- *Corresponding Author:
- Smita Rane

School of Pharmacy and Technology Management, Shirpur Campus, NMIMS University, Shirpur‑425 405, India

**E‑mail:**[email protected]

Date of Submission | 29 July 2010 |

Date of Decision | 09 April 2013 |

Date of Acceptance | 30 April 2013 |

Indian J Pharm Sci 2013;75(4):420-426 |

## Abstract

The aim of this study was to investigate the combined influence of 3 independent variables in the preparation of paclitaxel containing pH-sensitive liposomes. A 3 factor, 3 levels Box-Behnken design was used to derive a second order polynomial equation and construct contour plots to predict responses. The independent variables selected were molar ratio phosphatidylcholine:diolylphosphatidylethanolamine (X 1 ), molar concentration of cholesterylhemisuccinate (X 2 ), and amount of drug (X 3 ). Fifteen batches were prepared by thin film hydration method and evaluated for percent drug entrapment, vesicle size, and pH sensitivity. The transformed values of the independent variables and the percent drug entrapment were subjected to multiple regression to establish full model second order polynomial equation. F was calculated to confirm the omission of insignificant terms from the full model equation to derive a reduced model polynomial equation to predict the dependent variables. Contour plots were constructed to show the effects of X 1 , X 2 , and X 3 on the percent drug entrapment. A model was validated for accurate prediction of the percent drug entrapment by performing checkpoint analysis. The computer optimization process and contour plots predicted the levels of independent variables X 1 , X 2 , and X 3 (0.99, -0.06, 0, respectively), for maximized response of percent drug entrapment with constraints on vesicle size and pH sensitivity.

## Keywords

Box-Behnken design, pH-sensitive, liposomes, optimization, paclitaxel

Paclitaxel, the first of a new class of microtubule
stabilizing agents, has been hailed by National
Cancer Institute as the most significant advance
in chemotherapy during the past 20-25 years [1].
Paclitaxel is poorly soluble in aqueous medium,
and is currently formulated in a vehicle composed
of 1:1 blend of cremophor EL and ethanol which is
diluted with 5-20 fold in normal saline or dextrose
solution (5%) for administration [2,3]. One of the
substantial problems associated with this formulation
is that the ethanol:cremophor vehicle is toxic [4-7].
The primary goal of formulation development for
paclitaxel is to eliminate the cremophor vehicle
and also to provide the possibility of improving the
efficacy of paclitaxel based anticancer therapy. One
of the major obstacles in designing the formulation
of novel drugs is their limited aqueous solubility. This
problem can be overcome by entrapping the drug in
vesicular structures like liposomes. Liposomes that
can be triggered to release their contents or fuse in response to pH stimuli are of particular interest in
cancer therapy as they can potentially respond to
acidic environments *in vivo* [8].

However, successful development of these systems
requires careful consideration of a number of factors
influencing the performance of the formulation,
including the physicochemical properties of the
raw materials (both drug and excipients), the
composition and the component’s relative amounts
in the formulations, as well as the manufacturing
process parameters. Many experiments fail in
their purpose because they are not properly
thought out and designed, and even the best data
analysis cannot compensate for lack of planning.
Experimental design is thus the preferred strategy,
especially when complex formulations, such as
liposomes, are to be developed [9]. In particular, the
multi-varied strategy of experimental design allows
simultaneous investigation of the effects of several
variables, as well as their actual significance on the
considered response and possible interrelationship
among them, giving the maximum information with minimum number of experiments [10,11].
Traditional experiments require more effort, time,
and materials when a complex formulation needs
to be developed. Various experimental designs [12-15]
are useful in developing a formulation requiring
less experimentation and providing estimates of
the relative significance of different variables.
In the work reported here, Box-Behnken design
was used to optimize pH-sensitive liposomes
containing paclitaxel. Independent variables
selected were molar ratio of phosphatidylcholine:
diolylphosphatidylethanolamine (PC:DOPE) (X_{1}),
moles of cholesterylhemisuccinate (CHEMS) (X_{2}),
and the amount of drug (X_{3}) to evaluate their
separate and combined effects on percent drug
entrapment (PDE), pH-sensitivity, and vesicle size
expressed as the average vesicle perimeter (AVP).
Liposomes are of interest from a technical viewpoint
and allow a wider scope to study the influence of
various formulation variables, hence liposomes need
to be optimized for desired response. In the present
study thin film hydration method was used for the
preparation and optimization of paclitaxel liposomes
as this method is simple and capable of producing
small vesicles.

