Research Journal of Chemical Vol. 4(6), 18-22, June (2014) International Science Congress Association Comparative QSAR study of Chincholikar P.M. 1 Department of Applied Chemistry, BUIT, Barkatullah University, Bhopal, MP, INDIA 2 Department of Chemistry, SNGGPG College, Bhopal, MP, INDIA Available online at: Received 19th March Abstract Anthracycline, a potent anticancer molecule being used extensively in chemotherapy, though known for its efficacy known to cause adverse effects when administered. We are trying to analyse the variations in its analogues structurally. To study the QSAR of anthracycline analogues by using graph theoretical indices and physicochemical properties efforts have been made to derive a best mathematical model for the calculation of biological activity of the analogues in multivariate form. We have used mult iple linear regression method. Two equations were developed separately, 1. Containing graph theoretical indices with r value 0.7038 and 2. Containing physicochemical properties as predictor variable with r value 0.8162 both models are significant to under Keywords: Anthracycline analogues, QSAR, drug designing. Introduction Anthracyclines (viz., Doxorubicin, Daunorubicin, Epirubicin, valirubicin, etc) are the anti- cancer drugs used in chemotherapy. The use of these drugs have the common side effects that were observed, in majority they are: nausea, alopecia, bone marrow suppression, and vomiting. Doxorubicin induced bone marrow suppression can now be reduced by the use of hematopoietic growth factors . The partition coefficient is the ratio of the concentration of a chemical in octanol to that in water in a two phase system at equilibrium. The logarithm of this coefficient, log shown to be one of the key parameters in quantitativ activity/property relationship (QSAR/QSPR) studies hydrophobicity and hydrophilicity of a substance is measured by - octanol– water partition coefficient. It gives the idea of the tendencies of hydrophobic molecules or parts of molecu avoid water, because they are not readily accommodated in the highly ordered hydrogen bonded structure of water Thermodynamically Hydrophobic interaction is favoured because of it increases the entropy of the water molecules that accompanies the association of non- polar molecules, which squeeze out water. There are some reports about the applications of MLR 3-6 and artificial neural network 7-10 modeling to predict the octanol/water partition coefficient of anti some previous pap ers, reports on the application of QSAR techniques in the development of a new, simplified approach to prediction of compounds properties11–17 . Study of log its experimental determination is often complex and time consuming that can be done only f or already synthesized compounds11 . Hence, computational methods were applied for the prediction of this parameter. The family composed of 23 substituted anthracyclines have been studied, and activity analyzed is the logIC Chemical Sciences _________________________________ ______ International Science Congress Association Comparative QSAR study of Anthracycline Analogues Chincholikar P.M. , Amlathe S.1 and Joshi S.2 Department of Applied Chemistry, BUIT, Barkatullah University, Bhopal, MP, INDIA Department of Chemistry, SNGGPG College, Bhopal, MP, INDIA Available online at: www.isca.in, www.isca.me March 2014, revised 3rd May 2014, accepted 10th June 2014 Anthracycline, a potent anticancer molecule being used extensively in chemotherapy, though known for its efficacy known to cause adverse effects when administered. We are trying to analyse the variations in its analogues structurally. To QSAR of anthracycline analogues by using graph theoretical indices and physicochemical properties efforts have been made to derive a best mathematical model for the calculation of biological activity of the analogues in multivariate iple linear regression method. Two equations were developed separately, 1. Containing graph theoretical indices with r value 0.7038 and 2. Containing physicochemical properties as predictor variable with r value 0.8162 both models are significant to under stand QSAR of anthracycline analogues. Anthracycline analogues, QSAR, drug designing. Anthracyclines (viz., Doxorubicin, Daunorubicin, Epirubicin, cancer drugs used in of these drugs have the common side - effects that were observed, in majority they are: nausea, alopecia, bone marrow suppression, and vomiting. Doxorubicin - induced bone marrow suppression can now be reduced by the The -octanol/water partition coefficient is the ratio of the concentration of a octanol to that in water in a two phase system at equilibrium. The logarithm of this coefficient, log , has been shown to be one of the key parameters in quantitativ e structure activity/property relationship (QSAR/QSPR) studies 2,3. The hydrophobicity and hydrophilicity of a substance is measured by water partition