生物反应器课件 PAT Introduction.ppt

PAT Introduction PAT Process Analytical Technology What is PAT? What is PAT (and what isnt) ? PAT Enabled SCADA Some Key Elements of a PAT in combination with SCADA Spectrometry Metadata & Experimental context Getting more from your data Is PAT? Installing some shiny new equipment ? Connecting a new probe / cell counter to a bioreactor ? Already standard practice in Bioprocess development ? Little business benefit to early bioprocess development ? An important tool for Quality by Design? What is PAT Process analytical technology (PAT) has been defined by the FDA as a mechanism to design, analyze, and control pharmaceutical manufacturing processes through the measurement of Critical Process Parameters (CPP) which affect Critical Quality Attributes (CQA). PAT should address uncontrolled process variation to mitigate risk of uncontrolled product variability PAT Solutions should be an integral part of a Quality by Design (QbD) strategy Long term goals reduce production cycling time prevent rejection of batches enable real time release increase automation improve energy and material use facilitate continuous processing How do we get there? Define process and product quality What causes variance? What is critical? Understand the process How can we control the process to the desired end product quality? On line monitoring (near infrared) How do we get there? Eliminate primary sources of variance 1) Medium composition: from undefined medium (containing protein extracts), to chemically defined medium with balanced C/N ratio 2) Inoculum: reproducible seedlot preparation, freeze at fixed OD, during mid-log growth phase. Define Quality Where should we finish? Experimental design Design space Control space From: D. Low, Amgen BioProduction 2006, Dublin Tools: SCADA Is the technology fit for purpose ? How much data ? How will you store it ? How will you annotate data ? Is the PAT application going to be on-line ? Is software OPC compliant ? If not , have you assessed interfacing costs ? Tools: NIR Full Spectrum Near Infrared Vibrational spectroscopy (800 2500nm) Non-invasive optical sensing NIR has widespread acceptance in pharmaceutical manufacturing Operational & Technical Flexibility Cost Effective, Robust & Scalable Tools: NIR However, Chemometric models are required to extract biomass,metabolite & catabolite data (other methods are also possible) In addition, large datasets make model building difficult Results: Calibration sensor One CHO run (10 days) New Castle Univerity (UK) 1 Spectra every 2 min 5500 Spectra 2000 Abs reading per spectra 10-15 runs for each calibrations Each Bioreactor run Raw Export file 55 Mb Opus files 75 Mb Calibration to full scale NIR probe calibration data collected in multiple small scale vessel run in parallel Better integration of NIR calibration into Development lifecycle Augment calibrations over the development lifecycle Run 1 Run 2 Run 3 Run 4 Run 5 Small scale Benchtop Bioreactors Calibration set Validation set Calibration Model Transferred to Full scale Production bioreactor Temp pH DOT FT-NIR Full Scale Production Bioreactor Time PharmaManufacturing Benchmarking Study Data (2005) Broad survey of 104 Pharma companies. Limited IT support for transition from bench through process dev to commercialisation Unstructured information collection, storage & retrieval results in more time searching for data Limited coordination across groups and sites affects the ability to meet development cycle times (time to human, time to clinic, etc) and product cost targets Lack of information visibility & context limit the effectiveness of portfolio and project management processes 5hr per week on average looking for data Cant find data 25 % of time leading to repeating 8% of experiments and increased R&D costs Limited and inefficient traceability (paper based) Few IT systems facilitate data reuse & ability to “drill across” General lack of satisfaction with all Pharma IT systems PharmaManufacturing Benchmarking Study Data (2005) Future requirements Development of consistent annotation standards to make bioprocess data meaningful Metadata should enable drilling down and across datasets, filter and sift & batch genealogy creation Free text will not be appropriate for all fields Reporting software formats data to ease transfer into analysis software What should SCADA do? Recipe driven batch execution Recipe transfer between units Secure acquisition & storage of Bioreactor data Offline data storage Easy integration of new sensors through OPC Real time Process view What should Bioreactor Controller do? Metadata & Annotations entry Blank Tags for analyser values via OPC Generic PID controller with OPC Input and configurable outputs SCADA Level reporting 60-70% of analysis time is reformatting data for importing into analysis tool Most users have simple reporting requirements Process map of the data flow Search, Sift, Filter