This document presents information on process analytical technology (PAT) for pharmaceutical development, manufacturing, and quality assurance. It discusses the definition, principles, goals, framework, tools, and implementation of PAT. Case studies are provided that demonstrate how PAT tools like in-situ FTIR, Raman spectroscopy, and chemical imaging can be used for reaction monitoring, crystallization, granulation, blending, and identification of raw materials. Barriers to PAT implementation include historical, cultural, organizational challenges but benefits include reduced cycle times, prevention of rejects, continuous improvement, and real-time release. PAT facilitates building quality into pharmaceutical products through process understanding.

1. PRESENTED BY:
Ms. Sahoo Sumita Gopal
M. Pharm Sem III
Department of Quality Assurance Techniques
GUIDED BY:
Prof. Arun Maruti Kashid
Asst. Professor
Pharmaceutical Chemistry
STES’s
Sinhgad Institute of Pharmacy,
Narhe, Pune 41
Process Analytical Technology for Innovative
Development, Manufacturing and Quality Assurance
1

2. OUTLINE
1. Definition of PAT
2. Introduction to PAT
3. Principle of PAT
4. Goals of PAT
5. Industrial Analytical Culture
6. PAT Framework
A] PAT Tools
B] Elements of PAT Implementation
7. PAT in Pharmaceutical R&D
8. PAT in Pharmaceutical Manufacturing
9. Barriers to Implementing PAT
10.Benefits of PAT
11.Conclusion
2

3. PAT is a system for:
Designing, analyzing, and controlling manufacturing
Timely measurements (i.e., During processing)
Critical quality and performance attributes
Raw and in-process materials
Processes
Process Analytical Technology (PAT) [1,2]
3

4. RISK-
ANALYSIS
MATHE-
MATICAL
Quality cannot be tested into products; it should be built-in or
should be by design.
Introduction to PAT [1,2]
4

5. Principle of PAT [1,2]
5

6. The simple meaning of process understanding is expressed by,
Process understanding=design + predictability + capability
PAT = Process Understanding
• Process Understanding – Innovation
• Process Understanding – Validation
• Process Understanding – Justifying “Real Time Release”
Process Understanding [1,2,3]
6

7. Goals of PAT [2,3]
To design and develop well understood processes
Use of the latest scientific advances in pharmaceutical manufacturing and
technology
 Risk-based approach to encourages innovation in the pharmaceutical
manufacturing sector
Agency resources are used effectively and efficiently to address the most
significant health risks
Up-to-date concepts of risk management and quality systems.
7

8. Industrial quality control
traditionally performed in
industries
PAT modern
methodology
Industrial Analytical Culture [4]
8

9. Specification (CQA) Traditional test Time frame No. of samples per analyst
Dissolution Dissolution test 2 days 8
Disintegration Physical test 2 days 24
Assay HPLC 2 days 24
Hardness Physical test 2 days 24
Content uniformity HPLC 4 days 3
Impurity HPLC 3 days 24
Appearance Appearance 2 days 48
Identification IR/UV 2 days 12
Water Karl-Fischer 2 days 24
Microbiology Biological test 7-14 days 12
Industrial quality control traditionally performed in
industries [4]
9

10. Specification
(CQA) 21st century testing
Time
frame No. of samples per analyst per 8h
Dissolution NIR 1s (> 1 million samples per day)
Disintegration NIR 1s (> 1 million samples per day)
Assay NIR/NMR 1s (> 1 million samples per day)
Hardness NIR 1s (> 1 million samples per day)
Content uniformity NIR 1s (> 1 million samples per day)
Impurity On-line HPLC/HPLC 10 min 24 (off-line) – 24 (on-line)
Appearance Colorimetry/NIR 1s 300
Identification NIR 1s 300
Water NIR/NMR/TE 1s (> 1 million samples per day)
Microbiology RMM
12h – 14
days
24
Pat Modern Methodology [4]
10

11. The framework is founded on process understanding to facilitate innovation
and risk-based regulatory decisions by industry and the agency
PAT Framework [1,5]
11

12. Multivariate tools
for design ,data
acquisition and
analysis
• Identify and
measure
CM and process
attribute
• Design process control
• At line
• In line
• On line
PAT TOOLS [1,5,6]
Continuous
improvement and
knowledge
management
tools
12

13. Building a science-based knowledge base - Complete process understanding at
the mechanistic and first principle level
 Process monitoring and control - Determination of critical process parameters
and critical quality attributes and selection of measurement, analysis and control
mechanisms to adjust the process to provide the predicted quality of the product.
 Validation of a PAT system
 Regulatory strategies
Elements in PAT Implementation [3,7]
13

14. • A deeper scientific and engineering understanding of manufacturing processes
• Reduced product development times, more robust licensing packages, faster
scale up, faster new product to market .
• Implementation of innovative manufacturing and quality strategies
PAT in pharmaceutical R&D [8]14

