. 3D-QSAR study was performed using k-nearest neighbor molecular field analysis (kNN-MFA) approach for both electrostatic and steric fields. Three different kNN-MFA 3D-QSAR methods (SW-FB, SA, and GA) were used for the development of models and tested successfully for internal (q^{2} > 0.62) and external (predictive r^{2} > 0.52) validation criteria. Thus, 3D-QSAR models showed that electrostatic effects dominantly determine the binding affinities.
Conclusions
The QSAR models developed in this study would be useful for the development of new apoptosis inducer as anticancer agents.
Keywords
- 4-anilinoquinozaline
- apoptosis inducer
- anticancer
- QSAR
- multiple linear regression (MLR)
- principle component analysis (PCA)
- partial least square (PLS)
- k-nearest neighbor molecular field analysis (kNN-MFA)
1. Introduction
Cancer is a disease of cell characterized by progressive, persistent, abnormal, purposeless, and uncontrolled proliferation of tissues. Currently, cancer is most dominating cause of death in world [1, 2]. Apoptosis is a cellular process, that organisms use for digestion of excessive cells to control cell numbers. Caspases is a family of cysteine proteases, which are produced as zymogenes, plays a vital role in the beginning and ending of apoptosis [3]. Small drug molecules can inhibit or activate caspases [4]. In apoptosis, cell shrinks, deformed, and looses its contacts to neighboring cells that resulted into fragmented cells called "apoptotic bodies" [5]. Human body removes apoptotic bodies without causing any inflammatory response. Process of apoptosis is controlled by a diverse range of cell signals [6]. In humans during cell development, many cells are produced by mitosis in excess which eventually undergo programmed cell death and their by contribute to sculpturing many organs and tissues [7]. Apoptosis is an essential process for the maintenance of tissue homeostasis. During the last decade, a number of compounds are identified to induce apoptosis. Compounds that promote apoptosis are considered as important medicaments for the treatment of cancer. Anticancer efficacy of many therapeutic agents is correlated to their apoptosis inducer ability, so identification of apoptosis-inducing activity plays an important role in discovery and development of potential anticancer agent [8]. Many anticancer drugs like camptothecins, such as topotecan and irinotecan, [9] and vinca alkaloids, such as vincristine and vinblastine [10], kill tumors to some extent through induction of apoptosis [11]. Recently, extensive advances are achieved in the field of apoptosis-based therapeutics. Many new drug candidates are currently being developed and most of them are in clinical state as potential apoptosis inducer agents. In an effort for search of new potent apoptosis-inducing agents, we have performed 2D- and 3D-QSAR study on 4-anilinoquinozaline derivatives for quantifying the necessary structural and physicochemical requirements of this series of compounds as potent apoptosis inducer and efficacious anticancer agents.
2. Materials and methods
QSAR is the study of the quantitative relationship between the experimental activity of a set of compounds and their physicochemical properties using statistical methods. The experimental information associated with biological activity, which is used as dependent variables in building a QSAR model. In this study, all computational work (2D- and 3D-QSAR) was performed using Vlife MDS QSAR plus software on a HP computer with Core2 Duo processor and a window XP operating system.