coefficient. It gives the idea of the tendencies of hydrophobic molecules or parts of molecu les that avoid water, because they are not readily accommodated in the highly ordered hydrogen bonded structure of water 2 . Thermodynamically Hydrophobic interaction is favoured because of it increases the entropy of the water molecules that polar molecules, which squeeze out water. There are some reports about the applications modeling to predict octanol/water partition coefficient of anti -cancer drugs. In ers, reports on the application of QSAR techniques in the development of a new, simplified approach to . Study of log o/w and its experimental determination is often complex and time or already synthesized . Hence, computational methods were applied for The family composed of 23 substituted anthracyclines have been studied, and activity analyzed is the logIC 50 (50% inhibitory concentration). The topological and physicochemical descriptors were studied for the investigation of QSAR of the compound. Multiple linear regressions were performed to obtain correlation for systematic study on substituents of anthracycline (table 1). Figure - Parent structure of anthracyclines Methodology Two methodologies are adopted to develop mathematical models. In the first method graph theoretical descriptor are used for correlation with biological activity (logIC second method physicochemical properties are used for correlation with biological activity (log The multivariate regression analysis (MRA) was used to obtain a model used in QSAR studies. Typically in such studies, after selecting the compounds an d the activity to be analysed, one considers selection of potentially useful descriptors. The major descriptor used in QSAR are divided into two classes: theoretical descriptors, this class includes Electrotopological state(S), Balaban J Index(J), Szeged Index(Sz), Schultz molecular topological Index(MTI), Wiener Index(W), Randic connectivity Index(), etc. and ii. (descriptor), this includes logP, Molar refractivity (MR), Molar ______ _______ ISSN 2231-606X Res. J. Chem. Sci. 18 Anthracycline Analogues Department of Applied Chemistry, BUIT, Barkatullah University, Bhopal, MP, INDIA Anthracycline, a potent anticancer molecule being used extensively in chemotherapy, though known for its efficacy - but known to cause adverse effects when administered. We are trying to analyse the variations in its analogues structurally. To QSAR of anthracycline analogues by using graph theoretical indices and physicochemical properties efforts have been made to derive a best mathematical model for the calculation of biological activity of the analogues in multivariate iple linear regression method. Two equations were developed separately, 1. Containing graph theoretical indices with r value 0.7038 and 2. Containing physicochemical properties as predictor variable with r value inhibitory concentration). The topological and physicochemical were studied for the investigation of QSAR of the compound. Multiple linear regressions were performed to obtain correlation for systematic study on substituents of anthracycline - 1 Parent structure of anthracyclines Two methodologies are adopted to develop mathematical models. In the first method graph theoretical descriptor are used for correlation with biological activity (logIC 50) and in the second method physicochemical properties are used for correlation with biological activity (log IC50). The multivariate regression analysis (MRA) was used to obtain a model used in QSAR studies. Typically in such studies, after d the activity to be analysed, one considers selection of potentially useful descriptors. The major descriptor used in QSAR are divided into two classes: i. Graph theoretical descriptors, this class includes Electrotopological Szeged Index(Sz), Schultz molecular topological Index(MTI), Wiener Index(W), Randic Physicochemical properties (descriptor), this includes logP, Molar refractivity (MR), Molar Research Journal of Chemical Sciences ____ _ Vol. 4(6), 18-22, June (2014) International Science Congress Association volume(MV), Parachor(Pc), Index of refracti tension(ST) etc. Indicator parameters are also used for the presentation of substitutent effect. Descriptors Type Graph theoretical descriptors Wiener index Randic connectivity Index Balaban J Index Szeged index Schultz molecular topological index Electro-topological state Physicochemical descriptors Partition Coefficient Molar refractivity Molar volume Parachor Index of refraction Surface tension The topological indices in the study included: Wiener index (W)16, Randic connectivity index 17 , Balaban J index (J) Substituents on anthracyclines (ANTHs) _ _____________________________________________ _ International Science Congress Association volume(MV), Parachor(Pc), Index of refracti on(IR), Surface tension(ST) etc. Indicator parameters are also used for the Abbreviation