Open for submissionsPeer reviewed, fully open access, and a low article processing charge.Submit manuscript

Research ArticleOpen AccessGoogle Scholar indexed

Prediction of Anti-Inflammatory Activity of a Series of Pyrimidine Derivatives, by Multiple Linear Regression and Artificial Neural Networks

,
,
,
,
  1. 1 Laboratory of Thermodynamics and Environmental Physico-Chemistry, Nangui Abrogoua University, Abidjan, Ivory Coast
  2. 2 Training and Research Unit Biological Sciences, University Peleforo Gon Coulibaly, Korhogo, Ivory Coast
  3. 3 Ivorian Research Group in Disease Modeling (GIR2M), Abidjan, Ivory Coast
Computational Chemistry·Volume 10, Issue 4·Pages 186–202·Published 27 October 2022·DOI10.4236/cc.2022.104009
Download PDF

Abstract

Anti-inflammatory activity of a series of tri-substituted pyrimidine derivatives was predicted using two Quantitative Structure-Activity Relationship models. These relationships were developed from molecular descriptors calculated using the DFT quantum chemistry method using the B3LYP/6-31G(d,p) level of theory and molecular lipophilicity. Thus, the four descriptors which are the dipole moment μD, the energy of the highest occupied molecular orbital EHOMO, the isotropic polarizability α and the ACD/logP lipophilicity were selected for this purpose. The Multiple Linear Regression (MLR) and Artificial Neural Network (ANN) models are respectively accredited with the following statistical indicators: R2=91.28%, R2aj=89.11%, RMCE = 0.2831, R2ext=86.50% and R2=98.22%, R2aj=97.75%, RMCE = 0.1131, R2ext=98.54%. The results obtained with the artificial neural network are better than those of the multiple linear regression. However, these results show that the two models developed have very good predictive performance of anti-inflammatory activity. These two models can therefore be used to predict anti-inflammatory activity of new similar pyrimidine derivatives.

KeywordsAnti-Inflammatory ActivityMultiple Linear RegressionArtificial Neural NetworkQSAR
  1. Fokunang, C.N., Tembe, E., Frederick, K. and Ngadjui, B. (2018) Overview of Non-Steroidal Anti-Inflammatory Drugs (NSAIDs) in Resource Limited Countries. MOJ Toxicology, 4, 5-13. https://doi.org/10.15406/mojt.2018.04.00081
  2. Yaro, B., et al. (2015) Activités anti-inflammatoire, analgésique et antioxydante de l’extrait aqueux des tiges feuillées de Saba senegalensis Pichon (Apocynaceae) Anti-Inflammatory, Analgesic and Antioxidant Activities of an Aqueous Extract of Saba senegalensis Pichon Stems with. Phytothérapie, 14, 213-219. https://doi.org/10.1007/s10298-015-0992-5
  3. Martel, M.O., Finan, P.H., Dolman, A.J., Edwards, R.R., Wasan, A.D. and Robert, N. (2016) Self-Reports of Medication Side Effects and Pain-Related Activity Interference in Patients with Chronic Pain: A Longitudinal Cohort Study. Pain, 156, 1092-1100. https://doi.org/10.1097/j.pain.0000000000000154
  4. Hanna, M.M. (2012) New pyrimido[5,4-e]pyrrolo[1,2-c]pyrimidines: Synthesis, 2D-QSAR, Anti-Inflammatory, Analgesic and Ulcerogenicity Studies. European Journal of Medicinal Chemistry, 55, 12-22. https://doi.org/10.1016/j.ejmech.2012.06.048
  5. Shefali, A. and Vijay, S. (2011) QSAR Study on Some Newly Synthesized Pyrimidobenzimidazole Derivatives as Analgesic Agents. International Journal of Pharmacy and Pharmaceutical Sciences, 3, 457-461.
