process analytical technologies (pat) and quality by design · process analytical technologies...

Process Analytical Technologies (PAT) and Quality by Design

- the Impact for Process Systems Engineering

Symposium on New Frontiers in Chemical & Biochemical Engineering, Thessaloniki, 26-28 Nov 2009

“To Honour the Life-long Contribution ofProfessors Anastasios Karabelas and Stavros Nychas

to Chemical Engineering in Greece”

Julian Morris1, Zengping Chen2 and David Lovett3

1Centre for Process Analytics & Control Technology, Newcastle & Strathclyde Univ. UK2State Key Laboratory of Chemo/Biosensing & Chemometrics, Hunan University, China

3Perceptive Engineering, Daresbury Innovation Centre, Daresbury, Cheshire, UKwww.cpact.com

Overview of Presentation

An overview of some of the pharma-sector challenges

Process Analytical Technologies:Where have we come from? Where are we now?, Where are we going?

Some challenges in spectroscopic data pre-processing and modellingApplications in Crystallisation and other industrial problems

Variability - Different Product Formulations, Recipes, Processing Units, Production Sites, Spectroscopic Probe Locations, ….

Closing the Analytical Control Loop

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 and 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 Effectiveness (OEE) - this measures how effectively the manufacturing equipment is used.

92%74%30%OEE3.23.51 to 2CpK

99.4%96%85% to 95%RFT99.6%97.4%60% to 80%OTIF

50143 to 5Stock Turn

A World Class Pharmaceuticals

Manufacturing Plant

A Winning Pharmaceuticals

Plant

Present Pharmaceutical

Industry

KPI’s

PHARMACEUTICAL BENCHMARKS

92%74%30%OEE3.23.51 to 2CpK

99.4%96%85% to 95%RFT99.6%97.4%60% to 80%OTIF

50143 to 5Stock Turn

A World Class Pharmaceuticals

Manufacturing Plant

A Winning Pharmaceuticals

Plant

Present Pharmaceutical

Industry

KPI’s

PHARMACEUTICAL BENCHMARKS

PAT and QbD

Courtesy Staffan Folestad AstraZeneca, APACT09

Design Space and Quality


Real Time Release (RTR)


The Principles of a ‘Process’ Systems Base Approach


Where are we in

Process Analytics and Control Technologies?

Heterogeneity

Heterogeneity can be influenced by the manufacturing process.

Different product variants arising

• from varying raw materials• from upstream processing issues• from downstream processing changes• from utilities issues• ………

Impurities and Polymorphism (Where we were in 1998)

Impurities and Polymorphism

Impurities effect nucleation and growth processes, and hence canstabilise meta-stable polymorphic forms.

As product purity improves during process chemistry work-up, the “stable” polymorphic form can change !

e.g. RITONOVIR aids drug which changed from anhydrous to hydrate crystal after launch:

with lower solubility and hence bio-availability.

product was withdrawn for a year and reformulated.

new FDA approval needed – mega cost implication !

and Today (An FDA 483)

Variability with Different Scales,

Product Formulations / Recipes,

Different Spectroscopic Probe Locations,

Different Processing Units,

Across Different Production Sites, …

The Impact of Multi-dimensionality

Multi-Recipe (Formulation) Data

Industrial challenge – one weeks production: 50 different products. 40 different recipes, 5 different production units. Example application of multiple group PCA and PLS with two formulations. Each process mixer is also considered as a separate ‘formulation’, hence the process model has 4 distinct groups.

Note: Formulation 2 has fewer raw materials and fewer process variables than Formulation 1

Recipe Group Mixer Number ofBatches

RawMaterials

QualityVariables

1 1 ( ) 1 19 23 1

2 (+) 2 21 23 1

2 3 (o) 1 20 17 1

4 (x) 2 29 17 1

ProcessVariables

120

120

90

90

Recipe Group Mixer Number ofBatches

RawMaterials

QualityVariables

1 1 ( ) 1 19 23 1

2 (+) 2 21 23 1

2 3 (o) 1 20 17 1

4 (x) 2 29 17 1

ProcessVariables

120

120

90

90

Impact of Different Formulations

The model contains within and between group variation. Subtle process events cannot be detected; the greater the number of distinct groups in the data set, the greater the impact of between group variation.