2.1 2D-QSAR modeling and dataset
Structure, experimental, and predicted activity of 4-anilinoquinozaline derivatives
| ||||||||||||||||
---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
S. no. | R | R_{1} | R_{2} | R_{3} | R_{4} | A | B | D | EC_{50} (μm)^{a} | ExperimentalpEC_{50} ^{b} | Predicted pEC_{50} | |||||
MLR | PCR | PLS | SW-FB | GA | SA | |||||||||||
1 | H | Me | Cl | H | OMe | C | C | C | 0.002 | 2.699 | 2.695 | 1.215 | 2.496 | 2.197 | 2.196 | 1.838 |
2 | H | Me | OMe | H | OMe | C | C | C | 0.004 | 2.398 | 2.386 | 1.684 | 2.083 | 2.197 | 2.347 | 2.071 |
3 | H | Me | NMe_{2} | H | OMe | C | C | C | 0.015 | 1.823 | 1.716 | 2.152 | 2.01 | 1.107 | 0.865 | 1.422 |
4 | H | Me | NHMe | H | OMe | C | C | C | 0.008 | 2.097 | 2.386 | 1.684 | 2.083 | 2.215 | 1.37 | 2.229 |
5 | H | Me | NHNH_{2} | H | OMe | C | C | C | 0.023 | 1.639 | 1.441 | 1.215 | 1.216 | 1.052 | 1.755 | 2.431 |
6 | H | Me | NMeAc | H | OMe | C | C | C | 0.009 | 2.046 | 2.143 | 2.62 | 2.576 | 2.244 | 2.017 | 2.255 |
7 | H | Me | Me | H | OMe | C | C | C | 0.002 | 2.699 | 1.924 | 1.684 | 1.814 | 2.37 | 1.875 | 1.174 |
8 | H | Me | Et | H | OMe | C | C | C | 0.009 | 2.046 | 3.331 | 2.152 | 1.969 | 1.717 | 2.482 | 1.728 |
9 | H | Me | CH_{2}F | H | OMe | C | C | C | 0.002 | 2.699 | 3.484 | 1.684 | 3.567 | 1.991 | 0.95 | 1.964 |
10 | H | Me | CH_{2}Cl | H | OMe | C | C | C | 0.048 | 1.319 | 1.315 | 1.684 | 1.679 | 2.104 | 1.507 | 1.23 |
11 | H | Me | CH_{2}OH | H | OMe | C | C | C | 0.002 | 2.699 | 2.386 | 1.684 | 2.307 | 2.398 | 1.85 | 2.699 |
12 | H | Me | CH_{2}NMe_{2} | H | OMe | C | C | C | 1.8 | -0.255 | 0.158 | 1.684 | -0.061 | 0.366 | -0.338 | -0.208 |
13 | H | H | Me | H | OMe | C | C | C | 6.4 | -0.806 | 0.242 | 0.512 | 0.048 | -0.066 | 0.251 | -0.113 |
14 | H | Me | Me | H | NO_{2} | C | C | C | 0.74 | 0.131 | -0.074 | 1.215 | 0.377 | -0.546 | -0.23 | 0.628 |
15 | H | Me | Me | H | F | C | C | C | 0.42 | 0.377 | 0.518 | 1.215 | 0.679 | 0.986 | 1.541 | 1.711 |
16 | F | Me | Me | H | OEt | C | C | C | 0.004 | 2.398 | 2.869 | 2.152 | 1.872 | 2.071 | 2.351 | 2.071 |
17 | H | Me | Me | H | OCHF_{2} | C | C | C | 0.009 | 2.046 | 1.892 | 1.684 | 1.833 | 1.559 | 0.129 | 1.238 |
18 | H | Me | Me | H | SMe | C | C | C | 0.004 | 2.398 | 1.924 | 1.684 | 1.814 | 1.583 | 1.851 | 2.226 |
19 | H | Me | Me | H | Et | C | C | C | 0.031 | 1.509 | 2.397 | 1.918 | 0.976 | 1.878 | 1.355 | 1.222 |
20 | H | Me | Me | H | NMe_{2} | C | C | C | 0.016 | 1.796 | 1.815 | 2.152 | 1.742 | 1.731 | 1.615 | 0.927 |
21 | H | Me | Me | H | OH | C | C | C | 0.086 | 1.066 | 0.518 | 1.215 | 0.679 | 0.561 | 1.64 | 1.299 |
22 | H | Me | Me | H | NH_{2} | C | C | C | 0.18 | 0.745 | 1.079 | 1.215 | 0.679 | 0.78 | 2.02 | 1.40 |
23 | H | Me | Me | H | N_{3} | C | C | C | 0.011 | 1.959 | 2.102 | 1.215 | 2.055 | 1.433 | 1.913 | 2.58 |
24 | H | Me | Me | H | NHAc | C | C | C | 0.059 | 1.229 | 1.382 | 1.918 | 1.331 | 0.56 | 1.419 | 1.31 |
25 | H | Me | Me | H | OMe | C | C | C | 0.002 | 2.699 | 1.391 | 1.684 | 1.514 | 1.766 | 1.543 | 1.732 |
