Advanced process control in biopharmaceutical production

Process models form the basis for advanced control strategies in bio-pharmaceutical process. ABB helps set up PAT-enabled model-based control of a fed-batch mammalian cell culture. These advanced strategies can increase productivity and process robustness, as well as decrease process variation.

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A wide range of potential applications

The biopharmaceutical sector has become a significant and growing division of the pharmaceutical industry, with the the top-selling drugs being protein-based products. Mammalian cells, particularly Chinese hamster ovary (CHO) cells, and bacterial systems, such as Escherichia coli (E. coli), produce the bulk of the products on the market but alternative systems such as yeast and plant cells are also used.

Utilizing ABB’s xPAT product, ABB collaborated with Irish universities and leading biopharmaceutical players in an initiative funded by Enterprise Ireland to construct models to set up and evaluate the benefits of PAT-enabled, model-based control of a fed-batch mammalian cell culture.

There is a wide range of potential applications for models of the mammalian cell culture process: investigation of underlying process mechanisms; analysis and prediction of experimental results facilitating swifter process optimization; advanced control strategies; decision support systems; and soft sensors. Choice of model type depends on both the intended application and also the quantity, quality and nature of experimental data available upon which to build the model.

Biopharmaceutical production takes place in inordinately complex environments, with a nonlinear, dynamic mix of billions of cells and nutrients, vulnerable to temperature change, pH, inhomogeneity and so on.

Model types

Mathematical - mechanistic

Mathematical - black box

Statistical

Model types and control strategies

Process control of bioreactors seeks to influence the individual complex intracellular reactions of billions of cells by controlling their extracellular environment.

Process models form the basis for most of the modern or advanced control strategies. The discussion here will focus on model-based control for mammalian cell processes.

The following figure illustrates a nonexhaustive categorization of process models. They can be delineated into qualitative, mathematical and statistical models. Mathematical models can be further subdivided into mechanistic and black box models. For control applications, generally, quantitative models are required.

As such, the most useful model types for the control of bioprocesses are first-principle models, neural-network-based models and PCA- or PLS-based multivariate statistical models.

Mechanistic - first-principle models

The first-principle models discussed here refer to engineering, rather than biological, first principles. They combine mass balance, rate and yield equations to describe the dynamic profiles of biomass, nutrients and metabolites.

When these models move from treating the cell as a single unit to more detailed levels, considering for example, amino acids, nucleotides, proteins and lipid pools, the complexity, time for development and required computational power increase greatly.

To replicate the relationship between substrate and metabolite concentrations, cell growth and product formation, it is not necessary to use expensive theoretical models. Instead, parameters within the series of equations can be optimized in order to fit the equations to experimental data.

Typically, these semi-empirical models have some extrapolation capability and so first-principle models of this nature are most useful for optimization and control applications. Some experimental data is required but it is usually of the order of three to five batches depending on the number of parameters and range of operating conditions within the model.

Black box - neural networks

Neural networks involve a type of black box modeling, where little or no understanding of the underlying mechanisms of the process is required. The neural network is programmed to predict an per-output
or outputs based on a set of inputs by training it with a set of experimental data and/or deterministic models.

The neural network learns by processing data without rules. At its core it is a nonlinear regression. A series of data inputs are applied to a number of functions or nodes, which form a layer within the neural network. The inputs are weighted, summed and then processed by a transfer or threshold function before being output. This output may be the final result or simply data to be passed on to further layers within the neural network. During training, the weights applied to each of the inputs are adjusted in order to minimize the difference between the predicted and actual results.

The development of a robust neural network model requires a much greater number of batches than first-principle models, typically of the order of 20 to 30.

The quality of the experimental data is also very important. It is necessary to identify all inputs that affect the output to be predicted, and also to avoid correlated inputs. The training data set must contain enough variability to span the entire operating region and the data must be preprocessed effectively to remove outliers and poor-quality data as well as to scale and normalize the inputs.

However, in the case of bioprocesses where there is a high degree of process complexity, the low level of process understanding needed can be a considerable advantage, particularly if there is access to historical datasets.

Multivariate process control

Traditional manufacturing industries have used univariate statistical process control (SPC) charts to monitor and improve their processes for some time.

