PAT EXPERT platform

1

Presentation & goals of PAT-Expert

Partners

QbD

PAT

Validation

2

First PAT-EXPERT platform

• To increase knowledge in real time

• To ensure compliance with FDA and EMA standards

• To control the manufacturing process

• To deliver a drug product in a state of control

• To reduce the costs of poor quality

• To increase efficiency

We create the PAT EXPERT platform, the first complementary, innovative and operational competencies center for R & D, Industrialization and Production in Pharmaceutical and Chemical sector.It brings together the skills of • Inhalexpert, expert of QbD development,

• Nir-Industry, specialist of PAT

• Galenisys Pharmaceutical Consultants, leader in the validation and transfer of analytical methods.

3

QbDIdentify and explain the critical sources of variability that impact DP CQAs,

•to control the variability of raw materials,

•to understand, validate and control the manufacturing process,

•to predict and control the drug product

PAT

•reduce production cycling time

•prevent rejection of batches

•enable real time release

•increase automation and control

•improve energy and material use

•facilitate continuous processing

Development& analyticalvalidation

Process Validation

Our strategy: From R&D to production up to final release

4

Presentation & goals of Pat-Expert

Partners

QbD

PAT

Validation

5

Page 6

INHALEXPERT

Founded in 2012, by Pascal CAVAILLON and Grégoire LERY, INHALEXPERT provides:

References:

Inhalexpert brings 29 years of experience and expertise to provide consulting and training for the development of any type of complex product including inhaled product

Inhalexpert stands QbDtraining and project support for development strategyand concept understanding, integration of pragmaticapproach including tools, validation and regulatorydossier

We had led successful projects in the EU, US and international markets.

NIR-INDUSTRY

• More than 15 years of expertise in process analyticalcontrol & monitoring, off-line (laboratory) and on-field, within several companies:

SEPPAL, ISITEC-LAB and now NIR-INDUSTRY (since 2014)

• Focused on Consulting, sale & installation of analyticalkey-hand solution for Pharma, chemical & polymere, food & feed, beverages and environment.

• Installation, training and follow up in R&D, up-scale and routine production of spectroscopic technologies: NIR, NIR AOTF, RAMAN and FTIR…

• Sales, set-up and training on chemometrics database, with Unscrambler (CAMO).

Some Pharma References:

Galenisys Solutions

Galenisys consultants are THE EXPERTS in:

• Aseptic Manufacturing and Control, Sterility Assurance and Sterilisation

• Quality Management Systems

• Quality and Regulatory compliance

• Qualification and Validation

• FDA & EMEA inspections / Audits

8

Qualification & Validation:

• Processes

• Cleaning, IT, Equipment, facilities and utilities

• VMPs for New/modified products and routine Q&V programmes

Presentation & goals of Pat-Expert

Partners

QbD

PAT

Validation

9

Objective and benefit

Higher Drug Product control i.e. control of all pDP CQAs• Throughout product lifecycle• Throughout product supply

chain

COPQ* = Cost of Poor Quality

Benefit for the Patient• Higher level of assurance of product Quality linked to:

➔ Efficacy➔ Safety

Benefit for the Pharmaceutical Company:

• Higher Quality and Capability

• Cost saving and efficiency for industry

• More efficient regulatory oversight

• Higher regulatory flexibility linked to Design Space

• To enhance root cause analysis and postapproval change management

10

Situation in 2002?

Dr. Doug Dean & Frances BruttinPriceWaterhouseCoopers

Sigma level ppm defects Yield Cost of Poor Quality

2s 308 537 69,2% 25 – 35%

3s 66 807 93,3% 20 – 25%

4s 6 210 99,4% 12 – 18%

5s 233 99,98% 4 – 8%

6s 3,4 99,99966% 1 – 3%Targeted Quality

to patients

Observed➔ 5 to 15%

Rejects

QA InspectionOOS

Rework

2000s Mfg

Design ExploratoryDevelopment

Official Development

Production

LOW

High

X

X

X

X

€1€10

Cost of making changes

€1000

€100

Ability to Implementchanges

TIME

Rule of 10s

Hidden• Lost customer loyalty• Excess inventory• Cost of engineering changeovers ➔ 15 to 25%• Extra equipment• Extra headcount

