Skip to main content

Advertisement

Log in

D-Optimal Optimization and Data-Analysis Comparison Between a DoE Software and Artificial Neural Networks of a Chitosan Coating Process onto PLGA Nanoparticles for Lung and Cervical Cancer Treatment

  • Original Article
  • Published:
Journal of Pharmaceutical Innovation Aims and scope Submit manuscript

Abstract

Purpose

A common approach to “Quality-by-Design” is to employ quality software(s) to characterize the impacts of input parameters on output-critical quality attributes. In this study, paclitaxel (PTX), a common chemotherapeutic agent, was loaded into poly-lactic-co-glycolic acid (PLGA) nanoparticles (NPs), and the coating process of chitosan (CS) onto PLGA NPs was focused for optimization.

Method

Experiments were designed using Modde 8.0 to set up a D-optimal design for inputs (CS/PLGA ratios, temperature, and pH), and particle size (Z), zeta potential (Zeta), polydispersity index (PDI), encapsulation efficiency (EE), and loading capacity (LC) were the selected outputs. Data analysis was performed using Modde 8.0 concurrently with artificial neural networks such as INform 3.1 and FormRules 2.0. Furthermore, enhancement of cytotoxicity, cellular uptake, and apoptosis by CS coating were also determined.

Results

The results confirmed the influence of inputs on output ones (R2 > 90%). The optimized formulation showed Z of 161.53 ± 0.97 nm, PDI of 0.270 ± 0.007, Zeta of 41.87 ± 1.42 mV, and EE of 98.59 ± 0.22%; the results were close to the predicted calculations. The optimal formulation, CS-PLGA NPs, showed higher cytotoxicity than PLGA NPs in Hela and SK-LU-1 cell-lines (cell viability assay). Furthermore, apoptosis and intracellular uptake studies confirmed enhancement of the CS layer.

Conclusion

The data reveal the validity of optimization models and their potential in anti-cancer therapy, especially for lung and cervical cancer treatment.

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.

Institutional subscriptions

Fig. 1
Fig. 2
Fig. 3
Fig. 4
Fig. 5
Fig. 6
Fig. 7

Similar content being viewed by others

References

  1. Pandey P, Bharadwaj R, Chen X. Modeling of drug product manufacturing processes in the pharmaceutical industry. Predictive modeling of pharmaceutical unit operations: Elsevier; 2017. p. 1–13.

  2. Guideline IHT. Pharmaceutical development. Q8 (2R). As revised in august. 2009.

  3. Lawrence XY, Amidon G, Khan MA, Hoag SW, Polli J, Raju G, et al. Understanding pharmaceutical quality by design. AAPS J. 2014;16(4):771–83.

    Article  CAS  Google Scholar 

  4. Li Z, Cho BR, Melloy BJ. Quality by design studies on multi-response pharmaceutical formulation modeling and optimization. J Pharm Innov. 2013;8(1):28–44.

    Article  Google Scholar 

  5. Nguyen C, Christensen JM, Nguyen T. Application of D-optimal study design with contour surface response for designing sustained release gliclazide matrix tablets. Pharmacol Pharma. 2014;2014

  6. Ho HN, Tran TH, Tran TB, Yong CS, Nguyen CN. Optimization and characterization of artesunate-loaded chitosan-decorated poly (D, L-lactide-co-glycolide) acid nanoparticles. J Nanomater. 2015;2015:1–12.

    Google Scholar 

  7. Ren S, Mu H, Alchaer F, Chtatou A, Müllertz A. Optimization of self nanoemulsifying drug delivery system for poorly water-soluble drug using response surface methodology. Drug Dev Ind Pharm. 2013;39(5):799–806.

    Article  CAS  PubMed  Google Scholar 

  8. Liu Z, Bruwer M-J, MacGregor JF, Rathore SS, Reed DE, Champagne MJ. Modeling and optimization of a tablet manufacturing line. J Pharm Innov. 2011;6(3):170–80.

    Article  Google Scholar 

  9. Esnaashari SS, Amani A. Optimization of noscapine-loaded mPEG-PLGA nanoparticles and release study: a response surface methodology approach. J Pharm Innov. 2018:1–10.

  10. Turkoglu M, Aydin I, Murray M, Sakr A. Modeling of a roller-compaction process using neural networks and genetic algorithms. Eur J Pharm Biopharm. 1999;48(3):239–45.

