Electronic Chromatogram Review at Biogen Idec: Past, Present, ad Future Lilong Huang, Sarah Yuan, Doug Cecchini, and Andre Walker

ISPE Boston Area Chapter 20Jun2013

Outline

• Introduction

• Biogen Idec Multivariate Analysis Journey

• Case Studies

• Summary

Inundata-ed

Temperature UV

VCD

pH Osmo

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Temperature

UV

VCD

pH

Osmo

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DO

This Looks Better

Multivariate Analysis: What & Why

The two variables are correlated The information is found NOT in the individual signals, but in the correlation pattern through MVA A lot of outliers are not detected unless all the variables are analysed together

Biogen Idec Advanced Process Control

• Established an infrastructure for data centralization and real time process monitoring

– Achieved full scale utilization of Disoverant and SBOL • 2003: 1st Discoverant Hierarchy & 1st Cell Culture SBOL

• 2006: SBOL on Manufacturing Floor

• 2007: 1st Purification SBOL model

• 2013: Approved patent on “Systems and Methods for Evaluating Chromatography Column Performance”, US008410928B2, 02Apr2013

• Built a culture of advanced process monitoring

– Shift Trend Review + Routine Trend Review

Centralized Data Management System

Batch Record

Bioreactor Purification. Drug

Substance Inoculation Raw Material

Data/Information

Flow

Material Flow

OPM LIMS

Interface

System allows user to

access data from any

system

Built-in analytics

provide for control

charts, Cpk analysis,

etc.

PI

Data Management System (Discoverant)

User

Drug

Product

Multivariate Plot

Contribution Plot

Univariate Plot

Multivariate Plot

Multi-variate Monitoring System - SBOL

9

Multivariate Chromatogram Analysis

• Motivation

– Chromatograms contain a lot of information that we are not utilizing

– Exploit already available continuous data • UV, conductivity, pH, pressure, volumetric flow

• Goals

– Non-subjective, quantitative method for evaluating chromatograms

• Example: Analysis of elution peak UV tracing

– MVA model using discrete parameters that describe the characteristics of the elution UV peak

10

UV

0

0.1

0.2

0.3

0.4

0.5

0.6

0 200 400 600 800 1000 1200 1400 1600 1800 2000

UV

MVA of Chromatograms Elution Peak Parameters

10% Peak Height

Alias Description

PkHt Peak Height

VolMid Volume at Peak Height

Area Area Under Curve

PkWd10 Peak Width @10% Peak Height

Asy10 Asymmetry @10% Peak Height

VSta10 Volume Start to Ascending 10% Peak Height

VStd10 Volume Start to Descending 10% Peak Height

FrntSlp Front Slope

BkSlp10 Back Slope

TailSlp Tail Slope

InflxUV Inflection UV

InflxVol Inflection Volume

Aripk Area Under Curve Inflection to Peak

Ari10a Area Under Curve Inflection to 10% Peak Height

Ari10pka Area From 10% Peak Height to Peak Height Ascending

Ari10d Area Peak to 10% Peak Height Descending

Artail Area Under Tail

LastUV Last UV

Pk80Slpa Slope 80% Peak Height to Peak Ascending

Pk80Slpd Slope Peak to 80% Peak Height Descending

Ar80Pk Area Above 80% Peak Height

11

Case Study #1: AUP

• Area under peak (AUP) of elution UV peak was proposed as an alternative to quantify protein concentration

• Ratios of AUP to total offline A280 across manufacturing sites were shown to be different

• Investigation was Launched

– To understand root cause and harmonize practice

Background

• Current practice: offline sampling for TA280

• TA280 information is contained in the elution peak

– ∫A280 d V = TA280

– Concept has been used in the past to assess incorrect A280 sampling deviations

– Assumption: A280 signal is proportional to protein concentration

Volume

Inconsistent Ratios X-Site (Before)

1: Data from site A per “As Found” linearization curve

3. Data from site B – Linearization curve normal

Root Cause - Linearization Curve Not Linear

• “As Found” curve was not linear • “As Left” curve returned to linear after replacing

interference filter

Consistent AUP/TA280 X-Site (After)

1. Data from site A per “As Found” linearization curve

2. Data from site A per “As Left” linearization curve 3. Data from site B – Linearization curve normal

Lessons Learned

• Area under peak of elution could add significant value to real time chromatography performance monitoring

• Question: Is UV representative of protein concentration?

• Answer: Correct linearization table is critical to UV meter performance and success of Chromatogram analysis (including AUP)

17

Case Study #2 – Elution UV Chromatogram Analysis

• Residual host cell protein was showing a gradual upward trend in the drug substance

• AIEX step (column 2) was suspected root cause

• Investigation was launched

– Started by building a multivariate process model (approximately 60 variables covering cell culture and purification)

18

AIEX PLS Model to Predict HCP

1

2

3

4

5

1 2 3 4 5

YV

arP

S(H

CP

)

YPredPS[2](HCP)

HCP Model2.M10 (PLS), HCP Model, PS-HCP Model2

YPredPS[Last comp.](HCP)/YVarPS(HCP)

RMSEP = ---

y=0.9141*x+0.1952R2=0.8139

SIMCA-P+ 11.5 - 5/5/2008 3:48:16 PM

Q2=0.73

• Eight chromatogram parameters predicts well HCP level in the Drug Substance

Further correlation analysis indicated: Chromatogram shapes changed as resin aged

