quality by design – facilitating real time release...
TRANSCRIPT
Quality by Design – Facilitating Real Time Release (RTR) Practical Challenges and Opportunities during RTR Implementation
Carl E. Longfellow Ph.D.,Senior Director, New Product and Process Development,
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Discussion Topics
n IntroductionWhat is RTR?
n RTR – Essential ElementsPeopleScience
- Statistical Tools- Control Strategy
Quality Systems and Processes
n Challenges and Opportunitiesn Benefits
Major Takeaways
n RTR is not the goal of Quality by Design (QbD). It is a possible outcome of QbD development
n RTR is possible when there is a high level of product and process understanding, a robust control strategy (including PAT), and science and risk-based quality systems aligned with Q10
n QbD and RTR raise the bar on quality. Returning to routine sampling and testing for product release may not be possible.
Real Time Release (RTR)— Regulatory Definition
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n 2001 EMEA NOTE for GUIDANCE ON PARAMETRIC RELEASE (CPMP/QWP/3015/99)
System of Release that gives assurance that the product is of intended quality based on the information collected during the manufacturing process and on the compliance with specific GMP requirements related to parametric release
- It is therefore based on the successful validation of the manufacturing processand review of the documentation on process monitoring carried out during manufacturing to provide the desired assurance of the quality of the product
n FDA PAT GUIDANCE, September 2004 - RTR is the ability to evaluate and ensure the acceptable quality of in-process and/or final product based on process data
REAL TIME RELEASE
Science
SystemsPeople
Real Time Release – Essential Elements
ICH Q8 Pharmaceutical Development
ICH Q9 Quality Risk Management
ICH Q10 Quality Systems
Real Time Release Elements - PeopleMultidisciplinary and cross-functional teams are a key to making QbD a success
Technology
Regulatory Affairs
Quality Operations
Statistics
Formulation Development
Chemometrics
Analytical Development
Operations
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Real Time Release Elements - Science
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Real Time Release
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Quality Risk Management
Knowledge Management
Statistical Tools
n Sampling plan justificationn Estimation of acceptable coverage to demonstrate
product quality- raise the bar over USPOperational Characteristics (OC) CurveSimulations
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Statistical Tools-Development of Sampling Plansn How often do we sample and where do we sample?
- Statistical rationale for sampling (in combination with risk assessments/prior knowledge)
- Sampling of tablets during the compression unit operation for a low dose tablet (as it may be more prone to segregation) may be different than that for a high dose tablet
- Rationale for placement of PAT device in manufacturing equipment –
- Why is the NIR for blender placed in the bottom of the blender versus the top (or side) and is the sample representative of the batch?
Statistical Tools -Operating Characteristics Curves
Operating Characteristic (OC) Curves are often used to illustrate the performance of a lot acceptance test. These curves provide a way to compare the performance of different tests.
Pro
babi
lity
of L
ot A
ccep
tanc
e
A calculation relevant to the Acceptance Criterion
High probability means lots will typically be found acceptable by the test being evaluated
Steepness of the curve indicates the discrimination of the test
Thomas Pyzdek, Quality Engineering Handbook, Second Edition, Marcel Dekker Inc.
Statistical Tools -Operating Characteristics Curve for UDU Test
“Development of a content uniformity test suitable for large sample sizes” Sandell et. al., Drug Information Journal, Vol. 40, pp337-344, 2006.
Coverage is the proportion of dosage units within 85-115% LC and is considered a relevant measure of the uniformity of the batch. At 98% coverage, USP would pass the batch 90% of the time, but there is zero chance of the second plan passing the batch
Statistical ToolsSimulations – Monte Carlo Simulations
Monte Carlo Simulations* - A technique that converts uncertainties in input variables of a model into probability distributions. By combining the distributions and randomly selecting values from them, it recalculates the simulated model many times and brings out the probability of the output.
- MCS allows several inputs to be used at the same time to create the probability distribution of one or more outputs.
- Different types of probability distributions can be assigned to the inputs of the model. When the distribution is unknown, the one that represents the best fit could be chosen.
- The use of random numbers characterizes MCS as a stochastic method. The random numbers have to be independent; no correlation should exist between them.
- MCS is a sampling method that generates the output as a range instead of a fixed value and shows how likely the output value is to occur in the range.