26 | H | Me | Me | H | OMe | C | C | C | 0.004 | 2.398 | 1.903 | 1.684 | 1.821 | 1.667 | 1.289 | 2.02 |
27 | H | Me | Me | H | OMe | C | C | C | 0.010 | 2 | 2.164 | 2.152 | 2.271 | 2.248 | 2.551 | 2.252 |
28 | H | Me | Me | H | OMe | N | C | C | 0.011 | 1.824 | 1.541 | 1.215 | 1.316 | 1.217 | 0.865 | 1.413 |
29 | H | Me | Me | H | OMe | C | N | C | 0.016 | 1.357 | 0.979 | 1.215 | 1.316 | 1.591 | 1.355 | 2.174 |
30 | H | Me | Me | H | NMe_{2} | N | C | C | 0.015 | 1.482 | 1.432 | 1.684 | 1.244 | 0.989 | 1.293 | 1.18 |
31 | H | Me | Me | H | OMe | N | C | N | 0.053 | 1.125 | 0.596 | 0.747 | 0.819 | 1.663 | 2.329 | 1.277 |
32 | H | Me | Me | OMe | OMe | C | N | N | 0.15 | 0.824 | 0.035 | 0.747 | 0.819 | 1.901 | 1.54 | 1.433 |
2.2. Selection of training and test set
Unicolumn statistics of training and test sets for apoptosis inducing activity
Set | Average | Max | Min | Std dev. | Sum |
---|---|---|---|---|---|
2D | |||||
Training | 1.595 | 2.699 | -0.806 | 0.975 | 38.273 |
Test | 1.774 | 2.699 | 0.824 | 0.632 | 14.189 |
3D | |||||
Training | 1.477 | 2.699 | -0.806 | 0.918 | 35.449 |
Test | 2.127 | 2.699 | 0.824 | 0.648 | 17.012 |
2.3. Regression analysis
Dataset of 32 molecules was subjected to regression analysis using MLR, PCR, and PLS as model building methods. QSAR models were generated using pEC_{50} values as the dependent variable and various descriptors values as independent variables. The cross-correlation limit was set at 0.5, number of variables in the final equation at six in MLR and five in PCR and six in PLS, and term selection criteria as r^{2}, F-test 'in,' at 4 and 'out' at 3.99, r^{2}, and F-test. Variance cutoff was set at 0, scaling to autoscaling, and number of random iterations to 10. Statistical measures were used for the evaluation of QSAR models were the number of compounds in regression n, regression coefficient r^{ 2 }, number of descriptors in a model k, F-test (Fisher test value) for statistical significance F, cross-validated correlation coefficient q^{2}, predictive squared correlation coefficients pred_r^{ 2 }, coefficient of correlation of predicted data set pred_r^{ 2 } se and standard error (SE) of estimation r^{ 2 } se and q^{2} se.
2.4. MLR analysis
MLR is a method used for modeling linear relationship between a dependent variable Y (pEC_{50}) and independent variable X (2D descriptors). MLR is based on least squares: the model is fit such that sum-of-squares of differences of observed and a predicted value is minimized. MLR estimates values of regression coefficients (r^{2}) by applying least squares curve fitting method. The model creates a relationship in the form of a straight line (linear) that best approximates all the individual data points. In regression analysis, conditional mean of dependant variable (pEC_{50}) Y depends on (descriptors) X. MLR analysis extends this idea to include more than one independent variable.
where Y is dependent variable, 'b's are regression coefficients for corresponding 'x's (independent variable), 'c' is a regression constant or intercept [14, 15].
