Quantitative Structure Activity Relationship (QSAR) Investigations and Molecular Docking Analysis of Plasmodium Protein Farnesyltransferase Inhibitors as Potent Antimalarial Agents

Authors

  • Mebarka Ouassaf University of Biskra, Group of Computational and Medicinal Chemistry, LMCE Laboratory, Algeria.
  • Salah Belaidi University of Biskra, Group of Computational and Medicinal Chemistry, LMCE Laboratory, Algeria.
  • Amneh Shtaiwi Faculty of Pharmacy, Middle East University, Jordan.
  • Samir Chtita Faculty of Sciences Ben M’sik, Hassan II University of Casablanca, Morocco

DOI:

https://doi.org/10.35516/jjps.v15i3.407

Keywords:

QSAR, docking, benzophenone, PFT inhibitory, antimalarial

Abstract

The development of farnesyltransferase inhibitors based on the benzophenone scaffold directed against Plasmodium falciparum is considered a strategy in malaria treatment. In this work, quantitative structure–activity relationship (QSAR) was performed to predict the protein farnesyltransferase (PFT) inhibitory activities for a series of 36 benzophenone derivatives. The data set was divided into two subsets of training and test sets, and the best model using multiple linear regression (MLR), with the values of internal and external validity (R2 = 0.884, R2adj = 0.865, R2pred = 0.821, Q2cv =0.822 and R2p=0.811) was found in agreement with the Tropsha and Golbraikh criteria. The applicability domain (AD) was determined using the Williams plot to describe the chemical space for the model used in this study. The model shows that antimalarial activities of benzophenone depend on logP, bpol, MAXDn, and FMF descriptors. These indications prompted us to design new benzophenones PFT inhibitors and predict the value of their anti-malarial activities based on the MLR equation. Docking results reveal that the newly designed benzophenones bind to the hydrophobic pocket and polar contact with high affinity. The predicted results from this study can help to design novel benzophenone as inhibitors of human PFT with high antimalarial activities.

Author Biographies

Mebarka Ouassaf, University of Biskra, Group of Computational and Medicinal Chemistry, LMCE Laboratory, Algeria.

University of Biskra, Group of Computational and Medicinal Chemistry, LMCE Laboratory, Algeria.

Salah Belaidi, University of Biskra, Group of Computational and Medicinal Chemistry, LMCE Laboratory, Algeria.

University of Biskra, Group of Computational and Medicinal Chemistry, LMCE Laboratory, Algeria.

Amneh Shtaiwi, Faculty of Pharmacy, Middle East University, Jordan.

Faculty of Pharmacy, Middle East University, Jordan.

Samir Chtita, Faculty of Sciences Ben M’sik, Hassan II University of Casablanca, Morocco

 Laboratory Physical Chemistry of Materials

References

World malaria report 2020, World Health Organization, Geneva, Switzerland, 2020, p 300

Prakash N., Patel S., Faldu N.J., Ranjan R. and Sudheer D.V.N. Molecular Docking Studies of Antimalarial Drugs for Malaria. J. Comput. Sci.Syst. Biol. 2010; 3: 70-73. doi:10.4172/jcsb.1000059

Hameed A., Masood S., Hameed A., Ahmed E., Sharif A. and Abdullah M.I. J. Comput. Aided. Mol. Des. 2019; 33:677-688.

Sharma K. A. Review on Plasmodium Falciparum-Protein Farnesyltransferase Inhibitors as Antimalarial Drug Targets. Curr. Drug. Targets. 2017; 18:1676–1686. https://doi.org/10.2174/1389450117666160823165004

Singh J., Mansuri R., Vijay S., Sahoo G.C., Sharma A. and Kumar M. Docking predictions-based Plasmodium falciparum phosphoethanolamine methyl transferase inhibitor identification and in-vitro antimalarial activity analysis. BMC.Chem. 2019; 13:43. https://doi.org/10.1186/s13065-019-0551-5

Subramanian T., Liu S., Troutman J.M., Andres D.A. and Spielmann H.P. Protein Farnesyltransferase-Catalyzed Isoprenoid Transfer to Peptide Depends on Lipid Size and Shape, not Hydrophobicity. Chem.Bio.Chem. 2008; 9:2872-2882. https://doi.org/10.1002/cbic.200800248

Kumar S., Bhardwaj T.R., Prasad D.N. And Singh R.K. Drug targets for resistant malaria: Historic to future perspectives. Biomed. Pharmacother. 2018;104: 8–27. https://doi.org/10.1016/j.biopha.2018.05.009