*Sanford Bolton, Charles Bon– Pharmaceutical Statistics- Practical and Clinical Applications, Fourth Edition, Marcel Dekker,
Monte Carlo Simulations –Contour Plots for Potential Scenarios
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105
106
107W
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93 94 95 96 97 98 99 100 101 102 103 104 105 106 107
NIRmean
Indicates potential scenarios where a batch would have a high probability of passing planNote: Plus signs represents cases with probability between 6-8%, empty squares for probability between 8-10%, and solid squares for probability above 10%.
Simulations – Help provides an assessment of risk for chosen coverage
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Real Time Release Elements - Science
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Real Time Release
Science
Quality Risk Management
Knowledge Management
Control Strategy
n Control Strategy: A planned set of controls, derived from current product and process understanding, that assures process performance and product quality. The controls can include parameters and attributesrelated to drug substance and drug product materials and components, facility and equipment operating conditions, in-process controls, finished product specifications, and the associated methods and frequency of monitoring and control. (ICH Q10)
n Ensures input quality attributes and process parameters are maintained within the approved design space(s)---thus product should meet specifications without finished product testing.
n PAT is one of the key tools that enable RTRIts application should be based on a risk evaluation
What is RTR? Control Strategy example for a high dose, roller compaction process….
Particle Size Analyzer – Control of RC output within pre-established range helps control hardness
NIR –Uniformity of Blend
NIR for tablets online testing, At Line automatic tablet weight checking –Uniformity/weight control
Fette Control Loops – Weight / uniformity control
NIR –Uniformity of Blend
Robust control strategy = Increased assurance of quality = RTR
Blending Content for API and FE
0
10
20
30
40
50
60
70
80
90
100
%AP
I an
d %
FE
0 10 20 30 40 50 60 70 80 90 100 110Rotations
RSD
0
10
20
30
40
50
60
70
80
90
100
%RS
D
0 10 20 30 40 50 60 70 80 90 100 110Rotations
Particle Size Output
Compression – FT-NIR Interim ReportNIR Report for This Pull
------------------------------------------------------
Date(mm/dd/yy): 01/11/07
Time: 9:51:40
Operator: Administrator
Batch Number: XXXXX
Sample: XXX mg tablets - Pull No: x
Index FileName Id %API %FE %Target
1 B93052-01.0 xxx mg 53.48 30.69 98.7
2 B93052-01.1 xxx mg 53.79 30.83 99.2
3 B93052-01.2 xxx mg 53.77 30.87 99.2
4 B93052-01.3 xxx mg 52.75 31.63 97.3
5 B93052-01.4 xxx mg 53.58 31.24 98.9
6 B93052-01.5 xxx mg 53.72 31.01 99.1
7 B93052-01.6 xxx mg 53.91 30.73 99.5
8 B93052-01.7 xxx mg 54.11 30.99 99.8
9 B93052-01.8 xxx mg 53.72 30.86 99.1
10 B93052-01.9 xxx mg 54.86 30.25 101.2
------------------------------------------------------
Summary for API Conc (%):
Average: 99.2%
Minimum: 97.3%
Maximam: 101.2%
Std. Dev: 1.0
Note: Summary based on the actual (not rounded) individual results.
Blend Uniformity Monitoring Results
Initial blendingAverage (relative to target)
• This is the overall mean results of 124 batches (mean of last 12 rotations for each batch).
949596 979899 101 103 105 107 109
100.0%99.5%97.5%90.0%75.0%50.0%25.0%10.0%2.5%0.5%0.0%
maximum
quartilemedianquartile
minimum
108.16108.16106.66104.30102.65100.9799.0497.7495.0794.3994.39
QuantilesMeanStd DevStd Err Meanupper 95% Meanlower 95% MeanN
100.943952.60229170.2336927101.40653100.48137
124
Moments
Rel_DVS_Ave
Distributions Stage_By_Time_Num=1
Mean API Concentration 101%
Initial blendingRSD
Where( :Stage_By_Time_Num == 1)
Lower Spec LimitUpper Spec LimitSpec Target
Specification.
10.499.
Value Below LSLAbove USLTotal Outside
Portion.
0.00000.0000
% Actual
USL
-3s +3sMean
0 2 4 6 8 10 12
CPCPKCPMCPLCPU
Capability.
2.507..
2.507
Index.
2.188..
2.188
Lower CI.
2.825..
2.825
Upper CI
Below LSLAbove USLTotal Outside
Portion.
0.00000.0000
Percent.
0.00000.0000
PPM.
9.0219.021
Sigma Quality
Z BenchZ LSLZ USL
Benchmark Z..