Principal Component 1

-10 -5 0 105 15

5

0

-5

10

-15-10

Prin

cipa

l Com

pone

nt 2

Principal Component 1

-10 -5 0 105 15

5

0

-5

10

-15-10

Prin

cipa

l Com

pone

nt 2

Courtesy Unilever, Vlaardingen

The Impact of Multidimensionality in PAT, QbD(Hence a Process Systems approach)


Agitator

Location 1Location 2Location 3

Location 4

Spectroscopic Probe Location PCA Plot of Measured Spectra

Impact of Spectroscopic Probe Locationin a Reactor Vessel

A multi-group ‘generic’ calibration model can help overcome probe location issues

PC1

PC

2

Location 3

PC

2

Location 2

Location 4

Location 1

The Impact of Inter-probe Variability

Spectra from different probes are distinctInter-probe variability is the greatest source

Variability – Modelling and Calibration ChallengesProcess Issues:

Scaling up and reactor size and characteristics issuesMultiple or changing formulations (recipes)Cell improvement; cell line changes; media changesEquipment characteristics; site-to-site process differences, etcFluctuations in both control and external process variablesLimited experimental data due to time and cost limitations – hence small data sets from which to build models – learn from medical statistics and statistics used in clinical trials.

Analytical Issues:

Separating absorbance from multiplicative light scattering effects caused by the variations in optical path lengthInter 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?

Variability (or PAT) by Edwards Deming

Cease reliance on mass inspection to achieve quality.

Eliminate the need for mass inspection by building quality into the product in the first place.

Dr W. Edwards Deming (Circa 1980s)

“Learning is not compulsory, ……. Neither is survival”

In-Process Analytics & Process Control

CBB#2 Project: collaboration with

Leeds, Heriot-Watt and Newcastle University

PartnersAEA Technology

AstraZenecaBede Scientific Instruments

BNFLClairet Scientific

DTIEPSRC

GlaxoSmithKlineHEL

Malvern InstrumentsPfizer

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 •microscopyVideo microscopy

•MSZWUVvis

•Nucleation•kinetics

•Reactant•rheology

•Polymor•-•phic•form XRD

Issues in Down-Stream Production Processes(e.g. in Crystallisation)

Multi-sourced materials properties making up feed-stocks impact on product processability:

hence on the properties of any formulated products made downstream. I.e. variability in feedstock results in variability of products.

Important solid-form properties:physical - particle size / shape, hardness, density and plasticitychemical - purity, polymorphic form, crystallinity, hygroscopicity

Clear need for flexible processes developed through QbD and controlled using PAT to ensure reproducible processes producing highly consistent quality product suitable for Real-Time-Release into the Patient.

Reactor Scale-Up: Effects on Nucleation ProcessesNucleation promoted via sites within crystallisation reactor such as at:

Walls of vessel & at liquid free surfacesStirrer and reactor internals such as baffle surfaces and impellers

Hetero-nuclei, e.g. impurities, particulates, seeds etc.Particle/particle and reactor/particle collision attrition fragments Process in-homogeneities due hydrodynamics and reactor mixing

0

10

20

30

40

50

60

70

80

90

100

15 20 25 30 35 40 45 50 55 60 65 70 75 80

Temperature [°C]

Tran

smitt

ance

[%]

900 rpm

500rpm700rpm

E.g. in LGA crystallisation, stirrer speed effects MSZW with nucleation being enhanced by greater mixing

Cooling Profiles

The Impact of Cooling Profiles on CSD

Utilities are often the ‘forgotten’ area

Advanced Chemometric MethodsFor Assured Process Analytics Applications

Fluctuations in External Variables on Calibration ModelsIn 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, andcomposition-related influential mode

A new chemometric method, Extended Loading Space Standardization (ELSS), has been developed to explicitly model these two kinds of influential modes.

Spectral Calibration IssuesSpectroscopic measurements in chemical and pharmaceutical processes are always liable to fluctuations in both control and external process variables.

This can result in noisy spectra, non-linear shifts, broadening in spectral bands and multiplicative light scattering perturbations.