2.5 PCR method
PCR is a data compression method based on the correlation among dependent and independent variables. PCR provides a method for finding structure in datasets. Its aim is to group correlated variables, replacing the original descriptors by new set called principal components (PCs). These PCs uncorrelated and are built as a simple linear combination of original variables. It rotates the data into a new set of axes such that first few axes reflect most of the variations within the data. First PC (PC_{1}) is defined in the direction of maximum variance of the whole dataset. Second PC (PC_{2}) is the direction that describes the maximum variance in orthogonal subspace to PC_{1}. Subsequent components are taken orthogonal to those previously chosen and describe maximum of remaining variance, by plotting the data on new set of axes, it can spot major underlying structures automatically. Value of each point, when rotated to a given axis, is called the PC value. PCA selects a new set of axes for the data. These are selected in decreasing order of variance within the data. Purpose of principal component PCR is the estimation of values of a dependent variable on the basis of selected PCs of independent variables [16].
2.6 PLS regression method
PLS analysis is a popular regression technique which can be used to relate one or more dependent variable (Y) to several independent (X) variables. PLS relates a matrix Y of dependent variables to a matrix X of molecular structure descriptors. PLS is useful in situations where the number of independent variables exceeds the number of observation, when X data contain colinearties or when N is less than 5 M, where N is number of compound and M is number of dependant variable. PLS creates orthogonal components using existing correlations between independent variables and corresponding outputs while also keeping most of the variance of independent variables. Main aim of PLS regression is to predict the activity (Y) from X and to describe their common structure [17]. PLS is probably the least restrictive of various multivariate extensions of MLR model. PLS is a method for constructing predictive models when factors are many and highly collinear.
2.7. Validation of QSAR model
2.8. 3D-QSAR modeling and dataset
Dataset of 32 molecules was divided into training (24 compounds) and test (8 compounds) set by SE method having dissimilarities values of 7.9 with pEC_{50} activity field as dependent variable and various 3D descriptors calculated for the compounds as independent variables (Additional file 2).
2.9. Molecular modeling and alignment
Conformational search was carried out by systemic conformational search method (grid search), which generates all possible conformations, by systematically varying each of the torsion angles of a molecule by some increment, keeping the bond lengths and bond angles fixed and lowest energy conformers were selected. All the compounds were aligned by template-based method. In template-based alignment method, a template structure was defined and used as a basis for alignment of a set of molecules. In this study, all the compounds were aligned against minimum energy conformation of most active compound number using quinazoline ring as template.
2.10. Calculation of field descriptors
Electrostatic and steric field descriptors were calculated with cutoffs of 10.0 kcal/mol for electrostatic and 30.0 kcal/mol for steric, and charge type was selected as by Gasteiger-Marsili [19]. The dielectric constant was set to 1.0, considering distance-dependent dielectric function. Probe setting was carbon atom with charge 1.0. A total of 2,080 field descriptors (1,040 for each electrostatic and steric) were calculated for all the compounds in separate columns. 3D-QSAR analysis was performed after exclusion of all the invariable columns, as they do not contribute to QSAR.
2.11. k-Nearest neighbor molecular field analysis (kNN-MFA)
The kNN methodology relies on a simple distance learning approach whereby an unknown member is classified according to the majority of its kNN in training set. The nearness is measured by an appropriate distance metric. In kNN-MFA method, several models were generated for selected members of training and test sets. Once training and test sets are generated, kNN methodology is applied to descriptors generated over the grid [20]. The steric and electrostatic interaction energies are computed at lattice points of the grid using a methyl probe of charge +1. These interaction energy values are considered for relationship generation and utilized as descriptors to decide nearness between molecules.
2.12. kNN-MFA with stepwise forward-backward (SW-FB) variable selection method
kNN-MFA models were developed using SW-FB method with cross-correlation limit set to 0.5 and term selection criterion as q^{2}. F-test 'in' was set to 4.0, and F-test 'out' to 3.99. As some additional parameters, variance cutoff was set at 2 kcal/mol Å and scaling to autoscaling; additionally, kNN parameter setting was done within the range of 2-5 and prediction method was selected as the distance-based weighted average.
2.13. kNN-MFA with genetic algorithm (GA)
GA first described by Holland [21], mimics natural evolution and selection. In biological systems, genetic information that determines the individuality of an organism is stored in chromosomes. Chromosomes are replicated and passed onto the next generation with selection criteria depending on fitness.
