RHAZES: Green and Applied Chemistry
Vol. 13, 2021, pp. 29~42 ISSN: 2605-6895
The c-Src kinase inhibitors: 2D-QSAR study by Multiple Linear Regression method
Salma ELBAHI1*, Yassine KOUBI1, Meryem BOUTALAKA1, Halima HAJJI1, M'barek Choukrad1*, Abdelouahid Sbai1, Mohammed Aziz AJANA1, Tahar LAKHLIFI1 and
Mohammed BOUACHRINE1,2
1Molecular chemistry and Natural Substances Laboratory, Faculty of Science, University Moulay Ismail, Meknes, Morocco
2Higher School of Technology - Khenifra (EST-Khenifra), University of Sultan My Slimane, PB 170, Khenifra 54000 Morocco.
Article Info ABSTRACT
Article history:
Received 02 Aug 2021 Revised 26 Aug 2021 Accepted 11 Sep 2021
A two-dimensional quantitative structure-activity relationship (2D- QSAR) study was conducted on a series of 34 purine derivatives against c-Src tyrosine kinase. This analysis is affected by two statistical methods integrated with XLSTAT software: principal component analysis (PCA) and multiple linear regression (MLR).
The best model established by multiple linear regression (R2=0,802, Q2cv=0,757, R2test=0.696) showed very satisfactory results. The proposed QSAR model was verified by internal and external tests, such as Y randomization test and Golbraikh-Tropsha criteria. The domain of applicability of the MLR model has been investigated using the Williams diagram to detect the compounds that are outside this domain.
Keyword:
2D-QSAR Tyrosine kinase PCA
MLR
Corresponding Author:
Email: salma26elbahi@gmail.com Phone:0622187024
1. INTRODUCTION
Kinases are a large class of enzymes responsible for catalyzing the transfer of phosphate groups (such as ATP) to carbohydrates, lipids, proteins, or nucleotide substrates [1]. Src family kinases are the largest family of non-receptor protein tyrosine kinases, and they play an important role in multiple intracellular signal transduction pathways involved in cell growth, migration, survival, adhesion and differentiation [2,3]. In most normal cell types, the expression level of c-Src kinase is low. The activation of c-Src occurs through the phosphorylation of Tyr416 and the dephosphorylation of the second tyrosine Tyr530 located in the enzyme binding pocket [4, 5]. Although c-Src kinase is highly regulated in most normal cells and is only active at low levels, studies have shown that it is upregulated in many types of human tumors, including those derived from colon cancer, breast cancer, pancreatic cancer, liver cancer, Tumors of brain cancer and bladder cancer [6, 7, 8].
Therefore, in recent years, the search for new c-Src tyrosine kinase inhibitors for the treatment of cancer has attracted much attention. So far, various c-Src kinase inhibitors have been found, including several scaffolds: benzotriazine [9], pyrazolopyrimidine [10], 4-anilinoquinazoline [11] and others [12, 13].
In this study, we tried to establish a more reliable QSAR model for purine derivatives, which are analogs of c-Src tyrosine kinase, and used 2D-QSAR methods to determine the key structural factors that affect the inhibitory activity of c-Src tyrosine kinase.
2. RESEARCH METHOD 2.1. Data sources
To establish a quantitative structure-activity relationship, we studied a series of 34 purine derivatives against c-Src tyrosine kinase from the literature [14]. All experimental activity values pIC50 were converted to the negative logarithm of pIC50(pIC50=-log10(pIC50)) (Table 1). The data set is randomly divided into a training set (28 compounds) used to generate a 2D-QSAR model and a test set (6 compounds) used to verify the quality of the model.
Figure 1. General chemical structure of the studied compounds.
