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. 2005 Sep 30;6(2):E209–E222. doi: 10.1208/pt060231

Papain entrapment in alginate beads for stability improvement and site-specific delivery: Physicochemical characterization and factorial optimization using neural network modeling

Mayur G Sankalia 1, Rajshree C Mashru 1,✉, Jolly M Sankalia 1, Vijay B Sutariya 1
PMCID: PMC2750534  PMID: 16353980

Abstract

This work examines the influence of various process parameters (like sodium alginate concentration, calcium chloride concentration, and hardening time) on papain entrapped in ionotropically cross-linked alginate beads for stability improvement and site-specific delivery to the small intestine using neural network modeling. A 33 full-factorial design and feed-forward neural network with multilayer perceptron was used to investigate the effect of process variables on percentage of entrapment, time required for 50% and 90% of the enzyme release, particle size, and angle of repose. Topographical characterization was conducted by scanning electron microscopy, and entrapment was confirmed by Fourier transform infrared spectroscopy and differential scanning calorimetry. Times required for 50% (T50) and 90% (T90) of enzyme release were increased in all 3 of the process variables. Percentage entrapment and particle size were found to be directly proportional to sodium alginate concentration and inversely proportional to calcium chloride concentration and hardening time, whereas angle of repose and degree of cross-linking showed exactly opposite proportionality. Beads with >90% entrapment and T50 of <10 minutes could be obtained at the low levels of all 3 of the process variables. The inability of beads to dissolve in acidic environment, with complete dissolution in buffer of pH≥6.8, showed the suitability of beads to release papain into the small intestine. The shelf-life of the capsules prepared using the papain-loaded alginate beads was found to be 3.60 years compared with 1.01 years of the marketed formulation. It can be inferred from the above results that the proposed methodology can be used to prepare papain-loaded alginate beads for stability improvement and site-specific delivery.

Keywords: alginate beads, neural network, multilayer perceptron, optimization, papain

