Skip to main content
Log in

Interaction of drugs amlodipine and paroxetine with the metabolizing enzyme CYP2B4: a molecular dynamics simulation study

  • Original Paper
  • Published:
Journal of Molecular Modeling Aims and scope Submit manuscript

Abstract

The interactions of the drugs amlodipine and paroxetine, which are prescribed respectively for treatment of hypertension and depression, with the metabolizing enzyme cytochrome CYP2B4 as the drug target, have been studied by molecular dynamics (MD) simulation. Poly ethylene glycol was used to control the drugs’ interactions with each other and with the target CYP2B4. Thirteen simulation systems were carefully designed, and the results obtained from MD simulations indicated that amlodipine in the PEGylated form prescribed with paroxetine in the nonPEGylated form promotes higher cytochrome stability and causes fewer fluctuations as the drugs approach the target CYP2B4 and interact with it. The simulation results led us to hypothesize that the combination of the drugs with a specific drug ratio, as proposed in this work, manifests more effective diffusivity and less instability while metabolizing with enzyme CYP2B4. Also, the active residues in the CYP2B4 enzyme that interact with the drugs were determined by MD simulation, which were consistent with the reported experimental results.

Efficient drug-enzyme interactions, as a result of PEGylation.

This is a preview of subscription content, log in via an institution to check access.

Access this article

Price excludes VAT (USA)
Tax calculation will be finalised during checkout.

Instant access to the full article PDF.

Fig. 1
Fig. 2
Fig. 3
Fig. 4

Similar content being viewed by others

References

  1. Ortiz de Montellano PR (2015) Cytochrome P450: structure, mechanism, and biochemistry. Springer, New York. https://doi.org/10.1007/978-3-319-12108-6

  2. Hagen KD, Gillan JM, Im SC, Landefeld S, Mead G, Hiley M, Waskell LA, Hill MG, Udit AK (2013) Electrochemistry of mammalian cytochrome P450 2B4 indicates tunable thermodynamic parameters in surfactant films. J Inorg Biochem 129:30–34

    Article  CAS  Google Scholar 

  3. Guengerich FP (2003) Cytochromes P450, drugs, and diseases. Mol Interv 3:194–204

    Article  CAS  Google Scholar 

  4. Nebert DW, Dalton TP (2006) The role of cytochrome P450 enzymes in endogenous signalling pathways and environmental carcinogenesis. Nat Rev Cancer 6:947–960

    Article  CAS  Google Scholar 

  5. Edson KZ, Rettie AE (2013) CYP4 enzymes as potential drug targets: focus on enzyme multiplicity, inducers and inhibitors, and therapeutic modulation of 20-hydroxyeicosatetraenoic acid (20-HETE) synthase and fatty acid ω-hydroxylase activities. Curr Topics Med Chem. 13:1429–1440

    Article  CAS  Google Scholar 

  6. Beers MH, Passman LJ (1990) Antihypertensive medications and depression. Drugs 6:792–799

    Article  Google Scholar 

  7. Krousel-Wood MA, Frohlich ED (2010) Hypertension and depression: coexisting barriers to medication adherence. J Clin Hypertens 12:481–486

    Google Scholar 

  8. Coelho PV, Brum CA (2009) Interaction between antidepressants and antihypertensive and glucose lowering drugs among patients in the HIPERDIA program, coronel fabriciano, Minas Gerais state, Brazil. Cad Saude Publica 25:2229–2236

    Article  Google Scholar 

  9. Pohar A, Likozar B (2014) Dissolution, nucleation, crystal growth, crystal aggregation, and particle breakage of amlodipine salts: modeling crystallization kinetics and thermodynamic equilibrium, scale-up, and optimization. Ind Eng Chem Res 53:10762–10774

    Article  CAS  Google Scholar 

  10. Gotrane DM, Deshmukh RD, Ranade PV, Sonawane SP, Bhawal BM, Gharpure MM, Gurjar MK (2010) A novel method for resolution of amlodipine. Org Process Res Dev 14:640–643

    Article  CAS  Google Scholar 

  11. Caron G, Ermondi G, Damiano A, Novaroli L, Tsinman O, Ruell JA, Avdeef A (2004) Ionization, lipophilicity, and molecular modeling to investigate permeability and other biological properties of amlodipine. Bioorg Med Chem 12:6107–6118

