Research Article Experimental and Mathematical Studies on

Research ArticleExperimental and Mathematical Studies on the Drug ReleaseProperties of Aspirin Loaded Chitosan Nanoparticles

Yixiang Shi1 Ajun Wan12 Yifei Shi3 Yueyue Zhang1 and Yupeng Chen1

1 School of Chemistry and Chemical Engineering Shanghai Jiaotong University 800 Dongchuan Road Shanghai 200240 China2 State Key Laboratory of Pollution Control and Resources Reuse College of Environmental Science and EngineeringTongji University Shanghai China

3 School of Life Sciences and Biotechnology Shanghai Jiaotong University 800 Dongchuan Road Shanghai 200240 China

Correspondence should be addressed to Ajun Wan wanajunsjtueducn

Received 28 February 2014 Accepted 14 May 2014 Published 1 June 2014

Academic Editor Shyh-Dar Li

Copyright copy 2014 Yixiang Shi et al This is an open access article distributed under the Creative Commons Attribution Licensewhich permits unrestricted use distribution and reproduction in any medium provided the original work is properly cited

The study of drug release dynamic is aiming at understanding the process that drugs release in human body and its dynamiccharacteristics It is of great significance since these characteristics are closely related to the dose dosage form and effect of thedrugs The Noyes-Whitney function is used to represent how the solid material is dissolved into solution and it is well usedin study of drug dynamic In this research aspirin (acetylsalicylic acid (ASA)) has been encapsulated with different grades ofchitosan (CS) varying in molecular weight (Mw) for the purpose of controlled release The encapsulation was accomplished byionic gelation technology based on assembly of positively charged chitosan and negatively charged sodium tripolyphosphate (TPP)The encapsulation efficiency loading capacity and drug release behavior of aspirin loaded chitosan nanoparticles (CS-NPs) werestudied It was found that the concentration of TPP and Aspirin molecular weights of chitosan have important effect on the drugrelease patterns from chitosan nanoparticles The results for simulation studies show that the Noyes-Whitney equation can besuccessfully used to interpret the drug release characteristics reflected by our experimental data

1 Introduction

Percutaneous transluminal coronary angioplasty (PTCA) isan accepted treatment for providing relief of angina pec-toris in patients with single- and multivessel disease [1 2]Coronary restenosis remains the primary limitation of PTCA[3 4] Antiplatelet therapy with acetylsalicylic acid (aspirin)is well established as secondary prophylaxis in patientswith arterial thrombotic disorders [5 6] Studies have alsodemonstrated the prophylactic effect of low-dose aspirin onreducing the risk of future cardiovascular events in a varietyof clinical settings [7] Aspirin (ASA) is an anti-inflammatorypain killer which is one of the hydrophilic drugs It isextensively used for pain relief to reduce inflammation andtemperatures and to reduce the risk of heart attacks andstrokes [8]

Drug delivery systems (DDS) such as lipid- or polymer-based nanoparticles can be designed to improve the pharma-cological and therapeutic properties of drugs administered

parenterally [9] Chitosan is a naturally occurring biopolymermade up of 120573-(14)-linked glucosamine units [10 11] It isproduced by deacetylation of chitin extracted from shellsof crabs shrimps and krills [12] Being versatile due to itschemical structure chitosan has received increasing attentionas a renewable polymeric material Biodegradability lowtoxicity and good biocompatibility make chitosan partic-ularly suitable for use in biomedical and pharmaceuticalformulations [13ndash15] It has shown excellent properties asexcipient andhas beenused as a vehicle in compressed tabletsas a disintegrant as a binder and as a granulating agent ingroundmixtures as well as a cogrinding diluent to boost dis-solution rate and bioavailability of water-insoluble drugs [16ndash18] Chitosan has also been widely investigated in the devel-opment of controlled release drug delivery systems [19ndash27]Particularly it has mucoadhesive properties due to its pos-itive charge which gives it electrostatic interaction with thenegatively charged mucosal surface making drugs easier toabsorb transmucosally

Hindawi Publishing CorporationBioMed Research InternationalVolume 2014 Article ID 613619 8 pageshttpdxdoiorg1011552014613619

2 BioMed Research International

Although there has been considerable interest in devel-oping chitosan nanoparticles (NPs) as effective drug deliverydevices [6 13 28] a majority of these studies have dealtwith the preparation method of drug loaded CS-NPs Up todate the study of drug loaded chitosan nanoparticles wasfocused on release behavior of the drug from chitosan matrixnanoparticles Depending on the concentration deacetyla-tion degree and Mw of chitosan as well as the concentrationof sodium tripolyphosphate the liberation of drug from thechitosan nanoparticles varied from fast release to slow release[29ndash31] Therefore it is of great interest to investigate theinteraction mechanism of the factors which influenced therelease behavior of drug from CS-NPs

Pharmacokinetics is the study focusing on the releaseprofiles of drugs under different conditions Its most imme-diate application is to study the drug release process withinhuman body By pharmacokinetic studies we can determinethe best way for drug administration what is the suitabledosage form how many drugs per dose how many times thedrug needed to be given each day and its efficacy Thereforeit is important to set the right kinetic parameters for drugrelease Based on the experimental data of kinetic curves wemay sum up the mathematical model for the drug releaseprocess and thus be able to provide guidance for futureexperiments

In this study the chitosan nanoparticles (CS-NPs) wereobtained by ionic gelation technology based on assembly ofpositively charged chitosan and negatively charged sodiumtripolyphosphate (TPP) With aspirin (ASA) as the modeldrug the encapsulation efficiency loading capacity and drugrelease behavior in vitro were studied Morphology of theprepared aspirin CS-NPs was observed by transmission elec-tron microscopy (TEM) The characterization and thermalbehavior of aspirin in CS-NPs were studied by the Fouriertransform infrared (FT-IR) spectroscopy and differentialscanning calorimetry (DSC) The effect of the concentrationof TPP and ASA and molecular weight of chitosan werediscussed in detail In our computational simulations weused and tested three different mathematical models whichare probabilistic Boolean networkmodel themodel based onpolynomial fitting and the differential equation based on theNoyes-Whitney equation [32]

