DFT-Based Prediction of Anti-Leishmanial Activity of Carboxylates and Their Antimony(III) Complexes Against Five Leishmanial Strains
Abstract
Carboxylates and their antimony(III) complexes experimentally scanned earlier for anti-leishmanial activity (IC50) against five leishmanial strains viz., L. major, L. major (Pak), L. tropica, L. mex mex, and L. donovani. These activities have been theoretically predicted by DFT method along with quantitative structure-activity relationship (QSAR) study. Molecular modeling and geometry optimization of the all the eight compounds have been performed on workspace program of CAChe Pro software of Fujitsu by opting B88-PW91 (Becke '88; Perdew & Wang '91) GGA (generalized-gradient approximation) energy functional with DZVP (double-zeta valence polarized ) basis set in DFT (Density Functional Theory). For QSAR, multiple linear regression (MLR) analysis has been performed on Project Leader Program associated with CAChe. The reliability of correlation between experimental activities and predicted activities are r2 = 0.826, r2CV = 0.426 (L. major); r2 = 0.905, r2CV = 0.507 (L. major (Pak)); r2 = 0.980, r2CV = 0.932 (L. tropica); r2 = 0.781, r2CV = 0.580 (L. mex mex) and r2 = 0.634, r2CV = 0.376 (L. donovani), and a comparison of the experimental values and the values obtained by theoretical calculations has been presented pictorially that shows close resemblance.
1. Introduction
Survey of literatures exposed that organoantimony compounds show diversity in their biological activity [1, 2, 3, 4, 5, 6, 7, 8]. They show their antitumor, antimicrobial (viz., anti-fungal and anti-leishmanial) and insecticidal (viz., contact, stomach, anti-feedant and acaricidal) activities mostly in +III and +V oxidation states [1]. We know that experimental determination of various activity viz., pharmacological, toxicological, biochemical as well as human health end points of any compound are time and wealth-consuming. Hence, mathematical models were, are and will be of important use to predict these activities [9]. Gap between experimental and theoretical measurement is now bridged by computer simulation techniques [10]. In our previous work anti-fungal and insecticidal activities of perfluorophenyl antimony(III) and antimony(V) chlorides were rescaled by us using mathematical model after experimental work and the results were exciting [1]. This boost us to draw mathematical models for rescaling anti-leishmanial activities of carboxylates and their antimony(III) complexes. The quantum chemical parameters used in this study were well described in our previous publications [11, 12, 13].
2. Materials and Methods
Carboxylates and their antimony(III) complexes listed in Table 1 are the study materials for the present study [14]. The IC50 of these compounds were taken from the literature against five leishmanial strains viz., L. major, L. major (Pak), L. tropica, L. mex mex, and L. donovani (Table 2, Table 3, Table 4, Table 5). For prediction of biological activity (IC50) of these compounds, multiple linear regression analysis has been performed on Project Leader Program associated with CAChe [15, 16, 17]. The quantum chemical properties listed in Table 1 were used as independent variables and the experimental IC50 of the compounds listed in Table 2, Table 3, Table 4, Table 5, Table 6 as dependent variables. For this the molecular modeling and geometry optimization of the all the eight compounds have been performed on workspace program of CAChe Pro software of Fujitsu by opting B88-PW91GGA energy functional with DZVP basis set in DFT method [18].
3. Results and Discussion
Leishmania is a parasitic protozoan. There are more than twenty species of this. These different species are morphologically indistinguishable, but they can be differentiated by isoenzyme analysis, molecular methods, or monoclonal antibodies [19]. They are responsible for the disease leishmaniasis. Leishmaniasis is transmitted by the bite of infected female phlebotomine sand flies. Here anti-leishmanial activities are divided into five sets on the basis of their five different species.
For sake of simplicity, the anti-leishmanial activity of each leishmanial species has been studied separately as described below.
