From Design of Experiments to closed loop control Petter Mörée & Erik Johansson Umetrics

Umetrics, The Company Part of ~1Billion conglomerate The market leader in software for multivariate analysis (MVDA) & Design of Experiments (DOE) 25+ years in the market Off line analysis tools On-Line process monitoring and fault detection 700+ companies, 7,000+ users Pharmaceutical, Biotech, Chemical, Food, Semiconductors and more Worldwide Presence with MKS Offices: Umeå, Malmo, Sweden York, England Boston, San Jose, USA Singapore Frankfurt, Germany Close collaboration with universities in USA, Sweden, UK and Canada Partnership with Sartorius; global marketing, distribution, development and integration.

Building a capable process Manufacturing DOE Control Strategy Knowledge building DOE Analysis Design Space Error detection/ Knowledge building QRA: Quality Risk Assessment MVDA QFD Quality Function Deployment DOE is a knowledge building tool for process development MVDA is used both for process understanding and process monitoring

Processes and their data are never perfect Delegates at this meeting are of course excluded Multivariate data analysis (MVA) is a tool to learn from data Marek used MVA and NIR to predict glucose nad other parameters inside the reactor This talk will focus on process parameters Tightly controlled pH, pO2, Temperature Parameters used for keeping tightly controlled at their sepoint Stirring, airation, cooling, base addition .. Commonly measured CER, OUR … Monitor, interpret, control

Is this chart familiar? DJIA = x1*Merck + x2*J&J + x3*Pfizer + x4*DuPont + ....

t1= x1*Temperature + x2*Pressure + x3*Agitation speed + x4* pO2. MSPC – Multivariate Statistical Process Control Evolution Level – Monitoring Example of a fermentation t1= x1*Temperature + x2*Pressure + x3*Agitation speed + x4* pO2. Control limits Average (signature) of all good experiments New run/experiment assessed by the model

Statistical Process Control MULTIVARIATE CONTROL CHART Signature average of all good runs control limits (± 3s from avg.)

MVDA Objectives for the pharmaceutical & biopharmaceutical industry Increase of process understanding Identification of influential process parameters Identification of correlation pattern among the process parameters Generation of process signatures Relationship between process parameters and quality attributes Increase of process control Efficient on-line tool for Multivariate statistical control (MSPC) Analysis of process variability Enabling on-line early fault detection Support for time resolved design space verification real time quality assurance Predicting quality attributes based on process data Excellent tool for root cause, trending analysis and visualization Fundament for Continued Process Verification (CPV) Development Production Mechanistic understanding on how formulation and process parameters affect product performance

Work and Data flow For Method Development Reduction of Dimensionality Batch Level Evolution Level Aims: - Creation of batch signature Identify correlation patterns Fundament for CPV All Process Parameters Individual Probes Individual Probes …

Work and Data flow For Routine Use in Production Batch Level Evolution Level Aims: Conformity check Real time release testing - Trend analysis - Root cause analysis Identification of responsible Parameter(s) Increased of level of detail Answers: What? When? How? Investigation on process data

What makes Multivariate-SPC so powerful? The SIMCA product family uses a data compression technique Multivariate data analysis PCA and or PLS Data from all relevant process parameters are concentrated to a few highly informative graphs Simplifies overview, analysis and interpretation Enable use of data by increasing ease of use Simple drill-down functionality to transfer compressed information back to raw data for analysis The upper plot shows how 7 measured parameters change over the batch evolution for one batch. In the lower figure the 7 parameters have been concentrated into one. Using all data for analysis is one of the keys to success in Early fault detection. An issue with control of a CPP will show up earlier in the behavior of the actuators than in the CPP itself. The fault will be identified, analyzed and acted upon quicker to prevent effects on product quality.

Drill-down for analysis

Monitor Early fault detection Project dashboard Knowledge building SIMCA-online technology is acknowledged for its ability to detect process issues before they become critical Project dashboard Full drill-down to raw data for cause analysis Knowledge building Instant analysis of process changes improves understanding Process visibility Easy-to-grasp graphics makes the process status accessible to colleagues at all levels

Prediction and Continued Process Verification Product quality information Indirect information based on process behavior As long as a process behaves well, product should be according to specification Soft sensor modeling Predict hard-to-get process properties from online process data, spectral data etc. Predictive analytics Online prediction of product quality and properties Continued Process Verification Ongoing assurance is gained during routine production that the process remains in a state of control.

Motivation for QbD Reducing process variability is not necessarily desirable With variation in inputs Initial material qualities Environment Equipment Static process Results in variability in outputs

QbD and PAT Strategies Control strategy b) feedforward control Adjusting the process based on variations in the input Media and feed composition Used in pulp and paper and other industries with natural products with high variability Cheese production

QbD and PAT Strategies Control strategy c) PAT control Adjusting the process based on measurement of quality in the process Used in many processing industries using various methods Direct measurement of material quality Inferential control – estimation of quality from process measurements Spectral calibration

Important Process Parameter Monitoring Important Process Parameter Monitoring is used to detect and diagnose process deviations UMETRICS CONFIDENTIAL

Model Predictive Control (MPC) Important Process Parameter MPC is used to predict UMETRICS CONFIDENTIAL

Model Predictive Control (MPC) Important Process Parameter MPC is used to predict and optimize the process UMETRICS CONFIDENTIAL

Manipulated Variables Model Based Control Manipulated Variables Important Process Parameter UMETRICS CONFIDENTIAL

Novartis Biopharmaceutical

Chemometric portfolio

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