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\def\RunningAuthor{Ganesh Narayan Sharma et al.}
\firstPage{532}
\articleType{Original Article}
\receivedDate{05.08.2019}
\acceptedDate{22.11.2019}
\revisedDate{16.11.2019}
\journalVolume{2020, 11}
\journalIssue{1}
\journalDoi{ijrps.v11i1.1855}
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\def\authorCount{5}
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\title{Box-Behnken design for development and characterization of nanostructured chitosan-based nanoparticles containing sulfasalazine for the treatment of ulcerative colitis}
\author{Ganesh~Narayan~Sharma\textsuperscript{*}\textsuperscript{1},  
            Praveen~Kumar~C~H\textsuperscript{2},  
            Birendra~Shrivastava\textsuperscript{3},  
            Kumar~B\textsuperscript{4}, 
            Arindam~Chatterjee\textsuperscript{5}~\\[5pt]{\textsuperscript{1}Department of Pharmacology, School of Pharmaceutical Sciences, Jaipur National University, Jagatpura-302017, Jaipur, India}~\\{\textsuperscript{2}Research Scholar, School of Pharmaceutical Sciences, Jaipur National University, Jagatpura-302017, Jaipur, India}~\\{\textsuperscript{3}School of Pharmaceutical Sciences, Jaipur National University, Jagatpura-302017, Jaipur, India}~\\{\textsuperscript{4}Department of Pharmaceutics, Ratnam Institute of Pharmacy, Muthukur (M), Nellore -524346, Andhra Pradesh, India}~\\{\textsuperscript{5}Department of Pharmaceutics, School of Pharmaceutical Sciences, Jaipur National University, Jagatpura-302017, Jaipur, India}}

\begin{abstract}
The present research was designed to improve the permeability of sulfasalazine by loading it into chitosan nanoparticles using the ionic gelation method. The process parameters were screened and optimized through Box-Behnken design. 13 formulations containing sulfasalazine chitosan-based nanoparticles (SCSNPs) were optimized using particle size, zeta potential, and \% encapsulation efficiency as responses.  The effect of every factor on responses was statistically analyzed using ANOVA and p-Value, and the correlation coefficient of all the responses was found to be {\textgreater}0.99 and {\textgreater}0.96 for optimized CSNPs and optimized SCSNPs respectively with p{\textless}0.05. From the predicted and observed values of responses, the optimized formulation (SCSNPs) has a particle size of 261\ensuremath{\pm}3.06 nm, with an encapsulation efficiency of 81.3\ensuremath{\pm}5.3\%. Morphology of the particles using scanning electron microscopy reveals nearly spherical shaped particles with a zeta potential of +41.4\ensuremath{\pm}0.5 mV. In-vitro studies acknowledge that sulfasalazine was released in a sustained manner for about 24 hrs in simulated colonic fluid pH 7 and phosphate buffer pH 7.4, when compared to a simulated colonic fluid at fed (pH 6) and fasted state (pH 7.8).
\end{abstract}\def\keywordstitle{Keywords}

\begin{keywords}Sulfasalazine,\newline Chitosan,\newline Ulcerative colitis,\newline Nanoparticles,\newline Box-Behnken design
\end{keywords}
\twocolumn[ \maketitle {\printKwdAbsBox}]
\makeatletter{\raggedright\textsuperscript{*}Corresponding Author\par Name:\ Ganesh~Narayan~Sharma~\\ Phone:\ +917791832857~\\  Email:\ ganeshmph@gmail.com
        \par}\vspace*{-11pt}\hrulefill\par{\fontsize{12}{14}\selectfont ISSN: 0975-7538}\par% 
        \textsc{DOI:}\ \href{https://doi.org/10.26452/\@journalDoi}{\textcolor{blue}{\underline{\smash{https://doi.org/10.26452/\@journalDoi}}}}\par%
        \vspace*{-11pt}\hrulefill\\{\fontsize{9.12}{10.12}\selectfont Production and Hosted by}\par{IJRPS |~\href{www.ijrps.com}{www.ijrps.com}}\par%
        \vspace*{-2pt}{\fontsize{9.12}{10.12}\selectfont\textcopyright\ \@copyrightYear\ $|$ All rights reserved.}\par%
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        \makeatother
        
    
\section{Introduction}
Sulfasalazine is an aminosalicylate used in the short term and long term treatment of ulcerative colitis (UC) \unskip~\citep{645097:15145497,645097:15145476}. Sulfasalazine is BCS class-IV drug with least permeability and solubility and have many challenges for the researchers working on novel targeted drug delivery system \unskip~\citep{645097:15145481}. Many researchers found that sulfasalazine has a maximum percentage of remission in UC when compared to Crohn's disease, which were grouped together as inflammatory bowel disease \unskip~\citep{645097:15145479,645097:15145468,645097:15145496}.


\bgroup
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\centering \makeatletter\IfFileExists{images/cb75135b-6560-4393-b6ad-0830ad63fd78-upicture1.png}{\includegraphics{images/cb75135b-6560-4393-b6ad-0830ad63fd78-upicture1.png}}{}
\makeatother 
\caption{\boldmath {2D Response surface Contour plots showing desirability between factors and responses (CSNPs)}}
\label{f-08826cdde287}
\end{figure*}
\egroup
Hence in the present research, a safe and effective dosage form was prepared to target sulfasalazine into the colon. For this intention, chitoson was selected as a polymer due to its biodegradability, biocompatibility, and ability to sustain the drug release in colonic pH \unskip~\citep{645097:15145478}. The presence of primary amine at the C-2 position of glucosamine residue made chitosan as an important polysaccharide for the fabrication of functional drug delivery. The ability of chitosan to release the drug in a sustained manner is due to deprotonation of amines that undergo interpolymer associations leading to film and gel formation \unskip~\citep{645097:15145477,645097:15145503}. Ionic gelation method was used in the fabrication of SCSNPs due to the avoidance of organic solvents with less shear forces \unskip~\citep{645097:15145498}. From the literature, it was found that sulfasalazine nanoparticles were prepared using albumin \unskip~\citep{645097:15145500} mixed alginate-N,O-carboxymethyl chitosan \unskip~\citep{645097:15145485}, polymer-coated mesoporous silica nanoparticles \unskip~\citep{645097:15145489}, etc.

The present research was accomplished to optimize and characterize chitosan-based sulfasalazine nanoparticles using Box{\textendash}Behnken design. The prepared chitosan-based sulfasalazine nanoparticles were filled in capsules, which were further coated with pH-sensitive polymers like cellulose acetate phthalate and eudragit-S 100 using polyethylene glycol-300 as a plasticizer.
    
\section{Materials and Methods}
Sulfasalazine was kindly gifted by SP Accure Labs, Hyderabad. Chitosan, Tripolyphosphate (TPP), and Phosphotidylcholine was purchased from Sigma Aldrich, Mumbai. Eudragit S-100, Cellulose acetate phthalate was obtained from Drugs India, Hyderabad. Tris and Bovine serum albumin from Thermo Fisher Scientific, Hyderabad. Sodium phosphate dibasic, Sodium hydroxide, Dichloromethane, Potassium phosphate monobasic was obtained from New Himalaya Scientific Company, Nellore. All the chemicals used were of analytical grade.

\textbf{Fabrication of blank chitosan nanoparticles using ionic gelation method}

Chitosan (CS) solutions were prepared by dissolving chitosan in aqueous acetic acid, according to the formulation of Table~\ref{tw-b04b2dc77042}. Tripolyphosphate (TPP) aqueous solutions were prepared according to the stated quantities and added dropwise to the chitosan solutions under stirring at 400rpm for 100 minutes using IKA stirrer for the preparation of chitosan nanoparticles (CSNPs). If necessary, pH was adjusted to 4.5 using Mettler Toledo pH meter with 1M aqueous sodium hydroxide solution in order to neutralize the excess acid. Then CS-TPP suspension was ultrasonicated using PCI analytics ultrasonicator for 5 minutes to produce CSNPs with controlled sizes. CS-TPP suspension was vaccum filtered using a Millipore vaccum pump, and produced CSNPs were dried.