15. CASE STUDY 1 [8]
The goal is to identify and understand critical process parameters (CPP) while
a process is still in the development phase .
Determining CPP’s during development provide the opportunity to eliminate
or minimize these critical parameters through chemistry or engineering
solutions.
15

16. Experimental
A general description of the reaction, crystallization and drying operations for the
case study of an API scale up experiment are as follows:
Reaction:
Conversion of a secondary amine to an amide, reagent: butyl glycolate, base
catalyst:1,8-diazobicyclo[5.4.0]undec-7-ene (DBU), solvent: 1-methyl 2-
pyrrolidone (NMP), reaction temperature 110°C.
Crystallization:
Addition of anti-solvent, cool to 20°C, heat to reflux, hold at reflux 4 hours,
monitor for formation of desired polymorph.
Drying:
vacuum tumble drying at 50°c and 75 torr
16

17. Unit Operation Unit Operation Manufacturer
Reaction
Objective: determine
reaction end pt.
In-situ FTIR
Off-line LC
Mettler Toledo, ReactIR
Agilent 1100 LC
Crystallization
Objectives: determine
desaturation rate and
polymorphic conversion
In-situ FTIR
In-situ FBRM
In-situ PVM
Off-line LC
Mettler Toledo, ReactIR
Lasentec M200
Lasentec Model 900
Agilent 1100 LC
Drying
Objective: determine
drying rate and end point
Mass Spectrometry
Off-line GC
Stand-alone Agilent
5793 with MS Sensor
Software
(Diablo Analytical)
Agilent 6890 GC
Instrument involve in Study [8]
17

18. Using PCA to model the variability of the spectra collected from a system can be
a general approach to determining the end point of any operation where a change
in the spectral variability of the system is indicative of the endpoint of an
operation.
In-situ FTIR reaction endpoint determination using PCA.
STEP 1 : REACTION
18

19. IR during crystallization
Step 2 : Crystallization
19

20. Crystallization Desaturation Profile by LC
20

21. Lasentec FBRM chord length distributions change
during polymorph transition21

22. A. Lasentec PVM image during anti-solvent addition (undesired form).
 Figure 5 contains PVM images that show the change from poorly defined
solids after the anti-solvent addition transitioning to better defined bar-like
crystals formed during the reflux step.
22

23. B. PVM image during reflux C. PVM image at the end of
cool down
Conversion to desired form.
Desired form
23

24.  The objective of the dryer monitoring was to determine the drying rate for
final product.
 The drying curve plotted for selected ion abundance versus drying time.
Monitoring of vacuum tumble dryer by mass spectrometry.
STEP 3: DRYING
24

25.  The utilization of process analytical technologies (PAT) facilitates the integration
of chemistry, engineering and analytics during process development and scale-up
activities.
 The data-rich experiments performed with in-situ analytical measurements lead to
increased process knowledge through better understanding of critical process
parameters.
 The benefit to this development approach is that rework is minimized when
processes are transferred to manufacturing scale.
CONCLUSIONS
25

26. Reduced waste, right-first-time manufacturing, higher production asset
utilization
Real-time quality assurance and validation
Movement toward real-time release of products
Lean manufacturing practices for reduced raw material, work in progress, and
and finished good inventories
More robust product supply to the public
PAT in Pharmaceutical Manufacturing [9,10]
26

27. CONTROL STRATEGY [9]
Unit Operations
Attributes
Controls
Content Uniformity NIR
Water Content – NIR
Particle size – FBRM
Dispensation
Blending
Fluidized
Bed Dryer
Packaging
Tableting
Identity-NIR
Blend Homogeneity -NIR
Granulation
Extent of Wet
Massing - Power
Consumption
Air
Scale
Multivariate Model (predicts Disintegration)
Raw Materials
27

28. CASE STUDY 2 [2]
28

29.  Regulatory requirements state that the identity of every container of an API
delivery must be ascertained.
 Some deliveries may contain up to 100 (or even more) individual containers
and if the current method of identification requires between 10 and 15 min per
sample, the total analysis time for a single batch of material then equates to 2
days.
 In comparison the use of NIR results in a total analysis time of 2 hr. In terms
of timesaving, a reduction of 88% can be achieved on current testing using
NIR,
Raw Materials Identification and Conformity Analysis [10]
Raw materials identification
29

30. Conformity Analysis
(a) The conformity spectrum
 Usually 20–30 previous batches
of material are used to develop
the library and these materials
represent a wide range of
manufacture date, storage
conditions and sample packing
variations.
30

