How to cite this paper
Zaki, K., Fakir, F., Sbai, A., Maghat, H., Bouachrine, M & Lakhlifi, T. (2024). Inhibition activity of triazoles as a new family for the inhibition of the Indoleamine 2,3-dioxygenase 1 IDO1 protein using 2D-QSAR approach.Current Chemistry Letters, 13(3), 451-466.
Refrences
1. Tang, K., Wu, Y.-H., Song, Y., Yu, B. (2021) Indoleamine 2,3-Dioxygenase 1 (IDO1) Inhibitors in Clinical Trials for Cancer Immunotherapy. J Hematol Oncol. 14 (1), 68.
2. Pallotta, M. T., Rossini, S., Suvieri, C., Coletti, A., Orabona, C., Macchiarulo, A., Volpi, C., Grohmann, U. (2022) Indoleamine 2,3‐dioxygenase 1 (IDO1): An Up‐to‐date Overview of an Eclectic Immunoregulatory Enzyme. The FEBS Journal. 289 (20), 6099–6118.
3. Qian, S., Zhang, M., Chen, Q., He, Y., Wang, W., Wang, Z. (2016) IDO as a Drug Target for Cancer Immunotherapy: Recent Developments in IDO Inhibitors Discovery. RSC Adv. 6 (9), 7575–7581.
4. Bastikar, V., Bastikar, A., Gupta, P. (2022) Quantitative Structure–Activity Relationship-Based Computational Approaches. In Computational Approaches for Novel Therapeutic and Diagnostic Designing to Mitigate SARS-CoV-2 Infection, Elsevier. 191–205.
5. Myrko, I., Chaban, T., Demchuk, Y., Drapak, Y., Chaban, I., Drapak, I., Pankiv, M., Matiychuk, V. (2024) Current Trends of Chemoinformatics and Computer Chemistry in Drug Design: A Review. 10.5267/j.ccl. 13 (1), 151–162.
6. Zrinej, J., ElMchichi, L., Lakhlifi, T., Bouachrine, M. (2022) Curcumin and Derivatives against Human Colon Cancer (HCT-116). Computationnel. RHAZES: Green and Applied Chemistry. 83-102.
7. Ouabane, M., Hajji, H., Belhassan, A., Koubi, Y., Elbouhi, M., Badaoui, H., Sekkat, C., Lakhlifi, T. (2022) 2D-QSPR of the Retention/Release Property for Odorant Molecules in Pectin Gels of Different Concentration. RHAZES: Green and Applied Chemistry. 15-35.
8. Tropsha, A., Gramatica, P., Gombar, V. K. (2003) The Importance of Being Earnest: Validation Is the Absolute Essential for Successful Application and Interpretation of QSPR Models. QSAR Comb. Sci. 22 (1), 69–77.
9. Adeniji, S. E., Uba, S., Uzairu, A. (2018) Theoretical Modeling and Molecular Docking Simulation for Investigating and Evaluating Some Active Compounds as Potent Anti-Tubercular Agents against MTB CYP121 Receptor. Future Journal of Pharmaceutical Sciences, 4 (2), 284–295.
10. Adeniji, S. E., Uba, S., Uzairu, A., Arthur, D. E. (2019) A Derived QSAR Model for Predicting Some Compounds as Potent Antagonist against Mycobacterium Tuberculosis : A Theoretical Approach. Advances in Preventive Medicine, 2019, 1–18.
11. Saxena, A. K., Prathipati, P. (2003) Comparison of MLR, PLS and GA-MLR in QSAR Analysis. SAR and QSAR in Environmental Research, 14 (5–6), 433–445.
12. Mao, L., Wang, Y., Zhao, J., Xu, G., Yao, X., Li, Y.-M. (2020) Discovery of Icotinib-1,2,3-Triazole Derivatives as IDO1 Inhibitors. Front. Pharmacol.11, 579024.
13. Hou, X., Gong, X., Mao, L., Sun, G., Yang, J. (2022) Design, Synthesis and Biological Evaluation of Erlotinib-Based IDO1 Inhibitors. Front. Pharmacol. 13, 940704.
14. Xu, G.-Q., Gong, X.-Q., Zhu, Y.-Y., Yao, X.-J., Peng, L.-Z., Sun, G., Yang, J.-X., Mao, L.-F. (2022) Novel 1,2,3-Triazole Erlotinib Derivatives as Potent IDO1 Inhibitors: Design, Drug-Target Interactions Prediction, Synthesis, Biological Evaluation, Molecular Docking and ADME Properties Studies. Front. Pharmacol. 13, 854965.
15. Hou, X., Gong, X., Mao, L., Zhao, J., Yang, J. (2022) Discovery of Novel 1,2,3-Triazole Derivatives as IDO1 Inhibitors. Pharmaceuticals. 15 (11), 1316.
16. Zaki, K., Sbai, A., Bouachrine, M., Lakhlifi, T. (2022) Statistical QSAR Investigations Using QSAR Techniques to Study Aminopyrimidine-Based CXCR4 Antagonists. RHAZES: Green and Applied Chemistry.