The technique was optimized by Design-Expert software using Box Behnken design (BBD) with 13 run, 3-factor, 3-level, as shown in Table~\ref{tw-de939b3da3c1}. The design is suitable for investigating the quadratic response surface and constructing a second-order polynomial model. The dependent and independent variables with actual values were shown in the table. Temperature (35\ensuremath{^{0}}C), pH (4.5), and stirring speed (600rpm) was considered as constant variables for the preparation of chitosan-based nanoparticles.

\textbf{Fabrication of sulfasalazine loaded chitosan nanoparticles using ionic gelation method}

By using the optimized factors of blank CSNPs, formulation parameters for the preparation of sulfasalazine CSNPs were identified and screened through BBD. Thirteen batches were prepared according to the design, as shown in Table~\ref{tw-0e79bc9ae633} and evaluated for particle size, zeta potential, and entrapment efficiency (\%EE). The dependent and independent variables were shown in Table~\ref{tw-86fc4491b797}. The concentrations of chitosan, tripolyphosphate, and acetic acid was kept constant as per the optimized factors of blank CSNPs. The method of preparation is the same as the preparation of blank CSNPs.

\textbf{Morphology of CSNPs and SCSNPs}

Morphology and shape analysis of optimized formulations were evaluated using SEM (Hitachi S-4300 Microscope). The formulations were placed on the double-sided adhesive carbon tabs and adhered to aluminium stubs coated with gold/palladium alloy using the Emscope sputter coating system at 20\ensuremath{\mu}A for 1 minute under argon gas. An electronic beam at an accelerating voltage of 5-10kV was used at a working distance of 13-15mm. Using similar conditions, images were captured at several magnifications \unskip~\citep{645097:15145475}.


\begin{table*}[!htbp]
\caption{\boldmath {Variables with coded and actual values for Box behnken design (Formulation{\textendash} Blank CSNPs)} }
\label{tw-de939b3da3c1}
\def\arraystretch{1.1}
\ignorespaces 
\centering 
\begin{tabulary}{\linewidth}{p{\dimexpr.6134\linewidth-2\tabcolsep}p{\dimexpr.12370000000000001\linewidth-2\tabcolsep}p{\dimexpr.1461\linewidth-2\tabcolsep}p{\dimexpr.1168\linewidth-2\tabcolsep}}
\tbltoprule \rowcolor{kwdboxcolor}Independent variables & Low & Medium & High\\
\tblmidrule 
Coded Values &
  (-1) &
  (0) &
  (-1)\\
A = Chitosan (mg/ml) &
  2 &
  3.5 &
  5\\
B =Tripolyphosphate (mg/ml) &
  0.5 &
  1.25 &
  2\\
C = Acetic acid (mg/ml) &
  0.2 &
  0.5 &
  0.8\\
Dependent variables Constrains &
   &
   &
  \\
Y1 = Particle size (nm): Goal {\textendash}Minimize &
   &
   &
  \\
Y2 = Zeta potential (mV): Goal {\textendash} In range &
   &
   &
  \\
Y3= Poly dispersity Index: Goal {\textendash} Minimize &
   &
   &
  \\
\tblbottomrule 
\end{tabulary}\par 
\end{table*}

\begin{table*}[!htbp]
\caption{\boldmath {Formulations showing factors optimized by Box Behnken design (Formulation{\textendash} Blank CSNPs)} }
\label{tw-b04b2dc77042}
\def\arraystretch{1.1}
\ignorespaces 
\centering 
\begin{tabulary}{\linewidth}{p{\dimexpr.2967\linewidth-2\tabcolsep}p{\dimexpr.2275\linewidth-2\tabcolsep}p{\dimexpr.2621\linewidth-2\tabcolsep}p{\dimexpr.2137\linewidth-2\tabcolsep}}
\tbltoprule \rowcolor{kwdboxcolor}Formulation Code & Factor-1 \mbox{}\protect\newline Chitosan (mg/ml)  & Factor-2 \mbox{}\protect\newline Tripolyphosphate (mg/ml)  & Factor-3 \mbox{}\protect\newline Acetic acid (mg/ml) \\
\tblmidrule 
CNI -1 &
  5 &
  1.25 &
  0.8\\
CNI -2 &
  2 &
  2 &
  0.5\\
CNI -3 &
  3.5 &
  1.25 &
  0.5\\
CNI -4 &
  2 &
  1.25 &
  0.2\\
CNI -5 &
  2 &
  1.25 &
  0.8\\
CNI -6 &
  3.5 &
  2 &
  0.8\\
CNI -7 &
  3.5 &
  2 &
  0.2\\
CNI -8 &
  5 &
  0.5 &
  0.5\\
CNI -9 &
  3.5 &
  0.5 &
  0.8\\
CNI -10 &
  3.5 &
  0.5 &
  0.2\\
CNI -11 &
  5 &
  2 &
  0.5\\
CNI -12 &
  5 &
  1.25 &
  0.2\\
CNI -13 &
  2 &
  0.5 &
  0.5\\
\tblbottomrule 
\end{tabulary}\par 
\end{table*}

\begin{table*}[!htbp]
\caption{\boldmath {Variables with coded and actual values for Box Behnken design (Formulation{\textendash}Sulfasalazine CSNPs)} }
\label{tw-86fc4491b797}
\def\arraystretch{1.1}
\ignorespaces 
\centering 
\begin{tabulary}{\linewidth}{p{\dimexpr.60349999999999994\linewidth-2\tabcolsep}p{\dimexpr.11269999999999996\linewidth-2\tabcolsep}p{\dimexpr.1549\linewidth-2\tabcolsep}p{\dimexpr.1289\linewidth-2\tabcolsep}}
\tbltoprule \rowcolor{kwdboxcolor}Independent variables & Low & Medium & High\\
\tblmidrule 
Coded Values &
  (-1) &
  (0) &
  (-1)\\
A= Sulfasalazine (mg/ml) &
  1 &
  1.5 &
  2\\
B=Stirring Speed (rpm) &
  400 &
  600 &
  800\\
C= Temperature (0C) &
  10 &
  22.5 &
  35\\
Dependent variables Constrains &
   &
   &
  \\
Y1 = Particle size (nm) : Goal {\textendash} Minimize &
   &
   &
  \\
Y2 = Zeta potential (mV): Goal {\textendash} Maximize &
   &
   &
  \\
Y3= \% EE: Goal {\textendash} Maximize &
   &
   &
  \\
\tblbottomrule 
\end{tabulary}\par 
\end{table*}

\begin{table*}[!htbp]
\caption{\boldmath {Formulations showing factors optimized by Box Behnken design (Formulation{\textendash}Sulfasalazine CSNPs)} }
\label{tw-0e79bc9ae633}
\def\arraystretch{1.1}
\ignorespaces 
\centering 
\begin{tabulary}{\linewidth}{p{\dimexpr.2483\linewidth-2\tabcolsep}p{\dimexpr.2344\linewidth-2\tabcolsep}p{\dimexpr.2673\linewidth-2\tabcolsep}p{\dimexpr.25\linewidth-2\tabcolsep}}
\tbltoprule \rowcolor{kwdboxcolor}Formulation Code & Factor-1 \mbox{}\protect\newline Sulfasalazine (mg/ml)  & Factor-2 \mbox{}\protect\newline Stirring Speed (rpm)  & Factor-3 \mbox{}\protect\newline Temperature (0C) \\
\tblmidrule 
SCSNP -1 &
  2 &
  600 &
  10\\
SCSNP -2 &
  1 &
  600 &
  35\\
SCSNP -3 &
  2 &
  800 &
  22.5\\
SCSNP -4 &
  1.5 &
  400 &
  10\\
SCSNP -5 &
  1.5 &
  600 &
  22.5\\
SCSNP -6 &
  1 &
  600 &
  10\\
SCSNP -7 &
  1.5 &
  800 &
  10\\
SCSNP -8 &
  2 &
  400 &
  22.5\\
SCSNP -9 &
  1.5 &
  800 &
  35\\
SCSNP -10 &
  1 &
  400 &
  22.5\\
SCSNP -11 &
  2 &
  600 &
  35\\
SCSNP -12 &
  1 &
  800 &
  22.5\\
SCSNP -13 &
  1.5 &
  400 &
  35\\
\tblbottomrule 
\end{tabulary}\par 
\end{table*}

