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Drug Product Continuous Process Verification – A Case Study
1
CASSS 2016 Summer CMC 19 July 2016
Tom DamratoskiBristol-Myers SquibbDirector, Biologics Drug Product MS&T
BMS Proprietary and Confidential 2
Drug Product CPV In Practice - 3 Levels
3. Quarterly Portfolio- Sr. Management Scorecard looking at all sites and products
2. End to End Product Reviews--Cross functional team across Drug Substance, Drug Product, Analytical, Stability and Quality--Deeper Statistical Analysis applied to Hot Spots
1. Drug Product Manufacturing Site -Approximately Monthly interbatch data reviews-Voice of the Process at the Shop Floor-Site Focused…but also feeds data upward
Product A Product B
ACME ACME CMO
% Ppk>1.3 80%
% Ppk >1.0
RejectionRate
2/50 0/20
Yield
0
10
20
30
40
50
60
70
80
90
100
Attr
ibut
e X
BMS Proprietary and Confidential 3
1. Manufacturing Site CPV- Getting Started
1. Procedures and training • Statistical software and Training• Data mining practices- Manual, Automated, or Hybrid• Statistical Alert Rules And Procedures• Data review frequency and participants
2. Initial Monitoring – Beginning of Stage III Process Validation • Monitoring Plan Established Based on Risk Assessment• Monitor 1st ~ 30 lots, generate Time Series plots (too soon for CpK)• Lock Control Limits
3. Continuous Process Verification • Visualize Data using Time series, histograms, and PpK/CpK analysis• Generate statistical alert event reports where needed• Utilize Heat Map, and apply multivariate analyses when needed (Generally
multivariate utilized less for DP than in DS)• Data Feed upwards into E2E Product Reviews and Portfolio scorecards
BMS Proprietary and Confidential 4
Drug Product Monitoring Plan- Typical Profile
Critical Quality Attributes – pH– Concentration – HMW/LMW– Bioanalytical impurities– Particulate– Potency– Moisture (lyo)– Etc.
In Process Control– pH– Fill weight Performance
100 % Visual inspection Process – Valuable, often overlooked– % Particle defects– % Critical defects found– % Overall
Less is more at the start. Automated data capture and analysis enables larger data sets with faster alert response
BMS Proprietary and Confidential 5
6.3
6.2
6.1
6.0
5.9
5.8
5.7
Indi
vidu
al V
alue
_X=5.9933
UCL=6.1453
LCL=5.8414
LSL = 5.7
USL = 6.3
pH – 2 Learnings From a Basic Assay
Most Recent 15 Batches – PpK Increases to 1.63
pHN=30 BatchesPpK = 1.07 (Borderline Capable)
Learning 2: Assays with low sig figs, while
scientifically correct, often drive non-normal data sets
Learning 1: Data visualization is King…PpK is
a lagging indicator.
BMS Proprietary and Confidential 6
AAG4867AAD4371AAB27891H56677
10.9
10.8
10.7
10.6
10.5
Fill
Wei
ght,
g/Vi
al
Target=10.7
USL=10.9
LSL=10.5
Yervoy 50 mg/VialBoxplot of Net Fill Weight Checks by Lot
Fill Weight Control
•
• Box Plots are ideal…but data-intensivePer lot sigma is another measure
• Automated data acquisition from OEM check weigh system can be a challenge due to proprietary software
• Use side-by side to compare 2 different DP sites
BMS Proprietary and Confidential 7
Visual Inspection- Example Defect Trend
AAH9
194
AAH9
193
AAH7
744
AAG7
293R
AAJ5
501
AAG7
293
AAH5
397
AAJ1
246
AAH5
394
AAH7
734
AAH9
195
AAH5
393
AAH6
736
AAH5
395
AAE3
754R
AAE3
756
AAE3
754
AAE3
751
AAE3
748
AAE3
747R
AAE3
753
AAE3
747
AAG7
291
AAH2
522
AAG7
290
AAG7
289
AAG7
286
AAG9
564
AAE3
757
AAF3
932
AAE3
750
AAE3
752
AAE3
749
AAF7
195
AAD4
849R
AAF8
392
AAF8
393
AAF4
881
AAE8
730
AAF2
676
AAD2
076R
AAF0
208
AAF7
888
AAF5
108
AAE8
724
AAE8
714
AAE8
707
AAE8
686
AAE7
879
AAE7
586
AAE4
762
AAE4
762
AAE3
749
AAE3
746
AAE3
662
AAE0
418
AAE0
418
AAD7
169
AAD4
849
AAD4
848
AAD2
732
8.000000%
6.000000%
4.000000%
2.000000%
0.000000%
Lots
Indi
vidu
al V
alue
_X=0.335201%
UCL=1.461407%
LCL=-0.791005%
NMT=1.0%
1
I Chart of Major DefectsOrencia SC 125 mg/Syringe
AAH9
194
AAH9
193
AAH7
744
AAG7
293R
AAJ5
501
AAG7
293
AAH5
397
AAJ1
246
AAH5
394
AAH7
734
AAH9
195
AAH5
393
AAH6
736
AAH5
395
AAE3
754R
AAE3
756
AAE3
754
AAE3
751
AAE3
748
AAE3
747R
AAE3
753
AAE3
747
AAG7
291
AAH2
522
AAG7
290
AAG7
289
AAG7
286
AAG9
564
AAE3
757
AAF3
932
AAE3
750
AAE3
752
AAE3
749
AAF7
195
AAD4
849R
AAF8
392
AAF8
393
AAF4
881
AAE8
730
AAF2
676
AAD2
076R
AAF0
208
AAF7
888
AAF5
108
AAE8
724
AAE8
714
AAE8
707
AAE8
686
AAE7
879
AAE7
586
AAE4
762
AAE4
762
AAE3
749
AAE3
746
AAE3
662
AAE0
418
AAE0
418
AAD7
169
AAD4
849
AAD4
848
AAD2
732
8.000000%
6.000000%
4.000000%
2.000000%
0.000000%
Lots
Indi
vidu
al V
alue
_X=0.335201%
UCL=1.461407%
LCL=-0.791005%
NMT=1.0%
1
I Chart of Major DefectsOrencia SC 125 mg/Syringe
Simple but powerful tool to reduce complaint/quality risk• Lot defect rates compared to in-process control
limits (not specs)• Further defect pareto can be explored in regions
of concern (e.g. scratch, product on stopper)• Side by side control charts used for comparing
performance of 2 different DP sites.
