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

Cover all relevant quality attributes: Influence points identified across the process Checkmarks highlight where process understanding is required Same matrix used for Control Strategy CASSS CMC Strategy Forum QbD Tamas Blandl – July 19, 2010, Washington DC

Define Operating conditions Cover all relevant operational variables: steps in mapping Unit Operation Design Space Prioritize operational parameters for experimental evaluation; relevant quality attributes considered – FMEA Risk based filtering Screening studies, Interaction DOEs; relevant quality attributes studied – Evaluate main effects and interactions Generate Data Diagnostics and refinement to generate RSM equations - Data based statistical model building Analyze data Identify constraints Define operational parameter constraints based on impact to quality attributes - Design Space Operational ranges based on design space plus process performance – Simulate/confirm outcomes Define Operating conditions 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 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 CASSS CMC Strategy Forum QbD Tamas Blandl – July 19, 2010, Washington DC

Case Study: Impact of Multiple Unit Operations on Aggregate Column 1 Knowledge: Multivariate Impact: Medium Constraint: Equation 1 Viral Inactivation Knowledge: Multivariate Impact: High Constraint: Equation 2 Column 2 Knowledge: Multivariate Impact: High Constraint: Equation 3 TFF Knowledge: Univariate Impact: None Constraint: DS/DP Storage Knowledge: Univariate, Formulation robustness Impact: Low Constraint: Shelf life CASSS CMC Strategy Forum QbD Tamas Blandl – July 19, 2010, Washington DC

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 Design Space constraint: Aggregate (%) = function (pH, Temp, Protein conc, Time, load Aggregate) ≤ x% (numerical limit) Load aggregate level part of DOE CASSS CMC Strategy Forum QbD Tamas Blandl – July 19, 2010, Washington DC 8

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 output: Rate of excursions Simulation: Load aggregate at worst case Other parameters at observed values and distribution CASSS CMC Strategy Forum QbD Tamas Blandl – July 19, 2010, Washington DC 9

Assurance of quality at the boundaries of Design Space Statistical response surface models predict average response At boundary 50% of observations are out Use adjusted quality attribute limits 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 Set operational ranges based on Monte Carlo simulations at real life distribution of operational variables to predict frequency of excursions CASSS CMC Strategy Forum QbD Tamas Blandl – July 19, 2010, Washington DC

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

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 CASSS CMC Strategy Forum QbD Tamas Blandl – July 19, 2010, Washington DC 12

Storage/Stability Effect on Aggregate Univariate Approach: 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 CASSS CMC Strategy Forum QbD Tamas Blandl – July 19, 2010, Washington DC 13

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 CASSS CMC Strategy Forum QbD Tamas Blandl – July 19, 2010, Washington DC 14

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

Risk management throughout process design lifecycle Occurrence Matrix: 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 CASSS CMC Strategy Forum QbD Tamas Blandl – July 19, 2010, Washington DC 16

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

Capture process knowledge in risk matrix Updated occurrence scores after Process Characterization High RPN score: Opportunity to increase process capability Opportunity to enhance testing CASSS CMC Strategy Forum QbD Tamas Blandl – July 19, 2010, Washington DC 18

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

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