W Randic connectivity Index  J Sz Schultz molecular MTI S Log P MR MV Pc IR ST indices in the study included: Wiener index , Balaban J index (J) 18, Szeged index (Sz)19 , Electro topological state (S) Molecular topological index (MTI) indices were done using comp uter program Chemsketch 5.0 (from ACD labs). The physicochemical properties were estimated using Chemsketch 5.0 (from ACD labs), while the logP values were calculated using program ChemlogP. Multiple Linear Regression method22 was used for regression analysis. Results and Discussion The present study deals with the QSAR on benzodiazepines. The very first step is to calculate the desired topological indices, and the physicochemical properties and then investigate their utility. The topological indices viz. logW, physicochemical properties, Electrotopological state (S) and Schultz Molecular topological index (MTI), were calculated using software ACD lab (Chemsketch 5.0) and are presented in the parameters are also given in table 2Table-1 Substituents on anthracyclines (ANTHs) _ ________ ISSN 2231-606X Res. J. Chem. Sci. 19 , Electro topological state (S) 20, Schultz Molecular topological index (MTI) 21. Calculation of these uter program Chemsketch 5.0 The physicochemical properties were estimated using Chemsketch 5.0 (from ACD labs), while the logP values were calculated using program ChemlogP. Multiple Linear was used for regression analysis. The present study deals with the QSAR on benzodiazepines. The very first step is to calculate the desired topological indices, and the physicochemical properties and then investigate their The topological indices viz. logW, physicochemical properties, Electrotopological state (S) and Schultz Molecular topological index (MTI), were calculated using software ACD lab (Chemsketch 5.0) and are presented in the table 2. The indicator . Research Journal of Chemical Sciences ___________________________________________________________ ISSN 2231-606XVol. 4(6), 18-22, June (2014) Res. J. Chem. Sci. International Science Congress Association 20 Table-2 Topological indices, physicochemical properties and indicator parameters of anthracyclines (ANTHs) ANTHs MTI S logW logP ST Pol I 2 I 1 I 3,8 I 1,7 1 3173 48.35 2.8609 1.176 45.4 28.09 0 0 0 0 2 3173 49.85 2.8609 0.832 42.3 30.20 0 1 0 1 3 3543 56.02 2.9133 1.424 42.9 28.03 1 0 0 0 4 3543 57.52 2.9133 1.08 40.2 30.14 1 1 0 1 5 3173 44.46 2.8609 1.479 49.6 29.96 0 0 0 0 6 3173 49.85 2.8609 1.231 46.1 32.07 0 1 0 1 7 3543 52.13 2.9133 1.823 47.0 29.91 1 0 0 0 8 3543 48.24 2.9133 2.222 51.1 31.78 1 0 0 0 9 3543 51.64 2.9133 2.126 47.5 35.21 1 1 0 1 10 3543 65.6 3.0298 1.936 40.4 30.02 0 0 0 0 11 4173 45.95 2.9795 0.310 60.6 30.38 0 0 0 0 12 4523 53.62 3.0265 0.902 57.2 30.33 1 0 0 0 13 4523 55.12 3.0265 0.558 52.5 32.44 1 1 0 1 14 4523 49.73 3.0265 1.301 61.8 32.20 1 0 0 0 15 4523 51.23 3.0265 0.957 56.5 34.32 1 1 0 1 16 6119 70.87 3.1658 1.662 50.0 32.26 1 0 0 0 17 5111 58.02 3.0824 -0.58 54.4 32.16 1 1 0 1 18 4173 44.35 2.9795 -0.889 49.3 29.30 0 0 0 0 19 4173 45.85 2.9795 -0.71 48.7 31.26 0 1 0 1 20 4523 53.52 3.0265 -0.462 47.2 30.96 1 1 0 1 21 4523 48.13 3.0265 0.281 55.7 30.72 1 0 0 0 22 3629 50.35 2.9238 -0.058 48.5 32.83 0 1 0 1 23 4029 58.02 2.7931 0.19 46.1 32.78 1 1 0 1 = Indicator Parameter 1 If substituents on R, I = Indicator Parameter 1 If substituents on R', I3,8 = Indicator Parameter 1 If substituents on R or R, I1,7 = Indicator Parameter 1 If substituents on R, R or on both. The correlations that are low in values of R (0.50) are not considered being statistically insignificant. Individually indices were correlated in lesser manner with the biological activity (logIC50) and same was the case with physicochemical properties of observed biological activity. The results show that the topological, physicochemical properties and biological activity when studied for univariate correlation was insufficient to describe the SAR (structure activity relationship) in quantitative manner. All the univariate correlation gives very low correlation coefficient. Out of all univariate correlations the best statistics are shown by S (r = 0.2105, s = 0.7321). The correlation coefficient in the case of bivariate correlation showed little higher but insufficient to explain structure activity relationship quantitatively and the best statistics are shown by MTI and I2 (r = 0.6567, s = 0.5631), so is the case with trivariate correlation and best statistics are shown by MTI, log W and I2 (r =0.7038, s = 0.5492). In case of tetravariate combination, the best statistics are shown by MTI, logW, I2 and I1,7 (r =0.8162, s = 0.5468). In case of pentavariate combination, the best statistics are shown by MTI, logW, S, I2 and I1,7 (r = 0.7019, s = 0.549). In cases of hexavariate correlation the results are quite good and the correlation coefficient r (0.7063) is obtained as a best one from the correlation of MTI, S I2, I1, I1,7 