    Article  CAS  Google Scholar 

  12. Shah P, Patel J, Patel K, Gandhi T (2017) Development and validation of an HPTLC method for thesimultaneous estimation of clonazepam and paroxetine hydrochloride using a DOE approach. J Taibah Univ Sci 11:121–132

    Article  Google Scholar 

  13. Kapil RP, Friedman K, Cipriano A, Michels G, Shet M, Mondal SA, Harris SC (2015) Effects of paroxetine, a CYP2D6 inhibitor, on the pharmacokinetic properties of hydrocodone after coadministration with a single-entity, once-daily, extended-release hydrocodone tablet. Clin Ther 37:2286–2296

    Article  CAS  Google Scholar 

  14. Achanti S, Katta RR (2016) High-throughput liquid chromatography tandem mass spectrometry method for simultaneous determination of fampridine, paroxetine, and quinidine in rat plasma: application to in vivo perfusion study. J Food Drug Anal 24:866–875

    Article  CAS  Google Scholar 

  15. Yakubu MT, Jimoh RO (2015) Aqueous extract of carpolobia lutea root ameliorates paroxetine induced anti-androgenic activity in male rats. Mid East Fert Soc J 20:192–197

    Article  Google Scholar 

  16. Fujimori K, Takaki J, Shigemoto-Mogami Y, Sekino Y, Suzuki T, Sato K (2015) Paroxetine prevented the down-regulation of astrocytic L-Glu transporters in neuroinflammation. J Pharmacol Sci 127:145–149

    Article  Google Scholar 

  17. Shah MB, Kufareva I, Pascual J, Zhang Q, Stout CD, Halpert JR (2013) A Structural snapshot of CYP2B4 in complex with paroxetine provides insights into ligand binding and clusters of conformational states. J Pharmacol Exp Ther 346:113–120

    Article  CAS  Google Scholar 

  18. Shah MB, Wilderman PR, Pascual J, Zhang Q, Stout CD, Halpert JR (2012) Conformational adaptation of human cytochrome P450 2B6 and rabbit cytochrome P450 2B4 revealed upon binding multiple amlodipine molecules. Biochemistry 51:7225–7238

    Article  CAS  Google Scholar 

  19. Li JP, Guo JM, Shang EX, Zhu ZH, Liu Y, Zhao BC, Zhao J, Tang ZS, Duan JA (2017) Quantitative determination of five metabolites of aspirin by UHPLC–MS/MS coupled with enzymatic reaction and its application to evaluate the effects of aspirin dosage on the metabolic profile. J Pharm Biomed Anal 138:109–117

    Article  CAS  Google Scholar 

  20. Lei C, Cui Y, Zheng L, Chow PKH, Wang CH (2013) Development of a gene/drug dual delivery system for brain tumor therapy: Potent inhibition via RNA interference and synergistic effects. Biomaterials 34:7483–7494

    Article  CAS  Google Scholar 

  21. Ashour S, Omar S (2016) A modified high-performance liquid chromatographic method for the analysis of pantoprazole sodium in pharmaceutical dosage forms using lansoprazole as internal standard. Arabian Journal of Chemistry 9:S114–S119

    Article  CAS  Google Scholar 

  22. Dasgupta A, Wahed A (2014) Interferences in therapeutic drug monitoring, Chap. 15. Clinical chemistry, immunology and laboratory quality control. A comprehensive review for board preparation, certification and clinical practice. Elsevier, Amsterdam, pp 275-287

  23. Ha D, Yang N, Nadithe V (2016) Exosomes as therapeutic drug carriers and delivery vehicles across biological membranes: current perspectives and future challenges. Acta Pharmaceutica Sinica B 6:287–296

    Article  Google Scholar 

  24. Tsouris V, Joo MK, Kim SH, Kwon IC, Won YY (2014) Nano carriers that enable co-delivery of chemotherapy and RNAi agents for treatment of drug-resistant cancers. Biotechnology Advances 32:1037–1050

    Article  CAS  Google Scholar 

  25. Naeye B, Raemdonck K, Remaut K, Sproat B, Demeester J, Smedt SCD (2010) PEGylation of biodegradable dextran nanogels for siRNA delivery. Eur J Pharm Sci 40:342–351

