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-Timofei 1 , Ana Borota 1 , Alina Bora 1 , Sorin Simona Funar-Timofei 1 * Ana Borota 1 Alina Bora 1 Sorin Avram 1 , Daniela Ionescu 2 1 Institute of Chemistry of the Romanian Academy, Bv. Mihai y y, Viteazu 24, 300223 Timisoara, Romania 2 University 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 explored by the forced-swim test [1]. Some of the complexes l d b th f d i t t [1] S f th l 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. They also inhibit sterol 14 α –demethylase and hence considered Th l i hibit t l 14 d th l d h id d 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 oxysporum f sp cucumerinum fungi test was studied by oxysporum 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 O F F Cl S F O N N 1 N 10 N N N N N N N N N F S F F S F F F N F N N N 2 2 N N 11 11 N N N N N Cl N N N N Cl F S F F S O S Cl N O N N N N N N 3 12 N N O N N N N F F F F F F O F F S F O N N 4 13 N N N N N N N N N N C Cl F l F S F F F F F F F 5 14 N N N N Cl N N N N N N N N Cl Cl F S S S O F F N O F N N 6 N N 15 N N N N O N N N N Cl Cl F S F F Cl F F Cl F O N 7 16 N N S N N O N N N N N Cl N F S N F F C l S F F F F N N 8 N N 17 N N N N Cl N N N N Cl C l F F S F F F F S F Cl N N N N 9 18 N N O N N N N N N Cl Cl F O N F F S O

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 � optimized 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 r 2 ( ) test [7] was employed. It is considered that 2 r pred for a predictive QSAR model, its value should be higher than 0.5. 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: R 2 X(Cum) = 0.805, R 2 Y(cum) = 0.823, Q 2 (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 R 2 repeated 999 times. The extremely low calculated scrambled R (0.158) and Q 2 (-0.346) values indicate no chance correlation for the chosen model.

RESULTS AND DISCUSSION RESULTS AND DISCUSSION X Y 0.7 0 7 R7e+ 3.0 0.6 F10[C-F] 2.0 0.5 9 18 6 Mor26e 0.4 1.0 15 2 17 0.3 w*c[2] FO t[2] 0.0 3 0.2 12 1 5 5 -1.0 10 0.1 7 13 -0.0 -2.0 -0.1 RDF020v -3.0 RDF020p RDF020e RDF020u -0.2 0.00 0.05 0.10 0.15 0.20 0.25 0.30 0.35 0.40 0.45 -6 -5 -4 -3 -2 -1 0 1 2 3 4 5 6 w*c[1] t[1] Figure 1. Score scatter plot of the Figure 2. Loading scatter plot of the final PLS model final PLS model.

RESULTS AND DISCUSSION RESULTS AND DISCUSSION CoeffCS[2] VIP[2] 1.6 0.50 1.4 0.40 1.2 1.0 0.30 0 8 0.8 0.20 0.6 0.10 0.4 0.2 0.00 RDF020u RDF020v RDF020e RDF020p Mor26e F10[C-F] R7e+ 0.0 RDF020u RDF020v RDF020p F10[C-F] Mor26e RDF020e R7e+ Var ID (Primary) Var ID (Primary) ( y) Figure 3. PLS regression coefficients plot Figure 4. VIP plot of the x-variables of of the model with 2 components. The bars the two-component PLS model. indicate 95% confidence intervals based on jack-knifing. j k k ifi

RESULTS AND DISCUSSION RESULTS AND DISCUSSION � The data over fitting and model applicability was � 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 (RMSE tr = 0.096, MAE tr = g ( tr tr 0.079) and validation (RMSE ext = 0.178, MAE ext = 0.140) sets. � The calculated concordance correlation coefficient values for the training (CCC tr = 0.903), crossvalidation (CCC L7O = 0.832) and test (CCC ext = 0.853) sets indicate a L7O ext robust model with predictive power, which was 2 confirmed by the value of 0.681, too. r pred [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 0.9 2 0.8 13 0.7 17 18 9 0.6 Yexp 7 15 12 0.5 6 10 0.4 0.3 0.2 3 1 0.1 0.0 5 0.0 0.1 0.2 0.3 0.4 0.5 0.6 0.7 0.8 0.9 YPred[2] YPred[2] Figure 5. Experimental versus predicted RIR values obtained by the final PLS model. y