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A combined structure-based pharmacophore modeling and 3D-QSAR study on a series of N-heterocyclic scaffolds to screen novel antagonists as human DHFR inhibitors

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Abstract

Dihydrofolate reductase (DHFR) is an essential enzyme that participates in folate metabolism and purine and thymidylate synthesis in cell proliferation. It converts dihydrofolate (DHF) to tetrahydrofolate (THF) in the presence of nicotinamide adenine dinucleotide phosphate (NADPH). This enzyme is found within all organisms adjusting the cellular level of THF. Negligible DHFR activity leads to a deficiency of THF and cell death. This trait is used to inhibit the cancer cells. DHFR has been extensively studied as the therapeutic target for cancer treatment. Accordingly, there is an urgent need for the identification and development of novel effective inhibitors with higher selectivity, lower toxicity, and better potency than currently available drugs. Hence, we aimed to identify new compounds by utilizing the alignment-independent three-dimensional quantitative structure-activity relationship (3D-QSAR) and structure-based pharmacophore modeling. Using the results obtained from these approaches, several antagonists have been retrieved from the virtual screening by applying some filters such as Lipinski’s rule of five. Selected compounds were then docked into the binding site of the receptor for identification of ligand-receptor interactions, binding affinity prediction, and refinement based on GoldScore fitness values. Eventually, pharmacokinetic/drug-likeness features and toxicity profiles of novel compounds were predicted and evaluated. Four hits with PubChem CIDs of 136138676, 94182663, 60219817, and 133300845 were finally proposed as new candidates with potential inhibitory activity against human DHFR.

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References

  1. Sharma V, Gupta SK, Verma M (2019) Dihydropyrimidine dehydrogenase in the metabolism of the anticancer drugs. Cancer Chemother Pharmacol 84:1157–1166

    Article  CAS  PubMed  Google Scholar 

  2. Ray S (2014) The cell: a molecular approach. Yale J Biol Med 87:603–604

    PubMed Central  Google Scholar 

  3. Nussbaumer S, Bonnabry P, Veuthey JL, Fleury-Souverain S (2011) Analysis of anticancer drugs: a review. Talanta 85:2265–2289

    Article  CAS  PubMed  Google Scholar 

  4. Raimondi MV, Randazzo O, La Franca M, Barone G, Vignoni E, Rossi D, Collina S (2019) DHFR inhibitors: reading the past for discovering novel anticancer agents. Molecules 24:1140–1159

    Article  PubMed Central  Google Scholar 

  5. Vitaku E, Smith DT, Njardarson JT (2014) Analysis of the structural diversity, substitution patterns, and frequency of nitrogen heterocycles among US FDA approved pharmaceuticals: miniperspective. J Med Chem 57:10257–11074

    Article  CAS  PubMed  Google Scholar 

  6. Chiacchio MA, Iannazzo D, Romeo R, Giofrè SV, Legnani L (2019). Curr Med Chem 26:7166–7195

    Article  CAS  PubMed  Google Scholar 

  7. Prachayasittikul S, Pingaew R, Worachartcheewan A, Sinthupoom N, Prachayasittikul V, Ruchirawat S, Prachayasittikul V (2017) Roles of pyridine and pyrimidine derivatives as privileged scaffolds in anticancer agents. Mini-Rev Med Chem 17:869–901

    Article  CAS  PubMed  Google Scholar 

  8. Kerru N, Gummidi L, Maddila S, Gangu KK, Jonnalagadda SB (2020) A review on recent advances in nitrogen-containing molecules and their biological applications. Molecules 25:1909–1951

    Article  CAS  PubMed Central  Google Scholar 

  9. Parveen H, Hayat F, Salahuddin A, Azam A (2010) Synthesis, characterization and biological evaluation of novel 6-ferrocenyl-4-aryl-2-substituted pyrimidine derivatives. Eur J Med Chem 45:3497–3503

