IFPAC conference – Sept 2010 – Daniel keogh ABB ... · PDF fileRelease in the Pharmaceutical Industry IFPAC conference – Sept 2010 ... (~100 hrs), an ABB FTPA2000-260PH process

© ABB Group September 27, 2010 | Slide 1

ABB Measurement Products – Analytical Solutions Standard Solutions for QbD and Real Time Release in the Pharmaceutical Industry

IFPAC conference – Sept 2010 – Daniel keogh


What are Standard Solutions for QbD and Real Time Release in the Pharmaceutical Industry?

Application Product Picture

Laboratory FT-IR spectrometer MB3000-PH

FT-NIR tablet analyser - QC FTLA2000-PH60

Laboratory and at-line FT-NIR analyser MB3600-PH

Process FT-NIR platform FTPA2000-260PH

Pre-configured PAT FT-NIR analyser FTPA2000-PH40

Scalable PAT data integration solution xPAT


Pharmaceutical applications: drug substance


Laboratory reaction monitoringReal-time determination of reaction end point and intermediate products or contaminants concentration using in-situ probes

Technical choice for reaction monitoring: MIR versus NIRTechnology Advantages Challenges

MIR - Lower limits of detection (ppm level)- Ease of interpretation- Simple calibrations- Specificity of ATR probes for liquid phase in crystallisation reactions- Very good for qualitative applications

- Light pipe design not robust and reproducible for process- Expensive fibers and limited length- Often requires Sterling-cooled or liquid nitrogen cooled MCT- In crystallisation, coating of ATR may require ultrasonic cleaning

NIR - Allows use of long fiber optics (easier zoning)- Mature technology for process- No ultrasonic cleaning required

- Higher LOD than MIR- Problems with transmission probes in presence of particles/crystals- Small signal of NIR ATR --> Requires high S/N instrument- Requires chemometrics expertise


Process reaction monitoringExample: Real time monitoring of the second step of a ring closure reaction used for synthesis of an active pharmaceutical ingredient. As the reaction is very long (~100 hrs), an ABB FTPA2000-260PH process analyser with insertion transmission probe was used to trend absorption bands characteristic of the target product in first derivative spectra. A second degree polynomial equation was fitted based on several training runs and this equation was subsequently used for the early prediction of reaction end-point.


Solvent recovery The solvent recovery process involves the distillation of a “feed” or “crude”solvent from a low purity (e.g. 60%) to a pure product that can be re-used in the manufacturing process. Pharmaceutical and chemical companies face growing pressure from environmental groups and regulators to adopt sustainable manufacturing and green chemistry practices. It is critical for them to have a way of monitoring accurately and in real time the solvent recovery process, in order to avoid excessive water concentration in solvent and reduce the fees charged for incineration.

Example: real-time monitoring of water content in ethyl acetate/etanol solvent feed during distillation using an FTPA2000-260PH process analyser with insertion transmission probe (3 mm pathlength), 85m fiber optics length


Solvent recovery Background

Service engineer installed the full system with IQ/OQ in 3 days ( a week before the start of a new campaign)

The lab pre-calibration gave good results within the first day (14 samples)

Model development and training were finished in 4 days (lab model enriched with 30 process samples). Model was updated once after 1 month, and alarms lined to F-ratios

A new reference spectrum is collected at the start of a new campaign (every 10-14 weeks)

System has been accepted by an internal validation committee after 8 months of operation

, ( ), ( )

Actual Concentration ( C1 )Actual Concentration ( C1 )

Pred

icte

d Co

ncen

tratio

n ( F

5 C1

)Pr

edic

ted

Conc

entra

tion

( F5

C1 )

1

2

345

6

7

89

10

1112

13

14

15

161718

19

20

2122

23

2425

26

27

2829 30

31

32

33

3435

36

3738

39

40

41

42 43

44

45

46

4748

49

5051

52

-.2

.4

1

1.6

2.2

-.2 .4 1 1.6 2.2

0

.5

1

9000 8000 7000 6000 5000 (Wavenumber (cm )