    Article  CAS  Google Scholar 

  26. Keating E, Gonçalves P, Campos I, Costa F, Martel F (2009) Folic acid uptake by the human syncytiotrophoblast: interference by pharmacotherapy, drugs of abuse and pathological conditions. Reprod Toxicol 28:511–520

    Article  CAS  Google Scholar 

  27. Kyluik-Price DL, Scott MD (2016) Effects of methoxypoly (Ethylene glycol) mediated immunocamouflage on leukocyte surface marker detection, cell conjugation, activation and alloproliferation. Biomaterials 74:167–177

    Article  CAS  Google Scholar 

  28. Bunker A (2012) Poly(ethylene glycol) in drug delivery, why does it work, and can we do better? All atom molecular dynamics simulation provides some answers. Physics Procedia 34:24–33

    Article  Google Scholar 

  29. Banerjee SS, Aher N, Patil R, Khandare J (2012) Poly(ethylene glycol)-prodrug conjugates: concept, design, and applications. J Drug Deliv. https://doi.org/10.1155/2012/103973

  30. Magarkar A, Karakas E, Stepniewski M, Rog T, Bunker A (2012) Molecular dynamics simulation of PEGylated bilayer interacting with salt ions: a model of the liposome surface in the bloodstream. J Phys Chem B 116:4212–4219

    Article  CAS  Google Scholar 

  31. Yang C, Lu D, Liu Z (2011) How PEGylation enhances the stability and potency of insulin: a molecular dynamics simulation. Biochemistry 50:2585–2593

    Article  CAS  Google Scholar 

  32. Magarkar A, Róg T, Bunker A (2014) Molecular dynamics simulation of PEGylated membranes with cholesterol: building towards the DOXIL formulation. J Phys Chem C 118:15541–15549

    Article  CAS  Google Scholar 

  33. Han E, Lee H (2013) Effects of PEGylation on the binding interaction of magainin 2 and tachyplesin I with lipid bilayer surface. Langmuir 29:14214–14221

    Article  CAS  Google Scholar 

  34. Roberts MJ, Bentley MD, Harris JM (2012) Chemistry for peptide and protein PEGylation. Adv Drug Deliv Rev 64:116–127

    Article  Google Scholar 

  35. Pfister D, Morbidelli M (2014) Process for protein PEGylation. J Control Release 180:134–149

    Article  CAS  Google Scholar 

  36. Blaschke T, Varon J, Werner A, Hasse H (2011) Microcalorimetric study of the adsorption of PEGylated lysozyme on a strong cation exchange resin. J Chromatogr A 1218:4720–4726

    Article  CAS  Google Scholar 

  37. Li W, Zhan P, Clercq ED, Lou H, Liu X (2013) Current drug research on PEGylation with small molecular agents. Prog Polym Sci 38:421–444

    Article  CAS  Google Scholar 

  38. Xue X, Ji S, Mu Q, Hu T (2014) Heat treatment increases the bioactivity of C-terminally PEGylated staphylokinase. Process Biochemistry 49:1092–1096

    Article  CAS  Google Scholar 

  39. Yang X, Ding Y, Ji T, Zhao X, Wang H, Zhao X, Zhao R, Wei J, Qi S, Nie G (2016) Improvement of the in vitro safety profile and cytoprotective efficacy of amifostine against chemotherapy by PEGylation strategy. Biochem Pharmacol 108:11–21

    Article  CAS  Google Scholar 

  40. Reichert C, Borchard G (2015) Noncovalent PEGylation, an innovative subchapter in the field of protein modification. J Pharm Sci 105:386–390

    Article  Google Scholar 

  41. Yousefpour A, Modarress H, Goharpey F, Amjad-Iranagh S (2015) Interaction of PEGylated anti-hypertensive drugs, amlodipine, atenolol and lisinopril with lipid bilayer membrane: a molecular dynamics simulation study. Biochimica et Biophysica Acta 1848:1687–1698

    Article  CAS  Google Scholar 

  42. Yousefpour A, Modarress H, Goharpey F, Amjad-Iranagh S (2017) Combination of anti-hypertensive drugs: a molecular dynamics simulation study. J Mol Model 23:158. https://doi.org/10.1007/s00894-017-3333-9