\begin{table*}[!htbp]
\caption{\boldmath {Optimization of blank chitosan based nanoparticles using ionic gelation technique: (Formulation {\textendash}Blank CSNPs) n=3} }
\label{tw-734a36827049}
\def\arraystretch{1.1}
\ignorespaces 
\centering 
\begin{tabulary}{\linewidth}{p{\dimexpr.2344\linewidth-2\tabcolsep}p{\dimexpr.2656\linewidth-2\tabcolsep}p{\dimexpr.25\linewidth-2\tabcolsep}p{\dimexpr.25\linewidth-2\tabcolsep}}
\tbltoprule \rowcolor{kwdboxcolor}Formulation Code & Response-1 (Y1) \mbox{}\protect\newline Particle size (nm)  & Response -2 (Y2) \mbox{}\protect\newline Zeta potential (mV)  & Response -3 (Y3) \mbox{}\protect\newline PDI \\
\tblmidrule 
CNI -1 &
  359\ensuremath{\pm}6.31 &
  59.19\ensuremath{\pm}0.69 &
  0.231\ensuremath{\pm}0.003\\
CNI -2 &
  196\ensuremath{\pm}4.34 &
  21.13\ensuremath{\pm}1.02 &
  0.186\ensuremath{\pm}0.002\\
CNI -3 &
  245\ensuremath{\pm}6.45 &
  33.43\ensuremath{\pm}0.53 &
  0.193\ensuremath{\pm}0.001\\
CNI -4 &
  180\ensuremath{\pm}7.32 &
  19.32\ensuremath{\pm}0.49 &
  0.173\ensuremath{\pm}0.004\\
CNI -5 &
  183\ensuremath{\pm}8.12 &
  20.11\ensuremath{\pm}1.16 &
  0.197\ensuremath{\pm}0.002\\
CNI -6 &
  293\ensuremath{\pm}9.11 &
  39.61\ensuremath{\pm}0.59 &
  0.216\ensuremath{\pm}0.004\\
CNI -7 &
  289\ensuremath{\pm}8.13 &
  35.51\ensuremath{\pm}0.89 &
  0.177\ensuremath{\pm}0.003\\
CNI -8 &
  329\ensuremath{\pm}6.21 &
  49.12\ensuremath{\pm}1.32 &
  0.217\ensuremath{\pm}0.002\\
CNI -9 &
  231\ensuremath{\pm}6.34 &
  31.21\ensuremath{\pm}1.11 &
  0.232\ensuremath{\pm}0.003\\
CNI -10 &
  219\ensuremath{\pm}7.22 &
  26.17\ensuremath{\pm}1.34 &
  0.189\ensuremath{\pm}0.003\\
CNI -11 &
  392\ensuremath{\pm}3.45. &
  62.21\ensuremath{\pm}0.67 &
  0.206\ensuremath{\pm}0.002\\
CNI -12 &
  347\ensuremath{\pm}5.45 &
  52.31\ensuremath{\pm}0.78 &
  0.181\ensuremath{\pm}0.006\\
CNI -13 &
  173\ensuremath{\pm}6.34 &
  16.12\ensuremath{\pm}0.65 &
  0.192\ensuremath{\pm}0.003\\
\tblbottomrule 
\end{tabulary}\par 
\end{table*}

\begin{table*}[!htbp]
\caption{\boldmath {Summary of regression analysis of the responses (CSNPs)} }
\label{tw-0843e371e3b4}
\def\arraystretch{1.1}
\ignorespaces 
\centering 
\begin{tabulary}{\linewidth}{p{\dimexpr.24419999999999998\linewidth-2\tabcolsep}p{\dimexpr.0758\linewidth-2\tabcolsep}p{\dimexpr.16\linewidth-2\tabcolsep}p{\dimexpr.16\linewidth-2\tabcolsep}p{\dimexpr.16\linewidth-2\tabcolsep}p{\dimexpr.20\linewidth-2\tabcolsep}}
\tbltoprule \rowcolor{kwdboxcolor}Quadratic model & R2 & Adjusted R2 & SD & Adequate Precision & p-value\\
\tblmidrule 
Response-1 \mbox{}\protect\newline Particle size (nm)  &
  0.99 &
  0.98 &
  9.57 &
  27.19 &
  0.0020\\
Response -2 \mbox{}\protect\newline Zeta potential (mV)  &
  0.99 &
  0.99 &
  0.51 &
  98.00 &
  0.0001\\
Response -3 PDI &
  0.99 &
  0.97 &
  0.0028 &
  24.36 &
  0.0342\\
\tblbottomrule 
\end{tabulary}\par 
\end{table*}

\bgroup
\fixFloatSize{images/a5d17eff-e85e-4f0b-a8d1-196d2762296e-upicture2.png}
\begin{figure}[!htbp]
\centering \makeatletter\IfFileExists{images/a5d17eff-e85e-4f0b-a8d1-196d2762296e-upicture2.png}{\includegraphics{images/a5d17eff-e85e-4f0b-a8d1-196d2762296e-upicture2.png}}{}
\makeatother 
\caption{\boldmath {3D Response surface plots showing factors with particle size (CSNPs)}}
\label{f-d1ca111fc68c}
\end{figure}
\egroup

\bgroup
\fixFloatSize{images/de96fd1c-b608-4706-871d-44a83fe908ee-upicture3.png}
\begin{figure}[!htbp]
\centering \makeatletter\IfFileExists{images/de96fd1c-b608-4706-871d-44a83fe908ee-upicture3.png}{\includegraphics{images/de96fd1c-b608-4706-871d-44a83fe908ee-upicture3.png}}{}
\makeatother 
\caption{\boldmath {3D Response surface plots showing factors with zeta potential (CSNPs)}}
\label{f-c5b7fb214e7c}
\end{figure}
\egroup

\bgroup
\fixFloatSize{images/b3fa5616-41b9-40fd-8f26-44ea876c12fe-upicture4.png}
\begin{figure}[!htbp]
\centering \makeatletter\IfFileExists{images/b3fa5616-41b9-40fd-8f26-44ea876c12fe-upicture4.png}{\includegraphics{images/b3fa5616-41b9-40fd-8f26-44ea876c12fe-upicture4.png}}{}
\makeatother 
\caption{\boldmath {3D Response surface plots showing factors with PDI (Polydispersity index) (CSNPs)}}
\label{f-98737e1550df}
\end{figure}
\egroup

\bgroup
\fixFloatSize{images/dee88b02-78ec-479f-8ccf-0d1f413c922a-upicture5.png}
\begin{figure}[!htbp]
\centering \makeatletter\IfFileExists{images/dee88b02-78ec-479f-8ccf-0d1f413c922a-upicture5.png}{\includegraphics{images/dee88b02-78ec-479f-8ccf-0d1f413c922a-upicture5.png}}{}
\makeatother 
\caption{\boldmath {Average particle size of Optimized formulation (blank CSNPs)}}
\label{f-0f76cf628e12}
\end{figure}
\egroup
\textbf{Particle size and (Poly Dispersity Index) PDI}

Freshly prepared nanoparticles of sulfasalazine were diluted 200 times with deionized water and measured the particle size, PDI, and zeta potential using Malvern zeta sizer nano (ZS90). The average particle size was measured by dynamic light scattering at an angle of 90\ensuremath{^{0}}. The properties of dispersion and stability of nanoparticles was also measured using the same instrument. All the measurements were done in triplicate at 25\ensuremath{^{0}}C \unskip~\citep{645097:15145500,645097:15145487,645097:15145493,645097:15145470}.

\textbf{Entrapment efficiency (\%EE)}

The amount of sulfasalazine encapsulated in the nanoparticles were determined by separating the free drug using ultracentrifugation (Remi centrifuge). The formulations were centrifuged at around 18000rpm for 40min. The supernatant was collected, and the concentration of sulfasalazine incorporated in the formulations was analyzed separately using Shimadzu UV Spectrophotometer at 359 nm \unskip~\citep{645097:15145486,645097:15145488}.
\begin{eqnarray*}\%EE\;=\;(S_a-\;S_b)/S_a\ast100 \end{eqnarray*}
S\ensuremath{_{a}}= Total amount of drug in the system, S\ensuremath{_{b}}= Amount of drug in supernatant after centrifugation

\textbf{FT-IR studies for optimized formulation}

Cross-linking reaction between a phosphoric group of tripolyphosphate (TPP) and an amino group of chitosan was analyzed to confirm using Perkin Elmer spotlight 200i ft-ir. Homogenously dried formulation was used to prepare the KBr pellet, where the powder was compressed under vaccum using a round flat face punch. Samples were scanned from about 4000-400 cm\ensuremath{^{-1 }}\unskip~\citep{645097:15145491}.

