Structural Model Design of Xanthone Compounds as Antimalarial Activity

International Journal of Scientific and Research Publications, Volume 5, Issue 7, July 05 ISSN 505 Structural Model Design of Xanthone Compounds as Antimalarial Activity Dr.Amanatie, Prof. Dr.. Jumina, Prof. Dr Mustofa, M.Kes, Prof. Dr M.Hanafi 4, Dr. Ria Armunanto Department of Chemistry, Faculty Mathematics and Natural Sciences, Yogyakarta State University, Yogyakarta, Indonesia Department of Chemistry, Faculty of Mathematics and Natural Sciences, GadjahMada University, Yogyakarta, Indonesia, Jumina Prof. Dr. at Faculty Medicine of GadjahMada University, Yogyakarta, Indonesia 4 Prof. Dr. Sci at PK, LIPI Serpong Tangerang, Indonesia Abstract Xanthones and their derivatives have been reported to exhibit inhibitory activities towards Plasmodium falciparum. In order to provide deep insight into the correlation between inhibitory activities and structures of xanthones, linear regression method was employed to establish QSAR models for 6 xanthones derivatives with different structure. The accuracy and predictive power of the proposed QSAR model were verified, optimized, and validated by semi empiric PM method. The result of this research showed that the best QSAR model was model. Index Terms Antiplasmodialagents, Structural Models Design, xanthone. M I. INTRODUCTION alaria is the main health problems in subtropical and tropical countries. There are 05 countries in the world in malaria endemic and more than 500 million cases or more than.7 million people deaths by malaria each year. In Indonesia, malaria is still become a health problem, particularly in eastern Indonesia. In the year 00 the Annual ParactemiaIncidence (API) reported that there were 75 558 cases. Further, annual malaria incidents are more than.48 million and deaths among 7.5 million is people of Indonesian. The spread of malaria is so quickly and wide because of its parasites resistant, especially Plasmodium falciparum toward chloroquine.because of that, researchers want to find new malarial drugs which more potential. Several approaches had been conducted to find new antimalarial drugs, for examples through modification of chemical structure of antimalarial compounds so that they have higher antiplasmodium activity. Exploration of natural product as drugs traditionally has been applied for malaria treatment (Mustofa, 000). For instance, derivatives of xanthone (Figure. ) compounds have been known as potential antimalarial activity against Plasmodium falciparum. Based on description above and considering some factors as mentioned by previous researchers, therefore the writer conducted the QSAR analysis toward 6 compounds of xanthone derivatives (6) as shown at table (). The 6 of xanthone derivatives series have been tested the antiplasmodial activity by Likhitwitayawuidet al (998). The QSAR analysis in this research is different from that which has been conducted by Mustofa and Tahir (00). Mustofa and Tahir (00) used atomic netcharge as a descriptor with semi empiric AM method, this research used not only atomic netcharge as descriptors but also dipole moment, P and polarizability with semi empiric PM method. Then, by performing QSAR analysis towards 6 xanthone derivatives, model equations were determined to predict and to get information about antimalarial activity of xanthone compounds. The use of the electronic structure of the molecule based on semi empirical calculation have also been reported. This can lead to useful model equations for the prediction of antimalarial activity from the calculated atomic net charges (Mudasir, et al, 00 and Tahir, et al, 005). Furthermore, this approach has also been proven to be very helpful in obtaining an indication concerning the active center of molecules (Polman, et al, 998). II. METHODE Model Setup The atomic electron net charges, dipole moment, P, polarizability have been evaluated by Mulliken population analysis based on standard semi empiricaly PM method using chemical software HyperChem version 7,0. PM calculation is known to give quite satisfactory results for the chemistry of xanthone compounds derivatives containing hydroxyl groups. Geometries for stationary points were identified by medium of energy with respect to geometric parameters using PolakRibiere