OPTIMIZATION OF PHARMACEUTICAL PRODUCT FORMULATION BY FACTORIAL DESIGNS: CASE STUDIES AND RECENT RESEARCH

Abstract

Optimization of a product or process is determination of best possible composition or operating conditions resulting in its optimal performance. Optimization techniques are relatively new to the practice of pharmacy. Optimization using factorial designs is an efficient technique used in formulation optimization. Factorial experiments with two level factors are used widely because they are easy to design, efficient to run, straight forward to analyze, and full of information. Three case studies, in which optimization was performed by factorial design along with the review of recent research on optimization are discussed. [PUBLICATION ABSTRACT]

Headnote

ABSTRACT

Optimization of a product or process is determination of best possible composition or operating conditions resulting in its optimal performance. Optimization techniques are relatively new to the practice of pharmacy. Optimization using factorial designs is an efficient technique used in formulation optimization. Factorial experiments with two level factors are used widely because they are easy to design, efficient to run, straight forward to analyze, and full of information. Three case studies, in which optimization was performed by factorial design along with the review of recent research on optimization are discussed.

Keywords: Optimization; Factorial Design; Recent Research; Case Studies.

INTRODUCTION

The word "Optimize" means to make as perfect, effective or functional as possible. Optimization of product or process is determination of experimental conditions resulting in its optimal performance.1 Optimization has been defined as the implementation of systemic approaches to achieve the best combination of product and/or process characteristics under a given set of conditions.2

With respect to the drug formulations or pharmaceutical process, optimization is a phenomenon of finding "the best" possible composition or operating conditions. Although several optimization procedures are available to the pharmaceutical scientist, in general the procedure consists of preparing a series of formulations, varying the concentrations of formulation ingredients in some systemic manner. These formulations are then evaluated according to one or more attributes, such as hardness, dissolution, appearance, stability, taste and so on. Based on the results of these tests, a particular formulation (or series of formulations) may be predicted to be optimal.3

Optimization of pharmaceutical formulations involve choosing and combining ingredients that will result in formulation whose attributes conform to certain pre requisite requirements. The choice of the nature and quantities of additives (or) excipients to be used in a formulation has to be based on some rational. The optimization techniques will help in fixing the quantities or levels of the excipients, Optimization techniques are relatively new to the practice of pharmacy. In general the traditional procedure consists of preparing a series of formulation, varying the concentrations of the formulation ingredients in some systemic manner. These formulations were then evaluated according to one or more attributes such as hardness, dissolution, apperarance, stability, taste and so on, based on the results of these tests a particular formulation or series of formulations may be predicted to be optimal. The predicted optimal formulation has to be prepared and evaluated to conform its quality .The formulation is generally optimized according o a single attribute.

OPTIMIZATION BY FACTORIAL DESIGN

The modern approach for optimization is through the use of statistical techniques. Optimization using factorial designs is an efficient technique used in formulation optimization.

The optimization procedure is facilitated by construction of a mathematical equation that describes the experimental results as a function of the factor levels. A polynomial equation can be constructed in the case of a factorial design where the coefficients in the equation are related to effects and interactions of the factors.

The equation constructed from a 2n factorial experiment is as follows:

y=β0 + β1x1+β2x2+β3x3+.........+β12x1x2 +.........+β123x1x2x3

Where y is the measured response, xi is the level of the ith factor, β1, β2, β3........ represent coefficients computed from the responses of the formulations in the design and β0 represent intercept.

FULL FACTORIAL DESIGN (FFD)

Factorial experiments with two-level factors are used widely because they are easy to design, efficient to run, straightforward to analyze, and full of information. A full factorial design contains all possible combinations of a set of factors. This is the most fool proof design approach, but it is also the most costly in experimental resources. The full factorial designer supports both continuous factors and categorical factors with up to nine levels.

Factorial designs with only two-level factors have a sample size that is a power of two (specifically 2f where f is the number of factors). When there are three factors have a sample size that is a power of three.

N = Lk

Where, k = number of variables, L = number of variable levels, N = number of experimental trials, for example, in an experiment with three factors, each at two levels; we have eight formulations, a total of eight responses.

CASE STUDIES OF OPTIMIZATION USING FACTORIAL DESIGN

Case Study 1

Formulation of Combined Drug Products: A 22 factorial experiment was designed to develop a combination drug product to obtain the dose of each drug which would result in an optimal response. For this purpose, the 2 levels selected for drugA (x1) are 5 mg and 10 mg and for drug B (x2) the two levels are 50 mg and 100 mg. This study is an example of a 22 factorial study and involve four formulations with selected combinations of the two levels of drug A and drug B. The four formulations as per 22 factorial design are prepared and the response (y) i.e. time to reach anaesthesia in minutes is measured with each formulation. The formulations as per 22 factorial design, their responses (y) observed and potency transformations for developing the polynomial response equation are shown in Table 1.

