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 / PostGraduates

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 engineeringbased 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 2factor 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 Nonreplicated 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
 BoxBehnken 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