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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Outline

• Role of PAT for Continuous Manufacturing

• Process Monitoring

• Statistical Process Control (SPC)

• Lessons for SPC from Chemical Industry

• Real Time Release Testing

• 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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Role of PAT for Continuous Processes

• 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

• Commercial Manufacturing – Process Monitoring

• Assure desired product quality is being consistently manufactured • Identify non-conforming material

– Real Time Release Testing (RTRT)

• Evaluate the quality of final product based a combination of measured material attributes and process controls, ICH Q8 R(2)

• Continuous manufacturing naturally lends itself to RTRT approaches

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Sampling Considerations for PAT Measurements • Probe/sample location representative of the entire vessel

– Minimize the effect of the probe on the process

• Required measurement frequency based on process dynamics – Measurement resolution should sufficient for the detection of a

pulse of variability from a process disturbance

• Sample volume/mass – Representative of a unit dose

• Sample Interface – Remains constant during the process run (e.g., no fouling) – Environment factors (e.g., temperature, humidity)

• Evaluate reproducibility of online measurements – Include sampling considerations – On-line and in-line measurements may reduce but do not eliminate

sampling errors

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Conceptual Implementation of PAT for 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

In-Process Control Requirements

• 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 utilized to meet the regulatory requirements for in-process monitoring – Approaches may include multivariate process monitoring

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Approaches for Process Monitoring

• Statistical Quality Control (SQC) – Variability in quality attributes of the product are monitored over time – Appropriate limits are defined based on the statistical analysis of historical

operations – Does not guarantee that the process is in control (lagging indicator)

• Statistical Process Control (SPC) – The variability in critical process parameters are monitored over time – Monitoring the process 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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Multivariate Statistical Process Control (MSPC)

• Reduction in dimensionality

• Potential to enhance fault detection capabilities

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X1 & X2 are highly correlated

Control Strategy Implementation

• Process monitoring strategy may depend upon control strategy implementation

• MSPC strategy may be more suitable for Level 1 and 2 control strategies

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Level 3 End product testing + tightly constrained material

attributes and process parameters

Level 2 Reduced end product testing + Flexible

CMA & CPP within design space

Level 1 Real-time automatic

control + Flexible CPPs to respond to variability in CMAs

Control Strategy Implementation Options1

1. Yu, L et. al. AAPS J. 2014 Vol. 16 771-783

Multivariate Methods

• Latent Variable Models – Process data and product quality data are decomposed into scores (T) and

loading (P, W) • X= 𝑇𝑃𝑇 + 𝐸 • Y= 𝑇𝑄𝑇 + 𝐸

– Scores are related to samples and represent the projection of each sample into the space defined by the new latent variables

– Loading are related to variables and represent the relationship between the process variables and the new latent 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.)

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Process Monitoring Statistics

• MSPC model statistics can be calculated to assess the overall variability of the process – Alleviates the need to monitor all the combinations of latent variables on x-

y plots

• Hotelling T2

– The “distance” of the current operating point from the mean of the historical normal process data

– Able to detect process “stretches” • Process moves into new operating region; the relationship between the

process variables may have not changed

• Squared Predicted Error (SPEX) – The sum of the squared error between the measured process data

and the predicted process data – Able to detect a change in the relationship between process

variables

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• Hotelling T2 – Sum of the squared score values normalized by the amount of variation captured by

each latent variable; 𝑇2 = 𝑡2𝑖𝜆𝑖 𝐴

𝑖=1

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

variables; SPEX= 𝑥𝑚 − 𝑥𝑝 2𝑛

𝑖=1

Diagnosing Process Faults

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Perspective on MSPC for Continuous Processes from Chemical Industry • Statistical Quality Control routinely utilized to

assess performance of on-line analyzers – Focus on analyzers used to control end product quality – Control charts plot the error between on-line and periodic off-line

measurements

• MSPC utilized for process fault detection to address process safety and reliability issues – Complementary to the alarm and advance control systems

• Considerations for the implementation of MSPC for continuous process – Variable selection for MSPC models – Data selection for building MSPC models – Variable manipulations – 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

• Examine process variable cross-correlation matrix – 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

• All the data from process development studies may not be suitable for building MSPC models – 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 – 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

– 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

– Do not utilize mean updating to monitor long term issues (e.g., catalyst aging, fouling)

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Increased sensitivity over a process run

Variable Transformations Cont’d

• Weight variables to increase or decrease their influence in the MSPC model

– 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)

– Equivalent to manipulating the standard deviation for the process variable

• PCA/PLS multivariate models are linear – Utilize transformation to incorporate non-linear relationship

• PCA/PLS multivariate models are static – Need to compensate for time lags for 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

• Distribution of process diagnostic statistics have “fat tails” – 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 RTRT, Process Monitoring, and PAT

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

• Science and risk based approaches generally required for both MSPC and RTRT

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

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

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

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Benefits of RTRT

• Provides for increased assurance of quality – More process data collected

• Provides increased manufacturing flexibility and efficiency – Shorter cycle time – Reduced inventory – Reduction in end product testing – Reduction in manufacturing costs

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Consideration for the Point of Testing

• Is there a potential for the measured CQA to change downstream from the measurement point? For example, – Blend desegregation – Loss of weight (e.g., chipping) after weighting step – Hydrolytic degradation during aqueous film coating

• Is identity determined at a point that is visually unique? – Mitigation of potential human and/or system error – Unique identifiers on the intermediate when measured (e.g.,

embossing, size, shape)

• Risk assessment is valuable to exploring potential failure modes

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Considerations for RTRT Specification

• Specification still required in an RTRT approach – (CFR 314.50(d) and CFR 211.165(a))

• Should be representative of actual measurement – Can include in-process measurements (e.g., NIR measurements for

assay of uncoated tablets) – Can include surrogate measurements (e.g., models for dissolution) – Methods should be appropriately validated (including models used

as surrogate measurements)

• Alternatives can be included for stability testing • Utilization of appropriate statistical criteria for

large sample sizes

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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

• MSPC offers several advantages over univariate monitoring of process data

• MSPC is a complementary process control tool to an automatic process control system

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

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