Quality by Design approach in the development of Solid Lipid Nanoparticles of Linagliptin

 

D. Krishna Veni*, N. Vishal Gupta

Department of Pharmaceutics, JSS College of Pharmacy, Sri Shivrathreeswara Nagara, Mysuru,

JSS Academy of Higher Education and Research, JSS Medical Institution Campus, Karnataka, India

 

ABSTRACT:

 

 

INTRODUCTION:

Currently Quality by Design is the vital part of the pharmaceutical industry. It is used to ensure the predefined quality of the product during designing and developing and manufacturing any product. It is used for identification of risks associated with product development and to attain optimization through a scientific approach^[1-6]. It is a flexible process allowing continuous improvement in the process and product development mainly focuses on robustness and specifications of the product is based on performance requirements^[7-9].

 

Main elements in Pharmaceutical Quality by Design:

In pharmaceutical Quality by Design approach to product development manufacturer identifies characteristics that are critical to the quality of the product and establishes the relationship between critical material attributes (CMA’s), critical process parameters (CPP’s) and critical quality attributes (CQA’s) to consistently deliver a quality drug product.

·       Quality by Design consists of the following elements:

·       Quality target product profile

·       Identification of Critical Quality Attributes (CQA’s)

·       Initial risk assessment

·       Design of experiments

·       Design space

·       Control  strategy

·       Continuous improvement^[10-15].

 

 

Solid lipid nanoparticles (SLN’s) are potential drug-delivery systems for controlled drug delivery, by modifying the dissolution profile of the drug bioavailability enhancement is possible and targeted drug delivery is also possible [15-18].

 

The main objective of this study was to optimize the manufacturing process of solid lipid nanoparticles and to identify the impact of critical process parameters on critical Quality attributes using Quality by Design approach (QbD).

 

MATERIALS AND METHODS:

Materials used:

Linagliptin was gifted by MSN Laboratories, Hyderabad, India. Stearic acid was purchased from Loba chemie, Mumbai, Pluronic F-68 was purchased from Sigma-Aldrich. Remaining chemicals utilized in the experiment were of analytical reagent grade.

 

Method:

Quality by Design (QbD):

The main objective of Quality by design is to understand the influence of critical quality attributes (CQA’s) and critical process parameters (CPP’s) on quality of product and to ensure the quality of product during its shelf life.

 

Quality by approach begins with define Quality target product profile once it defined the second step is the identification of parameters which critically influence the QTPP that is CQA’s of the formulation. Most important step in QbD approach is Risk Assessment, it is science based approach used in QRM (Quality risk management). After initial risk assessment, there is an identification of potential factors that have maximum impact on CQA’s of the formulation to establish design space.

 

Quality target product profile:

It is starting step for formulation development, determines the design and extent of development. This is the performance based Quality attribute, every formulation should meet this attribute in order to achieve target product profile. The tests which are very crucial for formulation were Assay, impurities, drug loading and drug release, stability were included in Quality target product profile.

 

The primary step in the implementation of quality by design was Quality target product profile, it includes the type of drug delivery, type of dosage form and dosage strength, route of administration etc.

 

Identification of Critical Quality Attributes:

The second step in QbD approach was the identification of critical quality attributes of the Solid Lipid Nanoparticles. These quality attributes determine the performance of the formulation. These are originated from quality target product profile.

 

Initial risk assessment:

The combination of probability, occurrence and severity of risk was defined as risk assessment. Before starting the formulation by doing this step helps what studies should be carried out further and critical and noncritical variables can be defined to establish control strategy. Evaluated risks using the fishbone diagram or Ishikawa diagram. Ishikawa diagram was constructed to identify the effect of critical material attributes and critical process parameters for the development of optimized solid lipid nanoparticles.

 

Experimental Design and statistical analysis:

In this study, critical material attributes and critical process parameters were optimized using design expert® Software (StatEase, Inc., USA). 3 level factorial design, a response surface methodology was used in which 2 independent variables was laced at 3 levels (-1, 0, +1) like low, medium and high levels. X1: Amount of lipid X2: surfactant concentration were selected as independent variables and responses like Y1: particle size, Y2: entrapment efficiency and Y3: in-vitro drug release were selected as dependent variables. The interaction between each factor and its impact on dependent variables (responses) can be predicted by response surface method.