7.522
Index
Overall, Sigma = 0.88399
Capability Analysis
DVS_RSD_12
Distributions
Where( :Stage_By_Time_Num == 1)
USL
1 2 3 4 5 6 7 8 9 10 11
100.0%99.5%97.5%90.0%75.0%50.0%25.0%10.0%2.5%0.5%0.0%
maximum
quartilemedianquartile
minimum
5.65305.65305.24095.01154.55703.92243.26242.54892.03631.83691.8369
QuantilesMeanStd DevStd Err Meanupper 95% Meanlower 95% MeanN
3.84987730.88399330.07938494.00701493.6927396
124
Moments
DVS_RSD_12
DistributionsOverall RSD results plot, the max we have so far 6%
Ppk is 2.507
Initial blendingSpectral Distance
USL
.03 .04 .05 .06 .07 .08 .09 .1 .11 .12
100.0%99.5%97.5%90.0%75.0%50.0%25.0%10.0%2.5%0.5%0.0%
maximum
quartilemedianquartile
minimum
0.112310.112310.085910.077040.066710.057740.049490.043580.034660.033760.03376
QuantilesMeanStd DevStd Err Meanupper 95% Meanlower 95% MeanN
0.05884210.01335790.00123490.06128810.0563962
117
Moments
Last_SpcDist
Distributions Dose=50, Stage_By_Time_Num=1
Lower Spec LimitUpper Spec LimitSpec Target
Specification.
0.1.
Value Below LSLAbove USLTotal Outside
Portion.
0.85470.8547
% Actual
USL
-3s +3sMean
.02 .04 .06 .08 .1 .12
CPCPKCPMCPLCPU
Capability.
1.027..
1.027
Index.
0.881..
0.881
Lower CI.
1.172..
1.172
Upper CI
Below LSLAbove USLTotal Outside
Portion.
0.10310.1031
Percent.
1030.93381030.9338
PPM.
4.5814.581
Sigma Quality
Z BenchZ LSLZ USL
Benchmark Z..
3.081
Index
Overall, Sigma = 0.01336
Capability Analysis
Last_SpcDist
Distributions Dose=50, Stage_By_Time_Num=1
Overall distance results plot
Ppk is 1.03
Final blendingAverage (relative to target)
• This is the overall mean results of 128 batches (mean of last 12 rotations for each batch).
95 96 97 98 99 101 103 105 107
100.0%99.5%97.5%90.0%75.0%50.0%25.0%10.0%2.5%0.5%0.0%
maximum
quartilemedianquartile
minimum
107.33107.33105.84104.17102.78101.0499.2797.8696.2395.8595.85
QuantilesMeanStd DevStd Err Meanupper 95% Meanlower 95% MeanN
100.933182.45229820.2167546101.3621
100.50426128
Moments
Rel_DVS_Ave
Distributions Stage_By_Time_Num=3
Mean API Concentration 101%
Final blendingRSD
Lower Spec LimitUpper Spec LimitSpec Target
Specification.
10.499.
Value Below LSLAbove USLTotal Outside
Portion.
0.00000.0000
% Actual
USL
-3s +3sMean
0 2 4 6 8 10 12
CPCPKCPMCPLCPU
Capability.
2.533..
2.533
Index.
2.216..
2.216
Lower CI.
2.849..
2.849
Upper CI
Below LSLAbove USLTotal Outside
Portion.
0.00000.0000
Percent.
0.00000.0000
PPM.
9.0979.097
Sigma Quality
Z BenchZ LSLZ USL
Benchmark Z..
7.598
Index
Overall, Sigma = 0.92433
Capability Analysis
DVS_RSD_12
Distributions Stage_By_Time_Num=3
USL
1 2 3 4 5 6 7 8 9 10 11
100.0%99.5%97.5%90.0%75.0%50.0%25.0%10.0%2.5%0.5%0.0%
maximum
quartilemedianquartile
minimum
6.59066.59065.55674.68013.99543.42552.85202.38681.65741.46991.4699
QuantilesMeanStd DevStd Err Meanupper 95% Meanlower 95% MeanN
3.47621680.92432540.08169963.63788563.3145481
128
Moments
DVS_RSD_12
Distributions Stage_By_Time_Num=3Overall RSD results plot, the max we have so far 7%
Ppk is 2.533
Final blendingSpectral Distance
USL
.05 .1 .15 .2 .25 .3 .35
100.0%99.5%97.5%90.0%75.0%50.0%25.0%10.0%2.5%0.5%0.0%
maximum
quartilemedianquartile
minimum
0.374120.374120.095740.076900.068440.058940.051330.044690.039040.036810.03681
QuantilesMeanStd DevStd Err Meanupper 95% Meanlower 95% MeanN
0.06252890.03098720.0027940.06806
0.0569979123
Moments
Last_SpcDist
Distributions Dose=50, Stage_By_Time_Num=3
Lower Spec LimitUpper Spec LimitSpec Target
Specification.
