Skip to main content

Advertisement

SpringerLink
Log in

Prediction of the partition coefficients using QSPR modeling and simulation of paclitaxel release from the diffusion-controlled drug delivery devices

  • Original Article
  • Published:
Drug Delivery and Translational Research Aims and scope Submit manuscript

Abstract

An in silico approach is proposed to first predict the partition coefficient of the model drug, paclitaxel, in different biocompatible and biodegradable polymer versus the blood plasma using artificial neural networks (ANNs) and semi-empirical quantitative structure property relationships (QSPRs). A simplified molecular-input line-entry system (SMILES) notation is used to represent the structures of the different polymers and the drug. The SMILES notation is then used to calculate the various structure-based descriptors. These descriptors are then used in the ANNs and semi-empirical QSPRs to predict the properties for a given drug-polymer device. A fluid flow model is subsequently solved to simulate the controlled drug release in the blood plasma. The effects of various parameters are also studied on the drug release profiles from these devices. The proposed approach provides a systematic framework to simulate the controlled release of the drug from the diffusion-controlled drug-polymer release systems. The developed models can be used in a reverse engineer framework to design the controlled delivery devices for a target drug release profile in near future.

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

Access this article

Log in via an institution

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

Instant access to the full article PDF.

Institutional subscriptions

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

Similar content being viewed by others

References

  1. Wertheimer AI, Santella TM, Finestone AJ, Levy RA. Drug delivery systems improve pharmaceutical profile and facilitate medication adherence. Adv Ther. 2005;22:559–77.

    Article  PubMed  CAS  Google Scholar 

  2. Arifin DY, Lee LY, Wang CH. Mathematical modeling and simulation of drug release from microspheres: implications to drug delivery systems. Adv Drug Deliv Rev. 2006;58:1274–325.

    Article  PubMed  CAS  Google Scholar 

  3. Goyanes A, Robles Martinez P, Buanz A, Basit A Gaisford S. Effect of geometry on drug release from 3D printed tablets. Int J Pharm [Internet]. Elsevier; 2015 [cited 2017 Oct 23];494:657–63. Available from: https://www.sciencedirect.com/science/article/pii/S0378517315003890.

  4. Souto E, Wissing S, Barbosa C, Müller R. Development of a controlled release formulation based on SLN and NLC for topical clotrimazole delivery. Int J Pharm [Internet]. 2004 [cited 2017 Oct 23];278:71–7. Available from: http://www.ncbi.nlm.nih.gov/pubmed/15158950.

  5. Ranade VV, Cannon JB. Drug delivery systems. Boca Raton: CRC Press; 2011.

    Google Scholar 

  6. Peppas N, Sahlin JJ. A simple equation for the description of solute release. III. Coupling of diffusion and relaxation. Int J Pharm [Internet]. Elsevier; 1989 [cited 2017 Oct 23];57:169–72. Available from: http://www.sciencedirect.com/science/article/pii/0378517389903062.

  7. Hirschfelder J, Bird RB, Curtiss CF. Molecular theory of gases and liquids [Internet]. Wiley; 1964 [cited 2017 Oct 23]. Available from: http://cds.cern.ch/record/108119.

  8. Clairambault J. Modeling oxaliplatin drug delivery to circadian rhythms in drug metabolism and host tolerance. Adv Drug Deliv Rev. 2007;59:1054–68.

    Article  PubMed  CAS  Google Scholar 

  9. Geldof M, Freijer JI, Peletier LA, van Beijsterveldt L, Danhof M. Mechanistic model for the acute effect of fluvoxamine on 5-HT and 5-HIAA concentrations in rat frontal cortex. Eur J Pharm Sci. 2008;33:217–29.

    Article  PubMed  CAS  Google Scholar 

  10. Veng-Pedersen P. Noncompartmentally-based pharmacokinetic modeling. Adv Drug Deliv Rev. 2001;48:265–300.

    Article  PubMed  CAS  Google Scholar 

  11. Pramanik A, Garg S. Reverse engineering of diffusion-limited controlled drug delivery devices. New Delhi: Springer; 2017. p. 1391–400. [cited 2017 Oct 23]. Available from: http://link.springer.com/10.1007/978-81-322-2743-4_133.

