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

1. Introduction to Experimental Design

   • What is DOE?

   • Definitions

   • Sequential Experimentation

   • When to use DOE

   • Common Pitfalls in DOE

2. A Guide to Experimentation

   • Planning an Experiment

   • Implementing an Experiment

   • Analyzing an Experiment

   • Case Studies

3. Two Level Factorial Designs

   • Design Matrix and Calculation Matrix

   • Calculation of Main & Interaction Effects

   • Interpreting Effects

   • Using Center Points

4. Identifying Significant Effects

   • Variable & Attribute Responses

   • Describing Insignificant Location Effects

   • Determining which effects are statistically significant

   • Analyzing Replicated and Non-replicated Designs

5. Developing Mathematical Models

   • Developing First Order Models

   • Residuals /Model Validation

   • Optimizing Responses

6. Fractional Factorial Designs (Screening)

   • Structure of the Designs

   • Identifying an “Optimal” Fraction

   • Confounding/Aliasing

   • Resolution

   • Analysis of Fractional Factorials

   • Other Designs

7. Proportion & Variance Responses

   • Sample Sizes for Proportion Response

   • Identifying Significant Proportion Effects

   • Handling Variance Responses

8. Intro to Response Surface Designs

   • Central Composite Designs

   • Box-Behnken Designs

   • Optimizing several characteristics simultaneously

9. 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.

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