Table 1. Structure and biological activity of the studied compounds
N Comp n R1=1-2 and R=3-34 R2 R3 IC50 pIC50
1
H 2.43 5.614
2
H 2.42 5.616
3 3-Methoxy H H 1.21 5.917
4 4-Chloro H H 1.18 5.928
5 4-Bromo H H 1.76 5.754
6 4-Nitro H H 1.4 5.853
7 3-Nitro H H 0.95 6.022
8 4-Aminosulfonyl H H 1.82 5.739
9 3-Aminosulfonyl H H 0,1 7.000
10 4-Carbamoyl H H 2.2 5.657
11 4-Acetamido H H 1.05 5.978
12 H H H 0.75 6.124
13* 4-Methoxy H H 0.13 6.886
14 3-Methoxy H H 0.93 6.031
15 3-Methyl H H 0.83 6.08
16 4-Chloro H H 0.34 6.468
17 4-Bromo H H 0.45 6.346
18 4-Nitro H H 0.39 6.408
19 3-Nitro H H 0,13 6,886
20* 4-Aminosulfonyl H H 0.02 7.698
21 3-Qminosulfonyl H H 0,12 6.92
22* 4-Carbamoyl H H 2.01 5.696
23 4-Acetamido H H 0.65 6.187
24* H H H 3.14 5.503
25 4-Methoxy H H 1.33 5,876
26 3-Methoxy H H 1.2 5.92
27* 3-Methyl H H 2.55 5.602
28* 4-Chloro H H 0.69 6.161
29 4-Nitro H H 0.78 6.107
30 3-Nitro H H 0.34 6.468
31 4-Aminosulfonyl H H 0.26 6.585
32 3-Aminosulfonyl H H 0.34 6.468
33 4-Carbamoyl H H 1.22 5.913
34 4-Acetamido H H 1.59 5.798
*Test set molecules
2.2. Molecular descriptors
Several descriptors are calculated using Chemoffice 2016, ACD / ChemSketch [15], and MarvinSketch [16] to predict the correlation between the descriptors and the activity of the molecules studied (Tables 2, Tables 3).
Table 2. Software used to calculate the various descriptors
ACD / ChemSketch
Molar Volume (MV); Molecular Weight (MW); Parachor (Pc); Refractive Index (n); Surface Tension (γ);density(d) and Polarizability (α)
MarvinSketch Partition Coefficient (LogP) ; Polar Surface Area (PSA) Number of H-bond Acceptors (NHA); Number of H-bond donors (NHD); Van der Waals volume (VDWV);
Refractivity (R)
Chemoffice2016 Balaban Index (J); Weiner Index (W); Henry’s law (Hl)
Table 3. Calculated values of the different descriptors
MV Pc IR ɣ d α MW Hl w J HBA HBD VDWV PSA R LogP pIC50 1 239.8 700.8 1.696 72.8 1.356 36.63 325.376 21.66 1372 351754 8 3 285.76 90.99 91.98 1.83 5.614 2 232.1 680.4 1.708 73.8 1.332 35.89 309.38 20.39 1179 279309 7 3 276.61 81.76 90.56 2.5 5.616 3 239.8 700.8 1.696 72.8 1.356 36.63 325.38 21.66 1340 343904 8 3 285.87 90.99 91.98 1.83 5.917 4 227.8 679.3 1.726 79 1.447 35.92 329.79 20.56 1195 282920 7 3 273.44 95.75 90.32 2.59 5.928 5 232 693.2 1.738 79.6 1.612 37.03 374.25 20.83 1195 282920 7 3 277.81 81.76 93.14 2.76 5.754 6 236.1 719.6 1.725 86.2 1.508 37.18 356.35 23.43 1776 529539 8 3 291.55 134.13 93.19 1.85 5.853 7 236.1 719.6 1.725 86.2 1.508 37.18 356.35 23.43 1712 511062 8 3 291.59 134.13 93.19 1.85 6.022 8 244.8 749.8 1.717 87.9 1.529 38.23 374.42 23.72 1732 517103 9 4 307.47 141.92 97.67 0.59 5.739 9 244.8 479.8 1.717 87.9 1.529 38.23 374.42 23.72 1832 615061 9 4 307.51 141.92 97.67 0.59 7