Statistical Alert
BMS Proprietary and Confidential 8
Example – Product Related Impurity
AAH919
3
AAH5397
AAH6736
AAF8393
AAE8730
AAE87
07
AAD2076
AAC288
5
AAB6158
AAA506
6
6.0
5.5
5.0
4.5
4.0
Dea
mid
atio
n (%
)
USL=6.0
DPDS
Orencia SC Genealogy: Deamidation
Impu
rity
Variability….. Method or Process…or Both?
To Be Continued….
CpK = 1.0
BMS Proprietary and Confidential 9
Example Process PpK Heat Map
Parameter Ppk per Manufacturing StageDrug
SubstanceFormulation Filling Capping /
Sealing100% Visual Inspection
Finished DPThawing / Pooling
Mixing
Protein concentration
Purity
pH
SEC
Moisture
Reconstitution Time
Fill Dose
Machine Speed
Plunger Pressure
Height Adjustment
Critical Defects
Major Defects
Total Defects
Process Improvement
Initiatives Focused Here
BMS Proprietary and Confidential 10
Drug Product CPV In Practice - 3 Levels
3. Quarterly Portfolio- Sr. Management Scorecard comparing Products and Drug Substance and Drug Product Sites
2. End to End Product Reviews--Cross functional team across Drug Substance, Drug Product, Analytical, Stability and Quality--Deeper Statistical Analysis applied to Hot Spots
1. Drug Product Manufacturing Site -Approximately Monthly interbatch data reviews-Voice of the Process at the Shop Floor-Site Focused with MS&T participation
Product A Product B
ACME ACME CMO
% Cpk>1.3 80%
% Cpk >1.0
RejectionRate
2/50 0/20
Yield
0
10
20
30
40
50
60
70
80
90
100
Attr
ibut
e X
BMS Proprietary and Confidential 11
End to End Product Reviews
Each Cross-Functional Product team presents data package to technical management, approximately 3-4X per year
– Drug Substance– Drug Product – Analytical and Stability
• End to End view provides powerful insights– Most CQA’s are common from DS to DP– Analyses such as DS-to-DP genealogy assessments and Assay
Variation Ratio provide insights to source of variation (Assay or Process?)
– Selective multivariate analysis used as needed
• Input into Annual Product Quality Review
BMS Proprietary and Confidential 12
DS to DP Batch Genealogy-Product Related Impurity
AAH919
3
AAH5397
AAH6736
AAF8393
AAE8730
AAE87
07
AAD2076
AAC288
5
AAB6158
AAA506
6
6.0
5.5
5.0
4.5
4.0
Dea
mid
atio
n (%
)
USL=6.0
DPDS
Orencia SC Genealogy: Deamidation
Impu
rity
1. Variability not attributable to lab. Lower purity DS results in lower purity DP2. DP mfg process contribution should be explored: shorter Time out of
Refrigeration or light exposure? 3. Consider multivariate analysis
Both DP result and Input DS result(s) plotted against DP lot number
Batch of interest
BMS Proprietary and Confidential 13
DS to DP Batch Genealogy-Potency
AAH9193
AAH539
7
AAH6736
AAF839
3
AAE873
0
AAE87
07
AAD2076
AAC288
5
AAB615
8
AAA506
6
130
120
110
100
90
80
70
Assa
y %
LSL=70
USL=130
DPDS
Orencia SC Genealogy: B7 Binding
Random pattern of DS vs. DP result suggests analytical contributionMethod precision appears to be increasing with more recent lot history
BMS Proprietary and Confidential 14
Assay Variance Ratio (AVR)
• AVR is ratio of analytical system variance (based on reference standard results) to total process variance (DS or DP results)
• Indicates contribution of assay variability to total variability
Notes: • 0.45 cut off derived on the basis that analytical variability should not contribute more than 50% of the variability compared to
the process variability.
GuidelineCpk > 1.33 & AVR < 0.45 No Action Required
Cpk > 1.33 & AVR ≥ 0.45 May need further evaluation of analytical method. Medium risk
Cpk < 1.33 & AVR < 0.45 May need further evaluation of analytical method. Medium risk
Cpk < 1.33 & AVR ≥ 0.45 Needs further evaluation of analytical method. High risk
BMS Proprietary and Confidential 15
General Takeaways
• VP-level engagement and broad data transparency down to the shop floor are critical to a successful and sustainable CPV program
• Centralized Process Analytics team, led by Statistician, helps drive end-to-end product view, and provides second level statistical support (multivariate)
• When in doubt—use time-series plots to visualize the data
• CpK/PpK “heat maps” provide a good overview of process risk, and help drive further analyses and corrective actions
• Investment in automated data capture and analysis platforms (e.g. Discoverant) Provide the following benefits– Decreased data lag– Less labor– Increased data integrity
• While automation is good, CPV can be resourced and executed with manual data capture when necesary
BMS Proprietary and Confidential 16
Acknowledgements
Susan Abu-AbsiPeter Millili Syama AdhibhattaBryan ThurnauAbraham Diaz
Questions?