In bioprocesses, many variables are recorded and can potentially have an effect on the output of the system. Multivariate statistical methods have been developed to allow similar analysis of these large datasets. Detection of abnormal conditions and root cause analysis can be conducted by applying Principal Component Analysis (PCA) and Partial Least Squares or Projection on Latent Structures (PLS) methods.

Generally speaking, PCA is most often used to detect batch abnormalities, while PLS can be used to predict output parameters such as product quality or batch endpoint based on input variables and the current process states.

Statistical models are data driven and, therefore, it is important that the set of good reference batches captures the variation in the process. Too little variation results in false alarms and too much variation causes the model to be insensitive. A set of ten or more batches is necessary to build a multivariate statistical model.

Once the effort to build a good process model has been expended, it is desirable to exploit that model to maximize the benefit. Model-based control strategies represent one potential application. There are numerous forms of model-based control including model predictive control (MPC).

Model predictive control - inherently suited to optimization

Once the effort to build a good process model has been expended, it is desirable to exploit that model to maximize the benefit. Model-based control strategies represent one potential application. There are numerous forms of model-based control including model predictive control (MPC).

MPC is a multiple-input multiple-output (MIMO) control algorithm based on the repeated solution of a finite-horizon optimal control problem subject to a performance specification, constraints on states and inputs, and a system model. It can use a mathematical model such as first principle or neural network models, or statistical models such as PCA or PLS to create a future trajectory of the batch based on multiple measured process inputs.

It seeks to minimize the square of the error between the predicted trajectory and desired trajectory over a userdefined prediction horizon and then calculates a controller action for each of its outputs. In contrast to a traditional PID controller, which aims to minimize the instantaneous error between process variable and setpoint, the longer view taken by MPC reduces the impact of unknown disturbances, erratic signals and noise. MPC can also deal well with systems with a long dead time, though it is not robust in situations where the dead time changes significantly. MPC is inherently suited to optimization.

The presence of a dynamic optimizer, objective function and constraints within the framework means that MPC can predict future violations of constraints, handle complex interactions and smoothly adjust the manipulated variables. MPC has the widest application of all advanced control strategies in industrial applications.

The longer view taken by MPC reduces the impact of unknown disturbances, erratic signals and noise. MPC can also deal well with systems with a large dead time.

An online real-time process control system

The Process Analytical Technology (PAT) initiative launched by US Food and Drug Administration (FDA) aims to design quality into the process, rather than relying on testing the CQAs of the final product. Statistical models can be used as an aid for online process evaluation and decision making, as well as a tool for identifying variables likely to be responsible for deviations.

An industry-specific application built on ABB's System 800xA infrastructure, xPAT (eXtended PAT) is a next-generation PAT solution that harnesses the System 800xA operations and engineering environment and integration capability to provide significant improvements in the overall process and end-product quality. It provides life sciences users with a single system to access and examine online, real-time process data directly from the manufacturing operation.

The configurable Windows-based system collects data from ABB and/or third-party vendors’ analytical instruments and analyzes the data to determine the actual condition of the process. It then passes the resulting information to the ABB or third-party control system, and to other applications that support the drug manufacturing process.

Statistical models can be used as an aid for online process evaluation and decision making, as well as a tool for identifying variables likely to be responsible for deviations

