Quality by Design Approach in Qyy gpp s3. Mock Quality Overall Summary (QOS) - PMDA 14 Mock Quality Overall Summary (QOS) ... PMDA . 8 QbD and EMEA

1

Challenges in Global Product DevelopmentBar-Ilan University, April 2nd 2009

Quality by Design Approach in

1

Q y y g ppBiopharmaceutical Products

Dan Kenett (Teva), Ron Kenett (KPA)

OUTLINE

Background on Biopharmaceuticals

The Regulatory QbD status of Biopharmaceuticals

The QbD framework for the Biopharmaceuticals

2

2

OUTLINE




3

Biotech Process

Upstream Downstream

1. Develop host cell2. Establish a cell bank3. Protein production (Upstream)

4

http://www.ptemag.com/pharmtecheurope/Biopharmaceuticals/The-development-of-PAT-in-biotech-manufacturing/ArticleStandard/Article/detail/391530?contextCategoryId=37828&ref=25

p ( p )4. Purification (Downstream)5. API 6. DP

3

Biotech Process

5

0.3 ml BioreactorSimcell

Complexity of Biopharmaceutical

Small molecule drugSmall molecule drug

6

4

Biopharmaceuticals: 14% of Drug market

14%

7

IMS Health. IMS National Sales Perspectives:Retail and Non-Retail Combined Purchases,January–December 2006 (2007)

What is a Biosimilar?

It is a Biopharmaceutical which is similar to a Biological /Biopharmaceutical drug product that has already been authorized for marketing

The active drug substance of a Biosimilar is similar to the one of the Biological/ Biopharmaceutical reference (comparator)

8

Used at the same dose for the same indication.

5

Biosimilar development

9

OUTLINE




10

6

FDA Initiatives: A Quality Timeline

2004 2005 2006 2007

11

= FDA Workshops

FDA: Pilot for large Biomolecules

FDA promotes QbD for Biotech Therapies

The pilot should provide information that can help the FDA implement a QbD, risk-based approachfor complex products and to develop further guidance for industry

• CMC information in an expanded change protocol thatdescribes the implementation of QbD and risk management approaches for large molecules

• CDER's Office of Biotechnology Products (OBP)

12 http://www.fda.gov/ohrms/dockets/98fr/fda-2008-n-0355-n.pdf

• 5 original biologic license applications or new drug applications and 10 supplements

• Start 2008 , over 2 years

7

Case studies by Conformia Software

1. Mock Small Molecule Acetriptan (ACE) - 2008To exemplify how ICH Q8/Q9/Q10 might be applied to p y g ppPharmaceutical development programQbD CMC module available at http://www.conformia.com/Collaboration with Abbot, AstraZeneca, Eli Lilly, GlaxoSmithKline

2 Mock Monoclonal antibody - Initiated July 2008

13

2. Mock Monoclonal antibody Initiated July 2008CMC working group of 7 leading Biotech companies (Amgen, Genentech, Abbot Bio MedImmune, GlaxoSmithKline Bio, Eli Lilly , Pfizer Bio)

Other QbD fictitious case studies

• Examplain P2 (NCE) and Mockestuzumab S2 (Biotech) – EFPIA

• Analytical methods – EFPIA and PhRMA

• Product quality lifecycle implementation (PQLI) – ISPE

• Mock Quality Overall Summary (QOS) - PMDA

14

Mock Quality Overall Summary (QOS) - PMDA

EFPIA http://www.efpia.org/Content/Default.asp?PageID=450PhRMA http://www.phrma.org/ISPE http://www.ispe.org/PMDA http://www.pmda.go.jp/english/index.html

8

QbD and EMEA

From a presentation by Luigetti (EMEA) at Siena 2008:

• QbD approach is supported by the regulatory community in EU

• QbD implementation in the EU is ongoing

• The Design Space concept is included in the EU legislation

15 http://www.emea.europa.eu/Inspections/docs/Presentations/RL-QbDSiena081006.pdf

• The PAT team is the key for implementation of QbD in the EU

QbD and EMEA

16

9

QbD and Biosimilars

Pioneering Biosimilars ,Dr. Jorg Windisch 2007

17

Innovator’s “protective” arguments

18Robert Garnick Former SVP Regulatory, Quality, Compliance Genentech, Inc., November 2008

