PAT and QbD for BiopharmaAn academic perspective on the A-mAb case study

Dr. Mathieu Streefland

Assistant professor Bioprocess Engineering

Bioprocess Engineering Group WU

� Interaction bioreactor and cells� Biopharmaceuticals� Biofuels� Bio-pesticides� Healthy food� Bulk chemicals

� NEW� Medical Biotechnology

(manufacturing science)� Biorefinery (incl. pharma DSP)

Medical Biotechnology

9 Staff

2 Postdocs

~30 PhD students

The bloody origins of GMP regulation� 1902: 12 children die of tetanus contracted from contaminated diphteria vaccine

� Action: identity testing of product before release

� 1938: poisonous solvent in antibiotic sulfanilamide causes 107 deaths� Action: release testing includes test for impurities / reagents

� 1941: 300 people die after taking antibiotic sulfathiazole tainted with phenobarbital� Action: Separation of production lines, prevention of cross over and cross contamination regulation

� 1962: birth defects thousands European babies caused by thalidomide (Softenon)� Action: Enhance impurity testing, especially when enantiomeres are expected.

� 1976: many women injured/infertile caused by Dalkon Shield contraceptive device� Action: increased regulation for medical devices

� 1982: 7 deaths caused by cyanide poisoning of acetaminophen capsules� Actions: introduction of blisters for packaging and rules to prevent tampering with drugs

� 2004: FDA estimated 27,785 patients die of cardiac arrest after taking Vioxx

So, what does this tell us?� The GMP system does not assure quality; it

prevents stupidities� Risk will never be zero: things will go wrong in the

futureHowever:� The risk is manageable when the process

consistently delivers good quality product� The requires control over

� Input variability� Process variability

Product variability

Current manufacturing performance

� (Bio)pharmaceutical manufacturing currently operates at a 2-3 sigma level (FDA, 2005)

� This means 70-95% of all manufacturing batches meet the specifications; 5-30% faillure

� The ultimate goal: 6 sigma manufacturing

� 3.4 faulty productions in 1,000,000 batches

Current manufacturing excellence

Current manufacturing excellence

Long term Medium term Current

Process variation and PAT/QbD

� Process variation is reflected in product quality

� It is key to understand the sources of variance� It is key to understand the impact of variance on

quality

A process is well understood when all sources of variance are known and their impact on product quality is assessed

9

6 practical steps for QbD application

1. Determine the critical product attributes

2. Develop assays and analyses to measure critical product attributes

3. Understand the interaction between the process and the product and identify the critical process attributes that influence product quality

4. Incorporate PAT for monitoring of critical attributes during processing

5. Use DoE experimentation to explore the process design space

6. Develop a statistical model that describes the process design space

Creating a Biotech Case Study:“A-Mab”

� Based on a monoclonal antibody drug substance and drug product

� Companies shared actualmanufacturing data

� Publicly and freely available as a teaching tool for industry and agencies (Casss and ISPE websites)

Slide 11

The A-mAb process

VialShake Flasks Seed

Expansion Production Bioreactor

Harvest via centrifugation, depth filtration,

membrane filtration

Column 1 (Protein A)

Column 2 (Ion Exchange)

Viral FiltrationColumn 3 (Ion Exchange

or HIC)

Ultrafiltration/ Diafiltration

(UF/DF)

Bulk Filtration

Drug Product Processing/ Filling

Viral Inactivation

USP

DSP(Low pH)

Overall goals

� Identification of CQA’s

� Use of prior knowledge and platform tools

� Use of risk-based approach

� Use of DoE and statistic approaches� Linkage between CPP’s and CQA’s

� Approaches for design space definition� Rational approach for risk-based control strategy

Some examples: Risk based process characterizationStep 1: Ishikawa (fish-bone) diagram (brainstorm, prior knowledge)

Some examples: Risk based process characterization (2)Step 2: Rank parameters and attributes from Step 1 based on severity of

impact and control capability. Indentify interaction for DoE studiesQ u ality Attr ibute s Pro ce ss A ttr ib utes R is k M itig ation