    Article  Google Scholar 

  43. Kavyani Baghbaderani S, Amjad-Iranagh S, Dadvar M, Modarress H (2016) Hybrid dendrimers of PPI(core)-PAMAM(shell): a molecular dynamics simulation study. J Phys Chem B 36:9564–9575

    Article  Google Scholar 

  44. Rahimi A, Amjad-Iranagh S, Modarress H (2016) Molecular dynamics simulation of coarse-grained poly(L-lysine) dendrimers. J Mol Model 22:59–68

    Article  Google Scholar 

  45. Razmimanesh F, Amjad-Iranagh S, Modarress H (2015) Molecular dynamics simulation study of chitosan and gemcitabineas a drug delivery system. J Mol Model 21:165

    Article  Google Scholar 

  46. Kavyani Baghbaderani S, Amjad-Iranagh S, Modarress H (2014) Aqueous poly(amidoamine) dendrimer G3 and G4 generations with several interior cores at pHs 5 and 7: a molecular dynamics simulation study. J Phys Chem B 118:3257–3266

    Article  Google Scholar 

  47. Mousavi SZ, Amjad-Iranagh S, Nademi Y, Modarress H (2013) Carbon nanotube-encapsulated drug penetration through the cell membrane: an investigation based on steered molecular dynamics simulation. J Membr Biol 246:697–704

    Article  CAS  Google Scholar 

  48. Yousefpour A, Amjad-Iranagh S, Nademi Y, Modarress H (2013) Molecular dynamics simulation of nonsteroidal antiinflammatory drugs, naproxen and relafen, in a lipid bilayer membrane. Int J Quantum Chem 113:1919–1930

    Article  CAS  Google Scholar 

  49. Amjad-Iranagh S, Yousefpour A, Haghighi P, Modarress H (2013) Effects of protein binding on a lipid bilayer containing local anesthetic articaine, and the potential of mean force calculation: a molecular dynamics simulation approach. J Mol Model 19:3831–3842

    Article  CAS  Google Scholar 

  50. Nademi Y, Amjad-Iranagh S, Yousefpour A, Mousavi SZ, Modarress H (2013) Molecular dynamics simulations and free energy profile of Paracetamol in DPPC and DMPC lipid bilayers. J. Chem. Sci. 126:637–647

    Article  Google Scholar 

  51. Khajeh A, Modarress H (2014) The influence of cholesterol on interactions and dynamics of ibuprofen in a lipid bilayer. Biochimica et Biophysica Acta 1838:2431–2438

    Article  CAS  Google Scholar 

  52. Khajeh A, Modarress H (2014) Effect of cholesterol on behavior of 5-fluorouracil (5 FU) in a DMPC lipid bilayer, a molecular dynamics study. Biophys Chem 187-188:43–50

    Article  CAS  Google Scholar 

  53. Friedman R, Boye K, Flatmark K (2013) Molecular modelling and simulations in cancer research. Biochim Biophys Acta 1836:1–14

    CAS  Google Scholar 

  54. Tonel MZ, Martins MO, Zanella I, Pontes RB, Fagan SB (2017) A first-principles study of the interaction of doxorubicin with grapheme. Comput Theor Chem 1115:270–275

    Article  CAS  Google Scholar 

  55. Rissanen S, Kumorek M, Martinez-Seara H, Li YC, Jamróz D, Bunker A, Nowakowska M, Vattulainen I, Kepczynski M, Róg T (2014) Effect of PEGylation on drug entry into lipid bilayer. J Phys Chem B 118:144–151

    Article  CAS  Google Scholar 

  56. Shi S, Zhang S, Zhang Q (2014) Insight into the interaction mechanism of inhibitors P4 and WK23 with MDM2 based on molecular dynamics simulation and different free energy methods. Comput Theor Chem 1045:66–72

    Article  CAS  Google Scholar 

  57. Berkay S, Yurtsever M (2017) Adsorption of dihalogen molecules on pristine graphene surface: Monte Carlo and molecular dynamics simulation studies. J Mol Model 23:150. https://doi.org/10.1007/s00894-017-3311-2

    Article  Google Scholar 

  58. Sgarlata C, D’Urso L, Consiglio G, Grasso G, Satriano C, Forte G (2016) pH sensitive functionalized graphene oxyde as a carrier for delivering Gemcitabine: a computational approach. Comput Theor Chem 1096:1–6

