In Silico Prediction of Anticonvulsant Activity of
N-(2,2,2-Trichloro-1-hydroxyethyl)alkenyl- and –alkylarylcarboxamides
Pavlo V. Zadorozhnii*, Nina P. Popykhach, Vadym V. Kiselev, Ihor O. Pokotylo, Oxana V. Okhtina, Aleksandr V. Kharchenko
Department of Organic Substances and Pharmaceutical Preparations, Ukrainian State University of Chemical Technology, Gagarin Ave., 8, Dnipro 49005, Ukraine.
*Corresponding Author E-mail: torfp@i.ua
ABSTRACT:
In this paper, we have predicted the spectrum of the biological activity of N-(2,2,2-trichloro-1-hydroxyethyl)alkenyl- and -alkylarylcarboxamides using the PASS system, as well as their prospects as potential anticonvulsants. The probability of anticonvulsant activity has been shown to be considerably higher for -alkylarylcarboxamides than for -alkenylcarboxamides. For the structures analyzed, an acute toxicity assessment has been carried out in rats using the GUSAR program. We have selected the compounds leaders with the probability of anticonvulsant activity of more than 80%. The structures of the compounds leaders have been tested for compliance with Lipinski criteria.
KEYWORDS: PASS, GUSAR, Anticonvulsant, Toxic, LD50, Chloralcarboxamide, QSAR.
INTRODUCTION:
Epilepsy is a chronic disorder associated with abnormal electrical activity in the brain, which is characterized by recurrent, unprovoked seizures1. The search for new antiepileptic drugs is a very important and urgent task, which, on the one hand, is related to the practical needs of pharmacy and medicine, and on the other hand, allows a more detailed study of the biochemical and biophysical processes occurring in the brain2-6.
This article is a continuation of our series of studies on the search for potential antiepileptic drugs among N-(2,2,2-trichloro-1-hydroxyethyl)carboxamides7. At this stage of the research, the sample is taken from SciFinder library of structures using modern QSAR methods8. The prediction of anticonvulsant activity of N-(2,2,2-trichloro-1-hydroxyethyl)carboxamides is carried out using the PASS system9, whereas an acute toxicity estimate in rats is carried out using the GUSAR program10. The selected compounds leaders are tested for compliance with Lipinski criteria11.
MATERIAL AND METHODS:
The spectrum of biological activity is the whole complex of biological effects that a substance is able to induce under certain conditions of interaction with biological objects taking into account the peculiarities of specific experiments. It is this concept that the principle of operation of the computer system PASS is based on (Prediction of Activity Spectra for Substances)12. It predicts the spectrum of biological activity of organic compounds based on their structural formulas, i.e. biological activity of a substance is considered as its internal property depending only on its structure9.
The PASS system provides an estimate of "similarity / difference" of a testing compound towards the known biologically active substances. The results of prediction are presented as a list of possible types of activity with calculated estimates of the presence (Pa) and absence (Pi) having a value between 0 and 1. The higher the value (Pa) and the lower (Pi) for the specific activity, the greater is the probability of detection of this activity under the experimental conditions9.
Using the PASS system we have analyzed the spectrum of biological activity of N-(2,2,2-trichloro-1-hydroxyethyl)alkenyl- and -alkylarylcarboxamides. The prediction has been prepared for individual structures, taken from the international database SciFinder (https://scifinder.cas.org). We have introduced the structures of compounds for biological activity prediction using the graphical editor Marvin Sketch at the official website (http://www.pharmaexpert.ru). Then, these structures have been sent to the server as MNA-descriptors (Multilevel Neighborhoods of Atoms)9,12. The results of the prediction of biological activity spectrum have been visualized on the display and saved by "copy-paste".
RESULTS AND DISCUSSION:
Depending on the nature of the substituent of the amide fragment, all the compounds analyzed were divided into 2 groups: 1) alkenyl and 2) alkylaryl (Fig. 1). The probability of anticonvulsant activity of the testing compounds is 39.9% -86.9% (see Table 1-2).
Fig. 1: General view of
N-(2,2,2-trichloro-1-hydroxyethyl)carbox-amides.
Table 1: The results of prediction of anticonvulsant activity and acute toxicity of N-(2,2,2-trichloro-1-hydroxyethyl)alkenylcarboxamides.