10 234.5 715.3 1.74 86.4 1.442 37.52 338.38 26.82 1551 429649 8 4 290.06 124.85 94.59 0.84 5.657 11 248.9 753.9 1.736 84.1 1.415 39.64 352.4 26.37 1776 529539 8 4 307.27 110.86 100.38 1.23 5.978 12 210.8 634.2 1.727 81.8 1.405 33.29 296.33 22.12 1041 226762 7 2 256.5 78.96 83.83 2.31 6.124 13 234.8 692.9 1.698 75.7 1.389 35.94 326.36 19.95 1372 351754 8 2 282.64 88.19 90.3 2.15 6.886 14 234.8 692.9 1.698 75.7 1.389 35.94 326.36 19.95 1340 343904 8 2 282.47 88.19 90.3 2.15 6.031 15 227.1 672.5 1.71 76.8 1.366 35.2 310.36 22.08 1179 279309 7 2 273.31 78.96 88.88 2.82 6.08 16 222.8 671.4 1.729 82.4 1.484 35.23 330.78 22.25 1195 282920 7 2 270.1 78.96 88.64 2.91 6.468 17 227 685.3 1.741 82.9 1.652 36.34 375.23 22.52 1195 282920 7 2 274.65 78.96 91.46 3.07 6.346 18 222.7 691.3 1.747 92.8 1.532 35.88 341.33 21.42 1551 429649 8 2 279.29 122.1 90.15 2.25 6.408 19 222.7 691.3 1.747 92.8 1.532 35.88 341.33 21.31 1503 416817 8 2 279.32 122.1 90.15 2.25 6.886 20 239.8 741.9 1.717 91.5 1.565 37.47 375.41 22.01 1732 517103 9 3 304.23 139.12 95.99 0.91 7.698 21 239.8 741.9 1.717 91.5 1.565 37.47 375.41 22.01 1668 498518 9 3 304.27 139.12 95.99 0.91 6.92 22 229.5 707.4 1.743 90.1 1.478 36.83 339.36 28.51 1551 429649 8 3 286.74 122.05 92.91 1.16 5.696 23 243.9 746 1.738 87.4 1.448 38.95 353.39 28.06 1776 529539 8 3 303.82 108.06 98.7 1.54 6.187 24 245.9 737.4 1.716 80.07 1.379 38.37 339.4 24.4 1564 432673 8 3 303.01 93.2 97.1 1.68 5.503 25 269.9 796 1.692 75.5 1.368 41.02 369.43 25.63 1982 634027 9 3 329.17 102.43 103.57 1.52 5.876 26 269.9 796 1.692 75.5 1.368 41.02 369.43 25.63 1944 622358 9 3 328.97 102.43 103.57 1.52 5.92 27 262.2 775.7 1.702 76.5 1.347 40.29 353.43 24.36 1741 518907 8 3 319.64 93.2 102.14 2.2 5.602 28 257.9 774.5 1.718 81.3 1.449 40.31 373.85 24.53 1760 524325 8 3 317.04 93.2 101.91 2.29 6.161 29 257.8 794.4 1.734 90.1 1.49 40.97 384.4 27.1 2206 756354 9 3 326.05 136.34 103.42 1.62 6.107 30 257.8 794.4 1.734 90.1 1.49 40.97 384.4 26.99 2149 737445 9 3 325.91 136.34 103.42 1.62 6.468 31 274.9 845 1.71 89.2 1.522 42.6 418.48 27.69 2432 891911 10 4 350.79 153.36 109.26 0.29 6.585 32 274.9 845 1.71 89.2 1.522 42.6 418.48 27.69 2356 864741 10 4 350 153.36 109.26 0.29 6.468 33 264.6 810.6 1.73 87.9 1.444 41.91 382.43 30.79 2206 756354 9 4 333.3 136.29 106.18 0.53 5.913 34 279 849.2 1.727 85.7 1.42 44.03 396.46 30.34 2482 909319 9 4 350.58 122.3 111.97 0.92 5.798
2.3. Statistical analysi
2.3.1. Principal component analysis
Principal Component Analysis (PCA) is a method of data analysis used to reduce the dimensionality of such data sets and make information less redundant [17]. The results of the PCA are used to determine the correlation among descriptors and to identify the input variables of the QSAR.
2.3.2. Multiple Linear Regression (MLR)
Multiple linear regression (MLR) is one of the most transparent modeling methods due to its ease of use and ease of interpretation. It is based on the fact that there is a linear relationship between a dependent variable Y (the activity pIC50) and a series of independent variables Xi (the descriptors) [18] according to the following relation:
Where: Y is the dependent variable;
xi is the independent variables;
a0 is the constant of the model equation;
ai are the coefficients of descriptors in the model equation.
2.4. Validation of the QSAR model
To confirm the stability and predictive capacity of the established model a validation is required. There are two types of validation: internal and external.