2.14. kNN-MFA with simulated annealing (SA)
SA is the simulation of a physical process, 'annealing', which involves heating the system to a high temperature and then gradually cooling it down to a preset temperature (e.g., room temperature). During this process, the system samples possible configurations distributed according to the Boltzmann distribution so that at equilibrium, low energy states are the most populated.
3. Results and discussion
3.1. Generation of 2D-QSAR models
Molecular descriptors used in QSAR study
Descriptor | Description |
---|---|
AI descriptors | |
T_C_C_4 | T_C_C_4 is a count of number of carbon atoms (single, double or triple bonded) separated from any carbon atom (single or double bonded) by four bonds in a molecule (C_C_C_C_C_C) |
T_2_F_5 | T_2_F_5 is the count of number of double bounded atoms (i.e. any double bonded atom, T_2) separated from fluorine atom by five bonds in a molecule (C_C_C_C_C_C_F) |
T_2_Cl_1 | T_2_Cl_1 is the count of number of double bounded atoms (i.e. any double bonded atom, T_2) separated from chlorine atom by single bonds in a molecule (C_C_Cl) |
T_N_N_7 | T_N_N_7 is a count of number of nitrogen atoms (single, double or triple bonded) separated from any nitrogen atom (single or double bonded) by seven bonds in a molecule (N_C_C_C_C_C_C_C_N) |
T_N_O_5 | T_N_O_5 is a count of number of nitrogen atoms (single, double or triple bonded) separated from any oxygen atom (single or double bonded) by five bonds in a molecule (N_C_C_C_C_C_O) |
T_N_O_1 | T_N_O_1 is a count of number of nitrogen atoms (single, double or triple bonded) separated from any oxygen atom (single or double bonded) by single bonds in a molecule (N_C_O) |
T_2_Cl_2 | T_2_Cl_2 is the count of number of two bounded atoms (i.e. any double bonded atom, T_2) separated from chlorine atom by double bonds in a molecule (C_C_C_Cl) |
T_2_N_1 | T_2_N_1 is the count of number of double bounded atoms separated from nitrogen atom by one bonds in a molecule (C_C_N) |
Cluster | |
Chi3Cluster | Chi3Cluster which signifies simple 3^{rd} order cluster chi index in a compound contributed negatively to the model-1 |
Distance-based topological | |
RadiusOfGyration | RadiusOfGyration signifies size descriptor for the distribution of atomic masses in a molecule. |
MomInertiaY | This descriptor signifies moment of interia at Y-axes |
Hydrophobicity SlogpK | |
SKMostHydrophobic | SKMostHydrophobic is the most hydrophobic value on the van der Wall surface (vdWSA). VdWSA is the surface of the union of the spherical atomic surfaces defined by the van der Waal radius of each component atom in the molecule |
Electrotopological state | |
SssCH2E-index | SssCH2E-index indices for number of -CH_{2} group connected with two single bonds |
Model-1 (MLR)
pEC_{50} = +0.4723 (T_C_C_4) - 5.4583 (chi3Cluster) + 0.5491 (T_2_F_5) + 1.7150 (T_2_Cl_1) + 0.5612(T_N_N_7) - 1.0714 (T_C_Cl_2) + 0.0043
where n = 24_{training} and 8_{test}, DF = 17, r^{2} = 0.823, q^{2} = 0.763, F-test = 23.133, r^{2} se = 0.432, q^{2} se = 0.417, pred_r^{2} = 0.639
Model-2 (PCR)
pEC_{50} = +0.1170 5 (T_N_O_5) - 0.2894 (T_N_O_1) + 0.5052 (RadiusOfGyration) + 1.4150 (SKMostHydrophobic) - 1.0714 (MomInertiaY) - 2.3106
where n = 24_{training} and 8_{test} , DF = 19, r^{2} = 0.754, q^{2} = 0.667, F-test = 24.549, r^{2} se = 0.393, q^{2} se = 0.578, pred_r^{2} = 0.513