## Materials and Methods

Paclitaxel was received as a gift sample from Naprod life sciences, Mumbai. Phosphatidylcholine (PC) was received as a gift sample from Phospholipid GmbH, Nattermannalle, Germany. Cholesteryl hemisuccinate (CHEMS) was received as a generous gift from Merk Eprova AG Switzerland. Dioleoyl phosphatidylethanolamime (DOPE) was received as a gift sample from Lipoid GmbH, Germany. HPLC grade solvents and other chemicals were purchased from local supplier.

**Box–Behnken experimental design**

A Box–Behnken optimization design with three
variables was applied to find the optimum conditions
and to analyze how sensitive the responses were
to variations in the settings of the experimental
variables [16]. This design is a factorial design with
three levels, using middle points instead of corner
points and is useful for estimating the coefficients
in a second degree polynomial. A total of 15
experiments were performed including triplicates
of the center point. The center points improve the
assessment of the response surface curvature and simplify the estimation of the model error. The
traditional approach to develop a formulation is
to change one variable at a time. It is difficult to
develop an optimized formulation, as the method
reveals nothing about the interactions among the
variables. Hence, a Box–Behnken statistical design
with three factors, three levels was selected for the
optimization study. The dependent and independent
variables are listed in **Table 1**. The polynomial
equation generated by this experimental design
(Instat + software) is as follows:

Where Y_{i} is the dependent variable; b_{0} is the
intercept; b_{1} to b_{33} are regression coefficients: and X_{1},
X_{2} and X_{3} are the independent variable selected from
the preliminary experiments.

Levels | |||
---|---|---|---|

Low | Medium | High | |

Independent variables | |||

X_{1}=Molar ratio of PC: DOPE |
8:2 | 6:4 | 4:6 |

X_{2}=Molar concentration of CHEMS |
2 | 4 | 6 |

X_{3}=Amount of drug |
2.8 mg | 5.6 mg | 8.4 mg |

Transformed values | –1 | 0 | 1 |

Dependent variables | |||

Y_{1}=Percent drug entrapment |
|||

Y_{2}=Vesicle size |
|||

Y_{3}=pH‑sensitivity |

DOPE=Dioleoyl phosphatidylethanolamime

**Table 1:** Variables And Their Levels In Box-Behnken Design

**Preparation of liposomes**

Liposomes were prepared by the thin film hydration
method. Paclitaxel and the required quantities of
CHEMS, Phospholipon 90G, and DOPE were
dissolved in chloroform. All the batches were
prepared according to the experimental design in **Table 2**. Chloroform was evaporated using rotary
vacuum evaporator and kept overnight under
vacuum. Nitrogen gas was passed over the thin film.
Then it was hydrated with 5% dextrose solution,
above the phase transition temperature of lipids,
using glass beads. The suspension of liposomes was
sonicated to reduce the size of liposomes. This was
transferred to vials and stored at 4° [17].