10

QbD and BiosimilarsFeature Advantages Challenges

TPP Available at the beginning of the project (Innovator product)

CQA Identification of CQA by risk assessment can be achieved due to prior product knowledge

Complexity of biotech products

Product Design space Based partially on commercially available Innovator’s lots

Need for new systematic “knowledge culture”Use of risk assessment (FMEA) and statistical (multivariate analysis, DOE) toolsNew training requirements

Process monitoring and control Fewer problems in manufacturingBetter design of productsCost reduction due to lower waste

Investment in PAT technologyQuality controls shifted upstream

Product Specifications Based on desired product performance rather than on only

19

p ylimited batch data available at time of registration

Regulatory Reduction in manufacturing supplements for post-market changesAllows implementation of new technologyEnables continous improvements in products and manufacturing

OUTLINE




20

11

Scheme for implementing QbD to a Biopharmaceutical Product

21

Anurag S Rathore & Helen Winkle Nature Biotechnology volume 27 number 1 january 2009

mAb case study – Wyeth Biopharma

N=10-20 S.S. lots

N=20-40 S.S. lots

N=120-140 S.S.N=3-5 F.S.

22

Godavarti R. PDA workshop, Bethesda 2007

12


Dosage form and route of administrationDosage form strengthTherapeutic moiety release or deliveryPharmacokinetic characteristicsDrug product quality criteria (e.g. sterility, purity)

23


TPP of a Drug (ACE)

24

13


Product-related variantsProcess-related impuritiesProduct composition-strengthAventitious agents

25


CQA at CASSS CMC Strategy Forum, Bethseda 2008

“Critical quality attributes are the foundation of Quality by Design,” said Genentech’s Ron Taticek

Adapted risk assesment tools- Occurrence and detectability OUT- Impact and severity on safety and efficacy INCritical, Non Critical and Key Quality attributes categoriesCriticality of attribute linked to Process capability (control)?CQAs requires a lifecycle approach that evolves from development to commercial stages

26

Structure function relationship to evaluate potential impact (e.g. acidic forms , galactosylation) of CQA, confirm by bioassay

14


Specifications for:In process, drug substance and drug product attributes

Acceptable ranges for the key and critical process

parameters

27


Design Space

28

15

Risk identification

Which input variables have the potential to affect the analytical method performance?

Fishbone

29

Method Mapping1. Borman, P., Nethercote, P., Chatfield, M., Thompson, D., Truman, K. (2007)

Risk analysis by FMEA (Failure Mode and Effects Analysis)

30 P. Nethercote, GSK

16

1. Process mapping2. Preliminary Hazard Analysis (PHA)3. Hazard Analysis of Critical Control Points (HACCP)

Risk Assessment Tools

4. Hazard Operability Analysis (HAZOP)5. Fault tree analysis (FTA)6. Failure Mode Effects Analysis (FMEA)7. Failure Mode, Effects and Criticality Analysis (FMECA)8. Risk Ranking and Filtering9. Informal Risk Management

31

10. Taguchi, variation risk management method11. Ishikawa (Fishbone) Diagram12. IPO (Input Process Output)

In use for Biopharmaceuticals

Selection of Operating Parameters by RPN for Process characterization

32


17

Risk Evaluation output (ACE case study)

33

Design Space based on DOE at the heart of QbD

34

Illustration of outcome of an experimental study examining performance of a crystallization step in a pharmaceutical process using A) univariate experimentation and B) design of experiments

18

The Design of Experiments Strategy

ScopingScoping ScreeningScreening OptimizingOptimizing RobustnessRobustness

35

Initial assessment

Fractionaldesigns

Responsesurfaces

Robustdesigns

Process knowledge ProcessConfidence

© KPA Ltd., all rights reserved

DOE case study

Optimisation of an HPLC method used to determine eight dabsyl derivatives

36

Romero R., Gasquez, D., Sanshez, M., Rodriguez, L. and Bagur, M. (2002), “A Geometric Approach to Robustness testing in Analytical HPLC”, LC GC North America, 20, pp. 72-80.

