Design Space: Case Study for a Downstream Process Post Approval Tamas Blandl Amgen Process...

Design Space: Case Study for a Downstream Process Post Approval

Tamas Blandl

Amgen Process Development

Topics to be covered

• Sources of process knowledge: univariate and multivariate data

• Unit operation interactions

• Manufacturing data in model refinement

• Confidence level at design space boundaries

• Non-critical parameters

• How design space information is used in risk management

2CASSS CMC Strategy Forum QbD Tamas Blandl – July 19, 2010, Washington DC

Ideal state: Comprehensive process understanding

• Design space is comprehensive process understanding

• Product Quality Impact – QbD• Cover all relevant quality attributes• Cover all relevant operational variables

• Business impact • Cover process performance (titer, cell viability, yield, filterability)


Cover all relevant quality attributes: Influence points identified across the process


• Checkmarks highlight where process understanding is required

• Same matrix used for Control Strategy

Cover all relevant operational variables: steps in mapping Unit Operation Design Space

• Prioritize operational parameters for experimental evaluation; relevant quality attributes considered – FMEA

Generate Data

Risk based filtering

Analyze data

Identify constraints

Define Operatingconditions

• Screening studies, Interaction DOEs; relevant quality attributes studied – Evaluate main effects and interactions

• Diagnostics and refinement to generate RSM equations - Data based statistical model building

• Define operational parameter constraints based on impact to quality attributes - Design Space

• Operational ranges based on design space plus process performance – Simulate/confirm outcomes

CASSS CMC Strategy Forum QbD Tamas Blandl – July 19, 2010, Washington DC 5

Multiple sources of knowledge may form the basis of comprehensive process understanding


• Multivariate lab and/or pilot scale data• For unit operations with complex multi-parameter controls• Interactions between operational parameters may be reasonably expected• Quality attribute behavior can be modeled via process models

• Univariate lab and/or pilot scale data• For unit operations with limited complexity• Interactions between operational parameters are not expected• Quality attribute behavior can be modeled via process models

• Manufacturing scale process monitoring data• If sufficient run history is available to evaluate process variability• For quality attributes that are associated with facility specific microbial background levels,

such as endotoxin, bioburden, mycoplasma, etc, which can not be extrapolated from process models at lab or pilot scale

• Other molecules/processes, ie platform knowledge• Quality attribute behavior expected to be similar to prior molecules, other processes • Direct applicability of the data is confirmed

Case Study: Impact of Multiple Unit Operations on Aggregate


DS/DP Storage Knowledge: Univariate, Formulation robustnessImpact:Low Constraint:Shelf life

Column 2

Knowledge: Multivariate

Impact:High

Constraint:Equation 3

TFF

Knowledge: Univariate

Impact:None

Constraint:None

Viral InactivationKnowledge: Multivariate

Impact:High


Column 1

Knowledge: Multivariate

Impact:Medium


Viral Inactivation Unit Operation Design Space

• Column 1 pool aggregate level was part of DOE as input variable

• Multivariate constraint• Represented by multi-term equation• Term of Column 1 pool aggregate

level included


Load aggregate level part of DOE

• Design Space constraint:• Aggregate (%) = function (pH,

Temp, Protein conc, Time, load Aggregate) ≤ x% (numerical limit)

Use of manufacturing data

• Manufacturing and pilot scale data are used as additional center point replicates

• Opportunity to compare averages (center point responses) and variability (model error vs. error at mfg scale)

• Statistical treatment of scale as a variable allows adjusting trends to the manufacturing average (blocking)

• Design space models can be refined through the product lifecycle

Blocking:Trend centered on commercial scale mean


Simulation: Load aggregate at worst caseOther parameters at observed values and distribution

Simulation output: Rate of excursions

Assurance of quality at the boundaries of Design Space

• Design space equations expressed at upper/lower Individual Confidence Intervals

• Equations are adjusted to use ICI terms, ie 95%

• At boundary 95% of observations are in


0.5

1.5

2.5

Aggr

egate

s(%

)1.3

3348

5±0

.0881

21

10 15 20 25 30

20.27

Mass

Load (g/L)

Random

Uniform

Lower

Upper

10

25

-10 -5 0 5 10

-0.08

Elution

Molarity (%)

Random

Uniform

Lower

Upper

-5

5

3.9

4

4.1 4.2 4.34.1072

Elution pH

Random

Uniform

Lower

Upper

4.11

4.25

Aggregates (%)

Defect

0.0002

Rate

• Use adjusted quality attribute limits

• Set operational ranges based on Monte Carlo simulations at real life distribution of operational variables to predict frequency of excursions