 

Preparation of Linagliptin Solid Lipid Nanoparticles:

Solid lipid nanoparticles were made by Hot homogenization method. A specific amount of stearic acid was dissolved in 5ml of ethanol and melted using the water bath to get lipid melt and drug (5mg) was dispersed in a lipid melt further sonicated for 1 min. The organic phase was injected through an injection into the aqueous phase which contains a specific amount of surfactant and homogenized at 3000rpm for 1 hr. Continuous homogenization was performed for 3 times to get coarse emulsion. Further emulsion was sonicated using probe sonicator for 10 min to get the final suspension. Centrifugation of the final suspension was carried out for 30 min at 10000 rpm. The suspension was filtered and freeze dried using freeze dryer [19].

 

Evaluation and characterization of Solid Lipid Nanoparticles:

Determination of Particle size, polydispersity index and zeta potential:

Samples of solid lipid nanoparticles after adequate dilution were evaluated and characterized for particle size, polydispersity index analyzed by a particle size analyzer (Model Zetasizer Nano ZS, Malvern Ltd., UK) using Dynamic light scattering technique. Zeta Potential was performed through electrophoretic light scattering technique using particle size analyzer at 20 ºC and 150 V.

Determination of drug encapsulation efficiency and drug loading:

For determination of Linagliptin Entrapment efficiency, solid lipid nanoparticles were separated from the supernatant by centrifugation at 10000 rpm (REMI laboratory Instrument, Mumbai, India) for 30 min. Free Linagliptin concentration was measured in the supernatants by UV spectroscopy in λmax=209 nm. The concentration of free Linagliptin in the supernatant was obtained by comparing the absorption of the supernatant to the standard curve related to absorption and Linagliptin concentration.

 

Entrapment efficiency:

The entrapment efficiency of Linagliptin in SLN formulations were determined by dissolving the SLN in 10 ml of methanol and the dispersion was centrifuged at 10,000 rpm for 30 min. The supernatant was analyzed for free drug content.

 

       (Amount of Linagliptin in prepared formulation-amount of  Linagliptin in supernatant)

E E (%   =   -------------------------------------------------------------   × 100

    (Amount of drug in prepared formulation)

 

In- vitro dissolution studies:

This was performed using dialysis bag. In this estimated amount (50 mg) of Solid lipid nanoparticles were placed and this bag was soaked in a beaker containing fast stimulating intestinal fluid and stirred for 48 h using magnetic stirrer. Aliquots of 5 ml were withdrawn in regular time intervals and exact amount was replaced at regular intervals of 0, 2, 4, 8, 12, 24, 32, 48 h. Collected samples were analyzed using UV visible spectrophotometer at 209 nm.

RESULTS AND DISCUSSION:

Quality by Design:

The main objective of Quality by design is to understand the influence of critical quality attributes (CQA’s) and critical process parameters (CPP’s) on quality of product and to ensure the quality of product during its shelf life.

 

Quality by approach begins with define Quality target product profile once it defined the second step is the identification of parameters which critically influence the QTPP that is CQA’s of the formulation. Most important step in QbD approach is Risk Assessment, it is the science based approach used in QRM (Quality risk management). After initial risk assessment, there is an identification of potential factors that have maximum impact on CQA’s of the formulation to establish design space.

 

Quality Target Product Profile:

It is starting step for formulation development, determines the design and extent of development. This is the performance based Quality attribute, every formulation should meet this attribute in order to achieve target product profile. The tests which are very crucial for formulation were Assay, impurities, drug loading and drug release, stability were included in Quality target product profile.

 

The quality target product profile is summary of the quality attributes of a drug product that will be accomplished to ensure desired product quality, taking consideration of safety and efficacy of Solid lipid nanoparticles discussed in table 1.

 

Table 1: QTPP for Linagliptin loaded Solid Lipid Nanoparticles

Attributes	Target	Justification
A.     Physical Attributes
Type of drug delivery	Lipid based system	It helps in getting better absorption of drug and it leads to increase the bioavailability of a drug
Dosage form	Solid Lipid nanoparticles	Lipid based vascular system. It helps in increasing the bioavailability of a drug
Dosage strength	50 mg	Requires same strength as that of reference product
Route of Administration	Oral route	Highly recommended route for SLN administration and commercially available formulations also intended for oral route only
B.    Chemical Attributes
	Test	Result	Specification
I.	Identification by
	·       Infrared Absorption	The IR absorption spectrum matches with standard spectrum	The infrared absorption spectrum of the sample shall be concordant with that of Linagliptin standard spectrum
	·       HPLC	Sample RT matches with standard RT	The major peak retention time of the sample shall match with major peak retention time of the Linagliptin standard, as obtained in the chiral purity by HPLC
II.	Assay by HPLC	99.5 % w/w	Needed for clinical effectiveness
III.	Diffusion Profile	Media:6.8 buffer, 45 ml	Needed for clinical effectiveness
IV.	Residual solvents	0.06 % w/w	Not more than 0.10%
V.	Water content by KFR	0.49 % w/w	Not more than 1%
C.      Packaging and Storage related
Packaging	Capsules	The SLN’s can be easily delivered by filling in capsules with improved patient compliance and manufacturing ease
Stability	At least 6 months at room temperature	To retain therapeutic potential of the drug during storage