Model-3 (PLS)
pEC_{50} = +0.4335 (T_C_C_4) - 1.2055 (SssCH2E-index) - 3.1751 (chi3Cluster) + 0.7742 (T_2_Cl_1) + 0.3287 (T_2_F_5) + 0.3692 (T_2_N_1) - 3.0621
where n = 24_{training} and 8_{test} , DF = 30, r^{2} = 0.824, q^{2} = 0.622, F-test = 31.137, r^{2} se = 0.435, q^{2} se = 0.515, pred_r^{2} = 0.589
Statistical results of 2D-QSAR models
S. no. | Statistical Parameters | Model-1 (MLR) | Model-2 (PCR) | Model-3 (PLS) |
---|---|---|---|---|
1 | n | 24_{Training} and 8_{Test} | 24_{Training} and 8_{Test} | 24_{Training} and 8_{Test} |
2 | DF | 17 | 19 | 30 |
3 | r ^{2} | 0.823 | 0.754 | 0.824 |
4 | q ^{2} | 0.763 | 0.667 | 0.6224 |
5 | F test | 23.133 | 24.549 | 31.137 |
6 | r^{2} se | 0.432 | 0.393 | 0.435 |
7 | q^{2} se | 0.417 | 0.578 | 0.515 |
8 | pred_r^{2} | 0.639 | 0.513 | 0.589 |
9 | pred_r^{2}se | 0.561 | 0.562 | 0.648 |
3.2. Interpretaion of 2D-QSAR models
MLR (Model-1) and PLS (Model-3) indicate positive contribution of Baumann's [22] AI topological descriptors T_C_C_4, T_2_F_5, T_2_Cl_1 and negative contribution of chi3Cluster where as PCR model indicates positive contribution of RadiusOfGyration along with SKMostHydrophobic and negative contribution of T_N_O_1 and MomInertiaY.
AI descriptors can be generated considering topology of the molecule, atom type, and bond. For calculation of AI descriptors, every atom in the molecule was assigned at least one and at most three attributes. First attribute is 'T-attribute' to thoroughly characterize topology of the molecule. Second attribute is atom type, atom symbol is used here. Third attribute is assigned to atoms taking part in a double or triple bond. After all atoms have been assigned their respective attributes, selective distance count statistics for all combinations of different attributes are computed. A selective distance count statistic 'XY2' (e.g., TOPO2N3) counts all the fragments between start atom with attribute 'X' (e.g., '2' double-bonded atom) and end atom with attribute 'Y' (e.g., 'N') separated by graph distance 3. Graph distance can be defined as the smallest number of atoms along the path connecting two atoms in molecular structure. In this study, to calculate AI descriptors, we have used following attributes: 2 (double-bonded atom), 3 (triple-bonded atom), C, N, O, S, H, F, Cl, and Br the distance range of 0 to 7. RadiusOfGyration and MomInertiaY are distance-based topological descriptors. Topological indices are numerical values associated with chemical constitutions for the purpose of correlating chemical structure with biological activity. Distance-based topological descriptors are defined by their atom types and topological distance which signifies basic connectivity of atoms in the molecules. SKMostHydrophobic is a thermodynamic descriptor to characterize the hydrophobicity of a molecule. SlogP estimates logP by summing the contribution of atom-weighted solvent accessible surface areas and correction factors. SKMostHydrophobic contributed positively to the PCR model means the group, which increases hydrophobic nature, may cause increase apoptosis inducing activity. An estate contribution descriptor SssCH2E-index, which represents the electro-topological state indices for number of -CH_{2} group connected with two single bonds, is inversely proportional to the activity.
