PAT for the On-line Characterization of Continuous Manufacturing Systems

Thomas O’Connor, Ph.D.

Office of Pharmaceutical Science

FDA/PQRI Conference: Innovation in Manufacturing and Regulatory Assessment September 16th, 2014

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Disclaimer

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This presentation reflects the views of the author and should not be construed to represent FDA’s views or policies

Objectives and Outline

• Objectives – To delineate why PAT is critical for the monitoring and control of a

continuous process

– To illustrate the role of process monitoring in the control strategy for a

continuous process and the advantages of multivariate tools

• Outline – Role of PAT for Continuous Manufacturing

– Process Monitoring and Control

– Multivariate Statistical Process Control (MSPC)

– Considerations for the Implementation of MSPC

– Concluding Remarks

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Process Analytical Technology

• Process analytical technology (PAT) is “a system for designing, analyzing, and controlling manufacturing through timely measurements (i.e., during processing) of critical quality and performance attributes of raw and in-process materials and processes, with the goal of ensuring final product quality” PAT Guidance

• PAT tools – Multivariate tools for design, data acquisition, and analysis

– Process analyzers

– Process control tools

– Continuous improvement and knowledge management tools

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A Conceptual Example of Control Strategy in Continuous Manufacturing

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Receiving

Feeder

Blending Drying

Granulation

Blending

Feed Frame

Tablet Press Coating

Milling

At-line Chemical Properties & Physical Properties for Raw Materials

Concentration & Uniformity (Multi-component)

Particle size distribution Moisture content

Weight & Hardness Digital Imaging

Surrogate Dissolution

Model (release) Real-Time

Release Testing Online Assay

Chemometric model

Chemometric model

Raw material characterization, process data, & chemometric model outputs integrated into a supervisory control and data acquisition (SCADA) system

Role of PAT for Continuous Processes

• Necessary for Process Monitoring – Assure desired product quality is being consistently manufactured – Identify non-conforming material

• Supports Real Time Release (RTR) – Evaluate the quality of final product based a combination of measured

material attributes and process controls, ICH Q8 R(2)

– RTR facilitated by on-line measurements or surrogate assays (e.g. dissolution testing)

• Supports process development – Continuous and fast response of process to factor changes allows

efficient experimentation – Increases process understanding within the range of conditions

studied during development

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In-Process Controls

• To assure batch uniformity in-process controls shall be established – CFR 211.110(a)

– Examinations to be conducted on appropriate samples of in-process materials for each batch

• Requires higher frequency measurements for continuous processes compared to batch processes

– Controls shall monitor and validate the performance of the manufacturing processes that may cause variability in the drug product

• Valid in-process specifications shall be consistent with the release specification – CFR 211.110(b)

– Limits shall be derived from acceptable process variability estimates where possible

• Rejected in-process materials shall be identified and isolated – CFR 211.110(d)

• PAT tools can be utilized to meet the expectations for in-process monitoring

– Establish that the process is consistently producing the desired quality

– Approaches may include multivariate process monitoring

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Process Monitoring for the Prevention of Abnormal Events • Abnormal Events:

– An operating situation occurring within a processing unit that could result in adverse consequences (out of spec product, equipment malfunction, process safety incident etc.)

8 Reliability/Quality Pyramid

Process Monitoring and Control

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Items to Assist Operator Monitoring and Control Activities

Console Graphics

Advanced Controls

Process Monitoring Alarm System

Shutdown Systems

Process monitoring tools are an added layer of protection to help operations pro-actively intervene in the process and bridge the gap during the time between normal and alarm conditions

Approaches for Process Monitoring

• Statistical Quality Control (SQC) – Variability in quality attributes of the product are monitored

over time

• Statistical Process Control (SPC) – The variability in critical process parameters and in-process

quality measurements are monitored over time – Monitoring the process variables expected to supply more

information (e.g., detection and diagnosis) – May generate a large number of univariate control chart that

need to be monitored

• Multivariate Statistical Process Control (MSPC) – Takes advantage of correlations between process variables

– Reduces the dimensionality of the process into a set of independent variables

– May detect abnormal operations not observed by SPC

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Operator Overview Display

Multivariate Control Chart: Reduce Dimensionality/Enhance Fault Detection

Multivariate Statistical Process Monitoring Example

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F T

O

T F T

F D

Furnace Monitoring

Advanced Process Control Objectives 1. Maintain outlet temperature at SP 2. Maximize throughput Process Monitoring Objectives 1. Detect Furnace “Flooding” 2. ……

Out of Control Period – Heat Balance Deviation

Process Data is Highly Correlated

Multivariate Process Monitoring Methods

• Latent Variable Models – Process data and product quality data are decomposed into new a set of

independent variables

• Principal Component Analysis (PCA) – Determines the latent variables that maximize the variation in process data

captured by the model – Well suited for process monitoring applications

• Partial Least Squares (PLS) – Determines the latent variable that maximizes the correlation between the

process and product quality data – Well suited for predicting quality attributes from process data (e.g. soft

sensors, surrogate models etc.)