    Article  CAS  Google Scholar 

  59. Wang H, Meng F (2017) The permeability enhancing mechanism of menthol on skin lipids: a molecular dynamics simulation study. J Mol Model 23:279. https://doi.org/10.1007/s00894-017-3457-y

    Article  Google Scholar 

  60. Spiegela K, Magistrato A (2006) Modeling anticancer drug DNA interactions via mixed QM/MM molecular dynamics simulations. Org Biomol Chem 4:2507–2517

    Article  Google Scholar 

  61. Van Der Spoel D, Linahl E, Hess B, Groenhof G, Mark AE, Berendsen HJC (2005) GROMACS: fast, flexible and free. J Comput Chem 26:1701–1718

    Article  Google Scholar 

  62. Hess B, Kutzner C, Van der Spoel D, Lindahl E (2008) GROMACS4: algorithms for highly efficient, load-balanced, and scalable molecular simulation. J Chem Theory Comput 4:435–447

    Article  CAS  Google Scholar 

  63. Gromacs (2017) Gromacs 4.5. http://manual.gromacs.org/

  64. Zheng J, Jang H, Ma B, Tsai CJ, Nussinov R (2007) Modeling the Alzheimer Aβ 17-42 fibril architecture: tight intermolecular sheet-sheet association and intramolecular hydrated cavities. Biphys J:3046-3057

  65. Hess B, Bekker H, Berendsen HJC, Fraaije J (1997) Lincs: a linear constraint solver for molecular simulations. J Comput Chem 18:1463–1472

    Article  CAS  Google Scholar 

  66. Mohseni-Shahri FS, Housaindokht MR, Bozorgmehr MR, Moosavi-Movahedi AA (2016) Influence of taxifolin on the human serum albumin–propranolol interaction: multiple spectroscopic and chemometrics investigations and molecular dynamics simulation. J Solution Chem 45:265–285

    Article  CAS  Google Scholar 

  67. Periole X, Cavalli M, Marrink SJ, Ceruso MA (2009) Combining an elastic network with a coarse-grained molecular force field: structure, dynamics, and intermolecular recognition. J Chem Theor Comput 5:2531–2543

    Article  CAS  Google Scholar 

  68. Huet A, Derreumaux P (2006) Impact of the mutation A21G (Flemish variant) on Alzheimer’s β-amyloid dimers by molecular dynamics simulations. Biophys J 91:3829–3840

    Article  CAS  Google Scholar 

  69. Vieira DS, Degrève L, Ward RJ (2009) Characterization of temperature dependent and substrate-binding cleft movements in Bacillus circulans family 11 xylanase: a molecular dynamics investigation. Biochim Biophys Acta 1790:1301–1306

    Article  CAS  Google Scholar 

  70. Paris F, Flatters D, Caburet S, Legois B, Servant N, Lefebvre H, Sultan C, Veitia RA (2017) A novel variant of DHH in a familial case of 46,XY disorder of sex development: insights from molecular dynamics simulation. Clin Endocrinol (Oxf) 87:539–544

    Article  CAS  Google Scholar 

  71. Khan FI, Bisetty K, Wei DQ, Hassan MI (2016) A pH based molecular dynamics simulation of chitinase II isolated from themomyces lanuginosus SSBP. Cogent Biol 2:1168336. https://doi.org/10.1080/23312025.2016.1168336

    Google Scholar 

  72. Callegari D, Lodola A, Pala D, Revara S, Mor M, Rizzi A, Capelli AM (2017) Metadynamics simulations distinguish short- and long-residence-time inhibitors of cyclin-dependent kinase 8. J Chem Inf Model 57:159–169

    Article  CAS  Google Scholar 

  73. Duncan AL, Robinson AJ, Walker JE (2016) Cardiolipin binds selectively but transiently to conserved lysine residues in the rotor of metazoan ATP synthases. PNAS 113:8687–8692

    Article  CAS  Google Scholar 

  74. Mollica L, Theret I, Antoine M, Perron-Sierra F, Charton Y, Fourquez JM, Wierzbicki M, Boutin JA, Ferry G, Decherchi S, Bottegoni G, Ducrot P, Cavalli A (2016) Molecular dynamics simulations and kinetic measurements to estimate and predict protein-ligand residence times. J Med Chem 59:7167–7176

    Article  CAS  Google Scholar 

  75. Lu H, Tonge PJ (2010) Drug-target residence time: critical information for lead optimization. Curr Opin Chem Biol 14:467–474

    Article  CAS  Google Scholar 

Download references

Author information

Authors and Affiliations

Authors

Corresponding author

Correspondence to Hamid Modarress.