19

Factors and Levels

VariationUpper level (1)Lower level (-1)Nominal ValueFactor

-201Gradient profile

5423840Column Temp (C)

10443640Buffer Conc. (mM)

45.24.85Mobile-phase buffer pH

1451441446Detection wavelength (nm)

37

80.250.210.23Triethylamine (%)

510.59.510Dimethylformamide (%)

The Experimental Data

Buf Conc Buf pH Det Wave Trie perc Dim Perc1 1 1 1 1

peakArea peakHeight tRmin unad tRmin Res2365197 221351 18.331 1.1121 1.624

The experimental array The results

1 1 1 1 1-1 -1 1 -1 -11 1 -1 -1 -1-1 -1 -1 1 11 -1 -1 1 -1-1 1 -1 -1 11 -1 1 -1 1-1 1 1 1 -10 0 0 0 00 0 0 0 0

2375642 226029 18.370 1.1108 1.6732304629 226136 18.671 1.1087 1.7452329880 225052 18.524 1.1106 1.6412378082 221835 18.506 1.1101 1.6932383722 224268 18.561 1.1101 1.7362446272 234957 18.548 1.1132 1.7832384194 234699 18.853 1.1071 1.7872356666 221249 18.648 1.1086 1.6532311120 218445 18.639 1.1092 1.6892311696 219921 18 667 1 1088 1 733

38

0 0 0 0 0 2311696 219921 18.667 1.1088 1.733

20

Resolution Main Effects

Main Effects Plot (data means) for ResCenterpoint

1-1 1-1 1-1 1-1 1-1 1-1 1-1

1.75

1.73

1.71

1.69

Res

39

Dim PercTrie percDet WaveBuf pHBuf ConcCol TempGradient1.67

Interaction Plot

1-11-11-11-11-11-1

1.751.701.65

1.751 70

Gradient

Col Temp1

1-1

Interaction Plot (data means) for ResCenterpoint

No interactions

Interactions

1.701.65

1.751.701.65

1.751.701.65

1.751.701.65

1.751.701.65

Buf Conc

Buf pH

Det Wave

Trie perc

Dim Perc

1-1

1-1

1-1

1-1

-1No interactions

40

Dim Perc

Res= Y + a x Gradient + b x Col. Temp. + c x Buff. Conc. + d x Det. Wave+ e x interactions

Output: Mathematical function relating response to process parameters

21

Contour Plot of Resolution

Contour Plot of Res vs Col Temp, Det WaveContour Plot of Res vs Gradient, Col Temp

No Interactions between gradientand column temp.

Interactions between column temp. and detector wavelength

Col T

emp

1.00.50.0-0.5-1.0

1.0

0.5

0.0

-0.5

-1.0

> – – – – < 1.65

1.65 1.681.68 1.711.71 1.741.74 1.77

1.77

Res

Grad

ient

1.00.50.0-0.5-1.0

1.0

0.5

0.0

-0.5

-1.0

> – – – – < 1.65

1.65 1.681.68 1.711.71 1.741.74 1.77

1.77

Res

41

Det WaveCol Temp

Hi

Lo0.85585D

Optimal

Cur

peakArea

-1.0

1.0

-1.0

1.0

-1.0

1.0

-1.0

1.0

-1.0

1.0

-1.0

1.0Col Temp Buf Conc Buf pH Det Wave Trie perGradient

-1.0 -1.0 1.0 -1.0 1.0 -1.0

OptimizerControlable parameters

MinimumtRmin

d = 1.0000

MaximumpeakHeig

d = 1.0000

MaximumpeakArea

y = 234957.0

y = 2446272.0

lity

Attr

ibut

es

42

d = 0.24533

Minimumunad tRm

d = 0.84000

u

y = 1.1132

y = 18.5480

Qua

22

Optimizer

Response OptimizationParameters

Goal Lower Target Upper Weight ImportpeakArea Maximum 2300000 2400000 2400000 1 3peakHeight Maximum 220000 230000 230000 1 3p gtRmin Minimum 19 19 19 1 1unad tRmin Minimum 1 1 1 1 1Res Maximum 2 2 2 1 5Global SolutionGradient = -1Col Temp = -1Buf Conc = 1Buf pH = -1Det Wave = 1Trie perc = -1Dim Perc = 1