Wiesner J., Kettler K., Sakowski J., Ortmann R., Jomaa H. and Schlitzer M. Structure–Activity relationships of novel anti-Malarial agents: Part 5. N-(4-acylamino-3-benzoylphenyl)-[5-(4-nitrophenyl)-2-furyl] acrylic acid amides. Bioorg. Med. Chem. Lett. 2003; 13:361–363. https://doi.org/10.1016/S0960-894X(02)01003-X

Choudhari P. B., Bhatia M. S., Bhatia N. M. Application of pocket modeling and k-nearest neighbor molecular field analysis (kNN-MFA) for designing of some anticoagulants: potential factor IXa inhibitors. Med. Chem. Res. 2013; 22:976-985.

Roy K., Kar S., Das R.N. Background of QSAR and Historical Developments. Editors. Understanding the Basics of QSAR for Applications in Pharmaceutical Sciences and Risk Assessment . Boston: Academic Press, 2015; Chapter 1, pp 1–46. https://doi.org/10.1016/B978-0-12-801505-6.00001-6

Neves B.J., Braga R.C., Melo-Filho C.C., Moreira-Filho J.T., Muratov E.N. and Andrade C.H. QSAR-Based Virtual Screening: Advances and Applications in Drug Discovery. Front Pharmacol. 2018; 9:1275.https://doi.org/10.3389/fphar.2018.01275

Moukhliss Y., ElKhatabi K., Koubi Y., Maghat H., Sbai A, Bouachrine. and M.Lakhlifi T. 2D-QSAR modeling of novel pleconaril derivatives (isoxazole-based molecules) as antiviral inhibitors against Coxsackievirus B3 (CVB3). Jordan Journal of Pharmaceutical Sciences. 2021; 14:137- 156

Wiesner J., Kettler K., Sakowski J., Ortmann R., JomaaH.and Schlitzer M. Structure–Activity relationships of novel anti-Malarial agents: Part 5. N-(4-acylamino-3-benzoylphenyl)-[5-(4-nitrophenyl)-2-furyl] acrylic acid amides. Bioorg. Med. Chem. Lett. 2003;13: 361-363. doi:10.1016/S0960-894X(02)01003-X

Wiesner J., Fucik K., Kettler K., Sakowski J., Ortmann R. and Jomaa H. Structure–Activity relationships of novel anti-malarial agents. Part 6: N-(4-Arylpropionylamino-3 benzoylphenyl)-[5-(4-nitrophenyl)-2-furyl]acrylic acid amides; Bioorg. Med. Chem. Lett. 2003; 13:1539-1541. https://doi.org/10.1016/S0960-894X(03)00179-3

Wiesner J., Mitsch A., Wißner P., Krämer O., JomaaH.and Schlitzer M. Structure–Activity relationships of novel anti-Malarial agents. Part 4: N-(3-Benzoyl-4-tolylacetylaminophenyl)-3-(5-aryl-2-furyl)acrylic acid amides. Bioorg. Med. Chem.Lett. 2002;12: 2681-2683. doi:10.1016/S0960-894X(02)00555-3

Wiesner J., Mitsch A., Jomaa H. and Schlitzer M. Structure–activity relationships of novel anti-malarial agents. Part 7: N-(3-Benzoyl-4-tolylacetylaminophenyl)-3-(5-aryl-2-furyl)acrylic acid amides with polar moieties. Bioorg. Med. Chem. Lett. 2003; 13:2159-2161. doi:10.1016/S0960-894X(03)00353-6

Calculator Plugins, Marvin 6.3.0, 2014, ChemAxon (http://www.chemaxon.com).

HyperChem (Molecular Modeling System) (2007) Hypercube, Inc., 1115 NW, 4th Street, Gainesville, FL 32601, USA

Ruiz I.L. and Gómez-Nieto M.Á. Study of the Applicability Domain of the QSAR Classification Models by Means of the Rivality and Modelability Indexes. Molecules. 2018; 23:2756.

https://doi.org/10.3390/molecules23112756

Saxena A.K. and Prathipati P. Comparison of MLR, PLS and GA-MLR in QSAR analysis. SAR. QSAR. Environ. Res. 2003; 14:433–45.

https://doi.org/10.1080/10629360310001624015

Tropsha A. Best Practices for QSAR Model Development, Validation, and Exploitation. Mol. Info.2010; 29: 476–88.

https://doi.org/10.1002/minf.201000061

TROPSHA, A. Best Practices for QSAR Model Development, Validation, and Exploitation. Mol. Info. 2010; 29: 476 – 488.

