A Bayesian approach to the
Comparison of NIR vs HPLC
analytical methods in Continuous
Manufacturing Process Validation
Studies
Areti Manola1, Jyh-Ming Shoung1, Steven Novick2,
Tara Scherder3, Gilfredo Navarro1, Eric Sanchez1,
Stan Altan1
1Janssen
R&D, 2MedImmune, 3Arlenda
NCB2015 Conference
Villanova University
October 15, 2015
Outline
1. Overview of Continuous Manufacture
 Quantitative stakeholders:
Process Engineers, Chemometricians, Statisticians
2. Process Performance Qualification
 Verification of HPLC – NIR calibration
o Study Design
o Method Comparability/Equivalence
3. Relative Performance Index
 Comparison of ratios of probabilities
P(| y   |   | method)
2
 Bayesian approach
4. Case Study
5. Summary
Recent Industry Announcements
Continuous manufacturing offers QbD
And PAT advantages says ISP
By Gareth Macdonald 19-Apr-2011
Continuous manufacturing offers significant advantages
for drug production in terms of quality and efficiency, but
more research and regulatory input is needed to help the
pharmaceutical sector reap the benefits...
http://www.in-pharmatechnologist.com/Processing/Continuousmanufacturing-offers-QbD-and-PAT-advantages-says-ISP
Though making the switch from batch to
continuous manufacturing may be difficult,
costly and time consuming, pharma manufacturers and CMOs should begin to consider
the switch as in the long-run it will end up
saving companies time, money and space,
FDA’s CDER Director Janet Woodcock told
congressmen in a hearing Thursday.
http://www.in-pharmatechnologist.com/Processing/FDA-calls-on-manufacturers
-to-begin-switch-from-batch-to-continuous-production
3
http://www.fiercepharmamanufacturing.com/story/vertex-jj
-gsk-novartis-all-working-continuous-manufacturing-facilities/2015-02-09
Engineering Definition of Continuous
Manufacturing vs Batch Manufacturing
4
FDA Perspective on Continuous Manufacturing, Sharmista Chatterjee, Ph.D.
IFPAC Annual Meeting Baltimore, January , 2012
Example of Continuous Manufacturing
with On-line Monitoring
5
FDA Perspective on Continuous Manufacturing, Sharmista Chatterjee, Ph.D.
IFPAC Annual Meeting Baltimore, January , 2012
Ideal Future Vision of CM
6
Real Time
Release Approach
Traditional
Release Approach
Dissolution control
through DoE end point
Granulation
Milling
Control through LoD
Drying time by NIR
Drying
Milling
Blend Uniformity control
through NIR
Blending
Lubrication
Weight, Thickness,
Hardness, DT, friability
Compression



Coating
7
Standard DP release Tests
 Physical Description
 Composite
Assay(HPLC)
 Impurity (HPLC)
 Content Uniformty
(Mass)
Laboratory
NIR Composite Assay
Dosage Uniformity by
tablet weight control
Tablet Thickness
Advantages of Continuous Manufacture
Scientific/Engineering
 Integration of QbD concepts
 Cohesive development, quality
and technical operations
 Application of new
methodologies, technologies
and equipment
 Improved process capability
 Real time understanding of
process integrating process
engineering, chemometrics
and statistical considerations
Operational/Business
 Reduces costs
 Streamlined facility lay-out
 Reduction of raw materials and
intermediates inventories
 Flexibility in supply size
Payoff
 Reduction of overall drug
substance and drug product
development time
 Improve time to market
 Assures supply of high quality
product
Process Engineering Aspects
Unit Operations Model: Mixer
•
Residence Time Distribution Model
Pulse of Tracer
(i.e., API)
Critical process
parameters (CPPs)
Data (DEM predicted
particle velocities)
•
Unit: Mixer
Detector
(i.e., PAT tools)
Outputs
•
•
9
Powder properties
Unit responses:
• Flow Rate
• Mixing (RTD)
RTD models are used to determine:
1. Disturbance dissipation along process
2. Raw material traceability
3. Scale up requirements

MRT     t  E (t )dt
0
E (t) 
C (t )