0.1.
Value Below LSLAbove USLTotal Outside
Portion.
1.62601.6260
% Actual
USL
-3s +3sMean
0 .2 .4
CPCPKCPMCPLCPU
Capability.
0.403..
0.403
Index.
0.325..
0.325
Lower CI.
0.480..
0.480
Upper CI
Below LSLAbove USLTotal Outside
Portion.
11.328411.3284
Percent.
113284.34113284.34
PPM.
2.7092.709
Sigma Quality
Z BenchZ LSLZ USL
Benchmark Z..
1.209
Index
Overall, Sigma = 0.03099
Capability Analysis
Last_SpcDist
Distributions Dose=50, Stage_By_Time_Num=3
Overall distance results plot
Ppk is only 0.403If the extreme point is removed, Ppk is 1.057
Compression Monitoring Results
Summary by Batches (Spec on average: 95.0% – 105.0%)
Lower Spec LimitUpper Spec LimitSpec Target
Specification95
105100
Value Below LSLAbove USLTotal Outside
Portion0.00000.00000.0000
% Actual
LSL USLTarget
-3s +3sMean
92 96 100 104 108
CPCPKCPMCPLCPU
Capability1.8441.6851.6652.0021.685
Index1.6471.4981.5001.7821.498
Lower CI2.0411.8721.8302.2221.872
Upper CI
Below LSLAbove USLTotal Outside
Portion0.00000.00000.0000
Percent0.00090.21410.2150
PPM7.5076.5566.555
Sigma Quality
Z BenchZ LSLZ USL
Benchmark Z5.0556.0075.056
Index
Overall, Sigma = 0.9039
Capability Analysis
Mean(Rel.DVS%)
DistributionsPPK: 1.685
LSL USLTarget
94 95 96 97 98 99100 102 104 106
100.0%99.5%97.5%90.0%75.0%50.0%25.0%10.0%2.5%0.5%0.0%
maximum
quartilemedianquartile
minimum
102.94102.94102.28101.44101.00100.4699.9399.0798.5098.1298.12
QuantilesMeanStd DevStd Err Meanupper 95% Meanlower 95% MeanN
100.429890.90390130.0695309100.56716100.29263
169
Moments
Mean(Rel.DVS%)
Distributions
Mean: 100.4%
Summary by Sampling Points (Spec on average 92.5% – 107.5%)
LSL USLTarget
9394 96 9899 101 103 105 107
100.0%99.5%97.5%90.0%75.0%50.0%25.0%10.0%2.5%0.5%0.0%
maximum
quartilemedianquartile
minimum
105.07103.81102.79101.90101.15100.4099.7599.0498.4197.9897.40
QuantilesMeanStd DevStd Err Meanupper 95% Meanlower 95% MeanN
100.462381.10431940.0212408100.50403100.42073
2703
Moments
Mean(Rel.DVS%)
Distributions
Lower Spec LimitUpper Spec LimitSpec Target
Specification92.5
107.5100
Value Below LSLAbove USLTotal Outside
Portion0.00000.00000.0000
% Actual
LSL USLTarget
-3s +3sMean
90 100 110
CPCPKCPMCPLCPU
Capability2.2642.1242.0882.4032.124
Index2.2032.0662.0362.3382.066
Lower CI2.3242.1822.1412.4692.182
Upper CI
Below LSLAbove USLTotal Outside
Portion0.00000.00000.0000
Percent0.00000.00010.0001
PPM8.7107.8737.872
Sigma Quality
Z BenchZ LSLZ USL
Benchmark Z6.3727.2106.373
Index
Overall, Sigma = 1.10432
Capability Analysis
Mean(Rel.DVS%)
Distributions
Mean: 100.5%
PPK: 2.124
Summary by Sampling Points (FE: Spec 92.5 – 107.5)
LSL USLTarget
9394 96 9899 101 103 105 107
100.0%99.5%97.5%90.0%75.0%50.0%25.0%10.0%2.5%0.5%0.0%
maximum
quartilemedianquartile
minimum
103.76103.11101.92101.22100.1399.6099.1898.8298.4598.1597.55
QuantilesMeanStd DevStd Err Meanupper 95% Meanlower 95% MeanN
99.7765970.91054470.017513799.81093899.742255
2703
Moments
Mean(Rel.HPMC%)
Distributions
Lower Spec LimitUpper Spec LimitSpec Target
Specification92.5
107.5100
Value Below LSLAbove USLTotal Outside
Portion0.00000.00000.0000
% Actual
LSL USLTarget
-3s +3sMean
90 100 110
CPCPKCPMCPLCPU
Capability2.7462.6642.6672.6642.827