    Google Scholar 

  12. Siepmann J, Siegel RA, Rathbone MJ, editors. Fundamentals and applications of controlled release drug delivery [Internet]. Boston: Springer US; 2012. [cited 2017 Oct 23]. Available from: http://link.springer.com/10.1007/978-1-4614-0881-9

    Google Scholar 

  13. Higuchi T. Mechanism of sustained-action medication. Theoretical analysis of rate of release of solid drugs dispersed in solid matrices. J Pharm Sci. 1963;52:1145–9.

    Article  PubMed  CAS  Google Scholar 

  14. Weibull W. A statistical distribution function of wide applicability [Internet]. J Appl Mech. 1951:293–7. Available from: http://web.cecs.pdx.edu/~cgshirl/Documents/Weibull-ASME-Paper-1951.pdf

  15. Langenbucher F. Letters to the editor: linearization of dissolution rate curves by the Weibull distribution. J Pharm Pharmacol [Internet]. Blackwell Publishing Ltd; 1972 [cited 2017 Nov 5];24:979–81. Available from: http://doi.wiley.com/10.1111/j.2042-7158.1972.tb08930.x

  16. Goldsmith JA, Randall N, Ross SD. On methods of expressing dissolution rate data. J Pharm Pharmacol [Internet]. Blackwell Publishing Ltd; 1978 [cited 2017 Nov 5];30:347–9. Available from: http://doi.wiley.com/10.1111/j.2042-7158.1978.tb13253.x.

  17. Fu JC, Hagemeir C, Moyer DL, Ng EW. A unified mathematical model for diffusion from drug–polymer composite tablets. J Biomed Mater Res. 1976;10:743–58.

    Article  PubMed  CAS  Google Scholar 

  18. Singh SK, Fan LT, Hall D. A generalized model for swelling-controlled release systems. Biotechnol Prog. 1986;2:145–56.

    Article  PubMed  CAS  Google Scholar 

  19. Peppas NA, Reinhart CT. Solute diffusion in swollen membranes. Part I. A new theory. J Membr Sci. 1983;15:275–87.

    Article  CAS  Google Scholar 

  20. Reinhart CT, Peppas NA. Solute diffusion in swollen membranes. Part II. Influence of crosslinking on diffusive properties. J Membr Sci. 1984;18:227–39.

    Article  CAS  Google Scholar 

  21. Peppas NA, Moynihan HJ. Solute diffusion in swollen membranes. IV. Theories for moderately swollen networks. J Appl Polym Sci [Internet]. Wiley Subscription Services, Inc., A Wiley Company; 1985 [cited 2017 Nov 5];30:2589–606. Available from: http://doi.wiley.com/10.1002/app.1985.070300623.

  22. Ju RTC, Nixon PR, Patel MV. Drug release from hydrophilic matrices. 1. New scaling laws for predicting polymer and drug release based on the polymer disentanglement concentration and the diffusion layer. J Pharm Sci Wiley Subscription Services, Inc, A Wiley Company. 1995;84:1455–63.

    CAS  Google Scholar 

  23. Ju RTC, PR NN, Patel MV, Tong DM. Drug release from hydrophilic matrices. 2. A mathematical model based on the polymer disentanglement concentration and the diffusion layer. J Pharm Sci Wiley Subscription Services, Inc, A Wiley Company. 1995;84:1464–77.

    CAS  Google Scholar 

  24. Siepmann J, Peppas NA. Modeling of drug release from delivery systems based on hydroxypropyl methylcellulose (HPMC). Adv Drug Deliv Rev. 2001;48:139–57.

    Article  PubMed  CAS  Google Scholar 

  25. Kale SP, Garg S. Prediction of the mutual diffusion coefficient for controlled drug delivery devices. Comput Chem Eng [Internet]. Elsevier Ltd; 2012 [cited 2013 Nov 26];39:186–98. Available from: http://linkinghub.elsevier.com/retrieve/pii/S0098135411003383.

  26. Brignole EA, Bottini S, Gani R. A strategy for the design and selection of solvents for separation processes. Fluid Phase Equilib. 1986;29:125–32.

    Article  CAS  Google Scholar 

  27. Maranas CD. Optimal computer-aided molecular design: a polymer design case study. Ind Eng Chem Res. 1996;35:3403–14.

    Article  CAS  Google Scholar 

  28. Muro-Suñé N, Gani R, Bell G, Shirley I. Model-based computer-aided design for controlled release of pesticides. Comput Chem Eng. 2005;30:28–41.

    Article  CAS  Google Scholar 

  29. Churi N, Achenie LEK. Novel mathematical programming model for computer aided molecular design. Ind Eng Chem Res. 1996;35:3788–94.