Ethics declarations

Conflict of interest

The authors declare that they have no conflict of interest.

Electronic supplementary material

Fig. 1

The structures and chemical names of: (a) amlodipine, (b) paroxetine, and (c) PEG (GIF 86 kb)

High resolution image (TIFF 81 kb)

Fig. 2

The agglomeration of the drugs, AML and PAR (GIF 488 kb)

High resolution image (TIFF 276 kb)

Fig. 3

Root mean square fluctuation (RMSF) of CYP for simulation systems S1 (GIF 82 kb)

High resolution image (TIFF 128 kb)

Fig. 4

Root mean square fluctuation (RMSF) of CYP for simulation systems S2 (GIF 89 kb)

High resolution image (TIFF 146 kb)

Fig. 5

Root mean square fluctuation (RMSF) of CYP for simulation systems S4 (GIF 82 kb)

High resolution image (TIFF 128 kb)

Fig. 6

Root mean square fluctuation (RMSF) of CYP for simulation systems S5 (GIF 85 kb)

High resolution image (TIFF 136 kb)

Fig. 7

Root mean square fluctuation (RMSF) of CYP for simulation systems S7 (GIF 68 kb)

High resolution image (TIFF 98 kb)

Fig. 8

Root mean square fluctuation (RMSF) of CYP for simulation systems S8 (GIF 115 kb)

High resolution image (TIFF 1584 kb)

Fig. 9

Root mean square fluctuation (RMSF) of CYP for simulation systems S9 (GIF 79 kb)

High resolution image (TIFF 121 kb)

Fig. 10

Root mean square fluctuation (RMSF) of CYP for simulation systems S10 (GIF 69 kb)

High resolution image (TIFF 101 kb)

Fig. 11

Root mean square fluctuation (RMSF) of CYP for simulation systems S11 (GIF 79 kb)

High resolution image (TIFF 123 kb)

Fig. 12

Root mean square fluctuation (RMSF) of CYP for simulation systems S12 (GIF 77 kb)

High resolution image (TIFF 118 kb)

Fig. 13

Root mean square fluctuation (RMSF) of CYP for simulation systems S13 (GIF 86 kb)

High resolution image (TIFF 136 kb)

Fig. 14

Root mean square deviation (RMSD) of CYP for simulation systems S1 (PNG 15 kb)

Fig. 15

Root mean square deviation (RMSD) of CYP for simulation systems S2 (PNG 17 kb)

Fig. 16

Root mean square deviation (RMSD) of CYP for simulation systems S4 (PNG 16 kb)

Fig. 17

Root mean square deviation (RMSD) of CYP for simulation systems S5. (PNG 15 kb)

Fig. 18

Root mean square deviation (RMSD) of CYP for simulation systems S7 (PNG 15 kb)

Fig. 19

Root mean square deviation (RMSD) of CYP for simulation systems S8 (PNG 15 kb)

Fig. 20

Root mean square deviation (RMSD) of CYP for simulation systems S9 (PNG 15 kb)

Fig. 21

Root mean square deviation (RMSD) of CYP for simulation systems S10 (PNG 15 kb)

Fig. 22

Root mean square deviation (RMSD) of CYP for simulation systems S11 (PNG 15 kb)

Fig. 23

Root mean square deviation (RMSD) of CYP for simulation systems S12 (PNG 15 kb)

Fig. 24

Root mean square deviation (RMSD) of CYP for simulation systems S13 (PNG 16 kb)

Rights and permissions

Reprints and permissions

About this article

Check for updates. Verify currency and authenticity via CrossMark

Cite this article

Yousefpour, A., Modarress, H., Goharpey, F. et al. Interaction of drugs amlodipine and paroxetine with the metabolizing enzyme CYP2B4: a molecular dynamics simulation study. J Mol Model 24, 67 (2018). https://doi.org/10.1007/s00894-018-3617-8

Download citation

  • Received:

  • Accepted:

  • Published:

  • DOI: https://doi.org/10.1007/s00894-018-3617-8

Keywords

Navigation