QSAR MODELING OF FUNGICIDAL ACTIVITY OF MANNICH BASES ACTIVITY OF - - PowerPoint PPT Presentation

QSAR MODELING OF FUNGICIDAL ACTIVITY OF MANNICH BASES ACTIVITY OF MANNICH BASES

Simona Funar-Timofei1* Ana Borota1 Alina Bora1 Sorin Simona Funar-Timofei1 , Ana Borota1, Alina Bora1, Sorin

Avram1, Daniela Ionescu2

1Institute of Chemistry of the Romanian Academy, Bv. Mihai

y y, Viteazu 24, 300223 Timisoara, Romania

2University of Medicine and Pharmacy, Faculty of Pharmacy, P-

ta E. Murgu, 300034 Timisoara, Romania *e mail: timofei@acad icht tm edu ro e-mail: timofei@acad-icht.tm.edu.ro

INTRODUCTION INTRODUCTION

• 1,2,4-triazole and its derivatives represent one of the most
• 1,2,4 triazole and its derivatives represent one of the most

biologically active classes of compounds, possessing a wide spectrum of activities, including anti-inflammatory, antiviral, analgesic, antimicrobial, anticonvulsant, anticancer, antioxidant, antitumoral and antidepressant activity, the last usually being l d b th f d i t t [1] S f th l explored by the forced-swim test [1]. Some of the complexes containing 1,2,4-triazole ligands have rather peculiar structures and specific magnetic properties.

• Triazoles are used in the control of variety of fungal diseases in
• Triazoles are used in the control of variety of fungal diseases in

fruits, vegetables, legumes and grain crops, both as pre- and postharvest applications [2]. The biochemical mechanism of their antifungal effect is based on the inhibition of ergosterol biosynthesis thereby interfering with fungal cell-wall formation. Th l i hibit t l 14 d th l d h id d They also inhibit sterol 14α–demethylase and hence considered steroid demethylation inhibitor. 3- amino-1,2,4-triazole is an inhibitor of mitochondrial and chloroplast function.

[1]. M. Koparir, C. Orek, P. Koparir, K. Sarac, Spectrochim. Acta A, 2013, 105, 522–531. [2]. S.S. Kumar, H.P. Kavitha, Mini-Rev. Org. Chem., 2013, 10(1), 40-65.

AIM: AIM:

The fungicide activity of trifluoromethyl substituted The fungicide activity of trifluoromethyl-substituted

1,2,4-triazole Mannich bases containing substituted benzylpiperazine ring (Table 1), expressed by the mycelial growth inhibition activity against the Fusarium

• xysporum f sp cucumerinum fungi test was studied by
• xysporum f. sp. cucumerinum fungi test was studied by

partial least squares (PLS).

These fungicides were previously energy optimized [3]

g p y gy p [ ] by the RM1 semiempirical quantum chemical approach, using the Schrödinger software (Schrödinger, LLC, New York, NY, 2008). Structural descriptors of these compounds were correlated to the relative inhibition rate p (RIR) values.

[3]. S. Funar-Timofei, A. Borota, A. Bora, R. Curpan, S. Avram, Modeling of Mannich bases fungicidal activity by the MLR approach, 21st International Symposium on Analytical and Environmental Problems (ISAEP), Szeged, Hungary, 28 September, 2015.

METHODS

Table 1. Mannich bases structures including trifluoromethyl-substituted 1,2,4-triazoles g y , ,

No Structure No Structure 1

10

2

11

2

11

3

12

4

13

5

14

6

15

7

16

8

17

9

18

METHODS

Definition of target property and molecular structures

• A series of 18 Mannich bases having trifluoromethyl-substituted

1 2 4 triazole containing substituted benzylpiperazine ring was used 1,2,4-triazole containing substituted benzylpiperazine ring was used, having the fungicidal Fusarium oxysporum f. sp. Cucumerinum relative inhibitation rate (RIR, expressed in %) [4], as dependent variable.

• Quantum

chemical descriptors were derived for the energy

• ptimized structures using previously [3] the RM1 semiempirical

quantum chemical approach.

[4] B –L Wang X –H Liu X –L Zhang J –F Zhang H –B Song Z –M Li Chem Biol [4]. B.–L. Wang, X.–H. Liu, X.–L. Zhang, J.–F. Zhang, H.–B. Song, Z.–M. Li, Chem. Biol.

• Drug. Des. 2011, 78, 42–49.

METHODS

Compound descriptors were calculated by several programs:

Dragon (Dragon Professional 5.5/2007, Talete S.R.L., Milano, Italy), Instant JChem (Instant JChem v. 15.7.27, 2015, ChemAxon (http://www.chemaxon.com) ) and ChemProp (UFZ Department of Ecological Chemistry 2014. ChemProp 6.2, http://www.ufz.de/index.php?en=6738) software).

Partial Least Squares (PLS) [5] was employed to relate the Partial Least Squares (PLS) [5] was employed to relate the

structural descriptors to the mycelial growth inhibition activity against the Fusarium oxysporum f. sp. cucumerinum fungi test.

[5]. H. Wold, Partial Least Squares, in: S. Kotz and N. L. Johnson (Eds.), Encyclopedia of Statistical Sciences (Vol. 6), Wiley, New York, 1985, pp. 581-591.

METHODS

Model validation Model validation

The leave-seven-out cross-validation procedure was

employed for internal validation, the data over fit and model applicability was controlled by comparing the root-mean- applicability was controlled by comparing the root mean square errors (RMSE) and the mean absolute error (MAE) of training and validation sets and the predictive power of the model by the concordance correlation coefficient (CCC) [6].

