INTRODUCTION:

Computational chemistry is applications of computer and computer enable calculations in chemistry for various purposes. One most important scope of computational Chemistry is QSAR and QSPR followed by Drug Designing. The continuous development of structural and molecular descriptors those succeed to define the specification of molecules for particular biological activity with the help of statistical equation transform the designing into powerful and widely used mathematical model for the prediction of physiochemical and biological properties [1-6]. Acute toxicity is produced after administration of a single dose or multiple doses in a period not exceeding 24 hours, up to a limit of 2000 mg/k g. Objective of acute toxicity studies is to identify a dose causing major adverse effects and an estimation of the minimum dose causing lethality[7].

It is widely recognized that mathematical equations, whether they are derived in a purely empirical fashion from an arbitrary set of molecular descriptors, or from a pre-selected set of descriptors, expected on a theoretical grounds to have a connection with a particular property, can provide insight into the molecular and sub molecular origin of physical and biological properties[8].

In toxicology, the median lethal dose, LD50 (abbreviation for "lethal dose, 50%"), LC50 (lethal concentration, 50%) or LCt50 (lethal concentration and time) of a toxin, radiation, or pathogen is the dose required to kill half the members of a tested population after a specified test duration. LD50 figures are frequently used as a general indicator of a substance's acute toxicity. The test was created by J.W. Trevan in 1927. LD50 is usually determined by tests on animals such as laboratory mice.

LC stands for "Lethal Concentration". LC values usually refer to the concentration of a chemical in air but in environmental studies it can also mean the concentration of a chemical in water. Acute toxicity 12 h-log 1/LC50 values[9] of phenol derivatives to the tadpoles (Rana japonica) which used in the study.

MATERIAL AND METHODS:

To developing the model for Acute toxicity 12 h-log 1/LC₅₀ of phenol derivatives in we used ten descriptors Mor29p, Mor20e, Mor04m, Mor23m, FDI, RDF045m, MATS5p, R3e eHOMO and, eLUMO. There are 23 observations (molecules) are used to built first model for Acute toxicity 12 h-log 1/LC₅₀. By regression Statistics we get correlation coefficient is 0.821334, r² is 0.67459, Adjusted R Square is 0.488641, and Standard Error is 0.388744 for model which described by equation 1.

The dose of a drug that is pharmacologically effective for 50% of the population exposed to the drug or a 50% response in a biological system that is exposed to the drug.

Acute toxicity 12 h-log 1/LC50 valuesof phenol derivatives to the tadpoles (Rana japonica) which used in the study are given in Table (1.1). The effectivity (Acute toxicity) sequence of phenol’s derivatives is found as below. Table (1.1) 33< 1< 38< 11< 6< 35< 55< 36< 56< 14< 10< 52< 8< 58< 44< 22< 7< 20< 54< 53< 57<21<50

RESULT AND DISCUSSION:

Table (1.1) Observed and Predicted value of Acute toxicity using Eq. (1)

S. No.	Abbreviations	Acute toxicity 12 h-log 1/LC50	Predicted Acute toxicity 12 h-log 1/LC50	Residuals	Standard Residuals
1	phenol	2.769	2.883727	-0.11473	-0.36996
2	2-chlorophenol	3.011	2.889173	0.121827	0.392851
3	4-bromophenol	3.664	3.398017	0.265983	0.857702
4	4-chlorophenol	3.421	3.511381	-0.09038	-0.29145
5	4-fluorophenol	2.693	2.492745	0.200255	0.645754
6	2-methoxyphenol	2.654	2.839564	-0.18556	-0.59838
7	2-methylphenol	2.837	3.091757	-0.25476	-0.8215
8	4-methoxyphenol	2.624	2.325304	0.298696	0.96319
9	4-methylphenol	3.057	3.197222	-0.14022	-0.45217
10	4-tert-butylphenol	4.033	3.947582	0.085418	0.275444
11	2,6-Dimethylphenol	3.324	3.088079	0.235921	0.760764
12	2,4-Dichlorophenol	3.873	3.641049	0.231951	0.747961
13	2-Bromo-4-methyl-phenol	3.717	3.857046	-0.14005	-0.4516
14	benzene-1,3-diol	2.066	2.992516	-0.92652	-2.98769
15	Salicylic acid	2.84	3.067435	-0.22744	-0.7334
16	5-Chloro-salicylic acid	3.011	3.343654	-0.33265	-1.07269
17	4-hydroxy benzaldehyde	3.08	2.789969	0.290031	0.935249
18	2-nitrophenol	3.502	3.586645	-0.08464	-0.27295
19	3-nitrophenol	3.51	3.332383	0.177617	0.572754
20	4-nitrophenol	3.657	3.33442	0.32258	1.040208
21	4-Chloro-2-nitro-phenol	3.882	3.867456	0.014544	0.0469
22	2-Nitroresorcinal	3.492	3.78091	-0.28891	-0.93163
23	2,4-Dinitrophenol	4.306	3.764966	0.541034	1.744647