    Article  CAS  PubMed  Google Scholar 

  10. Martins P, Jesus J, Santos S, Raposo LR, Roma-Rodrigues C, Baptista PV, Fernandes AR (2015) Heterocyclic anticancer compounds: recent advances and the paradigm shift towards the use of nanomedicine’s tool box. Molecules. 20:16852–16891

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  11. Singh K, Kaur T (2016) Pyrimidine-based antimalarials: design strategies and antiplasmodial effects. Med ChemComm 7:749–768

    CAS  Google Scholar 

  12. Kaur R, Kaur P, Sharma S, Singh G, Mehndiratta S, Bedi PMS, Nepali K (2015) Anti-cancer pyrimidines in diverse scaffolds: a review of patent literature. Recent Pat Anticancer Drug Discov 10:23–71

    Article  CAS  PubMed  Google Scholar 

  13. Tosso RD, Andujar SA, Gutierrez L, Angelina E, Rodríguez R, Nogueras M, Baldoni H, Suvire FD, Cobo J, Enriz RD (2013) Molecular modeling study of dihydrofolate reductase inhibitors. Molecular dynamics simulations, quantum mechanical calculations, and experimental corroboration. J Chem Inf Model 53:2018–2032

    Article  CAS  PubMed  Google Scholar 

  14. Huennekens FM, Duffy TH, Vitols KS (1987) Folic acid metabolism and its disruption by pharmacologic agents. NCI monographs: a publication of the National Cancer Institute 5:1–8

    Google Scholar 

  15. Ingraham HA, Dickey L, Goulian M (1986) DNA fragmentation and cytotoxicity from increased cellular deoxyuridylate. Biochem 25:3225–3230

    Article  CAS  Google Scholar 

  16. Yoshioka A, Tanaka S, Hiraoka O, Koyama Y, Hirota Y, Ayusawa D, Seno T, Garrett C, Wataya Y (1987) Deoxyribonucleoside triphosphate imbalance. 5-Fluorodeoxyuridine-induced DNA double strand breaks in mouse FM3A cells and the mechanism of cell death. J Biol Chem 262:8235–8241

    Article  CAS  PubMed  Google Scholar 

  17. Singh A, Deshpande N, Pramanik N, Jhunjhunwala S, Rangarajan A, Atreya HS (2018) Optimized peptide based inhibitors targeting the dihydrofolate reductase pathway in cancer. Sci Rep 8:3190

    Article  PubMed  PubMed Central  Google Scholar 

  18. Abbat S, Jaladanki CK, Bharatam PV (2019) Exploring PfDHFR reaction surface: a combined molecular dynamics and QM/MM analysis. J Mol Graph Model 87:76–88

    Article  CAS  PubMed  Google Scholar 

  19. Schnell JR, Dyson HJ, Wright PE (2004) Structure, dynamics, and catalytic function of dihydrofolate reductase. Annu Rev Biophys Biomol Struct 33:119–140

    Article  CAS  PubMed  Google Scholar 

  20. Kinsella AR, Smith D (1998) Tumor resistance to antimetabolites. Gen Pharmac 30:623–626

    Article  CAS  Google Scholar 

  21. Jackson RC, Niethammer D (1977) Acquired methotrexate resistance in lymphoblasts resulting from altered kinetic properties of dihydrofoltate reductase. Eur J Cancer 13:567–575

    Article  CAS  PubMed  Google Scholar 

  22. Pignatello R, Guccione S, Forte S, Di Giacomo C, Sorrenti V, Vicari L, Barretta GU, Balzano F, Puglisi G (2004) Lipophilic conjugates of methotrexate with short-chain alkylamino acids as DHFR inhibitors. Synthesis, biological evaluation, and molecular modeling. Bioorg Med Chem 12:2951–2964