\textbf{Powder X-ray Diffraction (PXRD)}

Information regarding crystal lattice arrangements and the degree of crystallinity of optimized formulations were analyzed using PXRD. In order to analyze the physical state, PXRD spectra of dried nanoparticles were recorded at room temperature using Arex X-ray diffractometer with a voltage of 3Kv, 5Ma current with a scanning speed of 40/min. Samples were scanned from 0-600 (2\ensuremath{\Theta }) range with a step interval of 0.1 seconds \unskip~\citep{645097:15145490}.

\textbf{In-Vitro Drug release}

Drug release was performed in-vitro using a dialysis bag method. Dialysis bags were soaked in deionized water overnight prior to the experiment. Nanoparticle dispersion of 2ml is placed in the dialysis bag of 2000 Da and fixed two ends with the help of clamps. The bags were transferred into 250ml phosphate buffer pH 7.4 and in simulated colonic fluid (Fed and Fasted state) kept at 35\ensuremath{\pm}0.5\ensuremath{^{0}}C for the determination of drug release. Samples were withdrawn at specified time intervals for about 24 Hrs. At predetermined time intervals, 1ml of the sample was withdrawn by adding the fresh buffer/fresh simulated colonic fluid. The samples were analyzed using UV spectrophotometry at 359nm. All the measurements were done in triplicate. By using various kinetic models, the mechanism of drug release was noted based on R\ensuremath{^{2}} and `n' value \unskip~\citep{645097:15145469}.


\bgroup
\fixFloatSize{images/85ecb333-4c17-462b-84c6-06fd30e14d1a-upicture6.png}
\begin{figure*}[!htbp]
\centering \makeatletter\IfFileExists{images/85ecb333-4c17-462b-84c6-06fd30e14d1a-upicture6.png}{\includegraphics{images/85ecb333-4c17-462b-84c6-06fd30e14d1a-upicture6.png}}{}
\makeatother 
\caption{\boldmath {2D Response surface Contour plots showing desirability between factors andresponses (Sulfasalazine CSNPs)}}
\label{f-dbe5d25af019}
\end{figure*}
\egroup

\begin{table*}[!htbp]
\caption{\boldmath {Comparisonof predicted and observed values of blank CSNPs} }
\label{tw-6b381e31b4de}
\def\arraystretch{1.1}
\ignorespaces 
\centering 
\begin{tabulary}{\linewidth}{p{\dimexpr.2222\linewidth-2\tabcolsep}p{\dimexpr.2041\linewidth-2\tabcolsep}p{\dimexpr.2153\linewidth-2\tabcolsep}p{\dimexpr.1681\linewidth-2\tabcolsep}p{\dimexpr.1903\linewidth-2\tabcolsep}}
\tbltoprule \rowcolor{kwdboxcolor}Confirmation Location & Chitosan (A) & TPP (B) & \multicolumn{2}{p{\dimexpr(.3584\linewidth-2\tabcolsep)}}{\cAlignHack Acetic acid (C)}\\
\rowcolor{kwdboxcolor} & 3.30 & 1.36 & \multicolumn{2}{p{\dimexpr(.3584\linewidth-2\tabcolsep)}}{\cAlignHack 0.2}\\
\tblmidrule 
Response &
  Predicted Value &
  Observed value &
  Residuals &
  *Bias \%\\
Particle size (nm) &
  238.64 &
  233\ensuremath{\pm}13.3 &
  -5.64 &
  2.42\\
Zeta potential (mV) &
  30 &
  32.01\ensuremath{\pm}3.11 &
  2.1 &
  -6.27\\
PDI &
  0.175 &
  0.171\ensuremath{\pm}0.004 &
  -0.004 &
  2.33\\
\tblbottomrule 
\end{tabulary}\par 
\end{table*}

\begin{table*}[!htbp]
\caption{\boldmath {Optimization of sulfasalazine chitosan based nanoparticles using ionic gelation technique: (Formulation {\textendash}Sulfasalazine CSNPs)} }
\label{tw-f508987bc04b}
\def\arraystretch{1.1}
\ignorespaces 
\centering 
\begin{tabulary}{\linewidth}{p{\dimexpr.2171\linewidth-2\tabcolsep}p{\dimexpr.25869999999999997\linewidth-2\tabcolsep}p{\dimexpr.2742\linewidth-2\tabcolsep}p{\dimexpr.25\linewidth-2\tabcolsep}}
\tbltoprule \rowcolor{kwdboxcolor}Formulation Code & Response-1 (Y1) \mbox{}\protect\newline Particle size (nm)  & Response -2 (Y2) \mbox{}\protect\newline Zeta potential (mV)  & Response -3 (Y3) \mbox{}\protect\newline \% EE \\
\tblmidrule 
SCSNP -1 &
  354\ensuremath{\pm}9.34 &
  33.63\ensuremath{\pm}2.31 &
  81.93\ensuremath{\pm}1.78\\
SCSNP -2 &
  239\ensuremath{\pm}8.13 &
  33.56\ensuremath{\pm}3.22 &
  74.13\ensuremath{\pm}1.30\\
SCSNP -3 &
  319\ensuremath{\pm}9.54 &
  24.13\ensuremath{\pm}2.89 &
  67.89\ensuremath{\pm}1.34\\
SCSNP -4 &
  283\ensuremath{\pm}8.32 &
  34.59\ensuremath{\pm}1.86 &
  80.11\ensuremath{\pm}2.31\\
SCSNP -5 &
  289\ensuremath{\pm}9.15 &
  43.21\ensuremath{\pm}1.79 &
  84.17\ensuremath{\pm}1.45\\
SCSNP -6 &
  253\ensuremath{\pm}8.18 &
  31.39\ensuremath{\pm}2.78 &
  76.15\ensuremath{\pm}1.32\\
SCSNP -7 &
  316\ensuremath{\pm}7.21 &
  23.16\ensuremath{\pm}3.41 &
  66.73\ensuremath{\pm}1.31\\
SCSNP -8 &
  299\ensuremath{\pm}4.32 &
  41.16\ensuremath{\pm}3.33 &
  80.02\ensuremath{\pm}1.36\\
SCSNP -9 &
  293\ensuremath{\pm}8.34 &
  22.45\ensuremath{\pm}3.56 &
  64.19\ensuremath{\pm}1.34\\
SCSNP -10 &
  243\ensuremath{\pm}6.33 &
  36.32\ensuremath{\pm}2.78 &
  71.19\ensuremath{\pm}1.56\\
SCSNP -11 &
  286\ensuremath{\pm}7.82 &
  35.23\ensuremath{\pm}3.54 &
  80.67\ensuremath{\pm}2.31\\
SCSNP -12 &
  240\ensuremath{\pm}3.19 &
  21.31\ensuremath{\pm}3.36 &
  63.13\ensuremath{\pm}2.56\\
SCSNP -13 &
  276\ensuremath{\pm}6.14 &
  33.12\ensuremath{\pm}2.78 &
  78.16\ensuremath{\pm}3.12\\
\tblbottomrule 
\end{tabulary}\par 
\end{table*}

\begin{table*}[!htbp]
\caption{\boldmath {Summary of regression analysis of the responses (Sulfasalazine CSNPs)} }
\label{tw-78134f84ab91}
\def\arraystretch{1.1}
\ignorespaces 
\centering 
\begin{tabulary}{\linewidth}{p{\dimexpr.22459999999999997\linewidth-2\tabcolsep}p{\dimexpr.0816\linewidth-2\tabcolsep}p{\dimexpr.17380000000000003\linewidth-2\tabcolsep}p{\dimexpr.11040000000000001\linewidth-2\tabcolsep}p{\dimexpr.2546\linewidth-2\tabcolsep}p{\dimexpr.155\linewidth-2\tabcolsep}}
\tbltoprule \rowcolor{kwdboxcolor}Quadratic model & R2 & Adjusted R2 & SD & Adequate Precision & p-value\\
\tblmidrule 
Response-1 \mbox{}\protect\newline Particle size (nm)  &
  0.96 &
  0.86 &
  12.85 &
  9.63 &
  0.0472\\
Response -2 \mbox{}\protect\newline Zeta potential (mV)  &
  0.96 &
  0.87 &
  2.49 &
  10.10 &
  0.0398\\
Response -3  \mbox{}\protect\newline \% EE &
  0.98 &
  0.95 &
  1.50 &
  16.50 &
  0.0088\\
\tblbottomrule 
\end{tabulary}\par 
\end{table*}

