Mon 17.55 PAT and Quality by Design - A Process Systems View-Amended

8/10/2019 Mon 17.55 PAT and Quality by Design - A Process Systems View-Amended

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Agenda

Overview The FDA Process Analytical Technologies (PAT) and Process

Systems Engineering

Process Analytical Technologies:

Challenges in using spectroscopic data in PAT-based process

control and Real Time Release

Application Studies

Batch Cooling Crystallisation – from Lab to Industrial Pilot scale

Batch Endpoint Control using Process Analytical Data

Closure



The EU provides 32% of the worlds chemicals manufacturing

through some 25,000 enterprises of which 98% are SMEs which

account for 45% of the sectors ‘added value’, and 46% of all

employees are in SME

What does PAT, QbD & Real-Time-Release mean to an SME?



Benchmarks for Pharmaceuticals Companies

Benson R.S, From World Class Research to World Class Manufacturing: the Challenges,

Pharmaceutical Eng. Sept/Oct 2005

Stock Turn - this is the total turnover on the site at manufacturing price divided by all the stocks on the site on the

same basis. Stocks include finished goods, work in progress, and purchased raw materials; On Time in Full

(OTIF) delivery - this is the percentage of orders that are satisfied on time in full with zero defects; Right First

Time (RFT) - this is the percentage of the products that at the point of manufacture are delivered right first time

with no defects; CpK - is a statistical process measure on the variability of the product; Overall Equipment

Effect iveness (OEE) - this measures how effectively the manufacturing equipment is used.

92%74%30%OEE

3.23.51 to 2CpK99.4%96%85% to 95%RFT

99.6%97.4%60% to 80%OTIF

50143 to 5Stock Turn

A World Class

Pharmaceuticals

Manufacturing Plant

A Winning

Pharmaceuticals

Plant

Present

Pharmaceutical

Industry

KPI’sPHARMACEUTICAL BENCHMARKS

92%74%30%OEE

3.23.51 to 2CpK99.4%96%85% to 95%RFT

99.6%97.4%60% to 80%OTIF

50143 to 5Stock Turn

A World Class

Pharmaceuticals

Manufacturing Plant

A Winning

Pharmaceuticals

Plant

Present

Pharmaceutical

Industry

KPI’sPHARMACEUTICAL BENCHMARKS



Where are we in

Process Analytics and Control Technologies?



Inspection Observations (1)

Courtesy - Des Makohon Senior GMP Inspector,

PharmaIQ, London January 2010



Inspection Observations (2)





Consequences of Poor Development





Impurities and Polymorphism (Where we were in 1998)

The RITONOVIR aids drug

changed from anhydrous to

hydrate crystal after launch:

• Lower solubility and hence bio-

availability.

• Product was withdrawn for ayear and reformulated.

• New FDA approval needed –

mega cost implication !

As product purity improved

during process chemistry work-

up, the “stable” polymorphic form

changed



Closing the Analytical Control Loop

Some Challenges in using PAT-based

Sensors in Real Time Process Control







Incorporating PAT Sensors into BatchCooling Crystallisation Closed Loop

Process Control



The Principles of a ‘Process Systems’ Base Approach

Courtesy Staffan Folestad AstraZeneca, APACT09



In-Process Analytics & Process Control

CBB#2 Project: collaboration with

Leeds, Heriot-Watt and Newcastle University

Partners

AEA Technology

AstraZeneca

Bede Scientific InstrumentsBNFL

Clairet Scientific

DTI

EPSRC

GlaxoSmithKline

HEL

Malvern Instruments

Pfizer

Syngenta

•FTIR

•Supersa•-•turation

•Size

•Growth•kinetics•USS

•Batch•Batch•Process•process

•monitoring•monitoring•& control•& control

•Process

•conditions•Heat•transfer•LDA/PIV •Reaction

•Calorimetry

•Mixing &•scale•-•up

•CFD

•Shape•Video•microscopy

•MSZW

•UVvis•Nucleation•kinetics

•Reactant•rheology

•Polymor•-•phic•form

•XRD

FTIR

•Supersa•-•turation

•Size

•Growth•kineticsUSS

•Batch•Batch•Process•process

•monitoring•monitoring•& control•& control

•Process

•conditions•Heat•transfer•Heat•transfer•LDA/PIVLDA/PIV Reaction

Calorimetry

•Mixing &•scale•-•up•Mixing &•scale•-•up

•CFDCFD

•Shape•Video•microscopyVideomicroscopy

•MSZW

UVvis•Nucleation•kinetics

•Reactant•rheology

•Polymor•-•phic•form XRD



Fluctuations in External Variables on Calibration Models

In process analytical applications, spectral measurements can be subject to

changes in process temperature, flow turbulence, compactness, and other

external variations.