Move everything early on in the design stage

The only way to fix that is by design

Cost of Poor Quality

11

Voice of Customer: FDA/EMAA process is generally considered well understood when:

• All critical sources of variability i.e. API CQAs, CMAs, CDAs, QCPPs, CQAs are identified and explained ➔ Level 1

• Variability is managed by the process ➔ Level 2

• DP CQAs can be accurately and reliably predicted over the ranges of acceptance criteria established for API CQAs, CMAs, CDAs, QCPPs, CQAs and manufacturing environmental and other conditions ➔ Level 3➔ The ability to predict reflects a high degree of process understanding

Drug Product

Before QbD Variable Input

Fixed Process

Variable Output

Consistent output

Variable Input

Adapted Process

QbD/PAT/Design space

Control the Inputs (X's)………….……...Monitor the Output (Y's) 12

How to reach the objective?

Process step/unit operation

pCMAs, pCDAs and

API CQAs

pCQAs

pQCPPs

Process step/unitoperation

Process step/unitoperation

…..

pQCPPs pQCPPs

pCQAsDrug

ProductpDP CQAs

Mathematicalmodel, correlation➔ predictive

Need to understand and control raw materials and manufacturing process

To control Drug Product

i.e. all DP CQAs

Need to identifyand control linkbetween input

and output and DP CQAs

Understandand control

each unit operation

Understandand control

each unit operation

Understandand control

each unit operation

Understandand control

raw material

13

CQA1

CQA2

CQA3

Critical Quality Attributes

DP CQAs

DP CQA1

DP CQA2

DP CQA3

Drug Product

Intermediate CQAs

Objectives: identifying critical sources of variability and mapping the linkage to control DP CQAs

Inputs of unit operation Outputs of unit operation

Using prior knowledge, models and risk assessmentprocesses, relationships between input and output are

analyzed :CQA1 = function (API CQA1, CMA1)CQA2 = function (QCPP1, QCPP3)CQA3 = function (CMA1, CMA2, QCPP1)

QPP2 might not be needed in the establishment of design space

QCPP1

QPP2

QCPP3

Quality CriticalProcessParameters

CQA1

CQA2

CQA3

Critical Quality Attributes

Using prior knowledge, models and riskassessment processes, pCMAs, pCDAs, pAPICQAs and pQCPPs which could have an impact upon DP CQAs are analyzed.

CMA1

CMA2

Critical MaterialAttributes

API CQA1

Unit operation

Process variables:1. PSD ➔ API CQA12. PSD ➔ CMA13. Density➔ CMA24. Mixing speed ➔ QCPP15. Mxing time ➔ QCPP2

Intermediate CQAs:Blend uniformity

DP CQA 1: Content uniformityDP CQA 2: AssayDP CQA 3: Dissolution

DOE

Control strategy

Analyse de risques

14

OUTPUT = CQAs or DP CQAs

y

Inputs to the processcontrol variability

of the Output

People

Equipment

Measurement

Process

Materials

Environment

Observation

Ind

ivid

ua

l V

alu

e

4038363432302826242220

120

115

110

105

100

95

90

_X=102.37

UCL=116.68

LCL=88.05

I Chart

Observation

Ind

ivid

ua

l V

alu

e

6058565452504846444240

115

110

105

100

95

90

85

80

_X=97.94

UCL=112.65

LCL=83.23

I Chart

Observation

Ind

ivid

ua

l V

alu

e

8078767472706866646260

115

110

105

100

95

90

_X=99.63

UCL=111.55

LCL=87.71

I Chart

Observation

Ind

ivid

ua

l V

alu

e

10098969492908886848280

110

105

100

95

90

85

_X=98.76

UCL=111.17

LCL=86.35

I Chart

Observation

Ind

ivid

ua

l V

alu

e

6058565452504846444240

115

110

105

100

95

90

85

80

_X=97.94

UCL=112.65

LCL=83.23

I Chart

Observation

Ind

ivid

ua

l V

alu

e

8078767472706866646260

115

110

105

100

95

90

_X=99.63

UCL=111.55

LCL=87.71

I Chart

INPUTS

(X)High risk variables

y = ƒ(x)

DevelopmentDesign phase

Prior knowledgeRisk assessment

Experimental phaseDOEs

Mutivariate analysis

Production• Manufacturing transfer• Quality and cost

optimisation• Quality investigation• Validation

CAPABILITY = 3s

OUTPUT = CQAs or DP CQAs

y

Inputs to the processcontrol variability

of the Output

CAPABILITY = 6s

Process Robustness: ability of a process to tolerate variability of materials and changes of the process and equipment without negative impact on quality.

