An Overview on Design of Experiment in Product Formulation

Abstract

The pharmaceutical product development process requires specific domain knowledge and expertise in specific area. Design of Experiment is a systematic approach to development that begins with predefined objectives and emphasizes product and process understanding and process control, based on sound science and quality risk management.

Keywords

Design of Experiment, Product Formulation, Drug-Excipients, Tablet Granulation, Dissolution testing

Introduction

A drug candidate must be chemically, physically stable and manufacturable throughout the product life cycle and manufacturing process,). In addition, many quality standards and special requirements must be met to ensure the efficacy and safety of the product. It is always essential to establish the (target product profile) TPP so that the formulation effort will be effective and focused [1,2]. The TPP usually includes dosage form, route of administration, special-delivery requirement, maximum and minimum doses, and aspects of pharmaceutical elegance (appearance). The TPP guides formulation scientists to establish formulation strategies [3-12] and keep formulation effort focused and efficient. After the TPP is clearly defined, many studies must be conducted to develop a formulation. DOE is an effective tool for formulation scientists throughout the many stages of the formulation process and can help scientists make intelligent decisions. These steps include optimization of product, drug-excipient compatibility [13-19], process feasibility studies, formulation, and scale-up, and characterization of manufacturing process.

Design of Experiment

Design of Experiment [20-22] is a systemic approach to determine the relation between independent process/product variable and their effect on response variable (Figure 1).

Figure 1: Flow Chart in Design of Experiment.

Terminology used in Design of Experiment

1. Independent Variables: These variables are directly under the control of formulation scientist

2. Dependent variables: These variables are result or response

3. Factors: There are two types of factors are there Qualitative and Quantitative factors

4. Level: These are the values assigned or designated for factors

5. Responses surface plot: A 3-D graphical representation between independent factor and dependent factor

6. Interaction: It gives the overall effect of two or more variables means lack of additivity of factor effects

7. Effect: Magnitude in change in response

8. Contour plot: These are two dimensional representation of response obtained by plotting one independent variable against another keeping the response and other variable constant

9. Contour lines: curves obtained over counter plot to a response value

10. MLRA (Multiple Linear Regression Analysis): The technique which express mathematically in form of quadratic equation the linear relationship between various independent variable and dependent variable (Response)

11. Orthogonality: When effect is due to the main factor of interest and no interaction

12. Confounding: Lack of Orthogonality is termed as confounding or aliasing

13. Resolution: Measurement of degree of confounding

Types of Experimental Design

A. Completely randomized designs.

B. Full Factorial designs.

• Two-level full factorial designs.

• Full factorial example.

• Blocking of full factorial designs.

C. Fractional factorial designs.

• A 23-1 half-fraction designs.

• How to construct 23-1 designs.

• Confounding.

• Design resolution.

• Use of fractional factorial designs.

• Screening designs.

• Fractional factorial designs summary tables.

D. Randomized block designs.

• Latin squares.

• Graeco-Latin squares.

• Hyper-Graeco-Latin squares.

E. Plackett-Burman designs.

F. Response surface (second-order) designs.

• Central composite designs.

• Box-Behnken designs.

• Response surface design comparison.

• Blocking a response surface design.

G. Adding center points.

H. Improving fractional design resolution.

• Mirror-image foldover designs.

• Alternative foldover designs.

I. three-level full factorial designs.

J. Three-level, mixed level and fractional factorial designs

Application of Experimental Design

➢ Compatibility studies between Drug-Drug [23] and Drug-Excipients

➢ Granulation

➢ Pre Tablet Granulation [24-29]

➢ Oral-controlled release formulation [30-33]

➢ Modelling of properties of powder [34-41]

➢ Dissolution testing [42-62]

➢ Tablet formulation [63-78]

➢ Coating of tablets

➢ Extrusion-Spheronization

➢ Inhalation formulation [79-90]

Software used in Experimental Design

➢ DESIGN EXPERT

➢ FACTOP

➢ OPTIMA

➢ XTAP

➢ OMEGA

➢ ECHIP

➢ MULTI-SIMPLEX

➢ NEMRODW

➢ GRAPHPAD PRISM

➢ DoE PC IV

➢ MINITAB

➢ MODDE

Conclusion

Over the past decade the practice of optimization of formulation has increased. DoE optimization would further gain increased acceptance as a priceless developmental tool. DOE is an effective tool for formulation scientists starting from pre-formulation stage to various stages of clinical trial in product development. What is essentially needed for realization of this quality breakthrough is the workable basic knowledge of computers and statistics, coupled with persistence, patience, perseverance and passion. Different techniques used in optimization [91-100] mainly reduces the number of trial thereby cost and time consumed in product development.

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