Typically, variations of external variables influence spectral data in a non-linear manner which leads to the poor predictive ability of bilinear

calibration models on raw spectral data.

The influence of external variables on spectral data we classify into two

different modes:

multiplicative influential mode, and

composition-related influential mode

A new chemometric method, Extended Loading Space Standardization

(ELSS), has been developed to explicitly model these two kinds of

influential modes.





Process Analytics in Reactor Scale-Up

PAT from 1 to 20 to 250l Scales

Heating/Cooling unit

20 L Crystalliser

Heating/Cooling unitHeating/Cooling unit

20 L Crystalliser

Pump

Inlet and

return pipes

Reactor

Magnetic

stirrer

Flow loop

to flow cell

Water jacket

lines

Pump

Inlet and

return pipes

Reactor

Magnetic

stirrer

Flow loop

to flow cell

Water jacket

lines

Bede MONITORTM In process XRD



Bede MONITORTM In-process XRD

Crystal Polymorph Monitoring & Control

Temperature

readers

Temperaturemonitors

0

2000

4000

6000

8000

10000

12000

15 18 21 24 27 30

2Theta [degree]

I n t e n s i t y

t = 200 s

t = 600 s

t = 1000

t = 1400

t = 1800

t = 2200

t = 2600

t = 3000

t = 3400

t = 3800

t = 4200

t = 4400

t = 4600

peaks

peaks

0

2000

4000

6000

8000

10000

12000

15 18 21 24 27 30

2Theta [degree]

I n t e n s i t y

t = 200 s

t = 600 s

t = 1000

t = 1400

t = 1800

t = 2200

t = 2600

t = 3000

t = 3400

t = 3800

t = 4200

t = 4400

t = 4600

peaks

peaks

peaks

peaks

Typically circa 1 wt % detectable via

in-process XRD, much lower with

advanced chemometric analysis(Smoothed PCA)

System provides capability to monitor

polymorphic form “in-process”, i.e.

that unaffected by product separationprior to analysis.

Enhancing signal-to-noise ratio

iii k rQQIXrX )( TT

×+×= λ

+×= ],,[],,[ 2121 cc

rrrrrrF LL nsF FxFxFxx +==

mi ,,2,1 L=

Raw (a) and Processed (b) XRD profiles (by SPCA) for 6 XRD data sets

Smoothed Principal Component Analysis (SPCA)



Loading Space Standardisation (LSS)

Correcting temperature-induced spectral variations for ATR-FTIR data

in crystallization process monitoring

ATR-FTIR

FTIR

PC

Control

PCTemperature Probe

Thermo-stat Bath

Turbidity Probe

pH Probe

ATR-FTIR

Control PC

Condenser bath

1/2 L Crystalliser

Stirrer motor

Turbidity and pH Probes

Temperature probe ATR-FTIR PC

Heating/Cooling unit

20 L Crystalliser

PAT from 1/2 to 20 to 250l Scales

LSS

A b s o r b a n c

e ( A U )

0.00

0.02

0.04

0.06

0.08

0.10

0.12

Wavelength (nm)

850 900 950 1000 1050

A b s o r b a n c

e ( A U )

0.00

0.02

0.04

0.06

0.08

0.10

0.12

A b s o r b a n c

e ( A U )

0.00

0.02

0.04

0.06

0.08

0.10

0.12

Wavelength (nm)

850 900 950 1000 1050

Wavelength (nm)

850 900 950 1000 1050

Wavelength (nm)850 900 950 1000 1050

A b s o r b a n c e ( A U )