    Article  CAS  PubMed  Google Scholar 

  11. Pharmacists ASoH. AHFS drug information. Published by authority of the Board of Directors of the American Society of Hospital Pharmacists; 2012.

  12. Bishop JF, Dewar J, Toner GC, Smith J, Tattersall MH, Olver IN, et al. Initial paclitaxel improves outcome compared with CMFP combination chemotherapy as front-line therapy in untreated metastatic breast cancer. J Clin Oncol. 1999;17(8):2355–64.

    Article  CAS  PubMed  Google Scholar 

  13. Yang R, Yang SG, Shim WS, Cui F, Cheng G, Kim IW, et al. Lung-specific delivery of paclitaxel by chitosan-modified PLGA nanoparticles via transient formation of microaggregates. J Pharm Sci. 2009;98(3):970–84.

    Article  CAS  PubMed  Google Scholar 

  14. Gradishar WJ, Tjulandin S, Davidson N, Shaw H, Desai N, Bhar P, et al. Phase III trial of nanoparticle albumin-bound paclitaxel compared with polyethylated castor oil–based paclitaxel in women with breast cancer. J Clin Oncol. 2005;23(31):7794–803.

    Article  CAS  PubMed  Google Scholar 

  15. Weiss RB, Donehower RC, Wiernik PH, Ohnuma T, Gralla RJ, Trump DL, et al. Hypersensitivity reactions from taxol. J Clin Oncol. 1990;8(7):1263–8. https://doi.org/10.1200/jco.1990.8.7.1263.

    Article  CAS  PubMed  Google Scholar 

  16. Danhier F, Lecouturier N, Vroman B, Jérôme C, Marchand-Brynaert J, Feron O, et al. Paclitaxel-loaded PEGylated PLGA-based nanoparticles: in vitro and in vivo evaluation. J Control Release. 2009;133(1):11–7.

    Article  CAS  PubMed  Google Scholar 

  17. Fonseca C, Simoes S, Gaspar R. Paclitaxel-loaded PLGA nanoparticles: preparation, physicochemical characterization and in vitro anti-tumoral activity. J Control Release. 2002;83(2):273–86.

    Article  CAS  PubMed  Google Scholar 

  18. Tran BN, Nguyen HT, Kim JO, Yong CS, Nguyen CN. Developing combination of artesunate with paclitaxel loaded into poly-D, L-lactic-co-glycolic acid nanoparticle for systemic delivery to exhibit synergic chemotherapeutic response. Drug Dev Ind Pharm. 2017;43(12):1952–62.

    Article  CAS  PubMed  Google Scholar 

  19. Yang R, Shim W-S, Cui F-D, Cheng G, Han X, Jin Q-R, et al. Enhanced electrostatic interaction between chitosan-modified PLGA nanoparticle and tumor. Int J Pharm. 2009;371(1):142–7.

    Article  CAS  PubMed  Google Scholar 

  20. Parveen S, Sahoo SK. Long circulating chitosan/PEG blended PLGA nanoparticle for tumor drug delivery. Eur J Pharmacol. 2011;670(2):372–83.

    Article  CAS  PubMed  Google Scholar 

  21. Tran TH, Nguyen TD, Poudel BK, Nguyen HT, Kim JO, Yong CS, et al. Development and evaluation of artesunate-loaded chitosan-coated lipid nanocapsule as a potential drug delivery system against breast cancer. AAPS PharmSciTech. 2015;16(6):1307–16.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  22. Rowe RC, Sheskey PJ, Quinn ME, Association AP. Handbook of Pharmaceutical Excipients: Pharmaceutical Press; 2009.

  23. Martin-Banderas L, Duran-Lobato M, Munoz-Rubio I, Alvarez-Fuentes J, Fernandez-Arevalo M, A Holgado M. Functional PLGA NPs for oral drug delivery: recent strategies and developments. Mini-Rev Med Chem. 2013;13(1):58–69.

    Article  CAS  PubMed  Google Scholar 

  24. Abdelrahman AA, Salem HF, Khallaf RA, Ali AMA. Modeling, optimization, and in vitro corneal permeation of chitosan-lomefloxacin HCl nanosuspension intended for ophthalmic delivery. J Pharm Innov. 2015;10(3):254–68.