• Statistical response surface models predict average response

• At boundary 50% of observations are out

Column 2 aggregate multidimensional response surface: Design space constraint

• Complex multivariate constraint:• Represented by multi-term equation• Term of Load aggregate level

included as input variable


0

0.5

1

1.5

SE

C (

%

Agg

rega

te)

0.70

2258

[0.5

3129

,0.8

9704

]

520

540

560

580

600

620

570

Equil Wash

Mol. (10%) -mM

270

280

290

300

310

320

330

300

Elution Mol

(10%) -mM

1

1.05 1.1

1.15 1.2

1.11

Conditioning

Mol (10%) - M

5.4

5.6

5.8 6

6.2

6.4

6.6

6

pH equil/

w ash

5.4

5.6

5.8 6

6.2

6.4

6.6

6

pH

Conditioning5.

4

5.8

6.2

6.6

6

pH Elution

15 17 19 21 23 25

20

Temp

15 17 19 21

18.5

Mass Load

2 3 4 5 6

4

Load Aggregate

& LPA

Mfg

Sca

le

Pilo

t Sca

le

Sm

all S

cale

Mfg Scale

Block

1 2 3

1

Block 2

Prediction Profiler

• Design Space constraint:• Aggregate (%) = function(Equil Wash Mol, Conditioning

Mol, Elution Mol, Equil Wash pH, Conditioning pH, Elution pH, Temp, Mass load, load Aggregate) ≤ x% (numerical limit)

Separation not sensitive to load aggregate

TFF aggregate impact: univariate approach resulted in no constraint

• Screening study shows small reproducible increase in aggregate • Not sensitive to operating

parameters • Concentration• TMP• Pump passage #• Conversion ratio• Temperature

• pH adjustment/titration effect

• Univariate study at center point vs. worst case conditions comparable – No constraint


Storage/Stability Effect on Aggregate Univariate Approach: Shelf Life Constraint

• Shelf life constraint• Aggregate increase observed• Univariate constraint on storage

time

• Intermediate pool holds• No/minimal Aggregate increase

observed• Will not exceed knowledge

space: maximum hold times• Select operational ranges, ie

individual hold times, based on cumulative effects


Linking of unit operations• Quality attribute behavior across the whole

process is adequately described• Separate quality attribute DSp equation for each unit

operation• QA level in intermediates included as a variable for the

next step• Any univariate effects are accounted for

• Stability• Intermediate hold• TFF

• Unit operation acceptable levels are determined considering quality attribute behavior across the whole process

• Operational ranges (OR) are selected together• Cumulative effect modeled based on conditions• Ensure OR scenario provides acceptable level• Excursions can be modeled• Future state: can build in adaptive responses

• If unit operation OR changes• Evaluate impact to downstream steps


Non-critical parameters

• Variability does not impact product quality attribute outcomes• Not part of multivariate or univariate restrictions: not part of the

design space• Comprehensive approach used to identify them as non-critical

• Risk based screening• Data based screening

• Still controlled within a range• Range based on mfg procedure/equipment tolerances• Subject to change control

• Supporting data required• Change outcome monitored


Risk management throughout process design lifecycle

• Stage 1: Checkmarks• Relevant Quality Attributes for each unit

operation are identified• Initial identification based on platform

knowledge, Process Development results, scientific principles

• Stage 2: Occurrence Scores • Scoring definitions allow assignment of scores

with limited information• Scores range medium to high

• Stage 3: Updated Occurrence Scores• As comprehensive knowledge is built, scores

are updated to reflect more detailed understanding

• Low scores are given to robust unit ops, full range of scores used

Occurrence Matrix:


Decision tree developed to assign occurrence, based on yes/no answers

• Occurrence questions:• Is the quality attribute impacted• Is there comprehensive knowledge• Are there constraints• Is the process robust

• Is the process close to the edge of failure• Is a quality attribute excursion likely• Is there a low Cpk/Ppk observed/expected

• Is there process redundancy


SME evaluation of design space or available knowledge

Capture process knowledge in risk matrix

• Updated occurrence scores after Process Characterization

• High RPN score:• Opportunity to increase process capability• Opportunity to enhance testing


Summary

• Design space is comprehensive process understanding• Knowledge basis may be

• Multivariate studies• Univariate studies• Process history analysis• Platform knowledge

• Quality attribute behavior across the whole process is adequately described

• Risk management approach used throughout process design lifecycle


Acknowledgments

• Chulani Karunatilake

• Marc Better

• Toshi Mori Bajwa

• Ruoheng Zhang

• Megumi Noguchi

• Dongmei Szeto

• Ken Hamamoto

• Xinfeng Zhang

• Andy Howe

• Duane Bonam

• Bob Kuhn

• Abe Germansderfer


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