2 Materials and Methods

21 Materials Chitosan with different molecular weight(Mw 210KDa and 670KDa the degree of deacetylation was90)was obtained fromDacheng Biotech Co Ltd (WeifangChina) Aspirin was obtained from Lunan PharmaceuticalCo Ltd (Linyi China) Sodium tripolyphosphate (TPP) andother reagents were all analytical reagents grade Unlessotherwise stated the chitosan used in this research is mainly210 KDa

22 Preparation of Aspirin Loaded CS-NPs Chitosan withdifferent Mw was dissolved in 1 (vv) acidic aqueous solu-tion at the concentration of 2mgmL 10mg 15mg 25mgand 30mg of aspirin were added to the 25mL chitosan acidicsolution After dissolving completely Tween-80 (2 vv) was

added as a surfactant by using homogenizer at 500 rads for20min 10mL TPP solution with the different mass concen-tration (25mgmL 30mgmL 40mgmL 50mgmL) wasadded into the preformed aspirin chitosan solutions sepa-rately After cross-linking for 60min the aspirin loaded CS-NPs were washed with distilled water repeatedly and thenfreeze-dried

23 The Fourier Transform Infrared (FT-IR) SpectroscopyTransmission infrared spectra of CS-NPs loaded with variousconcentrations of aspirin pure aspirin and pure chitosanwere measured by using a Fourier transform infrared spec-trophotometer 430 (PEUSA) About 2mg of various sampleswas mixed with 100mg KBr and prepared pellets were usedfor studies

24 Differential Scanning Calorimetry (DSC) The DSC ther-mograms of pure aspirin and aspirin loaded CS-NPs wereprepared in the condition of 08mgmL aspirin and 25 30and 40mgmL TPP were recorded with a heating rate of20∘Cmin in nitrogen between 25∘C and 180∘C

25 Morphology Study The surface morphology of CS-NPswas observed by transmission electron microscopy (TEM)The CS-NPs solution was dropped on copper grids and driedovernight at room temperature for viewing

26 Evaluation of Drug Loaded Efficiency Aspirin encapsula-tion efficiency and loading capacity were studied by separat-ing CS-NPs from the aqueous medium containing free drugby ultracentrifugation with 9000 rpm at room temperaturefor 30min The amount of free aspirin was determined byUV spectrophotometer at 298 nm Dilutions of samples andcalibration curves were performed in phosphate bufferedsaline (pH 74) The aspirin encapsulation efficiency (EE)and the aspirin loading capacity (LC) of the process werecalculated from (1) and (2) indicated below

EE =(total amount of aspirin minus free aspirin)

total amount of aspirin times 100(1)

LC =(total amount of aspirin minus free aspirin)

nanoparticles weight times 100 (2)

27 In Vitro Drug Release Study The CS-NPs were collectedby ultracentrifugation at 9000 rpm for 30min They weredispersed in phosphate buffered saline (pH 74) at 37∘C undermagnetic stirring At various time points supernatants wereisolated by ultracentrifugation and 3mL sample was takenout instead of the same volume of freshmediumThe amountof released aspirin was analyzed with UV spectrophotometer(PE USA) at 298 nm

28 Mathematical Fitting Using the Noyes-Whitney Equa-tion The Noyes-Whitney equation was developed by somephysical chemists at the beginning of the 20th century to

BioMed Research International 3

characterize of the process of solid dissolution [33 34] Theequation is

119889119862

119889119905=119863119878

119881ℎ(119862119904minus 119862) (3)

where 119862 is the concentration of a substance in the solution 119905is time 119889119862119889119905 is the rate of dissolution 119862

119904is the solubility of

the substance in the solvent119863 is the diffusion coefficient 119878 isthe surface area of the solid119881 is the volume of themedium ℎis the thickness of the diffusion layer Since119863119881 and ℎ are allconstants usually 119870 is used in the place of 119863(119881 lowast ℎ) as aconstant Therefore it is possible for us to know how theconcentration will change through time by solving thisdifferential equation

The key for using the Noyes-Whitney fitting is to solvethe differential equation Since the dissolution rate is equalto the derivative of drug concentration over time we canreverse this operation to get the relationship between theconcentrations versus time First we combine the constantsin the formula and get 119889119862119889119905 = 119875(119862

119904minus119862) as 119875 = (119863119878)(119881ℎ)

so119889119862

(119862119904minus 119862)= 119875 lowast 119889119905 (4)

Solving this differential equation we getminus ln(119862119904minus119862) = 119875119905 + 119887

119887 is a constant so119862119904minus 119862 = 119890(minus(119875119905+119887))

119862 = 119862119904minus 119890(minus(119875119905+119887))

(5)

When 119905 = 0 119862 will be 0 too so we have

119862119904= 119890(minus119887)

119887 = minus ln (119862119904)

(6)

Then the final equation is

119862 = 119862119904minus 119890(minus119875119905) lowast 119862

119904= 119862119904(1 minus 119890(minus119875119905)) (7)

or

119862 = 119862119904(1 minus 119890(minus((119863119878)(119881ℎ))lowast119905)) (8)

Considering that the data we used reflect the drug releasepercentage over time and 119862 = 119898119881 = 119875 lowast 119898(total)119881 (119875is the drug release percentage and 119898(total) is the total drugamount) the equation can be further transformed into

119875 =119881

119898 (total)lowast 119862119904lowast (1 minus 119890(((minus119863119878)(119881ℎ))lowast119905)) (9)

While 119875 ge 0 and 119875 le 1 119905 ge 0

3 Results and Discussion

31 The Fourier Transform Infrared (FT-IR) SpectroscopyThe transmission infrared spectra of aspirin loaded CS-NPsare shown in comparison with chitosan-free NPs and pureaspirin in Figure 1 Compared with chitosan free NPs andpure aspirin the transmission infrared spectra of aspirinloaded CS-NPs is obviously different explaining that aspirinhave already been loaded to the CS-NPs

(a) Aspirin

(b) Chitosan

(c) Aspirin loaded CS-NPs

44000 4000 3500 3000 2500 2000 1500 1000 500

Wavenumber (cmminus1)