X-ray diffraction profiles of mannitol-methanol suspensions with the content of mannitol varying from 0.0% to 5.0% g/ml.

2θ

16.0 16.5 17.0 17.5 18.0 18.5 19.0

Inte

nsity

1200

1250

1300

1350

1400

1450b

Noisy spectra

Five NIR spectra for a ternary mixture sample measured at five different temperatures

Shift and broadening in spectral bands

Wavelength (nm)850 900 950 1000 1050

Abso

rban

ce (A

U)

-0.02

0.00

0.02

0.04

0.06

0.08

0.10

0.12

Wavelength (nm)850 900 950 1000 1050

Abso

rban

ce (A

U)

-0.02

0.00

0.02

0.04

0.06

0.08

0.10

0.12

Wavelength (nm)

850 900 950 1000 1050

Abs.

(AU

)

1.8

2.0

2.2

2.4

2.6

2.8

3.0

3.2

20 NIR spectra of a powder mixture measured under different sample compactness

Multiplicative light scattering

Smoothed Principal Component Analysis (SPCA)SPCA: Enhancing the Signal to Noise Ratio of X-ray Diffraction Spectra of Glutamic acid-methanol suspensions system.

L-Glutamic acid morphology: prismatic α-form and needle-like β-form crystals

Water jacketlinesThermocouples

Flow cell

Microsource®

INEL X-Ray Detector

α-form

β-form

Pump

Inlet and return pipes

Reactor

Magnetic stirrer

Flow loop to flow cellWater jacket

lines

Experimental set-up and on-line X-ray diffraction system

Smoothed Principal Component Analysis (SPCA)

Enhancing signal-to-noise ratio

iii k rQQIXrX )( TT ×+×= λ+×= ],,[],,[ 2121 cc rrrrrrF LL nsF FxFxFxx +==

mi ,,2,1 L=

SPCA

2θ16.0 16.5 17.0 17.5 18.0 18.5 19.0

Inte

nsity

1200

1250

1300

1350

1400

1450

16.0 16.5 17.0 17.5 18.0 18.51200

1250

1300

1350

1400

1450

16.0 16.5 17.0 17.5 18.0 18.5 19.01200

1250

1300

1350

1400

1450

16.0 16.5 17.0 17.5 18.0 18.51200

1250

1300

1350

1400

1450a

2θ2θ16.0 16.5 17.0 17.5 18.0 18.5 19.0

Inte

nsity

1200

1250

1300

1350

1400

1450

16.0 16.5 17.0 17.5 18.0 18.51200

1250

1300

1350

1400

1450

16.0 16.5 17.0 17.5 18.0 18.5 19.01200

1250

1300

1350

1400

1450

16.0 16.5 17.0 17.5 18.0 18.51200

1250

1300

1350

1400

1450a b

1.000%0.800%

0.533%0.389%

0.178%0.000%

16.0 16.5 17.0 17.5 18.0 18.5 19.0

Inte

nsity

1250

1300

1350

1400

1450b

1.000%0.800%

0.533%0.389%

0.178%0.0%

b

1.000%0.800%

0.533%0.389%

0.178%0.000%

16.0 16.5 17.0 18.0 18.5 19.01250

1300

1350

1400

1450

1.000%0.800%

0.533%0.389%

0.178%0.0%

b

2θ

b

1.000%0.800%

0.533%0.389%

0.178%0.000%

16.0 16.5 17.0 17.5 18.0 18.5 19.0

Inte

nsity

1250

1300

1350

1400

1450b

1.000%0.800%

0.533%0.389%

0.178%0.0%

b

1.000%0.800%

0.533%0.389%

0.178%0.000%

16.0 16.5 17.0 18.0 18.5 19.01250

1300

1350

1400

1450

1.000%0.800%

0.533%0.389%

0.178%0.0%

b

2θ2θ

Raw (a) and Processed (b) XRD profiles (by SPCA) for 6 XRD data sets of mannitol-methanol suspensions with the contents of mannitol equalling to 0.0%, 0.178%, 0.389%, 0.533%, 0.8% and 1.0% g/ml, respectively

