QSAR Analyses on the Cell Cytotoxicity of Some
N-phenyl-O-phenylthionocarbamate Derivatives Using Comparative Molecular Field Analysis (CoMFA) Based on Different Alignment Approaches and
Holographic Quantitative Structure-Activity Relationship (HQSAR)
Nack-Do Sung*, Pyung-Keun Myung1, Min-Gyu Seong, Seong-Jae Yu and Su-La Choi1 Division of Applied Biology and Chemistry, Research Center for Transgenic Cloned Pigs, College of Agriculture & Life Sciences, Chungnam National University, Daejeon 305-764, Korea
1Department of Pharmacology, College of Pharmacy, Chungnam National University, Daejeon 305-764, Korea Received October 30, 2003; Accepted December 15, 2003
A series of 22 new N-phenyl-O-(4-methoxyphenyl)thionocarbamate derivatives were synthesized and examined in vitro for cytotoxicity on human skin cancer cell line, SK-MEL-28 cells, by MTT assay. 3D- QSAR/CoMFA and HQSAR models were developed to guide the rational design of novel analogs exhibiting improved biological property. Two models exhibiting excellent self-consistency (r2> 0.90) and predictability (q2> 0.50) were developed. For alignment, atom fit (r2= 0.985 & q2= 0.526) and field fit (r2= 0.996 & q2= 0.495) were applied to compare the predictability of each model in CoMFA. Quality of HQSAR (r2= 0.967 & q2= 0.807) was slightly higher than that of CoMFA in terms of cross-validated predictive correlation coefficient, q2 values, but showed relatively poor predictability. Cytotoxicity (pI50) against SK-MEL-28 cells, based on CoMFA analysis results, exhibited a good correlation. Reliable results could be obtained using the two models for predicting in vitro cytotoxicity (pI50) against SK- MEL-28 cells of thionocabamate derivatives.
Key words: Cell cytotoxicity, human skin cancer cell line, SK-MEL-28 cells, 3D-QSAR/CoMFA, HQSAR, N-phenyl-O-(4-methoxyphenyl)thionocarbamates.
Recent studies have brought about several research strategies in anticancer drug discovery recognized as one of the important fields in human health and in pharmaceutical chemistry. Because cell cytotoxicity assay is widely used to evaluate the anticancer activity of newly synthesized compounds, in these studies cell cytotoxicity was measured for a series of 22 new N-phenyl-O-(4-methoxyphenyl) thionocarbamate derivatives using MTT method.
Thionocarbamate derivatives were examined for fungicidal activity in vitro against gray mold (Botrytis cinerea) and capsicum phytophthora (phytophthora capsici).1) One of the carbamates, pyrrolidine dithiocarbamate, was reported to have protective effect against oxidative stress in endothelial cells.2) In addition, thiuramdisulfide, a metabolite of dithiocarbamate, was found to induce apoptosis in the cell line, and prolinedithiocarbamate and diethyldithiocarbamate were revealed to be cancer chemopreventive agents.3) However, dithiocarbamates and diethyldithiocarbamate suppress
hematoiesis through copper-dependent mechanism4), and radiation-induced apoptosuism using zinc, respectively.5) Currently, the effect of disulfiram, a member of the dithiocarbamate family, on apoptosis of melanoma cells in vitro is being explored.6)
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In this study, on the basis of 3D-QSAR/CoMFA using different alignment scheme and HQSAR models for 22 new data set compounds, N-phenyl-O-(4-methoxyphenyl) thionocarbamate derivatives, we attempted to elucidate a structure-activity relationship to provide useful guidelines for the rational design of novel analogs exhibiting improved cytotoxicity on human skin cancer cell line, SK-MEL-28 cells.
*Corresponding author
Phone: +82-42-821-6737; Fax: +82-42-825-3306 E-mail: [email protected]
Abbreviations: 3D QSAR, Three dimensional quantitative structure activity relationship; CoMFA, Comparative molecular field analysis;
HQSAR, holographic quantitative structure-activity relationship; MTT, microculture tetrazolium; PLS, partial least- squares.