Figure 1 Transmission infrared spectra of aspirin chitosan andCS-NPs

60

50

40

30

20

30 60 90 120 150 180

Temperature (∘C)

Hea

t flow

endo

up

(mW

)

(a)

(b)

(c)

(d)

(a) Pure ASA

exo

(b) TPP at 25mgmL(c) TPP at 30mgmL(d) TPP at 40mgmL

Figure 2 DSC thermograms of (a) pure aspirin and 08mgmLaspirin loaded CS-NPs with (a) pure ASA (b) 25mgmL TPP (c)30mgmL TPP and (d) 40mgmL TPP

32 Differential Scanning Calorimetry (DSC) The DSC ther-mograms of aspirin and aspirin loaded CS-NPs with differentTPP concentrations are shown in Figure 2 The meltingpeak of pure aspirin (Figure 2(a)) occurred at 139∘C Amelting peak of aspirin loaded CS-NPs with 25mgmL TPP(Figure 2(b)) was found at 139∘C which is due to the physicalconnection between aspirin and CS-NPs and some littlepeaks were found during 150ndash160∘C which was probably dueto the chemical connection between aspirin and CS-NPsThe thermogram of aspirin loaded CS-NPs with 30mgmLTPP (Figure 2(c)) showed some little peaks during 150ndash160∘Cand the melting peak of aspirin at 139∘C was not presentconfirming the aspirin in CS-NPs at a molecular level with30mgmL TPP A thermogram of aspirin loaded CS-NPswith 40mgmL TPP (Figure 2(d)) showed a clear meltingpeak at 139∘C which is almost the same with Figure 2(b) andthe little peaks during 150ndash160∘C disappeared indicating thephysical connection between aspirin and CS-NPs


100nm

(a)

50nm

(b)

40nm

(c)

200nm

(d)

150nm

(e)

100nm

(f)

Figure 3 TEM of aspirin loaded CS-NPs ((a) ASA (04mgmL) and TPP (25mgmL) (b) ASA (08mgmL) and TPP (25mgmL) (c)ASA (12mgmL) and TPP (25mgmL) (d) ASA (04mgmL) and TPP (50mgmL) (e) ASA (08mgmL) and TPP (50mgmL) (f) ASA(12mgmL) and TPP (50mgmL))

33 Morphology Study The transmission electron photomi-crographs of aspirin loadedCS-NPs are illustrated in Figure 3The results revealed that the morphology of the preparedNPs were spherical in shape with a smooth surface But theparticle size is not symmetrical in distribution and someconglutination and congregation of NPs were found Figure 3shows that the particle size of CS-NPs was different with bothconcentrations of aspirin and TPP Photos (a) (b) and (c) inFigure 3 show the morphology of NPs which were preparedwith the same TPP and different aspirin concentrationsIt was found that the particle size decreased slightly withincreasing aspirin concentration The same phenomenonwas found in photos (d) (e) and (f) It revealed that theconcentration of aspirin has little influence on particle size ofCS-NPs By comparing photos (a) and (d) (b) and (e) and (c)and (f) it was observed that the particle size increased greatlywith increasing TPP and constant aspirin concentration Itproved that the TPP concentration has great influence onparticle size of CS-NPs

34 Drug Loading Efficiency

341 Effect of TPP Concentration The aspirin encapsulationefficiency from 37 to 90 was significantly affected bythe TPP concentration in Figure 4 and the higher theconcentration is the higher the encapsulation efficiency isAlso the loading capacity increased from 13 to 50 ingeneral by increasing the TPP concentration from 25 to50mgmL in Figure 5 The result indicated that the drugloading capacity of CS-NPs increased with increasing TPPconcentration

30

40

50

60

70

80

90

04 06 08 1 12ASA concentration (mgmL)

ASA

enca

psul

atio

n (

)

TPP at 25mgmLTPP at 30mgmL


Figure 4 Aspirin encapsulation efficiency with different concentra-tion of TPP and ASA

342 Effect of Aspirin Concentration As to different TPPconcentration the encapsulation efficiency and loadingcapacity of aspirin showed different variation trend in Figures4 and 5 In Figure 4 the line of the TPP concentration =


10

20

30

40

50


ASA

load

ing

()



Figure 5 Aspirin loading capacity with different concentration ofTPP and ASA

25mgmL shows that increasing the initial aspirin concen-tration will decrease the encapsulation efficiency of aspirinWhen the initial concentration of aspirin was 08mgmLthe line of the TPP concentration = 30mgmL shows theencapsulation efficiency is at the bottom while both lines ofthe TPP concentration gt 30mgmL show the encapsulationefficiencies are at the top Moreover the variation of loadingcapacity shown in Figure 5 was found to be similar to thatshown in Figure 4

343 Effect of Chitosan Molecular Weight As shown inFigures 6 and 7 the influence of chitosan Mw on the drugloading efficiency of aspirin was not significant when theconcentration of aspirin was lower than 10mgmL Theencapsulation efficiency increased by almost 2 and theloading capacity increased by about 1 with increasingchitosan Mw When the concentration of aspirin was higherthan 10mgmL the drug loading efficiency had a greatincreasewith higher chitosanMw Increasing chitosanMwupto 670 kDamade encapsulation efficiency and loading capac-ity increase by 75 and 5 respectivelyThe TPP concentra-tion used in this study is 25mgmL

35 In Vitro Drug Release The aspirin in vitro releasebehavior of chitosan is shown in Figures 8-9 All releasebehavior lasted for more than 12 hours It indicated thatthe CS-NPs showed a good performance of drug controlledrelease Furthermore all release profiles of the nanoparticlesexhibit a small burst release in the first 1 h and then slowrelease at constant but different rateThe results suggest that itis possible to control the release rate of aspirin by adjustingthe concentration of aspirin and molecular parameters of

35

40

45

50

55

60

65

70


ASA

enca

psul

atio

n (

)

CS Mw at 210 kDaCS Mw at 670kDa

Figure 6 Aspirin encapsulation efficiency with different chitosanMw

12

14

16

18

20

22

24

26

28

02 04 06 08 1 12 14ASA concentration (mgmL)

ASA

load

ing

()