Leishmania major
Prediction of anti-leishmanial of compounds listed in Table 1 against this strain in terms of 50% inhibition was scanned by using following equation:
In this case quantum chemical properties listed in Table 1 were used as independent variables and the experimental IC50 of the compounds listed in Table 2 as dependent variables. Various mathematical models were developed in which above model was selected as reliable. In this model (Eq.1) molecular electronegativity is the first descriptor and global softness is the second descriptor. r2 is the correlation coefficient (values higher than 0.5 have predictive power) and r2CV is the cross-validated correlation coefficient (values higher than 0.25 have predictive power). MLA is widely used method for building QSAR model. The coefficients of model have negative sign with respect to both χ and S. Thus, χ and S show inverse relationship with activity. A close examination of the table shows that there is little difference between the values of experimental and theoretical inhibitory activities as evident by their residual values (Δ).
Leishmania major (Pak)
Prediction of anti-leishmanial of compounds against this strain in terms of 50% inhibition was scanned by using following equation:
In this case quantum chemical properties listed in Table 1 were used as independent variables and the experimental IC50 of the compounds listed in Table 3 as dependent variables. Various mathematical models were developed in which above model was selected as reliable. In this model (Eq.2) dipole moment is the first descriptor and total energy is the second descriptor. The coefficients of model have positive sign with respect to both μ and ET. Thus, μ and ET show direct relationship with activity.
Leishmania tropica
Prediction of anti-leishmanial of compounds against this strain in terms of 50% inhibition was scanned by using following equation:
In this case quantum chemical properties listed in Table 1 were used as independent variables and the experimental IC50 of the compounds listed in Table 4 as dependent variables. Various mathematical models were developed in which above model was selected as reliable. In this model (Eq.3) ionization potential is the first descriptor and electron affinity is the second descriptor. The coefficients of model have positive sign with respect to I.P. and have negative sign with respect to E.A. Thus, I.P. shows direct relationship with activity, while E.A. shows inverse relationship with activity.
Leishmania mex mex
Prediction of anti-leishmanial of compounds against this strain in terms of 50% inhibition was scanned by using following equation:
In this case quantum chemical properties listed in Table 1 were used as independent variables and the experimental IC50 of the compounds listed in Table 5 as dependent variables. Various mathematical models were developed in which above model was selected as reliable. In this model (Eq.4) absolute hardness is the first descriptor and total energy is the second descriptor. The coefficients of model have negative sign with respect to η and have positive sign with respect to ET. Thus, η shows inverse relationship with activity, while ET shows direct relationship with activity.
Leishmania donovani
Prediction of anti-leishmanial of compounds against this strain in terms of 50% inhibition was scanned by using following equation:
In this case quantum chemical properties listed in Table 1 were used as independent variables and the experimental IC50 of the compounds listed in Table 6 as dependent variables. Various mathematical models were developed in which above model was selected as reliable. In this model (Eq.5) ionization potential is the first descriptor and global softness is the second descriptor. The coefficients of model have negative sign with respect to both I.P. and S. Thus, I.P. and S show inverse relationship with activity.
4. Conclusions
The reliability of correlation between experimental activities and predicted activities are r2 = 0.826, r2CV = 0.426 (L. major, Table 2); r2 = 0.905, r2CV = 0.507 (L. major (Pak), Table 3); r2 = 0.980, r2CV = 0.932 (L. tropica, Table 4); r2 = 0.781, r2CV = 0.580 (L. mex mex, Table 5) and r2 = 0.634, r2CV = 0.376 (L. donovani, Table 6), and a comparison of the experimental values and the values obtained by theoretical calculations has been presented pictorially (Figure 1, Figure 2, Figure 3, Figure 4, Figure 5) that shows close resemblance. The study reflected that descriptors derived from DFT methods have sufficient reliability to understand quantitative structure-activity relationship and also to predict the activity of new complex of this series from theoretically derived properties within limited time by QSAR models.
Acknowledgement
The authors are thankful to Dr. O.P. Mishra, Ex.Principal, M.L.K. (P.G.) College, Balrampur, for providing the laboratory facilities to conduct the calculation.