\begin{table*}[!htbp]
\caption{\boldmath {Comparison of predicted and observed values of SCSNPs} }
\label{tw-e334a893a293}
\def\arraystretch{1.1}
\ignorespaces 
\centering 
\begin{tabulary}{\linewidth}{p{\dimexpr.2915\linewidth-2\tabcolsep}p{\dimexpr.22200000000000003\linewidth-2\tabcolsep}p{\dimexpr.21679999999999996\linewidth-2\tabcolsep}p{\dimexpr.16389999999999997\linewidth-2\tabcolsep}p{\dimexpr.1058\linewidth-2\tabcolsep}}
\tbltoprule \rowcolor{kwdboxcolor}Confirmation Location & Sulfasalazine (mg/ml)  & Stirring speed \mbox{}\protect\newline (RPM) & \multicolumn{2}{p{\dimexpr(.2697\linewidth-2\tabcolsep)}}{Temperature (0C) }\\
\rowcolor{kwdboxcolor} & 1.29 & 525.16 & \multicolumn{2}{p{\dimexpr(.2697\linewidth-2\tabcolsep)}}{23.14}\\
\tblmidrule 
Response &
  Predicted Value &
  Observed value &
  Residuals &
  *Bias \%\\
Particle size (nm) &
  269.61 &
  261\ensuremath{\pm}3.06 &
  -8 &
  3.29\\
Zeta potential (mV) &
  43.21 &
  41.4\ensuremath{\pm}0.5 &
  -1.81 &
  4.37\\
\% EE &
  82.83 &
  81.3\ensuremath{\pm}5.3 &
  -1.53 &
  1.88\\
\tblbottomrule 
\end{tabulary}\par 
\end{table*}

\begin{table*}[!htbp]
\caption{\boldmath {In-Vitro Drug release studies of sulfasalazine CSNPs (n=3) } }
\label{tw-be0441d30740}
\def\arraystretch{1.1}
\ignorespaces 
\centering 
\begin{tabulary}{\linewidth}{p{\dimexpr.10579999999999998\linewidth-2\tabcolsep}p{\dimexpr.2346\linewidth-2\tabcolsep}p{\dimexpr.2153\linewidth-2\tabcolsep}p{\dimexpr.2263\linewidth-2\tabcolsep}p{\dimexpr.218\linewidth-2\tabcolsep}}
\tbltoprule \rowcolor{kwdboxcolor}Time (Hrs) & \% Drug release \mbox{}\protect\newline (phosphate buffer pH 7.4) & \% Drug release \mbox{}\protect\newline simulated colonic fluid - pH 7 & \% Drug release \mbox{}\protect\newline simulated colonic fluid  \mbox{}\protect\newline (Fed State) - pH 6 & \% Drug release \mbox{}\protect\newline simulated colonic fluid (Fasted State) - pH 7.8\\
\tblmidrule 
1 &
  15.15\ensuremath{\pm}1.35 &
  15.20\ensuremath{\pm}0.82 &
  17.67\ensuremath{\pm}1.39 &
  14.10\ensuremath{\pm}0.82\\
2 &
  22.70\ensuremath{\pm}1.60 &
  21.67\ensuremath{\pm}1.50 &
  24.20\ensuremath{\pm}1.05 &
  17.93\ensuremath{\pm}0.55\\
3 &
  25.70\ensuremath{\pm}1.40 &
  25.10\ensuremath{\pm}1.06 &
  37.30\ensuremath{\pm}1.73 &
  23.17\ensuremath{\pm}1.33\\
4 &
  30.10\ensuremath{\pm}1.50 &
  30.20\ensuremath{\pm}1.49 &
  45.83\ensuremath{\pm}2.28 &
  28.67\ensuremath{\pm}0.81\\
5 &
  34.70\ensuremath{\pm}0.40 &
  36.10\ensuremath{\pm}0.44 &
  50.53\ensuremath{\pm}1.30 &
  33.40\ensuremath{\pm}1.06\\
6 &
  40.80\ensuremath{\pm}1.70 &
  40.10\ensuremath{\pm}0.92 &
  58.30\ensuremath{\pm}0.10 &
  38.23\ensuremath{\pm}0.64\\
7 &
  44.75\ensuremath{\pm}2.15 &
  43.77\ensuremath{\pm}0.74 &
  62.87\ensuremath{\pm}1.21 &
  42.13\ensuremath{\pm}0.40\\
8 &
  49.65\ensuremath{\pm}0.55 &
  50.67\ensuremath{\pm}1.25 &
  70.73\ensuremath{\pm}1.46 &
  47.13\ensuremath{\pm}1.01\\
9 &
  56.90\ensuremath{\pm}0.50 &
  56.33\ensuremath{\pm}1.79 &
  77.50\ensuremath{\pm}1.64 &
  52.10\ensuremath{\pm}0.66\\
10 &
  61.10\ensuremath{\pm}1.20 &
  60.73\ensuremath{\pm}0.72 &
  81.93\ensuremath{\pm}0.64 &
  57.83\ensuremath{\pm}0.67\\
11 &
  65.35\ensuremath{\pm}0.55 &
  65.87\ensuremath{\pm}1.21 &
  89.97\ensuremath{\pm}1.17 &
  62.60\ensuremath{\pm}1.42\\
12 &
  70.10\ensuremath{\pm}1.20 &
  69.83\ensuremath{\pm}0.50 &
  99.03\ensuremath{\pm}0.80 &
  65.30\ensuremath{\pm}0.46\\
16 &
  82.60\ensuremath{\pm}0.70 &
  82.13\ensuremath{\pm}1.53 &
  - &
  79.74\ensuremath{\pm}1.38\\
24 &
  99.95\ensuremath{\pm}1.45 &
  99.70\ensuremath{\pm}0.72 &
  - &
  91.40\ensuremath{\pm}0.96\\
\tblbottomrule 
\end{tabulary}\par 
\end{table*}

\begin{table*}[!htbp]
\caption{\boldmath {Drug release kinetics of optimized SCSNPs} }
\label{tw-75863411d3e3}
\def\arraystretch{1.1}
\ignorespaces 
\centering 
\begin{tabulary}{\linewidth}{p{\dimexpr.1962\linewidth-2\tabcolsep}p{\dimexpr.16070000000000004\linewidth-2\tabcolsep}p{\dimexpr.14040000000000001\linewidth-2\tabcolsep}p{\dimexpr.1427\linewidth-2\tabcolsep}p{\dimexpr.16\linewidth-2\tabcolsep}p{\dimexpr.20\linewidth-2\tabcolsep}}
\tbltoprule \rowcolor{kwdboxcolor}SCSNPs & Zero Order (R2) & First Order (R2) & Higuchi \mbox{}\protect\newline  (R2) & \multicolumn{2}{p{\dimexpr(.36\linewidth-2\tabcolsep)}}{Korsmeyer Peppas}\\
\rowcolor{kwdboxcolor} &  &  &  & (R2) & (n)\\
\tblmidrule 
SCSNPs - PBS (pH 7.4) &
  0.94 &
  0.74 &
  0.98 &
  0.98 &
  0.62\\
SCSNPs - SCF FED (pH 6) &
  0.98 &
  0.72 &
  0.97 &
  0.99 &
  0.69\\
SCSNPs - SCF FASTED (pH 7.8) &
  0.93 &
  0.98 &
  0.97 &
  0.98 &
  0.65\\
SCSNPs - SCF (pH 7) &
  0.94 &
  0.80 &
  0.98 &
  0.98 &
  0.63\\
\tblbottomrule 
\end{tabulary}\par 
\end{table*}

    
\section{Results and Discussion}
\textbf{Statistical analysis (Box-Behnken design)}

The results of Box-Behnken design were analyzed, and the utility of this statistical design resulted in providing considerable information to optimize the formulation. All the responses were fitted to the quadratic model, and compatibility of the model was verified by ANOVA, lack of fit, and co-efficient of determination (R\ensuremath{^{2}}). To optimize the responses, every response should be interconnected with each other, and a most supportive zone must be required for every response to exclude bias. Desirability function was supported by many kinds of literature to optimize the multiple responses \unskip~\citep{645097:15145483,645097:15145471}.