3.3. Generation and interpretaion of 3D-QSAR models
3D-QSAR modeling was performed using kNN-MFA method that adopts a kNN principle for generating relationships between molecular fields and apoptosis-inducing activity. The kNN-MFA models (4-6) were generated using training set of 24 compounds and 3D-QSAR models were validated using a test set of 8 compounds. The steric (S) and electrostatic (E) descriptors specify the regions, where variation in the structural features of different compounds in training set leads to increase or decrease in activities. The number accompanied by descriptors represents its position in 3D MFA grid. The stepwise forward backward variable selection method resulted in several statistically significant models, of which model-4 is considered as the best one. The model selection criterion is the value of q^{2}, internal predictive ability of model, and that of pred_r^{2}, ability of the model to predict activity of external test set. Model-4 (SW-FB)
pEC_{50} = E_895 (-0.0321, 0.0124) E_805 (0.0448, 0.1181)
Model-5 (SA)
pEC_{50} = E_509 (0.0355, 0.0337) E_875 (0.2828, 1.2756) S_526 (-0.6662, -0.4031) E_147 (0.1935, 0.2030)
Model-6 (GA)
pEC_{50} = S_477 (30.000, 30.000) S_664 (-0.0690, -0.0457)
Statistical results of kNN-MFA method
S. no. | Statistical parameters | Model-4 (SW-FB) | Model-5 (SA) | Model-6 (GA) |
---|---|---|---|---|
1 | n | 24_{Training} and 8_{Test} | 24_{Training} and 8_{Test} | 24_{Training} and 8_{Test} |
2 | k | 2 | 2 | 2 |
3 | DF | 21 | 19 | 19 |
4 | q ^{2} | 0.653 | 0.693 | 0.622 |
5 | q^{2}_se | 0.54 | 0.526 | 0.516 |
6 | pred_r^{2} | 0.523 | 0.537 | 0.563 |
7 | pred_r^{2}se | 0.423 | 0.83 | 0.488 |
8 | Descriptors (Vn) | E_895 | E_509 | S_477 |
E_805 | E_875 | S_664 | ||
S_526 | ||||
E_147 |
4. Conclusions
Apoptosis plays a pivotal role in the cytotoxic activity of most chemotherapeutic drugs. QSAR modeling resulted in identification of common structural features responsible for prediction of apoptosis-inducing activity for 4-anilinoquinozaline derivatives. 2D-QSAR studies revealed that AI descriptors were major contributing descriptors. Descriptor values obtained in this study helped in quantification of the structural features of 4-anilinoquinozaline derivatives. The overall degree of prediction was found to be around 82% in case of MLR and PLS. Among the three 2D-QSAR models (MLR, PCR, and PLS), results of PLS analysis showed significant predictive power and reliability as compare to other two methods. The master grid obtained for the various kNN-MFA models showed positive value in electrostatic field descriptors, which indicates that positive electronic potential is required to increase apoptosis-inducing activity. Negative range in steric descriptors indicates that less bulky group is preferred in that region. 3D-QSAR results suggested the importance of some molecular characteristics, which should significantly affect the binding affinities of compounds. These results provide useful clues for designing novel apoptosis inducer for the treatment of cancer.
Declarations
Acknowledgements
The authors are thankful to the Vlife Science Technologies (Amit Bedi) Pvt. Ltd, Pune, India, for providing the software for the QSAR study.