15

How do we measure the control of drug product?The output of this measurement i.e. DP CQA, if normal distribution, is usually illustrated by a histogram and calculations that predict how many parts will be produced with specific mean (m) and standard deviation (s)

68,3

95,4

99,7

m

LCL UCL

• Two parts of process capability are: 1) measure the variability of the output of a process➔ voice of the process

Voice of the process

Variability around the target, about 1,5s ?

2) compare that variability with a proposed specification➔ voice of the customer

LSL USLVoice of the customer

3,5 s level

16

Presentation & goals of Pat-Expert

Partners

QbD

PAT

Validation

17

MONITORING SUPERVISION

CONTROL DIAGNOSTIC

“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”

ICH, ASTM and FDA definitions

➢PAT= Think global + act locally

➢PAT are focused on process stepsand analysis within a global process approach

Les PAT: Process Analytical Technology

More than only install online sensors

18

Process Quality Management System in Pharmaceutical facility

BENEFITS:• Facilitates process understanding.• Causes of product variability can be identified and

managed.• The increase in process understanding enables the

reduction in development and scale-up time.• Increases patent life of new and existing products.• Reduces production cycle times.• Reduces work in progress.• Enables the use of alternative (less expensive) raw

materials.

19

• Reduces wastage and rejects.

• Improves in quality and consistency of quality.• Enables rapid release of product.• Improves energy and material use.• Facilitates continuous processing.• Reduces manufacturing cycle times and staffing.• Reduces the manufacturing facilities required.• Creates potential for skid-/cellbased manufacturing for

rapid deployment as pre-built systems.• Meets the new FDA Process Validation Guidance.• Reduces development, manufacturing and quality costs

ACTIVE

DISP. 1EXCIPIENT

DISP.

ACTIVE

DISP. 2TABLET

PRESS1

Brimrose

SPECT1

FBD

Ethernet

Moisture

Optic Fibre

Ethernet

Optic Fiber

Optic Fiber

EthernetEthernet

NIRA SERVER

PACMAN

SERVER

Routeur

Site Ethernet

Optic Fiber

Weight

Management

Panel PC Panel PC Panel PC Panel PC Panel PC Panel PC

Barcode

Reader

EthernetEthernet

BLEND

SPECT

BLENDER

BIN

TABLET

ANALYZERLAB TABLET

ANALYZER

Panel PC

WIRELESS

RECEIVER

ETHERNET

Total quality control: Complete solution compliant with CFR21 part 11

20

Different industries, same QbD and PAT approaches

Biopharma (Mab) Chemistry (CPI)

Pharma Petrochemical➢ Formes solides➢ Formes Liquides➢ Formes semi-pâteuses

21

PROCESS & PAT: solid formsNIR

NIR

NIR

NIR

NIR

NIR

NIR

RAMAN

RAMAN

RAMAN

RAMAN

22

PAT application: solid form

Raw Material& API

Spray-DryerBlender, Mixing

FBD & Wurster

Tablet compression

Freeze drying monitoring

Solvant recovery

Control & sorting

23

PAT application : liquid & semi solid form

Raw Material& API

MixingSolvant

recoveryControl &

sorting

24

SUMMARY OF POSSIBILITIES

Step NIR & RAMAN Option 1 Option 2 Option 3

Raw materials Identification of API & excipient, (ok)Water contentPolymorphism state (ok)

Luminar 5030 with probes

Luminar 6000 lab with probe

Luminar 3060 multi channel

Blend Blend uniformityWater content

Luminar 4030

“Free Space”

Luminar 5030

in “Free Space”

mode

Wet granulation and drying

Moisture end pointMoisture profile controlBlend uniformityGranule size and distribution (ok)Polymorphism state (ok)

Luminar 4030

“Free Space”

Luminar 5030

in “Free Space”

mode

Luminar 3060

Multi channel with probes

Tablet analysis Identification of API & excipient (ok)Active & excipient content (ok)MoistureDissolutionHardnessDensity