Blank chitosan nanoparticles were formulated to optimize the concentration of chitosan, TPP, and acetic acid based on the dependent variables like particle size, zeta potential, and polydispersity index (PDI). The results for the responses were shown in Table~\ref{tw-734a36827049}. With an increase in the concentration of chitosan and TPP, particle size and zeta potential was increased and vice versa. PDI increased linearly with increase in the concentration of acetic acid which was in acceptance according to the literature \unskip~\citep{645097:15145473,645097:15145474,645097:15145482}. P-Values for the responses y\ensuremath{_{1}}, y\ensuremath{_{2}}, y\ensuremath{_{3}} was found to be 0.002, 0.001, 0.034. Hence the quadratic model is best fitted for all the responses with p{\textless}0.05. Table~\ref{tw-0843e371e3b4} shows a summary of the regression analysis of all the responses. Polynomial Equations~(\ref{dfg-9ea69d9741c1}), (\ref{dfg-56a1fefea6e7}) and~(\ref{dfg-74a03a1cd980}) for Y\ensuremath{_{1,\ }} Y\ensuremath{_{2,\ }} Y\ensuremath{_{3,\ }} explains the significant model terms with p{\textless}0.05. The variables with negative values represent negative effects on responses. Based on the desirability function, interaction effects between two factors and confirmation location was predicted using 2D contour and 3D response surface graphs shown in Table~\ref{tw-6b381e31b4de} and Figures~\ref{f-08826cdde287}, \ref{f-d1ca111fc68c}, \ref{f-c5b7fb214e7c} and~\ref{f-98737e1550df}. Among the responses, Y\ensuremath{_{1}} and Y\ensuremath{_{3}} were set in minimize, whereas Y\ensuremath{_{2}} in range. Confirmation location for the optimized formulation was achieved at A = 3.30 mg/ml, B\ensuremath{_{}} = 1.36mg/ml, C\ensuremath{_{}} = 0.2mg/ml with Y\ensuremath{_{1}} = 238.64 nm, Y\ensuremath{_{2\ }} =\ensuremath{_{}} 30 mV and Y\ensuremath{_{3\ }} = 0.175. The average particle size of the optimized CSNPs was given in Figure~\ref{f-0f76cf628e12}. Observed values for the confirmation location was found to be close to the predicted values indicating that Box Behnken Design can be considered as the best tool in formulating sulfasalazine chitosan nanoparticles.


\bgroup
\fixFloatSize{images/72f86c96-4e1d-4686-9148-5818a73eeb9c-upicture7.png}
\begin{figure}[!htbp]
\centering \makeatletter\IfFileExists{images/72f86c96-4e1d-4686-9148-5818a73eeb9c-upicture7.png}{\includegraphics{images/72f86c96-4e1d-4686-9148-5818a73eeb9c-upicture7.png}}{}
\makeatother 
\caption{\boldmath {3D Response surface plots showing factors with particle size (SCSNPs)}}
\label{f-f634d30a173e}
\end{figure}
\egroup

\bgroup
\fixFloatSize{images/233c2485-3de1-4dc9-afbc-17853307bd3a-upicture8.png}
\begin{figure}[!htbp]
\centering \makeatletter\IfFileExists{images/233c2485-3de1-4dc9-afbc-17853307bd3a-upicture8.png}{\includegraphics{images/233c2485-3de1-4dc9-afbc-17853307bd3a-upicture8.png}}{}
\makeatother 
\caption{\boldmath {3D Response surface plots showing factors with zeta potential (SCSNPs)}}
\label{f-0fe524344d75}
\end{figure}
\egroup

\bgroup
\fixFloatSize{images/7ddf7bde-a714-4578-b081-3b07ef559e51-upicture9.png}
\begin{figure}[!htbp]
\centering \makeatletter\IfFileExists{images/7ddf7bde-a714-4578-b081-3b07ef559e51-upicture9.png}{\includegraphics{images/7ddf7bde-a714-4578-b081-3b07ef559e51-upicture9.png}}{}
\makeatother 
\caption{\boldmath {3D Response surface plots showing factors with encapsulation efficiency (SCSNPs)}}
\label{f-00bdf2f8d999}
\end{figure}
\egroup

\bgroup
\fixFloatSize{images/a09c02ec-6778-45cb-b198-69edd9920bf0-upicture10.png}
\begin{figure}[!htbp]
\centering \makeatletter\IfFileExists{images/a09c02ec-6778-45cb-b198-69edd9920bf0-upicture10.png}{\includegraphics{images/a09c02ec-6778-45cb-b198-69edd9920bf0-upicture10.png}}{}
\makeatother 
\caption{\boldmath {Overlay contour plot for Sulfasalazine CSNPs (SCSNPs)}}
\label{f-7a4ab6008edf}
\end{figure}
\egroup

\bgroup
\fixFloatSize{images/9fa63fbc-cfd2-4d5f-ad44-8a6014de4fe1-upicture11.png}
\begin{figure}[!htbp]
\centering \makeatletter\IfFileExists{images/9fa63fbc-cfd2-4d5f-ad44-8a6014de4fe1-upicture11.png}{\includegraphics{images/9fa63fbc-cfd2-4d5f-ad44-8a6014de4fe1-upicture11.png}}{}
\makeatother 
\caption{\boldmath {Average particle size of optimized SCSNPs}}
\label{f-0557f5d0710e}
\end{figure}
\egroup

\bgroup
\fixFloatSize{images/b60adbe4-1855-480b-810b-68fed37d252d-upicture12.png}
\begin{figure}[!htbp]
\centering \makeatletter\IfFileExists{images/b60adbe4-1855-480b-810b-68fed37d252d-upicture12.png}{\includegraphics{images/b60adbe4-1855-480b-810b-68fed37d252d-upicture12.png}}{}
\makeatother 
\caption{\boldmath {SEM photographs of A) Optimized CSNPs, B) Optimized SCSNPs}}
\label{f-7e405d63b486}
\end{figure}
\egroup
\textbf{Polynomial equations with intercept and coded factors (CSNPs)}
\let\saveeqnno\theequation
\let\savefrac\frac
\def\dispfrac{\displaystyle\savefrac}
\begin{eqnarray}
\let\frac\dispfrac
\gdef\theequation{1}
\let\theHequation\theequation
\label{dfg-9ea69d9741c1}
\begin{array}{@{}l}\begin{array}{l}Y_1=+245+86.87A(P<0.05)+27.25B\\(P<0.05)+3.87C(P>0.05)+10AB\\(P>0.05)+2.25AC(P>0.05)-2BC\\(P>0.05)+18.37A^{2}(P<0.05)+9.12B^{2}\\(P>0.05)+3.87C^{2}(P>0.05).\end{array}\end{array}
\end{eqnarray}
\global\let\theequation\saveeqnno
\addtocounter{equation}{-1}\ignorespaces 

\let\saveeqnno\theequation
\let\savefrac\frac
\def\dispfrac{\displaystyle\savefrac}
\begin{eqnarray}
\let\frac\dispfrac
\gdef\theequation{2}
\let\theHequation\theequation
\label{dfg-56a1fefea6e7}
\begin{array}{@{}l}\begin{array}{l}Y_2=+33.43+18.26A(P<0.05)+4.48B\\(P<0.05)+2.10C(P<0.05)+2.02AB\\(P<0.05)+1.52AC(P<0.05)-0.23BC\\(P>0.05)+4.16A^{2}(P<0.05)-0.44B^{2}\\(P>0.05)+0.14C2(P>0.05).\end{array}\end{array}
\end{eqnarray}
\global\let\theequation\saveeqnno
\addtocounter{equation}{-1}\ignorespaces 

