(DOE) Design of Experiement in Pharmaceutical Development

Complete DoE, Types of Designs, OFAT, Plackett burman, Central Composite, Box-Behnken Designs, Surface Response Curve

What you'll learn
Pharmacy Graduates Students
Pharmacy PG Diploma
Diploma Pharmacy Students
Research scientists
Pharma Professional
Pharma R & D Students
Research Scholars
Project Interns
Pharmaceutical product Developer
Requirements
All Graduates / Post-Graduates
Pharmaceutical industry Researcher, formulators
Bachelor of pharmacy
Description
If you are looking for DOE for Pharmaceutical Development course so this is for you with cheap cost.

To learn design space creation and over all design of experiment, you also need some knowledge of Risk assessment and critical parameter assessment. There are plenty of books available for this topic but its better to go through research papers related to specific field of interest. That will give you a better perspective of it.

Alos there are plenty of softwares like JMP and sigma plot which offer a free trial where you can learn to creat Design space with simple clicks.

At the beginning of the twentieth century, Sir Ronald Fisher introduced the concept of applying statistical analysis during the planning stages of research rather than at the end of experimentation. When statistical thinking is applied from the design phase, it enables to build quality into the product, by adopting Deming's profound knowledge approach, comprising system thinking, variation understanding, theory of knowledge, and psychology.

The pharmaceutical industry was late in adopting these paradigms, compared to other sectors. It heavily focused on blockbuster drugs, while formulation development was mainly performed by One Factor At a Time (OFAT) studies, rather than implementing Quality by Design (QbD) and modern engineering-based manufacturing methodologies. Among various mathematical modeling approaches, Design of Experiments (DoE) is extensively used for the implementation of QbD in both research and industrial settings.

In QbD, product and process understanding is the key enabler of assuring quality in the final product. Knowledge is achieved by establishing models correlating the inputs with the outputs of the process. The mathematical relationships of the Critical Process Parameters (CPPs) and Material Attributes (CMAs) with the Critical Quality Attributes (CQAs) define the design space.

Consequently, process understanding is well assured and rationally leads to a final product meeting the Quality Target Product Profile (QTPP). This review illustrates the principles of quality theory through the work of major contributors, the evolution of the QbD approach and the statistical toolset for its implementation. As such, DoE is presented in detail since it represents the first choice for rational pharmaceutical development.

Keywords: Experimental design; design space; factorial designs; mixture designs; pharmaceutical development; process knowledge; statistical thinking.

The objective of Design of Experiments Training is to provide participants with the analytical tools and methods necessary to

Plan and conduct experiments in an effective and efficient manner

Identify and interpret significant factor effects and 2-factor interactions

Develop predictive models to explain process/product behavior

Check models for validity

Apply very efficient fractional factorial designs in screening experiments

Handle variable, proportion, and variance responses

Avoid common misapplications of DOE in practice

Participants gain a solid understanding of important concepts and methods to develop predictive models that allow the optimization of product designs or manufacturing processes. Many practical examples are presented to illustrate the application of technical concepts. Participants also get a chance to apply their knowledge by designing an experiment, analyzing the results, and utilizing the model(s) to develop optimal solutions. Minitab or other statistical software is utilized in the class.

CONTENT of course

Introduction to Experimental Design

What is DOE?

Definitions

Sequential Experimentation

When to use DOE

Common Pitfalls in DOE

A Guide to Experimentation

Planning an Experiment

Implementing an Experiment

Analyzing an Experiment

Case Studies

Two Level Factorial Designs

Design Matrix and Calculation Matrix

Calculation of Main & Interaction Effects

Interpreting Effects

Using Center Points

Identifying Significant Effects

Variable & Attribute Responses

Describing Insignificant Location Effects

Determining which effects are statistically significant

Analyzing Replicated and Non-replicated Designs

Developing Mathematical Models

Developing First Order Models

Residuals /Model Validation

Optimizing Responses

Fractional Factorial Designs (Screening)

Structure of the Designs

Identifying an “Optimal” Fraction

Confounding/Aliasing

Resolution

Analysis of Fractional Factorials

Other Designs

Proportion & Variance Responses

Sample Sizes for Proportion Response

Identifying Significant Proportion Effects

Handling Variance Responses

Intro to Response Surface Designs

Central Composite Designs

Box-Behnken Designs

Optimizing several characteristics simultaneously

DOE Projects (Project Teams)

Planning the DOE(s)

Conducting

Analysis

Next Steps

Recently, DoE has been used in the rational development and optimization of analytical methods. Culture media composition, mobile phase composition, flow rate, time of incubation are examples of input factors (independent variables) that may the screened and optimized using DoE.

Look for course description …. look for see you in the class….

Who this course is for
Research & Development
Bachelor of Pharmacy Students
Master of Pharmacy Students
Bachelors of Science
Master of Science
Career in Research and Development
Pharmacy Students
Pharma industry Professionals