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Acta Physico-Chimica Sinca  2018, Vol. 34 Issue (3): 314-322    DOI: 10.3866/PKU.WHXB201709042
Special Issue: Special issue for Chemical Concepts from Density Functional Theory
ARTICLE     
Toxicity Prediction of Organoph Osphorus Chemical Reactivity Compounds Based on Conceptual DFT
Xiaoqin DING1,*(),Junjie DING1,Dayu LI1,Li PAN1,Chengxin PEI2
1 Beijing Institute of Pharmaceutical Chemistry, Beijing 102205, P. R. China
2 State Key Laboratory of NBC Protection for Civilian, Beijing 102205, P. R. China
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Abstract  

Following the exceptional success of density functional theory (DFT) in the realm of quantum chemistry, the conceptual DFT (CDFT) method has been widely used for describing the dynamic reactivity index of reactive chemicals in recent years. Reactive chemicals refer to those that bind covalently to biological macromolecules; in other words, the binding of the ligand with the receptor or enzyme involved with the breakage of the old bond and the process of formation of the new bond. Organophosphorus AChE irreversible inhibitors are reactive chemicals. In the present work, we calculated the reactivity descriptors for AChE irreversible inhibitors (organophosphate compounds), including some pesticides and chemical warfare agents, by the CDFT method at the B3LYP/6-311++G(2d, 3p)/gas, B3LYP/6-311++G(2d, 3p)/CPCM/water, MP2/6-311++G(2d, 3p)/gas, MP2/6-311++G(2d, 3p)/CPCM/water levels, in order to analyze their reactivity and determine the optimal parameters for calculation. Reactivity descriptors such as chemical potential (μ), vertical ionization energy (I), vertical electronic affinity (A), molecular absolute hardness (η), electrophilicity (ω), condensed atomic Fukui function, and varied natural bond orbital (NBO) bond order, were used to identify changes in the reactivity of these compounds in the gas and aqueous phases with the conductor-like polarizable continuum model (CPCM) model. The values of the reactivity descriptors and quantitative structure-property relationship (QSPR) models indicated that: the center of the phosphor atom (P) was the nucleophilic reaction site with AChE for most of selected compounds; substituted tertiaryamine protonization in organophosphorus compounds greatly enhanced the electrophilic attackingability of the P reaction center; and as a whole, conformation did not have a significant effect on the reactivity for theDFT/B3LYP method, with an exception for the MP2 method which showed a comparative instability in results. The initial QSPR model in training sets of pLD50 with stepwise regression analysis shows that the B3LYP/6-311++G(2d, 3p)/gas level can provide a better result than the MP2 level and in the water phase, and provides a good representation of the molecular structure-toxicity relationship. These predictions for the compounds surpass those obtained by conventional QSPR equations, which do not consider electron transfer in the phosphorylated or aged process, thereby providing unreliable predictions. The proposed reactivity concept using the CDFT principle possesses a definite physical meaning, reflects the dynamic reactivity from the ground state of the molecular structure, and can be applied to toxicity predictions for AChE irreversible inhibitors with greater precision and stability.



Key wordsCDFT      Reactivity descriptors      Organophosphate      AChE irreversible inhibitors      QSPR     
Received: 04 August 2017      Published: 04 September 2017
MSC2000:  O641  
Corresponding Authors: Xiaoqin DING     E-mail: dingxiaoqin2008@126.com
Cite this article:

Xiaoqin DING,Junjie DING,Dayu LI,Li PAN,Chengxin PEI. Toxicity Prediction of Organoph Osphorus Chemical Reactivity Compounds Based on Conceptual DFT. Acta Physico-Chimica Sinca, 2018, 34(3): 314-322.