Abs

orba

nce

-1

2nd CH Overtone1st Overtone OH + Water

Water

FT-NIR Spectra of Water/Ethyl-Acetate-Ethanol

0

.5

1

9000 8000 7000 6000 5000 (Wavenumber (cm )

Abs

orba

nce

-1

2nd CH Overtone1st Overtone OH + Water

Water

FT-NIR Spectra of Water/Ethyl-Acetate-Ethanol

Range: 0-2%Temperature: 8-25 ºCSEP=0.045% w/w


Crystallization Properties of active pharmaceutical ingredients (API) such as solubility, dissolution rate, density, physical stability and melting point change depending on the type of crystalline forms. The various polymorphs of a given pharmaceutical compound will exhibit different drug release characteristics and biological activity upon inhalation or injection

Crystallization is usually the final stage in the manufacture of a bulk API. The rising demands for product quality are imposing to process engineers to have, not only a better process understanding (e.g. process dynamics, polymorphism) but also on-line monitoring techniques for quality assurance and control during this critical operation.

Absorbance spectra changes throughout crystallization

experiment


Crystallization Example: real-time monitoring of an API crystallisation process at industrial scale. The use of an in-situ transflectance probe (2 mm pathlength) connected to an ABB FT-NIR analyzer proved an accurate and reliable approach for:

1. Qualitative monitoring of the runs Detection of beginning of supersaturation in the crystallization experiment to decide on the on-set of reagent feeding

2. Quantitative characterization of the liquid phase Determination of residual API concentration in the mother liquor and prediction of crystal mass formed

PC1 score, 4 runs. (A) High feed rate, (B) Low feed rate.

A B

API crystals resulting from 2 different rates of reactant addition.

Magnification: 300x (A) High feed rate, (B) Low feed rate


Drying• Inferential dryer measurements rely on parameters like airflow, pressure

or inlet/outlet air or product temperature. Output variables do not always reflect exactly the critical quality attribute (moisture or solvent level), leading to long cycle times and risks of product degradation (e.g. dehydration).

• Alternatively, off-line analyses (loss on drying, Karl-Fisher titrations, gas chromatography) are more accurate but significantly increase cycle time. They require manual sampling of wet products during the drying phase, which leads to potential safety and industrial hygiene issues such as operator exposure to potent drugs. Moreover, manually collected samples are subject to physical effects like atmospheric humidity absorption or segregation that may alter the analytical determination. In addition, the delay associated with off-line analyses (from 15 min to several hours) makes them unpractical for efficient and accurate process control.


DryingExample: use of multi-channel FTPA2000-PH40 process analyser for real time monitoring of moisture content during drying of API (Range 30% to 4% w/w)

In-line probes inserted flush in the walls of 3 agitated pan dryers (~2mm from agitators)Water content displayed on operator terminalLong drying cycles (>24 hours)Reference method: Karl FisherFT-NIR showed that drying cycle could be reduced by 30%Also used for maintenance and early detection of process upsets

H2O DR1 (%w/w)

0

5

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25

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35

40

45

50

3/18/2003 9:36 3/18/2003 14:24 3/18/2003 19:12 3/19/2003 0:00 3/19/2003 4:48 3/19/2003 9:36 3/19/2003 14:24 3/19/2003 19:12 3/20/2003 0:00 3/20/2003 4:48

Calibration results

2

6

10

14

18

2 6 10 14 18

Reference value

FTIR

Static repeatability ( ) 0.004%Dynamic repeatability ( ) 0.04%SEC 0.19%SECV 0.22%SEP 0.10%r2 0.99


Pharmaceutical applications:drug product


.1

.2

.3

.4

1200 1100 1000 900 800

Fréquence (cm )

Abso

rban

ce

BomemAnalyseur FT-IR

Figure 2: Région phosphate des spectres ATR des échantillons

Contramid 3.25

Amidon (Hylon VII), Contramid 2.0 et 3.25

Amidon (Hylon VII)

-1

Contramid 2.0

Formulation

Deformulation

Example: Determination of the degree of cross-linking in a high amylose starch powder drug delivery matrix, using ATR spectroscopy. The drug release profile will be related to the level of cross-linking obtained in the starch matrix. It can be tailored to the drug being administered or even to the user.