 

Critical Quality Attributes:

These are physical, chemical or biological, microbiological attributes it should be within the range to get better quality Solid lipid nanoparticles were discussed in table 2.

 

Table 2: List of Critical Quality Attributes (CQAs) for SLN’s preparation

S. No	QTPP	TARGET	CQA’S	JUSTIFICATION
1	Colour Odor Appearance	It should be acceptable by the patient	No	Colour, odor & appearance of the product was not considered as crucial because they are not directly associated with the patient compliance
		No unpleasant odor
		Should be Acceptable by the patient
2	Particle Size	200-500 nm	Yes	This solid lipid nanoparticles are administered through oral route in capsule form and particle size shows impact on drug release
3	Entrapment Efficiency	30 %-80 %	Yes	Higher entrapment efficiency is directly proportional to drug release.
4	Drug Release	85 %-100 %	Yes	The amount of drug release Needed for clinical effectiveness

 

Table 3: Risk Estimation Matrix for assessing initial risk assessment of Linagliptin Solid Lipid Nanoparticles

Drug PDT CAQ’S	RISK ESTIMATION MATRIX
	Type of LIpid	Drug: Lipid ratio	Amount of surfactant	Homogenization Speed	Sonication time	Centrifugation speed	Freeze drying
Physical attributes	High	High	High	Medium	Low	Low	Low
Particle size	High	High	High	High	High	Medium	Low
Entrapment efficiency	High	High	High	High	Low	Low	Low
Drug release	High	High	High	Medium	Low	Low	Low
yield	High	High	High	Medium	Medium	Medium	Low

High - Risk Medium - Risk - Low- Risk

 

Initial risk assessment and factor screening study for SLN:

These studies were performed to find the critical material attributes (CMA’s) and critical process parameters (CPP’s) for Linagliptin solid lipid nanoparticles affecting the CQA’s of a drug product. Fishbone diagram or Ishikawa diagram (figure 1) was made to identify the primary risk factors which affect the quality of Linagliptin SLN.

 

Preliminary studies were performed to identify critical quality attributes with high risk by constructing risk estimation matrix were carried out for qualitative analysis of risk by assigning a low, medium, high risk levels discussed in table 3.

 

To identify the effect of each CMA’s and CPP’s on CQA’s of SLN’s, factors screening study were performed by altering the lipid amount 50, 100, 150 mg, altering the surfactant concentration 1 %, 2 %, 3 % were assessed for their effect on particle size, EE and in-vitro drug release.

 

Figure 1: Fishbone/Ishikawa diagram of Solid lipid nanoparticles

 

Evaluation and Characterization of Solid Lipid Nanoparticles:

Determination of Particle size:

Hot homogenization method was selected for preparation of solid lipid nanoparticles. The results indicated that particle size was enhanced with increasing lipid concentration and this increase was significant.

 

Particle size and shape of SLN’s was studied using particle size analyzer and scanning electron microscopy. Linagliptin loaded pH sensitive solid lipid nanoparticles were characterized for particle size. The results indicate lipid was directly proportional to particle size and surfactant concentration was inversely proportional to the particle size of the nanoparticles.

 

Determination of drug loading and entrapment efficiency:

It was observed from the results that the entrapment efficiency in the range of 41.23±1.21 to 57.35±1.59 %. It shows that as the lipid concentration increases entrapment efficiency decreases. It may be due to highly placed lipid coat and polymer coating, which limits its entrapment due to this time taken for lipid precipitation was more. Similarly entrapment efficiency increases when surfactant concentration increases this may be due to free drug available on the surface of nanoparticle in place of entrapment on nanoparticles.

 

In vitro dissolution studies:

In vitro drug dissolution studies were carried out for all formulations for 48 hours. The presence of stearicacid in the nanoparticles resulted in sustained drug release in intestine. Entrapment efficiency was directly proportional to the drug release higher concentration of stearic acid and lower concentration of Surfactant retarded the drug release and vice versa.

