Методология поиска противоопухолевого действия (2-оксо-2 h-[1, 2, 4]триазино[2, 3с]хиназолин-6-ил) тионов с помощью QSAR и докинговых исследований

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UDC 547. 873'-856. 1:615. 277. 3]-047. 72:167
I. S. Nosulenko, O. Yu. Voskoboynik, O. M. Antypenko, G. G. Berest, S. I. Kovalenko Methodology for prediction of anticancer action of (2-oxo-2H-[1,2,4]triazino[2,3-c]-quinazolin-6-yl)
thiones via QSAR and docking studies
Zaporizhzhia State Medical University Key words: Quinazolines, Triazines, Casein Kinase II, Quantitative Structure-A ctivity Relationship, Molecular Docking Simulation.
Aimed to elaborate new group of protein kinase inhibitors we conducted receptor-based screening (docking, QSAR modeling) and biochemical testing for derivatives of (2-oxo-2H-[1,2,4]triazino[2,3-c]quinazolin-6-yl)thiones.
Methods and results. This study allowed identifying of new potential anticancer compounds among (2-oxo-2H-[1,2,4]triazino[2,3-c]quinazolin-6-yl)thiones'- derivatives.
Conclusion. Obtained data may be used for the development of more active and selective inhibitors of protein CK2 kinase. Besides that QSAR-models which were created may be used for planning of chemical modification of structure aimed to creation of new anticancer agents.
Методолопя пошуку протипухлинноТ ди (2-оксо-2Д-[1,2,4]триазино[2,3-с]-хшазолш-6-ш)тюшв за допомогою i докшгових дослвджень
I. С. Носуленко, О. Ю. Воскобойник, О. М. Антипенко, Г. Г. Берест, С. I. Коваленко
З метою розробки ново! групи iнгiбiторiв протешкшаз виконали рецептор-орiентований вiргуальний скрингнг (докгнг, QSAR-моделювання) та бiохiмiчне тестування ряду похщних (2-оксо-2Н-[1,2,4]триазино[2,3-с]хшазолш-6-ш)тюну. Встановили, що щ спо-луки е перспективними об'-ектами для розробки активних i селективних iнгiбiторiв проте! н СК2 кiнази. Здшснене дослiдження дало можливiсть зробити значний внесок у пошук нових ефективних протипухлинних сполук у ряду (2-оксо-2Н-[1,2,4]триазино[2,3-с] хiназолiн-6-iл)тiонiв та може використовуватися як теоретичне пiдrрунтя для структурно! оптимiзацii, що спрямовано на створення нових лжарських засобгв.
Ключовi слова: хтазолти, триазини, казеш ктаза II, юльюсна характеристика взаемозв '-язку структура — активтсть, молекулярний докшг.
Запор1зький медичний журнал. — 2015. — № 1 (88). — С. 99−104
Методология поиска противоопухолевого действия (2-оксо-2Д-[1,2,4]триазино[2,3-с]хиназолин-6-ил)тионов с помощью и докинговых исследований
И. С. Носуленко, А. Ю. Воскобойник, А. Н. Антипенко, Г. Г. Берест, С. И. Коваленко
С целью разработки новой группы ингибиторов протеинкиназ провели рецептор-ориентированный виртуальный скрининг (докинг, QSAR-моделирование) и биохимическое тестирование для ряда производных (2-оксо-2Я-[1,2,4]триазино[2,3-с]хиназолин-6-ил)тиона. Установлено, что данные вещества являются перспективными для разработки более активных и селективных ингибиторов протеин СК2 киназы. Проведенное исследование позволило внести значительный вклад в поиск новых эффективных противоопухолевых соединений в ряду (2-оксо-2Я-[1,2,4]триазино[2,3-с]хиназолин-6-ил)тионов и может быть использовано в качестве теоретической базы для структурной оптимизации, направленной на создание новых лекарственных препаратов. Ключевые слова: хиназолины, триазины, казеин киназа II, количественная характеристика взаимосвязи структура — активность, молекулярный докинг.