    Article  CAS  PubMed  Google Scholar 

  23. Volk EL, Rohde K, Rhee M, McGuire JJ, Doyle LA, Ross DD, Schneider E (2000) Methotrexate cross-resistance in a mitoxantrone-selected multidrug-resistant MCF7 breast cancer cell line is attributable to enhanced energy-dependent drug efflux. Cancer Res 60:3514–3521

    CAS  PubMed  Google Scholar 

  24. MacGuire JJ (2003) Anticancer antifolates: current status and future directions. Curr Pharm Des 9:2593–2613

    Article  Google Scholar 

  25. Gubner R, August S, Ginsberg V (1951) Therapeutic suppression of tissue reactivity. 2. Effect of aminopterin in rheumatoid arthritis and psoriasis. Am J Med Sci 221:176–182

    Article  CAS  Google Scholar 

  26. Brown PM, Pratt AG, Isaacs JD (2016) Mechanism of action of methotrexate in rheumatoid arthritis, and the search for biomarkers. Nat Rev Rheumatol 12:731–742

    Article  CAS  PubMed  Google Scholar 

  27. Yuthavong Y, Tarnchompoo B, Vilaivan T, Chitnumsub P, Kamchonwongpaisan S, Charman SA, McLennan DN, White KL, Vivas L, Bongard E, Thongphanchang C (2012) Malarial dihydrofolate reductase as a paradigm for drug development against a resistance-compromised target. PNAS 109:16823–16828

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  28. Hong W, Wang Y, Chang Z, Yang Y, Pu J, Sun T, Kaur S, Sacchettini JC, Jung H, Wong WL, Yap LF (2015) The identification of novel mycobacterium tuberculosis DHFR inhibitors and the investigation of their binding preferences by using molecular modelling. Sci Rep 5:15328–15341

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  29. Shah K, Queener S, Cody V, Pace J, Gangjee A (2019) Development of substituted pyrido [3, 2-d] pyrimidines as potent and selective dihydrofolate reductase inhibitors for pneumocystis pneumonia infection. Bioorg Med Chem Lett 29:1874–1880

    Article  CAS  PubMed  Google Scholar 

  30. Zhao R, Goldman ID (2003) Resistance to antifolates. Oncogene 22:7431–7457

    Article  CAS  PubMed  Google Scholar 

  31. Bleyer WA (1978) The clinical pharmacology of methotrexate. New applications of an old drug Cancer 41:36–51

    CAS  PubMed  Google Scholar 

  32. Izbicka E, Diaz A, Streeper R, Wick M, Campos D, Steffen R, Saunders M (2009) Distinct mechanistic activity profile of pralatrexate in comparison to other antifolates in in vitro and in vivo models of human cancers. Cancer Chemother Pharmacol 64:993–999

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  33. Srinivasan B, Tonddast-Navaei S, Roy A, Zhou H, Skolnick J (2019) Chemical space of Escherichia coli dihydrofolate reductase inhibitors: new approaches for discovering novel drugs for old bugs. Med Res Rev 39:684–705

    Article  PubMed  Google Scholar 

  34. Lin JT, Bertino JR (1991) Clinical science review: update on trimetrexate, a folate antagonist with antineoplastic and antiprotozoal properties. Cancer Investig 9:159–172

    Article  CAS  Google Scholar 

  35. Yuan Y, Hu Z, Bao M, Sun R, Long X, Long L, Li J, Wu C, Bao J (2019) Screening of novel histone deacetylase 7 inhibitors through molecular docking followed by a combination of molecular dynamics simulations and ligand-based approach. J Biomol Struct Dyn 37:4092–4103

    Article  CAS  PubMed  Google Scholar 

  36. Mahajan P, Wadhwa B, Barik MR, Malik F, Nargotra A (2020) Combining ligand-and structure-based in silico methods for the identification of natural product-based inhibitors of Akt1. Mol Divers 24:45–60

    Article  CAS  PubMed  Google Scholar 

  37. Arooj M, Thangapandian S, John S, Hwang S, Park JK, Lee KW (2011) 3D QSAR pharmacophore modeling, in silico screening, and density functional theory (DFT) approaches for identification of human chymase inhibitors. Int J Mol Sci 12:9236–9264