  6. Abuhashem, A.A. and Youssef, M.M. (2011) Synthesis of New Visnagen and Khellin Furochromone Pyrimidine Derivatives and Their Anti-Inflammatory and Analgesic Activity. Molecules, 16, 1956-1972. https://doi.org/10.3390/molecules16031956
  7. Kumar, V., Sharma, S. and Husain, A. (2015) Synthesis and in Vivo Anti-Inflammatory and Analgesic Activities of Oxadiazoles Clubbed with Benzothiazole Nucleus. International Current Pharmaceutical Journal, 4, 457-461. https://doi.org/10.3329/icpj.v4i12.25597
  8. Giles, D., Roopa, K., Sheeba, F.R., Gurubasavarajaswamy, P.M., Divakar, G. and Vidhya, T. (2012) Synthesis Pharmacological Evaluation and Docking Studies of Pyrimidine Derivatives. European Journal of Medicinal Chemistry, 58, 478-484. https://doi.org/10.1016/j.ejmech.2012.09.050
  9. Nassar, E., Elbadry, Y.A. and El Kazaz, H. (2016) Synthesis, in Vivo Anti-Inflammatory, and in Vitro Antimicrobial Activity of New 5-Benzofuranyl Fused Pyrimidines. Chemical and Pharmaceutical Bulletin, 64, 558-563. https://doi.org/10.1248/cpb.c15-00922
  10. Khedekar, P. (2015) QSAR Studies of 6-Aminopyrimidin-4-One Derivatives as Anti-Inflammatory Agent. International Journal of Pharmacy & Biomedical Research, 2, 13-16.
  11. Kawade, D.P., Khedekar, P.B. and Bhusari, K.P. (2011) QSAR Studies of 6-Aminopyrimidin-4-One Derivatives as Anti-Inflammatory Agent. International Journal of Pharmacy & Biomedical Research, 2, 13-16.
  12. Traoré, Y., Koné, M.G.-R., Ouattara, O. and Ziao, N. (2018) QSAR Approach to Estimating the Analgesic Activity of a Series of Tri-Substituted Pyrimidine Derivatives. Journal of Computational Chemistry & Molecular Modelling, 3, 221-234. https://doi.org/10.25177/JCCMM.3.1.2
  13. Rekka, E.A. and Kourounakis, P.N. (2008) Chemistry and Molecular Aspects of Drug Design and Action. Taylor & Francis Group, Abingdon-on-Thames. https://doi.org/10.1201/9781420008272
  14. Zuvela, P., David, J. and Wong, M.W. (2018) Interpretation of ANN-Based QSAR Models for Prediction of Antioxidant Activity of Flavonoids. Journal of Computational Chemistry, 39, 953-963. https://doi.org/10.1002/jcc.25168
  15. Mitchell, J.B. (2014) Machine Learning Methods in Chemoinformatics. Wiley Interdisciplinary Reviews: Computational Molecular Science, 4, 468-481. https://doi.org/10.1002/wcms.1183
  16. Frisch, M.J., Trucks, G.W., Schlegel, H.B., Scuseria, G.E., Robb, M.A., Cheeseman, J.R., Scalmani, G., Barone, V., Mennucci, B., et al. (2009) Gaussian 09, Revision A.02. Gaussian, Inc., Wallingford.
  17. Erickson, C.B., Ankenman, B.E. and Sanchez, S.M. (2018) Comparison of Gaussian Process Modeling Software. European Journal of Operational Research, 266, 179-192. https://doi.org/10.1016/j.ejor.2017.10.002
  18. Gayathri, R. and Arivazhagan, M. (2014) Molecular Structure, Vibrational Investigation of 2-chloro-α-α-α-trifluoro-3,5-dinitrotoluene Using DFT (LSDA, B3LYP, B3PW91 and MPW1PW91) and UV-Vis Absorption Spectra in Organic Solvents: A IEF-PCM/TD-DFT Study. Spectrochimica Acta, Part A: Molecular and Biomolecular Spectroscopy, 123, 309-326. https://doi.org/10.1016/j.saa.2013.12.012
  19. Arnott, J.A., Kumar, R. and Planey, S.L. (2013) Lipophilicity Indices for Drug Development. Journal of Applied Biopharmaceutics and Pharmacokinetics, 1, 31-36.
