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PAT Best Practices:Learnings Across Industries

Dr. Charles E (Chuck) MillerChief Data ScientistCamo Analytics

• Process Analytical Technology (PAT)• History of “PAT”, Process Analytics • Scope (Revisited)

• Parallel Universes:• Pharma, Life Sciences• Food, Ag, Petrochem, Chemical• Key Contributions from All

• PAT Best Practices: across 7 categories

• Case Studies

Outline

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• September 2004• Broad Industry Audience:

• Highlight opportunities• Encourage innovation

• Recommendations, not enforceable• Voluntary Manufacturer-Regulatory

collaboration• Address industry hesitancy: Regulatory Risk,

technical concerns• Develop well-understood processes, RTRT,

efficiency• “…Facilitate Continuous Processing to

improve efficiency and manage variability…”

FDA’s PAT Guidance

Definition (FDA, 2004): “..a mechanism to design, analyze, and control pharmaceutical manufacturing processes through the measurement of Critical Process Parameters (CPP) which affect Critical Quality Attributes (CQA).”

• “Process Analytical Chemistry” had been a field since the 1980’s

• Chemical, Food/AG, Materials, Petrochemicals• CPAC: Center for Process Analytical Chemistry, U.

Washington

• Before that, plenty of cases of in-line analytical measurements

• DuPont Model 400: IR photometer: commercialized 1962• Ratio of two wavelengths• Over 5000 manufactured!

• DuPont Model 800: rotating filter wheel photometer• Vis, NIR, polarized vis, mid-IR

• 1970s: Food/Ag: At-line NIR• Grating, integrating sphere

But, was “PAT” Really New in 2004?

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Lehrer and Luft- “URAS”

• Safety and control driven• Butadiene/air mix• CO in pure H2 (NH3 synthesis)

• “Negative Filtering”:• Use target gas as a “filter”• Interference rejection

• Built-in reference cell• Engineers: URAS superior

vs. electrochemical instruments

• 30 gases! (CO, CO2, CH4, C2H2, C2H6, C3H6, NO, N2O...)

Radiationabsorber

Radiationabsorber

Indicator

Pneumatic detector

AmplifierDiaphragmcapacitor

Light Sources

Chopper

Filter Cells

SampleCell

ReferenceCell

Joseph W. (Bill) Worthington, “60 YEARS OF CO ANALYSIS BY NDIR GAS ANALYZERS”

Rapid, highly-relevant analytical measurements, associated with an industrial process

• Instrument Location: on-line, at-line, off-line

• Analytical Modality: Optical, electrochemical, chromatographic, others

• Sampling Mode: • Minimal manual intervention• Minimally invasive to the process• Limited access

• Level of Automation: generally high• Timescale: Process-dependent

• sufficient for process control, product disposition

Let’s Re-Group: Scope of “PAT”

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• Non-pharma industries:• Analytical technology relatively mature• Many value-adding applications: food/Ag, petrochem, materials• Sampling, sampling, sampling…..• Online MVA/analytics still developing…• Distributed/networked systems: food/Ag• Aerospace, Defense robust sensors, optics• Support structures in place: SHS system houses, model services, etc..

• Pharma:• Skeptical- still seen as “new” technology, can’t trust it• Deeply entrenched organizations and workflows- won’t “let it in”• QA/validation: hyper-scrutiny (vs. lab analytical, and processes themselves)!• Irony: “Regulation Excuse”, but US regulators had encouraged its use!• How to handle disagreements between lab/PAT/process model?• QbD (2008): encouraged use in R&D/commercialization (Mfg. use still lagging..)

2004 “Parallel Universes”

Today: Key Contributions from Both

• Instrument Technology: Optics, electronics

• Automated Referencing, Standardization schemes

• Sampling Systems engineering• Multivariate Calibration

• Model optimization• Data preprocessing

• Networked systems• Calibration Transfer• Method/model management

• Raw Material Effects

• Compliance, Quality Assurance of instrument and data systems

• Calibration model lifecycle management

• Risk assessment, mitigation• Change management

procedures• Theory of Sampling (TOS)

“Renaissance”• Platform Paradigm: “PAT-IT”

management systems

Non‐pharma  Pharma:

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Typical “PAT Landscape”• PAT Method:

includes sampling, instrument settings, data processing, model

• PAT Model: The MVA model used to convert instrument data to outputs

PAT Method

PAT Model

PAT Procedure: HOW outputs are to be used

“Guideline on the use of near infrared spectroscopy by the pharmaceutical industry and the data requirements for new submissions and variations”, EMA, 27 January 2014.