Materials and Methods
Chemistry & data set. Twenty-two new N-sub. phenyl- O-(4-ethoxyphenyl)thionocarbamate derivatives were synthesized by the reaction of N-(4-methoxy)phenylchloro- thionoformate and substituted anilines described previously.1) Cytotoxicity (IC50) of the synthesized derivatives was measured by the following cell culture method, and negative logarism (pI50) of IC50 values were used to construct a predictive QSAR model. Data set contains derivatives with various substituents (Sub.) on N-phenyl ring in phenylthionocarbamate derivatives (Table 1).
Cell culture. Human skin cancer cell line, SK-MEL-28 (ATCC, HTB72), was maintained in an RPMI-1640 medium supplemented with 10% fetal-bovine serum inactivated at 56oC for 30 min, 100 units · ml−1 penicillin, and 100µg · ml−1 streptomycin. Human cervical carcinoma HeLa cells were maintained in DMEM supplemented with 10% fetal bovine serum inactivated at 56oC for 30 min, 100 units · ml−1 penicillin, 100µg · ml−1 streptomycin, low glucose, L- glutamine, 110 mg · l−1 sodium pyruvate, and pyridoxine hydrochloride. All cells were grown at 37oC in a humidified atmosphere of 5% carbon dioxide.
Cytotoxicity for SK-MEL-28. Cell viability was evaluated by a microculture tetrazolium reduction assay using 3-(4,5-dimethylthiazol-2-yl)-2,5-diphenyltetrazolium bromide (MTT) method.11) Exponential growing cells (1 × 104 cells/
well) were inoculated into 96-well plates supplemented with
100µl RPMI-1640 for SK-MEL-28 cells. After 24 h, the cells were treated according to the concentrations of the derivatives.
After the treated cells were incubated for 48 h, 50µl MTT (5 mg · ml−1, Sigma) was added to the plates, which were then incubated at 37oC for 6. To dissolve formazan, the medium was removed, and 100µl of the acidified isopropanol was added, followed by 20µl sodium dodesyl sulfate (3%). The plates were then measured at 540 nm using a microplate reader (THERMO max, Molecular devices, USA). IC50 values were determined by plotting the concentration of the derivatives versus the survival ratio of the treated cells. The negative logarism (pI50) of IC50 values was used to construct the predictive QSAR model.
Molecular modeling. Conformations of the derivatives were examined by systemic search and decided based on low energy conformation using the “conformation search” module of SYBYL.12) They were fully optimized by molecular mechanics using MMFF94 force field13) for geometry optimization, and the MMFF94 charge was computed as an atomic charge in each case. In the absence of experimental evidences on the binding conformation, the structure, which is based on the global minimum conformation, was used, a standard practice in most cases of CoMFA studies.14) The conformations of other data set compounds were derived from the optimized conformation for compound 9 using the
“Building” module of SYBYL.12) Because a single conformation is assumed for a ligand binding to a receptor in CoMFA study, the binding conformation of the molecules Table 1. CoMFA- and HQSAR-predicted vs. experimentally observed cytotoxicity (pI50) against human skin cancer cell line (SK-MEL-28) of N-phenyl-O-phenylthionocarbamate derivatives
No. N-Phenyl Sub. M.P.(oC) Exp. CoMFA HQSAR
Pre.a Dev. Pre.b Dev. Pre. Dev.
