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Process Characterization/Process Validation

Based on the QbD concept, TOT BIOPHARM has established a comprehensive and audited process validation system, which consists of process characterization (PC), process validation (PV), and continuous process validation (CPV). With strong platform capabilities and flexible solutions, we can provide professional and efficient process validation services, especially with significant advantages in process validation of late-stage product. The core technical team has nearly 10 years of experience, and has completed the full process validation for multiple projects.

Process Characterization Workflow
Process definition
– Define manufacturing process
– Establish batch database
– Collect prior knowledge
Risk assessment
– Define CQA & KPAs
– Define process parameters
– Risk ranking
RExperiment design and execution
– Scaled-down model establishment
– Multivariate analysis (MVA)
– Design of Experiments (DOE)
Statistical analysis and forecasting
– Linear/nonlinear multiparameter model
– CPPs & KPPs classification
– Worst-case study
– Design space identification
Control strategies
– Robustness assessment
– Lifecycle qualification
– Process validation (PV)
Process validation - Workflow
  • Validation master plan
  • Risk assessment
  • Pre-validation qualification: Qualification of documents, equipment, detection instruments, analytical methods, personnel training, major materials etc. to meet requirements
  • Studies on critical quality attributes
  • Studies on critical process parameters
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Process characterization of two projects with high quality. One of the projects has already been launched on the market, while the other is currently in the process of compiling BLA data.

  • Process definition
  • Risk assessment
  • Experiment design and execution
  • – Define manufacturing process
  • – Define CQA &KPAs
  • – Scaled-down model establishment
  • – Establish batch database
  • – Define process parameters
  • – Multivariate analysis (MVA)
  • – Collect prior knowledge
  • – Risk ranking
  • – Design of Experiments (DOE)
  • Statistical analysis and forecasting
  • Control strategies
  • – Linear/nonlinear multiparameter model
  • – Robustness assessment
  • – CPPs & kPPs classification
  • – Lifecycle qualification
  • – Worst-case study
  • – Process validation (PV)
  • – Design space identification
Extensive platform experience
  • Comprehensive project experience
  • Well-experienced core team
Scientific and rigorous experimental arrangement
  • Learning organization
Strong data statistical analysis capability
  • Relying on knowledge and experience to deeply mine data, scientifically and rigorously assist BLA application
Digital systems
  • Adopt digital management tools to ensure data completeness, authenticity, and traceability.