0.00

0.02

0.04

0.06

0.08

0.10

0.12

850 900 950 1000 1050

0.00

0.02

0.04

0.06

0.08

0.10

0.12

LSSLSS

A b s o r b a n c

e ( A U )

0.00

0.02

0.04

0.06

0.08

0.10

0.12

Wavelength (nm)

850 900 950 1000 1050

A b s o r b a n c

e ( A U )

0.00

0.02

0.04

0.06

0.08

0.10

0.12

A b s o r b a n c

e ( A U )

0.00

0.02

0.04

0.06

0.08

0.10

0.12

Wavelength (nm)

850 900 950 1000 1050

Wavelength (nm)

850 900 950 1000 1050

Wavelength (nm)850 900 950 1000 1050


0.00

0.02

0.04

0.06

0.08

0.10

0.12

850 900 950 1000 1050

0.00

0.02

0.04

0.06

0.08

0.10

0.12

Wavelength (nm)850 900 950 1000 1050


0.00

0.02

0.04

0.06

0.08

0.10

0.12

850 900 950 1000 1050

0.00

0.02

0.04

0.06

0.08

0.10

0.12

Münchwilen Foxboro Control System as Set Up for



Münchwilen Foxboro Control System as Set-Up for

CBBII Trial on 250 Litre Reactor R-122

Heater

power

Control temperature

PLS quality factor

PLS concentration

Control valve

on chiller

Jackettemperatures

Jacket flow rate

Power on aggitator

Heater chiller oil bath unit

To & from

site cryo

services

Valve posit ion

Baffle temperature

Refluxcondenser

Water inlet

Turbidity

Heater

power

Control temperature

PLS quality factor

PLS concentration

Control valve

on chiller

Jackettemperatures

Jacket flow rate

Power on aggitator

Heater chiller oil bath unit

To & from

site cryo

services

Valve posit ion

Baffle temperature

Refluxcondenser

Water inlet

Turbidity



Supersaturation Control System Upgrade to IMC Capability

INPUT

Block

User

Define

Model

UserDefine “ S”

set point

HEL PC

Solubility

Model

Macro

S(t)

PI Controller

Control Block

Data Processing Block

C(t)

ENABLIR

SOFTWARE

WINISO

SOFTWARE

C(t)

FTIRPC

Spectra

PLS model

FTIR Spectrometer

Crystallisation Vessel

4 – 20 mA

INPUT

Block

User

Define

Model

UserDefine “ S”

set point

HEL PC

Solubility

Model

MacroMacro

S(t)

IMC Based PI Controller

Control Block

Data Processing Block

C(t)

ENABLIR

SOFTWARE

WINISO

SOFTWARE

C(t)

FTIRPC

Spectra

PLS model

C(t)

FTIRPC

Spectra

Cal. model

FTIR Spectrometer

Crystallisation Vessel

4 – 20 mA

Supersaturation Control of L Glutamic Acid



Supersaturation Control of L-Glutamic Acid

250 litre Plant Crystall iser

0

10

20

30

40

50

60

70

80

90

100

0 100 200 300 400 500 600 700 800 900 1000 1100 1200 1300

Time (min)

T e m p e r a t u

r e ,

C o n c e n t r a t i o n ,

S o l u b i l i t y & T u r b i d i t y

0.0250.0750.1250.1750.2250.275

0.3250.3750.4250.4750.5250.5750.6250.6750.7250.7750.8250.8750.9250.9751.0251.0751.1251.1751.225

S u p e r s a t u

r a t i o n ( S = C / C

* )

Temperature (°C) Concentration (g/500ml)

Solubility (g/500ml) Turbidity (%)

Supersaturation Slimit

Slimit

Smax = 1.125S = 1.1

Smin = 1.075Started Supersaturation

Control

5% seeds

added

0

10

20

30

40

50

60

70

80

90

100

0 100 200 300 400 500 600 700 800 900 1000 1100 1200 1300

Time (min)

T e m p e r a t u

r e ,

C o n c e n t r a t i o n ,

S o l u b i l i t y & T u r b i d i t y

0.0250.0750.1250.1750.2250.275

0.3250.3750.4250.4750.5250.5750.6250.6750.7250.7750.8250.8750.9250.9751.0251.0751.1251.1751.225