Compound
|
R |
CAS Number |
Anticonvulsant activity |
Toxic, LD50 mg/kg |
||
Pa |
Pi |
IV |
Oral. |
|||
1.1 |
|
16328-30-6 |
0.652 |
0.013 |
52.950 |
1460.000 |
1.2 |
|
14825-93-5 |
0.637 |
0.014 |
41.780 |
1394.000 |
1.3 |
|
13855-75-9 |
0.585 |
0.021 |
50.280 |
1652.000 |
1.4 |
|
863036-54-8 |
0.641 |
0.014 |
62.170 |
1922.000 |
1.5 |
|
118641-32-0 |
0.570 |
0.023 |
132.500 |
2072.000 |
1.6 |
|
902268-09-1 |
0.456 |
0.046 |
90.360 |
247.800 |
1.7 |
|
863036-55-9 |
0.690 |
0.010 |
87.140 |
1086.000 |
1.8 |
|
863036-53-7 |
0.642 |
0.014 |
76.590 |
2557.000 |
1.9 |
|
898472-67-8 |
0.631 |
0.015 |
51.000 |
2737.000 |
1.10 |
|
863036-63-9 |
0.616 |
0.017 |
77.790 |
1744.000 |
1.11 |
|
51249-46-8 |
0.616 |
0.017 |
77.790 |
1744.000 |
1.12 |
|
863036-56-0 |
0.747 |
0.008 |
80.130 |
3425.000 |
1.13 |
|
863036-60-6 |
0.667 |
0.012 |
119.300 |
3364.000 |
1.14 |
|
863036-62-8 |
0.536 |
0.028 |
170.600 |
1167.000 |
1.15 |
|
863036-57-1 |
0.502 |
0.035 |
107.500 |
1075.000 |
1.16 |
|
863036-61-7 |
0.580 |
0.022 |
48.850 |
3119.000 |
1.17 |
|
56737-15-6 |
0.558 |
0.025 |
58.340 |
1201.000 |
1.18 |
|
863036-64-0 |
0.399 |
0.065 |
187.600 |
1770.000 |
1.19 |
|
863036-63-9 |
0.433 |
0.052 |
136.700 |
1364.000 |
1.20 |
|
475045-74-0 |
0.608 |
0.018 |
44.040 |
1256.000 |
1.21 |
|
1322257-68-0 |
0.608 |
0.018 |
44.040 |
1256.000 |
Table 2: The results of prediction of anticonvulsant activity and acute toxicity of N-(2,2,2-trichloro-1-hydroxyethyl) alkylarylcarboxamides.
Compound |
R |
CAS Number |
Anticonvulsant activity |
Toxic, LD50 mg/kg |
||
Pa |
Pi |
IV |
Oral. |
|||
2.1 |
|
56737-19-0 |
0.769 |
0.007 |
117.600 |
1841.000 |
2.2 |
|
726-20-5 |
0.762 |
0.007 |
142.100 |
914.700 |
2.3 |
|
90842-52-7 |
0.814 |
0.005 |
159.500 |
498.500 |
2.4 |
|
98946-72-6 |
0.790 |
0.005 |
156.800 |
1337.000 |
2.5 |
|
99184-27-7 |
0.620 |
0.017 |
680.800 |
1695.000 |
2.6 |
|
5961-23-9 |
0.750 |
0.008 |
128.200 |
1743.000 |
2.7 |
|
1017522-73-4 |
0.663 |
0.012 |
56.380 |
2493.000 |
2.8 |
|
1179480-23-9 |
0.626 |
0.028 |
333.200 |
1488.000 |
2.9 |
|
102541-39-9 |
0.740 |
0.008 |
187.300 |
1365.000 |
2.10 |
|
924637-59-2 |
0.623 |
0.016 |
69.470 |
2143.000 |
2.11 |
|
351004-16-5 |
0.594 |
0.020 |
126.400 |
4709.000 |
2.12 |
|
24131-09-7 |
0.752 |
0.007 |
49.840 |
2357.000 |
2.13 |
|
105789-04-6 |
0.576 |
0.023 |
144.100 |
982.700 |
2.14 |
|
323591-85-1 |
0.678 |
0.011 |
96.560 |
2896.000 |
2.15 |
|
861053-13-6 |
0.869 |
0.004 |
91.520 |
2719.000 |
The probability of anticonvulsant activity is significantly higher for N-(2,2,2-trichloro-1-hydroxyethyl)alkylaryl carboxamides than for N-(2,2,2-trichloro-1-hydroxyethyl)alkenylcarboxamides (Table 1, 2, Fig. 2). For –alkylarylcarboxamides the calculated LD50 value for the intravenous route of administration is higher than for -alkenylcarboxamides, that is, the toxicity of -alkyl aryl carboxamides is lower (Fig. 3). A similar dependence is also observed for the toxicity in the oral route of administration (Fig. 4).
Fig. 2: Probability of anticonvulsant activity of N-(2,2,2-trichloro-1-hydroxyethyl)alkenyl- (black) and -alkylarylcarboxamides (red).
Fig. 3: The estimated LD50 value in intravenous administration of N-(2,2,2-trichloro-1-hydroxyethyl)alkenyl- (black) and -alkylarylcarboxamides (red).