2.4.1. Internal validation
The internal validation of a QSAR model was carried out using the LOO (leave-one-out) cross-validation which is quantified by the coefficient R2cv calculated by the following relation:
Another internal validation technique is often used: the Yrandomization test [19]. It consists in randomly mixing the activities / proprities for the learning series using the same descriptors. Hence, new models are obtained. These must-haves low performance.
However, internal validation is not enough to study the predictive power of a model, which is why the external validation of the model has become an obligatory part of QSPR / QSAR modeling [20, 21].
2.4.2. External validation
The external validation consists in predicting the activity / property of a series of tests that are not in the development series of the model, this validation is characterized by the parameters R2(test) Q2cv(test). Recently, several studies [22, 23] have shown the insufficiency of the parameters R2, R2cv to cofirm the predictive power of QSAR models. Therefore, other parameters should be checked for this purpose. These parameters are known as
"external validation criteria" or are often referred to as "Tropsha criteria" [22].
2.5. Applicability domain
The domain of applicability (DA) of a QSAR model defines the zone in which a compound can be predicted with confidence. The domain of applicability, therefore, corresponds to the region of chemical space including the compounds of the learning set and similar compounds close in this same space [24]. There are various methods for determining the scope of application of a QSAR model. Among these methods, we find the "leverage"
method which is based on the variation of the standardized residuals of the dependent
variable according to the leverage. If a compound has a residual and leverage that exceeds the threshold h * = 3(k+1)/ N (where K is the number of descriptors and N the number of molecules constitutes the learning set), this compound is considered to be outside the domain of applicability of the elaborate model [25].
3. RESULTS AND ANALYSIS 3.1. Principal component analysis
All the 16 descriptors coding for the 34 molecules were subjected to PCA. Figure 2 shows that the total number of variables has been reduced to 2 principal components (F1 and F2), with a total variance of 78.67%.
Figure 2. Correlation circle between descriptors
The correlation coefficients in the matrix obtained (table4) provide information on the low or high inter-relation between the descriptors. In general, a correlation coefficient greater than 0.5 shows a good correlation among the descriptors. Therefore, to reduce the redundancy in our database, the descriptors that strongly correlated (r ≥ 0.9) and had a low value of the correlation coefficient with the dependent variable pIC50 were excluded.
Table 4. The correlation matrix
MV Pc IR ɣ d α MW Hl WI J HBA HBD VDWV PSA R LogP pIC50
MV 1
Pc 0.751 1
IR -0.359 -0.106 1
ɣ 0.048 0.153 0.689 1
D -0.157 -0.075 0.593 0.708 1 Α 0.971 0.771 -0.125 0.228 -0.018 1 MW 0.757 0.597 0.080 0.517 0.525 0.824 1
Hl 0.699 0.621 0.227 0.410 -0.009 0.803 0.599 1 WI 0.884 0.704 -0.029 0.468 0.107 0.933 0.837 0.799 1
J 0.881 0.680 -0.036 0.466 0.121 0.928 0.845 0.790 0.996 1 HBA 0.784 0.566 -0.231 0.451 0.149 0.774 0.783 0.583 0.890 0.890 1 HBD 0.657 0.380 -0.026 0.257 0.040 0.695 0.602 0.655 0.668 0.670 0.618 1 VDWV 0.972 0.751 -0.212 0.275 -0.005 0.979 0.837 0.760 0.957 0.956 0.872 0.691 1
PSA 0.442 0.344 0.216 0.792 0.456 0.527 0.695 0.522 0.756 0.754 0.814 0.620 0.614 1 R 0.969 0.759 -0.131 0.258 0.010 0.997 0.842 0.797 0.937 0.935 0.800 0.716 0.987 0.556 1 LogP -0.608 -0.391 0.078 -0.493 -0.117 -0.629 -0.614 -0.650 -0.751 -0.751 -0.865 -0.813 -0.716 -0.824 -0.666 1 pIC50 -0.094 -0.175 0.083 0.483 0.504 -0.078 0.259 -0.199 0.107 0.128 0.302 -0.185 0.034 0.354 -0.035 -0.164 1
3.2. Multiple Linear Regression (MLR)
The best association obtained using MLR is is a linear combination of four descriptors:
surface tension (ɣ), polar surface area (PSA), density (d), and refractive index (n). Hence, the resulting equation is:
pIC
50= 60,209 - 40,438 n + 0,179 ɣ + 2,245 d - 2,489.10
-2PSA
According to table 5, the high value of R2 and R2adj and the low value of MSE indicate that the proposed model is statisticallysignificant.
