drug product continuous process verification – a case study

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


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


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


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.


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


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


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


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


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


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


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


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


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


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


Acknowledgements

Susan Abu-AbsiPeter Millili Syama AdhibhattaBryan ThurnauAbraham Diaz

Questions?

drug product continuous process verification – a case study

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