URL:

http://www.whxb.pku.edu.cn/10.3866/PKU.WHXB201709042     OR     http://www.whxb.pku.edu.cn/Y2018/V34/I3/314

Name pLD50(rat)/(μg·kg-1) HOMO/(a.u.) LUMO/(a.u.) (L-H)a/eV P-charge fP(r) b
sarin 3.66 -0.30853 -0.01552 7.97327 2.39323 0.04233
soman 4.01 -0.30182 -0.01629 7.76973 2.39693 0.03169
tabun 3.31 -0.27529 -0.02279 6.87093 2.30801 0.04865
amiton 3.22 -0.21574 -0.01174 5.55117 2.22079 -0.00280
amiton+NHc 3.22 -0.37238 -0.14426 6.20751 2.25657 0.03566
VX 4.43 -0.20997 -0.01374 5.33973 2.00131 -0.00064
VX+NH 4.43 -0.37102 -0.13840 6.32996 2.02916 0.04545
GV 3.72 -0.23216 -0.01579 5.88777 2.55768 0.01366
GV+NH 3.72 -0.37391 -0.16792 5.60532 2.56211 0.03655
GV+NH-Ninvertd 3.72 -0.37658 -0.16635 5.72069 2.55776 0.03711
methamidophos 1.82 -0.25407 -0.01860 6.40752 2.10542 0.04128
paraoxon 3.15 -0.27701 -0.10030 4.80856 2.59854 0.01961
parathion 2.70 -0.26027 -0.10055 4.34624 2.06917 0.01876
dichlorvos 1.82 -0.25291 -0.02184 6.28778 2.58576 0.02170
DMMPA -0.38 -0.24616 -0.01311 6.34166 2.53048 0.02865
leptophos 0.87 -0.25020 -0.06096 5.14952 1.83612 0.01947
diisopropyle 2.89 -0.30782 -0.01442 7.98388 2.61035 0.02460
EDMM 4.27 -0.22366 -0.01408 5.70301 1.99680 0.00515
EDMM+NH 4.27 -0.37686 -0.16074 5.88097 2.01884 0.04085
trichlorfon 0.80 -0.30221 -0.05833 6.63637 2.38787 0.03141
Table 1 The pLD50value, frontier orbital energy and gap, NBO charge and fp-(r) in training sets.
Name I/eV A/eV (I-A)/eV μ/eV η/eV ω/eV
sarin 10.62338 0.40002 10.22336 -5.51170 5.11168 2.97151
soman 10.12265 0.34783 9.77482 -5.23524 4.88741 2.80391
tabun 9.53891 0.32610 9.21280 -4.93250 4.60640 2.64084
amiton 7.72701 0.38711 7.33990 -4.05706 3.66995 2.24251
amiton+NHc 12.12186 -2.72825 14.85011 -4.69681 7.42506 1.48551
VX 7.47529 0.36253 7.11276 -3.91891 3.55638 2.15919
VX+NH 12.11138 -2.63416 14.74554 -4.73861 7.37277 1.52279
GV 8.28013 0.32166 7.95847 -4.30089 3.97924 2.32427
GV+NH 12.30365 -3.16866 15.47231 -4.56749 7.73616 1.34834
GV+NH-Ninvert 12.37832 -3.14093 15.51924 -4.61870 7.75962 1.37457
methamidophos 9.11244 0.43004 8.68240 -4.77124 4.34120 2.62194
paraoxon 9.32693 -0.82099 10.14792 -4.25297 5.07396 1.78241
parathion 8.77754 -0.86911 9.64666 -3.95422 4.82333 1.62085
dichlorvos 8.95219 0.36877 8.58343 -4.66048 4.29171 2.53047
DMMPA 8.57309 0.40989 8.16320 -4.49149 4.08160 2.47127
leptophos 8.23588 -0.33412 8.57000 -3.95088 4.28500 1.82141
diisopropyl 10.45402 0.38487 10.06914 -5.41944 5.03457 2.91687
EDMM 8.09136 0.36412 7.72724 -4.22774 3.86362 2.31309
EDMM+NH 12.32954 -3.06068 15.39022 -4.63443 7.69511 1.39556
trichlorfon 10.10123 0.06345 10.03778 -5.08234 5.01889 2.57330
Table 2 The global reactivity index for the training sets at B3LYP/6-311++G(2d, 3p)/gas level.