43

Dim Perc = 1Predicted ResponsespeakArea = 2446272, desirability = 1.00000peakHeight = 234957, desirability = 1.00000tRmin = 19, desirability = 0.84000unad tRmin = 1, desirability = 0.24533Res = 2, desirability = 0.91500Composite Desirability = 0.85585

The Design Space of the HPLC method:Reaction Temperature and pH Interactions

DESIGN-EXPERT Plot

Th b b llActual Factors:

X = pH

Y = temp

Actual Constants:

gl-7-aca = 45.00

enzyme = 7.000.000

0.240

0.479

0.719

0.959

Des

irabi

lity

The baseball hat

44 7.00

7.37

7.75

8.12

8.50

10.00

13.75

17.50

21.25

25.00

pH temp

23

Design Space

Surface Plot Contour Plot

45

ICH Q8 (R)

DOE for a Downstream Unit Process Characterization

Results from the process characterization studies performed at small scale.(a) Screening study involving examination of nineoperational parameters for a chromatographystep in a biotech process. Six of the operationalparameters were identified for further study viaDOE: temperature buffer A pH buffer B pHDOE: temperature, buffer A pH, buffer B pH,product loading, flow rate and bed height. Std,standard deviation. (b) Results from the DOEstudy showing the effect of different operationalparameters on % purity for a chromatography stepin a biotech process. Temperature and buffer ApH were found to have an effect that is significantboth statistically and in magnitude. The greenellipses indicate parameters that had an effectof significant magnitude (>20% for recoveryand >5% for purity). The red ellipses indicateparameters that had a statistically significantimpact (P < 0.05). Buffer B pH * load ratedenotes a first order interaction between thesetwo parameters. Term “t-ratio” stands for the testof significance and compares the difference withthe standard error of the difference

46


24

Process Design Space

47


Clinical Design spaceFor Innovator (from clinical lots)For Biosimilar (from Innovator lots)

Product Design space

1 Lot 5 Lots

Product Design space

Process Design space

48Anurag S Rathore & Helen Winkle Nature Biotechnology volume 27 number 1 january 2009

25

49

QbD scheme for a Biotech Product

Procedural controlsIn-Process controlsLot release testingProcess monitoringCharacterization testingComparability testingStability testing

50


26

Overview of Control strategy for ACE tablets

51

The diagram shows which unit operations impact each drug product CQA, the control points, control method and the intermediate quality attributes controlled.

BEFORE

AFTER

52

27


53


Process Validation

Aim to demonstrate:1. Process delivers a product of acceptable quality if operated p p q y p

within Design Space2. Small scale/Pilot system used for the establishment of the

Design Space performs like the manufacturing scale process

Validation acceptance criteria = limits of acceptable variability

54

Validation acceptance criteria = limits of acceptable variability in product quality and process performance

28


55


Raw material control

56

Lanan M. , PDA workshop, Bethesda 2007

29

PAT

57

PAT in Upstream

58

A biotech fermentor with a process MS and process FT-NIR attached. Each technique can be used to monitor

different aspects of the process as it progresses.

Davies B., 2006, Pharmaceutical Technology Europe, 18,11

30

PAT in Upstream

59


PAT in Upstream

60


31

PAT in Downstream

61

Rathore et al 2008 Biotechnol. Bioeng. 100, 306-16

PAT in Downstream

62

Rathmore 2009 Nature Biotechnology

32


63


Regulatory Filings

Includes:Process Design SpaceO i f D P dOperating space for Drug ProductRefined Product design spaceDescription of control strategyProcess Validation Plan for Process Monitoring

64

Plan for Process MonitoringComparability or Expanded Change Protocol (to allow future flexibility)

33

What is QbT?

Quality by Testing(Present)

Quality by Design

65

Yu, L. (2008), " Pharmaceutical Quality by Design: Product and Process Development, Understanding, and Control", Pharmaceutical Research, 25, 4, pp. 781-791.