Запорожский медицинский журнал. — 2015. — № 1 (88). — С. 99−104
It is well known, that derivatives of quinazoline have significant anticancer potential, that has been proved by our previous articles [2,10,11], and also by many other researchers. What is even more persuasive, that based on quinazoline skeleton a set of anticancer drugs is being used as an inhibitor of the tyrosine kinase activity associated with EGFR (epidermal growth factor receptor), HER2/neu (Human EGFR type 2), vascular endothelial growth factor receptor (VEGFR) and the RET-tyrosine kinase, (Erlotinib, Lapatinib, Vandetanib) [1,4−6]. In most cases such drugs are prescribed to treat Non-small lung cancer, generally in combination with other drugs (Capecitabine, Letrozole, Gemcitabine, others).
Such, without any doubt focused search of a new active anticancer compound, among quinazoline derivatives is a cutting-edge theme.
So the aim of our work was to reveal the probable mechanism of action based on QSAR-analysis, docking and interaction with
available protein kinase, namely CK2 [18]. To find out reliable QSAR-model is the task, solving with, would help a lot for future work not only for our research group, but for many others too.
Materials and methods
Anticancer activity. The library of compounds, that consists of 76 derivatives of (2-oxo-2^-[1,2,4]triazino[2,3-c]quina-zolin-6-yl)thiones was obtained, as a part of PhD research. A range of (2-oxo-2^-[1,2,4]triazino[2,3-c]quinazolin-6-yl) thiones is a promising object for a search of effective anticancer compounds. Cooperating with international research program (Development Therapeutic Program, DTP) of National Cancer Institute (NCI) these derivatives were preliminary tested in vitro for 60 cancer cell lines at a concentration of 10−5 M [3]. Some of them were investigated for dose dependent action in 5 concentrations (10−4-10−8 M). But the amount of those compounds is not enough to built QSAR-model. Detailed description of the procedure is written in http: //dtp. nci. nih. gov/. So data of
© I. S. Nosulenko, O. Yu. Voskoboynik, O. M. Antypenko, G. G. Berest, S. I. Kovalenko, 2015
NHRZ
RI = Me, Bz, Ar, hctaryl. R3 = Alk, Bz, Phenetyl, Ar, hetaiyl- R,=] [, I. Br- R4=l [, F
Fig. 1. Base core of structures used for QSAR calculations.
growth percent inhibition of cell lines in one concentration was used to build the QSAR model.
Main skeleton with radicals chosen for this work, to built QSAR models is displayed in fig. 1. The detailed description of the synthesis and structure elucidation is presented in our previous papers [2,10,11].
QSAR and statistical analysis. First of all, all molecules were built by MarvinSketch 6.3.0 [12]. Then they were preliminary optimized by program HyperChem8.0.8 using molecular mechanical MM+ algorithm combined with semi-empirical PM3 molecular modeling method with a maximum number of cycles and Polak-Ribiere (Conjugate Gradient) algorithm. Molecular mechanics has been used to produce more realistic geometry values for the majority of organic molecules owing to the fact of being highly parameterized. The next step was a re-optimization of the MM+ optimized structures by applying semi-empirical PM3 molecular modeling method. Obtained files were further used for calculations.
Descriptors were calculated using Dragon (& gt- 1600 descriptors). The definition of all used molecular descriptors and the calculation procedures were summarized elsewhere [16,17]. Optimized structures were also used for calculation of additional important quantum-chemical parameters (final heat of formation, total energy, electronic energy, core-core repulsion, ionization potential, homo, lumo), that were also used as descriptors. M0PAC2012 was used to do mentioned computations [15]. Besides, scoring functions obtained by Autodock4 to CK2 kinase was added as a separate descriptor. It is a crucial parameter as it estimates the free energy of ligand binding to the receptor.
The correlation coefficients for all pair of descriptor variables used in the models were evaluated to identify highly correlated descriptors in order to detect redundancy in the data set. Hence, descriptors with constant variables and near-constant variables were excluded from the further consideration (r& gt-0. 95).
