AAPS PharmSciTech

, Volume 13, Issue 2, pp 611–622 | Cite as

Evaluation of Anticancer Drug-Loaded Nanoparticle Characteristics by Nondestructive Methodologies

  • David Awotwe-Otoo
  • Ahmed S. Zidan
  • Ziyaur Rahman
  • Muhammad J. Habib
Research Article

Abstract

The purpose of this study was to utilize near-infrared (NIR) spectroscopy and near-infrared chemical imaging (NIR-CI) as non-invasive techniques to evaluate the drug loading in letrozole-loaded PLGA nanoparticle formulations prepared by the emulsification–solvent evaporation method. A Plackett–Burman design was applied to evaluate the main effects of amount of drug (X1), amount of polymer (X2), stirring rate (X3), emulsifier concentration (X4), organic to aqueous phase volume ratio (X5), type of organic solvent (X6), and homogenization time (X7) on drug entrapment efficiency. The influence of three different spectral pretreatment methods (multiplicative scatter correction, standard normal variate, and Savitzky–Golay second derivative transformation with third-order polynomial) and two different regression methods (PLS regression and principal component regression (PCR)) on model prediction ability were compared. PLS of spectra that were pretreated with Savitzky–Golay second derivative transformation provided better model prediction than PCR as it revealed better linear correlation (correlation coefficient of 0.991) for both calibration and prediction models. Relatively low values of root mean square errors of calibration (RMSEC = 0.748) and prediction (RMSEP = 0.786) and low standard errors of calibration (SEC = 0.758) and prediction (SEP = 0.589) suggested good predictability for estimation of the loading of letrozole in PLGA nanoparticles. NIR-CI analysis also revealed mutual homogenous distribution of both polymer and drug and was capable of clearly distinguishing the 12 formulations both quantitatively and qualitatively. In conclusion, NIR and NIR-CI could be potentially used to characterize anticancer drug-loaded nanoparticulate matrix.