  20. Costa, C. and Silva, B. (2017) Analysis and Validation of Dipole Moment Calculations in Chemistry Teaching. The Electronic Journal of Chemistry, 9, 360-368. https://doi.org/10.17807/orbital.v9i5.1020
  21. Peter, E., Teresa, M. and Catanho, J.D.A. (2008) Quantitative Structure-Activity Relationships (QSAR) of 4-Amino-2,6-diarylpyrimidine-5-carbonitriles with Anti-Inflammatory Activity. Journal of the Brazilian Chemical Society, 19, 337-343. https://doi.org/10.1590/S0103-50532008000200021
  22. Lopachin, R.M., Geohagen, B.C. and Nordstroem, L.U. (2019) Mechanisms of Soft and Hard Electrophile Toxicities. Toxicology, 418, 62-69. https://doi.org/10.1016/j.tox.2019.02.005
  23. Sabirov, D.S. (2013) Anisotropy of Polarizability of Fullerene Higher Adducts for Assessing the Efficiency of Their Use in Organic Solar Cells. The Journal of Physical Chemistry, 117, 9148-9153. https://doi.org/10.1021/jp401944x
  24. Prabavathi, N., Nilufer, A., Krishnakumar, V. and Akilandeswari, L. (2012) Molecular and Biomolecular Spectroscopy Spectroscopic, Electronic Structure and Natural Bond Analysis of 2-aminopyrimidine and 4-aminopyrazolo [3, 4-d] pyrimidine: A Comparative Study. Spectrochimica Acta Part A: Molecular and Biomolecular Spectroscopy, 96, 226-241. https://doi.org/10.1016/j.saa.2012.05.015
  25. Rutkowska, E. (2013) Review Lipophilicity—Methods of Determination and Its Role in Medicinal Chemistry. Acta Poloniae Pharmaceutica ñ Drug Research, 70, 3-18.
  26. Peter, S.C., et al. (2018) Quantitative Structure-Activity Relationship (QSAR): Modeling Approaches to Biological Applications. In: Ranganathan, S., et al., Eds., Encyclopedia of Bioinformatics and Computational Biology, Elsevier, Amsterdam, 661-676. https://doi.org/10.1016/B978-0-12-809633-8.20197-0
  27. Wang, T., Wu, M., Lin, J. and Yang, L. (2015) Quantitative Structure-Activity Relationship Promising Advances in Drug Discovery Platforms. Expert Opinion on Drug Discovery, 10, 1283-1300. https://doi.org/10.1517/17460441.2015.1083006
  28. Roy, K. and Kar, S. (2015) A Primer on Qsar/Qspr Modeling. Springer, Berlin, 37-59. https://doi.org/10.1007/978-3-319-17281-1
  29. Veerasamy, R., Rajak, H., Jain, A., Sivadasan, S., Varghese, C.P. and Agrawal, R.K. (2011) Validation of QSAR Models—Strategies and Importance. International Journal of Drug Design & Discovery, 2, 511-519.
  30. Khan, A.U. (2016) Descriptors and Their Selection Methods in QSAR Analysis: Paradigm for Drug Design. Drug Discovery Today, 21, 1291-1302. https://doi.org/10.1016/j.drudis.2016.06.013
  31. Qin, L., Zhang, X., Chen, Y., Mo, L., Zeng, H. and Liang, Y. (2017) Predictive QSAR Models for the Toxicity of Disinfection Byproducts. Molecules, 22, Article No. 1671. https://doi.org/10.3390/molecules22101671
  32. Lipiński, P.F.J. and Szurmak, P. (2017) Scramble “N” Gamble: A Tool for Fast and Facile Generation of Random Data for Statistical Evaluation of QSAR Models. Chemical Papers, 71, 2217-2232. https://doi.org/10.1007/s11696-017-0215-7
  33. Martin, T.M., et al. (2012) Does Rational Selection of Training and Test Sets Improve the Outcome of QSAR Modeling? Journal of Chemical Information and Modeling, 52, 2570-2578. https://doi.org/10.1021/ci300338w
  34. Roy, P.P., Paul, S., Mitra, I. and Roy, K. (2009) On Two Novel Parameters for Validation of Predictive QSAR Models. Molecules, 14, 1660-1701. https://doi.org/10.3390/molecules14051660