ABB’s integrated PAT solution

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URL	Title	Description	Industry	Solution	Topic	Country
https://new.abb.com/control-systems/industry-specific-solutions/pharmaceutical-and-life-sciences/pharmaceutical-manufacturing-award	800xA wins Pharmaceutical Manufacturing Award in Hungary	Automation and BMS solutions delivered to Gedeon Richter Plc’s biotechnological facility	Food and Beverage	ABB 800xA, ABB Operations Management Software	Energy Efficiency	Hungary
https://new.abb.com/control-systems/industry-specific-solutions/pharmaceutical-and-life-sciences/pharmaceutical-company	Pharmaceutical company identifies 14MUSD in savings with ABB Engineering	Assessment of assets and operating practices across the customer’s utilities network	Chemicals		ABB Partners Information Management Operator Effectiveness, Service	United States of America
https://new.abb.com/control-systems/industry-specific-solutions/pharmaceutical-and-life-sciences/controlling-the-living-biopharmaceutical-factory	System 800xA controls bioprocess for apPAT project in Ireland	Experimental work to implement, optimize and evaluate the practicalities and benefits of installing a PATenabled advanced control system for a bio-pharmaceutical mammalian cell process	Mining	ABB 800xA	Enterprise Connectivity	Ireland
https://new.abb.com/control-systems/industry-specific-solutions/pharmaceutical-and-life-sciences/apc-mpc-model-predictive-control-for-biopharmaceutical	Advanced process control in biopharmaceutical production	Model predictive control - inherently suited to optimization of bioprocess for predicting violations of constraints, handling complex interactions and smoothly adjusting manipulated variables	Cement	ABB 800xA	Electrical Control System Energy Efficiency Operator Effectiveness	United Kingdom
https://new.abb.com/control-systems/industry-specific-solutions/pharmaceutical-and-life-sciences/system-extension-at-the-dorog-site-of-gedeon-richter	System 800xA upgrade at Gedeon Richter, Hungary	Evolution of a MOD 300 system to 800xA and upgrading it continously over the years with control system extension at the Dorog Site	Chemicals Power Generation	ABB 800xA	ABB Partners Information Management Operator Effectiveness, Service	Hungary
https://new.abb.com/oil-and-gas/case-studies/refining-and-chemicals/beyond-lopa---safety-integrity-assessment	Beyond LOPA - safety integrity assessment for specialist pharmaceutical manufacturer	Demonstrating to the regulatory authorities that batch reaction process risks are being managed appropriately	Marine vessels Oil, Gas and Petrochemical	ABB High Integrity Safety, ABB Operations Management Software	Electrical Control System Energy Efficiency Operator Effectiveness, Service	United Kingdom
https://new.abb.com/control-systems/industry-specific-solutions/pharmaceutical-and-life-sciences/reliability-assessment	Reliability assessment brings 850,000 USD annual savings for pharmaceutical manufacturer	Many medicines can be destroyed by storage and transport at the wrong temperature. How proper monitoring solutions from ABB support best practice	Food and Beverage		ABB Partners Energy Efficiency Enterprise Connectivity, Service Operator Effectiveness	United States of America
https://new.abb.com/control-systems/industry-specific-solutions/pharmaceutical-and-life-sciences/control-and-monitoring-of-temperature-in-the-pharmaceutical-supply-chain	Control and monitoring of temperature in the pharmaceutical supply chain	Many medicines can be destroyed by storage and transport at the wrong temperature. How proper monitoring solutions from ABB support best practice	Oil, Gas and Petrochemical	ABB Compact Product Suite	Information Management	United Kingdom
https://new.abb.com/control-systems/industry-specific-solutions/pharmaceutical-and-life-sciences/freeze-drying-in-the-pharmaceutical-industry	Freeze drying in the pharmaceutical industry	ABB provides independent verification, validation monitoring of the freeze drying process, enables compliance with latest regulations and GAMP guidelines, eliminates unauthorized adjustment of recorded data	Food and Beverage	ABB Compact Product Suite	Enterprise Connectivity, Operator Effectiveness, Information Management	United Kingdom
https://new.abb.com/control-systems/system-integrators/success-stories/partner-spotlight-njcsi-south-africa	System 800xA with batch and integration with Oracle ERP for Adcock Ingram pharmaceuticals (by NJCSI)	Controlling five production suites as well as the bulk storage and finished goods storage facilities, integration with the packaging machinery and MOM software integration with Oracle ERP.	Chemicals Oil, Gas and Petrochemical Power T&D	ABB 800xA, ABB Operations Management Software	ABB Partners Enterprise Connectivity	South Africa
https://new.abb.com/life-sciences/references/plug-and-produce---the-next-level-towards-pharma-4.0-for-ge-healthcare	Plug and Produce - the next level towards Pharma 4.0 at GE Healthcare	The new solution of connecting software and equipment in Biotech production will reduce engineering efforts especially regarding qualification efforts to satisfy compliance requirements.	Pulp and Paper	ABB 800xA	Enterprise Connectivity, Information Management	Sweden
https://new.abb.com/life-sciences/references/abb-ability-system-800xa-smart-client-for-recordati-ireland-ltd	ABB Ability System 800xA Smart Client for pharmaceutical company Recordati Ireland	Production managers instantly view plant metrics and KPIs outside the control room based on real-time control system data and analyze historical data and events from their office PC.	Power Generation	ABB 800xA	Information Management	Ireland
https://new.abb.com/process-automation/case-studies/pharmaceutical/johnson-and-johnson-centocor	Computerized maintenance management for Johnson & Johnson biopharmaceuticals	Implementing a world-class CMMS automates and streamlines maintenance, reduces costs, increases reliability and productivity by 30%	Mining	ABB Operations Management Software	ABB Partners Service	United States of America
https://new.abb.com/life-sciences/references/modular-automation-solution-for-life-science-company-bayer-ag	Modular automation solution for life science company Bayer AG	Moving from monolithic automation systems to a more flexible and service oriented plug and produce solution to enable personalized medicine	Pulp and Paper	ABB 800xA	Operator Effectiveness	Germany
https://new.abb.com/control-systems/industry-specific-solutions/pharmaceutical-and-life-sciences/mammalian-cell-fermentation-production-at-the-debrecen-site-of-gedeon-richter	Mammalian cell fermentation production	A fully automated greenfield plant at the Debrecen site of Gedeon Richter, Hungary	Pulp and Paper	ABB 800xA	Simulation	Hungary