Name bo1_X a bo2_O-C b bo3_N c bo4_N+1 d bo5_N-1 e bo6_P-X f bo7_O-C g
sarin 0.6085 0.8214 0.6085 0.6021 0.6579 0.0006 -0.0020
soman 0.6082 0.8166 0.6082 0.6037 0.6505 0.0189 -0.0294
tabun 0.7699 0.8402 0.7206 0.7302 0.6850 0.1290 0.0376
amiton 0.9321 0.7019 0.7019 0.7081 0.7338 0.0454 0.0170
amiton+NH 0.8793 0.8220 0.7432 0.7191 0.7860 0.0825 0.0360
VX 0.8870 0.8554 0.6996 0.7073 0.7289 0.0403 0.0089
VX+NH 0.8384 0.8345 0.7417 0.7213 0.7213 0.1171 0.0259
GV 0.7210 0.8567 0.6230 0.6173 0.6470 0.0404 0.0141
GV+NH 0.6735 0.9076 0.6486 0.6341 0.6968 0.1520 0.0300
GV+NH-Ninvert 0.6849 0.9041 0.6169 0.6144 0.6639 0.1609 0.0380
methamidophos 0.9137 0.8821 0.6958 0.6971 0.7437 0.0910 0.0233
paraoxon 0.6623 0.8439 0.6623 0.7123 0.5891 0.0732 0.0168
parathion 0.6471 0.8432 0.6471 0.7005 0.6544 0.0250 0.0181
dichlorvos 0.6652 0.8686 0.6652 0.6572 0.5746 0.0906 0.0257
DMMPA 0.7097 0.8797 0.7097 0.7171 0.6922 0.0897 0.0272
leptophos 0.6379 0.8736 0.6379 0.6178 0.6304 -0.0412 0.0144
diisopropyl 0.6351 0.8212 0.6351 0.6282 0.6744 0.0034 0.0128
EDMM 0.9166 0.8564 0.6878 0.6955 0.7202 0.0714 0.0197
EDMM+NH 0.8734 0.8300 0.7321 0.7115 0.7758 0.0976 0.0395
trichlorfon 0.7195 0.8686 0.7195 0.7036 0.7569 0.0212 0.0194
Table 3 Pivotal NBO bond order and their variability after obtaining 1e or losing 1e.
B3LYP/gas B3LYP/water MP2/gas MP2/water
HOMO/(a.u.) -0.30782 -0.31809 -0.46857 -0.47438
LUMO/(a.u.) -0.01442 -0.01244 0.03743 0.04190
LUMO-HOMO/eV 7.98388 8.31723 13.76907 14.04880
P1-charge 2.61035 2.62587 2.83067 2.84698
fP1-(r) 0.02460 0.02079 0.09101 0.07436
fC6-(r) -0.00767 -0.01016 0.06992 0.06209
fC8-(r) -0.01089 -0.0093 -0.00059 0.00064
P1―F3 bond order 0.6351 0.6268 0.5985 0.5908
P1―O4 bond order 0.7582 0.7711 0.7217 0.7333
P1―O5 bond order 0.7497 0.7710 0.7126 0.7316
O5―C6 bond order 0.8212 0.8018 0.7992 0.7779
O4―C8 bond order 0.8176 0.7999 0.7948 0.7761
Table 4 Optimizing structure, atomic number of diisopropyl phosphofluoridate and comparison of calculation results with four different calculating level.
Compound pLD50(Exp.) Pred.1CDFT Residue1 Pred.2 2D-QSPR a Residue2
Training sets
sarin 3.66 3.67 0.01 3.64 -0.02
soman 4.01 3.94 -0.07 4.09 0.08
tabun 3.31 3.17 -0.14 3.25 -0.06
amiton+NH 3.22 3.65 0.43 3.18 -0.04
VX+NH 4.43 4.27 -0.16 4.53 0.10
GV+NH-Ninvert 3.72 3.73 0.01 3.69 -0.03
methamidophos 1.82 1.50 -0.32 1.99 0.17
paraoxon 3.15 3.03 -0.12 3.17 0.02
parathion 2.70 2.52 -0.18 2.62 -0.08
dichlorvos 1.82 1.96 0.14 1.91 0.09
DMMPA -0.38 -0.10 0.28 -0.15 0.23
leptophos 0.87 0.93 0.06 0.86 -0.01
diisopropyl 2.89 3.00 0.11 2.58 -0.31
EDMM+NH 4.27 4.15 -0.12 4.15 -0.12
trichlorfon 0.80 0.87 0.07 0.79 -0.01
Test sets
triazophos 0.97 0.83 -0.14 -0.08 -1.05
DCP 1.60 1.42 -0.18 1.63 0.03
TEP 0.10 1.33 1.23 2.74 2.64
DMMP -0.30 -0.28 0.02 1.59 2.01
24-optfreq25 - 6.39 1.15
27-optfreq25 - 5.69 0.67
Table 5 pLD50 prediction results for the training set and the test set with the established equations average based on CDFT method and conventional 2D-QSPR.