17. Eriksson, L., Jaworska, J., Worth, A. P., Cronin, M. T. D., McDowell, R. M., Gramatica, P. (2003) Methods for Reliability and Uncertainty Assessment and for Applicability Evaluations of Classification- and Regression-Based QSARs. Environ Health Perspect. 111 (10), 1361–1375.
18. Walker, J. D., Jaworska, J., Comber, M. H. I., Schultz, T. W., Dearden, J. C. (2003) GUIDELINES FOR DEVELOPING AND USING QUANTITATIVE STRUCTURE–ACTIVITY RELATIONSHIPS. Environ Toxicol Chem. 22 (8), 1653.
19. Gramatica, P. (2007)Principles of QSAR Models Validation: Internal and External. QSAR Comb. Sci. 26 (5), 694–701.
20. Baumann, K. (2003) Cross-Validation as the Objective Function for Variable-Selection Techniques. TrAC Trends in Analytical Chemistry. 22 (6), 395–406.
21. Yadav, M., Narasimhan, B., Kapoor, A. (2024) Development of 2-Dimensional and 3-Dimensional QSAR Models of Indazole Derivatives as TTK Inhibitors Having Anticancer Potential. 10.5267/j.ccl. 13 (1), 225–240.
22. Rücker, C., Rücker, G., Meringer, M. (2007) Y-Randomization and Its Variants in QSPR/QSAR. J. Chem. Inf. Model. 47 (6), 2345–2357.
23. Golbraikh, A., Tropsha, A. (2002) Beware of Q2! Journal of Molecular Graphics and Modelling. 20 (4), 269–276.
24. Elbouhi, M., Badaoui, H., Ouabane, M., Alaoui, M. A., Koubi, Y., Mokhlis, Y., ElKamel, K., Lakhlifi, T. (2022) Anti-Tumor Activity of Novel Benzimidazole-Chalcone Hybrids as Non-Intercalative Topoisomerase II Catalytic Inhibitors: 2D-QSAR Study. RHAZES: Green and Applied Chemistry. 62-75.
25. Netzeva, T. I., Worth, A. P., Aldenberg, T., Benigni, R., Cronin, M. T. D., Gramatica, P., Jaworska, J. S., Kahn, S., Klopman, G., Marchant, C. A., Myatt, G., Nikolova-Jeliazkova, N., Patlewicz, G. Y., Perkins, R., Roberts, D. W., Schultz, T. W., Stanton, D. T., Van De Sandt, J. J. M., Tong, W., Veith, G., Yang, C. (2005) Current Status of Methods for Defining the Applicability Domain of (Quantitative) Structure-Activity Relationships: The Report and Recommendations of ECVAM Workshop 52 ,. Altern Lab Anim. 33 (2), 155–173.
26. Garg, R., Smith, C. J. (2014) Predicting the Bioconcentration Factor of Highly Hydrophobic Organic Chemicals. Food and Chemical Toxicology. 69, 252–259.
2. Pallotta, M. T., Rossini, S., Suvieri, C., Coletti, A., Orabona, C., Macchiarulo, A., Volpi, C., Grohmann, U. (2022) Indoleamine 2,3‐dioxygenase 1 (IDO1): An Up‐to‐date Overview of an Eclectic Immunoregulatory Enzyme. The FEBS Journal. 289 (20), 6099–6118.
3. Qian, S., Zhang, M., Chen, Q., He, Y., Wang, W., Wang, Z. (2016) IDO as a Drug Target for Cancer Immunotherapy: Recent Developments in IDO Inhibitors Discovery. RSC Adv. 6 (9), 7575–7581.
4. Bastikar, V., Bastikar, A., Gupta, P. (2022) Quantitative Structure–Activity Relationship-Based Computational Approaches. In Computational Approaches for Novel Therapeutic and Diagnostic Designing to Mitigate SARS-CoV-2 Infection, Elsevier. 191–205.
5. Myrko, I., Chaban, T., Demchuk, Y., Drapak, Y., Chaban, I., Drapak, I., Pankiv, M., Matiychuk, V. (2024) Current Trends of Chemoinformatics and Computer Chemistry in Drug Design: A Review. 10.5267/j.ccl. 13 (1), 151–162.
6. Zrinej, J., ElMchichi, L., Lakhlifi, T., Bouachrine, M. (2022) Curcumin and Derivatives against Human Colon Cancer (HCT-116). Computationnel. RHAZES: Green and Applied Chemistry. 83-102.
7. Ouabane, M., Hajji, H., Belhassan, A., Koubi, Y., Elbouhi, M., Badaoui, H., Sekkat, C., Lakhlifi, T. (2022) 2D-QSPR of the Retention/Release Property for Odorant Molecules in Pectin Gels of Different Concentration. RHAZES: Green and Applied Chemistry. 15-35.
8. Tropsha, A., Gramatica, P., Gombar, V. K. (2003) The Importance of Being Earnest: Validation Is the Absolute Essential for Successful Application and Interpretation of QSPR Models. QSAR Comb. Sci. 22 (1), 69–77.