\let\saveeqnno\theequation
\let\savefrac\frac
\def\dispfrac{\displaystyle\savefrac}
\begin{eqnarray}
\let\frac\dispfrac
\gdef\theequation{3}
\let\theHequation\theequation
\label{dfg-74a03a1cd980}
\begin{array}{@{}l}\begin{array}{l}Y_3=+0.19+0.01A(P<0.05)-0.00B\\(P<0.05)+0.01C(P<0.05)-0.00AB\\(P>0.05)+0.00AC(P<0.05)-0.00BC\\(P>0.05)-0.00A^{2}(P>0.05)+0.00B^{2}\\(P<0.05)+0.00C^{2}(P>0.05).\end{array}\end{array}
\end{eqnarray}
\global\let\theequation\saveeqnno
\addtocounter{equation}{-1}\ignorespaces 
SCSNPs were formulated to optimize the concentration of sulfasalazine, stirring speed, and temperature based on the dependent variables like particle size, zeta potential, and encapsulation efficiency. Table~\ref{tw-f508987bc04b} shows the results for responses. p-Values for the responses y\ensuremath{_{1}}, y\ensuremath{_{2}}, y\ensuremath{_{3}} was found to be 0.047, 0.039, 0.008. Hence the quadratic model is best fitted for all the responses with p{\textless}0.05. Table~\ref{tw-78134f84ab91} shows a summary of the regression analysis of all the responses. Based on the desirability function, interaction effects between two factors and confirmation location was predicted using 2D contour and 3D response surface graphs shown in Table~\ref{tw-e334a893a293} and Figures~\ref{f-dbe5d25af019}, \ref{f-f634d30a173e}, \ref{f-0fe524344d75}, \ref{f-00bdf2f8d999} and~\ref{f-7a4ab6008edf}. Among the responses, Y\ensuremath{_{2}} and Y\ensuremath{_{3}} were set in maximize, whereas Y\ensuremath{_{1}} in minimizing. Confirmation location for the optimized formulation was achieved at A = 1.29 mg/ml, B\ensuremath{_{}} = 525.16 rpm, C\ensuremath{_{}} = 23.14\ensuremath{^{0}}C with Y\ensuremath{_{1}} = 269.61 nm, Y\ensuremath{_{2\ }} =\ensuremath{_{}} 43.21 mV and Y\ensuremath{_{3\ }} = 82.83\% (Desirability {\textendash} 0.88). Overlay contour plot shown in Figure~\ref{f-7a4ab6008edf} explains the most supportive zone for all the responses. Observed values were found to be very close to the predicted values of confirmation location, indicating the best optimization results using Box Behnken Design. 

\textbf{Particle Size}

There is an increase in particle size with increasing the concentration of sulfasalazine. Particle size\ensuremath{_{}}was found to be increased with an increase in stirring speed upto 600rpm and decreased thereafter, which may be due to the prevalence of high shearing rates that destroys the repulsive forces leading to aggregation \unskip~\citep{645097:15145499,645097:15145467}. Particle size decreased linearly with an increase in temperature, which is shown in 3D response surface graphs (Figure~\ref{f-f634d30a173e}). Polynomial equation with intercept and coded factors is as follows,
\let\saveeqnno\theequation
\let\savefrac\frac
\def\dispfrac{\displaystyle\savefrac}
\begin{eqnarray}
\let\frac\dispfrac
\gdef\theequation{4}
\let\theHequation\theequation
\label{dfg-feb69fd68ca9}
\begin{array}{@{}l}\begin{array}{l}Y_1\;=+289+35.37A(P<0.05)+8.37B\\(P>0.05)-14C(P<0.05)+5.75AB\\(P>0.05)-13.5AC(P>0.05)-4BC\\(P>0.05)-11.37A^{2}(P>0.05)-2.37B^{2}\\(P>0.05)+5.377C^{2}(P>0.05).\end{array}\end{array}
\end{eqnarray}
\global\let\theequation\saveeqnno
\addtocounter{equation}{-1}\ignorespaces 
From the Equation~(\ref{dfg-feb69fd68ca9})\textbf{, }independent variables like A and C was found to be significant as the p-value is less than 0.05. The observed value of particle size for the confirmation location was found to be 261\ensuremath{\pm}3.06, and the results were given in Table~\ref{tw-e334a893a293} and Figure~\ref{f-0557f5d0710e}.

\textbf{\% Encapsulation efficiency (EE)}

\%EE of all the SCSNPs ranged from 63.13\ensuremath{\pm}2.56 to 84.17\ensuremath{\pm}1.45\%. Polynomial equation with intercept and coded factors is as follows,
\let\saveeqnno\theequation
\let\savefrac\frac
\def\dispfrac{\displaystyle\savefrac}
\begin{eqnarray}
\let\frac\dispfrac
\gdef\theequation{5}
\let\theHequation\theequation
\label{dfg-65653fe488a1}
\begin{array}{@{}l}\begin{array}{l}Y_3=+84.17+3.23A(P<0.05)-5.94B\\(P<0.05)-0.97C(P>0.05)-1.0AB\\(P>0.05)+0.19AC(P>0.05)-0.14BC\\(P>0.05)-3.8A^{2}(P<0.05)-9.7B^{2}\\(P<0.05)-2.1C^{2}(P>0.05).\end{array}\end{array}
\end{eqnarray}
\global\let\theequation\saveeqnno
\addtocounter{equation}{-1}\ignorespaces 
From Equation~(\ref{dfg-65653fe488a1}), the independent variables like A, B, A\ensuremath{^{2}}, and B\ensuremath{^{2}} was found to be significant as the p-value is less than 0.05. Encapsulation efficiency was found to be increased up to 1.5mg/ml concentration of sulfasalazine and decreased thereafter. Further increase in sulfasalazine concentration leads to a decrease in \% EE, which may be due to the precipitation of chitosan molecules in the dispersion. \%EE was found to be increased up to 600 rpm and decreased thereafter, which may be due to the prevalence of high shearing rates that destroys the repulsive forces leading to aggregation \unskip~\citep{645097:15145499,645097:15145467}. These results were clearly shown in the 3D response surface graph, Figure~\ref{f-00bdf2f8d999}. Observed \% EE of the optimized batch was found to be 81.3\ensuremath{\pm}5.3, with particle size 261\ensuremath{\pm}3.06.

\textbf{Zeta potential}

Zeta potential of all SCSNPs ranged from 21.31\ensuremath{\pm}3.36 to 43.21\ensuremath{\pm}1.79 mV. Polynomial equation with intercept and coded factors is as follows,
\let\saveeqnno\theequation
\let\savefrac\frac
\def\dispfrac{\displaystyle\savefrac}
\begin{eqnarray}
\let\frac\dispfrac
\gdef\theequation{6}
\let\theHequation\theequation
\label{dfg-f77a193d8ffd}
\begin{array}{@{}l}\begin{array}{l}Y_2\;=+43.21+1.244A(P>0.05)-6.76B\\(P<0.05)+0.19C(P>0.05)+0.5AB\\(P>0.05)+0.14AC(P>0.05)+0.19BC\\(P>0.05)-3.6A^{2}(P>0.05)-8.8B^{2}\\(P<0.05)-6.0C^{2}(P<0.05).\\\end{array}\end{array}
\end{eqnarray}
\global\let\theequation\saveeqnno
\addtocounter{equation}{-1}\ignorespaces 
From Equation~(\ref{dfg-f77a193d8ffd}), independent variables like B, B\ensuremath{^{2}}, and C\ensuremath{^{2}} was found to be significant as the p-value is less than 0.05. Zeta potential was mainly affected by stirring speed and temperature. At higher speeds and temperature, a decrease in the viscosity of chitosan leads to structural instability, thereby decreasing zeta potential \unskip~\citep{645097:15145466}. The observed value of zeta potential for the confirmation location was found to be +41.4\ensuremath{\pm}0.5. This positive surface charge leads to interaction with mucin and has the characteristics of mucoadhesion \unskip~\citep{645097:15145492}.

\textbf{Morphology}

Scanning electron microscopy reveals that there is an increase in the particle size of SCSNPs when compared to CSNPs. From the micrographs (Figure~\ref{f-7e405d63b486}), it was evident that particles were rough in texture with a nearly spherical shape. 