Batch no. | X_{1} |
X_{2} |
X_{3} |
Y_{1} (PDE±SD)* |
Y_{2}(µm) |
Y_{3} (%) |
---|---|---|---|---|---|---|

1 | 0 | –1 | –1 | 84.1±0.68 | 6.4 | 70.69 |

2 | 0 | –1 | 1 | 80.31±1.17 | 6.9 | 73.27 |

3 | 0 | 1 | –1 | 82.26±1.54 | 2.6 | 81.00 |

4 | 0 | 1 | 1 | 79.7±1.23 | 2.7 | 79.56 |

5 | –1 | 0 | –1 | 85.52±1.92 | 6.2 | 60.43 |

6 | –1 | 0 | 1 | 78.22±0.86 | 3.8 | 65.30 |

7 | 1 | 0 | –1 | 92±1.41 | 4.4 | 83.12 |

8 | 1 | 0 | 1 | 89.64±1.52 | 4 | 85.11 |

9 | –1 | –1 | 0 | 84.04±1.87 | 6.3 | 66.91 |

10 | –1 | 1 | 0 | 80.62±2.12 | 3.3 | 75.14 |

11 | 1 | –1 | 0 | 94.68±0.98 | 6.5 | 79.87 |

12 | 1 | 1 | 0 | 88.73±1.28 | 2.4 | 94.77 |

13 | 0 | 0 | 0 | 90.37±1.04 | 4 | 86.07 |

14 | 0 | 0 | 0 | 91.14±1.59 | 3.8 | 88.90 |

15 | 0 | 0 | 0 | 92.53±1.33 | 4.1 | 85.18 |

PDE=Percent drug entrapment, n=3, Y2=average vesicular size in μm, Y3=percent drug release

**Table 2:** Box‑Behnken Experimental Design With Measured Responses

**Microscopy**

The liposomes were mounted on glass slides and viewed under a microscope (Motic) for morphological observation after suitable dilution. Particle size was measured as average object perimeter.

**Percent drug entrapment**

The amount of paclitaxel incorporated in liposomes was determined using HPLC (Perkin Elmer). Five hundred microliter of liposomal suspension was diluted with water and acetonitrile to 1 ml. Extraction of paclitaxel was accomplished by adding 4 ml of tert-butylmethyl ether, vortex mixing for 1 min, and centrifuging the mixture for 15 min. Three milliliter of the organic layer was separated and evaporated to dryness. Residue was reconstituted with 1 ml methanol. Twenty microliter of the above solution was injected into a C18 column, 5 µm. The column was eluted with acetonitrile/water (60/40). The drug was estimated by UV absorption measurement at 227 nm (flow rate 1 ml/min) [18,19].

*In vitro* release from pH-sensitive liposomes

Liposomal formulations were diluted with phosphate buffer pH 5 in 1:2 ratios, and incubated at 37° for 15 min. The drug released was separated from liposomal paclitaxel, extracted, and quantified using the same procedure described above for the determination of incorporated drug.

**Checkpoint analysis**

A checkpoint analysis was performed to confirm the role of the derived polynomial equation and contour plots in predicting the responses. Values of independent variables were taken at three points, one from each contour plot, and the theoretical values of PDE were calculated by substituting the values in the polynomial equation. Liposomes were prepared experimentally at three checkpoints, and evaluated for the responses.

**Optimum formula**

After developing the polynomial equations for the
responses PDE, AVP, and pH sensitivity with the
independent variables, the formulation was optimized
for the response PDE. Optimization was performed
to find out the level of independent variables
(X_{1}, X_{2}, and X_{3}) that would yield a maximum value
of PDE with constraints on AVP and pH sensitivity.

## Results and Discussion

Liposomes were observed under a microscope to
examine their morphology and were observed to be
mostly spherical, with a few being slightly elongated.
A Box–Behnken experimental design with three
independent variables at three different levels was used to study the effects on dependent variables. All
the batches of liposomes within the experimental
design were evaluated for PDE, pH sensitivity, and
vesicle size. A Box–Behnken experimental design
has the advantage of requiring fewer experiments
(15 batches) than would a full factorial design
(27 batches). Transformed values of all the batches
along with their results are shown in **Table 2**.
Formulations 7, 12, 13, 14, and 15 had the highest
PDE (>90%). **Table 3** shows the observed and
predicted values with residuals and percent error of
responses for all the batches.

