Optimization is selecting the most suitable element from available resources considering all the factors which influence decisions in any experiment. In the pharmaceutical industry, optimization has always meant changing one variable at a time to solve a problem formulation. To improve formulation irregularities, modern pharmaceutical optimization uses a systematic design of experiments (DoE). Quality by Design enhances the assurance of safe and effective drugs to consumer and promise to improve manufacturing quality performance and also product free of contamination and gives the desired benefit as in the label to the consumer.

1. Pharmaceutical Product
Development
Optimization techniques
By
Kaushal Kumar

2. TABLE OF CONTENTS
01 02 03 04
Introduction Design Component Process

3. Introduction
Optimization is selecting the most suitable element from available
resources considering all the factors which influence decisions in
any experiment.
In the pharmaceutical industry, optimization has always meant
changing one variable at a time to solve a problem formulation.
To improve formulation irregularities, modern pharmaceutical
optimization uses a systematic design of experiments (DoE).
Quality by Design enhances the assurance of safe and effective
drugs to consumer and promise to improve manufacturing quality
performance and also product free of contamination and gives the
desired benefit as in the label to the consumer.

4. Introduction
Optimization techniques and Experimental design
are used specifically to examine various problems
that occur during the research.
If the experiments in the production are carried out
randomly then results obtained will be random, so
we need to plan the experimental process such that
relevant information is obtained.

5. Experiment Design Process
Define the objectives
Planning the experiments
Factors which influence the study is screened
Selecting the experimental design
Formulation and evaluation
Search for the optimum by using computer aided modeling
DOE methodology should be validated
Scale up & the obtained entire process is implemented

6. Optimization Parameters
Parameters
Problem
Types
Constrained Unconstrained
Variables
Independent Dependent

7. Components Of Experimental Design

8. Components Of Experimental Design
► Factors
That define a process, Property is
identified
► Levels
That influence the effect of factors
► Responses
Identification of the design space
Factors Levels Responses
Micro oven Temperature
Ex – 20, 30, 40
Trail & Error
method
Or
One factor at a
time
Or
DOE
Sugars Number of cups
Ex – 1, 2, 3
cups
Eggs Number of Eggs
Ex – 1, 2, 3
eggs
Flour Number of cups
Ex – 1, 2, 3
cups

9. Process Of Experimental Design
Describe Specify Design Collect Fit Predict
► Describe
Identify goal, responses, and factors. A result that you care about & can measure well
► Specify
Identify effects for an assumed model.
► Design
Generate a design & evaluate it for suitability.
► Collect
Run trail using design settings, measure response for each run.
► Fit
Determine a model that best fit experimental data.
► Predict
Use the model to optimize factor settings or to predict process performance.

10. Experimental Design
Factorial Design Fractional Factorial Design
Full Factorial Design
Plackett-burman Design
Central Composite Design (CCD) Box-behnken Design
Taguchi Design Mixture Design
Star Design Box Design
Uniform Shell Design Simplex Lattice Design
D-optimal Design Sequential Optimization Design
Extreme Vertices Design

11. Uses
 In microencapsulation process.
 Provide solution to large scale manufacturing problems.
 Improvement of physical & biological properties by modification.
 Provides string assurances to regulatory agencies superior drug product quality.

12. THANKS!
Do you have any questions?
easyconceptkaushal@gmail.com
+91 9546688666