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References

  • 1.Bickerstaff GF. Immobilization of enzymes and cells: some practical considerations. In: Bickerstaff GF, editor. Immobilization of Enzymes and Cells. Totowa, NJ: Humana Press; 1997. pp. 1–12. [Google Scholar]
  • 2.Uhlig H. Industrial Enzymes and their Applications. New York: Wiley; 1998. [Google Scholar]
  • 3.Lin SY. Release kinetics of drug from different combined ratios of micropellets. J Microencapsul. 1993;10:319–322. doi: 10.3109/02652049309031521. [DOI] [PubMed] [Google Scholar]
  • 4.Ritschel WA. Targeting in the gastrointestinal tract: new approaches. Methods Find Exp Clin Pharmacol. 1991;13:313–336. [PubMed] [Google Scholar]
  • 5.Chen H, Langer R. Magnetically-responsive polymerized liposomes as potential oral delivery vehicles. Pharm Res. 1997;14:537–540. doi: 10.1023/A:1012124205524. [DOI] [PubMed] [Google Scholar]
  • 6.Park PG, Shalaby WSW, Park H. Biodegradable Hydrogels for Drug Delivery. Lancaster, PA: CRC Press; 1993. Physical Gels; pp. 99–140. [Google Scholar]
  • 7.Yotsuyanagi T, Ohkubo T, Ohhashi T, Ikeda K. Calcium-induced gelation of alginic acid and pH-sensitive reswelling of dried gels. Chem. Pharm Bull. 1987;35:1555–1563. [Google Scholar]
  • 8.Pillay V, Dangor CM, Govender T, Moopanar KR, Hurbans N. Drug release modulation from crosslinked calcium alginate microdises. 1. Evaluation of the concentration dependency of sodium alginate on drug entrapment capacity, morphology and dissolution rate. Drug Deliv. 1998;5:25–34. doi: 10.3109/10717549809052024. [DOI] [PubMed] [Google Scholar]
  • 9.Arica B, Calis S, Kas HS, Sargon MF, Hincal AA. 5-Fluorouracil encapsulated alginate beads for the treatment of breast cancer. Int J Pharmaceut. 2002;242:267–267. doi: 10.1016/S0378-5173(02)00172-2. [DOI] [PubMed] [Google Scholar]
  • 10.Gu F, Amsden B, Neufeld R. Sustained delivery of vascular endothelial growth factor with alginate beads. J Control Release. 2004;96:463–463. doi: 10.1016/j.jconrel.2004.02.021. [DOI] [PubMed] [Google Scholar]
  • 11.Almeida FP, Almeida AJ. Cross-linked alginate-gelatine beads: a new matrix for controlled release of pindolol. J Control Release. 2004;97:431–439. doi: 10.1016/j.jconrel.2004.03.015. [DOI] [PubMed] [Google Scholar]
  • 12.Agatonovic-Kustrin S, Zecevic M, Zivanovic LJ, Tucker IG. Application of artificial neural networks in HPLC method development. J Pharm Biomed Anal. 1998;17:69–76. doi: 10.1016/S0731-7085(97)00170-2. [DOI] [PubMed] [Google Scholar]
  • 13.Sathe PM, Venitz J. Comparison of neural network and multiple linear regression as dissolution predictors. Drug Dev Ind Pharm. 2003;29:349–355. doi: 10.1081/DDC-120018209. [DOI] [PubMed] [Google Scholar]
  • 14.Takayama K, Fujukawa M, Obata Y, Morishita M. Neural network based optimization of drug formulations. Adv Drug Del Rev. 2003;55:1217–1231. doi: 10.1016/S0169-409X(03)00120-0. [DOI] [PubMed] [Google Scholar]
  • 15.Agatonovic-Kustrin S, Beresford R. Basic concepts of artificial neural network (ANN) modeling and its application in pharmaceutical research. J Pharm Biomed Anal. 2000;22:717–727. doi: 10.1016/S0731-7085(99)00272-1. [DOI] [PubMed] [Google Scholar]
  • 16.Winkler DA, Burden FR. Bayesian neural nets for modeling in drug discovery. Drug Discovery Today: BIOSILICO. 2004;2:104–111. doi: 10.1016/S1741-8364(04)02393-5. [DOI] [Google Scholar]
  • 17.Turner JV, Maddalena DJ, Cutler DJ. Pharmacokinetic parameter prediction from drug structure using artificial neural networks. Int J Pharm. 2004;270:209–219. doi: 10.1016/j.ijpharm.2003.10.011. [DOI] [PubMed] [Google Scholar]
  • 18.Veng-Pedersen P, Modi NB. Application of neural networks to pharmacodynamics. J Pharm Sci. 1993;82:918–926. doi: 10.1002/jps.2600820910. [DOI] [PubMed] [Google Scholar]
  • 19.Kachrimanis K, Karamyan V, Malamataris S. Artificial neural networks (ANNs) and modeling of powder flow. Int J Pharm. 2003;250:13–23. doi: 10.1016/S0378-5173(02)00528-8. [DOI] [PubMed] [Google Scholar]
  • 20.Havlis J, Madden JE, Revilla AL, Havel J. High-performance liquid chromatographic determination of deoxycytidine monophosphate and methyldeoxycytidine monophosphate for DNA demethylation monitoring: experimental design and artificial neural networks optimisation. J Chromatogr B Biomed Sci Appl. 2001;755:185–194. doi: 10.1016/S0378-4347(01)00075-5. [DOI] [PubMed] [Google Scholar]
  • 21.Dow LK, Kalelkar S, Dow ER. Self-organizing maps for the analysis of NMR spectra. Drug Discovery Today: BIOSILICO. 2004;2:157–163. doi: 10.1016/S1741-8364(04)02409-6. [DOI] [Google Scholar]
  • 22.Lim CP, Quek SS, Peh KK. Prediction of drug release profiles using an intelligent learning system: an experimental study in transdermal iontophoresis. J Pharm Biomed Anal. 2003;31:159–168. doi: 10.1016/S0731-7085(02)00573-3. [DOI] [PubMed] [Google Scholar]
  • 23.Taskinen J, Yliruusi J. Prediction of physicochemical properties based on neural network modelling. Adv Drug Deliv Rev. 2003;55:1163–1183. doi: 10.1016/S0169-409X(03)00117-0. [DOI] [PubMed] [Google Scholar]
  • 24.Molnar L, Keseru GM, Papp A, Gulyas Z, Darvas F. A neural network based prediction of octanol-water partition coefficients using atomic5 fragmental descriptors. Bioorg Med Chem Lett. 2004;14:851–853. doi: 10.1016/j.bmcl.2003.12.024. [DOI] [PubMed] [Google Scholar]
  • 25.Jouyban A, Majidi M-R, Jalilzadeh H, Asadpour-Zeynali K. Modeling drug solubility in water-cosolvent mixtures using an artificial neural network. Farmaco. 2004;59:505–512. doi: 10.1016/j.farmac.2004.02.005. [DOI] [PubMed] [Google Scholar]
  • 26.Iannuccelli V, Coppi G, Bernabei MT, Cameroni R. Air compartment multiple-unit system for prolonged gastric residence. Part I. Formulation study. Int J Pharm. 1998;174:47–54. doi: 10.1016/S0378-5173(98)00229-4. [DOI] [Google Scholar]
  • 27.Wang K, He Z. Alginate-konjac glucomannan-chitosan beads as controlled release matrix. Int J Pharm. 2002;244:117–126. doi: 10.1016/S0378-5173(02)00324-1. [DOI] [PubMed] [Google Scholar]
  • 28.Kim C-K, Lee E-J. The controlled release of blum dextran from alginate beads. Int J Pharm. 1992;79:11–19. doi: 10.1016/0378-5173(92)90088-J. [DOI] [Google Scholar]
  • 29.Ostberg T, Lund EM, Graffner C. Calcium alginate matrices for oral multiple unit administration. IV Release characteristics in different media. Int J Pharm. 1994;112:241–248. doi: 10.1016/0378-5173(94)90360-3. [DOI] [Google Scholar]
  • 30.Wong TW, Chan LW, Kho SB, Heng PWS. Design of controlled-release solid dosage forms of alginate and chitosan using microwave. J Control Release. 2002;84:99–114. doi: 10.1016/S0168-3659(02)00237-7. [DOI] [PubMed] [Google Scholar]
  • 31.Fernandez-Hervas MJ, Holgado MA, Fini A, Fell JT. In vitro evaluation of alginate beads of a diclofenac salt. Int J Pharm. 1998;163:23–34. doi: 10.1016/S0378-5173(97)00333-5. [DOI] [Google Scholar]
  • 32.Skjak-Brae G, Grasdalen KH, Draget KI, Smidsrod O. Inhomogeneous poysaccharide ionic gels. Carbohydr Polym. 1989;10:31–54. doi: 10.1016/0144-8617(89)90030-1. [DOI] [Google Scholar]

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