Y-scrambling was used to check the model robustness and

predictive power.

In addition, to test the predictive power of the model, the

predictive r2 ( ) test [7] was employed. It is considered that for a predictive QSAR model, its value should be higher than 0.5. 2

pred

r 0.5.

[6]. N. Chirico, P. Gramatica, J. Chem. Inf. Model. 2011, 51, 2320-2335. [7]. P. P. Roy, S. Paul, I. Mitra, K. Roy, Molecules 2009, 14, 1660-1701.

RESULTS AND DISCUSSION RESULTS AND DISCUSSION

• PLS calculations were performed to correlate the RIR values with all

the calculated descriptors. Compounds: 4, 8, 11, 14, 16 were included in the test set.

• A two-components PLS model with acceptable statistical quality

was obtained: R2X(Cum) = 0.805, R2Y(cum) = 0.823, Q2(Cum) = 0.735.

• Y-randomization test and leave-seven-out crossvalidation runs

were performed to check the robustness and internal predictive ability of the PLS models. The Y-scrambling procedure, which was repeated 999 times. The extremely low calculated scrambled R2 repeated 999 times. The extremely low calculated scrambled R (0.158) and Q2 (-0.346) values indicate no chance correlation for the chosen model.

RESULTS AND DISCUSSION RESULTS AND DISCUSSION

0 7

X Y

0.0 1.0 2.0 3.0 t[2] 1 2 3 5 6 9 12 15 17 18 0.2 0.3 0.4 0.5 0.6 0.7 w*c[2] Mor26e R7e+ F10[C-F] FO

• 3.0
• 2.0
• 1.0
• 6
• 5
• 4
• 3
• 2
• 1

1 2 3 4 5 6 t[1] 5 7 10 13

• 0.2
• 0.1
• 0.0

0.1 0.00 0.05 0.10 0.15 0.20 0.25 0.30 0.35 0.40 0.45 w*c[1] RDF020u RDF020v RDF020e RDF020p

Figure 1. Score scatter plot of the final PLS model Figure 2. Loading scatter plot of the final PLS model.

RESULTS AND DISCUSSION RESULTS AND DISCUSSION

0.30 0.40 0.50 CoeffCS[2]

0 8 1.0 1.2 1.4 1.6 VIP[2]

0.00 0.10 0.20 RDF020u RDF020v RDF020e RDF020p Mor26e R7e+ F10[C-F] Var ID (Primary)

0.0 0.2 0.4 0.6 0.8 Mor26e RDF020u RDF020v RDF020e RDF020p F10[C-F] R7e+ Var ID (Primary)

Figure 3. PLS regression coefficients plot

• f the model with 2 components. The bars

indicate 95% confidence intervals based j k k ifi Figure 4. VIP plot of the x-variables of the two-component PLS model.

( y)

• n jack-knifing.

RESULTS AND DISCUSSION

The data over fitting and model applicability was

RESULTS AND DISCUSSION

The data over fitting and model applicability was

controlled by comparing the root-mean-square errors (RMSE) and the mean absolute error (MAE) [8] calculated for the training (RMSEtr = 0.096, MAEtr = g (

tr tr

0.079) and validation (RMSEext = 0.178, MAEext = 0.140) sets.

The calculated concordance correlation coefficient

values for the training (CCCtr = 0.903), crossvalidation (CCCL7O = 0.832) and test (CCCext = 0.853) sets indicate a

L7O ext

robust model with predictive power, which was confirmed by the value of 0.681, too.

2

pred

r

[8]. P. Gramatica, In: Reisfeld B, Mayeno AN, editors. Computational Toxicology, Volume II, Methods in Molecular Biology, Vol. 930, “On the Development and Validation of QSAR Models”, Springer, 2013, pp. 499-526.

RESULTS AND DISCUSSION RESULTS AND DISCUSSION

0.7 0.8 0.9 2 13 0.3 0.4 0.5 0.6 Yexp 6 7 9 10 12 15 17 18 0.0 0.1 0.2 0.0 0.1 0.2 0.3 0.4 0.5 0.6 0.7 0.8 0.9 YPred[2] 1 3 5

Figure 5. Experimental versus predicted RIR values

• btained by the final PLS model.

YPred[2]

y

CONCLUSIONS CONCLUSIONS

• The obtained two-components PLS model is satisfactory in the

fitting and has predictive power.

• In the final PLS model the selected molecular descriptors capture

3D information (3D-Morse, GETAWAY, RDF), supplying information about interatomic distances, topological distances, types of atoms about te ato c d sta ces, topo og ca d sta ces, types o ato s and which encode chemical information (2D-frequency fingerprints). The fungicidal activity can be raised by molecular conformation in

• The fungicidal activity can be raised by molecular conformation in

3D descriptors weighted by atomic van der Waals volumes, atomic Sanderson electronegativities and atomic polarizabilities, geometrical descriptors referring to the effective position of b tit t d f t i th l l substituents and fragments in the molecular space.

ACKNOWLEDGEMENTS ACKNOWLEDGEMENTS

The authors are indebted to Chemaxon Ltd. and Prof. Gerrit

Schüürmann from Helmholtz Centre for Environmental Research (UFZ Leipzig Germany) for giving access to Research (UFZ, Leipzig, Germany) for giving access to their programs.

This project was financially supported by Project 1.1 of This project was financially supported by Project 1.1 of

the Institute of Chemistry Timisoara of the Romanian Academy.