Risk management and statistical multivariate analysis approach for design and optimization of satranidazole nanoparticles

    Risk management and statistical multivariate analysis approach for design and optimization of satranidazole nanoparticles Shalaka Dhat, Swati Pund, Chandrakant Kokare, Pankaj Sharma, Birendra Shrivastava PII: DOI: Reference:

S0928-0987(16)30418-3 doi: 10.1016/j.ejps.2016.09.035 PHASCI 3741

To appear in: Received date: Revised date: Accepted date:

15 July 2016 26 September 2016 26 September 2016

Please cite this article as: Dhat, Shalaka, Pund, Swati, Kokare, Chandrakant, Sharma, Pankaj, Shrivastava, Birendra, Risk management and statistical multivariate analysis approach for design and optimization of satranidazole nanoparticles, (2016), doi: 10.1016/j.ejps.2016.09.035

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ACCEPTED MANUSCRIPT Risk management and statistical multivariate analysis approach for design and optimization of satranidazole nanoparticles

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Shalaka Dhata,c,1,* Swati Pundb,1, Chandrakant Kokarea, Pankaj Sharmaa, Birendra a

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Shrivastavaa

School of Pharmaceutical Sciences, Jaipur National University, Jaipur, Rajasthan -302017,

India.

Department of Biosciences and Bioengineering, Indian Institute of Technology, Powai,

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b

Mumbai- 400076, India

Department of Pharmaceutics, S.T.E.S’s Sinhgad Institute of Pharmacy, Narhe, Pune,

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c

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Maharashtra - 411041, India.

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Correspondence to:

Shalaka Dhat Department of Pharmaceutics S.T.E.S‟s Sinhgad Institute of Pharmacy Narhe, Pune - 411 041, Maharashtra, India. E-mail : [email protected] Tel.: +91 20 66831806 Fax: +91 20 66831816 1

These authors contributed equally to this work. 4

ACCEPTED MANUSCRIPT Abstract

Rapidly evolving technical and regulatory landscapes of the pharmaceutical product

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development necessitates risk management with application of multivariate analysis using

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Process Analytical Technology (PAT) and Quality by Design (QbD). Poorly soluble, high dose drug, Satranidazole was optimally nanoprecipitated (SAT-NP) employing principles of Formulation by Design (FbD). The potential risk factors influencing the critical quality

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attributes (CQA) of SAT-NP were identified using Ishikawa diagram. Plackett-Burman screening design was adopted to screen the eight critical formulation and process parameters

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influencing the mean particle size, zeta potential and dissolution efficiency at 30 min in pH 7.4 dissolution medium. Pareto charts (individual and cumulative) revealed three most critical

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factors influencing CQA of SAT-NP viz. aqueous stabilizer (Polyvinyl alcohol), release modifier (Eudragit® S 100) and volume of aqueous phase. The levels of these three critical formulation attributes were optimized by FbD within established design space to minimize

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mean particle size, poly dispersity index, and maximize encapsulation efficiency of SAT-NP. Lenth‟s and Bayesian analysis along with mathematical modeling of results allowed

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identification and quantification of critical formulation attributes significantly active on the selected CQAs. The optimized SAT-NP exhibited mean particle size; 216 nm, polydispersity

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index; 0.250, zeta potential; -3.75 mV and encapsulation efficiency; 78.3%. The product was lyophilized using mannitol to form readily redispersible powder. X-ray diffraction analysis confirmed the conversion of crystalline SAT to amorphous form. In vitro release of SAT-NP in gradually pH changing media showed < 20% release in pH 1.2 and pH 6.8 in 5 h, while, complete release (> 95%) in pH 7.4 in next 3 h, indicative of burst release after a lag time. This investigation demonstrated effective application of risk management and QbD tools in developing site-specific release SAT-NP by nanoprecipitation.

Keywords Design of experiments, Formulation by design, Ishikawa diagram, Plackett-Burman, Risk assessment

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ACCEPTED MANUSCRIPT 1. Introduction Process analytical technology (PAT) and Quality by Design (QbD) are true paradigm for enhancing quality, safety and efficacy of pharmaceutical products. PAT designs, analyses,

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controls and improves the understanding of a complex multivariate pharmaceutical process

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through timely measurements of critical quality and performance attributes of raw and inprocess materials and processes. Use of statistical multivariate analysis (MVA) and design of experiments (DoE) provide information on the challenges encountered by complex

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pharmaceutical process and continuously monitor the real-time process variation (Eriksson et al., 2013). Implementation of PAT can cut down amount of testing in downstream process

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whereas, QbD provides wide working margin and the flexibility to change downstream while

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in design space.

DoE combined with MVA is an efficient and powerful risk mitigating approach for continuous quality improvement. It facilitates thorough understanding of the process and

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manages the associated risk for total quality management. In addition, it supports rational study of impact of formulation and/or process variables on the predefined responses with

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fewer experiments in shorter time period (Joshi et al., 2008; Pund et al., 2010). QbD is a scientific approach promoted by Food and Drug Administration of the United States

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Department of Health and Human Services to enhance pharmaceutical development throughout a product‟s life cycle. DoE, an element of QbD, portrays a complete picture of variation of critical responses as a function of the factors influencing the target quality profile. It involves thorough understanding of the product as well as the process by generation of mathematical equations, extrapolation of data and plotting of the results (Singh et al., 2011a). A well-established statistical experimental design thus overrides the conventional approach of optimizing one variable at a time (Singh et al., 2011a). DoE simultaneously tests the effects of variables and their interactions and relates causative relationships between process parameters, input materials and quality attributes. It defines the design space of the process which is the largest volume within which one can vary important process factors without risking violation of specifications. MVA and DoE is needed to accomplish PAT/QbD (Eriksson et al., 2013).

Recently, a holistic concept of „Formulation by Design‟ (FbD), based on QbD has been adopted and precisely reported for DoE in drug formulation development. With the advent of 6

ACCEPTED MANUSCRIPT newer nanotechnology driven delivery systems and the recent regulatory quality initiatives, implementation of FbD is now an integral part of drug industry. Selection of appropriate experimental designs, accurate computer aided optimization, precise definition of design and

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control space, identification of critical quality attributes (CQAs), critical formulation

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attributes (CFAs) and critical process parameters (CPPs) are the key strengths to achieve the goal of FbD (Singh et al., 2011b).

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Orally delivered polymeric nanoparticles are focal point of today‟s therapeutic research and offer benefits of improved bioavailability and targeted delivery over the conventional drug

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delivery systems (Ravi et al., 2015). Development of such an impeccable polymeric nanoparticulate system is a multivariate process involving rational and judicious selection of

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process and formulation variables (Ribeiro et al., 2013, Yadav and Sawant, 2010). However, combined use of quality risk management tools, screening and experimental designs for

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optimization have been less explored. (Rahman et al., 2010; Yerlikaya et al., 2013).

Satranidazole (SAT), a high dose poorly soluble antiprotozoal drug is clinically superior to

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metronidazole for the treatment of intestinal amoebiasis (Nagarajan, 2006). Systemic absorption and distribution after oral administration before reaching to colon, results in low

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concentration of antiamoebic drug in colonic lumen (Kayser and Kinderlen, 2003). Delayed release into colonic lumen after oral administration ensures site specificity and sufficient drug concentration gradient for rapid and high absorption. Delayed delivery of nanoparticles will, in addition, assure the effective uptake by the colonic mucosa and will help to reduce the dose. Therefore, SAT was selected for formulating delayed release polymeric nanoparticles. Development of solid oral nanoformulation is a complex multivariate process and needs powerful risk mitigating approach for successful quality product. The present study pioneers the successful implementation of FbD for development of delayed release SAT polymeric nanoparticles (SAT-NP) using Ishikawa herringbone diagram, Plackett-Burman and 23 full factorial design. This study also illustrates further application of FbD for the validation of the developed mathematical model and search for optimized process and product composition.

2. Materials and methods 2.1 Materials SAT was a generous gift by Alkem laboratories Ltd. (Ankleshwar, India). Poloxamer 188 (P188) and mannitol were received as gift samples from BASF India Ltd (Mumbai, India) 7

ACCEPTED MANUSCRIPT and Roquette Pharma (Mumbai, India), respectively. Poly(lactic-co-glycolic) acid (PLGA) (L:G 50:50, average Mw. 24,000-38,000, Resomer® RG 503H with free carboxyl end groups) and Eudragit® S 100 (ES100) was generously gifted by Evonik Industries Ltd. (Mumbai,

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India). Polyvinyl alcohol (PVA average Mw. 13,000 - 23,000) was purchased from Sigma-

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Aldrich (Mumbai, India). Acetonitrile (HPLC-grade) was purchased from Thomas Baker (Mumbai, India). All other ingredients and reagents of analytical grade were purchased from Research-Lab Fine Chem. Industries (Mumbai, India) and used as received. Fresh double

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distilled water filtered through 0.22 µm membrane filter was used throughout the study.

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2.2 Methods 2.2.1 Preparation of SAT nanoparticles (SAT-NP)

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SAT-NP were prepared by the classical nanoprecipitation technique (Fessi et al., 1989). Briefly SAT and release modifying polymer (either PLGA or ES100) were dissolved in acetone. This organic phase was filtered through 0.45µ pore size membrane to remove any

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particulate matter and added drop wise into the aqueous phase containing a suitable stabilizer (either PVA or P188) under mechanical stirring (either 500 or 1500 rpm) for either 2 or 4 h.

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The resulting nanodispersion obtained was centrifuged at 18,000 rpm for 30 min (C-24 Plus, Remi Laboratory Instruments, Mumbai, India) at ambient temperature. The supernatant

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obtained was analyzed by reverse phase chromatography (RP-HPLC) method for free drug content. The nanoparticle bed was washed twice with distilled water to eliminate any unentrapped drug and residual surfactant. The resultant nanoparticles were reconstituted in 2 mL distilled water, with mannitol (1:1) as cryoprotectant. The nanoparticle dispersion was frozen at -20° C for 12 h (RQV-200 plus, Remi Laboratory Instruments, Mumbai) and lyophilized (Freezone, Lab Conco, USA) at 0.1 mbar at -50 ± 0.5° C for 24 h. The lyophilized nanoparticles were stored in desiccator at ambient temperature and used for further characterization.

2.2.2 Characterization of SAT-NP 2.2.2.1 Particle size, polydispersity index (PDI) and zeta potential Lyophilized nanoparticles were dispersed in distilled water and sonicated for 15 sec to obtain uniform dispersion before measurements. The mean particle size and PDI of the resulting nanosuspension were determined by photon cross-correlation spectroscopy (Nanophox, Sympatec, Germany). Sample temperature was set at 25° C and 3 runs of 60 s were performed. Detection was carried out at a scattering angle of 90°. From the resulting 8

ACCEPTED MANUSCRIPT correlation curves, a 2nd order analysis was performed to determine mean particle size and standard deviation (Pund et al., 2014, 2015). Zeta potential was measured on zeta meter

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(Nano Plus DLC, Micromeritics, USA).

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2.2.2.2 Entrapment efficiency (EE)

Lyophilized nanoparticles (10 mg) were added to acetone (10 mL) and sonicated for 1 min to facilitate complete dissolution of nanoparticle coat and SAT. The mixture was centrifuged

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and the supernatant was diluted suitably and analyzed by RP-HPLC (Shimadzu, Kyoto, Japan) which consisted of binary gradient pump (LC-20AD, Shimadzu, Kyoto, Japan) and

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diode array detector (SPD-M20, Shimadzu, Kyoto, Japan). The separation was performed on a Kromasil C18 (4.6 mm × 250 mm, 5µ) column. The mobile phase consisted of phosphate

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buffer (0.01 M, pH 3.5): acetonitrile (65:35, v/v), filtered through a 0.45 µm membrane filter and degassed prior to use. The mobile phase was delivered at a flow rate of 1 mL.min -1 and monitored at 320 nm, the wavelength of maximal absorption for SAT. The method was

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validated earlier for linearity, accuracy, precision and repeatability over a concentration range of 0.5-50 µg.mL-1 (Dhat et al., 2016). EE (%) of SAT-NP was calculated according to the

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formula

…Eq. 1

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EE =

2.2.2.3 Dissolution efficiency (DE30) Dissolution of lyophilized nanoparticles was carried out in 100 mL pH 7.4 phosphate buffer at 37 ± 0.5°C at 50 rpm under magnetic stirring. SAT or its equivalent formulation (25 mg) was added to dissolution medium and SAT released in the dissolution media was measured using validated RP-HPLC method described earlier, after centrifugation of dissolution aliquots at 18,000 rpm for 15 min. For each dissolution run, a mean of six determinations was recorded. Dissolution efficiency at the end of 30 min (DE30) was calculated from the area under dissolution curve at time t (estimated using trapezoidal rule) and the percentage of the area of the rectangle described by 100 % dissolution in the same time (Pund et al., 2014).

2.2.3 Cause and effect relationship- Ishikawa herringbone diagram To configure the risk analysis operation for determining the cause-effect relationship between potential formulation and process parameters and the CQAs, an Ishikawa herringbone (fishbone) diagram was constructed (Beg et al., 2015). In present study, mean particle size, 9

ACCEPTED MANUSCRIPT EE, zeta potential and DE30 were identified as key CQAs affecting the biopharmaceutical characteristics of polymeric nanoparticles, based on prior scientific knowledge (Yerlikaya et

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al., 2013).

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2.2.4 Risk assessment screening

An eight factor, 12 run Plackett - Burman screening design (PBSD) was adopted for screening high risk, CFAs and CPPs, influencing the identified CQAs. The selected critical

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formulation and process independent factors (CFAs and CPPs) and their respective levels are tabulated in table 1. The lower and higher levels for all the eight factors were decided on the

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basis of preliminary trial experiments and were coded as -1 and +1 for simplification. Mean particle size (Y1, nm), zeta potential (Y2, mV), EE (Y3, %) and DE30 (Y4, %) were selected as

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CQA i.e. dependent responses. The design was generated and randomized for statistical analysis using NEMRODW software (LPRAI SARL, Marseille, France). Experimental formulations were prepared in triplicate and the corresponding observed values of CQAs

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(mean of three determinations) are listed in table 2. The significance of the PBSD model and factor coefficients for each independent variables were tested using multiple regression

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analysis and analysis of variance (ANOVA) and are demonstrated graphically using Pareto charts of normalized squares of contribution (%) and cumulative normalized squares (%).

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2.2.5 23Full factorial design for optimization of SAT-NP A 23 full factorial design was constructed to evaluate the main and interaction effects of CFAs and CPPs identified using PBSD. The five factors showing statistically insignificant effect on the CQAs were kept fixed at low level as employed in PBSD (Table 3). Remaining three CFAs namely, concentration of PVA in aqueous phase (X1), volume of aqueous phase (X2) and the amount of ES100 in organic phase (X3) were systematically explored and optimized using 3 factor, 2 level, 23 full factorial design keeping the high and low levels same as in PBSD (Table 3). Experimental design was generated and evaluated using the NEMRODW software (LPRAI SARL, Marseille, France). Randomization was carried out to balance the effect of extraneous uncontrollable conditions that can affect the results of experiments. The CQAs studied were mean particle size of nanoparticles (Y1, nm), EE (Y2, %) and PDI (Y3). Batches produced (FD1-FD8) as per the experimental design in three replicates and the corresponding observations of CQAs are presented in table 4.

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ACCEPTED MANUSCRIPT Multiple linear regression analysis was carried out to generate polynomial models to determine explanatory CFA having significant impact on the selected three CQAs. The significance of the model was estimated by applying ANOVA at the 5% significance level. A

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model was considered significant if the p value is less than 0.05. The influence of the CFAs using Lenth‟s method and Bayesian analysis.

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2.2.6 Validation of 23full factorial model and optimization

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as well as interactions among them on each of the CQA, are also demonstrated graphically

The optimization was performed by putting constraints on levels of CFAs in the polynomial

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equations so as to minimize the particle size and PDI, and simultaneously maximize EE. For validation of the polynomial equations, one optimum formulation composition (A) and three

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random checkpoints (B, C and D) were selected by intensive grid search performed over the entire experimental domain. The selection of optimal check point was based on the predetermined goal of minimum mean particle size as well as PDI and maximum EE.

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Formulations corresponding to these four check points were prepared and evaluated for all the three responses (Y1-Y3). The resultant experimental data of response properties were

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calculated.

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subsequently compared quantitatively with the predicted values and the % bias was

2.2.7 Characterization of optimized SAT-NP 2.2.7.1 Powder X-ray diffraction (PXRD) of optimized SAT-NP PXRD patterns of pristine SAT, optimized SAT-NP and physical mixture of SAT, ES100, PVA, and mannitol, were recorded using (D8 Advance, Bruker, Germany) diffractometer equipped with Cu anode dermic X-ray tube and operated at a current of 30 mA and a voltage of 40 kV. The samples were irradiated at a scanning rate of 5° min-1 over a diffraction angle 2θ, in the range of 10-80° with a step size of 0.02°. 2.2.7.2 Site specific release profile of SAT-NP The in vitro dissolution was carried out by gradually changing pH of media (pH 1.2 for 2 h, pH 6.8 for 3 h, followed by pH 7.4) thereby, simulating the gastric and intestinal fluids in the gastrointestinal tract (Beloqui et al., 2014). pH of media was changed as per the method A of dissolution of delayed release dosage forms specified in USP. Briefly, optimized NP (equivalent of 25 mg of SAT) were suspended in the dissolution medium under magnetic stirring (50 rpm, 37°C). At prefixed time intervals (1, 2, 3, 4, 5, 6, 7, 8, 12, 18 and 24 h), 0.5 mL aliquot was withdrawn and centrifuged for 15 min. at 18,000 rpm at ambient temperature. 11

ACCEPTED MANUSCRIPT The resulting supernatants were analyzed by validated RP-HPLC method to quantify the amount of SAT released. For each dissolution run, a mean of six determinations was

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recorded.

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3. Results and Discussion 3.1 Risk identification: Cause –effect relationship

QbD approach in formulation development i.e. FbD involves identification of all the potential

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risk factors likely to influence the product quality. An Ishikawa herringbone is a qualitative graphical tool to explore systematically, the causes and sub-causes that can contribute to the

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effect and the cause-effect relationship (Mukharya et al., 2013; Singh et al., 2010). An Ishikawa diagram (Fig. 1) was constructed to identify all the potential formulation and

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process variables likely to influence the CQAs of SAT-NP. Factors such as technique for preparation of SAT-NP, type of stirrer, nature and amount of organic phase were fixed based on the reported literature and our preliminary experimentation. Acetone was a solvent of

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choice for preparing SAT-NP as it dissolves both SAT and the polymers. In addition, acetone gets easily evaporated, thereby reducing the overall processing time. Based on preliminary

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experimentation, prior knowledge and robust equipment specifications, eight influencing factors (CFAs and CPPs, X1-X8, Table 1) were prioritized and subjected to further screening,

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viz. amount of SAT in organic phase (mg, X1), type of polymer in organic phase (X2), type of stabilizer in aqueous phase (X3), concentration of stabilizer (%, X4), amount of polymer in organic phase (mg, X5), volume of aqueous phase (mL, X6), stirring time (h, X7) and stirring speed (rpm, X8).

3.2 Risk assessment screening PBSD also known as Hadamard design involves study of large number of factors in relatively few experimental runs. PBSD was used to identify the vital CFAs and CPPs influencing the CQAs. The therapeutic efficacy of SAT-NP depends upon rate and extent of uptake of SAT in the cellular environment. To achieve this, nanosized particles with good EE and rapid release from the polymeric carrier resulting in high drug concentration at the site of uptake is desirable. Nanoparticulate formulations are lyophilized and delivered as solid dosage forms for enhancing the stability and shelf life. However, it is necessary to ensure the physical stability of formulated nanoparticles till lyophilization. Zeta potential, a critical indicator of long term physical stability of nanodispersions (Sengel Turk et al., 2012), was considered as the CQA. As a thumb rule, for sterically stabilized systems, a zeta potential of at least ±20 12

ACCEPTED MANUSCRIPT mV guarantees physically stable nanoformulation. Hence, particle size (Y1), zeta potential (Y2), EE (Y3) and DE30 (Y4) were selected as primary CQAs.

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Pareto charts of normalized squares of contribution (%) and cumulative normalized squares

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(%) for particle size, zeta potential, EE, and DE30 are shown in fig. 2. The statistical parameters derived from ANOVA and regression analysis namely, model determination coefficient, F-ratio, model p value, coefficient estimates of all risk factors and their respective

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p values are tabulated in table 5. b1–b8 are the estimated coefficients for X1-X8 calculated from the observed experimental values of Y1-Y4, whereas, b0 is the arithmetic mean of all the

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quantitative outcomes of the 12 experimental runs. For particle size, the determination coefficient (R2) 0.971 indicates that 97.1% of the variation in response could be explained by

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the model and confirms the goodness of fit of the model. Low probability (p=0.0306) and Fratio greater than the theoretical (Fisher test critical value), indicate the significance of the regression model with a confidence level of 95% (Shah et al., 2013). It is observed from fig.

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2a, that concentration of stabilizer (X4), volume of aqueous phase (X6), type of stabilizer (X3) and amount of SAT in organic phase (X1) are the significant CFAs influencing the particle

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size (Y1). This is easily observed from the higher values of coefficient estimates and also from the significant p values (Table 5). The cumulative Pareto chart (Fig. 2b) shows 95.9%

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contribution of these four factors in influencing the particle size. Increase in the amount of SAT exhibited significant increase in the nanoparticle size. Increased drug concentration, increases the density of molecules in the dispersed phase, thereby, promoting the crystal growth (Mainardes and Evangelista, 2005). Poloxamer and PVA are widely used as stabilizers in nanoparticulate formulations (Woitiski et al., 2009, Pei et al., 2013, Makhlof et al., 2009). Both the stabilizers are nonionic and reduce the particle aggregation by providing a steric barrier (Lourenco et al., 1996). However P188 is bulkier than PVA (Keum et al., 2011) and its use results in nanoparticles with particle size greater than that when PVA is stabilizer (Feczko et al, 2008). Both PVA and P188 have surface active properties. Increasing the concentration of surfactant results in increased diffusion of the drug to the external phase causing a decrease in the particle size (Rahman et al., 2010). On the contrary, an increase in the volume of aqueous phase (X6) was found to increase the particle size. This is in contrast to the observations by Daspaul et al., (2013) wherein volume of aqueous phase showed inverse relationship with particle size owing to the increased diffusion of acetone in the aqueous phase. However, our observations are in agreement with Jeffery et al., (1991) who attributed increase in particle size to the reduction in agitation efficiency. 13

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The zeta potential was found to be significantly influenced only by the type of stabilizer (X3) used (Fig. 2c). The R2 and the p value of the main effects obtained from ANOVA were 0.985

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and 0.012 respectively (Table 5), indicative of significant model with a confidence level of

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95%. Zeta potential is due to the presence of charge on the particle surface. Fig. 2c and 2d clearly reveals the sole contribution of stabilizers (95.5%) for imparting the charge on the particle surface. The negative zeta potential obtained for all the formulations is attributed to

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the ionization of functional groups of stearic stabilizers PVA and P188 on the surface of nanoparticles (Sengel Turk et al., 2012). SAT alone exhibits a zeta potential of -29.2 mV.

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However, the differing zeta potential obtained with PVA and P188 is indicative of the differential shielding effect of these surfactants. Comparatively lower zeta potential observed

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with PVA as stabilizer (PBSD01, PBSD04, PBSD06, PBSD07, PBSD08 and PBSD12, Table 2) indicates effective stearic blanketing stabilization as compared to P188 (PBSD02,

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PBSD03, PBSD05, PBSD09, PBSD10 and PBSD11, Table 2).

EE was found to be significantly influenced by the type of stabilizer in aqueous phase (X3),

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type of polymer in organic phase (X2), and the amount of polymer in organic phase (X5) as observed from the highest bar length in Fig. 2e, 37.3%, 22.9% and 21.4%, respectively. The

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cumulative effect of all the three factors was found to be 81.51% (Fig. 2f). The confidence that the model can predict the observed value better than the mean was evident from the good correlation obtained between observed and the predicted values with R2, 0.972 and p-value, 0.029 (Table 5). The formulations prepared with ES100 as polymer and PVA as stabilizer, viz. PBSD06 and PBSD07 showed comparatively higher EE, 84.2% and 82.1%, respectively (Table 2). It has been observed that, PLGA exhibits surfactant like effects due to the acid terminal group introduced at the o/w interface and promote diffusion of drug from organic to aqueous phase of the emulsion, resulting in lower EE (Rahman et al., 2010) than ES100 which has ester functional groups. Use of PVA was found to increase EE whereas, P188 lowered it (Feczko et al., 2008). Increasing the amount of either of the polymer resulted in increase in EE. This was obvious because increase in polymer concentration increases the viscosity of the medium, resulting in decreased diffusion of drug to the external phase and increasing EE (Rahman et al., 2010).

DE30, though a model independent parameter is very useful for comparison of different dissolution profiles (Pund et al., 2014). It is widely used as a significant index of drug 14

ACCEPTED MANUSCRIPT dissolution performance. The DE30 (Y4) of the prepared formulation varies between 25% (PBSD01) - 86% (PBSD12) (Table 2). Statistical analysis results (Fig. 2g) revealed that the most significant factors affecting DE30 (Y4) were amount of polymer (X5), type of polymer in

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organic phase (X2), and amount of SAT in organic phase (X1). The cumulative contributory

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effect of all these three factors on the DE30 (X5) was 99.13% (Fig. 2h). The R2, 0.997 and p value, 0.010 indicate significant fit of the data in the model being tested. All the above three factors showed a negative effect on DE30. PLGA is a biodegradable polymer that releases the

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drug by diffusion and erosion. The initial drug release occurs by diffusion of drug from polymer matrix whereas, during the later stage the release is mediated by both diffusion and

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erosion of PLGA (Rahman et al., 2010). The release of drug from acid resistant ES100 matrix is pH dependent and hence DE30 was found to be higher in formulations prepared with ES100

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(PBSD03, PBSD05, PBSD06, PBSD07, PBSD11 and PBSD12, Table 2), when analyzed at pH 7.4. Increase in polymer concentration (X5), increases the viscosity and the diffusional path length for the entrapped drug, thereby decreasing the DE30. It has been reported that

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increase in the amount of drug in the nanoparticles increases the porosity of the system, resulting in increase in DE30 (Yadav and Sawant, 2010). However, we observed a decrease in

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DE30 with increase in drug loading with 1.98% contribution, although 97.15% significant

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contribution was by X5 and X2 (Fig. 2g and h). As observed in PBSD, nanoparticles with PLGA as polymer and P188 as stabilizer showed larger particle size and lower EE and hence, these two polymers were excluded from further studies. As increasing the amount of SAT also resulted in increase in the particle size, its level was kept unchanged as 25 mg. Stirring speed and time had no significant influence on any of the selected CQAs and hence, were fixed at 500 rpm and 2 h, respectively hereafter. The variability in zeta potential and DE30 was largely based on the nature of the polymer and the stabilizer and hence these parameters were eliminated as CQAs from the further design study.

3.3 Experimental design Based on the significance of factors screened in PBSDs, concentration of stabilizer in aqueous phase, volume of aqueous phase and amount of polymer in the organic phase were identified as the CFAs influencing the quality of SAT-NP. To systematically explore, the individual effects of varying three most significant factors; 23 full factorial design was

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ACCEPTED MANUSCRIPT applied. A first order polynomial regression model representing the quantitative effect of factors (X1, X2 and X3) upon each of the responses (Y1–Y3) that fitted to the data is as follows: …Eq. 2

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Y = b0 +b1X1 +b2X2 +b3X3 +b12 X1X2 +b13X1X3 + b23X2X3 +b123X1X2X3

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Where, b0 is the arithmetic mean of all the quantitative outcomes of the experimental runs; b1, b2 and b3 are the estimated coefficients from the observed experimental values of Y for X1, X2

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and X3.

The terms XiXj (i and j = 1, 2 and 3) represent the interaction terms. Coefficients with one

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factor represent the effect of that particular factor while the coefficients with more than one factor represent the interaction between those factors. A positive sign in front of the terms indicates synergistic effect while negative sign indicates antagonistic effect of the factors

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(Pund et al., 2010).

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Graphical analysis of responses using Lenth‟s method allowed easy identification of important CFAs active for the considered CQAs and the optimum CFA level to be selected. The graphical analysis also showed that changes of levels that are active on response

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correspond to bars that exceed the two dotted vertical reference lines. The two dotted reference lines are calculated according to the experimental variance (Fig. 3a, c, and e). In

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particular, the active factors are those, where, a level change determines a response variation which is statistically different from the variation due to experimental error. Bayesian analysis of coefficients was also carried out (Fig. 3b, d and f). This is a posteriori calculation of the probability that each of the effects is active and is represented graphically as box plots (Patil et al., 2011).

3.3.1 Effect on Particle size The size of the nanoparticles is an important CQA influencing the in vivo distribution, biological fate, toxicity, targeting ability of nanoparticle systems and also the drug loading (Singh and Lillard, 2009). The time required for nanoparticles to cross the intestinal mucosa, a site of colonization of trophozoites of E. histolytica, depends on the particle size; with smaller particles crossing faster than larger ones (Desai et al, 1996). Moreover, the uptake of drugs by amoeba is driven by passive diffusion of drug across the cell. The nanosized particles can attach better to mucus layers due to their easier penetration and small mass. In addition, increased mucus production in inflamed colon leads to thicker mucus layer allowing 16

ACCEPTED MANUSCRIPT higher amount of particle adherence (Lamprecht et al., 2001). Particle size of SAT-NP ranged between 210.6 nm (FD2) to 465.7 nm (FD7) (Table 4). Independent factors affecting particle size significantly (p < 0.05) were concentration of PVA (X1) and the amount of ES100 (X3)

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(Table 6). The individual and interaction effects of the factors on the particle size can be

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explained by the following regression equation;

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Y1= 348.20 -93.49X1 +1.588X2 +15.566X3 -4.889X1X2 -0.730X1X3 -15.64X2X3 -16.965X1X2X3 …Eq. 3

Increase in concentration of PVA tends to decrease the particle size whereas, reverse is

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observed with amount of ES100. The effect of increase in PVA concentration is however more profound as evident from the highest bar length (Fig. 3a) and the highest negative value

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coefficient of term X1 (b1); 93.49 in equation 3. PVA is a polymeric stabilizer with surfactant like properties. Increase in the concentration of PVA results in lowering of interfacial tension between the organic and the aqueous phases, causing formation of smaller droplets.

ED

Moreover, the steric stabilization produced avoids coalescence during evaporation of the organic solvent, thus preventing aggregation of the formed nanoparticles (Reibeiro et al,

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2013). Amount of ES100 had significant effect on particle size as observed from positive coefficient of X3 in equation 3 and significant bar length in fig. 3a. An increase in particle size with increase in the polymer concentration in the organic phase is observed for Poly-ɛ-

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caprolactone, PLGA and Eudragit® polymers (Daspaul et al., 2013, Budhian et al., 2007, Galindo-Rodriguez et al., 2004, Dinesh Kumar et al., 2015). Volume of aqueous phase (X2) had no significant effect on the particle size (p=0.634, Table 6), unlike the significant effect seen during the PBSD. The coefficient values for the interaction terms X1X2, X1X3 also showed that the interaction between these factors were statistically insignificant (p>0.05, Table 6), however, the interaction between X2X3 and X1X2X3 were statistically significant and the negative sign of their coefficients (b23 and b123) showed an antagonistic effect on the particle size. Bayesian analysis of the coefficients (Fig. 3b) shows that the effects b1, b3 and the interaction terms b23 and b123 are active, with probabilities of 99.80%, 38.35%, 38.50% and 41.45% respectively. The probability of insignificant effects, b2, b12 and b13, was found to be considerably lower 11.86%, 12.76% and 11.79% respectively.

3.3.2 Effect on EE Higher EE of nanoparticulates is always essential for reducing the dose. SAT is high dose drug and necessitates lowering of the dose to improve the efficacy and reduce the dose 17

ACCEPTED MANUSCRIPT related adverse effects. The EE for all the eight batches ranged from 65.5% (FD3) to 84.4% (FD6) as shown in table 4. The regression equation obtained for EE is as follows:

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Y2 = 74.33 +5.00X1 -1.291X2 +2.922X3 +0.139X1X2 +0.978X1X3 +0.176X2X3 -0.231X1X2X3

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..Eq. 4

Graphic analysis of CQAs (Fig. 3c) shows significant synergistic influence of concentration

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of PVA and amount of ES100 on EE (p<0.05, Table 6). Both, increase in PVA concentration and increase in the amount of ES100, tend to increase the EE, although the effect of PVA is

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more profound as evident from the highest bar length in Lenth‟s analysis (Fig. 3c) and highest positive value; 5.00, of the coefficient (b1) of term X1 in equation 4. As seen earlier,

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the increase in the amount of ES100, results in an increase in particle size leading to increased diffusional path for the drug to diffuse out. This reduces the drug loss by diffusion and increases EE (Daspaul et al., 2013). The coefficient values for the term X2 and the

ED

interaction terms X1X2, X2X3 and X1X2X3 show that their effect and interactions were statistically nonsignificant (p>0.05, Table 6). Bayesian analysis also shows that the activity of

PT

effect of PVA concentration has more probability (99.24%) than activity of effect of amount of ES100 (97.51%) (Fig. 3d). The probability of activity of b2 and activity of interaction

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terms b1b2, b1b3, b2b3 and b123 as shown by Bayesian analysis of coefficients (Fig. 3f) was found to be 87.27%, 12.01%, 80.87%,12.16% and 12.45% respectively.

3.3.3. Effect on PDI

PDI is an important parameter to determine the particle size distribution of the sample. It generally ranges from 0 to 1. PDI values in the range 0.1- 0.3 indicate a narrow size distribution, whereas those greater than 0.3 indicate a very broad distribution (Tang et al., 2013). Lower the PDI, better is the stability of the nanoparticulate dispersion (Yadav and Sawant, 2010). The PDI of the formulations ranged from 0.22 (FD2) to 0.34 (FD6) for various factor level combinations (Table 4). Presence of single peak in all the particle size measurement experiments indicates unimodal distribution. The regression equation for PDI is as follows:

Y3= 0.311 -0.012X1 +0.005X2 +0.014X3 +0.001X1X2 +0.016X1X3 -0.007X2X3 -0.008X1X2X3 …Eq. 5

18

ACCEPTED MANUSCRIPT The p values for all the individual and interaction factors are more than 0.05 indicative of their insignificant effect on PDI (table 6). This is in agreement with the graphical representation (Fig. 3e and f), in which none of the factors or their cross product terms were

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found to be significant. Sonication time has strong influence on particle size distribution

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(Mainardes and Evangelista, 2005). However, in present study, as all the formulations were processed for the same homogenization time, the PDI did not vary significantly.

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3.3.4 Optimization and validation of the developed mathematical model With the help of polynomial equations, the SAT-NP composition was optimized for all the

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three responses. The final optimal composition parameters were calculated by satisfying the requirements for each CFA in the set. Thus, to obtain a product with improved

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biopharmaceutical characteristics, it is desirable to minimize particle size along with PDI and maximize EE. In this study, optimization was performed with constraints for Y1 (< 225 nm), Y2 (> 76%) and Y3 (< 0.27). The optimal calculated levels were as follows,

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Concentration of stabilizer, PVA (X1) = 0.4% Volume of aqueous phase (X2) = 15.75 mL

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Amount of ES100 in organic phase (X3) = 26.88 mg

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The formulation prepared with the above-mentioned CFAs levels showed Y1Experimental as 216 nm (Y1Predicted, 221.3 nm; bias -2.5%), Y2Experimental as 78.3% (Y2predicted, 77%; bias, 1.7%), and Y3Experimental as 0.25 (Y3Predicted, 0.258; bias, -3%) as shown in Table 7. To assess the reliability of the developed mathematical model, formulations corresponding to optimum composition and three additional random compositions covering the entire range of experimental domain were prepared. For each of these three formulations, the responses (Y1-Y3) were estimated by the use of generated mathematical models (Eq. 3-5) and compared with that observed from experimental procedures. The formulation components of the optimum and the random check points, their experimental and predicted values for all the three responses variables are tabularized (Table 7). The lower magnitude of bias observed indicates reasonable agreement between the predicted and experimental values. It is an index of robustness and high extrapolative ability of the generated quantitative mathematical models (Joshi et al., 2008; Pund et al, 2014).

3.4 Characterization of optimized SAT-NP 3.4.1 PXRD 19

ACCEPTED MANUSCRIPT The PXRD analysis was carried out to investigate the change in the crystalline state of lyophilized SAT-NP. The diffraction spectrum of pristine SAT (Fig. 4a) shows sharp peaks at 2θ diffraction angles 16.7°, 18.7° and 20.6°. Pristine ES100 exhibits sharp peaks at 15° and

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30°, PVA exhibits single sharp peak around 20°, whereas mannitol shows intense peaks at

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14.66°, 18.79°. 23.27°, 28.34°, 31.73° and 38.73° as observed from the PXRD databank and the diffraction spectrum of physical mixture of SAT: ES100: PVA and mannitol (Fig. 4b). The PXRD spectrum of optimized SAT-NP (Fig.4c) shows reduction in the intensity of peaks

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in comparison to that of pristine SAT (Fig. 4a). This indicates reduction in crystallinity or transformation from crystalline to amorphous form confirming entrapment of SAT in

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polymeric carrier. The presence of few crystalline peaks between 10°-25° may be due to the presence of the ES100, PVA and mannitol, all of which exhibit crystallinity in the same

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region as that of SAT.

3.4.2 Site specific release of lyophilized nanoparticles

ED

The in vitro release profile of lyophilized SAT-NP was investigated in gradually pH changing buffer (Fig. 5). At acidic pH, nanoparticles exhibited very low release confirming sufficient

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protective barrier of ES100, an anionic copolymer based on methacrylate and methacrylic acid. The low permeability at acidic pH is due to hydrogen bonding between the carbonyl

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oxygen of ester group and hydroxyl groups of carboxylic moiety of ES100 (Coco et al., 2013). This ensures delaying of release of SAT till it reaches the target site with alkaline pH. At pH 7.4, corresponding to the ileocaecal region of the gastrointestinal tract, a rapid release of SAT was observed. ES100 is an enteric polymer that erodes at pH 7.0 due to the structural changes associated with ionization of carboxylic functional group. The pH dependent release of SAT in the colonic milieu is further more promising for SAT as nanosize will ensure more effective uptake by the colonic mucosa; the site of intestinal amoebiasis.

Conclusion This study justifies the modern philosophy of nanoformulation development that necessitates core regulatory application of PAT and MVA approach to gain insight into formulation and process variables using QbD. Plackett-Burman screening and 23 full factorial design as tools of FbD were successfully utilized to develop SAT-NP exhibiting site-specific release. Lenth‟s and Bayesian analysis along with mathematical modeling of results allowed identification and quantification of formulation variables active on the selected responses. CFAs significantly affecting the physicochemical behavior of SAT-NP were optimized within the 20

ACCEPTED MANUSCRIPT considered experimental domain and were found to be 0.4% PVA (X1) in 15.75 mL aqueous phase along with 26.88 mg of ES100 in organic phase. High degree of prediction power obtained for 23 full factorial design validated the applicability of FbD as an efficient tool in

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product optimization. It is evident that, the identification of critical levels of aqueous

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stabilizer as well as organic polymer could be of potential benefit in the development of site

Abbreviations

PT

ED

MA

NU

Critical formulation attributes Critical quality attributes Critical process parameters Dissolution efficiency Design of experiments Encapsulation efficiency Formulation by Design Factorial design Multivariate analysis Process Analytical Technology Plackett-Burman screening design Polydispersity index Poly(lactic-co-glycolic) acid Polyvinyl alcohol Powder X-ray diffraction Quality by Design Satranidazole Satranidazole nanoparticles

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CFAs CQAs CPPs DE30 DoE EE FbD FD MVA PAT PBSD PDI PLGA PVA PXRD QbD SAT SAT-NP

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specific release polymeric nanoparticles.

References

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Dhat, S., Pund, S., Kokare, C., Sharma, P., Shrivastava, B., 2016. Mechanistic investigation of biopharmaceutic and pharmacokinetic characteristics of surface engineering of satranidazole nanocrystals. Eur. J. Pharm. Biopharm. 100, 109-118.

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Galindo-Rodriquez, S., Allemann, E., Fessi, H., Doelkar, E., 2004. Physicochemical parameters associated with nanoparticle formation in the salting out, emulsion diffusion and nanoprecipitation methods. Pharm. Res. 21, 1428-1439.

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ACCEPTED MANUSCRIPT Mukharya, A., Patel, P.U., Shenoy, D., Chaudhary, S., 2013. Quality risk management of top spray fluidized bed process for antihypertensive drug formulation with control strategy engendered by Box-behnken experimental design space. Int. J. Pharm. Investig. 3, 15–28.

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Nagarajan, K., 2006. Creative research in the chemical industry – Four decades in retrospect. J. Chem. Sci. 118, 291-309.

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Pei, J., Lv, Q., Han, J., Li, X., Jin, S., Huang, Y., Jin S., Yuan, H., 2013. Schisandra lignansloaded enteric nanoparticles: preparation, characterization, and in vitro - in vivo evaluation. J. Drug target. 21, 180-187.

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Pund, S., Joshi, A., Vasu, K., Nivsarkar, M., Shishoo, C., 2010. Multivariate optimization of formulation and process variables influencing physico-mechanical characteristics of sitespecific release isoniazid pellets. Int. J. Pharm. 388, 64-72.

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Pund, S., Shete, Y., Jagadale, S., 2014. Multivariate analysis of physicochemical characteristics of lipid based nanoemulsifying cilostazol - Quality by Design. Colloids Surf. B Biointerfaces. 115, 29-36.

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Pund, S., Pawar, S., Gangurde, S., Divate, D., 2015. Transcutaneous delivery of leflunomide nanoemulgel: Mechanistic investigation into physicomechanical characteristics, in vitro anti-psoriatic and anti-melanoma activity. Int. J. Pharm. 487, 148-156.

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Ravi, P.R., Vats, R., Dalal, V., Gadekar, N., Aditya, N., 2015. Design, optimization and evaluation of poly-ɛ-caprolactone (PCL) based polymeric nanoparticles for oral delivery of lopinavir. Drug Dev. Ind. Pharm. 41, 131-140.

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Singh, B., Kapil, R., Nandi, M., Ahuja, N., 2011b. Developing oral drug delivery systems using formulation by design: vital precepts, retrospect and prospects. Expert Opin. Drug Deliv. 8, 1341-1360. Tang, X.J., Fu, Y.H., Meng, Q.H., Li, L.M., Ying, X.Y., Han, M., He, Q.J., Yang, B., Zeng, S., Hu, Y.Z., Sheng, X. X., Gao, J.Q., 2013. Evaluation of pluronic nanosuspensions loading a novel insoluble anticancer drug both in vitro and in vivo. Int. J. Pharm. 456, 243-250 Woitiski, C.B., Veiga, F., Ribeiro, A., Neufeld, R., 2009. Design for optimization of nanoparticles integrating biomaterials for orally dosed insulin. Eur. J. Pharm. Biopharm.73, 25-33.

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Yadav, K.S., Sawant, K.K., 2010. Formulation optimization of etoposide loaded PLGA nanoparticles by double factorial design and their evaluation. Curr. Drug Deliv. 7, 61-64.

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Yerlikaya, F., Ozgen, A., Vural, I., Guven, O., Karaagaoglu, E., Khan, M.A., Capan, Y., 2013. Development and evaluation of Paclitaxel nanoparticles using a quality-by-design approach. J. Pharm. Sci. 102, 3748-3761.

Table legends

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Table 1. Eight formulation and process parameters (X1-X8) along with their levels selected for analysis by 12 run Plackett-Burman Screening Design (PBSD) and the critical quality attributes (CQA; dependent responses Y1- Y4) measured.

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Table 2. Layout of composition of SAT nanoparticles (SAT-NP) showing the levels of CFAs and CPPs and the result data of mean values of observed CQAs for the 12 runs of PlackettBurman Screening design (PBSD) Table 3. Table 3. 23 full factorial design: Levels of three CFAs varied in experimental design along with fixed levels of other CFAs and CPPs. Table 4. 23 full factorial design matrix and result data of mean values of responses, namely, mean particle size (Y1), EE (Y2) and PDI (Y3) Table 5. Statistical parameters derived from regression analysis and ANOVA of 8 factor, 12 run PBSD for mean particle size (Y1), zeta potential (Y2), EE (Y3) and DE30 (Y4) Table 6. A Summary of p values for coefficients of selected CFAs and their interaction terms resulted from 23 full factorial analysis for the CQAs: Y1 (Mean particle size), Y2 (EE) and Y3 (PDI) Table 7. The experimental and predicted values for all the responses (Y1-Y3) along with biasa (%) observed for optimum SAT-NP (A) and three random SAT-NP compositions covering the experimental domain (B, C and D) Figure captions

24

ACCEPTED MANUSCRIPT Fig. 3. Graphical representation of effect of selected CFAs (X) on CQAs (Y) using Lenth's method; (a) Y1 (particle size), (c) Y2 (EE) and (e) Y3 (PDI). Bayesian analysis of the coefficients showing probability of each CFA's (X) activity for all the considered CQAs (Y), (b) Y1 (particle size), (d) Y2 (EE) and (f) Y3 (PDI).

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Fig. 4. X-ray powder diffraction analysis of (a) Pristine SAT, (b) Physical mixture of Pristine SAT: ES100: PVA:Mannitol (1:1:2.4:1) and (c) Lyophilized SAT-NP

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ED

MA

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Fig. 5. In vitro release of SAT-NP in media with gradually changing pH. (Each data point represents mean of 6 determinations and error bars represent SD)

25

ACCEPTED MANUSCRIPT Table 1. Eight formulation and process parameters (X1-X8) along with their levels selected for analysis by 12 run Plackett-Burman Screening Design (PBSD) and the critical quality attributes (CQA; dependent responses Y1- Y4) measured.

X2

Type of polymer in organic phase

X3

Type of stabilizer in aqueous phase

X4

Concentration of stabilizer (%)

X5

Amount of polymer in organic phase (mg)

X6

Volume of aqueous phase (mL)

X7

Stirring time (h)

X8

Stirring speed (rpm)

MA

CQA

T

Amount of SAT in organic phase (mg)

NU

X1

Level High (+1) Low (-1) 25

50

ES100

PLGA

PVA

P188

0.2

0.4

25

50

15

25

2

4

500

1500

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Critical formulation attributes (CFAs) and / critical process parameters (CPPs)

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Symbol

Unit nm

Mean particle size

Y2

Zeta potential

Y3

Entrapment efficiency (EE)

%

Y4

Dissolution efficiency at 30 min (DE30)

%

mV

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PT

ED

Y1

26

ACCEPTED MANUSCRIPT Table 2. Layout of composition of SAT nanoparticles (SAT-NP) showing the levels of CFAs and CPPs and the result data of mean values of observed CQAs for the 12 runs of PlackettBurman Screening design (PBSD) CQAsa

PBSD05

PBSD06

PBSD07

PBSD08

PBSD09

PBSD10

PBSD11

PBSD12 a

-1

-1

-1

1

Y4 (%) 25.2

± 3.1

± 0.07

± 2.6

± 0.8

531.4

-11.2

67.3

30.9

± 4.6

± 0.09

± 1.9

± 1.5

477.5

-13.1

47.2

77.8

± 3.6

± 0.22

± 1.5

± 2.8

278.9

-2.35

67.3

31.3

± 2.2

± 0.06

± 2.3

± 1.1

552.6

-10.5

58.3

73.4

± 3.7

± 0.09

± 2.2

± 1.4

387.7

-3.82

84.2

85.1

± 2.6

± 0.03

± 1.8

± 1.3

422.3

-1.04

82.1

61.3

± 3.7

± 0.02

± 1.7

± 2.2

591.2

-3.32

46.3

41.1

± 4.7

± 0.04

± 2.1

± 1.7

514.2

-12.2

29.9

42.3

± 4.4

± 0.19

± 1.5

± 1.6

379.8

-10.9

28.3

47.9

± 2.6

± 0.13

± 1.5

± 2.1

427.2

-14.3

59.9

70.6

± 3.3

± 0.22

± 2.4

± 1.5

398.9

-1.24

70.1

86.2

± 2.6

± 0.02

± 1.9

± 1.2

-1

1

1

-1

-1

-1

1

-1

1

1

-1

1

-1

-1

1

1

-1

-1

-1

1

1

1

-1

-1

-1

1

1

1

-1

1

-1

1

1

-1

-1

-1

1

1

-1

1

1

1

-1

1

-1

-1

-1

1

-1

1

-1

1

1

-1

1

-1

-1

-1

-1

-1

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1

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Y3 (%) 65.2

1

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PBSD04

1

1

Y2 (mV) -3.15

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PBSD03

-1

1

X4 X5 X6 X7 X8 Y1 (%) (mg) (mL) (h) (rpm) (nm) -1 1 1 1 -1 -1 411.5

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PBSD02

X2 X3

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PBSD01

X1 (mg) 1

PT

Formulation

T

CFAs and/CPPs

1

-1

1

1

-1

1

-1

-1

-1

1

1

1

-1

1

1

-1

1

-1

-1

Values are mean of three determinations ± SD.