Proc es s Pa ra m ete r in P ro duc tio n B iorea cto r

Agg

rega

te

aFuc

osyl

atio

n

Gal

acto

syla

tion

Dea

mid

atio

n

HC

P

DN

A

Pro

duct

Yie

ld

Via

bilit

y at

Har

vest

Turb

idity

at

harv

est

Ino cu lu m Via ble C el l C o nce ntr D O E

Ino cu lu m Via bi li ty Lin kag e S tud ie s

Ino cu lu m In V it ro C ell Ag e EO PC S tud y

N -1 B io rea cto r p H Lin kag e S tud ie s

N -1 B io rea cto r T em p era ture Lin kag e S tud ie s

O sm olal ity D O E

A nti fo am C o ncen tra tio n N ot R e qu ire d

N u trien t C o ncen tra tion in m e dium

D O E

M e dium s torag e tem pe ra tu re M e diu m H old Stu dies

M e dium ho ld tim e before f il trat io n

M e diu m H old Stu dies

M e dium Fi lt ration M e diu m H old Stu dies

M e dium Ag e M e diu m H old Stu dies

T im ing of Fe ed ad ditio n N ot R e qu ire d

V olum e of F ee d ad ditio n D O E

C o m po ne nt C on cen trat io n in F ee d

D O E

T im ing of glucose fe ed a dd it io n

D O E-In direc t

A mo un t o f G lucose fe d D O E-In direc t

D isso lve d O xyge n D O E

D isso lve d C a rbo n D iox ide D O E

T em p era ture D O E

p H D O E

C u lture D uration (d ays ) D O E

R e m na nt G lu co se C o nce nt ration

D O E-In direc t

Potential impact to significantly affect a process attribute such as yield or viability

Potential impact to QA with effectivecontrol of parameter or less robust control

Some examples: Risk based process characterization (3)

Examples of DoE results

3

4

5

Tite

r (g

/L)

3.74

3131

±0.0

7605

2

4

6

8

aFuc

osyl

atio

n6.

4399

33

±0.2

2694

8

24

28

32

Gal

acto

syla

tion

(%)

29.2

8939

±0.6

7458

2

4e+56e+5

8e+51e+6

HC

P (p

pm)

6955

38

±165

18.3

1500

2000

2500

DN

A (p

pm)

1935

.343

±89.

5590

8

24

28

32

CEX

% A

cidi

cV

aria

nts

27.6

6898

±0.4

8081

4

1.8

2.2

2.6

3.0

Agg

rega

tes

(%)

2.51

5119

±0.0

3524

34

34.5 35

35.5 36

35Temperature

(C)

30 40 50 60 70

50DO (%)

40 60 80 100

120

140

160

100CO2 (%)

6.6

6.7

6.8

6.9 7

7.1

6.85pH

.8 1

1.2

1.4

1.6

1.2[Medium]

(X)

360

380

400

420

440

400Osmo (mOsm)

9 10 11 12 13 14 15

12Feed (X)

.7 .8 .9 11.

11.

21.

3

1IVCC (e6cells/mL)

15 16 17 18 19

17Duration

(d)

Prediction Profiler

Strong points in the A-mAb case study

� The use of scale down models for determining the design space for the 15k L process

� Extension of the design space to include future scale up to 25k L

� Life cycle approach to validation, including PCA models to monitor shifts, trends and excursions

Lacking in the case study: biology� Correlation of macro-level parameters (pH, DO,

[glucose], VCD, etc) with product attributes (affinity, titer, glycosylation, oxidation, etc).

� OK for DSP; not for USP

� QbD can only start with building process understanding

� PAT tools need to be capable of capturing biological events� nIR, capacitance probes, fluorescence (bioview)

Available PAT tools for bioprocess monitoring

Available PAT tools for bioprocess monitoring

Bioprocess Understanding

� Integration of 4 process levels:� Process (pH, DO, T, stirrer speed, feed rate, etc)

� Cell (viability, cell cycle, apoptosis, growth rate)� Metabolism (nutrient concentrations, toxic metabolites,

metabolic modeling)� Genomics/transcriptomics (shifts in expression of

genes involved in glycosylation, protein excretion, protein folding and cell household)

� With critical product quality attributes (CQA’s)

Conclusions

� A bioprocess can only be fully understood through its biology

� The biology of the cell substrate is the link between CQA’s, CPP’s in biopharma USP

� Biopharma PAT tools for USP need to be able to capture biological events

Questions?

mathieu.streefland@wur.nl