 

The R² values of Zero order model for all the formulations were significantly greater indicating the release of drug is independent of concentration and the delivery system plays an important role in controlling the release of the drug.

Experimental design:

Results that were obtained from all the formulations were assessed to get appropriate study design. Contour plots and desirability plot were generated using Design expert® 11 software. Linear model was suggested for particle size and Quadratic model was used for entrapment efficiency and drug release. The factors which had significant effect on the responses was identified using ANOVA (Analysis of variance)

 

The results (Table 4) depicts that the selected independent variables have strong influence on the selected responses, as particle size (nm), entrapment efficiency (%) and in-vitro drug release values were in the range of 214-407 nm, 41. 23-57.35 % and 95.09- 85.10 % respectively.

 

 

Table 4: Experimental design and responses of Solid lipid nanoparticles

Runs	Factors		Responses
	Lipid (mg)	Surfactant (%)	Particle Size (nm)	EE (%)	Drug release(%)
F1	100	4	214	57	95.09
F2	50	2	326	41	85.1
F3	150	4	214	53	94.06
F4	150	3	248	49	90.07
F5	150	2	376	43	87.79
F6	50	3	271	47	89.33
F7	100	3	251	51	90.07
F8	50	4	221	52	92.63
F9	100	2	407	46	88.37

 

Response 1: Particle Size

The particle size and its distribution play a vital role in determining the physical stability of formulation. The impact of lipid and surfactant concentration on particle size was studied and the obtained responses were given in table 4. The linear model with F value of 14.75 implies that the model was significant (table 5). The model p value was 0.0048 indicated that the model terms were significant. P values for amount of lipid was 0.6682 and for surfactant concentration 0.0016.

 

 

Table 5: Particle size ANOVA Response for Linear model

Source	Sum of Squares	df	Mean Square	F Value	p-value Prob > F	Remarks
Model	32801.33	2	16400.67	14.75	0.0048	significant
A-Amount of the lipid	240.67	1	240.67	0.2164	0.6682
B-Surfactant concentration	32560.67	1	32560.67	29.28	0.0016
Residual	6671.56	6	1111.93
Cor Total	39472.89	8

 

The regression coefficient value R² value was 0.8310 and adjusted R² value was 0.7746 indicated that there was minimum variations in the experimental model.

The polynomial equation in terms of coded factors was used to make predictions about particle size for given levels of each factor.

Pareicle Size =282.89 + 6.33 A – 73.67 B

 

The individual factor A, amount of lipid had a positive effect on particle size as shown in polynomial equation. The particle size was increased with the increase in amount of lipid where as surfactant concentration shown negative effect. An increase in particle size was observed by increasing lipid and reduction in surfactant concentration.

 

A film of loosely floating surfactant molecules in nanodispersion was due to lesser amount of surfactant compared to amount of lipid. Medium lipid concentration with higher surfactant concentration resulted in smaller sized particles. The effects of factors on particle size were presented in the form of response surface plots (figure 2 &3).

 

Figure 2: Contour Plot of Particle Size

 

Figure 3: 3D Plot of Particle Size

 

Response 2: Entrapment efficiency:

The quadratic model with F value 141.61 indicated that the model was significant. All terms were significant with p value 0.0009.

 

 

Table 6: Entrapment efficiency ANOVA Response for Quadratic Model

Source	Sum of Squares	df	Mean Square	F Value	p-value Prob > F	Remarks
Model	203.22	5	40.64	141.61	0.0009	Significant
A-Amount of the lipid	4.63	1	4.63	16.13	0.0277
B-Surfactant concentration	168.01	1	168.01	585.37	0.0002
AB	0.3844	1	0.3844	1.34	0.3309
A2	29.98	1	29.98	104.45	0.0029
B2	0.2200	1	0.2200	0.766	0.4457
Residual	0.8610	3	0.2870
Cor Total	204.08	8

 

Regression coefficient value R² was found to be 0.9958 adjusted R² was 0.9887 indicated that least variations in experimental model.

 

The polynomial equation in terms of coded factors was

EE = +51.89 + 0.8783 * A + 5.29 * B – 0.3100 * AB – 3.87 * A^2*- 0.3317 *B²

 

The results of drug entrapment in solid lipid nanoparticles formulation was described in table 4. The term A amount of lipid and term B surfactant concentration showed positive coefficient means it had direct relationship with entrapment efficiency. The interaction effect of lipid with surfactant concentration was negative. Formulation with lipid and surfactant concentration 100 and 4 showed greater entrapment efficiency with 57.35 % as compared to other formulations. Response surface plots (figure 4&5) depict the effect of each factor on % entrapment efficiency.