Index2.6722.5922.5972.5922.751
Lower CI2.8192.7362.7362.7362.904
Upper CI
Below LSLAbove USLTotal Outside
Portion0.00000.00000.0000
Percent0.00000.00000.0000
PPM9.4929.4419.492
Sigma Quality
Z BenchZ LSLZ USL
Benchmark Z7.9927.9918.482
Index
Overall, Sigma = 0.91054
Capability Analysis
Mean(Rel.HPMC%)
Distributions
Mean: 99.8%
PPK: 2.664
Summary by Individual Tablets (Spec 90% – 110%)
LSL USLTarget
90 92 94 96 98 100102104106 109
100.0%99.5%97.5%90.0%75.0%50.0%25.0%10.0%2.5%0.5%0.0%
maximum
quartilemedianquartile
minimum
108.61105.15103.81102.53101.47100.3699.3698.4897.5896.7294.77
QuantilesMeanStd DevStd Err Meanupper 95% Meanlower 95% MeanN
100.454081.59546480.0098345100.47336100.4348
26319
Moments
Rel.DVS%
Distributions
Lower Spec LimitUpper Spec LimitSpec Target
Specification90
110100
Value Below LSLAbove USLTotal Outside
Portion0.00000.00000.0000
% Actual
LSL USLTarget
-3s +3sMean
90 100 110
CPCPKCPMCPLCPU
Capability2.0891.9942.0092.1841.994
Index2.0711.9771.9932.1651.977
Lower CI2.1072.0122.0262.2032.012
Upper CI
Below LSLAbove USLTotal Outside
Portion0.00000.00000.0000
Percent0.00000.00110.0011
PPM8.0527.4837.479
Sigma Quality
Z BenchZ LSLZ USL
Benchmark Z5.9796.5525.983
Index
Overall, Sigma = 1.59546
Capability Analysis
Rel.DVS%
Distributions
Mean: 100.5%
PPK: 1.994
Summary by Individual Tablets (FE: Spec 90% – 110%)
LSL USLTarget
90 92 94 96 98 100102104106 109
100.0%99.5%97.5%90.0%75.0%50.0%25.0%10.0%2.5%0.5%0.0%
maximum
quartilemedianquartile
minimum
105.89103.32102.31101.20100.2599.6299.1098.6598.1997.8096.75
QuantilesMeanStd DevStd Err Meanupper 95% Meanlower 95% MeanN
99.7761351.02458960.006315699.78851499.763756
26319
Moments
Rel.HPMC%
Distributions
Lower Spec LimitUpper Spec LimitSpec Target
Specification90
110100
Value Below LSLAbove USLTotal Outside
Portion0.00000.00000.0000
% Actual
LSL USLTarget
-3s +3sMean
90 100 110
CPCPKCPMCPLCPU
Capability3.2533.1813.1783.1813.326
Index3.2263.1533.1523.1533.297
Lower CI3.2813.2083.2053.2083.355
Upper CI
Below LSLAbove USLTotal Outside
Portion0.00000.00000.0000
Percent0.00000.00000.0000
PPM9.4419.4419.441
Sigma Quality
Z BenchZ LSLZ USL
Benchmark Z7.9419.5429.978
Index
Overall, Sigma = 1.02459
Capability Analysis
Rel.HPMC%
Distributions
Mean: 99.8%
PPK: 3.181
Summary by Individual Tablets (Run chart)
89
91
93
95
97
99
101
103
105
107
109
111
Y
-1000 1000 3000 5000 7000 9000 11000 13000 15000 17000 19000 21000 23000 25000 27000Rows
Y Rel.DVS% Rel.HPMC%
Overlay Plot
FE Active
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n ICH Q10 AlignmentScience and Risk based Approach to Quality
Disaster recovery plansChemometric Model MaintenanceHandling of outliersBatch release process in the RTR environmentQuality risk management (enabler)Tracking and trending of data
Real Time Release Elements –Quality Systems and Processes
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Quality Systems and Processes -Development of Disaster Recovery Systems
n Things to considerWhat do we do if a PAT measurement system stops functioning?What do we do when all the PAT measurement systems stop functioning?What do we do if the chemometric model is no longer appropriate?