The genetic algorithm (GA) and multiple linear regression analysis (MLRA) were used to select the descriptors and to generate the correlation models that relate the structural features to the cell growth percent of different cancer cell lines. The combination of the GA-MLRA technique was applied to obtain the best descriptors among 1671 calculated overall (DRAGON, M0PAC2012, Autodock4), and to construct QSAR models using the QSARINS 2.2.1 [8]
Calculation of QSAR-models was conducted separately for each line of non-small lung cancer (A549/ATCC, EKVX, HOP-62, HOP-92, NCI-H226, NCI-H23, NCI-H322M, NCI-H460, NCI-H522). Growth percent according to the NCI protocol wasn'-t converted to any other value, it was used in original version to built models. Some cell lines were given the value of -999, which means, that they were not tested.
Preliminary calculation was made to find the cancer line, which according to the statistical parameters correlated with the calculated descriptors most accurately. Thus, the amount of generation algorithm setup was set until 5 descriptors, and generation per size was established to the value of 500, and the division into training and test sets was performed automatically at a ratio of 80 to 20 percent relatively. Models, which showed statistical significance according to the parameters at a higher level (r2& gt-0. 5), were selected for a more thorough rendering. For these lines the following options were given: the amount of generation algorithm setup was set until 7 descriptors, and generation per size was established to the value of 10 000. Seventy-six derivatives of (2-oxo-2^-[1,2,4]triazino[2,3-c]quinazolin-6-yl) thiones were spited into training and test sets and the division, was made such, as to establish equal distribution of substances of high and moderate percentage of inhibition of cell growth.
Docking. Receptor-oriented flexible docking was performed by software package Autodock 4.2.6 [13]. Ligands and mac-romolecules were prepared by software packages Vega ZZ (command line) [14] and MGL Tools 1.5.6 [13]. Autodock works with ligands and receptor molecules of PDBQT format, containing the coordinates of atoms and partial charges. Mol2 format was converted to PDBQT by means of Vega program, hydrogen atoms from non-polar atoms were removed and force field AUTODOCK was added. Changing of the receptor format from PDB to PDBQT and formation of the cards for docking was carried out in programs MGL Tools and AutoGrid.
The catalytic subunit of protein kinase CK2 was chosen as the target for the docking, namely, CK2 kinase, that was crystallized with inhibitor CX-494 (PDB code 3NSZ) [7]. Water molecules, ions and ligands were deleted from original PDB file.
The following parameters were set for the docking: step of forward movement equal 2 A, quaternion angle — 50°, the torsion angle — 50°. The degree and coefficient of torsion freedom were 2 and 0. 274 respectively. Cluster tolerance — 2 A. External energy of the grid — 1000, the maximum initial energy — 0, the maximum number of attempts — 10 000. The number of structures in the population — 300, the maximum number of stages assessing energy — 1 000 000, the maximum number of generations — 27 000, the number of structures that move to the next generation — 1, the level of genetic mutations — 0. 02, crossover rate — 0. 8, way of crossover — arithmetic. a-Parameter of Gaussian distribution was equal to 0, p-parameter of Gaussian distribution — 1. The number of iterations of Lamarck genetic algorithm search is 10 for each ligand.
Visual analysis of compounds'- interaction with amino acid residues of ATP-binding pocket of protein kinase CK2 was performed in the program Discovery Studio Visualizer 4.0.
© I. S. Nosulenko, O. Yu. Voskoboynik, O. M. Antypenko, G. G. Berest, S. I. Kovalenko, 2015
Inhibition of protein kinase. Expressed in insect cells Sf21 (Upstate-Millipore) human CK2 kinase domain was used for in vitro test. Compounds'- inhibitory activity to protein kinase CK2 was determined by inclusion of radioactive phosphorus in the peptide substrate during its kinase phosphorylation in the presence of y-32P-ATP [9].