9. Adeniji, S. E., Uba, S., Uzairu, A. (2018) Theoretical Modeling and Molecular Docking Simulation for Investigating and Evaluating Some Active Compounds as Potent Anti-Tubercular Agents against MTB CYP121 Receptor. Future Journal of Pharmaceutical Sciences, 4 (2), 284–295.
10. Adeniji, S. E., Uba, S., Uzairu, A., Arthur, D. E. (2019) A Derived QSAR Model for Predicting Some Compounds as Potent Antagonist against Mycobacterium Tuberculosis : A Theoretical Approach. Advances in Preventive Medicine, 2019, 1–18.
11. Saxena, A. K., Prathipati, P. (2003) Comparison of MLR, PLS and GA-MLR in QSAR Analysis. SAR and QSAR in Environmental Research, 14 (5–6), 433–445.
12. Mao, L., Wang, Y., Zhao, J., Xu, G., Yao, X., Li, Y.-M. (2020) Discovery of Icotinib-1,2,3-Triazole Derivatives as IDO1 Inhibitors. Front. Pharmacol.11, 579024.
13. Hou, X., Gong, X., Mao, L., Sun, G., Yang, J. (2022) Design, Synthesis and Biological Evaluation of Erlotinib-Based IDO1 Inhibitors. Front. Pharmacol. 13, 940704.
14. Xu, G.-Q., Gong, X.-Q., Zhu, Y.-Y., Yao, X.-J., Peng, L.-Z., Sun, G., Yang, J.-X., Mao, L.-F. (2022) Novel 1,2,3-Triazole Erlotinib Derivatives as Potent IDO1 Inhibitors: Design, Drug-Target Interactions Prediction, Synthesis, Biological Evaluation, Molecular Docking and ADME Properties Studies. Front. Pharmacol. 13, 854965.
15. Hou, X., Gong, X., Mao, L., Zhao, J., Yang, J. (2022) Discovery of Novel 1,2,3-Triazole Derivatives as IDO1 Inhibitors. Pharmaceuticals. 15 (11), 1316.
16. Zaki, K., Sbai, A., Bouachrine, M., Lakhlifi, T. (2022) Statistical QSAR Investigations Using QSAR Techniques to Study Aminopyrimidine-Based CXCR4 Antagonists. RHAZES: Green and Applied Chemistry.
17. Eriksson, L., Jaworska, J., Worth, A. P., Cronin, M. T. D., McDowell, R. M., Gramatica, P. (2003) Methods for Reliability and Uncertainty Assessment and for Applicability Evaluations of Classification- and Regression-Based QSARs. Environ Health Perspect. 111 (10), 1361–1375.
18. Walker, J. D., Jaworska, J., Comber, M. H. I., Schultz, T. W., Dearden, J. C. (2003) GUIDELINES FOR DEVELOPING AND USING QUANTITATIVE STRUCTURE–ACTIVITY RELATIONSHIPS. Environ Toxicol Chem. 22 (8), 1653.
19. Gramatica, P. (2007)Principles of QSAR Models Validation: Internal and External. QSAR Comb. Sci. 26 (5), 694–701.
20. Baumann, K. (2003) Cross-Validation as the Objective Function for Variable-Selection Techniques. TrAC Trends in Analytical Chemistry. 22 (6), 395–406.
21. Yadav, M., Narasimhan, B., Kapoor, A. (2024) Development of 2-Dimensional and 3-Dimensional QSAR Models of Indazole Derivatives as TTK Inhibitors Having Anticancer Potential. 10.5267/j.ccl. 13 (1), 225–240.
22. Rücker, C., Rücker, G., Meringer, M. (2007) Y-Randomization and Its Variants in QSPR/QSAR. J. Chem. Inf. Model. 47 (6), 2345–2357.
23. Golbraikh, A., Tropsha, A. (2002) Beware of Q2! Journal of Molecular Graphics and Modelling. 20 (4), 269–276.
24. Elbouhi, M., Badaoui, H., Ouabane, M., Alaoui, M. A., Koubi, Y., Mokhlis, Y., ElKamel, K., Lakhlifi, T. (2022) Anti-Tumor Activity of Novel Benzimidazole-Chalcone Hybrids as Non-Intercalative Topoisomerase II Catalytic Inhibitors: 2D-QSAR Study. RHAZES: Green and Applied Chemistry. 62-75.
25. Netzeva, T. I., Worth, A. P., Aldenberg, T., Benigni, R., Cronin, M. T. D., Gramatica, P., Jaworska, J. S., Kahn, S., Klopman, G., Marchant, C. A., Myatt, G., Nikolova-Jeliazkova, N., Patlewicz, G. Y., Perkins, R., Roberts, D. W., Schultz, T. W., Stanton, D. T., Van De Sandt, J. J. M., Tong, W., Veith, G., Yang, C. (2005) Current Status of Methods for Defining the Applicability Domain of (Quantitative) Structure-Activity Relationships: The Report and Recommendations of ECVAM Workshop 52 ,. Altern Lab Anim. 33 (2), 155–173.
26. Garg, R., Smith, C. J. (2014) Predicting the Bioconcentration Factor of Highly Hydrophobic Organic Chemicals. Food and Chemical Toxicology. 69, 252–259.