1 Mekenyan O. G. ; Veith G. D. SAR and QSAR in Environ. Res. 1994, 2, 129.
2 Katagi K. Rev. Environ. Contam. Toxicol. 2002, 175, 79.
3 Karelson M. ; Lobanov V. S. Chem. Rev. 1996, 96, 1027.
4 Donald M. M. ; Karen M. B. ; Irwin K. ; Richard E. S. Arch. Toxicol. 2006, 80, 756.
5 DingJ. J.;DingX. Q.;PanL.;ChenJ. S.Acta. Phys. -Chim. Sin.2014,302157.
5 丁俊杰;丁晓琴;李大禹;潘里;陈冀胜.物理化学学报,2014,302157.
6 Ding J. J. ; Ding X. Q. ; Zhao L. F. ; Chen J. S. Acta Pharm. Sin. 2005, 40, 340.
6 丁俊杰; 丁晓琴; 赵立峰; 陈冀胜. 药学学报, 2005, 40, 340.
7 Katritzky A. R. ; Kuanar M. ; Slavov S. ; Dennis Hall C. Chem. Rev. 2010, 110, 5714.
8 Parr R. G. ; Yang W. Annu. Rev. Phys. Chern. 1995, 46, 701.
9 John C. H. J.Am. Chem. Soc. 2010, 132, 7558.
10 Geerlings P. K. ; De Profit F. ; Langenaeker W. Chem. Rev. 2003, 103, 1793.
11 Liu S.-B. Acta Phys. -Chim. Sin. 2009, 25, 5.
11 刘述斌. 物理化学学报, 2009, 25, 5.
12 Bueno P. R. ; Miranda D.A. Phys. Chem. Chem. Phys. 2017, 19, 6184.
13 James S. M. A. ; Junia M. ; Paul W. A. J.Chem. Theory Comput. 2007, 3, 358.
14 Pérez P. ; Yepes D. ; Jaque P. ; Chamorro E. ; Domingo L. R. ; Rojas R. S. ; Toro-Labbé A. Phys. Chem. Chem. Phys. 2015, 17, 10715.
15 Domingo. L. R. ; Ríos-Gutiérrez. M. ; Pérez P. Molecules 2016, 21, 748.
16 Chattaraj P. K. ; Roy D. R. Chem. Rev. 2007, 107, PR46.
17 Sablon N. ; Proft F. D. ; Ayers P. W. ; Geerlings P. K. J.Chem. Theory Comput. 2010, 6, 3671.
18 Tim F. ; Sablon N. ; Proft F. D. ; Ayers P. W. ; Geerlings P. K. J.Chem. Theory Comput. 2008, 4, 1065.
19 Semenyuk Y. P. ; Morozov P. G. ; Burov O. N. ; Kletskii M. K. ; Lisovin A. V. ; Kurbatov S. V. ; Terrier F. Tetrahedron 2016, 72, 2254.
20 Ayers P. W. ; Parr R. G. J.Chem. Phys. 2008, 128, 184108.
21 (accessed March 28, 2013).
22 FrischM. J.;TrucksG. W.;SchlegelH. B.;ScuseriaG. E.;RobbM. A.;CheesemanJ. R.;ScalmaniG.;BaroneV.;MennucciB.;PeterssonG. A.et al.Gaussian 09, Revision B.04; Wallingford CT, Pittsburgh,PA:Gaussian Inc.,2009.
23  Cerius2, Version 4.5;Accelrys Inc.:San Diego, CA 92121, USA,1999.
24  ACD lab 12.0 software;Advanced Chemistry Development, Inc.:Canada,2010.
25  HyperChem7.0(Beta1.04 for Evaluation copy) Software;Hypercube, Inc.:Gainesville,2002.
26 VictorE. K.;EugeneN. M.;AnatolyG. A.QSAR & Comb. Sci2009,6-7,664.
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