Confirmation via Lab Studies: Change in Chromatogram Shape due to Resin Age

Resin Wash (%) Recov (%) HCP Agg

Average 3.5 79.4 48 0.9 New

S.D. 0.8 4.4 8 0.1

Average 12.1 70.5 201 1.9 Aged

S.D. 0.3 2.2 29 0.1

Aged

Resin

New

Resin

Wash 1 Wash 2 Elution Strip

Chromatogram MVA Summary

• Chromatogram MVA enabled successful root cause investigation of the HCP upward shift

– Multivariate analysis of cell culture and chromatogram parameters indicated that chromatogram parameters were strongly correlated with HCP

– Further correlation analysis indicated that resin age contributed to the chromatogram shape shift

– Lab studies confirmed that resin age contributed to the column performance shift and the gradual upward trend of HCP

21

Summary and Conclusions

• Biogen Idec’s E-Chrom Review

– Our Goal is to develop non-subjective, quantitative method for evaluating chromatograms

• Data Accuracy is Key

– Adequate instrument calibration/maintenance ensures accurate data and successful electronic chromatogram analysis

• Multivariate Chromatogram Review

– More objective and quantitative than visual inspection

– Relates chromatogram characteristics to column performance and product quality attributes

Future of Chromatogram Review at Biogen Idec

Au

tom

ate

d

Re

al T

ime

C

hro

mat

ogr

am

An

alys

is AUP Alternative for offline total A280

measurement (enable real time yield estimation)

TA Real time evaluation of column packing performance

MVA 1) Quantitative E-Chrom review

(remove subjectivity) 2) Real time product quality

prediction 3) Real time column life cycle

validation

DEV Data

MFG Data

SBOL Operational Consistency

Challenges: Equipment design, Instrument, and interaction of solution to resin

Acknowledgement

• Doug Cecchini • Joydeep Ganguly • Robert Genduso • Ben Gilbert • John Pieracci • Jeff Simeone • Jorg Thommes • Andre Walker • Sarah Yuan

24

Excess slides

UV Absorbance

UV LIGHT

SOURCE

REFERENCE

DETECTOR

AND FILTER

MEASUREMENT

DETECTOR

AND FILTERPROCESS

STREAM

WINDOWS

MVA Principal Component Analysis

Variables Scores

N

1 X1 X2 X3 X4 t1-1 t2-1

2 X1 X2 X3 X4 t1-2 t2-2

3 X1 X2 X3 X4 t1-3 t2-3

4 X1 X2 X3 X4 t1-4 t2-4

5 X1 X2 X3 X4 t1-5 t2-5

6 X1 X2 X3 X4 t1-6 t2-6

7 X1 X2 X3 X4 t1-7 t2-7

8 X1 X2 X3 X4 t1-8 t2-8

9 X1 X2 X3 X4 t1-9 t2-9

10 X1 X2 X3 X4 t1-10 t2-10

Weight

P1-1 P1-2 P1-3 P1-4

P2-1 P2-2 P2-3 P1-4

SIMCA-P+ (Umetrics)

Var 3

Var 2

Var 1

Project data onto two dimensional plane

PC 2

Fit 2nd Principal components (least squares)

Data is scaled and centered

Plotted in K dimensional space

Fit 1st Principal components (least squares)

PC 1

27

Score Plot (t1 vs t2)

- 6

- 4

- 2

0

2

4

6

- 9 - 8 - 7 - 6 - 5 - 4 - 3 - 2 - 1 0 1 2 3 4 5 6 7 8 9

t[2

]

t[1]

HBuNew.M1 (PC), PC1, Work set

Scores: t[1]/t[2]

Ellipse: Hotelling T2 (0.05)

1 2

3 4 5

6 7 8

9 10 11

12 13 14 15

16

17 18 19 20 21 22 23 24 25 26 27 28 29

30 31 32 33

34

35 36 37 38

39 40

41 42 43

44

45

46

47

48

49 50

51

52 53 54 55

56

57 58 59

60 61 62 63 64

65 66 67 68 69

70 71 72

73 74 75 76

77 78 79

80 81

82

83

84 85 86 87 88

89

90 91

92 93 94 95 96

97 98

99

100

101 102 103

104 105 106

107 108 109 110 111

112 113 114

115 116

117 118 119

120 121 122 123

124 125 126

127 128

129

130

131 132

133

134 135

136 137 138

139

140 141 142

Simca-P 7.0 by Umetri AB 2000-03-30 12:07

95 % Confidence

limit

- 0 . 2 0

- 0 . 1 0

0 . 0 0

0 . 1 0

0 . 2 0

0 . 3 0

- 0 . 2 0 - 0 . 1 0 0 . 0 0 0 . 1 0 0 . 2 0

p[2

]

p[1]

HBuNew.M1 (PC), PC1, Work set

Loadings: p[1]/p[2]

90Feed

90Rflx 90OH

90DF

90RR

90SSto57

90SSF

90BT

90t20T

90t30T 90TT

57H2 57H2toF

57L

57KOPL

57FeedP

57FeedT

183NO3F

183NO3to

183CircF 183L 183Vol

183VF 183VNO3r

183T 35Rflx

35OH 35RR

35TP

35t10T

35BT

35t30T 35T50T

35TT

35t20T

33Resto3

33OH

33Rflx

33DF

33RR

33STM

33SpecF

33SSF

33BL 33BT

33t31T 33t41T 33t60T Assayo%

iHBu

Simca-P 7.0 by Umetri AB 2000-03-30 12:17