The total volume of the reaction mixture was 30L. First to 3 pL of reaction buffer (200 mM of Tris-HCl (pH 7. 5), 500 mM KCl, 100 mM MgCl2) was added 0.5L of peptide substrate solution (RRRDDDSDDD (New England Biolabs), 135 ^M), 15.5L of water and 0. 05L of protein solution (0. 01 protein kinase relative activity). Then 1 microliter of inhibitor was added and after 3 minutes the reaction was initiated by adding to 20L of reaction mixture volume 10L 150M ATP solution, which also contained 1 microcurie of y-32P-ATP. The final concentration of ATP in the reaction mixture was 50M. The reaction mixture was incubated for 30 min at 30 oC. Reaction was stopped by adding 8l of 5% phosphoric acid. The entire volume of sample was carried over onto a P-cellulose filter «Whatman P81», which were washed three times for 5 min with 0. 75% phosphoric acid. Filters were dried, and their radioactivity was measured on a scintillation counter PerkinElmer Tri-Carb 2800-TR. As a negative control we used a sample of 1L DMSO (final concentration was 3. 8%) instead of the inhibitor. The degree of inhibition of protein kinase was determined by the ratio of 32P in samples with inhibitor and in his absence.
Results and Discussion
According to the GA-MLRA we have obtained two good predictive models of non-small lung cancer (cell line EKVX and NCI-H522). The obtained equations consist of 6 descriptors. Most of the descriptors, used in models are among 3D ones (RDF, 3D-MoRSE, WHIM and GETAWAY descriptors). Such, it is clear, that not only presence of pharmacophore is important for biological activity, but also its spatial arrangement.
GP = 1 92. 6738(±98. 2228) xSIC2 + 28. 0662 (±20. 2474) xEEig08r-8. 185 9(±2. 23 95) xRDF130u-145. 1481(±57. 7604) xE3p-45. 6237(±12. 5782) x nThiazoles+5 1. 2009(±22. 3854) xB07[N-u]-132. 9902(±102. 6973) (Eqn. 1)
Statistical data: training set («=49- r2=0. 7583- RMSE tr=13. 3049- 5=14. 3709- F= 21. 9629- Q2LOO= 0. 6945) — prediction set («=12- r2=0. 6951- RMSE ext = 61. 788O), where GP — growth percent, n — number of studied compounds, r2 — squared regression coefficient, RMSE — root mean square error, F — variance ratio, Fisher coefficient, Q2LOO — weighted correlation coefficient by leave-one out method, and s — standard error.
According to the equation, higher value of SIC2, EEig08r and B07[N-O] is responsible for higher growth percent and responsively for lower anticancer activity. While higher value of RDF130u, E3p and nThiazoles decreases growth percent.
S1C2 EEtgOBr RDFl30u ?3p nThtuolK B07{N-0] Ext. tndSMIt
Fig. 2. Correlation of predicted versus experimental GP for model of non-small cell lung cancer, cell line NCI-H522 (Eqn. 1)
Fig. 3. Correlation of predicted versus experimental GP for model of non-small cell lung cancer, cell line EKVX (Eqn. 2)
GP = 3. 9 8 9 7 (± 2. 2 4 4 6) x RD F 1 4 5 u -4. 6468(±0. 808) xRDF080e-27. 9718 (±17. 9189) xMor16v+ 47. 6055(±21. 3013) xMor19v-767. 041 (±377. 4656)*G2m-30. 5949(±13. 5572) xH-048+267. 3181(±69. 2583) (Eqn. 2)
Statistical data: training set (n=48- r2= 0. 7878- RMSE tr= 10. 0414- s=10. 8460- F= 25. 9905- Q2LOO= 0. 7098) — prediction set (n=13- r2=0. 7177- RMSE ext = 22. 0403).
Significance of descriptor contribution can be seen in table 1. The QSAR model containing only one descriptor has value of r2=0. 3981. It consists of RDF080e descriptor, that corresponds to radial distribution function — 8. 0/weighted by atomic Sanderson electronegativities. It is among the RDF descriptors, obtained by radial basis functions centered on different interatomic distances (from 0. 5A to 15.5 A).
Ranking ligand binding was performed by the energy of the kinase domain. It uses a scoring function program of Autodock4.