1 4-Acetyl 151~153 4.20 4.22 -0.02 4.21 -0.01 4.27 -0.07
2 4-Cyano 152~154 4.06 4.07 -0.01 4.08 -0.02 4.03 0.03
4 4-Nitro 118~120 4.04 3.98 0.06 4.08 -0.04 4.10 -0.06
5 4-Hydroxyl 166~168 4.42 4.51 -0.09 4.37 0.05 4.34 0.08
6 4-Methyl 110~112 4.35 4.40 -0.05 4.40 -0.05 4.31 0.04
7 4-Methoxy 164~166 4.30 4.39 -0.09 4.29 0.01 4.19 0.11
8 4-Chloro 159~161 4.50 4.43 0.07 4.46 0.04 4.45 0.05
9 4-Bromo 135~137 4.55 4.42 0.13 4.43 0.12 4.39 0.16
10 4-Fluoro 151~153 4.26 4.42 -0.16 4.30 -0.04 4.32 -0.06
11 3-Methoxy 138~141 4.26 4.26 0.00 4.27 -0.01 4.14 0.12
12 3-Cyano 124~126 4.11 4.15 -0.04 4.08 0.03 4.00 0.11
13 3-Hydroxyl 131~133 4.30 4.37 -0.07 4.28 0.02 4.34 -0.04
14 3-Chloro 110~112 4.13 4.25 -0.12 4.19 -0.06 4.23 -0.10
16 3-Bromo 87~89 4.32 4.17 0.15 4.32 0.00 4.35 -0.03
17 3-Fluoro 135~137 4.28 4.20 0.08 4.31 -0.03 4.27 0.01
18 2-Trifluoromethyl 72~74 1.46 1.53 -0.07 1.43 0.03 1.51 -0.05
20 2-Nitro 70~72 2.95 2.93 0.02 2.97 -0.02 2.77 0.18
21 2-Hydroxyl 146~148 3.52 3.42 0.10 3.53 -0.11 3.91 -0.39
22 2-Bromo 119~121 3.95 3.92 0.03 4.00 -0.05 4.08 -0.13
Abbreviation: Exp.: observed value; Pre.: predicted value; Dev.: difference in observed and predicted values.
aatom-fit alignment, bfield-fit alignment, test set: 3, 15, and 19 in CoMFA and HQSAR models.
being studied must be determined. In the absence of experimental evidences on the binding conformation of N- phenyl-O-phenylthionocarbamate, we generated structures based on the global minimum conformation, a standard practice in most CoMFA studies.
CoMFA-PLS. CoMFA requires each molecule to be aligned to ensure maximal superposition of steric and electrostatic fields of the training set compounds. In this study, two different CoMFA alignment schemes, atom fit and field fit, were applied.15,16) In CoMFA study most alignment rules employ a least-squares fitting of pharmacophoric elements between a designated template molecule and the other molecule in the training set.
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PLS method18) was applied for constructing predictive 3-D QSAR models to correlate variations in biological activity with those in the descriptors described in the previous section. The optimum number of principal components (PCs) was determined by the Leave-One-Out (LOO) cross-validated procedure.19) This procedure generates the smallest standard error of prediction by systematically excluding each compound from the training set, after which activity of each excluded compound is predicted by a model derived from the remaining compounds. The predicted activities of the left-out compounds allow the calculation of q2 and cross-validated standard error.
Based on the optimal number of PCs obtained, the final PLS analysis was carried out without cross-validation to generate a predictive QSAR model with a conventional correlation coefficient r2 and a non cross-validated standard error.
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Results and Discussion
CoMFA models for cytotoxicity. CoMFA generates a 3D-QSAR model by correlating the known biological/
physical activities for a set of compounds with their calculated steric (van der Waals) and electrostatic (Coulombic) field descriptors sampled on a regular 3D grid around each molecule. In these predictive QSAR models, the molecular field descriptors were used for CoMFA models. Once statistically validated, the 3D-QSAR models can be applied to predict the activities of untested compounds. To generate a CoMFA model, the molecules must be fully optimized and aligned (superimposed) with a common structural basis. The CoMFA models were constructed by applying cytotoxicity data (pI50) of the 19 training set compounds, based on the atom-fit and field-fit alignment schemes. In CoMFA study one of the most important processes is the alignment of molecule, which is a time-consuming step for determining conformation.7) To align our training set compounds, N-(4- bromophenyl)-O-(4-methoxyphenyl)thionocarbamate, 9, was used as a template molecule, and each training set compound was aligned based on O-phenyl ring and thionocarbamate backbone. The atom and field fit alignment schemes are shown in Fig. 1 (A: atom-fit alignment; B: field-fit alignment).
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CoMFA contour maps were generated as scalar products of coefficients and standard deviation associated with each
CoMFA column. Steric and electrostatic contour plots associated with the CoMFA prediction model using atom-fit and field-fit alignment schemes are shown in Fig. 2; the template compound, 9, was inserted to facilitate interpretation.