Influence of the degree of cross-linking on spectra

Laboratory FT-IR


Laboratory FT-IRMaterial identification (raw materials or finished products)

Example: discrimination of active pharmaceutical ingredients, using a laboratory FT-IR spectrometer with diffuse reflectance accessory and Kubelka-Munk correction to account for the diffuse light scattering effect in powders

0

.5

1

1.5

2

2.5

3

3.5

5000 4500 4000 3500 3000 2500 2000 1500 1000 500

Kubelka-Munk / Wavenumber (cm-1) Paged X-Zoom CURSOR

File # 1 = AMP02KM 23/08/1995 08:53 Res=4 cm-1

MB102 \DTGS \ DRIFT \ Ampicillin sodium ~ 40% in CsI \ 20 scans

0

1

2

3

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5000 4500 4000 3500 3000 2500 2000 1500 1000 500


File # 2 = PIPER2KM 23/08/1995 09:21 Res=4 cm-1

MB102 \DTGS \ DRIFT \ Piperacillin Sodium ~ 40% in CsI \ 20 scans

0

5

10

15

5000 4500 4000 3500 3000 2500 2000 1500 1000 500


File # 2 : SULB1KM 23/08/1995 09:37 Res=4 cm-1

MB102 \DTGS \ DRIFT with pure Sulbactam Sodium \ 20 scans

Ampicillin sodium Piperacillin sodium Sulbactam sodium


Impurity and foreign materials detection in products

Quality Analysis (QA) / Quality Control (QC)

Example: QC check of the content of ranitidine (API for gastric diseases) and phenol (preservative) in an injection formulation using FT-IR spectrometer with Horizontal ATR accessory. Objective: decrease the time per analysis with respect to chromatography in order to increase the number of samples tested by the Quality Control laboratory

% Ranitidine in injection Formulation of Zantac

2.3

2.35

2.4

2.45

2.5

2.55

2.6

2.65

2.7

2.3 2.35 2.4 2.45 2.5 2.55 2.6 2.65 2.7

Laboratory FT-IR


At-line analysis

PAT method development

Example: Quantitative determination of moisture content in lactose powder using a laboratory FT-NIR analyser with fiber optics launcher and diffuse reflectance probe

Laboratory FT-NIR


Laboratory FT-NIRDetermination of active ingredient content in tablets (optionally moisture)

Example: Development of API content calibration model for antidepressor tablets on a master FT-NIR instrument and transfer of the calibration to a second instrument. Hardness, excipient lots and concentrations varied. Reference values determined by HPLC.

Uncertainty: 0.7 mg activeRepeatability: 0.15 mg activey = 0.9176x + 1.5802R2 = 0.9137

12.00

14.00

16.00

18.00

20.00

22.00

24.00

26.00

12.00 14.00 16.00 18.00 20.00 22.00 24.00 26.00

HPLC Reference mg Active

PLS-

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icte

d m

g A

ctiv

e

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1 2 3 4 5 6 7 8 9 101112131415161718192021222324252627282930Tablet #

Pred

icte

d m

g ac

tive

System 1System 2

Example results: 350mg uncoated tablets, 20mg API

SECV: 0.70mg

SEP: 0.54mg

SDD transfer: 0.26mg


Counterfeit drug detection

Example: Detection of counterfeit tablets. Straightforward detection using FT-NIR laboratory analyser in diffuse reflection mode.

Laboratory FT-NIR

FT-NIR spectra showing signatures of original ED drug and 3 counterfeits

PCA score plot showing discrimination between orginal and counterfeit drugs

original

CF2

CF 1

CF3


Deformulation / competition analysis

Example: Discrimination of paracetamol tablets between 4 suppliers (2 branded, 2 generics) using FT-NIR laboratory analyser in diffuse transmission mode. Tablets have similar size, shape, and color with API as the main component.