    Article  CAS  Google Scholar 

  30. Camarda KV, Bonnell BW, Maranas CD, Nagarajan R. Design of surfactant solutions with optimal macroscopic properties. Comput Chem Eng [internet]. Elsevier Science Ltd; 1999;23:S467–70. Available from: https://doi.org/10.1016/S0098-1354(99)80115-X.

  31. Siepmann J, Kranz H, Peppas NA, Bodmeier R. Calculation of the required size and shape of hydroxypropyl methylcellulose matrices to achieve desired drug release profiles. Int J Pharm. 2000;201:151–64.

    Article  PubMed  CAS  Google Scholar 

  32. Vrentas JS, Duda JL. Diffusion in polymer—solvent systems. I. Reexamination of the free-volume theory. J Polym Sci Polym Phys Ed [Internet]. Wiley; 1977 [cited 2017 Nov 5];15:403–16. Available from: http://doi.wiley.com/10.1002/pol.1977.180150302.

  33. Vrentas JS, Duda JL. Diffusion in polymer–solvent systems. II. A predictive theory for the dependence of diffusion coefficients on temperature, concentration, and molecular weight. J Polym Sci Polym Phys Ed [Internet]. Wiley; 1977 [cited 2017 Nov 5];15:417–39. Available from: http://doi.wiley.com/10.1002/pol.1977.180150303.

  34. Flory PJ. Principles of polymer chemistry. Ithaca: Cornell University Press; 1953. p. 672.

    Google Scholar 

  35. Muro-Suñé N, Gani R, Bell G, Shirley I. Predictive property models for use in design of controlled release of pesticides. Fluid Phase Equilib. 2005;228–229:127–33.

    Article  CAS  Google Scholar 

  36. Holten-Andersen J, Eng K. Activity coefficients in polymer solutions. Prog Org Coat. 1988;16:77–97.

    Article  CAS  Google Scholar 

  37. Voelkel A, Fall J. Inverse gas chromatography. Relationship between mass activity coefficient and Flory-Huggins interaction parameter in examination of petroleum pitches. Chromatographia. 1997;44:197–204.

    Article  CAS  Google Scholar 

  38. Lindvig T, Michelsen ML, Kontogeorgis GM. A Flory-Huggins model based on the Hansen solubility parameters. Fluid Phase Equilib. 2002;203:247–60.

    Article  CAS  Google Scholar 

  39. Hansen CM. Hansen solubility parameters: a user’s handbook. Boca Raton: CRC Press; 2007.

    Book  Google Scholar 

  40. Votano JR, Parham M, Hall LHMH, Kier LB, Mark Hall L, Hall LHMH. Prediction of aqueous solubility based on large datasets using several QSPR models utilizing topological structure representation. Chem Biodivers [Internet]. 2004 [cited 2016 Jul 6];1:1829–41. Available from: http://www.ncbi.nlm.nih.gov/pubmed/17191819.

  41. Abbott MM, Smith JM, Van Ness HC. Introduction to chemical engineering thermodynamics. McGraw-Hill, Bost 2001. p. 619–26.

  42. Haines RIS, Sandler SI. Aqueous Solubilities and infinite dilution activity coefficients of several polycyclic aromatic hydrocarbons. J Chem Eng Data. 1995;40:833–6.

    Article  CAS  Google Scholar 

  43. Weininger D. SMILES, a chemical language and information system. 1. Introduction to methodology and encoding rules. J Chem Inj Comput Sci. 1988;28:31–6.

    Article  CAS  Google Scholar 

  44. Bicerano J. Prediction of polymer properties [Internet]. Marcel Dekker; 2002 [cited 2017 Oct 23]. Available from: https://books.google.co.uk/books?hl=en&lr=&id=5L1USsiq6KcC&oi=fnd&pg=PP1&dq=bicerano&ots=iKdk05DwYi&sig=iAbJmDPUCPx61nIftYbQPAKBhjQ#v=onepage&q=bicerano&f=false.

  45. Cho YI, Kensey KR. Effects of the non-Newtonian viscosity of blood on flows in a diseased arterial vessel. Part 1: steady flows. Biorheology. 1991;28:241–62.

    Article  PubMed  CAS  Google Scholar 

  46. Kleven M, Melaaen MC, Reimers M, Røtnes JS, Aurdal L, Djupesland PG. Using computational fluid dynamics (CFD) to improve the bi-directional nasal drug delivery concept. Food Bioprod Process. 2005;83:107–17.