Figure 7 Aspirin loading capacity with different chitosan Mw

chitosan In contrast microsphere-based drug delivery sys-tem may have a higher percentage of burst release thereforehigher dosage is generally needed [35]

Moreover we noticed that the drug release behaviorwith different TPP concentration is different when the initialaspirin concentration is 08mgmL As shown in Figures 8-9 the line of ASA 10mgL and 08mgL showed a releaseplateau during 2 to 6 hours when the TPP concentration is


0

10

20

30

40

50

60

70

80

0 1 2 3 4 5 6 7 8 9 10 11 12Time (h)

Cum

ulat

ive r

elea

se (

)

ASA at 10mgmLASA at 08mgmL

ASA at 06mgmL

Figure 8 Drug release from CS-NPs when the TPP concentrationis 25mgmL with different initial aspirin concentration

lower than or equal to 30mgmL As the TPP concentrationis higher than 30mgmL the release plateau disappeared asshown in Figure 9 Comparing the three lines of 10mgL to06mgL to the other two lines (the aspirin concentration is10 and 06mgmL) respectively in Figures 8-9 we found thatthe sequence of the drug release speed is ASA 10mgLgtASA06mgL gt ASA 08mgL

36 Mathematical Simulation Studies Using the data in Fig-ures 8-9 we find that the Noyes-Whitney equation approachcan be used to interpret the original release curve First itexplains why the release rate will be slowing down With theincrease of 119905 the growing rate of 119890(((minus119863119878)(119881ℎ))lowast119905) will dropSecond it explains why there is an upper limit for the finalrelease percentage It will approach zero when 119905 is infinite so119875 will reach its maximum value 119881119898(total) lowast 119862

119904

In addition the formula also reflects some propertieswhich are not exhibited in the experiment The dissolutionprocessmayhave twodifferent end resultsOne is to approachcertainmaximum value as shown in the experiments and theother is to continue the drug release process along the curveeventually release completely so that 119875 = 1 The key decisionfactor for the difference between these two results is the totaldosage119898(total) If the total dosage is small119898(total)119881 lt 119862

119904

and t is infinite themaximum value of 119875 = 119881119898(total)lowast119862119904is

greater than 1 and the drug will be completely released Oth-erwise it approaches the maximum value It is worth men-tioning that119898(total)119881 is the concentration of the drug whenit is fully released

This formula also tells us when a drug is released insidethe body because of the very large amount of body fluid that

0

5

10

15

20

25

30

35

40

45

0 1 2 3 4 5 6 7 8 9 10 11 12Time (h)

Cum

ulat

ive r

elea

se (

)


ASA at 06mgmL


is 119881 is large the value of 119875 will soon reach its maximumTherefore the drug release profile in the body is mostly thefirst part of the experiment curve and is almost linear Ifwe want to control its speed according to the equation themeasures we may take include reducing the surface area ofthe drug or changing the nature of the drug itself

We also tested two other mathematical models theprobabilistic Boolean network model and the polynomialfitting The initial purpose of designing the probabilisticBoolean network model [36 37] is intended to be used inbiological networks in which during biological activities thechanges among each state are dependent on certain factors Itsbasic idea is the Markov chain that is the state in which thenetwork will be at the next moment depends on the knownstates at this moment Each of the network nodes representsa state of the actual biological events The simulation resultsobtained from the probabilistic Boolean network model arenot satisfactory We first built a state transition table (notshown) According to the table all the state transitions arestiff and determined (because all nonzero values are 1) whichdoes notmeet our expectationsThere are two possible expla-nations for thisThis model is most suitable for discrete statesrather than continuous values and this model requires a largeamount of raw data Although we could forcibly discrete thecontinuous values all the data are still concentrated in certainkind of state Most of the positions are 0 and only a handfulhas nonzero values Therefore to improve the usefulnessof the probabilistic Boolean network in the drug releasestudies we may have to either further refine the data classifi-cation so that there are more differences that can be reflectedamong them or increase the amount of raw data


Results for our polynomial fittings are not ideal eitherPolynomial fitting is the modeling process in which a seriesof polynomial functions is used It is based on the Taylorformula inwhich any kind of expression can be approximatedby the sum of infinite polynomial functions as shown below

119891 (119909) = 119891 (119886) +1198911015840 (119886)

1(119909 minus 119886) +

119891(2) (119886)

2(119909 minus 119886)

2

+ sdot sdot sdot +119891(119899) (119886)

119899(119909 minus 119886)

119899 + 119877119899(119909)

(10)

Therefore it is always possible to improve the fittingaccuracy by adding more items The purpose of the poly-nomial fitting is to get the relationship between the drugconcentrations versus time The first step is to determineusing which mathematical formula to fit the individual dataWe tried 119862 = 119905119909 and to determine at what value of 119909 therelationship between the drug concentration and time ismostclosely mirroring the experiment data Our simulation (datanot shown) indicates that when the number of power is at13 to 14 or so the result is the closest to the experiment dataHowever all the results for other power values are in fact closeto experiment data too And considering that this is just forone experiment we cannot draw the conclusion that it is opti-mal Even if this is indeed the numerically optimal solutionit may not be able to explain the mathematical laws behindthis physical process Therefore the conclusions obtainedby this method are not very convincing To improve thecredibility of polynomial fitting we need a lot of data toimprove Even so it cannot solve a more fundamental prob-lem Is this the scientificwayDoes it reflect themathematicalrelationship behind the physical processes Therefore to usethismodelingmethod we not only need a lot of data to derivean appropriate equation of the fitting form but also need datato validate the model

4 Conclusions

The drug-polymer delivery system of aspirinCS-NPsexhibits well sustained release performance Encapsulationefficiency loading capacity thermal behavior and drugrelease behavior of aspirin loaded CS-NPs were affectedby TPP and aspirin concentration as well as the molecularweight of chitosan Physicochemical characterization ofall CS-NPs loaded with aspirin could reveal the drugphysical state and drug-polymer interaction The data ofDSC demonstrated the drug-polymer interaction betweenaspirin and CS-NPs The drug-polymer interaction affectedthe release of aspirin from the CS-NPs resulting in sustainedrelease action It was summarized that chitosan could interactwith negatively charged drugs when encapsulated into CS-NPs We also use mathematical models to simulate the drugrelease process and the Noyes-Whitney equation approachis successful and can well explain the experiment data weobtainedThis may provide us with a more convenient way tovalidate our data and predict experiment results in the futurestudies