Laboratory FT-NIR

Overlaid FT-NIR diffuse transmission spectra of tabets from the 4 suppliers

PCA score plot showing discrimination between the 4 suppliers

B2

G1

G2

B1


Granulation - DryingDetermination of residual moisture content and product uniformity during granulation and drying

Example: Use of multi-channel FT-NIR process analyser for real-time monitoring of moisture content during drying (Range 5 to 1% w/w). In-line probes inserted in the dollies of 2 fluid bed dryers. Short drying cycles (30mn)

A c t u a l v s p re d ic te d

y = 0 . 9 9 8 9 x

1

1 .5

2

2 .5

3

3 .5

1 1 .5 2 2 .5 3 3 .5

Ac t u a l c o n c e n t r a t i o n

Pre

dic

ted

conc

entr

atio

n

S e r ie s1L i n e a r ( S e r ie s1 )

Legend

Beige: Continuous line: % moisture

Red: Outlet air temperature

White with big jumps: air flow

Green: Inlet Air Temperature

SEP (w/w) Dynamic repeatability (w/w)0.09% 0.02%


Blend uniformityAPI level and ribbon tensile strength during roller compaction

API level and blend uniformity during high shear mixing

API level and blend uniformity during compression feed frame analysis

Example: FT-NIR real time measurements of ibuprofen in flowing powder formulation in a hopper at the entrance of a compression unit. A process analyser coupled to a non-contact probe has been used to understand the effect of the flow level and initial active ingredient concentration on the homogeneity of the blends, using designed experiments.

R2 = 0.97Uncertainty = 4.2%

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Reference Concentration (%)

Con

cent

ratio

n pr

edic

ted

by P

LS (%

)


Extrusion In pharmaceutical industry hot-melt extrusion (HME) can be used to extrude combinations of drugs, polymers and plasticizers into various solid dosage forms (tablets, transdermal patches, granules, capsules, etc.). It offers a number of advantages over traditional pharmaceutical processing techniques:

No solvents and water required less processing steps than wet granulation and no drying required

Ability to mass large volume units requires less energy than high-shear granulation

Intense mixing results in uniform dispersion

Continuous process more efficient than batch processes

Anhydrous nature of the process eases formulation for the increasing number of molecular entities (>40%) that have poor aqueous solubility and target lipophilicreceptors.

Usual control parameters for HME processes are torque, drive motor amperage and melt pressure. However, for direct measurements of the Critical Quality Attibutes such as API, excipients or contaminants content, FT-NIR is the technology of choice as it allows real-time and non-destructive analysis of the extrudate with specially designed extruder probes inserted in the die.


Extrusion Example: Use of FTPA2000-260PH process analyser for monitoring residual water content in the hot melt of a sustained release pharmaceutical formulation, using a diffuse reflectance probe inserted in extruder die.

H2O Combination Region

1

2

1 = No water added startup condition

2 = 1.8, 1.9, 2.0, 2.1, 2.2 wt% water added

High absorbance due to different gains settings for re ference and sample

All spectra from run

H2O Combination Region

1

2

1 = No water added startup condition

2 = 1.8, 1.9, 2.0, 2.1, 2.2 wt% water added

High absorbance due to different gains settings for re ference and sample

All spectra from run


Tablet coating Coating thickness is one of the key parameters influencing the dissolution rate of drug in human body. The complexity of the coating unit operation arises from the fact that multiple variables may influence the quality and degree of coating. These variables are associated with the heat and mass transfer characteristics, the coating curing, the spray configuration, the nature of the coating material, the geometry of the system, the nature of the core, and the rate and extent of coating accumulation.

Often the extent of film coating of tablets is determined by either applying a specified amount of coating material, by measuring coating material mass with HPLC or by measuring the weight gain of tablets until the desired end point is achieved. These methods are time-consuming, require sample preparation steps and can only be performed after the coating process has been completed. They do not allow real time control of the process.


Tablet coating Example 1: Use of a FTPA2000-260PH process analyzer with transflectanceprobe to monitor the homogeneity of tablet coating solutions in a hold tank before transfer to fluid-bed coater. Three calibration models were built for % solids, % hydroxypropyl methylcellulose and % ethyl cellulose. The three modelsallowed real-time monitoring of homogeneity of the coating solutions beforestarting the coating process.