Case – SPCA in Morphology Monitoring

Concentration (%)0 2 4 6 8 10

Pea

k he

ight

-400

-200

0

200

400

600

800

R= 0.9540

2θ =29.8272

0 2 4 6 8 10-400

-200

0

200

400

600

800

R= 0.9540

2θ =29.8272


Pea

k he

ight

-400

-200

0

200

400

600

800

R= 0.9540

2θ =29.8272

0 2 4 6 8 10-400

-200

0

200

400

600

800

R= 0.9540

2θ =29.8272


Pea

k he

ight

-400

-200

0

200

400

600

800

R = 0.9951

2θ =29.8272

0 2 4 6 8 10-400

-200

0

200

400

600

800

R = 0.9951

2θ= 29.8272


Pea

k he

ight

-400

-200

0

200

400

600

800

R = 0.9951

2θ =29.8272

0 2 4 6 8 10-400

-200

0

200

400

600

800

R = 0.9951

2θ= 29.8272

The raw (a) and pre-processed (b) XRD profiles of the beta form of GA-methanol slurries with concentration varying from 0.02% to 8.00%; The relationships between concentrations and peak heights at peaks B3 of the raw (c) and processed (d) spectra.

2θ

10 12 14 16 18 20 22 24 26 28 30 32 34

Inte

nsity

1000

2000

3000

4000

5000β - form

B3

2θ

10 12 14 16 18 20 22 24 26 28 30 32 34

1000

2000

3000

4000

5000β - form

B3

B2B1

2θ

10 12 14 16 18 20 22 24 26 28 30 32 34

Inte

nsity

1000

2000

3000

4000

5000β - form

B3

2θ

10 12 14 16 18 20 22 24 26 28 30 32 34

1000

2000

3000

4000

5000β - form

B3

B2B1

10 12 14 16 18 20 22 24 26 28 30 32 34

Inte

nsity

1000

2000

3000

4000

5000

6000

B1B2

B3

? - form

2θ

10 12 14 16 18 20 22 24 26 28 30 32 34

1000

2000

3000

4000

5000

6000B1

B2

B3

β - form

10 12 14 16 18 20 22 24 26 28 30 32 34

Inte

nsity

1000

2000

3000

4000

5000

6000

B1B2

B3

? - form

2θ

10 12 14 16 18 20 22 24 26 28 30 32 34

1000

2000

3000

4000

5000

6000B1

B2

B3

β - form

Bede MONITORTM In-process XRDCrystal Polymorph Monitoring & Control

Typically circa 1 wt % detectable via in-process XRD, much lower with advanced chemometric analysis (Smoothed PCA)

Temperaturereaders

Temperaturemonitors

0

2000

4000

6000

8000

10000

12000

15 18 21 24 27 302Theta [degree]

Inte

nsity

t = 200 s

t = 600 s

t = 1000 s

t = 1400 s

t = 1800 s

t = 2200 s

t = 2600 s

t = 3000 s

t = 3400 s

t = 3800 s

t = 4200 s

t = 4400 s

t = 4600 s

αpeaks

βpeaks

0

2000

4000

6000

8000

10000

12000

15 18 21 24 27 302Theta [degree]

Inte

nsity

t = 200 s

t = 600 s

t = 1000 s

t = 1400 s

t = 1800 s

t = 2200 s

t = 2600 s

t = 3000 s

t = 3400 s

t = 3800 s

t = 4200 s

t = 4400 s

t = 4600 s

αpeaksαpeaks

βpeaksβpeaks

System provides capability to monitor polymorphic form “in-process”, i.e. that unaffected by product separation prior to analysis.

Loading Space Standardisation (LSS)Correcting temperature-induced spectral variations for ATR-FTIR data in crystallization process monitoring.