Fig. 4. The estimated LD50 value in oral administration of N-(2,2,2-trichloro-1-hydroxyethyl)alkenyl- (black) and -alkylarylcarboxamides (red).
Compounds leaders were assigned to the structures having the probability of anticonvulsant activity more than 80%. Of all the compounds studied, two structures (2.3) and (2.15) will have the greatest probability of anticonvulsant activity. The compounds leaders were tested for compliance with Lipinski criteria using the Molinspiration web resource (http://www.molinspiration.com/cgi-bin/properties). All the compounds meet the criteria (Table 3).
Table 3: The test of the compounds leaders for compliance with Lipinski criteria
Entry |
Compound |
Mr |
logP |
Rot.Bond |
Hdonor |
Hacceptor |
1 |
2.3 |
317.00 |
3.09 |
4 |
2 |
3 |
2 |
2.15 |
324.63 |
3.83 |
6 |
2 |
3 |
CONCLUSION:
In this paper, using the PASS system, we have predicted the spectrum of the biological activity of N-(2,2,2-trichloro-1-hydroxyethyl)alkenyl- and -alkylarylcarboxamides. They are shown to be potential anticonvulsants. It is shown that for -alkylarylcarboxamides the probability of anticonvulsant activity is significantly higher than for -alkenylcarboxamides. Acute toxicity in rats has been evaluated for the structures analyzed with the use of the GUSAR program. We have selected the compounds leaders, which have the probability of anticonvulsant activity more than 80%. The structures of the compounds leaders have been tested for compliance with Lipinski criteria.
CONFLICT OF INTEREST:
The authors declare no conflict of interest.
REFERENCES:
1. Rowles J, Olsen M. Perspectives on the development of antioxidant antiepileptogenic agents. Mini Rev. Med. Chem. 2012; 12(10): 1015−1027. doi:10.2174/138955712802762266
2. Smyth MD, Barbaro NM, Baraban SC. Effects of antiepileptic drugs on induced epileptiform activity in a rat model of dysplasia. Epilepsy Res. 2002; 50(3): 251-264. doi:10.1016/S0920-1211(02)00051-7
3. Cross JH. Neurodevelopmental effects of anti-epileptic drugs. Epilepsy Res. 2010; 88(1): 1-10. doi:10.1016/j.eplepsyres.2009.08.015
4. Cook AM, Bensalem-Owen MK. Mechanisms of action of antiepileptic drugs. Therapy 2011; 8(3): 307–313.
5. Lee SK. Old versus New: Why Do We Need New Antiepileptic Drugs? J. Epilepsy Res. 2014; 4(2): 39-44. doi:10.14581/jer.14010
6. Videman M, Tokariev A, Stjerna S, Roivainen R, Gaily E, Vanhatalo S. Effects of prenatal antiepileptic drug exposure on newborn brain activity. Epilepsia 2016; 57(2): 252–262. doi:10.1111/epi.13281
7. Zadorozhnii PV, Kiselev VV, Pokotylo IO, Okhtina OV, Kharchenko AV. In silico prediction of anticonvulsant activity of N-(2,2,2-trichloro-1-hydroxyethyl)alkylcarboxamides. J. Chem. Pharm. Sci. 2017; 10(3): 1099-1105.
8. Ambulgekar Sh. QSAR models and guidance: a review. Asian J. Res. Chem. Pharm. Sci. 2016; 4(1): 1-10.
9. Filimonov DA, Lagunin AA, Gloriozova TA, Rudik AV, Druzhilovskii DS, Pogodin PV, Poroikov VV. Prediction of the biological activity spectra of organic compounds using the PASS online web resource. Chem. Heterocycl. Comp. 2014; 50(3): 444-457. doi:10.1007/s10593-014-1496-1
10. Lagunin A, Zakharov A, Filimonov D, Poroikov V. QSAR modelling of rat acute toxicity on the basis of PASS prediction. Mol. Inf. 2011; 30(2-3): 241-250. doi:10.1002/minf.201000151
11. Lipinski CA, Lombardo F, Dominy BW, Feeney PJ. Experimental and computational approaches to estimate solubility and permeability in drug discovery and development settings. Adv. Drug Delivery Rev. 1997; 23(1-3): 3-25. doi:10.1016/S0169-409X(00)00129-0
12. Sadym AV, Lagunin AA, Filimonov DA, Poroikov VV. Internet System Predicting the Spectrum of Biological Activity of Chemical Compounds. Pharmaceut. Chem. J. 2002; 36(10): 538-543. doi:10.1023/A:1022402425883
Received on 18.09.2017 Modified on 18.10.2017
Accepted on 01.11.2017 © RJPT All right reserved
Research J. Pharm. and Tech 2018; 11(2):711-716.
DOI: 10.5958/0974-360X.2018.00134.8