Batch no. | Observed PDE | Predicted PDE | Residuals | %Error |
---|---|---|---|---|

1 | 84.1 | 85.169 | –1.069 | 1.27 |

2 | 80.31 | 81.167 | –0.857 | 1.07 |

3 | 82.26 | 82.213 | 0.047 | 0.06 |

4 | 79.7 | 78.211 | 1.489 | 1.87 |

5 | 85.52 | 83.667 | 1.853 | 2.17 |

6 | 78.22 | 79.665 | –1.445 | 1.85 |

7 | 92 | 92.829 | –0.829 | 0.90 |

8 | 89.64 | 88.827 | 0.813 | 0.91 |

9 | 84.04 | 83.817 | 0.223 | 0.27 |

10 | 80.62 | 80.861 | –0.241 | 0.30 |

11 | 94.68 | 92.979 | 1.701 | 1.80 |

12 | 88.73 | 90.023 | –1.293 | 1.46 |

13 | 90.37 | 91.477 | –1.107 | 1.22 |

14 | 91.14 | 91.477 | –0.337 | 0.37 |

15 | 92.53 | 91.477 | 1.053 | 1.14 |

PDE=Percent drug entrapment

**Table 3:** Observed And Predicted Values With Residuals Of The Response Y1(Pde)*

The PDE (dependent variable) obtained at various levels
of the three independent variables (X_{1}, X_{2}, and X_{3}) was subjected to multiple regression to yield a second-order
polynomial equation (full model):

The value of the correlation coefficient (r2) of
Eqn. 2 was found to be 0.9755, indicating good
fit. The PDE values measured for the different
batches showed wide variation (i.e., values ranged
from a minimum of 79.7 to a maximum of 94.68).
The results clearly indicate that the PDE value
is strongly affected by the variables selected for
the study. This is also reflected by the wide range
of values for coefficients of the terms of Eqn 2.
The main effects of X_{1}, X_{2}, and X_{3} represent the
average result of changing one variable at a time
from its low level to its high level. The interaction
terms (X_{1}X_{2}, X_{1}X_{3}, X_{2}X_{3}, X_{1}^{2}, X_{2}^{2}, and X_{3} ^{2}) show
how the PDE changes when two variables are
simultaneously changed. Positive coefficient of
X_{1} (molar ratio of PL 90 and DOPE) indicate
favorable effect on PDE, while the negative
coefficient of X_{2} and X_{3} indicate unfavorable effect
on PDE. Among the three independent variables
the lowest coefficient value is for X_{2} (b_{2} = –1.478)
indicating that this variable is not significant in
predicting PDE.

The standardized effect of the independent variables
and their interaction on the dependent variable was
investigated by preparing a pareto chart (**fig. 1)** which
depicts the main effect of the independent variables and interactions with their relative significance on
the PDE. The length of each bar in the chart indicates
the standardized effect of that factor on the response.
The fact that the bar for X_{1}X_{2}, X_{1}X_{3}, X_{2}X_{3}, and X_{1}^{2} remains inside the reference line in **fig. 1** and the small
coefficients for these terms in Eqn. 2 indicate that these
terms contribute the least in prediction of PDE. Hence,
these terms are omitted from the full model to obtain a
reduced second-order polynomial equation (equation 3)
by multiple regressions of the PDE and the significant
terms (P<0.05) of Eqn. 2:

To confirm the omission of nonsignificant terms, F
statistic was calculated after applying analysis of
variance for the full model and the reduced model
(**Table 4**). The F calculated value (1.04) is less than
the tabled value of F (4.95) at a 0.05 confidence
interval. Hence, it is concluded that the omitted
terms do not significantly contribute to predicting the PDE. This implies that the main effect of the
amount of drug and the molar ratio of phospholipids
is significant, as is evident from their high coefficients
and the fact that the bars corresponding to variables
X_{1}, X_{2}, X_{3}, X_{2}^{2},
X_{3}^{2} extend beyond the reference line
in **fig. 1**.