 

Figure 4: Contour Plot of Entrapment Efficiency

 

Figure 5: 3D Plot of Entrapment Efficiency

 

Response 3: Drug release:

The release pattern was almost similar in all the formulations this could be due to the ratio of lipid and surfactant for homogenous dispersion of Linagliptin in lipid matrix for maximal release.

 

Table 7: Drug release ANOVA response for Quaratic Model

Source	Sum of Squares	df	Mean Square	F-value	p-value	Response
Model	79.07	5	15.81	30.70	0.0089	Significant
A-Amount of Lipid	3.94	1	3.94	7.64	0.0699
B-Surfactant concentration	70.18	1	70.18	136.23	0.0014
AB	0.3969	1	0.3969	0.7704	0.4447
A²	3.63	1	3.63	7.04	0.0768
B²	0.9339	1	0.9339	1.81	0.2709
Residual	1.55
Cor Total	80.62	3	0.5152

 

The model F value was 30.70 implies that the model was significant and p value was 0.0089 indicates model was significant. In this case term A was not significant and term b was significant.

 

The regression coefficient value R² was 0.9808 and adjusted R² was 0.9489 indicates minimal variations in the experiment model. Polynomial equation in terms of coded factors was given below

 

Drug release =+90.7211+0.81 * A + 3.42 * B + -0.315 * AB-1.34667 * A²+0.68333B²

As per polynomial equation term A amount of lipid and term B surfactant concentration was positive effect. All interaction effects showed negative coefficient. The response surface plots were illustrated in figure 6&7.

 

To understand the mechanism of drug release results of various formulations were fitted with different kinetic models like zero order, first order, Higuchi Matrix, peppas models. The kinetic studies revealed that SLN’s followed Zero and First order and showed diffused sustained release.

 

Figure 6. Contour Plot of Drug release

 

Figure 7. 3D Plot of Drug release

 

Overlay Plot:

Contour plot results are superimposed on each other at three different levels to get best results as depicted in the figure: 8. Minimum and Maximum values are set to get optimized values amount of the lipid is X₁: 102.68 mg and surfactant concentration X₂: 3.06 to get the particle size of 278.20, EE: 52.28 and drug release 90.99 % after performing the experiment practically the obtained results are shown in table 8.

 

Figure 8: Overlay plot of Solid Lipid Nanoparticles

 

Table 8: Optimisation of Linagliptin loaded pH sensitive Solid lipid nanoparticle Formulation (F10)

Value	Lipid (mg)	Surfactant (%)	Particle Size (nm)	EE (%)	Drug release (%)
Predicted	102.68	3.68	278	52.20	90.99
Actual (F-10)	102	3	269	50.32	87.91
Realtive error(%)	0.68	0.8	9	1.88	3.08

 

Design space:

Desirability is used for optimization of multiple response processes. Desirability value ranges from zero to 1 for any given response. The value of 1 represents the ideal case and zero indicates undesired value means one or more responses are falling outside the desirable limits. In this case as depicted in figure 9 desirability of solid lipid Nanoparticle formulation is 0.951. Hence there are chances of 0.049 errors.

 

Figure 9: Desirability graph of SLN

 

Control strategy:

A planned set of controls that are raw materials control, in process control and finished product can be derived from current product (Solid lipid nanoparticles) and current process understanding that assures process performance and Solid Lipid nanoparticles quality.

 

Continuous improvement:

Once the method validation is completed method performance can be monitored. This can be performed by using control charts and tracking systems.

 

CONCLUSION:

The present study revealed the optimization of Solid lipid nanoparticles production by solvent injection technique and identifying the affect of critical process parameters and critical material attributes on critical Quality attributes. First step was defining Quality target product profile followed by application of response surface methodology and regression methods to describe the relation between dependent and independent variables, desirability approach was used for optimization. All formulations of solid lipid nanoparticles were found to be residing within the design space. It can be concluded that all the formulations of SLN’s were within the design space there are less errors in production of solid lipid nanoparticle.

 

CONFLICT OF INTEREST:

None .

 

REFERENCES:

1.      Zhang L, Mao S. Applications of quality by design and its tools in drug delivery. ‎Asian Jouranl of pharmaceutical sciences. 11; 2016: 144-145.