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  38. Mattioni BE, Jurs PC (2003) Prediction of dihydrofolate reductase inhibition and selectivity using computational neural networks and linear discriminant analysis. J Mol Graph Model 21:391–419

    Article  CAS  PubMed  Google Scholar 

  39. Wilson GL, Lill MA (2011) Integrating structure-based and ligand-based approaches for computational drug design. Future Med Chem 3:735–750

    Article  CAS  PubMed  Google Scholar 

  40. Gramatica P (2020) Principles of QSAR modeling: comments and suggestions from personal experience. IJQSPR 5:1–37

    Google Scholar 

  41. Moro S, Bacilieri M, Deflorian F (2007) Combining ligand-based and structure-based drug design in the virtual screening arena. Expert Opin Drug Discov 2:37–49

    Article  CAS  PubMed  Google Scholar 

  42. Hariri S, Shirini F, Ghasemi JB, Rasti B (2019) Design of pyrimidine-based scaffolds as potential anticancer agents for human DHFR: three-dimensional quantitative structure–activity relationship by docking derived grid-independent descriptors. J Iran Chem Soc 16:2365–2378

    Article  CAS  Google Scholar 

  43. Hariri S, Ghasemi JB, Shirini F, Rasti B (2019) Probing the origin of dihydrofolate reductase inhibition via proteochemometric modeling. J Chemom 33:e3090

    Article  Google Scholar 

  44. Chen X, Liu M, Gilson MK (2001) BindingDB: a web-accessible molecular recognition database. Comb Chem High Throughput Screen 4:719–725

    Article  CAS  PubMed  Google Scholar 

  45. Liu T, Lin Y, Wen X, Jorissen RN, Gilson MK (2007) BindingDB: a web-accessible database of experimentally determined protein–ligand binding affinities. Nucleic Acids Res 35:D198–D201

    Article  CAS  PubMed  Google Scholar 

  46. Momany FA, Rone R (1992) Validation of the general purpose QUANTA® 3.2/CHARMm® force field. J Comput Chem 13:888–900

    Article  CAS  Google Scholar 

  47. Pastor M, Cruciani G, McLay I, Pickett S, Clementi S (2000) GRid-INdependent descriptors (GRIND): a novel class of alignment-independent three-dimensional molecular descriptors. J Med Chem 43:3233–3243

    Article  CAS  PubMed  Google Scholar 

  48. Duran A, Martínez GC, Pastor M (2008) Development and validation of AMANDA, a new algorithm for selecting highly relevant regions in molecular interaction fields. J Chem Inf Model 48:1813–1823

    Article  CAS  PubMed  Google Scholar 

  49. Rasti B, Namazi M, Karimi-Jafari MH, Ghasemi JB (2017) Proteochemometric modeling of the interaction space of carbonic anhydrase and its inhibitors: an assessment of structure-based and sequence-based descriptors. Mol Inform 36:1600102

    Article  Google Scholar 

  50. Zhao H, Moroni E, Yan B, Colombo G, Blagg BS (2012) 3D-QSAR-assisted design, synthesis, and evaluation of novobiocin analogues. ACS Med Chem Lett 4:57–62

    Article  PubMed  PubMed Central  Google Scholar 

  51. Kennard RW, Stone LA (1969) Computer aided design of experiments. Technometrics 11:137–148

    Article  Google Scholar 

  52. Rasti B, Shahangian SS (2018) Proteochemometric modeling of the origin of thymidylate synthase inhibition. Chem Biol Drug Des 91:1007–1016

    Article  CAS  PubMed  Google Scholar 

  53. Hariri S, Rasti B, Mirpour M, Vaghar-Lahijani G, Attar F, Shiri F (2020) Structural insights into the origin of phosphoinositide 3-kinase inhibition. Struct Chem 31:1505–1522

