QSER modeling of half-wave oxidation potential of indolizines by theoretical descriptors

Yıl 2022, Cilt: 9 Sayı: 3, 709 - 720, 31.08.2022

Nabil Bouarra Nawel Nadji Soumaya Kherouf Loubna Nouri Amel Boudjemaa Khaldoun Bachari Djelloul Messadi

https://doi.org/10.18596/jotcsa.1065043

Cited By: 2

Öz

Indolizine derivatives hold essential biological functions and have been researched for hypoglycemic, antibacterial, anti-inflammatory, analgesic, and anti-tumor actions. Indolizine scaffold has intrigued conjecture and continuous attention and has become an effective parent system for generating powerful novel medication candidates. This research focused on applying the quantitative structure-electrochemistry relationship (QSER) approach to the half-wave potential (E1/2) for Indolizine derivatives using theoretical molecular descriptors. After calculating the descriptors and splitting the data into both sets, training and prediction. The QSER model was constructed using the Genetic Algorithm/Multiple Linear Regression (GA/MLR) technique, which was used to choose the optimal descriptors for the model. A four-parameter model has been established. Many assessment procedures, including cross-validation, external validation, and Y-scrambling testing, were used to assess the model's performance. Furthermore, the applicability domain (AD) was investigated using the Williams and Insubria graphs to assess the correctness of the established model's predictions. The constructed model exhibits great goodness-of-fit to experimental data, as well as high stability (R²=0.893, Q²LOO= 0.851, Q²LMO=0.843 RMSEtr= 0.052, s= 0.056). Prediction results show a good agreement with the experimental data of E1/2 (R²ext= 0.912, Q²F1= 0.883, Q²F2= 0.883, Q²F3= 0.919, CCCext= 0.942, RMSEext=0.045).