ATR-FTIR

FTIRPC

Control PC

Temperature Probe

Thermo-stated 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

Loading Space Standardisation (LSS)Correcting non-linear shift and broadening in spectral bands caused by temperature fluctuations:

)()()(1

ii

K

kikkiii ttct esx += ∑

=,

)()(

)()(

m

11

tt

tt

PCAm

PCA

'

'

TPX

TPX

⎯⎯→⎯

⎯⎯→⎯

MMM LSStt ii ⇒~)(P )()( refLSS

test tt xx ⎯⎯→⎯

LSS

Abs

orba

nce

(AU

)0.00

0.02

0.04

0.06

0.08

0.10

0.12

Wavelength (nm)850 900 950 1000 1050

Abs

orba

nce

(AU

)0.00

0.02

0.04

0.06

0.08

0.10

0.12

Abs

orba

nce

(AU

)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

Ternary mixture 5 spectra measured at 5 different temperatures (black solid line: 30oC, red dash line: 40oC, green dash-dot line: 50oC, pink dotted line: 60oC, blue dash-dot-dot line: 70oC)

Wavelength (nm)850 900 950 1000 1050

Abso

rban

ce (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

250l Pilot Plant Batch Agitated Vessel

Outlet (8mm) port Inlet (12mm) port

α-sizer

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

Jacket temperatures

Jacket flow rate

Power on aggitator

Heater chiller oil bath unit

To & from site cryoservices

Valve position

Baffle temperature

Refluxcondenser

Waterinlet

Turbidity

Heater power

Control temperature

PLS quality factor

PLS concentration

Control valve on chiller

Jacket temperatures

Jacket flow rate

Power on aggitator

Heater chiller oil bath unit

To & from site cryoservices

Valve position

Baffle temperature

Refluxcondenser

Waterinlet

Turbidity

Supersaturation Control System Upgrade to PI Capability

INPUTBlock

User DefineModel

User Define “S”set point

HEL PC

Solubility Model

Macro

S(t)

PI ControllerControl Block

Data Processing Block

C(t)

ENABLIR

SOFTWARE

WINISO

SOFTWARE

C(t)

FTIR PC Spectra

PLS model

FTIR Spectrometer

Crystallisation Vessel

4 – 20 mA

INPUTBlock

User DefineModel

User Define “S”set point

HEL PC

Solubility Model

MacroMacro

S(t)

IMC Based PI ControllerControl Block

Data Processing Block

C(t)

ENABLIR

SOFTWARE

WINISO

SOFTWARE

C(t)

FTIR PC Spectra

PLS model

C(t)

FTIR PC Spectra

Cal. model

FTIR Spectrometer

Crystallisation Vessel

4 – 20 mA

Supersaturation Control of L-Glutamic Acid250 litre Plant Crystalliser

0

10

20

30

40

50

60

70

80

90

100

0 100 200 300 400 500 600 700 800 900 1000 1100 1200 1300Time (min)

Tem

pera

ture

, Con

cent

ratio

n, S

olub

ility

& T

urbi

dity

0.0250.0750.1250.1750.2250.2750.3250.3750.4250.4750.5250.5750.6250.6750.7250.7750.8250.8750.9250.9751.0251.0751.1251.1751.225

Sup

ersa

tura

tion

(S =

C/C

*)

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

Solubility (g/500ml) Turbidity (%)Supersaturation SlimitSlimit

Smax = 1.125S = 1.1

Smin = 1.075Started SupersaturationControl

5% seedsadded

0

10

20

30

40

50

60

70

80

90

100

0 100 200 300 400 500 600 700 800 900 1000 1100 1200 1300Time (min)

Tem

pera

ture

, Con

cent

ratio

n, S

olub

ility

& T

urbi

dity

0.0250.0750.1250.1750.2250.2750.3250.3750.4250.4750.5250.5750.6250.6750.7250.7750.8250.8750.9250.9751.0251.0751.1251.1751.225

Sup

ersa

tura

tion

(S =

C/C

*)





Smax = 1.125S = 1.1

Smin = 1.075Started SupersaturationControl

5% seedsadded

Optical Path-Length Estimation and Correction (OPLEC)Separating absorbance from multiplicative light scattering effects caused by the variations in optical path length

i

J

jjjiii cb εsx += ∑

=1,

], , ,[] , , ,[),( 2121 mjjjjmji cccOPLECb LL === cxxxXcX ,,

multiplicative parameter-ib

OPLEC has recently been applied in Raman Scattering applications

OPLEC

NIR spectra of five gluten/starch mixtures in 20 replicates (left) with different compactness and the corresponding NIR spectra processed by OPLEC (right)