and logW with the BA (biological activity). The low value of standard deviation (0.5479) and high value of F-ratio (8.129) supports the findings. The mathematical model from above variables is shown in equation (1). logIC50 = 7.3644 10-4 MTI – 0.0173 S – 0.9519 I – 0.3466 I + 0.5664 I1,7 – 3.508 LogW + 10.0314 (1) The estimation of logIC50 values were essential to confirm our findings between the best suited model and the observed values. The observedand calculated logIC50 values obtained from equation (1) are presented in table III. Using the equation (1) we can describe the effect of substituents. It is observed that the substituents at 1 and 7 positions and together increase the biological activity, while the substituents at 1 and 2 positions have a negative impact on the activity quantitatively. Similarly, in case of physicochemical properties best results of univariate correlation are shown by logP (r = –0.574, s = 0.6056). Similarly for bivariate correlation the best correlation coefficient is shown by logP and I (r = 0.6776, s = 0.5491) and Research Journal of Chemical Sciences ___________________________________________________________ ISSN 2231-606XVol. 4(6), 18-22, June (2014) Res. J. Chem. Sci. International Science Congress Association 21 the best results for trivariate correlation are shown by logP, Pol and I(r = 0.7186, s = 0.5242). In the case of tetravariate correlation compatible results are obtained by the correlation of logP, ST, Pol, and I2 (r = 0.7695, s = 0.4860). The pentavariate correlation shows more compatibility than the tetravariate correlation by logP, ST, Pol, I, and I3,8 (r = 0.7948, s = 0.4665). For the hexavariate correlation the results are encouraging but the better results are obtained with I, I3,8, I1,7, logP, surface tension (ST) and polarizability (Pol) with the r value of 0.8153. The high value of F-ratio (16.192) and the low value of standard error (0.4496) supports the above finding. The mathematical model obtained from above variables can be given by equation (2): logIC50 = – 0.5520 logP – 0.0628 ST + 0.1792 Pol – 0.6754 I2 + 0.4947 I3,8 – 0.4340 I1,7 – 0.1839 (2) The observedand calculated values of logIC50 obtained from equation, (2) are presented in table 3. Using the equation (2) we can describe the effect of substituent. It is observed that the substituent at 3 or 8 position increases the biological activity, whereas the substitutions at position-2 and together at 1 and 7-position have a negative impact on biological activity. The results are little contrary to the one observed with the graph theoretical descriptors but can very well be seen that the negative impact is comparatively very low in case of substituent at positions-1 and 7. Conclusion The indicator parameters were successfully used to explore the substituent effect. On the basis of equation (2) we conclude that the substituent at 2nd position has a negative impact on the biological activity quantitatively, i.e, presence of substituent at position decreases the biological activity (logIC50) and the substituent at 3rd or 8th position show the direct relationship and will help increase in biological activity quantitatively, i.e., the presence of the substituent at positions-3 and 8 play a significant role to enhance the biological activity (logIC50). Based on the results and discussion made above conclusion may be drawn - no single molecular descriptors or physicochemical properties could be used individually for QSAR studies. In multivariate correlations more than the graph theoretical descriptors the physicochemical properties have shown significant correlation. Thus, physicochemical property studies are most suited for understanding QSAR of anthracyclines. Table-3 Observed and calculated logIC50 of substituted anthracyclines (ANTHs) ANTHs logIC50(Obs.) logIC50 a Residual logIC50 b Residual 1 1.602 1.4956 0.1063 1.4366 0.1653 2 1.23 1.0841 0.1458 1.7131 -0.4836 3 0.869 1.105 -0.236 0.7767 0.0922 4 0.708 0.6935 0.0144 1.0548 -0.3468 5 0.973 1.5629 -0.5899 1.4095 -0.4365 6 0.908 1.0841 -0.1761 1.6672 -0.7592 7 0.301 1.1722 -0.8712 0.721 -0.42 8 0.255 1.2395 -0.9845 0.6805 -0.4255 9 0.462 0.7952 -0.3332 0.6001 -0.1381 10 1.114 0.8771 0.2368 1.5741 -0.4601 11 1 1.8575 -0.8575 1.3394 -0.3394 12 0.176 1.4711 -1.2951 0.5934 -0.4174 13 0.58 1.0596 -0.4796 0.9646 -0.3846 14 0.255 1.5384 -1.2834 0.5339 -0.2789 15 0.342 1.1269 -0.7849 0.9296 -0.5876 16 0.544 1.8593 -1.3153 0.8485 -0.3045 17 1.982 1.2464 0.7355 1.3508 0.6311 18 2.587 1.8852 0.7017 2.3358 0.2511 19 2.663 1.4737 1.1892 2.2361 0.4268 20 1.813 1.0873 0.7256 1.4195 0.3934 21 1.875 1.566 0.3089 1.0361 0.8388 22 2.58 1.1906 1.3893 2.276 0.3039 23 1.477 1.4644 0.0125 1.6139 -0.1369 Research Journal of Chemical Sciences ___________________________________________________________ ISSN 2231-606XVol. 4(6), 18-22, June (2014) Res. 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