• Scope of PAT Usage• Sampling• Instrument• Data Handling• Calibration• Quality Management• Personal/

Organizational

PAT Best Practices (IMHO):The Seven Categories

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Scope- Use, or Don’t Use?

• Cost of installation, testing, on-going verification

• Cost of ownership• Level of automation, and reliability• Consumables, utilities required• Frequent model maintenance

• Personnel costs/efforts• Manufacturing Sites running lean!

• Data costs- security and storage:• R&D is not “Manufacturing-Lite”,

often more data volume!

• Reduced lab costs, COGS• Improved QA, process control• Process uptime, energy savings• Retrospective investigation

support• Improved Culture- more

engagement in process monitoring

• PM- process and control loops• Utilities support

PROs: Value Proposition CONs: Costs, Risks

Balance the PROs and CONs!

Unexpected, unpublicized!

• Location: driven by intended use, value proposition

• Interface/Probe Engineering:• Materials, optics, design• Uptime (minimize fouling)• Single or Multi-phase?

• Theory of Sampling (TOS)• Highly automated, if possible• Sampling system monitoring:

Temperatures, Pressures, flows, etc…

Sampling

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• Signal/Registration Stability data quality

• Access to instrument “health” data• Supply power, PC resources, reference arrays

• Supporting utilities, NEMA enclosures• Remote and automated referencing

workflows• Preventive Maintenance Program• Access to instrument certificates,

validations (audits)• Control software/system stability

• Sufficient resources

Instrument

• Infrastructure sufficient?• …to “build on the top of”?...

• Site Automation & IT engagement• Data aggregation, alignment

• Enables advanced diagnostics• Data Integrity• Avoid Network latency (speed)

issues• From system backups, other jobs

• Connectivity robustness• Avoid “dropped” signals• Rigorous IQ/OQ testing protocols

Data Handling

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• Simpler is better (MLR, PLS..)• Model optimization

• Data Preprocessing scheme• Sample, variable subset selection

• Validation and Verification• Commensurate with intended use

• Model monitoring and updates• Reliability, security and

configurability of model execution system

• System for Model maintenance and change management

• Manage Regulatory risks• Post-approval changes (Regulatory

Opening?..)

Calibration Model

Model Data

Data Import

Data “cleaning”

Exploratory Modeling

Model Assessment (cross validation RMSECV, test

set RMSEP, NAS,..)

Final Model GenerationData Pre-

processing (2nd d., MSC, SNV,…)

Final Model “Packaging”

Final Model Package

DOE

Lab Work

Prior Knowledge

Process Sampling

Modeling Tools (PCA, PLS, MLR, SIMCA, KNN,…)

Lab Analyzer

Field Analyzer

Visualization Tools

Commercial Model

Development software

Model development

process

Miller, Wise, Shaver, IFPAC 2007

• Outlier Diagnostics: • High-frequency, lLow-specificity• Low Cost!

• Comparison to reference (lab) method

• Higher-cost, higher-specificity• Sampling protocol• Data alignment- metadata &

timestamps• Comprehensive QA/

Performance Specifications• Not just prediction error (RMSEP),

but also outlier diagnostics (in-space and out-of-space)!

• Utilize existing site QA management systems whenever possible

PAT Quality Management

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• The PAT “Hot Potato”• On-site expertise

• Inclusive Principles:• PAT is highly interfacial

• Set high standards on data for method development, testing

• For Capital Projects: FEL! (don’t miss the boat….)

• Sampling hardware • Instrument utilities• Data, network systems

Personal/Organizational

OperationsQA

Tech Ops

LabMaintenance

PATEngineering CMC

Math/StatsProduce Dev Team

Site 

R&D

Corporate

• Case 1: Soymeal NIR – since the 1970’s• Case 2: In-line NIR Reaction Monitoring: Since 1989• Case 3: Pharma Examples:

• RTRT, since 2006• Precision Coating IPC, since 2011• Continuous Manufacturing, in progress….

Case Studies

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Case 1: Soymeal At-line NIR

• 1974: first filter NIR• 1987: Network of 6 “tilting filter” NIRs• 1989: first dispersive NIR

• 1989 Calibration data still used today !• 1992: First usage of PLS models, w/dispersive NIR• 2009: Platform conversion started• Robustness testing:

• Eight Constituents for Five Products Tested using Three Platforms: Foss 6500, XDS, Diode Array

• Reference method changes• Crop year effects• Select a Minimum Diversity of Samples (100-1000) to Represent

the Population• Model “refresh” strategy: Eliminate newest crop years,

or eliminate oldest crop years?