Gramatica P. Principles of QSAR models validation: internal and external. QSAR. Comb. Sci. 2007;26 :694–701. https://doi.org/10.1002/qsar.200610151

Golbraikh A. and Tropsha A. Predictive QSAR modeling based on diversity sampling of experimental datasets for the training and test set selection. J. Comput. Aided. Mol. Des. 2002 ;16: 357–369.

https://doi.org/10.1023/A:1020869118689

Roy K., Kar S. and Ambure P. On a simple approach for determining applicability domain of QSAR models. Chemom. Intell. Lab. Syst. 2015; 145:22–29.

https://doi.org/10.1016/j.chemolab.2015.04.013

Lin L.I. A concordance correlation coefficient to evaluate reproducibility. Biometrics. 1989; 45:255–68.

Golbraikh A., Wang X.S., Zhu H., Tropsha A., Predictive QSAR Modeling: Methods and Applications in Drug Discovery and Chemical Risk Assessment: J. Leszczynski (Ed.), Handbook of Computational Chemistry, Springer Netherlands, Dordrecht, 2016

Rücker C., Rücker G.and Meringer M. y-Randomization and its variants in QSPR/QSAR. J. Chem. Inf. Model. 2007; 47:2345–2357. https://doi.org/10.1021/ci700157b

Ouassaf M., Belaidi S., Benbrahi̇m İ., Belai̇di̇ H. and Chti̇ta S. Quantitative Structure-Activity Relationships of 1.2.3 Triazole Derivatives as Aromatase Inhibition Activity. Turkish Comp. Theo. Chem. 2020; 4:1–11. https://doi.org/10.33435/tcandtc.545369

Ouassaf M., Belaidi S., Lotfy K., Daoud I. and Belaidi H. Molecular Docking Studies and ADMET Properties of New 1.2.3 Triazole Derivatives for Anti-Breast Cancer Activity. J. Bionanosci. 2018; 12: 26-36. DOI: https://doi.org/10.1166/jbns.2018.1505

Dermeche K., Tchouar N., Belaidi S., Salah T. Qualitative Structure-Activity Relationships and 2D-QSAR Modeling of TNF-α Inhibition by Thalidomide Derivatives. J. Bionanosci. 2015; 9: 395-400. DOI: https://doi.org/10.1166/jbns.2015.1320

Almi Z., Belaidi S., Segueni L., Structural Exploration and Quantitative Structure-Activity Relationships Properties for 1.2. 5-Oxadiazole Derivatives, Rev. Theo. Sci. 2015; 3: 264-272

Weaver S. and Gleeson M.P. The importance of the domain of applicability in QSAR modeling. J.Mol. Graph. Model. 2008; 26:1315–1326. https://doi.org/10.1016/j.jmgm.2008.01.002

Chtita S., Belhassan A., Bakhouch M., Taourati A.I., Aouidate A., Belaidi S., Moutaabbid M., Belaaouad S., Bouachrine M. and Lakhlifi T. QSAR study of unsymmetrical aromatic Disulfides as potent avian SARS-CoV main protease inhibitors using quantum chemical descriptors and statistical methods. Chemometr. Intell. Lab. Syst. 2021; 210:104266. https://doi.org/10.1016/j.chemolab.2021.104266

Chtita S., Ghamali M., Ousaa A., Aouidate A., Belhassan A., Taourati A. I., Masand V. H., Bouachrine M. and Lakhlifi T. QSAR study of anti-Human African Trypanosomiasis activity for 2-phenylimidazopyridines derivatives using DFT and Lipinski’s descriptors. Heliyon, 2019; 5:01304.

Al-Shar’i N.A., Hassan M.A., Al-Barqi H.M., Al-Balas Q.A. and El-Elimat T. Discovery of Novel Glyoxalase-I Inhibitors Using Computational Fragment-Based Drug Design Approach. Jordan Journal of Pharmaceutical Sciences. 2020; 13:225-245

Ouassaf M., Belaidi S., AlMogren M.M., Chtita S., UllahKhan S. and ThetHtar T. Combined docking methods and molecular dynamics to identify effective antiviral 2, 5-diaminobenzophenonederivatives against SARS-CoV-2. J. King Saud Univ. Sci. 2021; 33:101352. https://doi.org/10.1016/j.jksus.2021.101352