Authors’ Affiliations
References
- Varmus H: The new era in cancer research. Science 2006, 312: 1162–1165. 10.1126/science.1126758View ArticleGoogle Scholar
- Kingston DG, Newman DJ: Taxoids: cancer-fighting compounds from nature. Curr Opin Drug Disc Dev 2007, 10: 130–144.Google Scholar
- Wyllie AH, Kerr JF, Currie AR: Cell death: the significance of apoptosis. Int Rev Cyt 1980, 68: 251–306.View ArticleGoogle Scholar
- Fischer U, Schulze-Osthoff K: New approaches and therapeutics targeting apoptosis in disease. Pharmacol Rev 2005, 57: 187–215. 10.1124/pr.57.2.6View ArticleGoogle Scholar
- Fuentes-Prior P, Salvesen GS: The protein structures that shape caspase-activity, specificity, activation and inhibition. Biochem J 2004, 384: 201–232. 10.1042/BJ20041142View ArticleGoogle Scholar
- Green DR, Kroemer G: The pathophysiology of mitochondrial cell death. Science 2004, 305: 626–629. 10.1126/science.1099320View ArticleGoogle Scholar
- Schulze-Osthoff K, Ferrari D, Los M, Wesselborg S, Peter ME: Apoptosis signaling by death receptors. Eur J Biochem 1998, 254: 439–459. 10.1046/j.1432-1327.1998.2540439.xView ArticleGoogle Scholar
- Fesik SW: Promoting apoptosis as a strategy for cancer drug discovery. Nat Rev Cancer 2000, 5: 876–885.View ArticleGoogle Scholar
- Grivicich I, Regner A, da Rocha AB, Grass LB, Alves PA, Kayser GB, Schwartsmann G, Henriques JA: Irinotecan/5-fluorouracil combination induces alterations in mitochondrial membrane potential and caspases on colon cancer cell lines. Oncol Res 2005, 15: 385–392.Google Scholar
- Kolomeichuk SN, Bene A, Upreti M, Dennis RA, Lyle CS, Rajasekaran M, Chambers TC: Induction of apoptosis by vinblastine via c-Jun autoamplification and p53-independent down-regulation of p21WAF1/CIP1. Mol Pharmacol 2008, 73: 128–136. 10.1124/mol.108.039750View ArticleGoogle Scholar
- Kolenko VM, Uzzo RG, Bukowski R, Finke JH: Caspase-dependent and -independent death pathways in cancer therapy. Apoptosis 2000, 5: 17–20. 10.1023/A:1009677307458View ArticleGoogle Scholar
- Cai SX, Sirisoma N, Pervin A, Zhang H, Jiang S, Willardsen AJ, Anderson MB, Mather G, Pleiman CM, Kasibhatla S, Tseng B, Drewe J: Discovery of N -(4-Methoxyphenyl)- N ,2-dimethylquinazolin-4-amine, a potent apoptosis inducer and efficacious anticancer agent with high blood brain barrier penetration. J Med Chem 2009, 52: 2341–2351. 10.1021/jm801315bView ArticleGoogle Scholar
- Hudson BD, Hyde RM, Ra hr E, Wood J: Parameter based methods for compounds selection from chemical databases. Quant Struct Act Relat 1996, 15: 285–289. 10.1002/qsar.19960150402View ArticleGoogle Scholar
- Croux C, Joossens K: Influence of observations on the misclassification probability in quadratic discriminant analysis. J Multivar Anal 2005, 96: 348–403.View ArticleGoogle Scholar
- Devillers J: Neuronal network in QSAR and drug design. Academic Press, London; 1996.Google Scholar
- Doucet JP, Barbault F, Xia H, Panaye A, Fan B: Nonlinear SVM approaches to QSPR/QSAR studies and drug design. Curr Comput Aided Drug Des 2007, 3: 263–289. 10.2174/157340907782799372View ArticleGoogle Scholar
- Huberty CJ: Applied discriminant analysis. Willey, New York; 1994.Google Scholar
- Kubyani H: Variable selection in QSAR studies: an evolutionary algorithm. Quant Struct Act Relat 1994, 13: 285–294.Google Scholar
- Gasteiger J, Marsili M: Iterative partial equalization of orbital electronegativity--a rapid access to atomic charges. Tetrahedron 1980, 36: 3219–3228. 10.1016/0040-4020(80)80168-2View ArticleGoogle Scholar
- Shen M, LeTiran A, Xiao Y, Golbraikh A, Kohn H, Tropsha A: Quantitative structure-activity relationship analysis of functionalized amino acid anticonvulsant agents using k nearest neighbor and simulated annealing PLS methods. J Med Chem 2002, 4: 2811–2823.View ArticleGoogle Scholar
- Holland J: Adaptation in natural and artificial systems. University of Michigan Press; 1976.Google Scholar
- Balaban AT: Highly discriminating distance-based topological index. Chem Phys Lett 1982, 89: 399–404. 10.1016/0009-2614(82)80009-2View ArticleGoogle Scholar
Copyright
This article is published under license to BioMed Central Ltd. This is an Open Access article distributed under the terms of the Creative Commons Attribution License (http://creativecommons.org/licenses/by/2.0), which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.