\bgroup
\fixFloatSize{images/21b7787c-8908-4bec-a5db-8384f3bf58af-upicture13.png}
\begin{figure}[!htbp]
\centering \makeatletter\IfFileExists{images/21b7787c-8908-4bec-a5db-8384f3bf58af-upicture13.png}{\includegraphics{images/21b7787c-8908-4bec-a5db-8384f3bf58af-upicture13.png}}{}
\makeatother 
\caption{\boldmath {FT-IR of Sulfasalazine (Pure Drug), Optimized blank chitosan nanoparticles (CSNPS), Optimized sulfasalazine nanoparticles (SCSNPS)}}
\label{f-049162041ffd}
\end{figure}
\egroup

\bgroup
\fixFloatSize{images/b7f05e86-ae75-4799-a6d1-cac66eb4a1ab-upicture14.png}
\begin{figure}[!htbp]
\centering \makeatletter\IfFileExists{images/b7f05e86-ae75-4799-a6d1-cac66eb4a1ab-upicture14.png}{\includegraphics{images/b7f05e86-ae75-4799-a6d1-cac66eb4a1ab-upicture14.png}}{}
\makeatother 
\caption{\boldmath {DSC of Sulfasalazine (Pure Drug), Blank chitosan nanoparticles (CSNPs), Optimized sulfasalazine nanoparticles (SCSNPs)}}
\label{f-53c28e3cf847}
\end{figure}
\egroup

\bgroup
\fixFloatSize{images/6c31f380-c6d0-416b-ad7a-2c0934a54d95-upicture15.png}
\begin{figure}[!htbp]
\centering \makeatletter\IfFileExists{images/6c31f380-c6d0-416b-ad7a-2c0934a54d95-upicture15.png}{\includegraphics{images/6c31f380-c6d0-416b-ad7a-2c0934a54d95-upicture15.png}}{}
\makeatother 
\caption{\boldmath {PXRD of Sulfasalazine and optimized sulfasalazine nanoparticles (SCSNPS)}}
\label{f-21ca085839b1}
\end{figure}
\egroup

\bgroup
\fixFloatSize{images/fafe53cf-e4e6-4b56-8081-101f43e9a2c6-upicture16.png}
\begin{figure}[!htbp]
\centering \makeatletter\IfFileExists{images/fafe53cf-e4e6-4b56-8081-101f43e9a2c6-upicture16.png}{\includegraphics{images/fafe53cf-e4e6-4b56-8081-101f43e9a2c6-upicture16.png}}{}
\makeatother 
\caption{\boldmath {Cumulative \% drug release of optimized sulfasalazine nanoparticles (SCSNPS) in various fluids (SCF-Simulated Colonic Fluid, PBS {\textendash} Phosphate buffer)}}
\label{f-8c574926f380}
\end{figure}
\egroup
\textbf{FT-IR Spectroscopy}

Characteristic peaks for sulfasalazine, optimized blank CSNPs, and optimized SCSNPs were shown in Figure~\ref{f-049162041ffd}. Characteristic peaks for chitosan at 3361 cm\ensuremath{^{-1}} (N-H and O-H stretch) shifted to a higher number of 3420 cm\ensuremath{^{-1}} with broadband in CSNPs. Other peaks at 1658 cm\ensuremath{^{-1}} (C=O) and 1577 cm\ensuremath{^{-1}} (NH\ensuremath{_{2}} bending) shifted to lower numbers of 1628 cm\ensuremath{^{-1 }} and 1560 cm\ensuremath{^{-1}}, respectively \unskip~\citep{645097:15145494,645097:15145501}. These shifts may be due to the interaction between chitosan and TPP. CSNPs showed new characteristic peaks of tripolyphosphate at 1280 cm\ensuremath{^{-1}} (P=O stretching) and 1036 cm\ensuremath{^{-1}} (P-O bending). Characteristic peaks for sulfasalazine at 1340 cm\ensuremath{^{-1}} (asymmetric stretching mode of SO\ensuremath{_{2}}), 1385 cm\ensuremath{^{-1}} (O-H bending), 1410 cm\ensuremath{^{-1}} (symmetric stretching mode of COO\ensuremath{^{-}}), 1582 cm\ensuremath{^{-1}} (stretching mode of N=N) slightly shifted to lower wavenumbers in SCSNPs indicating the interactions between chitosan/tripolyphosphate matrix and sulfasalazine \unskip~\citep{645097:15145495}. Broadband at 3460 cm\ensuremath{^{-1 }} and less intense band at 1339 cm\ensuremath{^{-1 }} reveals the possible interaction between {\textendash}OH group of sulfasalazine and {\textendash}NH group of chitosan for hydrogen bonding, and this may be the reason for increased encapsulation efficiency of optimized SCSNPs.

\textbf{Differential Scanning Calorimetry}

The broad endothermic peak was observed at 244.3\ensuremath{^{0}}C for sulfasalazine, corresponding to its melting point shown in Figure~\ref{f-53c28e3cf847}. CSNPs experienced two endothermic peaks at 56.8\ensuremath{^{0}}C and 330.2\ensuremath{^{0}}C, in relation to evaporation of water and degradation of chitosan, respectively \unskip~\citep{645097:15145480,645097:15145472}. The endothermic peak of sulfasalazine was shifted from 244.3\ensuremath{^{0}}C to 310.3\ensuremath{^{0}}C in SCSNPs, indicating superior thermal stability of sulfasalazine.

\textbf{X-Ray diffraction}

Powdered X-ray diffraction patterns for pure sulfasalazine and optimized SCSNPs were shown in Figure~\ref{f-21ca085839b1}. Pure sulfasalazine showed larger Lin (counts) when compared to optimized SCSNPs. Less intensities of sulfasalazine in SCSNPs indicates that the drug is in disordered crystalline or in amorphous form. This studies indicate the improvement of bioavailability of sulfasalazine in SCSNPs \unskip~\citep{645097:15145502}.

\textbf{In-Vitro drug release studies}

Cumulative drug release for optimized SCSNPs was conducted for 24 hrs in phosphate buffer and simulated colonic fluids (SCF), as shown in Table~\ref{tw-be0441d30740} and Figure~\ref{f-8c574926f380}. Cumulative drug release of sulfasalazine CSNPs in phosphate buffer pH 7.4, SCF pH 7, SCF pH 6 (Fed state), SCF pH 7.8 (Fasted state) was found to be 99.95\ensuremath{\pm}1.45, 99.70\ensuremath{\pm}0.72, 99.03\ensuremath{\pm}0.80 and 91.40\ensuremath{\pm}0.96 \% respectively. 

In SCF pH 6 (Fed state), more than 99\% of the drug was released within 12 Hrs, which may be due to the solubility of chitosan in acidic and slightly acidic pH \unskip~\citep{645097:15145484}. In SCF pH 7.8 (Fasted state), drug release was retarded, and only 91.40\% of the drug was released in 24 hrs, which may be due to the slow gelling capacity of chitosan at higher pH values \unskip~\citep{645097:15145484}. From the results, it was found that more than 99\% of the drug was released in 24 hrs in phosphate buffer pH 7.4 and SCF pH 7, which was found to be the best when compared to the SCSNPs release in other fluids.

Results were fitted with various kinetic models, as shown in Table~\ref{tw-75863411d3e3}. Korsemeyer-Peppas was found to be the best-fitted model with the mechanism of drug release as non-fickian diffusion with `n' value ranging from 0.62-0.69.
    
\section{Conclusions}
Chitosan nanoparticles containing sulfasalazine was formulated and optimized using Design Expert: Quality by Design (QbD) - software. PXRD and DSC of optimized SCSNPs revealed the improvement of bioavailability and stability by converting its low soluble crystalline forms of sulfasalazine to highly soluble amorphous forms. The encapsulation efficiency of SCSNPs was found to be increased due to the interaction between amine and hydroxyl group of chitosan and sulfasalzine, respectively. Increased encapsulation efficiency made SCSNPs to release the drug for about 24 hrs. Although the drug was released for about 24 hrs in-vitro, the stability of SCSNPs in gastric pH should be improved by coating the nanoparticles using pH-sensitive polymers, thereby targeting the drug release to the colon.
    

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