From: David J. Ryan, “Learning How to Break, So You Will Not Break, Your Calibrations: A Study of Crop Year Effects”, IDRC 2014

Dickey John GAC III

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469

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Isolate Protein Crop Year Distribution

Isolate PAI

Case 1, Learnings and Deliverables• Key Learnings:

• “multi-year robust calibrations do not fall apart overnight, given an ever evolving landscape of variables, models still require routine updates”

• Models including 5-6 crop years work best• Cal samples older than ~8 yr add less value to models,

due to response bias and non-random distribution• Food/ag DBs require ~300 samples to enable good

transfer to new platform

• Deliverables:• Crop year effects on CQAs: mean and distribution• Proactive Model Robustness Testing

• ~3x actual model update frequency• Diagnostics: Not just SEP!

• T2, Q-residual, and score scatter• Optimal “refresh scheme” for cal samples• Impact of crop year on NIR methods• Method transfers between platforms

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200020012002200320042005200620072008200920102011201220132014

Eliminate Newest: Bias

IsoPAI IsoDM WFPDB WFNSI LecAI SMCF DAPDB DAFAT

From: David J. Ryan, “Learning How to Break, So You Will Not Break, Your Calibrations: A Study of Crop Year Effects”, IDRC 2014

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Case 2: In-line FTNIR Reaction Monitoring

• Purpose: Reactor control in a continuous process • Redundant with on-line GC, process model

• 4 production units across 2 plant sites• 100s of product grades, each with different composition space• 31 PLS models total

• 1989: Instruments and Sample System installed• 1992: PLS models deployed, Outlier diagnostics 1994• Sampling: Slip stream transmittance, 5000 PSI, flammable fluid,

with entrained “wax”• Phase separation risk• T, P and flow sensors on sampling system

• 20 sec. analysis frequency• Instrument: Analect “wedge” FT-NIR

• NEMA enclosure, in “shaky” location• 1996: High Pressure Calibrator: injected DOE standards for

calibration development• Custom real-time chemometrics• Still running today!

FEED

REACTOR

PRODUCT

ANALYZER

C. Miller, et al,  “Multivariate Outlier Diagnostics: A Critical Component of  NIR/PAT Method QA”, IDRC2014

Case 2: Multivariate Diagnostics-The Story

• 1992: Highly divergent feed composition resulted in a runaway reaction!

• No injuries, but >5 days downtime• Root cause difficult to determine

• ..but many blamed the NIR

• Model diagnostics:• Custom coded: “leverage ratio” and “residual ratio”• Support at least 3 functions:

• Model maintenance• Process control (disable NIR PV usage)• System reliability

• Non-specific, but can infer issues “upstream” (instrument, process)

C. Miller, et al,  “Multivariate Outlier Diagnostics: A Critical Component of  NIR/PAT Method QA”, IDRC2014

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Case 2: Other Diagnostics

• Instrument diagnostics:• Custom card: enclosure T, power supply

voltages, currents, etc….• Proactive maintenance, reliability

• Sampling System diagnostics:• P, T, flow sensors on the sampling system• Univariate “Fouling Factor”

• Remote access (PCAW)• With access protocols

C. Miller, et al,  “Multivariate Outlier Diagnostics: A Critical Component of  NIR/PAT Method QA”, IDRC2014

Case 2: Summary• PAT Value to Operations:

• Redundancy support for closed-loop composition controller

• “Window” into process dynamics, product transitions• System Safety and Utility

• Key PAT Learnings:• NIR H/W: Improved robustness to sampling, and

environment• Cal sample sets: can “mix” DOE and on-line samples

effectively• Real-time Diagnostics:

• For Model, Sampling and Instrument monitoring• Supports process control, system robustness and reliability• Especially critical for continuous process

• Criticality of IT: software and networking• COPA, PAT-IT

• Developed Lifecycle Management systems:• Model monitoring, administration, change management

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• NIR diffuse transmittance: In service since 2006• Uses Bruker’s OPUS S/W• Three dosages (A, B, C)• “Mixed” PLS Calibration Models:

• DOE data: provides robustness• Process data: provides relevance

• Outlier diagnostics in OPUS: • “M-distance” or MD (IS)• “F-value” (OS)

• Diagnostic limits:• “2K/N” (ASTM) limit on MD, • 99% CL for F-value (hard-wired)

• Both periodic, and event-based comparisons of NIR to reference analytical method

Case 3: Real Time Release Testing (RTRT) Application

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Design Variables:

• %LC• particle size• hardness• weight

Charles E. Miller, Manoharan Ramasamy, John P Higgins, Nathan Pixley, IFPAC 2018

RTRT Method Events

MAR, JUL 06: 3 NIR models put into service

10.3.2010 (A): 132 MD alarms over 8 batches! All negatives!