References
- Singhal K, Sahu, VK, Singh P, Raj, P (2014) DFT-Based Prediction of Antifungal and Insecticidal Activities of Perfluorophenyl-antimony(III) and –antimony(V) chlorides. Med Chem Res 23: 1758-1767. DOI 10.1007/s00044-013-0752-8[CrossRef]
- Yang N, Sun H (2010) Biological chemistry of antimony and bismuth, in biological chemistry of arsenic, antimony and bismuth (ed H. Sun), John Wiley & Sons, Ltd, Chichester, UK. doi: 10.1002/9780470975503.ch3[CrossRef]
- Kant R, Amresh G, Chandrashekar K, Shukla SK (2009) Biological studies of some pentafluoroantimony compounds. Phosphorus, Sulfur Silicon Relat Elem 184(9): 2453-2464. doi: 10.1080/10426500 802498646[CrossRef]
- Sharma P, Perez D, Cabrera A, Rosas N, Arias JL (2008) Perspectives of antimony compounds in oncology. Acta Pharmacol Sin. 29(8): 881-889. doi: 10.1111/j.1745-7254.2008.00818.x.[CrossRef] [PubMed]
- Kant R, Singhal K, Shukla SK, Chandrashekar K, Saxena AK, Ranjan A, Raj P (2008) Synthesis and biological activity of a novel compound: (C6F5)2SbPh. Phosphorus, Sulfur Silicon Relat Elem 183(8): 2029-2039. doi: 10.1080/10426500701841763[CrossRef]
- Tiekink ERT (2002) Antimony and bismuth compounds inoncology. Critical Reviews in Oncology/Hematology 42(3): 217-224. http://dx.doi.org/10.1016/S1040-8428(01)00217-7 [CrossRef]
- Socaciu C, Pasca I, Silvestru C, Bar A, Haidue I (1994) Antitumor organometallics. IV. The mutagenic potential of some diphenylantimony(III) dithiophosphorus derivatives. Met-Based Drugs 1(4): 291-297. doi: 10.1155/MBD.1994.291a[CrossRef] [PubMed]
- Raj P, Saxena AK, Aggarwal AK (1989) The preparation of pentafluorophenyl antimony(III) and antimony(V) halides and mixed halides. J Fluorine Chem 42(2): 163-172. http://dx.doi.org/10.1016/S0022-1139(00)82746-3[CrossRef]
- Hansch C, Leo A (1995) Exploring QSAR: Fundamentals and applications in chemistry and biology, American Chemical Society, Washington, DC. ISBN 10: 0841229872 / 0-8412-2987-2
- Dearden J (2003) In silico prediction of drug toxity. Journal of Computer-Aided Molecular Design 17(2-4), 119-127. DOI:10.1023/A:1025361621494[CrossRef] [PubMed]
- Singh RK, Khan AKR, Sahu VK, Singh PP (2009) Comparative QSTR study of a series of alcohol derivatives against tetrahymena pyriformis. Int J Quantum Chem 109: 185-195. doi: 10.1002/qua.21790[CrossRef]
- Sahu VK, Singh RK (2009) Prediction of bioconcentration factor of organic compounds in fish. Clean-soil, air, water 37(11): 850-857. doi: 10.1002/clen.200900170[CrossRef]
- Soni AK, Singh P, Sahu VK (2020) DFT-Based Prediction of Bioconcentration Factors of Polychlorinated Biphenyls in Fish Species Using Molecular Descriptors. Advances in Biological Chemistry 10: 1-15. https://www.scirp.org/journal/abc[CrossRef]
- Khan MI, Gul S, Hussain I, Khan, MA, Ashfaq M, Inayat-Ur-Rahnan, Ullah, F, Durrani, GF, Baloch IB, Naz R (2011) In vitro anti-leishmanial and antifungal effects of new SBIII carboxylates. Org & Med Chem Lett 1 (2): 1-7.[CrossRef] [PubMed]
- Leach, A.R. (2001) Molecular Modelling. Principle and Applications. Second Edition, Pearson Education Limited, Harlow.
- Montgomery DC, Peck EA (1992) Introduction to linear regression analysis 2nd edition. John Wiley. New York, p. 672. ISBN: 978-0-470-54281-1
- http://www.CACheSoftware.com
- Parr RG, Yang W (1989) Density functional theory of atoms and molecules, Oxford University Press, New York, p. 127.
- https://www.cdc.gov/parasites/leishmaniasis/biology.htm
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