Real-time monitoring of solution homogeneity

Example spectra and calibration curve


PAT in biopharmaceutical manufacturing


FT-NIR features for biomanufacturingFast Allows real-time measurements

FlexibleFiber optics allows in-situ analysis

Different sampling options allow to accommodate different fermentation media (clear, opaque) Multiplex instruments

No sample preparation required

Accurate Can be used as an alternative method to laboratory at-line measurements

Information richDetermination of chemical and physical characteristics of samples can be used for in-line qualitative and quantitative analysis (nutrients, products, by-products)

Can pick-up information on the complete matrix (not chemically characterized)

For R&D, can screen a large number of fermentation process attributes

For manufacturing, can monitor critical quality attributes

NIR spectroscopy applied to fermentation


FT-NIR spectra: in-line monitoring of Saccharomyces Cerevisiae yeast growth on malt extract (ethanol production)

NIR spectroscopy applied to fermentation


NIR for fermentation – Technical aspectsProbe optimisation

Transflectance probesCan accommodate clear (transmission) and optically dense (reflectance) media Useful for exploratory R&D work from lag phase to exponential and stationary phases (alternative: multiplex measurement)Adjustable pathlength useful for method development but issues in manufacturing (cleaning, reproducibility and stray light issues)

Transmission probesUseful for most fermentation processes except filamentous fermentations (e.g. antibiotic production)Require aseptic design

Reflectance probesUseful for some filamentous fermentationsNon-contact versions available

Important constraints related to sterility regardless of probe typeSurface finish of wetted partsSpecial fiber and connector design if autoclaving requiredSpecial window design if CIP/SIP required (temperature gradient)


Monitoring of the fermentation or cell culture process

Determination of fermentation analytes concentration (nutrients, metabolites, by-products)

Example: monitoring of total sugars and ethanol from FT-NIR spectra during very-high-gravity corn mash fermentation (NCSU-BTEC)

NIR spectroscopy applied to fermentation processes


Monitoring of the fermentation process

Cell density and biomass content

Example: determination of biomass growth by correlation of FT-NIR spectra with cell density during very-high-gravity corn mash fermentation (NCSU-BTEC)

NIR spectroscopy applied to fermentation processes


Monitoring of downstream purification process

Example: Monitor the elution process and fraction collection to reduce loss of API through ionic exchange column (9 cycles, alternative to assay that takes 10 minutes)3

NIR spectroscopy applied to downstream processes

Eluted samples


Data Management xPAT


What are the Data Handling Challenges ? Managing Analyzers

Analyzer integration for solutions such as the Process Analytical Technology (PAT) Initiative presents a unique set of integrationchallenges for data exchange and control.

There is a large variety of analyzers types, from various vendors with many different types of data, including complex arrays and structures.

There are many different ways to integrate analyzers and execute control from external systems.

New process optimization opportunities exist that require unit and system level coordination and control of multiple analyzers from different vendors.

Open Standards are required to streamline integration and remove requirement for purpose-built integration.

Standard interfaces drive down the validation costs for project implementation in the pharmaceutical industry.


What are the Data Handling Challenges ?Analyzers produce multiple disparate formats e.g. Spectra, Bar Graphs, Chromatographs, Images etc.

An analyzer model can be used to predict many different properties.

Not all analyzer measurements are in real time.

In order to interpret data from multiple analyzers the measurements need to be correlated.

Sensor data needs to be correlated

Process Data required

Data collected needs to be made available to various offline platforms e.g. modelling packages, LIMS, EBRS etc


Integration at Integration at Process Controller is Process Controller is expensive and expensive and proprietaryproprietary

Limited data types Limited data types from analyzer from analyzer controller to process controller to process controller in noncontroller in non--standard formatstandard formatHot Air

Granules

Filters

FBRM Analyzer

NIR used to monitor moisture

Determine end-pointMoisture profile used for asset monitoringProvide data to allow closed loop control to optimize drying

FBRM used to monitor particle size

Detect prevent clumpingDetect presence of fines that can clog filtersProvide data to allow closed loop control to optimize drying