Table 5. Statistical results of the 2D-QSAR equation generated by MLR
Statistical parameter Value
N train 26
R 0.895
R2 0.802
R2adj 0.767
MSE 0.055
F 23.236
The surface tension and density positively influence the activity while the refractive index and polar surface area hurt the activity.
To compare the degree of influence of each descriptor (n; ɣ; d; PSA) on the pIC50 of purine derivatives, it is necessary to identify the standardized coefficient or the values of the t-test of those descriptors in the model equation. The bigger the absolute value of the t-test, the greater is the influence of the descriptor. According to table 6, the t-test value of the refractive index is greater than that of the other three descriptors. This indicates that in this model, the influence of the refractive index on the activity is greater than that of others.
Table 6. Characteristics of the MLR model parameters
Descriptors value Standard error t Pr ˃ t
n - 40.438 7.548 - 5.357 ˂ 0.0001
ɣ 0.179 0.036 4.988 ˂ 0.0001
d 2.245 0.820 2.739 0.012
PSA - 0.025 0.006 - 3.877 0.001
In conclusion, these results illustrate that if we want to increase the value of the activity, we must increase the surface tension and density values and decrease the refractive index and polar surface area values.
The correlation between the observed and predicted pIC50 of the two sets(training and test set) established by RLM is shown in Figure 3.
Figure 3. The correlations between observed and predicted pIC50 for MLR
3.3. Validation of QSAR model developed by 2D-QSAR 3.3.1. y-randomization
To validate the MLR model, we used a y-randomization test.100 randomized trials were performed for the MLR model. Tables 7 and 8 show the low Q2cv (LOO) Rand and R2Rand
values obtained. The results indicate that the original model is not due to a chance correlation.
Table 7.values of y-randomization test results
Rand R R2 Q2 Rand R R2 Q2 Rand R R2 Q2 Rand R R2 Q2
1 0.271 0.073 -0.393 26 0.577 0.333 0.006 51 0.192 0.037 -0.605 76 0.317 0.101 -0.356
2 0.453 0.205 -0.078 27 0.490 0.240 -0.090 52 0.265 0.070 -0.349 77 0.382 0.146 -0.230
3 0.493 0.243 -0.158 28 0.557 0.311 -0.045 53 0.355 0.126 -0.371 78 0.341 0.117 -0.222
4 0.427 0.182 -0.279 29 0.314 0.099 -0.577 54 0.288 0.083 -0.281 79 0.434 0.188 -0.272
5 0.376 0.141 -0.312 30 0.478 0.228 -0.086 55 0.534 0.285 -0.190 80 0.533 0.284 -0.111
6 0.401 0.161 -0.177 31 0.400 0.160 -0.273 56 0.365 0.133 -0.495 81 0.530 0.281 -0.208
7 0.253 0.064 -0.444 32 0.607 0.368 0.054 57 0.466 0.217 -0.091 82 0.307 0.094 -0.291
8 0.349 0.122 -0.256 33 0.489 0.239 -0.126 58 0.345 0.119 -0.274 83 0.305 0.093 -0.392
9 0.428 0.183 -0.237 34 0.359 0.129 -0.334 59 0.355 0.126 -0.531 84 0.325 0.106 -0.265
10 0.393 0.155 -0.227 35 0.301 0.091 -0.254 60 0.430 0.185 -0.146 85 0.634 0.402 0.120
11 0.331 0.110 -0.225 36 0.258 0.067 -0.392 61 0.345 0.119 -0.260 86 0.181 0.033 -0.504
12 0.237 0.056 -0.394 37 0.583 0.339 0.044 62 0.227 0.051 -0.732 87 0.419 0.176 -0.336
13 0.528 0.279 0.025 38 0.403 0.163 -0.244 63 0.629 0.396 0.137 88 0.200 0.040 -0.860
14 0.311 0.097 -0.532 39 0.371 0.138 -0.279 64 0.415 0.172 -0.497 89 0.437 0.191 -0.163
15 0.499 0.249 -0.085 40 0.449 0.201 -0.336 65 0.265 0.070 -0.330 90 0.157 0.025 -0.481