2.      Shah B, et.al. Application of quality by design approach for intranasal delivery of rivastigmine loaded solid lipid nanoparticles:effect on formulation and characterization parameters. European Journal of Pharmaceutical Sciences. 12; 2015: 54-66.

3.      Politis SN, et al. of experiments in pharmaceutical development. Drug Development and Industrial pharmacy. 43; 2017: 889-901.

4.      Amasya G, Badilli U, Aksu B, Tarimci N. Quality by design case study 1: Design of 5-fluorouracil loaded lipid nanoparticles by the W/O/W double emulsion- Solvent evaporation method. European Journal of Pharmaceutical Sciences. 10; 2016: 92-102.

5.      Pramod K, Tahir MA, Charcoo NA, Ansari SH, Ali J. Pharmaceutical product development: A quality by design approach. International journal of pharmaceutical investigation. 6; 2016: 129-138.

6.      Patil H, Feng X, Ye X, Majumdar S, Repka M A. Continuous production of fenofibrate solid lipid nanoparticles by hot melt extrusion technology: a systematic study based on a quality by design approach. AAPS Jurnal. 2015; 17: 194-205.

7.      Fahmy R, Kona R, Dandu R, Xie W, Claycamp G, Hoag S W. Quality by design Application of failure mode effect analysis and Plackett Burman Design of Experiments in the identification of main factors in the formulation and process design space for roller compacted ciprofloxacin hydrochloride immediate release tablets. AAPS Pharm Sci Tech. 13; 2012:1243-1254.

8.      Kumar VP, Gupta N V. A review on quality by design approach for pharmaceuticals. Interantional journal of drug delivery and research. 7; 2015: 52-60.

9.      Darwish M K. Application of quality by design principle to study the effect of coprocessed materials in the preparation of mitrazapine orodispersible tablets. Jouranl of Drug Delivery. 5; 2013: 309-322.

10.   Patel GM, Shelat PK, Lalwani AN. QbD based development of proliposome of lopinavir for improved oral bioavailability. European journal of pharmaceutical sciences. 108; 2017: 50-61.

11.   Yerlikaya F, Ozgen A, Vural I, Guven O, Karaaaoglu E, Khan MA. Development and evaluation of paclitaxel nanoparticles using Quality by design approach. Journal of Pharmaceutical Sciences. 109; 2013: 3748-3761.

12.   Tureli N G, Tureli AE, Schneider M. Optimization of ciprofloxacin complex loaded PLGA nanoparticles for pulmonary treatment of cystic fibrosis infections: Design of experiments approach. International Journal of pharmaceutics. 2016;515:343-351.

13.   Hales D, et al. A quality by design study on enoxaparin sodium loaded polymeric microspheres for colon specific delivery. European Journal of pharmaceutical sciences 100; 2017: 249-261.

14.   Garg NK, Sharma G, Singh B, Nirbhavane P, Tyagi RK, Katare O.P. Quality by Design enabled development of aceclofenac loaded nano structured lipid carriers: an improved dermatokinetic profile for inflammatory disorders. International Journal of pharmaceutics. 2017; 517: 413-431.

15.   Dhat S, Pund S, Kokare C, Sharma P, Shrivastava B. Risk management and statistical multivariate analysis approach for design and optimization of satranidazole nanoparticles. European Journal of pharmaceutical sciences. 96; 2017: 273-283.

16.   Jog R, Unachukwu K, Burgess DJ. Formulation design space for stable, pH sensitive crystalline nifedipine nanoparticles. International Journal of pharmaceutics. 2016; 514: 81-92.

17.   Rahman Z, Zidan AS, Habib MJ, Khan MA. Understanding the quality of protein loaded PLGA nanoparticles variability by placket burman design. International Journal of pharmaceutics. 1; 2010:186-194.

18.   Turk CTS, Hascicek C. Design of lipid polymer hybrid nanoparticles for therapy of BPH: part I. Formulation optimization using a design of experiment approach. Journal drug delivery science technology. 41; 2017:16-27.

19.   Raju KK, Sudhakar B, Murthy KVR. Factorial design studies and biopharmaceutical evaluation of simvastatin loaded solid lipid nanoparticles for improving the oral bioavailability. ISRN Nanotechnology. 2014; 2014: 1-8.

 

 

 

 

Accepted on 10.02.2019           © RJPT All right reserved

Research J. Pharm. and Tech 2019; 12(9):4454-4462.