Proprietary interface

Proprietary Interface

Current Analyzers in the ProcessProprietary Interfaces – Non Standard Data

NIR Analyzer

AnalyzerController

AnalyzerController

Process Controller(PLC, DCS)

ProprietaryProprietaryinterfaceinterface

ProprietaryProprietaryInterfaceInterface


Integration at Process Controller is standard and easier

Complex data types available from analyzer controller to process controller in a standard format

Hot Air

Granules

Filters

FBRM Analyzer

NIR used to monitor moisture

Determine end-pointMoisture profile used for asset monitoringProvide data to allow closed loop control to optimize drying

FBRM used to monitor particle size

Detect prevent clumpingDetect presence of fines that can clog filtersProvide data to allow closed loop control to optimize drying

Proprietary interface

OPC-ADI Analyzers in the ProcessStandard Interfaces – Standard Data

Smart NIRAnalyzer

AnalyzerController

Process Controller(PLC, DCS)

ADI interface

ADI Interface

Managing Analyzers – OPC-ADI


Managing Process and Analytical Data

Granulator Fluid Bed Dryer Tablet Press Coater

NIR : Monitoring end point predication of moisture content for drying phase.

FBRM : Measures particle sizes.

NIR : Monitoring coating thickness

PVM: Particle Size

RAMAN: API Concentration

xPAT System


Managing Process and Analytical Data – Data Access

Data rich Information available and compatible with many chemometric PackagesModelling for continuous manufacturing

BatchManager

SCADAHistorian

SCADAOPC Server

SCADAPLC

SCADA I/O Analogs & Digitals

•Time Based Trends • Alarm & Event

OPC/DA (Analogs & Dig.)

Dryer

NIR Analyzer

VisualizationClients

Analyzer DataAcquisition

PaticleAnalyzer

Particle Size Model Moisture Model

PATData Manager

Combined Particle / Moisture Model

Data By “Batch ID”

ChemometricAnalysisWorkstations

Hot Air

Granules

Filters


Control Network

xPAT Data Manager

BatchManager

SCADAOPC Server

SCADAHistorian

OtherServer Visualization

Clients &Chemometric tools

ABB or 3rd Party Analyzer

xPAT Analyzer Controller

Unit 1

Unit 1

(Wireless link)

PAT and network architectureFrom small …


Router

/ Firewall

Batch

Manager

SCADA

Historian

Other

Server

Batch

Manager

SCADA

OPC Server

SCADA

Historian

Other

Server

Visualization

Client &

Chemometric tools



Unit 1ABB or 3rd Party Analyzer


Unit 6

…………

Visualization

Client &

Chemometric tools



Unit 1ABB or 3rd Party Analyzer


Unit 6

…………SCADA

OPC Server

PAT and network architecture… To large

Corporate Network

Additional

Visualization Clients &

Chemometric tools

xPAT Data

Manager

Control NetworkControl Network

xPAT Data

Manager


BatchManager

SCADAHistorian

SCADAOPC Server

SCADAPLC

SCADA I/O Analogs & Digitals

•Time Based Trends

• Alarm & EventOPC/DA (Analogs & Dig.)

Fermentor

FT-NIR Analyser

Operator workplace

FTSW800

PAT Data Manager

Data Stored by Batch ID or other unique identifierRemote Client SupportData Accessible by Open Interfaces (OPC, ODBC etc)Multi Analyzer Model Support Time based, A&E, Spectral & method data Stored & archivedCentralized analyzer control

Data By “Batch ID”

The ABB PAT Data Manager is used to manage, store and serve up the data derived from and the configurations for all the instrument platforms and link the data with that captured by the SCADA control system.

Chemometricanalysis

workstations

Integration

MM

Process mass spectrometer

Off-gas

Air/O2/CO2

pH, T

Air flow, agitation


Documents

IFPAC conference – Sept 2010 – Daniel keogh ABB ... · PDF fileRelease in the Pharmaceutical Industry IFPAC conference – Sept 2010 ... (~100 hrs), an ABB FTPA2000-260PH process