Enhanced potency is associated with adding/subtracting steric bulk and positive charge to the green/yellow and blue/red regions, respectively. The steric and electrostatic field contributions to the CoMFA model using atom fit alignment
were fixed at 57 and 43%, respectively. Plots of predicted versus observed pI50 values for CoMFA models using different alignment scheme are shown in Fig. 3. Each plot showed very good correlation between observed and predicted pI50 values, an indication that the constructed CoMFA model has good predictability.
Fig. 1. Orientation of representative molecules with respect to the template compound 9 achieved by atom fit (A) and field fit (B) alignment schemes.
Table 2. Statistical parameters of CoMFA models using different alignment schemes and HQSAR model.
Statistics CoMFA
HQSAR
Atom Fita Field Fita
Principal component 5.0 5.0 3.0
Number of data set compounds 19 19 19
q2 0.526 0.495 0.807
r2 0.985 0.996 0.967
Standard error 0.100 0.050
F 181.90 727.05
Stericb 56.80 54.10
Electrostaticb 43.20 45.90
Number of fragments 4~9
H-atom off
Best hologram length (bin) 61
aAlignment scheme, bRelative contribution ratio (%).
Fig. 2. CoMFA steric and electrostatic contour plots for inhi- bition activity (pI50) with the structure of compound 9 shown inside the field. Enhanced relative potency is associ- ated with adding/subtracting steric bulk from green/yellow regions and positive charge in the blue/red regions. (A) atom fit, (B) field fit.
Test Set Compounds. Three compounds, 3, 15, and 19, were excluded from the data set to serve as test set compounds in order to evaluate the predictability of the constructed CoMFA and HQSAR models. In this study constructed 3D- QSAR model using CoMFA showed good statistical quality and predictive QSAR results. Although the approximate nature of the pI50 values for these compounds precluded their use in the training set, these pI50 values could still be compared with those predicted by each QSAR model to evaluate the
constructed QSAR models. The observed pI50 values are examined along with the corresponding pI50 values predicted by the CoMFA models using different alignment schemes.
The pI50 values predicted by CoMFA model using atom fit alignment scheme were the most consistent with the experimental data. The results of the test-set compounds are summarized in Table 3, in which the observed pI50 values are listed along with the corresponding pI50 values predicted by the CoMFA models using atom fit alignment scheme. Figure 3 presents the plot of the predicted values by the resultant CoMFA model derived from the training set versus the observed values both for the compounds in the training (point) and test sets (circle). Results show that the predicted values by the model for both compounds are very close to the observed cytotoxicity.
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Table 3. Predicted pI50 values for test-sets compounds using HQSAR and CoMFA models.
No. N-Phenyl Sub. M.P. (oC) Exp.
CoMFA
HQSAR
Atom-fit Field-fit
Pre.a Dev. Pre. Dev. Pre. Dev.
3 4-Trifluoromethyl 155~157 4.10 4.22 -0.12 4.38 -0.28 2.95 1.15
15 3-Methyl 94~96 4.27 4.00 0.27 3.36 0.91 4.30 -0.03
19 2-Cyano 72~74 3.51 3.12 0.39 3.85 -0.34 3.75 -0.24
Dev.: difference in observed (Exp.) and predicted values (Pre).
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References
1. Sung, N. D. and Soung, M. K. (1999) Phenyl substituent effect on the fungicidal activity of N-phenyl-O-phenylthion- ocarbamate derivatives, Kor. J. Pestic., Sci., 3, 29-36.
2. Moellering, D., McAndrew, J., Jo, H. and Darley-Usmar, V.
M. (1999) Effect of pyrolidine dithiocarbamate on endothe- lial cells: Protection against oxidative stress, Free Radical Biol. Med., 26, 1138-1145.
3. Liu, G. Y., Frank, N., Bartsch, H. and Lin, J. K. (1998) Induction of apotosis by thiuramdisulfides, the reactive metabolites of dithiocarbamates, through coordinative modu- lation of NF-kappa B, c-fos/c-jun, and p53 proteins, Mol.
Carcinog. 22, 235-246.