    Article  CAS  Google Scholar 

  54. Rogers D, Hopfinger AJ (1994) Application of genetic function approximation to quantitative structure-activity relationships and quantitative structure-property relationships. J Chem Inf Comput Sci 34:854–866

    Article  CAS  Google Scholar 

  55. Hou TJ, Wang JM, Liao N, Xu XJ (1999) Applications of genetic algorithms on the structure activity relationship analysis of some cinnamamides. J Chem Inf Model 39:775–781

    CAS  Google Scholar 

  56. Leardi R (2000) Application of genetic algorithm–PLS for feature selection in spectral data sets. J Chemom 14:643–655

    Article  CAS  Google Scholar 

  57. Whitley DC, Ford MG, Livingstone DJ (2000) Unsupervised forward selection: a method for eliminating redundant variables. J Chem Inf Comput Sci 40:1160–1168

    Article  CAS  PubMed  Google Scholar 

  58. Bush BL, Nachbar RB (1993) Sample-distance partial least squares: PLS optimized for many variables, with application to CoMFA. J Comput Aided Mol Des 7:587–619

    Article  CAS  PubMed  Google Scholar 

  59. Eriksson L, Jaworska J, Worth AP, Cronin MT, McDowell RM, Gramatica P (2003) Methods for reliability and uncertainty assessment and for applicability evaluations of classification-and regression-based QSARs. Environ Health Perspect 111:1361–1375

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  60. Gasteiger J (2003) Handbook of chemoinformatics. Wiley-VCH, Weinheim 4:1532–1554

    Google Scholar 

  61. Tropsha A, Gramatica P, Gombar VK (2003) The importance of being earnest: validation is the absolute essential for successful application and interpretation of QSPR models. QSAR Comb Sci 22:69–77

    Article  CAS  Google Scholar 

  62. Roy K, Ambure P, Aher R (2017) How important is to detect systematic error in predictions and understand statistical applicability domain of QSAR models? Chemom Intell Lab Syst 162:44–54

    Article  CAS  Google Scholar 

  63. de Souza LM, Mitsutake H, Gontijo LC, Neto WB (2014) Quantification of residual automotive lubricant oil as an adulterant in Brazilian S-10 diesel using MIR spectroscopy and PLS. Fuel 130:257–262

    Article  Google Scholar 

  64. Roy K, Das RN, Ambure P, Aher RB (2016) Be aware of error measures. Further studies on validation of predictive QSAR models Chemom Intell Lab Syst 152:18–33

    CAS  Google Scholar 

  65. Gramatica P (2007) Principles of QSAR models validation: internal and external. QSAR Comb Sci 26:694–701

    Article  CAS  Google Scholar 

  66. Gramatica P, Cassani S, Roy PP, Kovarich S, Yap CW, Papa E (2012) QSAR modeling is not “push a button and find a correlation”: a case study of toxicity of (benzo-) triazoles on algae. Mol Inform 31:817–835

    Article  CAS  PubMed  Google Scholar 

  67. Politi A, Durdagi S, Moutevelis-Minakakis P, Kokotos G, Mavromoustakos T (2010) Development of accurate binding affinity predictions of novel renin inhibitors through molecular docking studies. J Mol Graph Model 29:425–435

    Article  CAS  PubMed  Google Scholar 

  68. https://www.ccdc.cam.ac.uk/support-and-resources/ccdcresources/gold.pdf

  69. Shiri F, Pirhadi S, Ghasemi JB (2019) Dynamic structure based pharmacophore modeling of the Acetylcholinesterase reveals several potential inhibitors. J Biomol Struct Dyn 3(7):1800–1812

    Article  Google Scholar 

  70. Shiri F, Pirhadi S, Rahmani A (2018) Identification of new potential HIV-1 reverse transcriptase inhibitors by QSAR modeling and structure-based virtual screening. J Recept Signal Transduct Res 38:37–47