Conflict of Interests

The authors declare that there is no conflict of interestsregarding the publication of this paper

Acknowledgments

This work was financially supported by the Chinese NationalPostdoctoral Support Fund (2013M540370) the NationalNatural Science Foundation (20376045 20676079 21076124and 51173104) and the Nanometer Technology Program ofScience andTechnologyCommittee of Shanghai (0452nm037and 11nm0503500)

References

[1] L Lee E R Bates B Pitt J A Walton N Laufer and WW OrsquoNeill ldquoPercutaneous transluminal coronary angioplastyimproves survival in acute myocardial infarction complicatedby cardiogenic shockrdquo Circulation vol 78 no 6 pp 1345ndash13511988

[2] J Kaehler R Koester W Billmann et al ldquo13-year follow-up of the German angioplasty bypass surgery investigationrdquoEuropean Heart Journal vol 26 no 20 pp 2148ndash2153 2005

[3] P W Serruys H E Luijten K J Beatt et al ldquoIncidenceof restenosis after successful coronary angioplasty a time-related phenomenon A quantitative angiographic study in 342consecutive patients at 1 2 3 and 4monthsrdquoCirculation vol 77no 2 pp 361ndash371 1988

[4] S G Ellis G S Roubin S B King III J S Douglas Jr and WR Cox ldquoImportance of stenosis morphology in the estimationof restenosis risk after elective percutaneous transluminal coro-nary angioplastyrdquo The American Journal of Cardiology vol 63no 1 pp 30ndash34 1989

[5] E R Edelman and C Rogers ldquoPathobiologic responses tostentingrdquoTheAmerican Journal of Cardiology vol 81 no 7A pp4Endash6E 1998

[6] W Ajun S Yan G Li and L Huili ldquoPreparation of aspirin andprobucol in combination loaded chitosan nanoparticles and invitro release studyrdquo Carbohydrate Polymers vol 75 no 4 pp566ndash574 2009

[7] E Minar A Ahmadi R Koppensteiner et al ldquoComparison ofeffects of high-dose and low-dose aspirin on restenosis afterfemoropopliteal percutaneous transluminal angioplastyrdquoCircu-lation vol 91 no 8 pp 2167ndash2173 1995

[8] P A Gum K Kottke-Marchant E D Poggio et al ldquoProfile andprevalence of aspirin resistance in patients with cardiovasculardiseaserdquo The American Journal of Cardiology vol 88 no 3 pp230ndash235 2001

[9] TMAllen andP R Cullis ldquoDrug delivery systems entering themainstreamrdquo Science vol 303 no 5665 pp 1818ndash1822 2004

[10] I Aranaz M Mengıbar R Harris et al ldquoFunctional characteri-zation of chitin and chitosanrdquo Current Chemical Biology vol 3no 2 pp 203ndash230 2009

[11] K V Harish Prashanth and R N Tharanathan ldquoChitinchitosan modifications and their unlimited applicationpotential-an overviewrdquo Trends in Food Science and Technologyvol 18 no 3 pp 117ndash131 2007

[12] M N V Ravi Kumar ldquoA review of chitin and chitosan applica-tionsrdquo Reactive and Functional Polymers vol 46 no 1 pp 1ndash272000


[13] O Felt P Buri and R Gurny ldquoChitosan a unique polysac-charide for drug deliveryrdquo Drug Development and IndustrialPharmacy vol 24 no 11 pp 979ndash993 1998

[14] A Di Martino M Sittinger and M V Risbud ldquoChitosana versatile biopolymer for orthopaedic tissue-engineeringrdquoBiomaterials vol 26 no 30 pp 5983ndash5990 2005

[15] E Reverchon and A Antonacci ldquoChitosan microparticlesproduction by supercritical fluid processingrdquo Industrial ampEngineering Chemistry Research vol 45 no 16 pp 5722ndash57282006

[16] T Kristmundsdottir K Ingvarsdottir and G SaemundsdottirldquoChitosan matrix tablets the influence of excipients on drugreleaserdquoDrugDevelopment and Industrial Pharmacy vol 21 no13 pp 1591ndash1598 1995

[17] L Illum ldquoChitosan and its use as a pharmaceutical excipientrdquoPharmaceutical Research vol 15 no 9 pp 1326ndash1331 1998

[18] J Kristl J Smid-Korbar E Struc M Schara and H RupprechtldquoHydrocolloids and gels of chitosan as drug carriersrdquo Interna-tional Journal of Pharmaceutics vol 99 no 1 pp 13ndash19 1993

[19] S A Agnihotri and T M Aminabhavi ldquoControlled release ofclozapine through chitosan microparticles prepared by a novelmethodrdquo Journal of Controlled Release vol 96 no 2 pp 245ndash259 2004

[20] W E Rudzinski and T M Aminabhavi ldquoChitosan as a carrierfor targeted delivery of small interfering RNArdquo InternationalJournal of Pharmaceutics vol 399 no 1-2 pp 1ndash11 2010

[21] K S V Krishna Rao B Vijaya Kumar Naidu M C S SubhaM Sairam and T M Aminabhavi ldquoNovel chitosan-based pH-sensitive interpenetrating network microgels for the controlledrelease of cefadroxilrdquo Carbohydrate Polymers vol 66 no 3 pp333ndash344 2006

[22] H H Sokker A M Abdel Ghaffar Y H Gad and A S AlyldquoSynthesis and characterization of hydrogels based on graftedchitosan for the controlled drug releaserdquo Carbohydrate Poly-mers vol 75 no 2 pp 222ndash229 2009

[23] S G Kumbar K S Soppimath and T M AminabhavildquoSynthesis and characterization of polyacrylamide-grafted chi-tosan hydrogel microspheres for the controlled release ofindomethacinrdquo Journal of Applied Polymer Science vol 87 no 9pp 1525ndash1536 2003

[24] S A Agnihotri and TM Aminabhavi ldquoChitosan nanoparticlesfor prolonged delivery of timolol maleaterdquo Drug Developmentand Industrial Pharmacy vol 33 no 11 pp 1254ndash1262 2007