Wavelength (nm)850 900 950 1000 1050

Rel

ativ

e va

lue

-0.15

-0.10

-0.05

0.00

0.05

0.10

0.15

850 900 950 1000 1050

0.00

0.05

0.10

0.15

Wavelength (nm)850 900 950 1000 1050

Abs

. (A

U)

1.8

2.0

2.2

2.4

2.6

2.8

3.0

3.2

850 900 950 1000 10501.8

2.0

2.2

2.4

2.6

2.8

3.0

3.2

9-component PLS Model on Raw Spectral Data 2versus 2-component PLS Model Pre-processed by OPLEC

Expected concentration0.0 0.2 0.4 0.6 0.8 1.0

Pred

icte

d co

ncen

tratio

n

0.0

0.2

0.4

0.6

0.8

1.0

Expected concentration0.0 0.2 0.4 0.6 0.8 1.0

Pred

icte

d co

ncen

tratio

n

0.0

0.2

0.4

0.6

0.8

1.0

Predictions for the calibration set Predictions for the independent test set

Expected concentration0.0 0.2 0.4 0.6 0.8 1.0 1.2

Pred

icte

d co

ncen

tratio

n

-0.2

0.0

0.2

0.4

0.6

0.8

1.0

1.2

Expected concentration0.0 0.2 0.4 0.6 0.8 1.0 1.2

Pred

icte

d co

ncen

tratio

n

-0.2

0.0

0.2

0.4

0.6

0.8

1.0

1.2

Predictions for the calibration setPredictions for the independent test set

Predictive Performance of the PLS Models Standard PLS model; Calibration spectra pre-processed by OPLECMartens EMSC Selection 1; Martens EMSC Selection 2; EISC

Number of PLS components0 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16

RM

SE

P

0.00

0.02

0.04

0.06

0.08

0.10

Number of PLS components0 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16

RM

SE

P

0.00

0.02

0.04

0.06

0.08

0.10

EMSC - Extended Multiplicative Scatter Correction; EISC - Extended Inverted Signal Correction

Closing the Analytical Control Loop

Incorporating PAT Sensors into Real Time Process Control

PAT and Advanced Process Control - Why we’re here

PAT is part of a tool box to optimise the way pharmaceuticals are manufacturedProvide greater understanding of the process and what to controlPotentially provide a means to control “Critical Attributes” by monitoring and adjusting “Critical Parameters” in real timeProvides some of the ability to reduce the risk of process variability effecting process capability and product quality

Variable Process

Variable Process Model

Consistent OutputVariable Raw Material

Input to process

Continuous Quality Verification

Current Model

Fixed ProcessVariable Raw MaterialInput to process Variable Output

Univariate Data Quality Monitor:

Individual Signal ValidationLogical ChecksStatistical checks

Multivariate: Using Robust PCA

Outlier detectionOutlier IdentificationData Quality Monitoring

Data Quality Monitoring

Data Quality Records Underpin the Validity of the System– Critical in a Validated Environment

21 CFR Part 11 Records

Courtesy Perceptive Engineering

Clean Data Set

Real Time Quality Control (Using Spectral Data)

Spectral Data

Spectral Data

Data Quality Monitor

Process Data

Discrete Data

21 CFR Part 11 Records

Imported fromModel Development File

Real TimePre-

Processing

Design Space

Control Space

PLS/PCA Calibration

Model

DynamicPCA

Controller

Spectral Data

Process Data

Discrete Data

Real time pre-processed data • Continuously measured OR• a-periodically measured OR• real time value Inferred from

calibration model OR• end-point value inferred from

calibration model OR• scores of calibration model

are C-t-Q parameters

C-t-Q Parameters

Real Time Quality Control and Integrated Data Management

Courtesy Perceptive Engineering

PAT in Closed Loop Process Control - Some ChallengesReal-time management of process and spectroscopic data.

Real time robust fit-for-purpose ‘transferable’ calibration models.

No control system is going to control a spectrum of several hundred simultaneous values; so what is important?