KEY WORDS

imaging letrozole nanoparticle near-infrared PCR PLGA PLS 

REFERENCES

  1. 1.
    Cohen MH, Johnson JR, Li N, Chen G, Pazdur R. Approval summary: letrozole in the treatment of postmenopausal women with advanced breast cancer. Clin Cancer Res. 2002;8(3):665–9.PubMedGoogle Scholar
  2. 2.
    Chen D, Reierstad S, Lu M, Lin Z, Ishikawa H, Bulun SE. Regulation of breast cancer-associated aromatase promoters. Cancer Lett. 2009;273:15–27.PubMedCrossRefGoogle Scholar
  3. 3.
    Bai JL, Wu H, Lui J, Guo G, Chen J. Paclitaxel-loaded poly(d, l-lactide-co-glycolide) nanoparticles for radiotherapy in hypoxic human tumor cells in vitro. Cancer Biol Ther. 2008;7:911–6.PubMedCrossRefGoogle Scholar
  4. 4.
    Nair LS, Laurencin CT. Biodegradable polymers as biomaterials. Prog Polym Sci. 2007;32:762–98.CrossRefGoogle Scholar
  5. 5.
    Reich G. Near-infrared spectroscopy and imaging: basic principles and pharmaceutical applications. Adv Drug Deliv Rev. 2005;57:1109–43.PubMedCrossRefGoogle Scholar
  6. 6.
    Gowen AA, O’Donnell CP, Cullen PJ, Bell SEJ. Recent applications of chemical imaging to pharmaceutical process monitoring and quality control. Eur J Pharm Biopharm. 2008;69:10–22.PubMedCrossRefGoogle Scholar
  7. 7.
    Guidance for Industry: PAT—a framework for innovative pharmaceutical development, manufacturing and quality assurance. http://www.fda.gov/downloads/Drugs/GuidanceComplianceRegulatoryInformation/Guidances/UCM070305.pdf. Last accessed, October 5, 2011.
  8. 8.
    Herkert T, Prinz H, Kovar KA. One hundred percent online identity check of pharmaceutical products by near-infrared spectroscopy on the packaging line. Eur J Pharm Biopharm. 2005;51:9–16.CrossRefGoogle Scholar
  9. 9.
    Zidan AS, Rahman Z, Habib MJ, Khan MA. Spectral and spatial characterization of protein loaded PLGA nanoparticles. J Pharm Sci. 2009;99(3):1180–92.CrossRefGoogle Scholar
  10. 10.
    Chalus P, Roggo Y, Walter S, Ulmschneider M. Near-infrared determination of active substance content in intact low-dosage tablets. Talanta. 2005;66:1294–302.PubMedCrossRefGoogle Scholar
  11. 11.
    Camacho W, Valles-Lluch A, Ribes-Greus A, Karlsson S. Determination of moisture content in nylon 6,6 by near infrared spectroscopy and chemometrics. J Appl Polym Sci. 2003;87(13):2165–70.CrossRefGoogle Scholar
  12. 12.
    Plackett RL, Burman JP. The design of optimum multifactorial experiments. Biometrika. 1946;33:305–25.CrossRefGoogle Scholar
  13. 13.
    Sastry SV, Khan MA. Aqueous based polymeric dispersion: Placket–Burman design for screening of formulation variables of atenolol gastrointestinal therapeutic system. Pharm Acta Helv. 1998;73(2):105–12.PubMedCrossRefGoogle Scholar
  14. 14.
    Mainardes RM, Evangelista RC. PLGA nanoparticles containing praziquantel: effect of formulation variables on size distribution. Int J Pharm. 2005;290:137–44.PubMedCrossRefGoogle Scholar
  15. 15.
    Rahman Z, Zidan AS, Habib MJ, Khan MA. Understanding the quality of protein loaded PLGA nanoparticles variability by the Plackett–Burman design. Int J Pharm. 2010;389(1–2):186–94.PubMedCrossRefGoogle Scholar
  16. 16.
    Quintanar-Guerrero D, Fessi H, Allemann E, Doelker E. Influence of stabilizing agents and preparatives variables on the formation of poly (d, l-lactic acid) nanoparticles by an emulsification-diffusion technique. Int J Pharm. 1996;143:133–41.CrossRefGoogle Scholar
  17. 17.
    International Conference on Harmonization (ICH) Guidelines for Industry, ICH Q2(R1), Validation of Analytical Procedures, Methodology, 2005.Google Scholar
  18. 18.
    Lammertyn J, Nicolay B, Ooms K, DeSemedt V, De Baerdemaeker J. Non- destructive measurement of acidity, soluble solids and firmness of Jonagold apples using NIR-spectroscopy. Trans ASAE. 1998;41(4):1089–94.Google Scholar
  19. 19.
    Roggo Y, Chalus P, Maurer L, Lema-Martinez C, Edmond A, Jent N. A review of near infrared spectroscopy and chemometrics in pharmaceutical technology. J Pharm Biomed Anal. 2007;44:683–700.PubMedCrossRefGoogle Scholar
  20. 20.
    Pizarro C, Esteban-Diez I, Nistal AJ, Gonzalez-Saiz JM. Influence of data pre-processing on the quantitative determination of the ash content and lipids in roasted coffee by near infrared spectroscopy. Anal Chim Acta. 2004;509:217–27.CrossRefGoogle Scholar
  21. 21.
    Azzouz T, Puigdoménech A, Aragay M, Tauler R. Comparison between different data pre-treatment methods in the analysis of forage samples using near-infrared diffuse reflectance spectroscopy and partial least-squares multivariate calibration method. Anal Chim Acta. 2003;484:121–34.CrossRefGoogle Scholar
  22. 22.
    Dhanoa MS, Lister SJ, Sanderson R, Barnes RJ. The link between multiplicative scatter correction (MSC) and standard normal variate (SNV) transformations of NIR spectra. J Near Infrared Spectrosc. 1994;2:43–7.CrossRefGoogle Scholar
  23. 23.
    Barnes RJ, Dhanoa MS, Lister SJ. Standard normal variate transformation and de-trending of near-infrared diffuse reflectance spectra. Appl Spectrosc. 1989;43(5):772–7.CrossRefGoogle Scholar
  24. 24.
    Ravn C, Skibsted E, Bro R. Near-infrared chemical imaging (NIR-CI) on pharmaceutical solid dosage forms-comparing common calibration approaches. J Pharm Biomed Anal. 2008;48:554–6.PubMedCrossRefGoogle Scholar

Copyright information

© American Association of Pharmaceutical Scientists 2012

Authors and Affiliations

  • David Awotwe-Otoo
    • 1
    • 2
  • Ahmed S. Zidan
    • 2
  • Ziyaur Rahman
    • 2
  • Muhammad J. Habib
    • 1
  1. 1.Department of Pharmaceutical Sciences, College of PharmacyHoward UniversityWashingtonUSA
  2. 2.Division of Product Quality ResearchFood and Drug AdministrationSilver SpringUSA

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