    Article  Google Scholar 

  47. du Bois A, Lück H-J, Meier W, Adams H-P, Möbus V, Costa S, et al. A randomized clinical trial of cisplatin/paclitaxel versus carboplatin/paclitaxel as first-line treatment of ovarian cancer. CancerSpectrum Knowl Environ [Internet]. Oxford University Press; 2003 [cited 2016 Jul 10];95:1320–9. Available from: http://jnci.oxfordjournals.org/cgi/doi/10.1093/jnci/djg036.

  48. Miller K, Wang M, Gralow J, Dickler M, Cobleigh M, Perez EA, et al. Paclitaxel plus Bevacizumab versus paclitaxel alone for metastatic breast cancer. N Engl J Med Massachusetts Medical Society. 2007;357:2666–76.

    Article  CAS  Google Scholar 

  49. Strauss GM, Herndon J, Maddaus MA, Johnstone DW, Johnson EA, Watson DM, et al. Randomized clinical trial of adjuvant chemotherapy with paclitaxel and carboplatin following resection in stage IB non-small cell lung Cancer (NSCLC): report of Cancer and leukemia group B (CALGB) protocol 9633. ASCO Meet Abstr. 2004;22:7019.

    Google Scholar 

  50. Lao LL, Venkatraman SS, Peppas NA. Modeling of drug release from biodegradable polymer blends. Eur J Pharm Biopharm [Internet]. Elsevier B.V.; 2008;70:796–803. Available from: https://doi.org/10.1016/j.ejpb.2008.05.024

  51. Piringer O-G, Baner AL. Plastic packaging : interactions with food and pharmaceuticals. 2nd ed. Hoboken: Wiley; 2008.

    Book  Google Scholar 

  52. Mu L, Feng SS. A novel controlled release formulation for the anticancer drug paclitaxel (taxol((R))): PLGA nanoparticles containing vitamin E TPGS. J Control Release [Internet]. 2003;86:33–48. Available from: http://www.ncbi.nlm.nih.gov/entrez/query.fcgi?cmd=Retrieve&db=PubMed&dopt=Citation&list_uids=12490371.

  53. Feng S-S, Zeng W, Teng Lim Y, Zhao L, Yin Win K, Oakley R, et al. Vitamin E TPGS-emulsified poly(lactic-co-glycolic acid) nanoparticles for cardiovascular restenosis treatment. Nanomedicine [Internet]. 2007 [cited 2017 Oct 23];2:333–44. Available from: http://www.ncbi.nlm.nih.gov/pubmed/17716178.

  54. Kuh HJ, Jang SH, Wientjes MG, Au JL. Computational model of intracellular pharmacokinetics of paclitaxel. J Pharmacol Exp Ther. 2000;293:761–70.

    PubMed  CAS  Google Scholar 

Download references

Acknowledgements

The authors would like to thank S. Kale and A. Kumar for their valuable feedback and suggestions during the early stages of this work.

Author information

Authors and Affiliations

  1. Department of Chemical Engineering, Indian Institute of Technology, Kanpur, Uttar Pradesh, 208 016, India

    Anurag Pramanik & Sanjeev Garg

Authors
  1. Anurag Pramanik
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. Sanjeev Garg
    View author publications

    You can also search for this author in PubMed Google Scholar

Corresponding author

Correspondence to Sanjeev Garg.

Ethics declarations

Conflict of interest

The authors declare that they have no conflict of interest.

Additional information

Highlights

▪ Developed semi-empirical QSPR models for partition coefficient of the drug in polymer and blood plasma

▪ In silico modeling of the drug release from the controlled drug delivery devices to mimic in vivo drug release

▪ Simulated the effects of different design parameters on the controlled drug release profiles

Electronic supplementary material

ESM 1

(DOCX 734 kb)

Rights and permissions

Reprints and permissions

About this article

Check for updates. Verify currency and authenticity via CrossMark

Cite this article

Pramanik, A., Garg, S. Prediction of the partition coefficients using QSPR modeling and simulation of paclitaxel release from the diffusion-controlled drug delivery devices. Drug Deliv. and Transl. Res. 8, 1300–1312 (2018). https://doi.org/10.1007/s13346-018-0530-8

Download citation

  • Published:

  • Issue Date:

  • DOI: https://doi.org/10.1007/s13346-018-0530-8

Keywords

Search

Navigation