Is there is a robust fit-for-purpose calibration model to infer specific product properties?Are there particular features / segments of the spectrum of interest?

What is the impact of process control on spectroscopic calibration & modelling? e.g. temperature, light scattering effects, etc on control loop performance?

What is the impact of auto correlated data on the reliability and fitness-for-purpose of spectroscopic calibrations and on-line statistical performance monitoring models on product and process understanding and manufacture

the usual assumptions that the observations are Independent and Identically Distributed (IID), are inappropriate

Process Analytics – An Observation

Process Analysis is restricted to large companies while 98% of chemicals manufactured in Europe is by SME’s, primarily in Batch Plants

While

Lambda SensorMercedes S Class costs €100!

Courtesy Professor Roger Benson EUROPACT 2008

A Mercedes S Class -€100,000

Costs the same as

A FTIR – €100,000

and a Process Oxygen Analyser costs €15,000

Smart PAT, QbD and RTRis Smart Process Systems Engineering

And Needs

Smart Miniaturised Process AnalyticsSmart Modelling / Smart Chemometrics

Smart Process Control and Optimisation

& Smart (Responsive, Flexible, Intensified) Processing

So, Where do We Need to Go ?

Process Analytics - 2020

Miniaturised, self-cleaning, selective wavelength spectroscopy systems the standard at very competitive costs.

Open spectral data handling software across all analysers.

Much better use made of the spectral data integrated with process data.

Specific calibration built into each analysers with the ability to identify changes and proactively update.

and for SME manufacturers, all remotely monitored, checked, validated and re-calibrated.e-Pharmaceuticals Manufacturing

Opportunities for measurements and standards - Molecules to Manufacturing.

Motivated by Professor Roger Benson EUROPACT 2008

Advances in PAT Tools

Time Resolved NIR Spectral Data extends spectroscopy into the time domain


Intensified Processing - Bioproduction

Liquid feed INGas IN

Liquid collector(outlet/recycle)

Liquid feed distributorAir distributor

Packing

Liquid OUT (into recycle loop)

Gas OUT

Slip ring assembly

134 mm

Rotor unit

Rotor shaft

244 mm

Liquid feed INGas IN

Liquid collector(outlet/recycle)

Liquid feed distributorAir distributor

Packing

Liquid OUT (into recycle loop)

Gas OUT

Slip ring assembly

134 mm

Rotor unit

Rotor shaft

244 mm

Knitted wire mesh packing

Liquid distributors

Stainless steel rotor

Gas distributor

Rotating Packed Bed Hi-G Bioreactors

Intensified Processing - Bioproduction

Oscillatory Baffled Bio-reactorCourtesy CPI

Potential Savings with PAT in Bio-pharma

Courtesy P Galliher, Potential Benefits of PAT for Biomanufacturing, IFPAC 2005

Closure and ThanksIn this talk, I have tried to…

Introduced the concept of PAT, QbD and RTR within a Process Systems Engineering framework leading to faster scale-up (scale-down), assured product quality and reproducibility and enhanced manufacturing.

Highlighted some Process Analytical Technologies through work aiming to improve the understanding of process variability resulting in enhanced calibration models, enhanced process understanding and assured closed loop process monitoring and control.

Shown the utility of combining a number of process analytical technologies for real-time closed loop batch cooling crystallisation monitoring and control in an industrial pilot plant, and other key industrial issues.

…. And I hope, shown how they might contribute to moving:

“From Shake Flask to Multi-kilos – Faster, Better and SustainablyThrough a Process Systems Approach”

Many thanks to the organisers for their kind invitationand of course you, the audience, for your kind attention

Best wishes to Professors Anastasios Karabelas& Stavros Nychas on their well deserved retirement

I will be most happy to attempt to answer questions

Acknowledgements: EU FP6 NMP Sustainable Microbial and BiocatalyticProduction of Advanced Functional Materials (BIOPRODUCTION) and all my CPACT research colleagues past and present

Proteins Grown in Space Can Help Understand Disease

Courtesy Prof Al Sacco & Dr Catherine Coleman, Space Scientists

process analytical technologies (pat) and quality by design · process analytical technologies...

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