Investigation of outlier metric limits (too “tight”???)

18.3.2011 (all): new MD limits set using 95% CL

30.6.2011: A model updateB and C model 

updates

29.7.2009 (B): 6 MD alarms over 2 weeks, all negatives

B :3 MD alarms, all confirmed positives!

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F va

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27.1.2009 (A): 2 F alarms, confirmed positives!

•A model generated useful metrics right away; B and C models did so after a model update• Since 2006, only 7 confirmed positive tablets (out of >120k!)

• All were flagged by the outlier detector!• None resulted in tablet quality issues!

• However, 132 false alarms for model A in 2010

B model update effect on F value

12.2.2007 (A): 2 F alarms, confirmed positives!

XRCT confirmed “lumps” in tablet!

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RTRT : Outlier Diagnostics• ODs always a key component of model QA• Method transfer: Different operating

platform• OD nomenclature

• From 2006-2011, only 6 cases of confirmed “positive” tablets (out of >120k!)

• All of which were flagged by the outlier detector!• All of which were within Quality specifications

• OCT 2010: 132 false alarms • Led to an “OD System Review”• Stats-based adjustment of OD limits• F-distribution assumption, and 95% “default” OD limit, is

actually close to optimal!• OD system management!

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frequ

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132 MD‐based false alarms 

M‐distances (N > 100000)

At-line IPC- Spray Coating

• API Spray coating process• 3 NIR methods (1 per

dosage)• Thermo Antaris II NIR

• At-Line

• PLS regression (Y= HPLC assay)

• Relevant Y range limited to valid range for control

• SIPAT “PAT-IT” Solution• Camo (UNSC) Models

Charles Miller, Nathan Pixley, Bruce Thompson, Manoharan Ramasamy, John Higgins, IFPAC 2016

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NIR Model for IPC• NIR Model (PLS):

• Using “Mixed” Calibration Population:• Full scale development• Stability studies

• Using GA-selected variables

• Model Robustness Assessment:

• X: NIR spectra of tablets at same fixed levels of spray coating

• Y: Spray coating process parameter• Fit?: Infers some sensitivity of NIR

spectrum to process parameter1.8 2 2.2 2.4 2.6 2.8 3 3.2 3.4 3.6

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Y Measured 5 Air:Water Ratio [CF/g]Y

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Samples/Scores Plot of MK0431AXR_CUMASTER_051410

FSSFSD3BBFSD41:1

Air/water ratio

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Samples/Scores Plot of MK0431AXR_CUMASTER_051410

FSSFSD3BBFSD41:1fit95% Confidence Level

R2 = 0.8524 Latent VariablesRMSEC = 1.2586RMSECV = 1.6607Bias = 0CV Bias = 0.14508

Bed T

• Risk-based %CLs on ODs (using theoretical method)

• Outlier alarm triggers investigation

• NIR Predictions from flagged samples disqualified for NIR control application

• Outlier Diagnostics aid in: • NIR usage decisions, • NIR model “QA”, and• Addressing NIR sampling issues

IPC: Outlier Diagnostics

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Hotelling T2: 12 batches

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Continuous Manufacturing Application- Pushing the Limit?...

• Supports high-frequency spectral collection (~1Hz)

• Fully-integrated with process automation system

• Automated PAT “health-check” workflows

• Metadata-rich: Supports full traceability of blend & tablet data

• Network architecture optimized for reliability, speed

• Incorporated into Enterprise-wide PAT-IT system

Charles E. Miller, John P. Higgins, Louis Obando, Manoharan Ramasamy, IFPAC2018

Continuous Manufacturing, Continued:

• Very different usage cases for 2 PATs:• Tablet PAT:

• “Passive” collector• Traceability of results• Several “fault” scenarios• Reporting for QA/release

• Blend PAT:• High frequency: collection-model application-results• In-line workflows• Instrument, and sampler control• Reference array/metadata capture• Real-time trends to process HMIs

• Opportunity for Advanced and Targeted Diagnostics!

• Automated data archival32

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Histogram of time intervals (N=48388)

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• “PAT” (Process Analytical) is NOT new!• Both Pharma and non-Pharma have made key

contributions to PAT• “Best Practices” span a wide range of diverse categories

• Good practice well-positioned for new advances in PAT technology

Summary