Continuous Manufacture: Real-time Multivariate process monitoring

Transcription

1 Continuous Manufacture: Real-time Multivariate process monitoring Melanie Dumarey, Peter Hamilton, Christian Airiau GlaxoSmithKline, 9SEP15

2 Introduction GSK is investing in Continuous Manufacture for drug substance & drug product Acknowledge the required investment in technology to support CM Mechanistic modeling, PAT, MVA are an integral part of the CM analytics eco-system Case study focuses on 3 consecutive chemical steps run in continuous mode Currently in R&D, to be transferred in Manufacture in 1QR16 Multivariate Analysis (MVA) models presented here will be used for information only on the process

3 Process and scope 2 nd generation process Chemistry developed for flow Purification in batch, No change to API formation step Challenges Single solvent, degassing, fast kinetics, no solids Drivers Greater control of process impurities, enhanced consistency and robustness, reduced footprint Perceived blockers Existing batch technology vs. initial investment required, resistance to change, risk to supply chain, benefits in footprint difficult to quantify

4 From Lab to Plant Three chemistry stages run in continuous flow Lab scale facility, UK (25% of plant capability) Plant scale facility, Singapore Significantly reduced footprint compared to Batch processes

5 Ready now technology Improving existing equipment Molecular spectroscopy During development, in R&D Gain process insight during development activities Influence the control strategy definition Liquid chromatography (In R&D and in Manufacture) COTS fast LC Bring off-line resolution on-line Analysis time ~ 10 min

6 Advanced Analytics for Continuous Process Monitoring Objectives and Scope Implement Advanced Analytics capability for real-time monitoring of continuous chemistry process. Enable early fault process deviation and on-the-fly root cause analysis. Approach: Implement real-time process modeling as a core capability for continuous primary manufacture (Model Type: Multivariate Statistical Process Control (MSPC)) Integrate in real-time process and PAT (Conductivity + uhplc) data using SiPAT 4.1 Operator Supervisor Technical Manufacturing Processes How was our batch? Investigation & RCA Batch-to-batch? Review observation Risks with my process? Continuous Improvement Support operator? Go to next phase? Continuous process Deviation observed? Record observation Continuous Continuous Reactor Reactor 1 Continuous Continuous Reactor Reactor 2 Make correction? Escalate issue? Process PAT SIPAT Simca On-Line

7 Temperature MSPC score MSPC model: Objective One MSPC model summarizes and visualizes variability of multiple process sensor readings over time (e.g. Pressure, T, flow etc) Understand natural versus impactful process variation Enhanced process control resulting in more consistent quality Early drift detection can trigger correction actions and prevent forced shut down Facilitates root cause analysis Pressure Time

8 MSPC model: approach to model building Define section(s) of the process running in State of Control Product of known overall quality is produced by the line Build off line (SIMCA) a PCA based model Optimize number of latent variables Use Independent test sets of typical and atypical process condition Confirm that the model is able to differentiate State of Control versus atypicality Transfer the SIMCA model to Simca-On-Line (SOL) for real-time process monitoring SOL is pre-configured to read data from the SIPAT platform

9 MSPC model: Model Lagging evaluation More meaningful correlation patterns are summarized by correcting for lag time between sensors The model identifies correlation patterns between the same reaction volumes. Lagging enables to follow propagation of effects through the system, but causes monitoring delay e.g. The inlet conductivity of the reactor is linked to outlet conductivity measured 20 min later. This enables to study how change propagates in the continuous system. No correction for lags between sensors Corrected for lags between sensors Development campaign data, no quality impact Model not corrected for lag between sensors flags a large excursion out of model space 3 minutes before the flagged deviation, whilst the corrected model flags no excursion before.

10 Model output: Process Insight Stage 2-3 model (10-days) coloured by time (start end): score plot (Left) Loadings plot (right) Corrected for lags between sensors R2X=0.34, Q2=0.24 Contribution of sensor variability to model (Flow pump speed pressure - conductivity temperature)

11 MSPC overview, confirm steady state Process at steady state: no trend/drift: 3 hr of confirmed steady state (Blue dots, projected time points onto model) Process consistent, confirm steady state No pattern / trend / drift model prediction 3 Model statistical limit 1

12 Early Fault Detection - example Review of process against steady state model 14 hr into the process Process shift from model window Contribution plot indicates process param. responsible for the drift Main parameter plotted over time showing minor increase, outside statistical limits Reactor 2 Pump 2 Suction Pressure Minor increase Statistical limits, not Quality critical 1

13 Advanced Analytics for Continuous Process Real-time deployment Screen shot from real-time process monitoring on development campaign data in R&D Process health-check Model limit On-the-fly investigation Parameter X responsible for model drift Alarm setting Model drift Confirm parameter X drift,

14 Conclusions Development phase for Real-Time MVA supporting Continuous Manufacture Deployment of Sipat/SOL in Manufacturing environment in 1QR16 Similar solution considered for our Drug product Continuous Manufacture Feedback from shift team will be key to define best operating model User Interface (what to display for whom) Mobile App. Provide model output from SOL on tablet/smart phone for remote process monitoring MVA on the go!

15 Acknowledgements Melanie Dumarey Lead chemometrician Peter Hamilton Lead PAT Hanna Robinson Pete Shapland Malcolm Berry Clarence Wong Nora Pataut Ian Barylski Rob Hughes

16 Thank you