algorithm included in HyperChem package with convergence limit of 0.0 kcal mol A. Many possible linearrelationships between calculated atomic charges and antimalarial activity were evaluated by using SPSS statistical software. The main objective is to obtain a model with a few variables descriptors as possible but still describing the antimalarial activity satisfactorily. Hanschequation of these relations are: IC 50 = A(P) B (P) +C (E)+ D (ρσ ) + E + () III. RESULT AND DISCUSSION The calculated PM descriptors, such as atomic net charges, dipole moment, P and polarizability, of xanthone derivatives used in models are determined based on eq. aqnd listed in table (). This allows the exclusion of less relevant atoms and provides opportunity for a gradual evaluation of the atoms influencing significantly of the antimalarial activity of molecules. Selection for the Best Design Model

International Journal of Scientific and Research Publications, Volume 5, Issue 7, July 05 ISSN 505 In order to obtain the best design model that correlates with independent variables (descriptors) and dependent variable (antimalarial activity), regression analysis using SPSS software package have been performed. Twelve independent variables consisting of 5 atomic net charges qc, qc, qc, qc 4, qc 5, qc 6, qc 8, qc 9, qc 0, qc, qc, qc, qc 4, qc 5 and qo 7. As well as net charge of xanthone and dipole moment, P and polarizability were included in the model set up. At the first step, all variables are included in the model and the less relevant variables were then eliminated gradual from the model. This procedure finally gave QSAR models andvalue R, F, Fhit/Ftab are as listed in table (). The statistical parameters obtained Standard Deviation (SD) model and model from linear according to Eq. (). collected in table (4). From table it is evaluated that some models can immediately be eliminated because their F elevated /F table value are lower than unity, which means that the models are statistically not exist or rejected. In Table 5, content Model, qc,qc,qc, moment dipole, P, IC 50eval and IC 50exp. In Table. 6, content Model, qco7,qc,qc, moment dipole, IC50eval and IC50 exp. In table. 7. Content, IC 50 eval and IC 50 exp Model In table 8.content IC 50 eval and IC 50 exp Model and in t able9.conten Value PRESS or Value Y On the other hand, it is clearly shown that there are selected models which show a relatively good correlation between antimalarial activity of the best model among models selected as listed in table and 4 is not adequate only by comparing the R size, especially for model, and because their R values are 0,970. and 0,84. Therefore we should also consider other statistical parameter of F (model significance) and SD (Standard Deviation), it seems that model it the best model from the viewpoint of statistical parameters, Standard Deviation (SD) and F value (model significance). However, this model consisted of almost all descriptors avoilable and thus it is not the simplest model and therefore this model is excluded from selection. Now, the remaining possible models are model and. By comparing the parameters F and SD of two models, it is easily observed that model is the best model because it has highest f and relatively lower SD and contains only 4 variables which means that the model is the quite sample, thus model is selected as the best QSAR model. This model could therefore retionalize the search for new xanthonederivatives which have been antimalarial activity of these series xanthone derivatives, necessary due to the rapid resistance development of Plasmodium falciparum. Model Validation It has been conducted that model is the best model from the point of view of statistical parameters of the linear analysis. To see how good the model predict the activity of the xanthone series, calculation of the antimalarial activity for each xanthoneanalogs has been performed using model and results (predicted (IC 50 ) was plotted against those obtained by experiments (observed IC 50 ) by regression method to see how well they correlate each other (Fig ). It is obvious from this figure that model predicts very well the activity of the series of xanthone as indicated by the values of the slope and correlation coefficient (R) of the plot which is approaching