Hast M.A., Fletcher S., Cummings C.G., Pusateri E. E., Blaskovich., M. A, Rivas K., Gelb M H., Van Voorhis W. C., Sebti S. M., Hamilton A D. and Beese L. S. Chem. Biol. 2009;16:181-192

Ouassaf M., Belaidi S., Khamouli S., Belaidi H. and Chtita S. Combined 3D-QSAR and Molecular Docking Analysis of Thienopyrimidine Derivatives as Staphylococcus aureus Inhibitors. Acta Chim. Slov. 2021; 68:289–303. https://doi.org/10.17344/acsi.2020.5985

Cherkasov A., Muratov E.N., Fourches D., Varnek A., Baskin I.I. and Cronin M. QSAR modeling: where have you been? Where are you going to? J. Med. Chem. 2014; 57:4977–5010. https://doi.org/10.1021/jm4004285

Timmerman H., Mannhold R., Krogsgaard LP, Chemometric methods in molecular design, John Wiley & Sons, Hoboken, 2008

Bakdash J.Z. and Marusich L.R. Repeated Measures Correlation. Front Psychol. 2017; 8:456.https://doi.org/10.3389/fpsyg.2017.00456

Akinwande M.O., Dikko H.G. and Samson A. Variance Inflation Factor: As a Condition for the Inclusion of Suppressor Variable(s) in Regression Analysis. Open J. Stat. 2015; 5:754–67.

https://doi.org/10.4236/ojs.2015.57075

Kier L.B.and Hall L.H. An Electrotopological-State Index for Atoms in Molecules. Pharm. Res. 1990; 7:801–807. https://doi.org/10.1023/A:1015952613760

Galvez J., Garcia-Domenech R., De Julian-Ortiz J.V. and Soler R. Topological Approach to Drug Design. J. Chem. Inf. Comput. Sci. 1995; 35:272-284 https://doi.org/10.1021/ci00024a017

Yang Y., Engkvist O., Llinàs A. and Chen H. Beyond Size, Ionization State, and Lipophilicity: Influence of Molecular Topology on Absorption, Distribution, Metabolism, Excretion, and Toxicity for Druglike Compounds. J. Med. Chem. 2012; 26; 55:3667–77. https://doi.org/10.1021/jm201548z

Mitroy J., Safronova M.S. and Clark C.W. Theory and applications of atomic and ionic polarizabilities. J. Phys; B: At. Mol. Opt. Phys. 2010; 43: 202001. https://doi.org/10.1088/0953-4075/43/20/202001

Martin Y.C., Quantitative Drug Design: A Critical Introduction, Second Edition, CRC Press, 2010, Boca Raton, Floride, USA

Arnott J.A.and Planey S.L. The influence of lipophilicity in drug discovery and design. Expert. Opin. Drug Discov. 2012;7: 863–75.

https://doi.org/10.1517/17460441.2012.714363

Jalali-Heravi M. and Konuze E. Use of quantitative structure property relationships in predicting the Kraft point of anionic surfactants, Elec. J. Mol. Des. 2002; 1:410–417.

Roy K., MitraI., Kar S., Ojha P. K., Das R. N., and Kabir H. J. Chem. Info. and Mod. 2012 ;52: 396-408. DOI: 10.1021/ci200520g

Tropsha A. Best Practices for QSAR Model Development, Validation, and Exploitation. Mol. Info. 2010; 29:476–88.

https://doi.org/10.1002/minf.201000061

Roy K., Kar S. and Ambure P. On a simple approach for determining applicability domain of QSAR models. Chemom. Intell. Lab. Syst. 2015; 145:22–9. https://doi.org/10.1016/J.CHEMOLAB.2015.04.013

Kalirajan R., Gowramma B., Gomathi S. and Vadivelan R. Activity of Isoxazole substituted 9-aminoacridines against SARS CoV-2 main protease for COVID19: A computational approach. Jordan Journal of Pharmaceutical Sciences. 2021; 14:403-416.

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Published

2022-09-01

How to Cite

Ouassaf, M. . ., Belaidi, S. . ., Shtaiwi, A. . ., & Chtita, S. . (2022). Quantitative Structure Activity Relationship (QSAR) Investigations and Molecular Docking Analysis of Plasmodium Protein Farnesyltransferase Inhibitors as Potent Antimalarial Agents. Jordan Journal of Pharmaceutical Sciences, 15(3), 315–340. https://doi.org/10.35516/jjps.v15i3.407

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