    Article  CAS  PubMed  Google Scholar 

  71. Sunseri J, Koes DR (2016) Pharmit: interactive exploration of chemical space. Nucleic Acids Res 44:W442–W448

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  72. Lipinski CA, Lombardo F, Dominy BW, Feeney PJ (1997) Experimental and computational approaches to estimate solubility and permeability in drug discovery and development settings. Adv Drug Deliv Rev 23:3–25

    Article  CAS  Google Scholar 

  73. Meena A, Yadav DK, Srivastava A, Khan F, Chanda D, Chattopadhyay SK (2011) In silico exploration of anti-inflammatory activity of natural coumarinolignoids. Chem Biol Drug Des 78:567–579

    Article  CAS  PubMed  Google Scholar 

  74. Shukla A, Sharma P, Prakash O, Singh M, Kalani K, Khan F, Bawankule DU, Luqman S, Srivastava SK (2014) QSAR and docking studies on capsazepine derivatives for immunomodulatory and anti-inflammatory activity. PLoS One 9:e100797

    Article  PubMed  PubMed Central  Google Scholar 

  75. Cao D, Wang J, Zhou R, Li Y, Yu H, Hou T (2012) ADMET evaluation in drug discovery. 11. PharmacoKinetics Knowledge Base (PKKB): a comprehensive database of pharmacokinetic and toxic properties for drugs. J Chem Inf Model 52:1132–1137

    Article  CAS  PubMed  Google Scholar 

  76. Ertl P, Schuffenhauer A (2009) Estimation of synthetic accessibility score of drug-like molecules based on molecular complexity and fragment contributions. J Cheminform 1:8–18

    Article  PubMed  PubMed Central  Google Scholar 

  77. Fukunishi Y, Kurosawa T, Mikami Y, Nakamura H (2014) Prediction of synthetic accessibility based on commercially available compound databases. J Chem Inf Model 54:3259–3267

    Article  CAS  PubMed  Google Scholar 

  78. Daina A, Michielin O, Zoete V (2017) SwissADME: a free web tool to evaluate pharmacokinetics, drug-likeness and medicinal chemistry friendliness of small molecules. Sci Rep 74:2717

    Google Scholar 

  79. Zhao H, Moroni E, Yan B, Colombo G, Blagg BS (2013) 3D-QSAR-assisted design, synthesis, and evaluation of novobiocin analogues. ACS Med Chem Lett 4:57–62

    Article  Google Scholar 

  80. Chong IG, Ju CH (2005) Performance of some variable selection methods when multicollinearity is present. Chemometr Intell Lab Syst 78:103–112

    Article  CAS  Google Scholar 

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Authors and Affiliations

  1. Department of Chemistry, College of Science, University of Guilan, Rasht, Iran

    Safoura Hariri & Farhad Shirini

  2. Department of Microbiology, Faculty of Basic Sciences, Lahijan Branch, Islamic Azad University (IAU), Lahijan, Guilan, Iran

    Behnam Rasti

  3. Faculty of Chemistry, University of Tehran, Tehran, Iran

    Jahan B. Ghasemi

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  1. Safoura Hariri
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  2. Behnam Rasti
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  3. Farhad Shirini
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  4. Jahan B. Ghasemi
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Contributions

S. H.—investigation, formal analysis, methodology, project administration, validation, writing original draft; B. R.—partial analysis of results, data curation, reviewing and editing; F. Sh.—project administration, supervision, resources, validation, reviewing, and editing; JB. Gh.—project administration, supervision, validation, resources.

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Correspondence to Farhad Shirini or Jahan B. Ghasemi.

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Hariri, S., Rasti, B., Shirini, F. et al. A combined structure-based pharmacophore modeling and 3D-QSAR study on a series of N-heterocyclic scaffolds to screen novel antagonists as human DHFR inhibitors. Struct Chem 32, 1571–1588 (2021). https://doi.org/10.1007/s11224-020-01705-7

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