© I. S. Nosulenko, O. Yu. Voskoboynik, O. M. Antypenko, G. G. Berest, S. I. Kovalenko, 2015
Table 1
Statistical characteristics of multi-variable model (cell line EKVX)
Desc. amount Descriptors Training set
r2 RMSE tr s F Q2, OO
1 1RDF080e 0,3981 16,9119 17,2679 31,09 0,3446
2 RDF080e 2R6p+ 0,5168 15,1532 15,6396 24,5989 0,443
3 FINAL HEAT OF FORMATION RDF080e 3HATS2p 0,5798 14,1317 14,7464 20,6932 0,5081
4 4MATS2p 5RDF080u 6RDF010v 7Hypertens-80 0,6076 13,6547 14,4097 17,0355 0,5295
5 8BELp2 9RDF100u RDF080e 10E1s 11R1v 0,7276 11,3769 12,1447 22,9742 0,6359
6 12RDF145u RDF080e 13Mor16v 13Mor19v 14G2m 15H-048 0,7878 10,0414 10,846 25,9905 0,7098
Note: 1RDF080e — Radial Distribution Function — 8. 0/weighted by atomic Sanderson electronegativities- 2R6p+ - R maximal autocorrelation of lag 6/weighted by atomic polarizabilities- 3HATS2p — leverage-weighted autocorrelation of lag 2/weighted by atomic polarizabilities- 4MATS2p — Moran autocorrelation — lag 2/weighted by atomic polarizabilities- 5RDF080u — Radial Distribution Function — 8. 0/unweighted- 6RDF010v — Radial Distribution Function — 1. 0/weighted by atomic van der Waals volumes- 7Hypertens-80 — Ghose-Viswanadhan-Wendoloski antihypertensive-like index at 80%- 8BELp2 — Lowest eigenvalue n. 2 of Burden matrix/weighted by atomic polarizabilities- 9RDF100u — Radial Distribution Function — 10. 0/unweighted- 10E1s — 1st component accessibility directional WHIM index/weighted by atomic electrotopological states- 11R1v — R autocorrelation of lag 1/weighted by atomic van der Waals volumes- 12RDF145u — corresponds to: Radial Distribution Function — 14. 5/unweighted- 13Mor16v, Mor19v — 3D-MoRSE — signal 16/19/weighted by atomic van der Waals volumes- 14G2m — 2st component symmetry directional WHIM index/weighted by atomic masses- 15H-048 — H attached to C2(sp3)/C1(sp2)/C0(sp).
Scoring function Autodock4 evaluates the free energy of ligand binding to the receptor in kcal/mol, smaller values correspond to more potent inhibitors.
In the table 2 ten compounds with the best affinity are present. We also show hydrogen bonds that were observed in the docking study with the residues of CK2 kinase.
For in vivo test on CK2 kinase we have selected two compounds. Namely, ^-(2-fluorobenzyl)-2-((3-methyl-2-oxo-2H-[1,2,4]triazino[2,3-c]quinazolin-6-yl)thio)acetamide (MTB-67) with mean value of scoring function and 2-((2-oxo-
3-phenyl-2^-[1,2,4]triazino[2,3-c]quinazolin-6-yl)thio) acetamide (MTB-13) with moderate scoring function. Second one turned to be quite active. Such, the percentage of rest of kinase activity, in concentration 33M is 80 and 4 respectively. Cell growth percent of EKVX cell line according to the NCI protocol of N-(2-fluorobenzyl)-2-((3-methyl-2-oxo-2#-[1,2,4] triazino[2,3-c]quinazolin-6-yl)thio)acetamide is 89. 22 and predicted by equation is 75. 51. Such figures are comparable to measurements with CK2 kinase.
Table 2
Some of the active compounds according to the docking study
№ Comp. (№ NCI) Chemical formula Scoring function Hydrogen bond cell line EKVX cell line NCI-H522
Exp. Pred. Exp. Pred.
MTB-97 754 975 o -10,96 LYS68, VAL116, ASN118 101,15 91,28 85,87 92,29
MTB-100 754 976 o } cl -10,53 VAL116 77,80 73,11 41,24 56,87
MTB-36 752 628 o -10,27 VAL116 25,61 16,93 -49,92 74,97
MTB-62 753 035 -10,22 HIS160, ASP175, GLU114 97,36 104,32 104,33 76,90
© I. S. Nosulenko, O. Yu. Voskoboynik, O. M. Antypenko, G. G. Berest, S. I. Kovalenko, 2015
Table 2 (Continued)
№ Comp. (№ NCI) Chemical formula Scoring function Hydrogen bond cell line EKVX cell line NCI-H522
Exp. Pred. Exp. Pred.