  35. Rooki, R., Ardejani, F.D. and Moradzadeh, A. (2014) Hole Cleaning Prediction in Foam Drilling Using Artificial Neural Network and Multiple Linear Regression. Geomaterials, 4, 47-53. https://doi.org/10.4236/gm.2014.41005
  36. Affi, T.S., Ziao, N., Kone, M.G.-R., Bamba, K. and Ouattara, O. (2017) Can Empirical Descriptors Reliably Predict Molecular Lipophilicity? A QSPR Study Investigation. International Journal of Engineering Research and Applications, 7, 50-56. https://doi.org/10.9790/9622-0705015056
  37. Ge, B., Ishaku, M.M. and Lewu, H.I. (2021) Research on the Effect of Artificial Intelligence Real Estate Forecasting Using Multiple Regression Analysis and Artificial Neural Network: A Case Study of Ghana. Journal of Computer and Communications, 9, 1-14. https://doi.org/10.4236/jcc.2021.910001
  38. Baskina, I.I., Winklerb, D. and Tetko, I.V. (2016) A Renaissance of Neural Networks in Drug Discovery. Expert Opinion on Drug Discovery, 11, 785-795. https://doi.org/10.1080/17460441.2016.1201262
  39. Bahia, I.S.H. (2013) Using Artificial Neural Network Modeling in Forecasting Revenue: Case Study in National Insurance Company/Iraq. International Journal of Intelligence Science, 3, 136-143. https://doi.org/10.4236/ijis.2013.33015
  40. Wesolowski, M. and Suchacz, B. (2012) Artificial Neural Networks: Theoretical Background and Pharmaceutical Applications: A Review. Journal of AOAC International, 95, 652-668. https://doi.org/10.5740/jaoacint.SGE_Wesolowski_ANN
  41. Sutariyaa, V., Grosheva, A., Sadanab, P., Bhatiab, D. and Pathak, Y. (2013) Artificial Neural Network in Drug Delivery and Pharmaceutical Research. The Open Bioinformatics Journal, 7, 49-62. https://doi.org/10.2174/1875036201307010049
  42. Joshi, V.D., Kshirsagar, M.D. and Singhal, S. (2012) Synthesis and Pharmacological Study of Some Novel Pyrimidines. Der Pharmacia Sinica, 3, 343-348.
  43. Yejella, R.P. and Atla, S.R. (2011) A Study of Anti-Inflammatory and Analgesic Activity of New 2,4,6-Trisubstituted Pyrimidines. Chemical and Pharmaceutical Bulletin (Tokyo), 59, 1079-1082. https://doi.org/10.1248/cpb.59.1079
  44. Datar, P. (2015) QSAR and Synthesis of a Novel Biphenyl Carboxamide Analogue for Analgesic Activity. Modern Chemistry and Applications, 3, 1-7. https://doi.org/10.4172/2329-6798.1000148
  45. Adeniji, S.E., Uba, S., Uzairu, A. and 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, Article ID: 5173786. https://doi.org/10.1155/2019/5173786
  46. Dral, P.O., et al. (2016) Semiempirical Quantum-Chemical Orthogonalization-Corrected Methods: Theory, Implementation, and Parameters. Journal of Chemical Theory and Computation, 12, 1082-1096. https://doi.org/10.1021/acs.jctc.5b01046
  47. Edraki, N., Das, U. and Hemateenejad, B. (2016) Comparative QSAR Analysis of 3,5-bis (Arylidene)-4-Piperidone Derivatives: The Development of Predictive Cytotoxicity Models. Iranian Journal of Pharmaceutical Research, 15, 425-437.
  48. Kausar, S. and Falcao, A.O. (2018) An Automated Framework for QSAR Model Building. Journal of Cheminformatics, 10, Article No. 1. https://doi.org/10.1186/s13321-017-0256-5
  49. Murtagh, F. (2014) Ward’s Hierarchical Agglomerative Clustering Method: Which Algorithms Implement Ward’s Criterion? Journal of Classification, 31, 274-295. https://doi.org/10.1007/s00357-014-9161-z