Figure 1: Correlation of Observed and Predicted value of ‘Acute toxicity 12 h-log 1/LC50’ Using Eq. (1)

Correlation analysis (multiple linear regression analysis ‘MLR’):

In Quantitative Structure Activity Relationship (QSAR) models in which physicochemical parameters of drugs and the other compounds are correlated with biological activities, lipophilicity (partition coefficient, chromatographic parameters) has a major role.

In order to build linear relationship and test model for log 1/LC50, the 23 compound data sets was used as training to build model. Finally with the selected eight different descriptors, we will build linear models using the training data sets and following equations (1) were obtained.

Predicted log 1/LC50 =

(-6.65596 x Mor29p) + (0.033907 x Mor20e) +
(-0.02084 x Mor04m) + (-3.23507 x Mor23m) +
(1.754007 x FDI) + (-0.05399 x RDF045m) +
(-0.11927 x MATS5p) + (0.173157 x R3e) +
0.846725 …..(1)

Statistical analysis:

In order to confirm most powerful predictable Model for log 1/LC50 we have apply some statistical parameter. These statistical parameters are support Model for log 1/LC50 due to low value of LSE (2.116) and PE (0.573) is much greater than R (0.821) for model (Eq.1) is the best model. The cross-validated PRESS (2.116) and SSY (4.386); SSY is higher than PRESS indicate statistically significant of model (Eq.1) for log 1/LC50 And according to SPRESS (0.389) and PSE (0.303) values model (Eq.1) is a better model and will also give excellent result.

CONCLUSION:

From the above result and discussion we conclude that model developed and shown by Eq. (1) is excellent to predict the LC50 of phenol’s derivatives. Statistical approach PRESS and SSY, SPRESS and PSE values support this Model. Higher PE and lower LSE values give it to best predictive power.

Observed value of Acute toxicity 12 h-log 1/LC50 was plotted against and Predicted values Using Eq. (1) shown in Figure1 below. The figure clearly indicates there is a significant co-relation between Observed and Predicted values of Acute toxicity 12 h-log 1/LC50. Only 1,3-Dihydroxybenzene, 2,4-Dinitrophenol shows deviation. Other molecule shows excellent co-relation for Acute toxicity 12 h-log 1/LC50.

ACKNOWLEDGEMENT:

Authors are very thankful to Mr. C. G. Dhabu, Principal M. J. P. Govt. Polytechnic College, Khandwa for providing facilities and motivation in the work. The authors would like to thank Huang Hong, Wang Xiaodong for their excellent work on Acute toxicity which help us to this work.

REFERENCES:

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2. Kier L B and Hall L H Molecular. Connectivity in Structure Activity Analysis. Research Studies Press, UK. 1986. 367–422.

3. Trinajstic N Chemical Graph Theory. CRC Press Boca Raton, FL. 1992. 225-273.

4. Diudea M V and Ivanciue O. Molecular Topology. Comprex, Romania. 1995. 320.

5. Balaban A T. Chemical Topology to Three-Dimensional Geometry, New York. 1997.1-24.

6. Diudea M V. Mathematical model for the prediction of physiochemical and biological properties. Croat. Chem. Acta. 1999; 72: 835-851.

7. Robinson, S., Ockert, D., Stei, P. and Dreher, D et al.(2007). Challenging the regulatory requirement for conventional acute toxicity studies in pharmaceutical drug development. Toxicology. 231(2-3):96

8. Katritzky A R, Petrukhin R, Tatham D, Basak S, Benfenati E, Karesaon M, Maran U. Structurally Diverse Quantitative Structure-Property Relationship Correlations of Technologically Relevant Physical Properties. J. Chem. Inf. Comput. Sci. 2000; 1: 40.

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Received on 30.06.2017 Modified on 28.07.2017

Asian J. Research Chem. 2017; 10(5): 626-628.

DOI: 10.5958/0974-4150.2017.00105.5

Modeling of Acute Toxicity of Phenol Derivatives using Computational Methods

INTRODUCTION: