QUALITY-BY-DESIGN APPROACH FOR DEVELOPMENT AND OPTIMIZATION OF NEFOPAM HYDROCHLORIDE LOADED POLY-(ε-CAPROLACTONE) AND POLY-3-HYDROXYBUTYRATE MICROSPHERES

QUALITY-BY-DESIGN APPROACH FOR DEVELOPMENT AND OPTIMIZATION OF NEFOPAM HYDROCHLORIDE LOADED POLY-(ε-CAPROLACTONE) AND POLY-3-HYDROXYBUTYRATE MICROSPHERES

Neelam Sharma^{*1, 2}, Sukhbir Singh ¹, Sandeep Arora ¹ and Jitender Madan ³

Chitkara College of Pharmacy ¹, Chitkara University, Chandigarh Patiala National Highway (NH-64), Patiala - 140401, Punjab, India.

Department of Research ², Innovation and Consultancy, IKG Punjab Technical University, Jalandhar-Kapurthala Highway, Kapurthala - 144603, Punjab, India.

Chandigarh College of Pharmacy ³, Landran, Kharar, Sahibzada Ajit Singh Nagar - 140307, Punjab, India.

ABSTRACT: The objective of present study was to investigate optimized nefopam hydrochloride loaded microspheres (NPH-MS) by investigating the relationship between independent and dependent variables using response surface methodology. Central composite design with thirty three batches was constructed using drug: polymer (X₁), PHB: PCL (X₂), stirring speed (X₃), stirring time (X₄) and polyvinyl alcohol (PVA) (X₅) level as independent factors. NPH-MS were manufactured using polyhydroxybutyrate (PHB) and poly ε-caprolactone (PCL) by double emulsion solvent evaporation technique. The response variables were % entrapment efficiency (Y₁), mean diameter (Y₂), % drug loading (Y₃), and % yield (Y₄). Second-order polynomial equations for (Y₁-Y₄) were developed by Design-expert® 9.0.5.1 software. Positive and negative signs of regression coefficient indicated synergistic and antagonistic effect on response variables, respectively. Optimized NPH-MS estimated by design-expert software have highest desirability function, D = 0.911 and X₁, X₂, X₃, X₄ and X₅ were 1: 2.86, 1:1.19, 1501 rpm, 2.98 h and 0.54 % w/v, respectively. The model predicted values of Y₁, Y₂, Y₃ and Y₄ for optimized NPH-MS were 83.80%, 100.78μm, 21.50% and 77.99% respectively. Check point batch analysis validated the authenticity of predictive power of designed model as % bias between experimental and model predicted values was < 5%. It was concluded that quality-by-design approach has great utility in formulation optimization.

Response surface methodology, Second-order polynomial equations, Desirability function, Quality - by -design, Central composite design, Optimization

INTRODUCTION: Novel drug delivery system involves formulations, technologies, and systems for transporting a pharmaceutical compound in the body as needed to safely achieve its desired therapeutic effect. Recurrent dosing, systemic toxicity and reduced bioavailability are major obstacles in delivery of an active pharmaceutical entity.

Polymeric microspheres have been outstanding approach in domain of sustained drug delivery with superior safety and biocompatibility ^{1, 2}. Nefopam hydrochloride is a non-opioid, non-steroidal, centrally acting analgesic drug having chemical name 5-methyl-1-phenyl-1, 3, 4, 6-tetrahydro-2, 5-benzoxazocine hydrochloride ^{3, 4}.

It is cyclized analog of orphenadrine and diphenhydramine representing a new class of centrally acting skeletal muscle relaxants. It has oral bioavailability approximately 30-40% and an elimination half life of about 3-5. The adverse effects of drug such as nausea, vomiting, dizziness and patient non-compliance has limited its application ^{5, 6}.

In this research, nefopam hydrochloride loaded microspheres (NPH-MS) were synthesized employing mixture of poly-3-hydroxybutyrate (PHB) and poly-ε-caprolactone (PCL) by double emulsion solvent evaporation method ⁷. Central composite quality-by-design (QbD) approach has been applied for optimizing critical formulation / process variables with least number of experiments. Selection of appropriate regression model was performed through statistical analysis i.e. model F-value, lack of fit F-value and correlation coefficient (r²). The purpose of present research was to study effect of independent variables on response prameters and numerical optimization to investigate optimized NPH-MS ^8-11. Skeletal muscle relaxant activity of optimized NPH-MS was investigated using rota-rod apparatus.

MATERIALS AND METHODS:

Materials: Nefopam hydrochloride (C₁₇H₂₀ClNO, 5- methyl- 1- phenyl -1, 3, 4, 6- tetrahydro- 2, 5-benzoxazocine hydrochloride, Mw 289.8 g mol^-1, CAS NO-23327-57-3, 99.57% purity) was purchased from Hangz Hou- Daying- Chem. Company Ltd., China. Poly ε-caprolactone (CAS NO-24980-41-4, (C₆H₁₀O₂)_n, average Mw ~ 14,000, average M_n ~ 10,000 by GPC) and polyhydroxybutyrate (CAS NO-29435-48-1, poly-(R)-3-hydroxybutyric acid) were purchased from Sigma-Aldrich Chemie, Gmbh, Steinhelm, USA. All other chemicals used were of analytical grade.

Quality-by-Design: CCD was utilized for optimizing critical formulation/process variables in production of NPH-MS ^{12, 13}. CCD design consists of 2ⁿ two-level full-factorial or 2^n-1 fractional factorial design points which forms backbone of experiment, 2n axial (±α) points to check curvature and n_o center points to estimate experimental reproducibility. In this research, CCD with fractional factorial design was utilized for investigation of effect of five independent variables (n = 5) on response parameters of NPH-MS (Table 1). A 2^5-1 (sixteen experimental runs) fractional factorial design formed backbone of experiments with variables taken at low and high levels, which were depicted by coded values -1 and +1, respectively. Seven replicate runs (n_o) were performed at centre point, coded value 0 and ten additional runs (2n) were carried out at axial (star) points, coded α (in this study, α = 2 using following formula):

In present investigation, thirty three batches of NPH-MS were manufactured according to CCD experimental design matrix (Table 2) as generated by Design-Expert software (Trial Version 9.0.5.1, Stat-Ease Inc., MN). Second order polynomial model (Eq. 2) was generated by multiple regression analysis using Design-Expert software (Montgomery, 2008).

Where, Y denotes observed response variable, β₀ is constant coefficient and ϵ is residual correlated to experiments. β_i, β_ii and β_ij represents coefficients of linear, quadratic parameter and interaction parameters, respectively. X_i represents average outcome of changing one variable at a time from low to high, polynomial terms X_i² was employed to assess non-linearity effect of variable X_i, interaction terms X_iX_j illustrated how the response transforms when two variables X_i and X_j were altered concurrently.

TABLE 1: INDEPENDENT AND DEPENDENT VARIABLES FOR NPH-MS

TABLE 2: CENTRAL COMPOSITE DESIGN MATRIX FOR NPH-MS

Production Methodology: Double emulsion solvent evaporation method was employed for manufacturing NPH-MS ⁷. NPH dispersed in distilled water: acetone (1:1) was extruded gradually through syringe #20 to solution of PHB, PCL and sorbitan monooleate (0.5% w/v) in dichloromethan. Subsequently, sonicated for 2 min via an ultrasonic probe sonicator (PCI analytical, India) to produce primary emulsion. The gradual addition of primary emulsion to 0.5% w/v PVA solution containing Tween 20 (1% w/v) produced double emulsion. Hardening of microspheres was accomplished through continuous stirring at 37 ± 0.5 °C on magnetic stirrer (REMI, Mumbai, India). Hardened NPH-MS were collected by membrane filtration through 0.45μm Millipore filter (Merck Life Science Pvt. Ltd, Mumbai, India), and ultra-centrifuged using cooling centrifuge (Remi, RIS-24 BL, Mumbai, India) at 10,000 rpm for 30 min at 4 °C. Lyophilization was achieved using D-mannitol as lyoprotectant at - 65 °C and 0.5 kPa for 24 h in lyophilizer (Allied Frost, India) (Fig. 1).

FIG. 1: PRODUCTION METHODOLOGY FOR NPH-MS

Evaluation of Response Variables of Microspheres (Y₁-Y₄): NPH-MS (50mg) was dispersed in phosphate buffer, pH 7.4 for 24 h followed by centrifugation (Remi, RIS-24 BL, Mumbai, India) at 5,000 rpm for 10 min. Supernatant was analyzed spectrophotometrically at 266nm using double beam UV-visible spectrophotometer (Systronics AU-2701, Ahmedabad, India) ^{14, 15}. Entrapment efficiency (% EE, w/w) and drug loading (% DL, w/w) of NPH-MS were calculated using following equations:

Mean diameters of NPH-MS were determined by optical microscopy employing light microscope (Adeltavision Microscopes, India) at × 400 magnifications. Images were captured with APCAM USB2 digital cameras system (APCAM, India) and processed with Adelta Optec’s APView imaging software. Measurement was executed in triplicate (n = 3) to obtain mean diameter ⁵. The completely dried NPH-MS were accurately weighed and % yield of microspheres was calculated using following equation:

Response Surface Analysis of Variables (Y₁-Y₄) by Design-Expert Software: Three-dimensional response surface plots and corresponding two-dimensional contour plots were generated using model graphs tool of Design-Expert software to analyze transformation in response due to changes in level of independent variables ^{11, 16, 17}.

Search for Optimum Batch of NPH-MS by Numerical Optimization Method: Optimal values of formulation and process variables for fabrication of NPH-MS were acquired on previously mentioned constrained criterion through numerical optimization method using Design-Expert software ¹⁶. It involved setting optimization criteria followed by running optimization to generate desirability bar graph. Report of optimized composition and processing conditions for optimal formulation of NPH-MS along with predicted values of response variables was generated by Design-expert® 9.0.5.1 software. To acquire graphical view of region corresponding to highest overall desirability function, three-dimensional response surface graph and corresponding contour plot were generated for desirability coefficient as a function of changes in formulation variables.

Check Point Batch Analysis: Optimal values of formulation and process variables for synthesis of optimized NPH-loaded biodegradable microspheres with highest desirability coefficient were acquired using Design-expert® 9.0.5.1 software on set constrained criterion. For validation of optimization strategy, check point batch of NPH-MS was synthesized using optimal values of variables and evaluated for Y₁-Y₄ as per aforementioned procedures. Eq. 6 was used for calculating percent bias between experimental and model predicted values of response variables ¹⁰.

Statistical Analysis: All the experiments were performed in triplicate, and results were expressed as mean ± SD (n = 3). Statistical analysis of other data was performed using using GraphPad Prism version 5.01 (GraphPad Software, San Diego California, USA). Data were evaluated by Bonferroni post-test, and value of P < 0.05 was considered statistically significant.

RESULTS AND DISCUSSION:

Response Surface Analysis of Variables (Y₁-Y₄) by Design-Expert Software:

Entrapment Efficiency (% EE) (Y₁): A polymeric drug delivery system must be capable to entrap utmost possible quantity of drug with minimum amount of polymer, therefore, optimum drug: polymer for highest % EE should be acquired by evaluating the effects of formulation and process variables on % EE (Y₁). The effect of design variables on % EE can be described by following second-order polynomial equation:

Y₁ = 84.37 + 6.26X₁ + 0.99X₂ + 0.54X₃ + 0.041X₄ - 1.12X₅ + 0.19X₁X₂ - 0.60X₁X₃ - 0.41X₁X₄ + 0.31X₁X₅ -0.60X₂X₃ + 1.73X₂X₄ - 1.10X₂X₅ - 0.48X₃X₄ - 0.34X₃X₅ - 0.88X₄X₅ - 8.23X₁² - 0.67X₂² - 6.99X₃² - 6.86X₄² - 3.24X₅² (r² = 0.9902)…Eq.(7)

Model F-value and lack of fit F-value for % EE was found 55.29 (p < 0.0001) and 43.65 (p > 0.05), respectively, which implies that model is significant and lack of fit is non-significant relative to pure error indicating accuracy of data. Correlation coefficient (r²=0.9902) for polynomial quadratic equation revealed that model fitted the data significantly.

TABLE 3: REPORT OF ANALYSIS OF VARIANCE FOR % EE (Y₁), MEAN DIAMETER (Y₂), % DL (Y₃), AND % YIELD (Y₄) OF NPH-MS

*Statistically significant (p < 0.05).

Positive signs of regression coefficient X₁, X₂, X₃ and X₄ illustrated that % EE of NPH-MS increased with increasing values of these design variables. The factors affecting % EE with strong linear effect were X₁ (F-value, 182.39) and X₅ (F-value, 5.87) (p < 0.05). X₂ and X₄ had significant interaction effect on % EE (F-value, 9.24; p < 0.05). X₁, X₃, X₄ and X₅ had significant quadratic influence. The effect of design variables on % EE according to level of significance was X₁ > X₅ > X₂ > X₃ > X₄ (Table 3).

FIG. 2: RESPONSE SURFACE (3D) AND CORRESPONDING COUNTER (2D) PLOTS SHOWING THE EFFECT OF (A) DRUG: POLYMER AND PVA CONCENTRATION ON % EE, (B) DRUG: POLYMER AND STIRRING SPEED ON % EE

Fig. 2 suggested that increasing drug: polymer directly increased drug entrapment in NPH-MS which can be due to increased viscosity of droplets accommodating larger amount of drug. Secondly, viscous solution forms a barrier that might reduce the diffusion of NPH from into external aqueous phase. Furthermore, increasing amount of polymer would produce particles of larger volume to entrap greater amount of drug ^{11, 18-21}. Negative effect of PVA concentration on % EE can be illustrated by Fig. 2a. The increased concentration of PVA might result in reduction in free energy at interface leading to increased drug adsorption on particle surface, therefore reduced drug entrapment in microspheres ²².

Mean Diameter (Y₂): A second-order polynomial quadratic Eq. (8) developed by multiple regression analysis of mean diameter values can adequately describe the effect of selected variables on mean diameter.

Y₂ = 108.73 + 68.46X₁ - 0.58X₂ - 6.94X₃ - 9.19X₄ + 2.28X₅ - 0.074X₁X₂ + 1.26X₁X₃ - 5.72X₁X₄ +  3.41X₁X₅ - 0.47X₂X₃ - 1.65X₂X₄ + 1.79X₂X₅ - 2.71X₃X₄ + 0.97X₃X₅ + 1.71X₄X₅ + 21.38X₁² + 6.74X22 + 5.49X32 + 2.74X42 + 7.99X52 (r2 = 0.9886)    .....Eq. (8).

F-test was performed to determine significance of regression model and F-value 47.91 (< 0.0001) indicated that model was statistically significant. The goodness of fit of model was revealed by non-significant lack of fit F-value 1.05 (p > 0.05) ^{23, 24}. From values of coefficient, it appeared that drug: polymer (X₁) and PVA concentration (X₅) positively affected the mean diameter. In contrast, PHB: PCL (X₂), stirring speed (X₃) and stirring time (X₄) had antagonistic effect on particle size of NPH-MS. None of the significant interaction effect of variables was observed for mean diameter. The effect of selected variables on mean diameter of NPH-MS can be arranged in sequence X₁ > X₄ > X₃ > X₅ > X₂ (Table 3).

Mean diameter of NPH-MS decreased dramatically with an increase in PVA concentration (Fig. 3a). The relationship between PVA concentration and mean diameter was quadratic as indicated by significant p-value of X₅² (p < 0.05). PVA molecules exist as monomers at lower concentration producing coalescence of globules to generate microspheres having greater mean diameter. At higher concentration (above CMC), PVA molecules self-associate to produce micelles which can align at interface to yield stable emulsion with fine droplets ^25-27. Mean diameter of microspheres decreased significantly with an increase in stirring speed (Fig. 3a). The shear force exerted at high rpm might result in formation of particles with smaller size ¹¹.

FIG. 3: RESPONSE SURFACE (3D) AND CORRESPONDING COUNTER (2D) PLOTS DISPLAYING THE EFFECT OF (A) STIRRING SPEED AND PVA CONCENTRATION ON MEAN DIAMETER, (B) DRUG:POLYMER AND STIRRING TIME ON MEAN DIAMETER

Fig. 3b demonstrated that an increase in drug: polymer linearly increased mean diameter of NPH-MS which could be owing to improved viscosity of organic phase which provide larger resistance to sonication, consequently promoting fabrication of larger droplets ²⁵. Furthermore, PVA was insufficient to stabilize the emulsion droplets at higher polymer concentration leading to coalescence of droplets ^19-21.

% Drug Loading (% DL) (Y₃): Model proposed quadratic polynomial equation for estimating the effect of selected variables on % DL (Y₃). Synergistic effects of X₂, X₃, X₄ and antagonistic effects X₁, X₅ was observed for % DL as illustrated by following equation:

Y₃ = 21.10 - 2.25X₁ + 0.25X₂ + 0.16X₃ + 0.022X₄ - 0.31X₅ - 0.020X₁X₂ - 0.17X₁X₃ - 0.071X₁X₄ + 0.16X₁X₅ - 0.10X₂X₃ + 0.49X₂X₄ - 0.26X₂X₅ - 0.055X₃X₄ - 0.21X₃X₅ - 0.19X₄X₅ - 1.48X₁² - 0.17X₂² - 1.75X₃² - 1.72X₄² - 0.82X₅² (r² = 0.9919) ...Eq.(9)

Model F-value and lack of fit F-value for % EE was found 67.27 (p < 0.0001) and 2.23 (p > 0.05), respectively, which implies that model is significant and lack of fit is non-significant relative to pure error indicating accuracy of data. % DL was significantly influenced by X₁, X₂, X₅, X₂X₄, X₁², X₃², X₄², and X₅² (p < 0.05) (Table 3). Fig. 4 depicted that increase in drug: polymer and stirring speed initially increased the % DL followed by decline implying maximum % DL can be achieved at center level ^{19-21, 28}.

FIG. 4: RESPONSE SURFACE (3D) AND CORRESPONDING COUNTER (2D) PLOTS DISPLAYING THE EFFECT OF (A) STIRRING SPEED AND DRUG : POLYMER ON % DL, (B) STIIRING SPEED AND STIRRING TIME ON % DL

% Yield (Y₄): Results in Table 3 signified that independent factors significantly affecting % yield were drug: polymer (X₁), PHB: PCL (X₂), stirring time (X₃) and PVA concentration (X₅) (p < 0.05). The effect can be elucidated through mathematical relationship depicted in following equation:

Y₄ = 78.47 + 5.57X₁ - 0.22X₂ - 0.95X₃ - 2.07X₄ - 2.34X₅ + 0.77X₁X₂ + 1.04X₁X₃ + 0.79X₁X₄ -0.081X₁X₅ - 0.23X₂X₃ - 0.72X₂X₄ - 0.61X₂X₅ - 0.88X₃X₄ + 1.13X₃X₅ + 0.63X₄X₅ - 2.69X₁² - 0.17X₂² - 2.45X₃² - 2.33X₄² - 3.70X₅² (r² = 0.9793)..Eq. (10).

F-test was performed to determine significance of regression model and F-value 26.02 (< 0.0001) indicated that model was statistically significant. The goodness of fit of model was revealed by non-significant lack of fit F-value 0.83 (p > 0.05) ^{23, 24}. All the factors produced antagonistic effects on % yield of NPH-MS except drug: polymer as implied by positive sign of coefficient X₁. Fig. 5 represented that increase in drug: polymer produced positive effect while stirring speed and stirring time illucidated negative impact on % yield of NPH-MS ^{15, 19-21, 28}.

FIG. 5: RESPONSE SURFACE (3D) AND CORRESPONDING COUNTER (2D) PLOTS DISPLAYING THE EFFECT OF (A) DRUG: POLYMER AND PVA CONCENTRATION ON % YIELD, (B) STIRRING SPEED AND STIRRING TIME ON % YIELD

Optimized Batch of NPH-MS by Numerical Optimization Method: Optimal values of formulation and process variables producing highest overall desirability function, D = 0.911 was obtained by numerical optimization method using Design-Expert software (Table 4). Predicted values of response variables for optimized formulation have been represented in Table 5. Fig. 6 represented desirability bar graph which showed that partial desirability function of response variables (d₁-d₄) as well as overall desirability function (D) for optimized NPH-MS predicted by Design Expert Software. Fig. 7 depicted response surface plot and corresponding counter plot for overall desirability coefficient as a function of change in drug: polymer (X₁) and PHB: PCL (X₂) keeping stirring speed, stirring time and PVA concentration constant.

FIG. 6: DESIRABILITY FUNCTION BAR GRAPH FOR OPTIMIZED NPH-MS

FIG. 7: RESPONSE SURFACE (3D) AND CORRESPONDING COUNTER (2D) PLOTS FOR DESIRABILITY FUNCTION OF OPTIMIZED MICROSPHERES

TABLE 4: REPORT OF FORMULATION AND PROCESS VARIABLES FOR OPTIMIZED BATCH OF NPH-MS PREDICTED BY DESIGN-EXPERT® 9.0.5.1 SOFTWARE

Check Point Analysis and % Bias of Optimized Batch of NPH-MS: Comparison of experimental and model predicted values of response variables for optimized microspheres validated the authenticity of predictive power of designed model as indicated by % bias value < 5% (Table 5).

TABLE 5: COMPARISON OF EXPERIMENTAL AND MODEL PREDICTED VALUES OF RESPONSE VARIABLES FOR OPTIMIZED NPH-MS

CONCLUSION: Nefopam hydrochloride loaded poly-(ε-caprolactone) and poly-3-hydroxybutyrate microspheres were efficiently manufactured by double emulsion solvent evaporation technique. Central composite design response surface methodology could be successfully utilized for investigating the relationship between independent and dependent variables of formulation. Positive and negative signs of regression coefficient in second-order polynomial equations developed by Design-expert software indicated synergistic and antagonistic effect of selected factors on response variables, respectively. Optimized NPH-MS estimated by design-expert software have highest desirability function, D = 0.911. The present study conclusively manifested that the optimal formulations of NPH-MS contains drug: polymer (1: 2.86, w/w), PHB: PCL (1: 1.19, w/w), stirring speed (1501 rpm), stirring time (2.98 h) and PVA concentration (0.54%, w/v). The model predicted values of % entrapment efficiency (Y₁), mean diameter (Y₂), % drug loading (Y₃), and % yield (Y₄) for optimized NPH-MS were 83.80%, 100.78μm, 21.50% and 77.99%, respectively. Check point batch analysis illustrated that % bias between experimental and model predicted values was < 5% which authenticated the legitimacy of predictive power of designed model. Conclusively, it was manifested that quality-by-design approach could be effectively utilized for formulation optimization.

ACKNOWLEDGEMENT: The authors are thankful to IKG Punjab Technical University, Jalandhar for providing access to ScienceDirect and anti-plagiarism software. The authors express gratitude to Chitkara University for infrastructural support to carry out this work.

CONFLICT OF INTEREST: The authors have reported no conflicts of interest in this work.

REFERENCES:

1. Avella M, De Vlieger JJ, Errico ME, Fischer S, Vacca P and Volpe MG: Biodegradable starch/clay nano-composite films for food packaging applications. Food Chemistry 2005; 93: 467-474.
2. Hamishehkar H, Emami J, Najafabadi AR, Gilani K, Minaiyan M, Mahdavi H and Nokhodchi A: The effect of formulation variables on the characteristics of insulin-loaded poly (lactic-co-glycolic acid) microspheres prepared by a single phase oil in oil solvent evaporation method. Colloids and Surfaces B: Bio-interfaces 2009; 74: 340-349.
3. Alfonsi P, Passard A, Gaude-Joindreau V, Guignard B, Sessler DI and Chauvin M: Nefopam and alfentanil additively reduce the shivering threshold in humans whereas nefopam and clonidine do not. Anesthesiology 2009; 111: 102-109.
4. Podranski T, Bouillon TW, Riva T, Kurz AM and Oehmke MJ: Compartmental pharmacokinetics of nefopam during mild hypothermia. British Journal of Anaesthesia 2012; 108: 784-791.
5. Singh S, Singla Y and Arora S: Development and statistical optimization of nefopam hydrochloride loaded nano-spheres for neuropathic pain using Box–Behnken design. Saudi Pharmaceutical Journal 2016; 24: 588-599.
6. Verleye M, André N, Heulard I and Gillardin JM: Nefopam blocks voltage-sensitive sodium channels and modulates glutamatergic transmission in rodent. Brain Research 2004; 1013: 249-55.
7. Sharma N, Arora S and Madan J: Nefopam hydrochloride loaded microspheres for post-operative pain management: synthesis, physicochemical characterization and in-vivo evaluation. Artificial Cells, Nano-medicine, and Biotechnology 2017.
8. Govender S, Pillay V, Chetty DJ, Essack SY, Dangor CM and Govender T: Optimization and characterization of bio-adhesive controlled release tetracycline microspheres. International Journal of Pharmaceutics 2005; 306: 24-40.
9. Xie X, Yang Y, Chi Q, Li Z, Zhang H, Li Y and Yang Y: Controlled release of dutasteride from biodegradable microspheres: In-vitro and in-vivo studies. PLoS One 2014; 9: 1-23.
10. Yang X, Patel S, Sheng Y, Pal D and Mitra AK: Statistical design for formulation optimization of hydrocortisone butyrate-loaded plga nano-particles. AAPS Pharm Sci Tech 2014; 15: 569-587.
11. Zhou YZ, Alany RG, Chuang V and Wen J: Optimization of PLGA nano-particles formulation containing L-DOPA by applying the central composite design. Drug Development and Industrial Pharmacy 2013; 39: 321-330.
12. Box GEP and Wilson KB: On the experimental attainment of optimum conditions. Journal of the Royal Statistical Society. Series B (Methodological) 1951; 13: 1-45.
13. Myers RH, Montgomery DC and Anderson-Cook CM: Response Surface Methodology: Process and Product Optimization Using Designed Experiments. John Wiley and Sons, New York, Third Edition 2009.
14. Natarajan V, Krithica N, Madhan B and Sehgal PK: Formulation and evaluation of quercetin polycaprolactone microspheres for the treatment of rheumatoid arthritis. Journal of Pharmaceutical Sciences 2011; 100: 195-205.
15. Ranjan AP, Mukerjee A, Helson L and Vishwanatha JK: Scale up, optimization and stability analysis of curcumin C3 complex-loaded nanoparticles for cancer therapy. Journal of Nano-biotechnology 2012; 10: 1-18.
16. Lewis GA, Mathieu D and Phan-Tan-Luu R: Pharmaceutical Experimental Design. Marcel Dekker, Inc., New York, First Edition 1999.
17. Myers WR: Response Surface Methodology In: Chow SC: Encyclopedia of Biopharmaceutical Statistics. Marcel Dekker Inc., New York 2003: 858-869.
18. Freytag T, Dashevsky A, Tillman L, Hardee GE and Bodmeier R: Improvement of the encapsulation efficiency of oligonucleotides containing biodegradable microspheres. Journal of Controlled Release 2000; 69: 197-207.
19. Hao J, Fang X, Zhou Y, Wang J, Guo F, Li F and Peng X: Development and optimization of solid lipid nano-particle formulation for ophthalmic delivery of chloramphenicol using a box-behnken design. International Journal of Nano-medicine 2011; 6: 683-692.
20. Mao S, Shi Y, Li L, Xu J, Schaper A and Kissel T: Effects of process and formulation parameters on characteristics and internal morphology of poly (D, L-lactide-co-glycolide) microspheres formed by the solvent evaporation method. European Journal of Pharmaceutics and Bio-pharmaceutics 2008; 68: 214-223.
21. Shah M and Pathak K: Development and statistical optimization of solid lipid nanoparticles of simvastatin by using 2³ full-factorial designs. AAPS Pharm Sci Tech 2010; 11: 489-496.
22. Song X, Zhao Y, Hou S, Xu F, Zhao R, He J, Cai Z, Li Y and Chen Q: Dual agents loaded PLGA nanoparticles: systematic study of particle size and drug entrapment efficiency. European Journal of Pharmaceutics and Bio-pharmaceutics 2008; 69: 445-453.
23. Box GEP, Hunter JS and Hunter WG: Statistics for Experimenters: An Introduction to Design, Data Analysis and Model Building. John Wiley and Sons, Inc., Hoboken, NJ, Second Edition 2005.
24. Cochran WG and Cox GM: Experimental Designs. John Wiley and Sons Inc., New York, 1957.
25. Budhian A, Siegel SJ and Winey KI: Haloperidol-loaded PLGA nanoparticles: systematic study of particle size and drug content. International Journal of Pharmaceutics 2007; 336: 367-375.
26. Galindo-Rodriguez S, Allémann E, Fessi H and Doelker E: Physicochemical parameters associated with nanoparticle formation in the salting-out, emulsification-diffusion, and nano precipitation methods. Pharmaceutical Research 2004; 21: 1428-1439.
27. Zambaux MF, Bonneaux F, Gref R, Maincent P, Dellacherie E, Alonso MJ, Labrude P and Vigneron C: Influence of experimental parameters on the characteristics of poly (lactic acid) nanoparticles prepared by a double emulsion method. Journal of Controlled Release 1998; 50: 31-40.
28. Ko JA, Park HJ, Park YS, Hwang SJ and Park JB: Chitosan microparticle preparation for controlled drug release by response surface methodology. Journal of Microencapsulation 2004; 20: 791-797.
    

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    How to cite this article:

    Sharma N, Singh S, Arora S and Madan J: Quality-by-design approach for development and optimization of Nefopam hydrochloride loaded poly-(ε-caprolactone) and poly-3-hydroxybutyrate microspheres. Int J Pharm Sci Res 2017; 8(12): 5111-21.doi:10.13040/IJPSR.0975-8232. 8(12).5111-21.

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