ANOVA | Df | SS | MS | F value |
P value |
---|---|---|---|---|---|

Regression | |||||

A | 9 | 392.575 | 43.619 | 22.16 | 0.0016 |

B | 5 | 384.33 | 76.866 | 38.25 | 0.0000 |

Residuals | |||||

A | 5 | 9.842 (C_{1}) |
1.9685 (D_{1}) |
||

B | 9 | 18.087 (C_{2}) |
2.0097 |

ANOVA indicates analysis of variance; PDE, percent drug entrapment; A, full model; B, reduced model; Df, degrees of freedom; SS, sum of squares; MS, mean of squares; F, Fischers ratio. FCAL = [(C2-C1)/NTO]/D1 = 1.040, where NTO is the number of terms omitted (having a P value more than 0.05)

**Table 4:** Results Of Anova of Full And Reduced Models for PDE of Paclitaxel in Liposomes

Vesicle size of liposome batches, measured by using microscopy (Motic), was found to be in the range of 2.4 to 6.9 µm. A polynomial equation was also developed for AVP which is given as

The value of correlation coefficient (r2) of
Eqn. 4 was found to be 0.9499 indicating good
fit. Among the independent variables selected
and their interactions only X_{2} was found to be
significant (P<0.05), indicating a major contributing
effect of X_{2} on AVP. A negative value of the
coefficient for X_{2} (molar concentration of CHEMS)
indicates a favorable effect on AVP. Vesicles obtained
at high molar concentration of CHEMS are smaller
than are those obtained at low molar concentration
of CHEMS. CHEMS improves the fluidity of
bilayer membrane. The smaller size of vesicles may
result from this property of CHEMS. The effect of
CHEMS on the liposome bilayer structure can be
mainly ascribed to its charge inducing properties
and presumably to a minor extent to its molecular
geometry, or to a combination of both.

pH sensitivity (Y3) of liposomes measured as % drug release at pH 5 was found to be in the range of 60.43 to 94.77%. A polynomial equation was developed for % drug release:

The value of correlation coefficient (r2) of Eqn. 5
was found to be 0.9799 indicating good fit. Among
the independent variables selected X_{1}, and X_{2}, X_{1}^{2},
and X_{3}^{2} were found to be significant (P<0.05),
indicating a major contributing effect of molar ratio
of phospholipids and CHEMS on pH sensitivity of
liposomes. The positive values of coefficients for X_{1} and X_{2} indicate a favorable effect on pH sensitivity.
Liposomes with high level of DOPE and CHEMS
exhibit good pH sensitivity.

The three replicated center points in the Box–Behnken experimental design made it possible to assess the pure error of the experiments and enabled the model’s lack of fit to be checked. In this study, the model was checked for lack of fit for all the three responses. For lack of fit (F test) P values obtained were 0.969, 0.949, 0.909 for Y1, Y2 and Y3, respectively, and hence the current model provided a good fit to the data (P>0.05) and had no lack of fit.