27

ACCEPTED MANUSCRIPT Table 3. 23 full factorial design: Levels of three CFAs varied in experimental design along with fixed levels of other CFAs and CPPs. Levels of CFAs used in the formulation X1: Concentration of PVA in aqueous phase (%)

0.2

X2: Volume of aqueous phase (mL) X3: Amount of ES100 in organic phase (mg) 3

+1

T

-1

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CFAs analyzed in 23 factorial design

0.4

15

25

25

50

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Fixed levels of CFAs and CPPs used in 2 full factorial design Amount of SAT in organic phase (mg)

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Polymer in organic phase Stabilizer in aqueous phase Stirring time (h)

ES100 PVA 02 500

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PT

ED

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Stirring speed (rpm)

25

28

ACCEPTED MANUSCRIPT Table 4. 23 full factorial design matrix and result data of mean values of CQAs, namely, mean particle size (Y1), EE (Y2) and PDI (Y3) CQAsb Y2 (%) 69.2 ± 2.4 76.5 ± 2.2 65.5 ± 2.5 74.3 ± 2.9 72.3 ± 2.4 84.4 ± 1.3 70.3 ± 2.7 82.0 ± 1.2

Y3 0.32 ± 0.010 0.22 ± 0.021 0.33 ± 0.013 0.29 ± 0.014 0.32 ± 0.011 0.34 ± 0.013 0.33 ± 0.009 0.32 ± 0.007

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CFAsa X1 (%) X2 (mL) X3 (mg) Y1 (nm) FD1 -1 (0.2) -1 (15) -1 (25) 420.3 ± 3.6 FD2 1 (0.4) -1 (15) -1 (25) 210.6 ± 2.1 FD3 -1 (0.2) 1 (25) -1 (25) 430.6 ± 3.2 FD4 1 (0.4) 1 (25) -1 (25) 269.2 ± 2.7 FD5 -1 (0.2) -1 (15) 1 (50) 450.2 ± 3.1 FD6 1 (0.4) -1 (15) 1 (50) 305.5 ± 2.9 FD7 -1 (0.2) 1 (25) 1 (50) 465.7 ± 3.8 FD8 1 (0.4) 1 (25) 1 (50) 233.6 ± 2.4 a Values in the brackets are actual amounts. b Values are mean of three determinations ± SD. Formulation Run

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ACCEPTED MANUSCRIPT Table 5. Statistical parameters derived from regression analysis and ANOVA of 8 factor, 12 run PBSD for particle size (Y1), zeta potential (Y2), EE (Y3) and DE30 (Y4)

T

b0 Constant

Particle size Zeta potential EE (Y3) DE30 (Y4) (Y1) (Y2) Coefficie Coefficie Coefficien Coefficie p value p value p value p value nt nt t nt < <0.000 0.0002 <0.00 447.76 7.254 58.835 56.0 0.01 1 4 1 0

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Coefficie nt (b)

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Amount of b1 SAT in 0.18 0.11 0.05 0.02 26.22 0.594 -3.743 -3.0 organic 8 1 0 4 phase (X1) Type of b2 polymer in 0.70 0.85 0.016 0.00 3.40 -0.067 -8.128 -19.667 organic 9 9 5 1 phase (X2) Type of b3 stabilizer in 0.19 0.02 0.008 32.68 4.768 0.0084 1.167 aqueous 10.367 8 9 5 phase (X3) Concentratio b4 n of 0.14 0.00 -54.0 0.678 -0.158 0.93 0.333 0.67 stabilizer 8 7 (X4) Amount of b5 polymer in 0.29 0.64 0.001 < -10.45 -0.177 7.848 -7.333 organic 6 7 8 0.010 phase (X5) Volume of b6 0.51 aqueous 44.22 0.0128 0.258 2.582 0.22 -0.5 0.53 5 phase (X6) Stirring time b7 0.92 0.15 -12.43 0.23 0.036 4.157 0.08 -1.33 (X7) 5 6 Stirring b8 0.77 0.34 0.41 2.62 -0.393 -3.920 0.10 -0.667 speed (X8) 3 3 5 Coefficient of determination for 0.971 0.985 0.972 0.997 model (R2) 0.0306 0.012 0.029 0.012 Model p value F-ratio 12.6 24.34 12.94 113.37 Significant effects of factors (p < 0.05) on individual responses, R2> 0.90 and F-ratio greater than theoretical value for the applied model are shown in bold type.

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Intercept

Y2

Y3

b0

<0.0001

<0.0001

<0.0001

X1

b1

<0.0001

<0.0001

0.215

X2

b2

0.634

0.065

0.546

X3

b3

0.0013

0.0013

0.136

X1X2

b12

0.166

0.824

0.887

X1X3

b13

0.826

X2X3

b23

0.00123

X1X2X3

b123

<0.00074

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0.107

0.778

0.412

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0.144

0.712

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p values for coefficients Factor

0.376

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Significant effects of factors (p < 0.05) on individual CFAs are shown in bold type.

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Y2

Y3

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Response

RI P

Compositio Experimenta Predicte Bias Experimenta Predicte Bias Experimenta Predicte Bias l value d value a (%) l value d value a (%) l value d value a (%) n

26.88)b

216.0

221.3

2.5

309.2

302.6

2.1

1.7

0.250

0.258

3.0

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15.75,

77.9

3.4

0.320

0.316

1.3

69.0

70.2

1.7

0.315

1.5

78.3

75.4

B (0.35, 43.75)b C (0.225, 16.25, 408.0

400.7

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28.13)b

77.0

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22.5,

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A (0.4,

D (0.375, 23.75,

0.310

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266.2 271.8 2.1 77.1 79.9 3.7 0.330 0.319 3.5 a Bias ={ (predicted value-experimental value)/experimental value} x 100 b The values shown in the bracket are concentration of PVA (%), volume of aqueous phase (mL) and amount of ES100 (mg) 46.88)b

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Fig. 1. An Ishikawa herringbone diagram portraying the cause-effect relationship among the various formulation and process variables affecting critical quality parameters of SAT-NP

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Fig. 2. Pareto charts showing influence of selected CFAs and CPPs on CQAs of SAT-NP. Fig. a, c, e, and g represent normalized squares of contribution (%) and b, d, f, and h represent cumulative plot of normalized squares for particle size, zeta potential, EE, and DE30 respectively.

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Fig. 3. Graphical representation of effect of selected CFAs (X) on CQAs (Y) using Lenth's method; (a) Y1 (particle size), (c) Y2 (EE) and (e) Y3 (PDI). Bayesian analysis of the coefficients showing probability of each CFA's (X) activity for all the considered CQAs (Y), (b) Y1 (particle size), (d) Y2 (EE) and (f) Y3 (PDI).

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Fig. 4. X-ray powder diffraction analysis of (a) Pristine SAT, (b) Physical mixture of Pristine SAT: ES100: PVA:Mannitol (1:1:2.4:1) and (c) Lyophilized SAT-NP

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Fig. 5. In vitro release of SAT-NP in media with gradually changing pH . (Each data point represents mean of 6 determinations and error bars represent SD)

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