    Article  Google Scholar 

  25. Nguyen HT, Tran TH, Kim JO, Yong CS, Nguyen CN. Enhancing the in vitro anti-cancer efficacy of artesunate by loading into poly-D, L-lactide-co-glycolide (PLGA) nanoparticles. Arch Pharm Res. 2015;38(5):716–24.

    Article  CAS  PubMed  Google Scholar 

  26. Rowe R, COLBOURN E. Formulating Knowledge. Innov Pharm Technol. 2006;20:70–4.

    Google Scholar 

  27. Colbourn E, Roskilly S, Rowe R, York P. Modelling formulations using gene expression programming–a comparative analysis with artificial neural networks. Eur J Pharm Sci. 2011;44(3):366–74.

    Article  CAS  PubMed  Google Scholar 

  28. Abbasi S, Afrasiabi A, Zarchi AAK, Faramarzi MA, Tavoosidana G, Amani A. Preparation and optimization of N-acetylcysteine nanosuspension through nanoprecipitation: an artificial neural networks study. J Pharm Innov. 2014;9(2):115–20.

    Article  Google Scholar 

  29. Malvern_Instruments. Zetasizer nano series user manual. Worcestershire: Malvern Instruments Ltd. 2004.

  30. Malvern_Instruments. Dynamic light scattering: an introduction in 30 minutes. Technical Note Malvern, MRK656–01. 2012:1–8.

  31. Tran TH, Choi JY, Ramasamy T, Truong DH, Nguyen CN, Choi H-G, et al. Hyaluronic acid-coated solid lipid nanoparticles for targeted delivery of vorinostat to CD44 overexpressing cancer cells. Carbohydr Polym. 2014;114:407–15.

    Article  CAS  PubMed  Google Scholar 

  32. Tran TH, Nguyen HT, Pham TT, Choi JY, Choi H-G, Yong CS, et al. Development of a graphene oxide nanocarrier for dual-drug chemo-phototherapy to overcome drug resistance in cancer. ACS Appl Mater Interfaces. 2015;7(51):28647–55.

    Article  CAS  PubMed  Google Scholar 

  33. Hughes JP, Rees S, Kalindjian SB, Philpott KL. Principles of early drug discovery. Br J Pharmacol. 2011;162(6):1239–49.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  34. Scudiero DA, Shoemaker RH, Paull KD, Monks A, Tierney S, Nofziger TH, et al. Evaluation of a soluble tetrazolium/formazan assay for cell growth and drug sensitivity in culture using human and other tumor cell lines. Cancer Res. 1988;48(17):4827–33.

    CAS  PubMed  Google Scholar 

  35. Wu X, Liu H, Liu J, Haley KN, Treadway JA, Larson JP, et al. Immunofluorescent labeling of cancer marker Her2 and other cellular targets with semiconductor quantum dots. Nat Biotechnol. 2003;21(1):41–6.

    Article  CAS  PubMed  Google Scholar 

  36. Park E-J, Yi J, Chung K-H, Ryu D-Y, Choi J, Park K. Oxidative stress and apoptosis induced by titanium dioxide nanoparticles in cultured BEAS-2B cells. Toxicol Lett. 2008;180(3):222–9.

    Article  CAS  PubMed  Google Scholar 

  37. Oyaizu H, Adachi Y, Taketani S, Tokunaga R, Fukuhara S, Ikehara S. A crucial role of caspase 3 and caspase 8 in paclitaxel-induced apoptosis. Mol Cell Biol Res Commun. 1999;2(1):36–41.

    Article  CAS  PubMed  Google Scholar 

  38. Kim JA, Åberg C, Salvati A, Dawson KA. Role of cell cycle on the cellular uptake and dilution of nanoparticles in a cell population. Nat Nanotechnol. 2012;7(1):62–8.

    Article  CAS  Google Scholar 

  39. Liebmann J, Cook JA, Lipschultz C, Teague D, Fisher J, Mitchell JB. The influence of Cremophor EL on the cell cycle effects of paclitaxel (Taxol®) in human tumor cell lines. Cancer Chemother Pharmacol. 1994;33(4):331–9.