S u p e r s a t u

r a t i o n ( S = C / C

* )




Slimit




Slimit

Smax = 1.125S = 1.1

Smin = 1.075Started Supersaturation

Control

5% seeds

added



Variability – Modelling and Calibration Challenges

Process Issues:

Multiple or changing formulations (recipes)

Cell improvement; cell line changes; media changes

Equipment characteristics; site-to-site process differences, etc

Fluctuations in both control and external process variables

Small data sets are an issue but can be enhanced through Bootstrap

Aggregation and Bootstrap Aggregated Regression.

Analytical Issues:

Separating absorbance from multiplicative light scattering effectscaused by the variations in optical path length

Inter probe variability: impact of component variance on PLS calibration

– can probe differences be accomodated or eliminated?

Can calibration models be made generic for different production unit

operations / production lines?



Closing the Analytical Control Loop

Incorporating PAT Sensors into Real

Time Process Control

Batch Endpoint Control



Batch Endpoint Control

using Process Analytical Data

Batch Endpoint Control Concept

Process Analytical Technology - Calibration PLS Model

Controlling “Scores”

Industrial Examples

Drug Product Granulation Control

Results



Batch Endpoint Control Concept

1. End-point value of CTQ parameter continuously estimated throughout the batch

2. MV trajectories modified by controller to make estimated end-point hit target

3. Unfolded PLS model - predicts end-point value based on MV and process variabletrajectories over the entire batch

4. At each point in the batch, computes trajectory of MV moves over entire batch tominimise the error between the predicted end-point value and end-point target

Start of batch End of batch

TIME

Controller calculates futureMV moves over the

whole future windowto the end of the batch

Current time

Trajectory of CTQ parameter

CTQ end-point target

Manipulated variable (MV)



Process Analytical Technology




Calibration PLS Model

Spectrum

NIR absorbance

Evolution of absorbance at a selected wave number

Batch Evolution (25 mins)

Using PAT sensors in control offers some exciting possibilities:

Using modern PAT devices capable of 1-2 second or sub-second

measurement rates, real-time control based on PAT measurement is a reality.

Richness of measurement: not just a single data point per sample but a vector

of data per sample that exposes a broad amount of information about theprocess “State”.

Sensor calibration models can give real time inference of product property





Calibration PLS Model

AND also throws some challenges:

Real time pre-processing – Management of outliers in real time.

No control system is going to control a spectrum of several hundred

simultaneous values. So must define which aspect of the spectrumprovides the most important / critical information.

Ideally there could be a calibration model to determine the product

property?

Or Alternatively - Are there particular features/segments of the spectrum of

interest?

Can the scores of the PCA/PLS calibration model be used to control





Industrial Example: Controlling “ Scores”

Source: Example from Published Industrial Applications: Pfizer’s HSWG Real-time Control

System (IFPAC 09 – Mojgan Moshgbar).





Conclusions

PAT is not just Analytics – it is sophisticated sensors and analysers together

with smart chemometrics and modelling.

Variability issues around process plants, process analytics and multiple

or changing formulations (recipes) – A Process Systems Challenge.

The Quality by Design initiative within the Pharmaceutical Industry has used Advanced Control techniques, combined with chemometric models to monitor

and control Critical Quality and Process-ability parameters effectively.

1. Spectral data contains much information that may be converted into newvariables that capture the key aspects of the process character as it progresses

through a batch.

2. Process insight into unit operation may be obtained using Process Analytical

Devices.3. Periodic adjustment of the process variables at discrete “decision points” to

nudge the process is enough to modify the final quality parameters effectively.

4. Robust data quality and control of the whole system – not just analytical but

conventional measured systems must be quality monitored to ensure thatbehaviour outside the bounds of understanding is identified in real time.



Thanks and Acknowledgements

Many thanks to the organisers

for their kind invitation

and of course you, the audience,

for your kind attention

Julian Morris and Zengping Chen acknowledge their CPACT research

colleagues and the CPACT member companies for their R&D challenges

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Mon 17.55 PAT and Quality by Design - A Process Systems View-Amended

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