MTB-70 753 040 -10,05 VAL116 103,47 98,28 83,69 89,31
MTB-52 753 027 -9,95 GLU114 67,95 92,36 91,22 73,05
MTB-60 753 033 rW^nO -9,88 HIS160, ASP175 31,44 22,80 62,5 67,99
MTB-59 753 032 -9,86 ASP175, GLU114 111,06 92,99 92,27 69,22
MTB-66 752 624 … $/XL -9,86 GLU114 73,85 82,89 — 77,43
MTB-67 753 037 -9,77 VAL116, ASN118, GLU114 89,22 75,51 113,34 95,68
MTB-128 754 998 -9,30 LYS68, VAL116, HIS160 95,89 99,58 107,94 90,87
MTB-121 (-) -9,01 LYS68, VAL116 — 110,98 — 3,73
MTB-123 754 989 O N-Д -8,63 LYS68, VAL116 91,32 95,34 77,43 86,13
MTB-13 (-) O N S l -8,13 LYS68, ARG47 — 80,48 — 118,55
© I. S. Nosulenko, O. Yu. Voskoboynik, O. M. Antypenko, G. G. Berest, S. I. Kovalenko, 2015
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Information about authors:
Nosulenko I.S., Post-graduate Student of Department of Organic and Bioorganic Chemistry, Zaporizhzhia State Medical University. Voskoboynik O.Y., Associate Professor of Department of Organic and Bioorganic Chemistry, Zaporizhzhia State Medical University, PhD. Antypenko O.M., Post-graduate Student of Department of Organic and Bioorganic Chemistry, Zaporizhzhia State Medical University. Berest G.G., Associate Professor of Department of Pharmacognosy, Pharmaceutical Chemistry and Medicinal Preparations Technology, Post-graduate Education Faculty, Zaporizhzhia State Medical University, PhD.
Kovalenko S.I., Head of Department of Organic and Bioorganic Chemistry, Zaporizhzhia State Medical University, Professor, Doctor of Pharmaceutical Sciences, E-mail: kovalenkosergiy@gmail. com. BidoMocmi про aemopie:
Носуленко 1.С., асшрант каф. оргашчно! i бюоргашчно! ximi, Зап^зький державний медичний ушверситет. Воскобойник О. Ю., к. фарм. н., доцент каф. оргатчно! i бюоргашчно! хжц, Заж^зький державний медичний утверситет. Антипенко О. М., астрант каф. оргашчно! i бiооргaнiчноI xiMii, Зап^зький державний медичний ушверситет. Берест Г. Г., к. фарм. н., ст. викладач каф. фармакогнози, фармацевтично! xirni! та технологи лшв ФПО, Зап^зький державний медичний ушверситет.
Коваленко С. 1. д. фарм. н., професор, зав. каф. органчик! i бiооргaнiчноI xiмiI, Зaпорiзький державний медичний уншерситет, E-mail: kovalenkosergiy@gmail. com. Сведения об авторах:
Носуленко И. С., аспирант каф. органической и биоорганической химии, Запорожский государственный медицинский университет. Воскобойник А. Ю, к. фарм. н., доцент каф. органической и биоорганической химии, Запорожский государственный медицинский университет.
Антипенко А. Н., аспирант каф. органической и биоорганической химии, Запорожский государственный медицинский университет. Берест Г. Г., к. фарм. н., ст. преподаватель каф. фармакогнозии, фармацевтической химии и технологии лекарств ФПО, Запорожский государственный медицинский университет.
Коваленко С. И., д. фарм. н., профессор, зав. каф. органической и биоорганической химии, Запорожский государственный медицинский университет, E-mail: kovalenkosergiy@gmail. com.
Поступила в редакцию 25. 11. 2014 г.

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