Luminar 3070 for transmission and reflectance

Luminar 4030

For reflectance only(*)

(*) Diffuse reflectance may be used for high dosage tablets where distribution of active in the tablet can be safely assumed to be uniform. At low dosage, where active may be distributed non-uniformly, transmission is critical

25

SUMMARY OF POSSIBILITIES

Step NIR & RAMAN Option 1 Option 2 Option 3

Suspensions Identification of API & excipient, (ok)ContentPolymorphism state (ok)

Luminar 5030 with probes

Luminar 6000 lab with probe

Luminar 3060 multi channel

Creams, pastes Identification of API & excipient, (ok)Content

Luminar 4030

“Free Space”

Luminar 5030

in “Free Space”

mode

Clear liquids

Eye drops, syrups

Identification of API & excipient, (ok)Content

Luminar 4030

“Free Space”

Luminar 5030

in “Free Space”

mode

Luminar 3060

Multi channel with probes

Transdermal patches

Identification of API & excipient, (ok)Content

Luminar 3070 for transmission and reflectance

Luminar 4030

For reflectance only(*)

26

RAW MATERIALS

27

• Same Multiplexed Analyzer can be used for:

- Multiple Excipient and ActiveDispensaries

- Fluid Bed Dryer

- Warehouses

ID Raw Materials – Receiving and Dispensaries operations

28

Absorption spectra of some raw material

Solid & liquid

PlasdoneK-90, Lactose anhydrous, Lactose Mono-hydrous,

Sulfamethoxazole, Atenolol, Sulfasalzine,Trazodone-HCl,

Metronidazole, Metroformin-HCl, Doxycycline-HCl

1 - Anhydrous IP

2. Ethanol

3. Methanol

4. Acetone

5. Ethanol

6. Glycerin

29

Absorption Spectra - Pure Polymorphs

1461

1477

1970

1978

2164

2181

1732

1736

Similar chemically

Activities different…

Crystallisation form different

Polymorph H

Increasing amount

Polymorph A

Increasing amount

polymorph A

1st Derivative - Polymorphs in Tablets

WET GRANULATION: FBD, WURSTER, etc…

31

CQAs for wet granulation unit operationPotential CQA

End point measurement (e.g. power consumption, torque)

Blend uniformity

Flow

Moisture content

Granule size and distribution

Granule strength and uniformity

Bulk/tapped/true density

API polymorphic form

Cohesive/ adhesive properties

Electrostatic properties

Granule brittleness

Granule elasticity

PAT monitoring with NIR &/or RAMAN

End point measurement (e.g. power consumption, torque)

Blend uniformity

Flow

Moisture content

Granule size and distribution

Granule strength and uniformity

Bulk/tapped/true density

API polymorphic form

Cohesive/ adhesive properties

Electrostatic properties

Granule brittleness

Granule elasticity

PAT

32

Granulation – FBD/LAF

❖ The Simple – moisture end point

❖ The Smart – moisture profile control

❖ The Advanced – Excipient bead coating control

Granulation – FBD/LAF: Granulation Basics

Granulation is a process where two opposing effects take place simultaneously

- Agglomeration due to presence of “adhesive” compounds & moisture.

- Attrition of agglomerates due to collisions with other particles and the chopper. This effect occurs all the time until end point is reached

To achieve a reproducible granulate, we need to control the following:- Moisture profile with time, from beginning to end of spraying.- Rate of drying to end point- End point 33

Granulation – FBDSimple - End point by LOD only

34

Granulation – FBD

Simple - End point by NIR

Granulation – FBDSimple - End point by NIR

36

0.00

0.50

1.00

1.50

2.00

2.50

3.00

13:2

0:35

13:2

9:42

13:3

6:58

13:4

4:44

13:5

2:00

13:5

9:16

14:0

6:17

14:1

3:48

14:2

1:04

14:2

8:19

14:3

5:50

14:4

2:51

14:5

0:43

14:5

8:38

15:0

6:33

15:1

4:11

15:2

1:35

15:2

9:28

15:3

7:08

15:4

4:48

15:5

2:29

15:5

9:26

16:0

6:44

16:1

4:50

16:2

2:26

16:3

1:07

16:3

8:29

16:4

6:37

LOD

Time

Predicted

Reference

Granulation – FBDWhat can go wrong?