C
out
0
(t )dt
Chemometric Aspects
Process Analytical Technology (PAT)
• Near Infra Red (NIR) calibration modeling for PAT
during Process Design

PAT PLS = assess comparability to HPLC method
• Gage R&R designs relating HPLC to NIR during
development and subsequently during process validation
Statistical Aspects
 Process Validation
• Stage 1 Process Design – DoE, data analysis and
interpretation
• Stage 2 Process Performance Qualification
Statistical sampling protocols for Large n sampling plans
 IPC sampling


Verification of HPLC – NIR calibration
• Stability protocol and sampling designs
• Definition of sampling frequency and sample size
• Stage 3 Continued ProcessVerification (PV) - Process
Capability
Verification of HPLC – NIR calibration
 Calibration model is developed during Process Design, Gage
R&R to assess equivalence
 Blocked
design is 3 concentrations, at target tablet weight,
tablets are the blocks leading to essentially a paired comparison
design
 During the stages of PV, the calibration model will be tested
in production
 Equivalence

measures will be calculated
Schuirmann’s test
 We propose a Relative Performance Index using a Bayesian
approach to the assessment of an individual analytical
determination falling within a prespecified limit of the true
value for comparing NIR vs HPLC
12
Relative Performance Index
 Assuming the HPLC is the gold standard method, the probability of a
single analytical determination y from HPLC (or NIR) falling within
some interval  of the true value µ is calculated as follows:
 Δ 
 Δ 
  Φ 

Pr_ H  P(| y   |   | HPLC)  Φ 
 σ HPLC 
 σ HPLC 
 Δ  bias 
  Δ  bias 
  Φ 

Pr_ N  P(| y   |   | NIR)  Φ 
 σ NIR 
 σ NIR 
where (•) is the CDF of standard normal distribution.
 The Relative Performance Index is defined as follows:
Rel_Pfm  Pr_ N / Pr_ H
13
Method Comparison using the Relative Performance
Index
Given delta, Bias and MethodVariability
 OC Curves of probability of falling within delta of true value
Pr_ NIR
Pr_ HPLC
 Rel_Pfm across delta of true value
 Criterion for equivalence
 Pr(Rel_Pfm≥1)≥PC, where PC is a desired probability level
14
Case Study - Data Description
 A single CM batch was sampled as follows:
 20 locations chosen equispaced throughout the CM run
 3 tablets per location
 Tested by both NIR and HPLC methods
HPLC
Location = 1
NIR
Location = 2
HPLC
Location = 3
NIR
Location = 4
Location = 5
102
100
98
102
Location = 6
Location = 7
Location = 8
Location = 9
Location = 10
Location = 11
Location = 12
Location = 13
Location = 14
Location = 15
Assay
100
98
102
100
98
102
Location = 16
Location = 17
Location = 18
Location = 19
Location = 20
100
98
HPLC
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NIR
HPLC
NIR
Method
HPLC
NIR
Tablet
1
2
3
Statistical Model
Variance component model :
y j ( i ),k  M k  Li  T j ( i )   j ( i ),k
where
Yj(i),k = assay of jth tablet (j=1,2,3) from ith (i=1,2,…,20) location
from kth (k=HPLC, NIR) method,
Mk = overall mean from kth method,
Li
= random effect of ith location: ~ N(0, L2)
Tj(i) = random effect of jth tablet from ith location: ~ N(0, T2)
j(i),k = residual error from kth method: ~ N(0, k2), k = 1, 2
Preliminary analysis showed no location effect, therefore the
random effect of location was dropped from final model.
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REML Parameter Estimates
Effect
Fixed
Random
(SD)
Parameter
Estimate (se)
Lower
Upper
HPLC
100.01 (0.10)
99.81
100.32
NIR
100.18 (0.05)
100.08
100.29
Bias* (NIR-HPLC)
0.17 (0.09)
-0.02
0.36
Tablet
0.35
0.26
0.53
Residual (HPLC)
0.70
0.58
0.86
Residual (NIR)
0.20
0.11
1.04
*The 90% confidence interval for Bias = (0.01, 0.33)
17
95% Confidence
Interval
JAGS – Posterior Samples
 A Bayesian simulation of the posterior distribution and credible
intervals based on the previous model was done using JAGS with
vague priors:
 Mean[HPLC], Mean[NIR] ~ N( Mean=100, SD=10 )
 SD_Tablet ~ U(0, 5)
 SD_HPLC, SD_NIR ~ U(0, 5)
 60,000 posterior samples:




18
Number of chains=3
Burn-in = 20000
No. of sample = 20000
thin=25
JAGS – Parameter Estimates and Credible
Intervals
Effect
Fixed
Random
(SD scale)
19
Parameter
Mean (Median)
95% Credible
Interval
Lower
Upper
HPLC
100.02 (100.02)
99.81
100.22
NIR
100.19 (100.19)
100.08
100.29
Bias* (NIR-HPLC)
0.17 (0.17)
-0.02
0.36
Tablet
0.36 (0.36)
0.21
0.47
Residual (HPLC)
0.72 (0.71)
0.59
0.89
Residual (NIR)
0.19 (0.20)
0.02
0.37
*The 90% credible interval for Bias = (0.01, 0.33)
Normal Density Plots of HPLC and NIR centered on
the true mean
Given Estimated mean bias and median of sigmas for HPLC and NIR methods
NIR Bias=0.17
Bias SD
0
0.7144
0.17 0.1973
2.0
Normal Density
1.5
1.0
0.5
0.0
20
-2
-1
0
X
1
2
OC Curves of Probability of Falling within delta of
True Value
Given Estimated mean bias and median of sigmas for HPLC and NIR methods
21
Relative Performance Index across delta values
Given Estimated mean bias and median of sigmas for HPLC and NIR methods
22
Summary of Posterior Distribution (JAGS) of Relative
Performance Index with Various deltas
23
delta
0.05
0.10
0.15
0.20
0.25
0.30
0.35
0.40
0.45
0.50
0.55
0.60
0.65
0.70
0.75
0.80
0.85
0.90
0.95
1.00
Mean
2.21
2.25
2.29
2.31
2.28
2.21
2.11
2.01
1.90
1.79
1.70
1.61
1.53
1.47
1.41
1.35
1.31
1.26
1.23
1.20
Median
2.01
2.05
2.11
2.15
2.17
2.15
2.10
2.02
1.91
1.80
1.70
1.61
1.53
1.46
1.40
1.34
1.30
1.26
1.22
1.19
Maximum
24.06
12.13
8.46
6.64
5.78
4.91
4.24
3.72
3.33
3.01
2.75
2.54
2.36
2.21
2.08
1.97
1.87
1.79
1.71
1.64
Minimum
0.00
0.00
0.00
0.00
0.00
0.00
0.00
0.00
0.00
0.00
0.86
0.89
0.92
0.95
0.97
0.99
1.01
1.02
1.02
1.02
Pr(Rel_Pfm ≥ 1)
0.819
0.849
0.890
0.929
0.959
0.980
0.991
0.996
0.999
0.999
>0.999
>0.999
>0.999
>0.999
>0.999
>0.999
1.000
1.000
1.000
1.000
Comparison of Schuirmann’s Test and Relative
Performance Index for Method Comparison
Test
Criterion
delta= 0.25
delta = 0.50
Schuirmann’s
90%Credible
Interval of Bias
90%CI =
(0.01, 0.33)
90%CI =
(0.01, 0.33)
Fail
Pass
Pr(RPI ≥ 1) = 0.96
Pr(RPI ≥ 1) = 1.0
Pass
Pass
Pr(RPI ≥ 1) ≥ 80%
Relative
Performance Index
24
Summary
 CM is being actively encouraged by the FDA; companies are now
engaged in weighing its costs/benefits
 CM offers many scientific and business advantages, major
quantitative stakeholders are process engineers, chemometrician,
statisticians working together to ensure quality
 Equivalence of NIR to gold standard HPLC can be established
through a Relative Performance Index evaluated through
Bayesian calculations
• Made possible because Tablet dispersion can be removed
orthogonally given the paired comparison design
• Provides a natural interpretation of method performance
25