Anahtar Kelimeler

QSER, cyclic voltammetry, indolizines, molecular descriptors, MLR

Kaynakça

• 1. Georgescu E, Dumitrascu F, Georgescu F, Draghici C, Barbu L. A Novel Approach for the Synthesis of 5‐Pyridylindolizine Derivatives via 2‐(2‐Pyridyl) pyridinium Ylides. Journal of Heterocyclic Chemistry. 2013;50(1):78-82.
• 2. Borrows E, Holland D. The Chemistry of the Pyrrocolines and the Octahydropyrrocolines. Chemical reviews. 1948;42(3):611-43.
• 3. Katritzky A R, Rees C W, Scriven E F V, Lohray B B, Bhushan V., Comprehensive Heterocyclic Chemistry II. Pergamon Press;1996 .11628 p. ISBN: 0-08-042072-9.
• 4. Kitadokoro K, Hagishita S, Sato T, Ohtani M, Miki K. Crystal structure of human secretory phospholipase A2-IIA complex with the potent indolizine inhibitor 120–1032. The Journal of Biochemistry. 1998;123(4):619-23.
• 5. De Bolle L, Andrei G, Snoeck R, Zhang Y, Van Lommel A, Otto M, et al. Potent, selective and cell-mediated inhibition of human herpesvirus 6 at an early stage of viral replication by the non-nucleoside compound CMV423. Biochemical pharmacology. 2004;67(2):325-36.
• 6. Sonnet P, Dallemagne P, Guillon J, Engueard C, Stiebing S, Tangue J, Bureau B, Rault S, Auvray P, Moslemi S, Sourdaine P, Séralini G E, New aromatase inhibitors. Synthesis and biological activity of aryl-substituted pyrrolizine and indolizine derivatives, Bioorg Med Chem. 2000;8 (5):945-955.
• 7. Campagna F, Carotti A, Casini G, Macripo M. Synthesis of new heterocyclic ring systems: indeno [2, 1-b]-benzo [g] indolizine and indeno [1', 2': 5, 4] pyrrolo [2, 1-a] phthalazine. Heterocycles (Sendai). 1990;31(1):97-107.
• 8. Lillelund VH, Jensen HH, Liang X, Bols M. Recent developments of transition-state analogue glycosidase inhibitors of non-natural product origin. Chemical reviews. 2002;102(2):515-54.
• 9. Das A, Banik BK. Chapter 5 - Microwave-assisted synthesis of N-heterocycles. In: Das A, Banik B, editors. Microwaves in Chemistry Applications: Elsevier; 2021. p. 143-98.
• 10. Keyzer H, Eckert GM, Gutmann F. Electropharmacology. CRC Press; 1990. 432 p. ISBN:978-0-8493-5409-0.
• 11. Eberson L. Electron-Transfer Reactions in Organic Chemistry. In: Gold V, Bethell D, éditeurs. Advances in Physical Organic Chemistry [Internet]. Academic Press; 1982. p. 79‑185.
• 12. Guengerich FP, Willard RJ, Shea JP, Richards LE, Macdonald TL. Mechanism-based inactivation of cytochrome P-450 by heteroatom-substituted cyclopropanes and formation of ring-opened products. Journal of the American Chemical Society. 1984;106(21):6446-7.
• 13. Scholz F. Electroanalytical Methods: Guide to Experiments and Applications. Springer Science & Business Media; 2009. 366 p. ISBN:978-3-642-02915-8.
• 14. Macchiarulo A, Costantino G, Fringuelli D, Vecchiarelli A, Schiaffella F, Fringuelli R. 1, 4-Benzothiazine and 1, 4-benzoxazine imidazole derivatives with antifungal activity: a docking study. Bioorganic & medicinal chemistry. 2002;10(11):3415-23.
• 15. Todeschini R, Consonni V. Handbook of Molecular Descriptors. John Wiley & Sons; 2000. 692 p. ISBN: 9783527613106.
• 16. Hemmateenejad B, Shamsipur M. Quantitative structure-electrochemistry relationship study of some organic compounds using PC-ANN and PCR. Internet Electronic Journal of Molecular Design. 2004;3(6):316-34.
• 17. Nesmerak K, Nemec I, Sticha M, Waisser K, Palat K. Quantitative structure–property relationships of new benzoxazines and their electrooxidation as a model of metabolic degradation. Electrochimica acta. 2005;50(6):1431-7.
• 18. Fatemi MH, Hadjmohammadi MR, Kamel K, Biparva P. Quantitative structure–property relationship prediction of the half-wave potential for substituted nitrobenzenes in five nonaqueous solvents. Bulletin of the Chemical Society of Japan. 2007;80(2):303-6.
• 19. Hemmateenejad B, Yazdani M. QSPR models for half-wave reduction potential of steroids: A comparative study between feature selection and feature extraction from subsets of or entire set of descriptors. Analytica Chimica Acta. 2009;634(1):27-35.
• 20. Goudarzi N, Goodarzi M, Hosseini MM, Nekooei M. QSPR models for prediction of half wave potentials of some chlorinated organic compounds using SR-PLS and GA-PLS methods. Molecular Physics. 2009;107(17):1739-44.
• 21. Teklu S, Gundersen L-L, Rise F, Tilset M. Electrochemical studies of biologically active indolizines. Tetrahedron. 2005;61(19):4643-56.
• 22. ChemDraw Utra “Ultra-chemical structure drawing standard”. Version 7. 2002. Copyright Cambridge Soft Corporation.