[25] S C Angadi L S Manjeshwar and T M Aminabhavi ldquoStearicacid-coated chitosan-based interpenetrating polymer networkmicrospheres controlled release characteristicsrdquo Industrial ampEngineering Chemistry Research vol 50 no 8 pp 4504ndash45142011

[26] S C Angadi L S Manjeshwar and T M Aminabhavi ldquoCoatedinterpenetrating blendmicroparticles of chitosan and guar gumfor controlled release of isoniazidrdquo Industrial amp EngineeringChemistry Research vol 52 no 19 pp 6399ndash6409 2013

[27] P B Kajjari L S Manjeshwar and T M Aminabhavi ldquoNovelinterpenetrating polymer network hydrogel microspheres ofchitosan and poly(acrylamide)- grafted -guar gum for con-trolled release of ciprofloxacinrdquo Industrial amp Engineering Chem-istry Research vol 50 no 23 pp 13280ndash13287 2011

[28] S A Agnihotri N N Mallikarjuna and T M AminabhavildquoRecent advances on chitosan-based micro- and nanoparticlesin drug deliveryrdquo Journal of Controlled Release vol 100 no 1pp 5ndash28 2004

[29] Y Xu and Y Du ldquoEffect of molecular structure of chitosan onprotein delivery properties of chitosan nanoparticlesrdquo Interna-tional Journal of Pharmaceutics vol 250 no 1 pp 215ndash2262003

[30] Y Wu J Guo W Yang C Wang and S Fu ldquoPreparation andcharacterization of chitosan-poly(acrylic acid) polymer mag-netic microspheresrdquo Polymer vol 47 no 15 pp 5287ndash52942006

[31] J A Ko H J Park S J Hwang J B Park and J S LeeldquoPreparation and characterization of chitosan microparticlesintended for controlled drug deliveryrdquo International Journal ofPharmaceutics vol 249 no 1-2 pp 165ndash174 2002

[32] P Costa and J M Sousa Lobo ldquoModeling and comparisonof dissolution profilesrdquo European Journal of PharmaceuticalSciences vol 13 no 2 pp 123ndash133 2001

[33] A Dokoumetzidis and P Macheras ldquoA century of dissolutionresearch from Noyes and Whitney to the biopharmaceuticsclassification systemrdquo International Journal of Pharmaceuticsvol 321 no 1-2 pp 1ndash11 2006

[34] A Dokoumetzidis V Papadopoulou and P Macheras ldquoAnal-ysis of dissolution data using modified versions of Noyes-Whitney equation and the Weibull functionrdquo PharmaceuticalResearch vol 23 no 2 pp 256ndash261 2006

[35] D C Forbes and N A Peppas ldquoOral delivery of small RNA andDNArdquo Journal of Controlled Release vol 162 no 2 pp 438ndash4452012

[36] I Shmulevich E R Dougherty S Kim and W Zhang ldquoProb-abilistic Boolean networks a rule-based uncertainty model forgene regulatory networksrdquoBioinformatics vol 18 no 2 pp 261ndash274 2002

[37] I Shmulevich E R Dougherty and W Zhang ldquoGene per-turbation and intervention in probabilistic Boolean networksrdquoBioinformatics vol 18 no 10 pp 1319ndash1331 2002

Submit your manuscripts athttpwwwhindawicom

PainResearch and TreatmentHindawi Publishing Corporationhttpwwwhindawicom Volume 2014

The Scientific World JournalHindawi Publishing Corporation httpwwwhindawicom Volume 2014

Hindawi Publishing Corporationhttpwwwhindawicom

Volume 2014

ToxinsJournal of

VaccinesJournal of

Hindawi Publishing Corporation httpwwwhindawicom Volume 2014

Hindawi Publishing Corporationhttpwwwhindawicom Volume 2014

AntibioticsInternational Journal of

ToxicologyJournal of


StrokeResearch and TreatmentHindawi Publishing Corporationhttpwwwhindawicom Volume 2014

Drug DeliveryJournal of



Advances in Pharmacological Sciences

Tropical MedicineJournal of


Medicinal ChemistryInternational Journal of


AddictionJournal of



BioMed Research International

Emergency Medicine InternationalHindawi Publishing Corporationhttpwwwhindawicom Volume 2014


Autoimmune Diseases


Anesthesiology Research and Practice

ScientificaHindawi Publishing Corporationhttpwwwhindawicom Volume 2014

Journal of


Pharmaceutics


MEDIATORSINFLAMMATION

of


Although there has been considerable interest in devel-oping chitosan nanoparticles (NPs) as effective drug deliverydevices [6 13 28] a majority of these studies have dealtwith the preparation method of drug loaded CS-NPs Up todate the study of drug loaded chitosan nanoparticles wasfocused on release behavior of the drug from chitosan matrixnanoparticles Depending on the concentration deacetyla-tion degree and Mw of chitosan as well as the concentrationof sodium tripolyphosphate the liberation of drug from thechitosan nanoparticles varied from fast release to slow release[29ndash31] Therefore it is of great interest to investigate theinteraction mechanism of the factors which influenced therelease behavior of drug from CS-NPs

Pharmacokinetics is the study focusing on the releaseprofiles of drugs under different conditions Its most imme-diate application is to study the drug release process withinhuman body By pharmacokinetic studies we can determinethe best way for drug administration what is the suitabledosage form how many drugs per dose how many times thedrug needed to be given each day and its efficacy Thereforeit is important to set the right kinetic parameters for drugrelease Based on the experimental data of kinetic curves wemay sum up the mathematical model for the drug releaseprocess and thus be able to provide guidance for futureexperiments

In this study the chitosan nanoparticles (CS-NPs) wereobtained by ionic gelation technology based on assembly ofpositively charged chitosan and negatively charged sodiumtripolyphosphate (TPP) With aspirin (ASA) as the modeldrug the encapsulation efficiency loading capacity and drugrelease behavior in vitro were studied Morphology of theprepared aspirin CS-NPs was observed by transmission elec-tron microscopy (TEM) The characterization and thermalbehavior of aspirin in CS-NPs were studied by the Fouriertransform infrared (FT-IR) spectroscopy and differentialscanning calorimetry (DSC) The effect of the concentrationof TPP and ASA and molecular weight of chitosan werediscussed in detail In our computational simulations weused and tested three different mathematical models whichare probabilistic Boolean networkmodel themodel based onpolynomial fitting and the differential equation based on theNoyes-Whitney equation [32]