The relationship between the dependent and
independent variables was further elucidated by
constructing contour plots. The effects of X_{1} and
X_{3} with their interaction on PDE at a fixed level of
X_{2} (medium level) are shown in **fig. 2**. The plots
were found to be linear up to 97% PDE, indicating
the linear relationship between X_{1} and X_{3}. It was
determined from the contour plot that a higher value
of PDE (97%) could be obtained with an X_{1} level
range from 0.8 to 1, and an X_{3} level range from –0.5
to -1. It is evident from the contour that the low
level of X_{3}, and the high level of X_{1} favors PDE of
liposomes. PC is present in a lower proportion at
the high level of X_{1}, and therefore it is evident that
PC decreases the entrapment efficiency. When the
coefficient values of two key variables, X_{1} and X_{3},
were compared, the value for variable X_{1} (b2=4.581)
was found to be higher, indicating that it contributes
the most to predict the PDE. **Fig. 3** shows the
contour plot drawn at 0 level of X_{3}. The contours
of all PDE values were found to be curvilinear
and indicated that a high value of PDE (96%) can
be obtained for a combination of two independent
variables, X_{1} level 1 and X_{2} level in the range
of -0.06 to -0.245, indicating that CHEMS decreases
PDE. Similarly, **fig. 4** shows the contour plot at
0 level of X_{1}. The plot is found to be curvilinear
up to 91% PDE, but above this value, the plots
were found to be nonlinear indicating a nonlinear
relationship between X_{2} and X_{3}. This may be due
to the interaction between X_{2} and X_{3}. High value
of PDE could be obtained with an X_{2} level range
of -0.25 to 0.99 and an X_{3} level range of 1 to -0.14.
All the contour plots for the high value of PDE
were found to be nonlinear. This signifies that there
is no direct linear relationship among the selected
independent variables. A high value of PDE can be
obtained up to a certain level of all three independent
variables, but above this an increase in the level of
independent variables leads to a decrease in the PDE
of liposomes.

Three checkpoint batches were prepared and evaluated
for PDE, as shown in **Table 5**. Results indicate that
the measured PDE values were as expected. When
measured PDE values were compared with predicted
PDE values using student t-test, the differences were
found to be insignificant (P>0.05) indicating that the
obtained mathematical equation is valid for predicting
the PDE.

Batch no. | X_{1} |
X_{2} |
X_{3} |
PDE | |
---|---|---|---|---|---|

Measured | Predicted | ||||

16 | 0 | –0.5 | 0.5 | 87.92 | 88.76 |

17 | 0.5 | 0 | –0.5 | 91.7 | 93.46 |

18 | –0.5 | 0.5 | 0 | 89.15 | 87.31 |

PDE = Percent drug entrapment

**Table 5:** Checkpoint Batches with their Predicted and Measured Value of PDE

After studying the effect of the independent variables
on the responses, the levels of these variables
that give the optimum response were determined.
It is evident from the polynomial equation and
contour plots that CHEMS decreases the PDE
within liposomes. Also CHEMS is known to abolish
the gel-to-liquid phase transition of liposomes
and the resulting liposomes are less leaky. Hence,
medium level was selected as optimum for the
molar concentration of CHEMS (X_{2}), as up to
this level high value of PDE can be obtained. The
optimum formulation is one that gives a high value
of PDE (=91%), high pH sensitivity and a low
AVP (=4 µm) along with a high total amount of drug
entrapped and low amount of carrier in the resultant
liposomes. Using a computer optimization process
and the contour plot shown in **fig. 3**, for X_{1} the level of 0.99, for X_{2} level of –0.06 which gives the
theoretical values of 96.08% PDE, 90.61% release,
and 3.58 µm AVP were selected. A decrease in the
level of amount of drug (X_{3}) below the selected level
leads to a decrease in the total amount of entrapped
drug and increase in the level above the selected
level leads to low entrapment efficiency. Hence, a 0
level of the amount of drug was selected as optimum.
For confirmation, a fresh formulation was prepared
at the optimum levels of the independent variables,
and evaluated for responses. The observed values of
PDE, % release and AVP were found to be 94.37%,
89.6% and 3.24 µm, respectively, which were in close
agreement with the theoretical values.

Multicomponent liposomal formulations may include more factors during their preparation, making the interpretation of the system extremely complicated. In order for all the factors to be used at their optimal level and the best responses to be achieved, a lot of experiments must be performed, including all the possible combinations between the different factors. The use of fractional factorial design as described in the present study, can decrease the number of experiments, give useful conclusions for the main effects and interactions between the examined factors, and clarify complicated interactions through graphical presentations.

## Acknowledgements

Authors thank Naprod Life Sciences, Mumbai, for the gift sample of paclitaxel, Phospholipid GmbH, Nattermannalle, Germany for Phospholipon 90G, Merk Eprova AG Switzerland for CHEMS, Lipoid GmbH, Germany for DOPE.

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