strategies for monitoring and troubleshooting biopharmaceutical

Strategies for Monitoring and Troubleshooting Biopharmaceutical

Manufacturing Operations

Jack Priorjohn.prior@genzyme.comDirector, Manufacturing Technical SupportGenzyme Corporation

Strategies for Monitoring and Troubleshooting Biopharmaceutical Manufacturing Operations

OUTLINEAssessing the Data Bottleneck for a ProcessStatistical Process Control Case StudyChromatography Troubleshooting ApproachesFive Keys to Successful Process Data Systems

Assessing the Data Bottleneck for a Process

Not Measured

Not Recorded

Process Data

Not AccessibleNot Acted On

Not Analyzed

Not Trusted

Process Understanding and Improvement

PhysicalBarriers

OrganizationalBarriers

What hinders improvement/troubleshooting most?

Process Troubleshooting Overview

Process and Data Investigation

MFG Deviation

Specification or IPC Violation

SPC Alert

Lot Disposition

Preventative Actions

Process Insight

Outlier Detection

Resolution

Process and Data Investigation

MFG Deviation

Specification or IPC Violation

SPC Alert

Lot Disposition

Preventative Actions

Process Insight

Outlier Detection

Resolution

How to Monitor Large Quantities of Data? ->Statistical Process Control Rules

Run Date

Var

iabl

e

µ -3σ

µ -2σ

µ -1σ

µ

µ +1σ

µ +2σ

µ +3σ

0.135% 2.14% 13.6% 34.1% 34.1% 13.6% 2.14% 0.135%

Western Electric Rules

1 outside 3 σ

2 out of 3 outside 2 σ

4 out of 5 outside 1 σ

8 or more same side of µ

SPC Case Study: Purification Column Recovery

Most data fall within formal alert limits

60

70

80

90

100

110

120

130

140

Date

Rec

over

y

SPC Case Study: Purification Column Recovery

60

70

80

90

100

110

120

130

140

Date

Rec

over

y Most data fell within formal alert limitsLow recovery triggered formal investigation

SPC Case Study: Purification Column Recovery

60

70

80

90

100

110

120

130

140

Date

Rec

over

y Most data fell within formal alert limitsLow recovery triggered formal investigationSPC Alert ( 8 < mean ) visible 10 runs earlier~10% of each batch went to drain for 20 batchesAllston Automated SPC System:

Immediate e-mail notificationsHyperlinked trends & lot trees

Chromatography System Overview

Pump

Column

Flowmeter

F UVpH TCond P

pH TCond P

Bubble Trap Bypass Valve

Level Sensors Bubble Trap

Vent Valve

Vent Filter

DATA • Data entry• Assay• Sampling• Sensor• Assumptions

What Can Go Wrong in Chromatography?

60 cm Chromatography Column, Skid, and Piping

EQUIPMENT • Control system• Valve leaks• Bed channeling• Bed compression• Other…

PROCESS &

MATERIALS• Mixing• Material stability• Precipitation• Parameter sensitivity• Procedural gaps• Operator errors• Raw material issues

Chromatography Investigative Options

Equipment Checks

Assay Checks Experiments

·Pressure check system·Examine valves·Check/Recalibrate sensors·Test HETP/asymmetry

·Review raw QC data·Trend assay control·Re-assay retain·Resample from process

·Buffer/Raw material testing·Scale-down experiments·Stability studies·Resin life/cleaning studies

Data Compilation·Paper batch records·Chart traces ·MRP (lot tracking & usage)·Raw material attributes

Is Data Consistent with Adjacent Operations and Batches?

Component Balances Across Adjacent

Process Operations

Historical Online Profile

Comparison

Internal Consistency

Checks

Historical Runs

Adjacent Operations Column

B

Column A

Column C

product recoveryprotein recoveryvolume balance

pHUV

pressure flow rate

conductivitytemperature

Is Data Internally Consistent?

0 10 20 30 400

5

10

15

20

25

30

35

40

y = 0.97x

R2 = 0.99

AUFC

(L)

0 50 100 150 2000

20

40

60

80

100

120

140

y = 0.78x

R2 = 0.74

Measured Total Protein (mg)

AU

C

Component Balances Across Adjacent

Process Operations

Historical Online Profile

Comparison

Internal Consistency

Checks

Elution ProfileAttributes

Total Protein vs. Area under curve

Total Flow vs. Volume

?

Systematically Choosing Investigative Steps…

SPC Alert: Unusually Low Product Recovery

Protein recovery also low?

Preceding column product recovery high?

Online data available?

YESNO

Request retest of load

YES NO

Following column product recovery high?

YES NO

Protein accounted for in

load/wash?

YES NO

Request retest of eluate

Perform experimental investigation

Check equipment

Request resample

Initial Assessment Activities

YES NO

Dig for more data

??

? ?

Allston Landing Process Data Flows

ProcessOperations

UtilitiesSIP/CIP

Media PrepBuffer PrepCell Culture

MicrofiltrationChromatographyLyophilization

Control SystemsRosemount RS3Intellution FIX

AB PLCDelta V

Manual DataQC Results

Monitoring Logs

Data HistoriansAIM

Rapid Pharma

MRPLIMS

MS Access

Manual AnalysisExcelJMP

MATLABAutomated Analysis

MATLAB Web Trends/Reports

SPC Alerts

Five Keys to Successful Data-Driven Process Improvement

#1: Create broad awareness of how the process is running

Five Keys to Successful Data-Driven Process Improvement

#2: Ensure manual data entry has both immediate and long-term benefits.

#3: Exploit Metcalfe’s Law: the value of a network grows by the square of the number of its users

Five Keys to Successful Data-Driven Process Improvement

#4: Lower barriers (activation energy) to explore ideas and confirm theories

Five Keys to Successful Data-Driven Process Improvement

#5: Create efficient cross-functional teams to drive and close-out investigation and improvement initiatives

Five Keys to Successful Data-Driven Process Improvement