31.  NIR has been widely applied in the field of chemical industry.
 Use of fiber-optic probes enables noninvasive measuring in the reflectance
mode directly from the process stream.
 Wet granulation of pharmaceutics was the first process analytical application
of NIR in the pharmaceutical field
 Interfaced NIR spectroscopy with blending process equipment are used for
on-line blend analysis. The air flow rate was measured using flow tube
design, and an NIR set-up with a multichannel detector was used for
measurement of moisture during granulation.
Granulation using PAT tool [10,11]
31

32. NIR spectra over granulation time [11]
32

33. 33
NIR absorbance at wave number of 10,000 cm−1 and particle size against
granulation time

34. SEM photomicrographs at ×50 magnification
A] before granulation, B] 20 min granulation, C] 60 min granulation, D] 120 min
granulation, at ×2,000 magnification E] before granulation F]120 min granulation
SEM as PAT tool in wet granulation [11]
34

35. CASE STUDY 3
35

36. Chemical Imaging (CI)
36
Typical components of a Chemical Imaging system

37. In these images the positions where the spectra show the highest correlation with
that that of a sample having the required API and binder/excipient band intensities
are white/pink. Positions where there is poorer correlation are green/blue.
RAMAN CHEMICAL IMAGEING
37

38. Barriers to Implementing PAT [5]
“ If it is not broken, do not fix it ”.
 Barriers in general are categorized as: historical, cultural, organizational,
regulatory and technical
38

39. Benefits of PAT [3]
 To reduce manufacturing cycle times by minimizing the waiting periods
 Decisions are made based on science in PAT
 Preventing out-of-specifications, rejects, and reprocessing
 Continuous process improvement to enhance efficiency & quality
 Shorten the validation/development cycles
 Continuous process improvement to enhance efficiency & quality
39

40.  Manufacturing of drug products
 Increase process automation and on-line quality control systems
 Predicting of dissolution results
 Industry concerns with respect to PAT
 Risk-based approach to regulatory scrutiny
 Building quality into pharmaceutical products
 Real time release as an alternate to final product release
40

41.  PAT offers the pharmaceutical engineer exposure and usage to more
disciplines outside of traditional pharmaceutical production.
 PAT eliminates the bottleneck of laboratory testing.
 On-line instrumentation for PAT facilitates quality product
 Knowledge of Process is imperative, quality is built in the process, process is
not assessed at the end but during production.
CONCLUSIONS41

42. KEY REFERENCES
1. U.S. Department of Health and Human Services Food and Drug
Administration, Guidance for Industry PAT — A Framework for Innovative
Pharmaceutical Development, Manufacturing, and Quality Assurance,
Pharmaceutical CGMPs, 2004.
2. Ajaz Hussain, The Subcommittee on Process Analytical Technologies (PAT):
Closing Remarks Document for the FDA’s Advisory Committee for
Pharmaceutical Science, 2002.
3. Kinjal Sheliya, Ketan Shah, Process Analytical Technique, International
Journal of Pharmaceutical Sciences, 2014, 21-38
4. James Munson, Freeman Stanfield and Bir Gujral, A Review of Process
Analytical Technology (PAT) in the U. S. Pharmaceutical Industry, Current
Pharmaceutical Analysis, 2006, 405-414.
42

43. Ravindra Kamble, Sumeet Sharma, Venus Varghese, KR Mahadik; Process
Analytical Technology (PAT) in Pharmaceutical Development and its
Application, International Journal of Pharmaceutical Sciences Review and
Research, 2013, 212-223
Winskill , Hammond , An Industry Perspective on the Potential for
Emerging Process Analytical Technologies, FDA Science Board, Rockviile,
MD, 2001,169-180.
Paul David, Robert Roginski, Steve Doherty, Jodi Moe, The impact of
process analytical technology in pharmaceutical Chemical process
development , journal of Process Analytical Chemistry, 2014,1-5.
Arani Chanda,etl, Industry Perspectives on Process Analytical Technology:
Tools and Applications in API Development, Organic Process Research &
Development, 2015, 63−83.
5.
6.
7.
8.
43

44. Ai Tee Tok, Xueping Goh, Wai Kiong Ng, and Reginald B. H. Tan,
Monitoring Granulation Rate Processes Using Three PAT Tools in a Pilot-
Scale Fluidized Bed, American Association of Pharmaceutical Scientists,
2008, 1083-1091.
Margot Fonteyne, Jurgen Vercruysse, Fien De Leersnyder, Bernd Van
Snick, Chris Vervaet, Jean Paul Remon, Thomas De Beer ,Process
Analytical Technology for Continuous Manufacturing of Solid-dosage
Forms, Trends in Analytical Chemistry 2015,1-20.
Brad Swarbrick, Process Analytical Technology: A Strategy For Keeping
Manufacturing Viable in Australia, Vibrational Spectroscopy. 2007,
171–178.
9.
10.
11.
44