2 Materials and Methods

21 Materials Chitosan with different molecular weight(Mw 210KDa and 670KDa the degree of deacetylation was90)was obtained fromDacheng Biotech Co Ltd (WeifangChina) Aspirin was obtained from Lunan PharmaceuticalCo Ltd (Linyi China) Sodium tripolyphosphate (TPP) andother reagents were all analytical reagents grade Unlessotherwise stated the chitosan used in this research is mainly210 KDa

22 Preparation of Aspirin Loaded CS-NPs Chitosan withdifferent Mw was dissolved in 1 (vv) acidic aqueous solu-tion at the concentration of 2mgmL 10mg 15mg 25mgand 30mg of aspirin were added to the 25mL chitosan acidicsolution After dissolving completely Tween-80 (2 vv) was

added as a surfactant by using homogenizer at 500 rads for20min 10mL TPP solution with the different mass concen-tration (25mgmL 30mgmL 40mgmL 50mgmL) wasadded into the preformed aspirin chitosan solutions sepa-rately After cross-linking for 60min the aspirin loaded CS-NPs were washed with distilled water repeatedly and thenfreeze-dried

23 The Fourier Transform Infrared (FT-IR) SpectroscopyTransmission infrared spectra of CS-NPs loaded with variousconcentrations of aspirin pure aspirin and pure chitosanwere measured by using a Fourier transform infrared spec-trophotometer 430 (PEUSA) About 2mg of various sampleswas mixed with 100mg KBr and prepared pellets were usedfor studies

24 Differential Scanning Calorimetry (DSC) The DSC ther-mograms of pure aspirin and aspirin loaded CS-NPs wereprepared in the condition of 08mgmL aspirin and 25 30and 40mgmL TPP were recorded with a heating rate of20∘Cmin in nitrogen between 25∘C and 180∘C

25 Morphology Study The surface morphology of CS-NPswas observed by transmission electron microscopy (TEM)The CS-NPs solution was dropped on copper grids and driedovernight at room temperature for viewing

26 Evaluation of Drug Loaded Efficiency Aspirin encapsula-tion efficiency and loading capacity were studied by separat-ing CS-NPs from the aqueous medium containing free drugby ultracentrifugation with 9000 rpm at room temperaturefor 30min The amount of free aspirin was determined byUV spectrophotometer at 298 nm Dilutions of samples andcalibration curves were performed in phosphate bufferedsaline (pH 74) The aspirin encapsulation efficiency (EE)and the aspirin loading capacity (LC) of the process werecalculated from (1) and (2) indicated below

EE =(total amount of aspirin minus free aspirin)

total amount of aspirin times 100(1)

LC =(total amount of aspirin minus free aspirin)

nanoparticles weight times 100 (2)

27 In Vitro Drug Release Study The CS-NPs were collectedby ultracentrifugation at 9000 rpm for 30min They weredispersed in phosphate buffered saline (pH 74) at 37∘C undermagnetic stirring At various time points supernatants wereisolated by ultracentrifugation and 3mL sample was takenout instead of the same volume of freshmediumThe amountof released aspirin was analyzed with UV spectrophotometer(PE USA) at 298 nm

28 Mathematical Fitting Using the Noyes-Whitney Equa-tion The Noyes-Whitney equation was developed by somephysical chemists at the beginning of the 20th century to



119889119862

119889119905=119863119878

119881ℎ(119862119904minus 119862) (3)





119904minus119862) as 119875 = (119863119878)(119881ℎ)

so119889119862

(119862119904minus 119862)= 119875 lowast 119889119905 (4)



119862 = 119862119904minus 119890(minus(119875119905+119887))

(5)


119862119904= 119890(minus119887)

119887 = minus ln (119862119904)

(6)



119904= 119862119904(1 minus 119890(minus119875119905)) (7)

or



119875 =119881


While 119875 ge 0 and 119875 le 1 119905 ge 0



(a) Aspirin

(b) Chitosan


44000 4000 3500 3000 2500 2000 1500 1000 500



60

50

40

30

20

30 60 90 120 150 180

Temperature (∘C)

Hea

t flow

endo

up

(mW

)

(a)

(b)

(c)

(d)

(a) Pure ASA

exo





100nm

(a)

50nm

(b)

40nm

(c)

200nm

(d)

150nm

(e)

100nm

(f)





30

40

50

60

70

80

90


ASA

enca

psul

atio

n (

)






10

20

30

40

50


ASA

load

ing

()







35

40

45

50

55

60

65

70


ASA

enca

psul

atio

n (

)



12

14

16

18

20

22

24

26

28


ASA

load

ing

()






0

10

20

30

40

50

60

70

80

0 1 2 3 4 5 6 7 8 9 10 11 12Time (h)

Cum

ulat

ive r

elea

se (

)


ASA at 06mgmL




119904


119904




0

5

10

15

20

25

30

35

40

45

0 1 2 3 4 5 6 7 8 9 10 11 12Time (h)

Cum

ulat

ive r

elea

se (

)


ASA at 06mgmL






119891 (119909) = 119891 (119886) +1198911015840 (119886)

1(119909 minus 119886) +

119891(2) (119886)

2(119909 minus 119886)

2

+ sdot sdot sdot +119891(119899) (119886)

119899(119909 minus 119886)

119899 + 119877119899(119909)

(10)


4 Conclusions




Acknowledgments


References











































Volume 2014

ToxinsJournal of

VaccinesJournal of















AddictionJournal of






Autoimmune Diseases




Journal of


Pharmaceutics



of



119889119862

119889119905=119863119878

119881ℎ(119862119904minus 119862) (3)





119904minus119862) as 119875 = (119863119878)(119881ℎ)

so119889119862

(119862119904minus 119862)= 119875 lowast 119889119905 (4)



