Course Name: Pharmaceutical IndustryCMC Practical Statistics
Course duration:3天
Course Introduction:
The rapid development of the pharmaceutical industry, the intensification of competition in the pharmaceutical market and the improvement of regulatory requirements have made the value of statistical analysis more and more important to pharmaceutical companies. The application of statistical analysis can enable pharmaceutical companies to conduct research, development, production and quality control more accurately and scientifically, helping companies better understand and control product quality, optimize production processes, reduce costs and improve market competitiveness. This training course starts from practical applications and introduces the statistical analysis methods commonly used in CMC in the pharmaceutical industry, including experimental design, quality process control, stability analysis, data management and visual data exploration, so that students can master practical statistics. technologies and use these techniques to better apply statistical analysis in the pharmaceutical industry.
Course training purpose:
- Systematically and comprehensively study various experimental design types and other statistical analysis methods.
- Ensure that students master the application steps and precautions of statistical analysis methods and analysis tools through practical case exercises, and guide students to integrate statistical methods into actual work.
- Help companies effectively improve R&D efficiency, optimize process flows, achieve quality improvements, and reduce improvement costs.
Course participants:
- Scientific researchers, R&D engineers and managers who focus on process development and optimization, analytical method development and evaluation.
- Engineers and managers responsible for corporate quality management, process improvement, and process improvement.
Course content outline:
- Introduction to JMP
- Data management: efficient data integration and cleaning
- retrieve data
- Integrate data
- Clean data
- Define data
- Data visualization: quality analysis and quality improvement, rapid/ dynamic presentation of results; deviation management; rapid identification of major quality issues, etc.
- graph generator
- bubble chart
- Fishbone Diagram
- Pareto chart
- Descriptive statistical analysis: annual product quality review, weekly report, monthly report statistical summary of key parameters
- Calculation of basic statistics
- Normality test of data
- Interactive correlation analysis
- Hypothesis testing: change management of substrates/ processes / sites /materials; instrument/ equipment status verification, substitution; determination of product storage period; consistency comparison of original drugs and generic drugs; consistency comparison of method transfer, etc.;
- Basic knowledge of hypothesis testing
- One-sample mean test
- Two-sample mean test
- equivalence test
- One-way ANOVA
- Introduction to nonparametric testing
- Regression analysis (single factor/ multiple factors): correlation between various parameters and critical quality attributes, verification management, etc.;
- Basic principles of regression analysis
- Construction of regression model
- Diagnostics of regression models
- Regression model predictions
- Experimental design: process/ analytical method development, optimization, method tolerance evaluation, design space determination, etc.
- Basic principles of experimental design
- full factorial design
- Classic screening design
- response surface design
- Mixed design
- custom design
- deterministic screening design
- Extended design
- Stability analysis:
- Stability extrapolation based on Q1E
- Batch consistency comparison
- Statistical process control: monitoring of key quality parameters/specification parameters ; formulation of corporate internal control standards ; annual product quality review; preventive management, etc.
- Basic knowledge of control charts
- Measurement type control chart
- Count control chart
- process capability analysis
- Multivariate analysis: confirmation of reduced model, process monitoring and fault diagnosis, batch consistency comparison
- Principal Component Analysis PCA
- Data Mining: Discovery of Golden Batch, Problem Batch
- Introduction to Workflows: Automating Data Analysis
course features:
- The theory is combined with practice, and a large number of examples are used to assist the teaching, making it easy to understand.
- The content is extensive and covers commonly used statistical analysis methods in the field of pharmaceutical CMC.
- During the training period, you can enjoy JMP genuine software for free to ensure the training effect.
- After training, you can optionally participate in JMP certification and obtain the corresponding qualification certificate.