Drug product Specifications Issues

Under QbTProduct specs defined on the basis of limited number of batches available at time of registrationregistrationRisk of setting inappropiate Specs Product specs are the principal means of controlChallenge for comparability (following manufacturing change)

Under QbDBased on desired product performance

66

For Innovative products complex challengeFor Biosimilar products more straightforward (availability of Innovator lots which

are approved for clinical use)

34

SUMMARY

The Biotech industry and the FDA, EMEA are joining efforts to promote QbD for Biotech products

The QbD approach for Biotech is becoming more technically feasile due to:

The advent of technologies compatible with PAT and DOE for Upstream and Downstream operations.The availability of statistical software for DOE with

67

multivariate analysis and for the determination of Design spaceSoftware for Process monitoring and control

SUMMARY (continuation)The benefits of QbD for the Biotech industry are tangible and significant

Nevertheless the implementation of QbD in the biotech sector will require a major mindset change, investment in PAT technology, training in statistics, risk management and to overcome apprehension from change at all levels.

The complexity of Biotech products and the difficulty of clearly linking CQA to clinical outcome, will slow the QbD implementation

68

The QbD approach may be more straightforward to apply in the Biotech for Biosimilar /Follow on biologics due to the extensive clinical and product knowledge available with the Innovative products.

35

References1. Burnett Gail, Genentech,“Quality by Design in Biotechnology”, BioProcess International analytical and Quality summit, Boston 20082. Moheb Nasr, FDA, “Quality by Design- A modern system approach to Pharmaceutical Development and manufacturing- FDA perspective “ FDA

workshop, North Betsheda 20073. Shacter Emily, FDA, “FDA perspectives on the analytical characterization of Follow-on Protein Products” CASSS Mass Spec 20084. Luigetti Riccardo, EMEA, “QbD a global Implementation Perspective- The EU Perspective. “The Siena conference on product and process

optimization 20085. Windisch Jorg, Sandoz “Pioneering Biosimilars- Perspective of a company that develops Biopharmaceuticals as well as Biosimilars” Vienna

20076. Rathore Anurag, Amgen, “Quality by design for Biopharmaceuticals” Nature Biotechnology ,2009, 27, 26-347. ACE case study, Conformia software, http://www.conformia.com/8. Rathmore Anurag et al., Amgen, “Quality by Design: Industrial case studies on defining and implementing Design space for Pharmaceutical

Processes – Part 1” BioPharm International December 2008, 37-479. Rathore Anurag et al., Amgen,”Case study and application of PAT towards bioprocessing” Biotechnol. Bioeng. 2008,100,306-1610. Rathmore Anurag et al. Amgen, “Quality by Design: Industrial case studies on defining and implementing Design space for Pharmaceutical

Processes – Part 1” BioPharm International January 2009, 36-4411. Ho K. ,EMEA, “Quality by Design Application and Prspectives for Biologicals”12. Levine Howard, BioProcess technology consultants “The use of critical process analysis to reduce risk and increse Biologics product quality” FIP

quality conference, London 2007.13. Davies Brian, “The development of PAT in biotech manufacturing’ Pharmaceutical technology, 2006, vol 18, issue 1114. Q8(R) Pharmaceutical Development, ICH guideline , http://www.ich.org/cache/compo/276-254-1.html15. Q9 Quality risk management, ICH guideline, http://www.ich.org/cache/compo/276-254-1.html16. Q10 Pharmaceutical quality System, ICH guideline, http://www.ich.org/cache/compo/276-254-1.html17. Yu, L. " Pharmaceutical Quality by Design: Product and Process Development, Understanding, and Control", Pharmaceutical Research, 25, 4,

pp. 781-791, 2008.

69

18. Romero R., Gasquez, D., Sanshez, M., Rodriguez, L. and Bagur, M. “A Geometric Approach to Robustness testing in Analytical HPLC”, 2002 , LC GC North America, 20, pp. 72-80

19. Godavarti R. Wyeth, PDA workshop, Bethesda 200720. Lanan M. , Wyeth, PDA workshop, Bethesda 200721. Borman, P., Nethercote, P., Chatfield, M., Thompson, D., Truman, K. (2007), "The Application of Quality by Design to Analytical Methods",

Pharmaceutical Technology.

70

Documents

Quality by Design Approach in Qyy gpp s3. Mock Quality Overall Summary (QOS) - PMDA 14 Mock Quality Overall Summary (QOS) ... PMDA . 8 QbD and EMEA