    Article  CAS  PubMed  Google Scholar 

  40. Arıca Yegin B, Benoît J-P, Lamprecht A. Paclitaxel-loaded lipid nanoparticles prepared by solvent injection or ultrasound emulsification. Drug Dev Ind Pharm. 2006;32(9):1089–94.

    Article  PubMed  CAS  Google Scholar 

  41. Shao Q, Rowe RC, York P. Comparison of neurofuzzy logic and neural networks in modelling experimental data of an immediate release tablet formulation. Eur J Pharm Sci. 2006;28(5):394–404.

    Article  CAS  PubMed  Google Scholar 

  42. Aksu B, Paradkar A, de Matas M, Özer Ö, Güneri T, York P. Quality by design approach: application of artificial intelligence techniques of tablets manufactured by direct compression. AAPS PharmSciTech. 2012;13(4):1138–46.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  43. Ofir R, Seidman R, Rabinski T, Krup M, Yavelsky V, Weinstein Y, et al. Taxol-induced apoptosis in human SKOV3 ovarian and MCF7 breast carcinoma cells is caspase-3 and caspase-9 independent. Cell Death Differ. 2002;9(6):636–42.

    Article  CAS  PubMed  Google Scholar 

  44. Chu C, Xu J, Cheng D, Li X, Tong S, Yan J, et al. Anti-proliferative and apoptosis-inducing effects of camptothecin-20 (s)-O-(2-pyrazolyl-1) acetic ester in human breast tumor MCF-7 cells. Molecules. 2014;19(4):4941–55.

    Article  PubMed  PubMed Central  CAS  Google Scholar 

  45. Darzynkiewicz Z, Bruno S, Del Bino G, Gorczyca W, Hotz M, Lassota P, et al. Features of apoptotic cells measured by flow cytometry. Cytometry Part A. 1992;13(8):795–808.

    Article  CAS  Google Scholar 

  46. Rowinsky EK, Cazenave LA, Donehower RC. Taxol: a novel investigational antimicrotubule agent. JNCI: J Ntnl Cancer Institute. 1990;82(15):1247–59.

    Article  CAS  Google Scholar 

  47. Colbourn EA, Rowe RC. Advanced neural computing software systems: data mining in processing and formulation. 2006.

    Google Scholar 

  48. Shao Q, Rowe RC, York P. Investigation of an artificial intelligence technology—model trees: novel applications for an immediate release tablet formulation database. Eur J Pharm Sci. 2007;31(2):137–44.

    Article  CAS  PubMed  Google Scholar 

  49. Jain RA. The manufacturing techniques of various drug loaded biodegradable poly (lactide-co-glycolide)(PLGA) devices. Biomaterials. 2000;21(23):2475–90.

    Article  CAS  PubMed  Google Scholar 

  50. Singla A, Chawla M. Chitosan: some pharmaceutical and biological aspects-an update. J Pharm Pharmacol. 2001;53(8):1047–67.

    Article  CAS  PubMed  Google Scholar 

  51. Xiong S, Zhao X, Heng BC, Ng KW, Loo JSC. Cellular uptake of poly-(D, L-lactide-co-glycolide)(PLGA) nanoparticles synthesized through solvent emulsion evaporation and nanoprecipitation method. Biotechnol J. 2011;6(5):501–8.

    Article  CAS  PubMed  Google Scholar 

  52. Mehrishi J. Effect of lysine polypeptides on the surface charge of normal and cancer cells. Eur J Cancer (1965). 1969;5(5):427–35.

    Article  CAS  Google Scholar 

Download references

Author information

Authors and Affiliations

Authors

Corresponding author

Correspondence to Chien Ngoc Nguyen.

Ethics declarations

Conflicts of Interest

The authors declare that they have no conflict of interest.

Rights and permissions

Reprints and permissions

About this article

Check for updates. Verify currency and authenticity via CrossMark

Cite this article

Nguyen, C.N., Tran, B.N., Do, T.T. et al. D-Optimal Optimization and Data-Analysis Comparison Between a DoE Software and Artificial Neural Networks of a Chitosan Coating Process onto PLGA Nanoparticles for Lung and Cervical Cancer Treatment. J Pharm Innov 14, 206–220 (2019). https://doi.org/10.1007/s12247-018-9345-x

Download citation

  • Published:

  • Issue Date:

  • DOI: https://doi.org/10.1007/s12247-018-9345-x

Keywords

Navigation