Optimization techniques

Apr 15, 2023 1

H.R.Patel Institute of Pharmaceutical Education & Research, Shirpur.

Presented by :Bachchhao Kunal B..M. Pharm 2nd Semester

Guided by :Mr. G. B. Patil.Department of Quality Assurance

Apr 15, 2023 OPTIMIZATION TECHNIQUES 2

Contents :Introduction Definition Parameter Classic optimization Statistical designApplied optimization metheodDesign of experimentsTypes of experimental design Advantages and applications Conclusion References


INTRODUCTION

The term Optimize is defined as “to make perfect”.

It is used in pharmacy relative to formulation and processing

Involved in formulating drug products in various forms

It is the process of finding the best way of using the existing resources while taking in to the account of all the factors that influences decisions in any experiment


Final product not only meets the requirements from the bio-availability but also from the practical mass production criteria

Pharmaceutical scientist- to understand theoretical formulation.

Target processing parameters – ranges for each excipients & processing factors

In development projects , one generally experiments by a series of logical steps, carefully controlling the variables & changing one at a time, until a satisfactory system is obtained

It is not a screening technique.

Continue….


How we can make Formulation perfect ?

What should be characteristics?

What should be the conditions?

Questions Should be in mind


Optimization parameters

Problem types Variable

Constrained Unconstrained Independent Dependent

Formulating Processing Variables Variables


Independent variables or primary variables :

Formulations and process variables directly under control of the formulator.

These includes ingredients

Dependent or secondary variables :

These are the responses of the inprogress material or the resulting drug delivery system. It is the result of independent variables .

Optimization Parameters


General optimization

By MRA the relationships are generated from experimental data , resulting equations are on the basis of optimization.

These equation defines response surface for the system under investigation

After collection of all the runs and calculated responses ,calculation of regression coefficient is initiated.

Analysis of variance (ANOVA) presents the sum of the squares used to estimate the factor main effects.


General optimization technique3

INPUTS

OPTIMIZATION PROCEDURE

RESPONSEMATHEMATICAL MODEL OF SYSTEMINPUT FACTOR LEVEL

OUTPUTREAL SYSTEM


TERMS USED FACTOR: It is an assigned variable such as

concentration , Temperature etc.., Quantitative: Numerical factor assigned to it Ex; Concentration- 1%, 2%,3% etc.. Qualitative: Which are not numerical Ex; Polymer grade, humidity condition etc LEVELS: Levels of a factor are the values or

designations assigned to the factor

FACTOR LEVELS

Temperature 300 , 500

Concentration 1%, 2%

E.g.


APPLIED OPTIMIZATION METHODS3

EVALUTIONARY OPERATION

SIMPLEX METHOD

LAGRANGAIN METHOD

SEARCH METHOD

CANONICAL ANALYSIS


Continued………

Evolutionary Method: Constant , Repetitive and Care full planning of production process to move towards better process.

Simplex Method: It is simplex algorithm i.e. mathematical process which is adopted for simplex process & generally represented in geometrical figers.

LAGRANGAIN METHOD: It is extension of classical method for simplifying the formulae & equations .the disadvantage of this method is that it is applicable to for only two variable problems.


CLASSIC OPTIMIZATION3

• Application to unconstrained problem• Finding maximum or minimum of a function of independent

variable

Y= f(X) , where Y- Response X- Single independent variableY= f(X1, X2)


Design Of Experiment4 (DOE)

It is a structured, organized method used to determine

relationship between the factor affecting a process and

output of that process.

Reduce experiment time

Reduce experimental cost


Phases Of DOE4

Determine the goal

Identifying affecting factors

Selection of Experimental design

Generating a Design Matrix

Conducting an Experiment

Finding the optimum

Results


Types of Experimental Design1-4

Completely Randomized Design Randomised block Design

Factorial Design

Response surface design

Three level full factorial design

Full Factorial DesignFractional Factorial Design

Central Composite Design

Box- Behnken Design


Factorial Design

For evaluation of multiple factors simultaneously.