16 0.277 0.077 -0.354 41 0.501 0.251 -0.177 66 0.311 0.097 -0.314 91 0.433 0.188 -0.206
17 0.287 0.083 -0.294 42 0.356 0.127 -0.225 67 0.424 0.180 -0.241 92 0.236 0.056 -0.446
18 0.294 0.086 -0.384 43 0.290 0.084 -0.407 68 0.378 0.143 -0.298 93 0.600 0.360 0.024
19 0.388 0.151 -0.218 44 0.211 0.045 -0.381 69 0.272 0.074 -0.355 94 0.236 0.056 -0.365
20 0.284 0.081 -0.349 45 0.426 0.181 -0.721 70 0.446 0.199 -0.170 95 0.395 0.156 -0.609
21 0.390 0.152 -0.190 46 0.413 0.171 -0.247 71 0.434 0.189 -0.302 96 0.394 0.155 -0.195
22 0.255 0.065 -0.696 47 0.175 0.031 -0.443 72 0.318 0.101 -0.424 97 0.483 0.233 -0.062
23 0.486 0.236 -0.060 48 0.326 0.106 -0.334 73 0.465 0.216 -0.211 98 0.359 0.129 -0.358
24 0.387 0.149 -0.322 49 0.251 0.063 -0.575 74 0.376 0.142 -0.279 99 0.556 0.309 -0.055
25 0.243 0.059 -0.384 50 0.152 0.023 -0.405 75 0.566 0.320 0.025 100 0.398 0.158 -0.233
Table 8. Random models parameters
Average R Average R2 Average Q2CV cRp2
0.378 0.156 - 0.285 0.604
3.3.2. External validation
The results of Table 9 confirm the reliability and robustness of our proposed model.
Table 9. Comparison of the statistical parameter with Golbraikh and Tropsha criteria
Fitting criteria
Parameter Equation Model
score
Threshold
R2 0.802 ˃ 0.6
R2adj 0.767 ˃ 0.6
MSE 0.055 A low value
F 23.236 A high value
Internal validation
Q2CV 0.7565 ˃ 0.5
RRand Average of the 100 Rrand(i) 0.378 ˂ R R2Rand Average of the 100 R2rand(i) 0.156 ˂ R2 Q2CVLOO(Rand) Average of the 100 Q2CVLMO(Rand)(i) - 0.285 ˂ Q2CV
cR2P 0.604 ˃ 0.5
External validation
R2test 0.696 ˃ 0.5
0.173 ˃ 0.5
Δ r2test 0.001 ˂ 0.2
Δ r20(test) 0.004 ˂ 0.3
(r2 – r20) / r2 -0.538 ˂ 0.1
(r2 – r’20) / r2 -0.544 ˂ 0.1
K 1.011 0.85 ≤ K ≤ 1.15
K’ 0.984 0.85 ≤ K’≤ 1.15
and : refer to the observed and calculated/predicted response values.
and : refer to the mean of the observed and calculated/predicted response values.
N and p refer to the number of data points (compounds) and descriptors.
3.4. Applicability domain
The figure below displays the Williams plot of the MLR model. The value of the leverage (h*) was set at 0.536 and the standardized residual is ±3for the developed QSAR model.
According to this value, there is only one compound N° =13 falling outside the defined area of applicability. Therefore, this compound is considered an outlier. Finally, the results
obtained by the domain of applicability confirm the reliability of the QSAR model to predict the activity of the compounds with very high confidence.
Figure 4. Williams diagram for the MLR model (h*=0.536 and residual limits=±3)
4. CONCLUSION
In the present study, a 2D-QSAR was used to study a series of derivatives purine as c-Src tyrosine kinase inhibitors. The results obtained by the MLR indicated that the most important descriptors for predicting inhibitor activity are: surface tension (ɣ), polar surface area (PSA), density (d), and refractive index (n). In addition, our result shows a significant negative influence of n and PSA on pIC50 values, while ɣ and d have a positive effect. The information currently obtained will help predict the activity of new c-Src tyrosine kinase inhibitor candidates.
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