unity, is 0.970 and 0.84, respectively. Validation of the best model may be evaluated by comparing PRESS (predictive residual sum of square) value of the model in table (5) and the model in table (6). This parameter is defined as sum of square of difference between observed activity and predicted activity calculated by the corresponding model. The model with the smallest value of PRESS is the best model because it implies that the difference between the observed and the predict activities is minimal. Results of PRESS calculation for each model as listed in table (9) again confirms that model is the most reliable model because in induces the smallest PRESS value. Evaluation Model and Prediction of Most Influencing Atoms. According to the fitting coefficient values obtained by the variation of descriptors included as summarized in table, we may concluded that atom C, C, C and qring seem to be the most responsible atoms for the antiplasmodium inhibition activity of the molecules. Although some models listed in table (5 and 6) also include dipole moment and Carbon atoms of xanthone, the values of their coefficient are relatively small indicating that these atoms are of rather less importance and therefore can be eliminated from the model. By we immediately observed that C, C and C assume prominent role in most as compared with the others, and that either O 7, C and C has a significant influence, respectively. IV. CONCLUSION We have used a semi empirical molecular orbital calculation PM to study the correlation between atomic net charge and the antimalarial activity of a series antiplasmodium against wild type of the xanthone derivatives. The best overall correlation is given by the computed molecular properties of atomic net charges of xanthone derivatives and moment dipole, P, polarity. It is gratifying to observe that the most influencing atoms of the xanthone, in term of possible mode of docking of the molecule well to the targeted xanthone derivatives. It has also been demonstrated from this result that semiempirical PM, although induces possible error sources, still seem to be a necessary and acceptable compromise for quantum calculations on series of xanthone derivatives of this size, including the search for most influencing atoms. O O Figure. The molecular xanthone compounds

International Journal of Scientific and Research Publications, Volume 5, Issue 7, July 05 ISSN 505 Table. The 6 of xanthone derivatives series and the antiplasmodial activity No Compounds IC 50 4 5 6 7 8 9 0 4 5 6 4 5 6 7 8 9 0 4 5 6.70.00.88.00.0.60.70.40.00.00.00.0.40.48.8.48 Fig 8.Compound 7 Fig 9.Compound 8 Fig0Compound 9 FigCompound 0 Fig. Compound Fig. Compound Fig Compound Fig Compound Fig 4 Compound Fig 5 Compound 4 Fig4 Compound Fig 5Compound 4 Fig 6 Compound 5 Fig 7. Compound 6

International Journal of Scientific and Research Publications, Volume 5, Issue 7, July 05 4 ISSN 505 Fig 6. Compound 5 Fig 7Compound 6 Table.Value atomic net charges, dipole moment, P and polarizability of xanthone derivatives used in models qc qc qc qc4 qc5 qc6 qo7 0.06 0.08 0.08 0. 0. 0.0 0.9 0.6 0.6 0.0 0.6 0.6 0.0 0.6 0. 0. 0.6 0. 0.7 0.0 0.9 0.6 0.6 0.0 0.5 0.6 0.0 0. 0.07 0.0 0.0 0.05 0.09 0.05 0.0 0.06 0.0 0.9 0. 0. 0.7 0. 0.09 0.00 0.05 0. 0.09 0.05 0.0 0.05 0.0 0.9 0. 0.09 0.6 0. 0. 0.6 0.0 0.8 0.07 0.05 0.0 0.06 0.0 0.9 0. 0.08 0.0 0.09 0.00 0.0 0. 0.04 0.07 0.04 0. 0.04 0. 0.0 0.0 0.08 0.0 0.5 0.5 0.04 0. 0.4 0. 0.0 0.4 0.4 0.06 0.5 0.4 0. 0.0 0.5 0.4 0.05 0.5 0.4 0. 0.0 0.5 0.5 0.05 0.5 0.4 0. qc8 qc9 qc0 qc qc qc qc4 qo5 0. 0.9 0.5 0.04 0.5 0.0 0. 0. 0.40 0.5 0.04 0.5 0.0 0. 0. 0.4 0.5 0.04 0.5 0.00 0.8 0.5 0.6 0.40 0.0 0.6 0.4 0.04 0.4 0.09 0.9 0.9 0.06 0.4 0.0 0.05 0. 0. 0.40 0.5 0.04 0.5 0.0 0. 0. 0.40 0.0 0.06 0.9 0.00 0. 0. 0.40 0.0 0.06 0.0 0.00 0. 0.08 0.4 0.06 0.0 0.0 0.0 0.8 0.08 0.40 0.05 0.0 0.0 0.0 0. 0. 0.5 0.40 0.0 0. 0.0 0. 0. 0. 0.9 0.0 0. 0.4 0.0 0. 0.0 0.4 0.4 0.0 0. 0.4 0.0 0.0 0.4 0.4 0.0 0. 0.4 0.0 0.0 0.4 0.4 0.0 0. 0.4 0.0 0.6 0.7 0.4 0. 0.6 0. 0. 0.0 moment P polarz massa volume IC 50.54 0.45.5.0 60.56.70.4 0.45.5.5 60.8.00.96.47.5 8.0 64.9.88.5.47.5 8.0 6.66.00.99.47.5 9.0 69.8.0.0.50.79 44.0 640. 0.60.75.5.5 60.0 659.74 0.70 4.08 4.5 5.06 76.0 679.77 0.40.46 4.55 5.06 76.0 67.94 0.00.7.57 5.70 9.0 699.08.00.80 4.9 4.4 46.4 946.99.00 5. 5.6 4.84 486.9,8.0.0 5.45.4 58.7 50.67,9.6 0.40 5.49.5 56.88 506.64,489.98 0.48.90 0.5 57.5 5.69,6.67.68 0.9 49.7 45.55,.75 0.48