23 means 2 is level while 3 is factor

Factorial Design is divided into two types-- Full Factorial Design- Fractional factorial design


Full Factorial Design

– Simplest design to create, but extremely inefficient

– Each factor tested at each condition of the factor

– Number of runs (N) N = yx

Where, y = number of levels, x = number of factors

E.g.- 3 factors, 2 levels each, N = 23 = 8 runs


2X Design

2 = LevelX = Input Factors

x2

Number of factors

Number of runs

2 43 84 165 32 x1

x3


Fractional factorial design

– Means “less than full”– Levels combinations are chosen to

provide sufficient information to determine the factor effect

– More efficient– Used for more than 5-factors

x1

x2x3


Summary

Design Merits Limitations

Full Factorial Screening of factors

Limited runs

Fractional Factorial Design

For maximum number of factors

Effects are not uniquely estimated

Response surface design

Curves of response graphically

Become complex if maximum number of factors


Types of Fractional Factorial Design4

• Homogeneous fractional

• Mixed level fractional

• Box-Hunter

• Plackett-Burman


Homogenous fractional Useful when large number of factors must be

screened

Mixed level fractional Useful when variety of factors need to be evaluated

for main effects and higher level interactions can be assumed to be negligible.

Box-hunter Fractional designs with factors of more than

two levels can be specified as homogenous fractional or mixed level fractional

TYPES OF EXPERIMENTAL DESIGN


Plackett-Burman It is a popular class of screening design.

These designs are very efficient screening designs when only the main effects are of interest.

These are useful for detecting large main effects economically ,assuming all interactions are negligible when compared with important main effects

Used to investigate n-1 variables in n experiments proposing experimental designs for more than seven factors and especially for n*4 experiments.

TYPES OF EXPERIMENTAL DESIGN


Two most common designs generally used in this response surface modelling are

Central composite designs Box-Behnken designs

Box-Wilson central composite Design This type contains an embedded factorial or

fractional factorial design with centre points that is augemented with the group of ‘star points’.

These always contains twice as many star points as there are factors in the design

Continued………


The star points represent new extreme value (low & high) for each factor in the design

To picture central composite design, it must imagined that there are several factors that can vary between low and high values.

Central composite designs are of three types Circumscribed(CCC) designs-Cube points at the

corners of the unit cube ,star points along the axes at or outside the cube and centre point at origin

Inscribed (CCI) designs-Star points take the value of +1 & -1 and cube points lie in the interior of the cube

Faced(CCI) –star points on the faces of the cube.

Continued………


Box-Behnken design

They do not contain embedded factorial or fractional factorial design.

Box-Behnken designs use just three levels of each factor.

These designs for three factors with circled point appearing at the origin and possibly repeated for several runs.


Software's for Optimization

• Design Expert 7.1.3

• SYSTAT Sigma Stat 3.11

• CYTEL East 3.1

• Minitab

• Matrex

• Omega

• Compact


Advantages

Helps to determine important variables

Helps to measures interactions. Allows extrapolations of the data and

search for the best possible product . Allows plotting of graphs to depict how

variables are related.


Application1,5

Formulation development

Dissolution testing

Tablet coating

Capsule preparation


Conclusion

Immense potential in development of pharmaceutical product and processes

Less involvement of men, material, machine and

money.

Improvement in formulation characteristics.


REFERENCE

Modern pharmaceutics- vol 121

Textbook of industrial pharmacy by sobha rani R.Hiremath.

Pharmaceutical statistics

Pharmaceutical characteristics – Practical and clinical applications

www.google.com


REFERENCES

1) Bolton S, BonC.Pharmacutical statistics practical & clinical application, 5th ed. New York London ;informa healthcare publishing ; 2010.p. (223- 39,424-51).

2) Jain NK,Pharmaceutical Product Development, New Delhi ; CBS Publisher ; 2010. p. 295-340.

3) Schwartz JB,Connere RE,Schnaar RL,In: Banker GS & Rodes CJ , editor . Modern Pharmaceutics, 4thed. informa healthcare publishing ; 2010.p. 727-728.

4)Hirmanth RR,Vanjaka KI , Textbook of Industry Pharmacy ;Drug Delivery System and Cosmetics and Herbal Drug Technology ; 2009.p.148-68.

5) Lewis GH, Mathieu DG, Pharmaceutical experimental design; Dekker series publishing;Vol-92; 2008. p. 237-240.


Thank You

Optimization techniques

Health & Medicine

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