4. Pyatt, D. W., Yang, Y., Le, A., Stillman, W. S. and Irons, R.
D. (2000) Dithiocarbamates inhibit hematopoiesis via a cop- per-dependent mechanism, Biochem. Biophys. Res. Commu.
274, 513-518.
5. Mathiue, J., Ferlat, S., Ballester, B., Platel, S., Herodin, F., Chancerelle, Y., Mestries, J. C. and Kergonou, J. F. (1996) Radiation-induced apotosis in thymocytes: inhibtion by diethyldithiocarbamate and zinc, Radiat. Res., 146, 652-659.
6. Cen, D., Gonzalez, R. I., Buckmeier, J. A., Kahlon, R. S., Tohidian, N. B. and Meyskens, F. L. Jr. (2002) Disulfiram Fig. 4. Plots of HQSAR-predicted vs. observed values of
cytotoxicity (pI50) for SK-MEL-28 cell line.
Fig. 5. HQSAR contribution map for active compound, 4- chloro substituent, 8.
induces apoptosis human melanoma cell via redox-related process, Mol. Cancer Ther. 1, 197-204.
7. Cramer, III, R., Patterson, D. E. and Bunce, J. D. (1998) Comparative molecular field analysis (CoMFA). 1. Effect of shape on binding of steroids to carrier proteins. J. Am.
Chem. Soc., 110, 5959-5967.
8. HQSAR Tripos Associates Inc., St. Louis, MO, USA.
9. Lowis, D. R. (1997) A new highly predictive QSAR tech- niqu. Vol. 1, No. 5., Tripos Techical Notes.
10. Sung, N. D. (2003) Development of new agrochemicals by quantitative structure-activity relationship (QSAR) methodol- ogy. III. 3D QSAR methodologies and computer-assisted molecular design (CAMD). Kor. J. Pesticide Sci., 7, 1-11.
11. Varmicheal, J., Degraff, W. G., Gazdar, A. F., Minna, J. D.
and Mitchell, J. B. (1987) Evalution of a tetrazolium-based semiautomated colorimetric assay: assessment of chemosen- sitivity testing, Cancer Res., 47, 936-942.
12. SYBYL molecular modeling system, Version 6.5, Tripos, Inc., St. Louis, MO 63144. USA, http://www.tripos.com/.
13. Halgren, T. A. (1996) Mercker molecular force field. I. Basis, form, scope, parameterization and performance of MMF94. J.
Comput. Chem. 17, 490-519.
14. Tong, W., Perkins, R., Strelitz, R., Collantes, E. R., Keenan, S., Welsh, W. J., Branham, W. S. and Sheehan, D. M. (1997) Quantitative structure-activity relationships (QSARs) for estro-
gen binding to estrogen receptor: predictions scross species.
Environ. Health Perspect, 105, 1116-1124.
15. Marshall G. R., Barry, C. D., Bosshard, H. E., Dammkoe- hler, R. A., Dunn, D. A. (1979) The conformational param- eter in drug design: active analog approach. In Olson, E. C., Christoffersen, R. E., (eds.) Computer-assisted drug design.
American Chemical Society, Washington, D.C. pp. 205-226.
16. Cramer, R. D., Clark, M., Simeroth, P., Patterson, D. E.
(1991) Recent developments in comparative molecular field analysis (CoMFA). In Silipo, C., Vitoria, A. (eds). QSAR:
rational approaches to the design of bioactive compounds.
Elsevier, Amsterdam, pp. 239-242.
17. Folkers, G., Merz, A., Rognan, D. (1993) CoMFA: Scope and Limitations. In 3D-QSAR in Drug Design; Kubiny, H.
Ed. ESCOM, Leiden, p. 443-485.
18. Cramer, III. R. (1993) Partial least squares (PLS): Its strengths and limitation. Perspect. Drug Discovery Des., 1, 269-278.
19. Malinowski, E. R. and Howery (1980) Factor Analyses in Chemistry, Wiely, New York.
20. Heritage, T. W. and Lowis, D. R. (1999) Molecular Holo- gram QSAR. Rational Drug design. Novel Methodology and Practical Applications. American Chemical Society.
Washington, DC. pp. 212-225.