International Journal of Scientific and Research Publications, Volume 5, Issue 7, July 05 5 ISSN 505 N o No Table. Value R, F, Feval/Ftable Model R Feval Feval/Ftable I 0.97 8.87 0.00 II 0.65.8 0.96 III 0.84 4.5 0.045 IV 0.7.4 0.4 V 0.786.46 0.8 VI 0.757. 0.46 VII 0.854.48 0.96 VIII 0.9.67 0.6 IX 0.65.8 5 X 0.8.8 0.68 XI 0.9 4.689 0.056 XII 0.84.746 0.6 Table 4. Standard Deviation (SD) model and model No Model Standard Deviation (SD) Model SD= 4.490 Model SD=0.878 Com pou nd Com poun d qc 0. 4 0. 5 0. 4 6 0. qo7 qc Table 5. Model Table. 6 Model qc qc 0.0 0. 0.0 0. 0.0 0. 0.6 0. qc Mo m dip ol P Mo m dipo l IC 50ev IC 50eval IC 50ex.54.078.7 0.0 0.04 0.5.4.98008.0 0.0 0.04 0.5 5 0.0.90.9078 0. 0. 4 6 0.6.68 0.8098 0.48 al 5.45.4 8.75 08 5.49.5 8.550 8.90 0.5 4.645 9.68 5.8 0.9 88 IC 50e xp 0.40 0.48 0.5 0.48 p No 0. 0. Table. 7. IC 50 eval and IC 50 exp Model IC 50eval IC 50 exp Δ Δ 8.7508 0.40 8.506 69.40806 8.5508 0.48 8.0708 65.85474 4.6459 0.5 4.4959 0.68 4 5.888 0.48 5.04488 5.4508444 No Table 8. IC 50 eval and IC 50 exp Model IC 50eval IC 50 exp Δ Δ.078.7.78.8856.98008.0 0.099 0.0009.9078.778.9858 4 0.8098 0.48 0.98 0.0496 Table9. Value Y No. Model Value (Y) Model Y = 4.7408 Model Y = 0.96578 V. ACKNOWLEDGE. Research and Technology Ministry, KNRTRI, Focus Field of Health and Medicine Techniques Jakarta, Indonesia.. Doctor Grant, Gadjah Mada University, Indonesia. REFERENCES [] Hansch,C and Leo A. (995), Exploring QSAR, Fundamentals and applications in Chemistry and Bioy, ACS Professional Reference.Book, Americant. Chemical Society: Washington, DC, p9. [] Karelson, M and Lebanov, V.S., QuantumChemical Descriptors in QSAR/ QSPR studies. J. Chem.Rev. 96.0704. [] Kimura, T., Miyashita,Y., Funatsu, K. And Sasaki S, (996) Quantitative Structur Activity relationships of The Synthetic Substrates for Elastase Enzyme Using Nonlinear least square regression. J.Chem.Inf.Comput.Sci.6, 8589. [4] Likhitwitayawuid,K.Chanmahasathien,W.Ruangrungsi,N.Krungkrai,J., 998, Planta medica,, vol 64, Issue, 88. [5] Likhitwitayawuid, K,Phadungcharosen,T,Krungkrai,J., 998, Plata Medica, vol 64,Issue,707. [6] Mudasir., Herawati, Ethica,SN, and Wijaya,K (00a) QSAR analysis of antimalariaprimaquine and mefloquineana based on quantum chemical parameters. The 9 IUPAC Congres and 86 Conference of Canadian Sosiety for Chemistry, Ottawa, Canada, August 05, 00, p. 95 [7] Mudasir., Wijaya,K and Hadinugroho, D( 00 b). QSAR analysis of substituted,4 benzoquinnone herbicides Using Atomic NetCharge As Descriptors [8] Mudasir, tahir,i and Astuti,IP (00c), QSAR analysis of,,4 Thiadiazoline fungides, based on molecular descriptor calculated Using AM method. Ind.J.Chem,.947. [9] Pranowo,HD, 00 Computation of Chemistry, Department of Chemistry, Faculty of Mathematics and Natural Sciences, GadjahMada University, Yogyakarta, Indonesia

International Journal of Scientific and Research Publications, Volume 5, Issue 7, July 05 6 ISSN 505 [0] Mustofa 000 In vitro and invivo activity of the divers of natural and syntesicantimalarial:effect of potentialisator and the possibility of mechanism of actions. DisertasiUniversity of Montpellier I, France. [] Tahir, I., Mudasir, Yulistia,I, and Mustofa (005). Quantitative Structure Activity Relationship Analysis (QSAR) of VincadifformineAnaous as the Antiplasmodial Compounds of the Chloroquinesensible strain, Indon, J.Chem, 5, 5560. AUTHORS First Author Dr.Amanatie, Department of Chemistry, Faculty Mathematics and Natural Sciences, Yogyakarta State University, Yogyakarta, Indonesia, amanatie@uny.ac.id, Phone: 087944477 Telp. (074)896605 Second Author Prof. Dr.. Jumina, Department of Chemistry, Faculty of Mathematics and Natural Sciences, GadjahMada University, Yogyakarta, Indonesia, Jumina @ugm.ac.id Third Author Prof. Dr Mustofa, M.Kes, Prof. Dr. at Faculty Medicine of GadjahMada University, Yogyakarta, Indonesia, mustofa jogja@yahoo.com Fourth Author Prof. Dr M.Hanafi, Prof. Dr. Sci at PK, LIPI Serpong Tangerang, Indonesia, hanafi 4@yahoo.com Fifth Author Dr. Ria Armunanto, Department of Chemistry, Faculty of Mathematics and Natural Sciences, GadjahMada University, Yogyakarta, Indonesia, ria.armunanto@ugm.ac.id