• 23. Stewart JJ. Optimization of parameters for semiempirical methods VI: more modifications to the NDDO approximations and re-optimization of parameters. Journal of molecular modeling. 2013;19(1):1-32.
• 24. MOPAC2016, Stewart James J P, Stewart Computational Chemistry, Colorado Springs, CO, USA, (2016).
• 25. Todeschini R, Consonni V, Mauri A, Pavan M, DRAGON Software – version 5.4-TALETE srl, (2005).
• 26. Liu H, Gramatica P. QSAR study of selective ligands for the thyroid hormone receptor β. Bioorganic & medicinal chemistry. 2007;15(15):5251-61.
• 27. Karakaplan M, Avcu FM. A parallel and non-parallel genetic algorithm for deconvolution of NMR spectra peaks. Chemometrics and Intelligent Laboratory Systems. 2013;125:147-52.
• 28. Avcu FM, Karakaplan M. Finding exact number of peaks in broadband UV-Vis spectra using curve fitting method based on evolutionary computing. Journal of the Turkish Chemical Society Section A: Chemistry. 2020;7(1):117-24.
• 29. Organisation for Economic Co-operation and Development, Guidance Document on the Validation of (Quantitative) Structure-Activity Relationships [(Q)SAR] Models,ENV/JM/MONO (2007) 2, OECD Publishing, Paris.
• 30. Tropsha A, Gramatica P, Gombar VK. The importance of being earnest: validation is the absolute essential for successful application and interpretation of QSPR models. QSAR & Combinatorial Science. 2003;22(1):69-77.
• 31. De Lima Ribeiro FA, Ferreira MMC. QSPR models of boiling point, octanol–water partition coefficient and retention time index of polycyclic aromatic hydrocarbons. Journal of Molecular Structure: THEOCHEM. 2003;663(1-3):109-26.
• 32. Gramatica P. External evaluation of QSAR models, in addition to cross‐validation: verification of predictive capability on totally new chemicals. Molecular informatics. 2014;33(4):311-4.
• 33. Schüürmann G, Ebert R-U, Chen J, Wang B, Kühne R. External validation and prediction employing the predictive squared correlation coefficient-Test set activity mean vs training set activity mean. Journal of Chemical Information and Modeling. 2008;48(11):2140-5.
• 34. Consonni V, Ballabio D, Todeschini R. Comments on the definition of the Q2 parameter for QSAR validation. Journal of chemical information and modeling. 2009;49(7):1669-78.
• 35. Consonni V, Ballabio D, Todeschini R. Evaluation of model predictive ability by external validation techniques. Journal of chemometrics. 2010;24(3‐4):194-201.
• 36. Chirico N, Gramatica P. Real external predictivity of QSAR models: how to evaluate it? Comparison of different validation criteria and proposal of using the concordance correlation coefficient. Journal of chemical information and modeling. 2011;51(9):2320-35.
• 37. Lawrence I, Lin K. A concordance correlation coefficient to evaluate reproducibility. Biometrics. 1989:255-68.
• 38. Chirico N, Gramatica P. Real external predictivity of QSAR models. Part 2. New intercomparable thresholds for different validation criteria and the need for scatter plot inspection. Journal of Chemical Information and Modeling. 2012;52(8):2044-58.
• 39. Gramatica P, Chirico N, Papa E, Cassani S, Kovarich S, QSARINS, Software for the Development and validation of QSAR MLR Models, available on request.
• 40. Kherouf S, Bouarra N, Bouakkadia A, Messadi D. Modeling of linear and nonlinear quantitative structure property relationships of the aqueous solubility of phenol derivatives. Journal of the Serbian Chemical Society. 2019;84(6):575-90.
• 41. Bouarra N, Nadji N, Nouri L, Boudjemaa A, Bachari K, Messadi D. Predicting retention indices of PAHs in reversed-phase liquid chromatography: A quantitative structure retention relationship approach. Journal of the Serbian Chemical Society. 2021;86(1):63-75.
• 42. Gramatica P, Cassani S, Roy PP, Kovarich S, Yap CW, Papa E. QSAR modeling is not “push a button and find a correlation”: a case study of toxicity of (benzo‐) triazoles on algae. Molecular Informatics. 2012;31(11‐12):817-35.
• 43. Todeschini R, Consonni V. Molecular descriptors for chemoinformatics: volume I: alphabetical listing/volume II: appendices, references: John Wiley & Sons; 2009. ISBN: 3527628770.
• 44. Consonni V, Todeschini R, Pavan M. Structure/response correlations and similarity/diversity analysis by GETAWAY descriptors. 1. Theory of the novel 3D molecular descriptors. Journal of chemical information and computer sciences. 2002;42(3):682-92.