37

1. Diffuse Reflectance Probe inserted at same level and penetration as temperature probes2. Real time results during drying cycle3. Operators see moisture value on Glatt control panel, feed back can be used.4. Use multiplexer for up to 12FBD with same unit

Granulation - Glatt FBD, Fluid Air, Probe (Free Space available)

38

Miniature Luminar 5030 Hand Held installed

on a CPCG1

Miniature Luminar 4030 Process Analyzer installed

on a NIRO FBD

Granulation – FBDHow to do it? From R&D to Production

39

Absorption Spectra from Glatt FBD& PLS1 Regression of Absorption Spectra

40

FBD Operation by AOTF NIR via Sapphire window

Analyzer is integrated into Glatt 500 FBD Control system

NIR Moisture

Vacuum Oven LOD

*

Drying Curve: Batch XXXXX, Load X

Removed spectrometer

to inspect window

41

The Smart Way – Active coating on Excipient BeadsThis is typically done on Wurster Coater

Area of change

42

Second Derivative – very useful in Understanding the full Process.

43

Blender & Mixer

44

CQAs for wet granulation unit operation

PAT

Potential CQAs

Blend uniformity

Particle size distribution

Bulk/tapped/true density

Moisture content

Flow properties

Cohesive/adhesive properties

Powder segregation

Electrostatic properties

PAT monitoring with NIR &/or RAMAN

Blend uniformity

Particle size distribution

Bulk/tapped/true density

Moisture content

Flow properties

Cohesive/adhesive properties

Powder segregation

Electrostatic properties

45

FROM LAB TO PRODUCTION SIZE IN ONE EASY STEP

R&D SCALE

PRODUCTION 500 LITER

46

On-line Blend Analysis

4030 on 25cu.ft. double cone

5030 on a 5 liter V-blender

5030 on a lid

47

On-line Blend Analysis - The ultimate way

N = 1-30 N = 31- 60

Blend Run 3

0

2

4

6

8

10

12

0 20 40 60 80 100 120 140 160

Rotation Number

Diff

eren

ces

Differences

48

Blend profiles: moving block spectral standard deviation

Large block size

– Suitable endpoint based on y-axis value (1 point) or profile slope

Small block size

– Suitable endpoint based on consecutive y-axis values (multiple points) by minimal profile variability

Moving block blend profiles using different block sizes

0

0.02

0.04

0.06

0.08

0.1

0.12

0.14

0.16

0.18

0.2

0 10 20 30 40 50 60 70

Revolutions (block number)

me

an

sp

ec

tra

l s

tan

da

rd d

ev

iati

on

block = 3

block = 20

49

Blending – Quantitative Mg-Stearate

Peak of Mg-St 0.5 to 2%

2nd derivative

50

Blending – Quantitative Mg-Stearate

SEP (standard error of prediction) = 0.06

51

Blend Behavior – Stable or De-blending?

0.00E+00

5.00E-05

1.00E-04

1.50E-04

2.00E-04

2.50E-04

3.00E-04

3.50E-04

4.00E-04

4.50E-04

1 6 11 16 21 26 31 36 41 46 51 56 61 66 71 76 81 86 91 96 101 106 111

MB

SD

Rotation

Comparison Phenit vs. CMZ

XR1308 6block MBSD

phen2408 block6 MBSD

PhenetoinCarbamazapine

52

TABLET COMPRESSION

53

CQAs for tablet compression operation

PAT

Potential CQA

Tablet appearance

Target weight

Weight uniformity

Content uniformity

Hardness/tablet breaking force/ tensilestrength

Thickness/dimensions

Tablet porosity/density/solid fraction

Friability

Moisture content

Disintegration

Dissolution

PAT monitoring with NIR

Tablet appearance

Target weight

Weight uniformity

Content uniformity

Hardness/tablet breaking force/ tensilestrength

Thickness/dimensions

Tablet porosity/density/solid fraction

Friability

Moisture content

Disintegration

Dissolution

54

Off the Press Tablet Analyzer

55

Off the Press Tablet Analyzer

Tablet passing over detector

Reflectance detector inside

Software renders spacing between tablets totally insignificant

56

Transmission or Diffuse Reflectance measurements of Tablets

©1999-2000 Brimrose Corp of America

The following parameters can be measured in Tablets:

•I.D. of active (& excipient)

•Concentration of active & excipient)

•Moisture

•Dissolution

•Hardness

•Density

•Dosage Level

Laboratory with Tablet

Carousel

With Tablet Conveyer belt

57

Absorption Reflectance Spectra of Coating on Tablets

1394nm

58

PLS1 Regression of Spectra of Moving Coated Tablets

LOADING WEIGHTS FOR COATING WEIGHT

Bound water in coating oxide

1394nm

59

1st Derivative Spectra of 9 Dosage Levels in Transmission

1138nm

60

PLS1 Regression of 2nd Derivative Spectra

SEP = 0.15

61

LYOPHILISATION

62

CQAs for lyophilisation operation

PAT

Potential CQA

Appearance

Weight mean

Weight uniformity

Content uniformity and mean

Moisture content

API polymorph

Reconstitution time

PAT monitoring with NIR

Appearance

Weight mean

Weight uniformity

Content uniformity and mean

Moisture content

API polymorph

Reconstitution time

63

Lyophilization – Experimental set up

Brimrose Diffuse reflectance Remote sensing head

Rotating table

64

Freeze drying Process: Lyophilisation Monitoring

65

SOLVANT RECOVERY

66

Solvent Recovery Experience - A 10 Channel System

New Solvent Recovery

Plant

Commissioned in 1997

First MDC Recovery Sept. 97

Multipurpose Columns A and B

67

Solvent Recovery Experience - Flow Cell

•Liquid Side Draw Column B

•Flow Cells installed in by-

pass

•Sample take off point close to

probes

•Measure at LSD, Vapor Side

Draw, Reflux and Feed on

each column (8 total)

Flow cell68

82% MDC

15% Toluene

2.5% Acetone

0.2% Water

Trace heptane and methanol (<0. 1%)

Solvent Recovery Experience - A 10 Channel System

Typical MDC Feed Composition Typical Composition - Recovered MDC

99.7% MDC

0.1% Acetone

0.04% Methanol

0.03% Water

69

Solvent Recovery Experience - Methanol Validation

70

Solvent Recovery Experience - Process Screen

71

MDC Liquid Side Draw

98

98.2

98.4

98.6

98.8

99

99.2

99.4

99.6

99.8

100

6/9/99

14:24

6/9/99

16:48

6/9/99

19:12

6/9/99

21:36

6/10/99

0:00

6/10/99

2:24

6/10/99

4:48

6/10/99

7:12

6/10/99

9:36

6/10/99

12:00

Date/Time

%Co

mpo

nent

-0.05

0

0.05

0.1

0.15

0.2

MDC

Methanol

Ethanol

Water

Solvent Recovery Experience - Liquid Side Draw

72

OTHER APPLICATIONS

73

Luminar 3030 - Moving Vials Inspection linked to cap color

Cap color reader

Luminar 3030 “Free Space” optical module

Good separation between

2 products with different

active concentration with

speed up to 200 per

minute

74

PCA Analysis - Blue Caps (1%) vs. Red Caps (2%)1. Excellent separation of the two products at speeds of up to 80 per minute.

2. No need for trigger, system determines presence of sample by itself, and collects only relevant spectra.

75

NIR Project Status 1st production line fully commissioned with 16 channel Luminar 3060

Excellent performance in determining on-line moisture content

5 plants were equipped by 2002

76

Absorbance Spectra - Moving Transdermal Patches

77

1st Derivative Spectra - Moving Transdermal Patches

78

Regression for Active in Moving Transdermal Patches

SEC = 1.33

SEP = 1.65

79

Presentation & goals of Pat-Expert

Partners

QbD

PAT

Validation

80

VALIDATION

81

1) Definition of the Master Plan for the implementation

of the NIR method

2) Input to and Verification of the "Justification of the

Change of Method and the Equivalence"

3) Input to and Verification of the qualification protocol

4) Verification of the completed qualification

protocol

5) Input to, assesment of, and verification of both and the validation protocol for the

NIR method and the report of the equivalence with the

current method

6) Verification of the completed NIR method

validation protocol

7) Verification of the NIR method SOP

8) Verification of the method registration amendment with

health agencies

9) Verification of the completed Change Control

with respect to the NIR Equipment and the new

analytical methods

82

Galenisys expertise in NIR