800xA wins Pharmaceutical Manufacturing Award in Hungary

Automation and BMS solutions delivered to Gedeon Richter Plc’s biotechnological facility

Pharmaceutical company identifies 14MUSD in savings with ABB Engineering

Assessment of assets and operating practices across the customer’s utilities network

System 800xA controls bioprocess for apPAT project in Ireland

Experimental work to implement, optimize and evaluate the practicalities and benefits of installing a PATenabled advanced control system for a bio-pharmaceutical mammalian cell process

Advanced process control in biopharmaceutical production

Model predictive control - inherently suited to optimization of bioprocess for predicting violations of constraints, handling complex interactions and smoothly adjusting manipulated variables

System 800xA upgrade at Gedeon Richter, Hungary

Evolution of a MOD 300 system to 800xA and upgrading it continously over the years with control system extension at the Dorog Site

Beyond LOPA - safety integrity assessment for specialist pharmaceutical manufacturer

Demonstrating to the regulatory authorities that batch reaction process risks are being managed appropriately

Reliability assessment brings 850,000 USD annual savings for pharmaceutical manufacturer

Many medicines can be destroyed by storage and transport at the wrong temperature. How proper monitoring solutions from ABB support best practice

Control and monitoring of temperature in the pharmaceutical supply chain

Many medicines can be destroyed by storage and transport at the wrong temperature. How proper monitoring solutions from ABB support best practice

Freeze drying in the pharmaceutical industry

ABB provides independent verification, validation monitoring of the freeze drying process, enables compliance with latest regulations and GAMP guidelines, eliminates unauthorized adjustment of recorded data

System 800xA with batch and integration with Oracle ERP for Adcock Ingram pharmaceuticals (by NJCSI)

Controlling five production suites as well as the bulk storage and finished goods storage facilities, integration with the packaging machinery and MOM software integration with Oracle ERP.

Plug and Produce - the next level towards Pharma 4.0 at GE Healthcare

The new solution of connecting software and equipment in Biotech production will reduce engineering efforts especially regarding qualification efforts to satisfy compliance requirements.

ABB Ability System 800xA Smart Client for pharmaceutical company Recordati Ireland

Production managers instantly view plant metrics and KPIs outside the control room based on real-time control system data and analyze historical data and events from their office PC.

Computerized maintenance management for Johnson & Johnson biopharmaceuticals

Implementing a world-class CMMS automates and streamlines maintenance, reduces costs, increases reliability and productivity by 30%

Modular automation solution for life science company Bayer AG

Moving from monolithic automation systems to a more flexible and service oriented plug and produce solution to enable personalized medicine

Mammalian cell fermentation production

A fully automated greenfield plant at the Debrecen site of Gedeon Richter, Hungary

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