Duration: 3 days
Course Introduction:
The rapid development of the pharmaceutical industry, the intensification of competition in the pharmaceutical market, and the increasing regulatory requirements have made the value of statistical analysis in pharmaceutical enterprises increasingly valued. The application of statistical analysis can enable pharmaceutical companies to conduct research and development, production, and quality control more accurately and scientifically, helping them better understand and control product quality, optimize production processes, reduce costs, and improve market competitiveness. This training course starts from practical applications and introduces commonly used statistical analysis methods in the CMC end of the pharmaceutical industry, including experimental design, quality process control, stability analysis, data management, and visualization data exploration. This will enable students to master practical statistical techniques and better apply these techniques to statistical analysis in the pharmaceutical industry.
Training objectives:
• Systematically and comprehensively learn various types of experimental designs and other statistical analysis methods.
• Ensure mastery of the application steps and precautions of statistical analysis methods and tools through practical case drills, and guide students to integrate statistical methods into practical work.
• Help enterprises effectively improve research and development efficiency, optimize process flow, achieve quality improvement, and reduce improvement costs.
Participants:
• Research personnel, R&D engineers, and managers who focus on process development and optimization, analytical method development and evaluation.
• Engineers and managers responsible for enterprise quality management, process improvement, and process improvement.
Content：
• Introduction to JMP
• Data management: Efficient data integration and cleaning
• Obtain
• Integrate
• Cleaning
• Define
• Data visualization: quality analysis and improvement, rapid/dynamic presentation of results; Deviation management; Quickly identify major quality issues, etc
• Graph builder
• Bubble chart
• Fish bone chart
• Pareto chart
• Descriptive statistical analysis: Statistical summary of key parameters for product annual quality review, weekly and monthly reports
• Calculation of basic statistics
• Normal distribution test
• Interactive analysis
• Hypothesis testing: substrate/process/site/material change management; Instrument/equipment status verification, replacement; Determination of product storage period; Comparison of consistency between original and generic drugs; Comparison of consistency in method transfer, etc
• Basic knowledge of hypothesis testing
• Single sample mean test
• Double sample mean test
• Single factor analysis of variance
• Equivalence test
• Regression analysis (single/multiple factor): correlation between various parameters and key quality attributes; Verification management, etc
• Basic principles
• Construction
• Diagnosis
• Predictions
• DOE: Process/analysis method development, optimization, method tolerance evaluation, design space determination, etc
• Basic principles of DOE
• Full factorial design
• Classic screening design
• Response surface design
• Custom design
• Definitive screening design
• Stability analysis:
• Stability test based on Q1E
• Batch consistency comparison
• Statistical process control: monitoring of key quality parameters/specification parameters; Formulation of internal control standards for enterprises, etc
• Basic knowledge of control charts
• Control chart drawing
• Process capability analysis
• Multivariate analysis: confirmation of scaled down models, process monitoring and fault diagnosis, batch consistency comparison
• Principal Component Analysis (PCA)
• Nonlinear fitting: Biological assay and comparison of similarity with dissolution curve
• Parallelism test, F2 value calculation
• Data Mining: Discovery of Gold Batch and Problem Batch
• Partition (decision tree)
• Introduction to workflow: Implementing automated data analysis
• Question and Discussion
Course features:
• Combining theory with practice, with a large number of examples to assist teaching, easy to understand.
• The content is extensive, covering commonly used statistical analysis methods in the field of pharmaceutical CMC.
• During the training period, enjoy free access to JMP genuine software to ensure the effectiveness of the training.
• After training, you can choose to participate in JMP certification and obtain corresponding qualification certificates.
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