The polynomial response equation to be developed is of the type

y=β0 + β1x1+β2x2+β3x3+.........+β12x1x2 +.........+β123x1x2x3

The polynomial equation describing that relationship between the response (y) and the variables x1 and x2 based on the observed data was found to be

y=7.35-1.7(x1)-1.1(x2) -0.45(x,1x2)

Based on the above relationship the optimized formulation with response (y) as 5 min shall contain +0.5 of A (8.75 mg) and +1 of B (100 mg) and hence this combination of drug A and B is the optimized formulation which would produce anaesthesia in 5 minutes. Hence the optimized combined drug formulation should contain 8.75 mg of drug A and 100 mg of drug B.

Case Study 2

Optimization of Diclofenac SR Tablet Formulation by Factorial Design: The study is to design diclofenac SR tablets employing a combination of HPMC K 100 M (hydrophilic polymer) and ethyl cellulose (lipophilic polymer) for better controlled release. Diclofenac SR tablet formulation was optimized by 22 factorial design. Diclofenac SR tablets were formulated employing the selected combinations of HPMC (Factor A) and EC (Factor B) as per 22 factorial study and prepared by wet granulation method. The SR tablets were evaluated for drug release kinetics and mechanism. For optimization, time for 50 % release (T50) was taken as response (Y) and the percent of HPMC as X1 and percent of EC as X2. The polynomial equation describing the relationship between the response Y and the variables X1 and X2 based on the observed data was worked out.

The polynomial equation describing the relationship between the response Y (T50) and the variables X1 (% HPMC) and X2 (% EC) based on the observed data was found to be

Y = 2.95 + 1.05 X1 - 0.25 X2- 1.75 (X1X2)

Based on the above polynomial equation the optimized diclofenac SR tablets with a T50 of 4 hours could be formulated employing 50 % HPMC and 5.5 % ethyl cellulose as release retarding polymers.The optimized SR formulation prepared gave slow release of diclofenac over 12 h with a T50 of 4 h indicating validity of the optimisation technique employed. Diclofenac release from the optimized SR formulation was diffusion controlled and release was by non-fickian (anomalous) diffusion mechanism. Based on pharmacokinetics, diclofenac SR tablets for b.i.d administration should contain a total dose of 100 mg of diclofenac and the desired release rate (K0) is 8.66 mg/h. The drug release rate of optimised SR tablets formulated was found to be 8.54 mg/h, which is very close to the theoretical desired release rate. Hence the optimised formulation is considered as the best diclofenac SR formulation developed.

Case Study 3

Optimization of Valsartan Tablet Formulation by 23 Factorial Design: The objective of the study is to optimize valsartan tablet formulation by 23 factorial design for selecting the best combinations of diluent, binder and disintegrant giving fast dissolution of valsartan, a BCS class II drug.

For formulation of valsartan tablets as per 23 factorial design the three factors involved are binder, diluent and disintegrant. The two levels of the factor A (binder) are acacia and PVP at 2% concentration each and the two levels of the factor B (disintegrant) are potato starch (15%) and Primogel (5%). The two levels of the factor C (diluent) are lactose and DCP. Eight valsartan tablet formulations each containing 50 mg of valsartan were prepared employing selected combinations of the three factors i.e. binder, disintegrant and diluent as per 23 factorial design. The tablets were prepared by wet granulation method.

Much variations were observed in the disintegration and dissolution characteristics of the valsartan tablets prepared employing various combinations of binder (factor A), disintegrant (factor B) and diluent (factor C) as per 23 factorial design. Valsartan tablets formulated employing lactose as diluent (F1, Fa, Fb, Fab) disintegrated rapidly within 1 min whereas tablets formulated with DCP disintegrated relatively slowly in 3-5 min. Tablets formulated employing lactose as diluent gave higher dissolution rates (K1) and DE30 values when compared to the tablets formulated employing DCP. Formulation Fab (tablets prepared employing lactose, PVP and Primogel), F1 (tablets prepared employing lactose, acacia and potato starch) and Fabc (tablets prepared employing DCP, PVP and Primogel) gave higher dissolution rates and DE30 values and fulfilled the official (IP 2010) dissolution rate test specification of valsartan tablets. Hence combinations of (i) Lactose, PVP, Primogel, (ii) Lactose, acacia, potato starch and (iii) DCP, PVP, Primogel are the best combinations of diluent, binder and disintegrant recommended for formulation of valsartan tablets giving rapid and higher dissolution of valsartan, a BCS class II drug.

RECENT RESEARCH ON OPTIMIZATION BY FACTORIAL DESIGNS

Literature on optimization by factorial designs is rather scanty. A summary of recent research on optimization by factorial designs is given in Table 2.

CONCLUSION

Optimization by factorial designs is a promising technique in formulation development of dosage forms and drug delivery systems. It is the central component of Quality by Design (QbD), which emphasizes the systematic development of pharmaceutical products based on sound scientific principles.

References

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