119862 = 119862119904minus 119890(minus(119875119905+119887))

(5)


119862119904= 119890(minus119887)

119887 = minus ln (119862119904)

(6)



119904= 119862119904(1 minus 119890(minus119875119905)) (7)

or



119875 =119881


While 119875 ge 0 and 119875 le 1 119905 ge 0



(a) Aspirin

(b) Chitosan


44000 4000 3500 3000 2500 2000 1500 1000 500



60

50

40

30

20

30 60 90 120 150 180

Temperature (∘C)

Hea

t flow

endo

up

(mW

)

(a)

(b)

(c)

(d)

(a) Pure ASA

exo





100nm

(a)

50nm

(b)

40nm

(c)

200nm

(d)

150nm

(e)

100nm

(f)





30

40

50

60

70

80

90


ASA

enca

psul

atio

n (

)






10

20

30

40

50


ASA

load

ing

()







35

40

45

50

55

60

65

70


ASA

enca

psul

atio

n (

)



12

14

16

18

20

22

24

26

28


ASA

load

ing

()






0

10

20

30

40

50

60

70

80

0 1 2 3 4 5 6 7 8 9 10 11 12Time (h)

Cum

ulat

ive r

elea

se (

)


ASA at 06mgmL




119904


119904




0

5

10

15

20

25

30

35

40

45

0 1 2 3 4 5 6 7 8 9 10 11 12Time (h)

Cum

ulat

ive r

elea

se (

)


ASA at 06mgmL






119891 (119909) = 119891 (119886) +1198911015840 (119886)

1(119909 minus 119886) +

119891(2) (119886)

2(119909 minus 119886)

2

+ sdot sdot sdot +119891(119899) (119886)

119899(119909 minus 119886)

119899 + 119877119899(119909)

(10)


4 Conclusions




Acknowledgments


References











































Volume 2014

ToxinsJournal of

VaccinesJournal of















AddictionJournal of






Autoimmune Diseases




Journal of


Pharmaceutics



of


100nm

(a)

50nm

(b)

40nm

(c)

200nm

(d)

150nm

(e)

100nm

(f)





30

40

50

60

70

80

90


ASA

enca

psul

atio

n (

)






10

20

30

40

50


ASA

load

ing

()







35

40

45

50

55

60

65

70


ASA

enca

psul

atio

n (

)



12

14

16

18

20

22

24

26

28


ASA

load

ing

()






0

10

20

30

40

50

60

70

80

0 1 2 3 4 5 6 7 8 9 10 11 12Time (h)

Cum

ulat

ive r

elea

se (

)


ASA at 06mgmL




119904


119904




0

5

10

15

20

25

30

35

40

45

0 1 2 3 4 5 6 7 8 9 10 11 12Time (h)

Cum

ulat

ive r

elea

se (

)


ASA at 06mgmL






119891 (119909) = 119891 (119886) +1198911015840 (119886)

1(119909 minus 119886) +

119891(2) (119886)

2(119909 minus 119886)

2

+ sdot sdot sdot +119891(119899) (119886)

119899(119909 minus 119886)

119899 + 119877119899(119909)

(10)


4 Conclusions




Acknowledgments


References











































Volume 2014

ToxinsJournal of

VaccinesJournal of















AddictionJournal of






Autoimmune Diseases




Journal of


Pharmaceutics



of


10

20

30

40

50


ASA

load

ing

()







35

40

45

50

55

60

65

70


ASA

enca

psul

atio

n (

)



12

14

16

18

20

22

24

26

28


ASA

load

ing

()






0

10

20

30

40

50

60

70

80

0 1 2 3 4 5 6 7 8 9 10 11 12Time (h)

Cum

ulat

ive r

elea

se (

)


ASA at 06mgmL




119904


119904




0

5

10

15

20

25

30

35

40

45

0 1 2 3 4 5 6 7 8 9 10 11 12Time (h)

Cum

ulat

ive r

elea

se (

)


ASA at 06mgmL






119891 (119909) = 119891 (119886) +1198911015840 (119886)

1(119909 minus 119886) +

119891(2) (119886)

2(119909 minus 119886)

2

+ sdot sdot sdot +119891(119899) (119886)

119899(119909 minus 119886)

119899 + 119877119899(119909)

(10)


4 Conclusions




Acknowledgments


References











































Volume 2014

ToxinsJournal of

VaccinesJournal of















AddictionJournal of






Autoimmune Diseases




Journal of


Pharmaceutics



of


0

10

20

30

40

50

60

70

80

0 1 2 3 4 5 6 7 8 9 10 11 12Time (h)

Cum

ulat

ive r

elea

se (

)


ASA at 06mgmL




119904


119904




0

5

10

15

20

25

30

35

40

45

0 1 2 3 4 5 6 7 8 9 10 11 12Time (h)

Cum

ulat

ive r

elea

se (

)


ASA at 06mgmL






119891 (119909) = 119891 (119886) +1198911015840 (119886)

1(119909 minus 119886) +

119891(2) (119886)

2(119909 minus 119886)

2

+ sdot sdot sdot +119891(119899) (119886)

119899(119909 minus 119886)

119899 + 119877119899(119909)

(10)


4 Conclusions




Acknowledgments


References











































Volume 2014

ToxinsJournal of

VaccinesJournal of















AddictionJournal of






Autoimmune Diseases




Journal of


Pharmaceutics



of



119891 (119909) = 119891 (119886) +1198911015840 (119886)

1(119909 minus 119886) +

119891(2) (119886)

2(119909 minus 119886)

2

+ sdot sdot sdot +119891(119899) (119886)

119899(119909 minus 119886)

119899 + 119877119899(119909)

(10)


4 Conclusions




Acknowledgments


References











































Volume 2014

ToxinsJournal of

VaccinesJournal of















AddictionJournal of






Autoimmune Diseases




Journal of


Pharmaceutics



of































Volume 2014

ToxinsJournal of

VaccinesJournal of















AddictionJournal of






Autoimmune Diseases




Journal of


Pharmaceutics



of





Volume 2014

ToxinsJournal of

VaccinesJournal of















AddictionJournal of






Autoimmune Diseases




Journal of


Pharmaceutics



of

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