• Model statistics can be calculated to assess the overall health of the process – Hotelling T2: Able to detect process “stretches” – SPEX: Able to detect a change in the relationship between process variables

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• Hotelling T2 – The “distance” of the current operating point from the mean of the

historical normal process data

• Squared Predicted Error – Sum of the squared error between the model predicted and measured

process variables

Diagnosing Process Faults

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Considerations for the implementation of MSPC for continuous process • Continuous manufacturing facilitates the

adoption of MSPC for process monitoring – Simplifies the model building process – do not need account for changes

in the process with respect to time – There is generally an increase in the amount of process and quality data

available for analysis

• Best practice considerations for the implementation of MSPC – Variable selection for MSPC models – Data selection for building MSPC models – Variable transformations – MSPC model validation – On-line implementation

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Variable Selection for MSPC Models

• Utilize process knowledge to select variables – Risk assessment approaches may be used to identify failure modes

– Identify process variables that signal each failure mode

• Identify correlated process parameters – Statistical approach used to identify process variables that are related

– Dependent upon the process data selected for analysis

• Investigate control variables as well as process variables – Tightly controlled process variables typically provide little information

– Information may be contained in the manipulated variable (e.g., valve opening etc.)

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L

The % valve opening contains information about the variation in the process

Data Selection for MSPC Model Building

• Historical data utilized should capture the normal variations expected during the process lifecycle

– Should include data from routine operations (e.g. feeder refills, etc.)

• Process data from disturbances should be removed from the model building data set

• Minimize amount of steady-state data included in the model building data set

– Steady state data masks the relationship between variables (variation is mostly due to noise)

– Statistic filters can be used to remove steady state data

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Majority of variation reflects noise

Majority of variation reflects physical relationship

Data Selection Considerations Cont’d

• Relationships between variables are influenced by the process design and the process control structure – Control structure may not be finalized till the end of process development – May impact the applicability of process development data for model building – MSPC models may need to be re-vamped after process control projects are

implemented

• MSPC models may be initially constructed from process performance qualification data – Process qualification combines the actual facility, utilities, equipment with the

commercial manufacturing process, control procedures, and components to the end product

• MSPC models may be updated as part continuous process verification – Unlikely that all the sources of normal variation will be experienced during

the process qualification process

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Considerations for Variable Transformations • Process data is typically auto-scaled prior to model building

– Mean-centered to focus on variation (i.e. 𝑥 =0)

– Scaled by the standard deviation to equally weight each variable (i.e. s=1)

• The mean and std. dev. of each process variable is calculated from the model building data set

• A constant mean may not be suitable for monitoring a continuous process

– Filter mean to remove the impact of slow process shifts or a change in operating condition (e.g., high vs low flow rates)

– Filter constant should be based on the process time constant

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Increased sensitivity over a process run

Variable Transformations Cont’d

• Weight variables to increase model sensitivity to failure modes based on prior knowledge and risk assessment – Considering increasing the weight of variables that are early indicators of

faults identified by the risk analysis – Considering decreasing the weight of redundant measurements (e.g.,

multiple temperature measurements in an unit operation)

• PCA/PLS multivariate models are static and linear – Utilize transformations to incorporate non-linear relationship – Need to compensate for time lags in upstream and downstream process

variables

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The MSPC models should utilize the dynamically compensated variable

MSPC Model Validation

• Utilize independent data sets for validation studies • Validate models with normal operating data

– Goal is to minimize false positive alerts

• Validate models with operating data during process disturbances (if available) – Assess detection ability and timing for process faults

• Set the model alert thresholds balancing the risk between missed events, false positives, and detection timing – Better performance obtained by setting SPEX and T2 limits based on

validation studies rather than the std. dev. obtained form normal operating data

• Minimizes false positives with minimal impact on detection capability or detection timing

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On-line Implementation Considerations

• Ensure reading of process variables are synchronized – Facilitated be integrating all process and quality data into a single source

(e.g., process historian, supervisory control system, etc.)

• Ensure process data are not compressed – Process historians may utilize compression for storing data – Data compression can mask the relationship between process variables

• Need to account for missing and bad data – May be able to use model estimates for the missing data – Need to establish criteria when the status of the model will become bad

(e.g., number of bad inputs, missing critical inputs) – Utilize bumpless transfers when missing process data becomes available

• Incorporate MSPC model into the Quality Management System – Establish work process for responding to alerts – Establish work process for assessing the health of the MSPC model

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Relationship between Real Time Release, Process Monitoring, and PAT

• Real-time-release (RTR), when used, is part of the Control Strategy – Can include some or all of then finale product CQAs

• Not all Process Analytical Technology leads to RTR – PAT systems can be designed to control CQAs of raw materials or in-

process materials and not contribute to RTR – Some PAT tools may be utilized for both process monitoring and RTR

• MSPC approaches that establish a process signature are an evolving approach for RTR – Science and risk based approaches generally required for MSPC to ensure

that it monitors the relevant failure modes

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Concluding Remarks

• Continuous manufacturing process facilitate the adoption of PAT tools for the development of process understanding, process monitoring, and real time release testing

• PAT may required to meet the regulatory requirements for in-process monitoring

• Process monitoring is a complementary control tool for the detection and diagnosis of deviations from normal operating conditions

• MSPC offers several advantages over univariate monitoring of process data

• MSPC approaches that establish a process signature are an evolving approach for RTR

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