- What are the alternative procedures and sampling plans for sample/batch analysis and release?
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Decision Tree for Failure Modes– PAT Failure During the Manufacturing Process
Does PAT pass System Suitability?
NO
Proceed with unit operation/manufacturin
g process
YES
YES
NO
Is PAT functional during the Process?
YES
Can the instrument be repaired in a suitable time frame?
Can the instrument be replaced with a spare instrument?
Generate Event Report Form, Fix/replace
instrument
NO
Are there alternative controls to ensure/control process variability?
Are there measurements downstreamwhich could be used to correlate data?
Do we need further sampling?
Generate Event Report Form, Capture Process/
Action Items
Revert to Testing using Regulatory Analytical Procedure
Generate Investigation/ERF to identify root cause
Capture Process/Action Items
YES
Stop Process*,Evaluate Instrument
*: Please note that it may not be practical to stop some unit operations in themanufacturing process during the middle of the run. For eg: Blending
Predefining reaction ensures proactive quality as compared to thinking of reaction after event -reactive quality
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Quality Systems and ProcessesChemometric Models- Establishment and Maintenance
n How do you transfer small scale models to large scale equipment?
- Need to assess variability due to equipment, personnel, environment, measurement systems, materials etc. and refine models as necessary
n What are the procedures for chemometric model maintenance?
n How often would a periodic check on the model performance be performed?
n What are the criteria for the revision of models in the RTR environment and how does this differ from the R&D/monitoring environment?
Quality Systems and Processes-Handling of Outliers
Development of mechanisms/predefined systems to handle outliers in the measurement systems (proactive quality)
- Should use a holistic assessment of the process measurements (in-process + final product) to assess product/process performance and impact to quality
- Reaction to outlier’s must be risk based - # of occurrences dictate reaction to outliers(setting of zero
tolerance criteria critical)- The reaction to an outlier after significant process/product history
should be different than an outlier observed when the amount of historical information is minimal
- Consider potential impact of an outlier to patient safety and efficacy
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Quality Systems and Processes-Batch Disposition and RTR
n Points to consider for batch disposition in a RTR environment…
Use of electronic batch records and identification of exceptions (flagging) that foster easier batch release Development of SPCs and a process/product monitoring system provide a real time assessment of process/product performance
Quality Systems and Processes-Batch Disposition Points to Consider, Continued…
n What is the relationship between the PAT attribute measured and the acceptance criteria for the drug product?
Dissolution of an extended release product – if attribute measured as a surrogate for dissolution is polymer concentration, need to establish correlation between polymer concentration and dissolution (models)Need to define strategy for defining dissolution (or other quality attribute) in a Certificate of Analysis (CoA). Options include:
- Generate a dissolution result based on model developed to demonstrate correlation to polymer and use in CoA. Indicate that the dissolution is a calculated value and not a measured value
- Defining polymer concentration in CoA and indicate that this is a surrogate for dissolution
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Illustration of how constituent content can affect dissolution behavior
X-ray Imaging of tablets
Correlation of Density (X-ray) to AUC (PK)
API Particle Size Affect on Dissolution
D50=3.2D50=20
Quality Systems and Processes-Quality Risk Management – What it is…
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NO RISK NO REWARD
KNOW RISK KNOW REWARD
Quality Systems and Processes-Quality Risk Management – Points to Consider
n Procedures for the implementation of QRM uniformly across the entire organization
Use of the same language (terminology), processEstablish criteria for re-evaluation of risks and mitigation plans –time, event or knowledge based
n Training program Various levels – awareness, participant, facilitator, team leader) to ensure effective utility of the toolChoice of the right QRA/QRM approach (Risk filter, FMEA, HACCP)Utilize tool in a proactive manner, not in a reactive fashion
Quality Systems and Processes-Tracking and Trending
n Procedures (and processes) for tracking and trending of data
Identify what needs to be tracked and trended (and Why?)- Process inputs (including raw material characteristics, parameters),
process outputs, process capability measurements (cycle times, yields, process capabilities)
Identify tools/process for tracking and trending- Establishing procedures/systems within quality systems
Establish rules for tracking and trending- When are we going to react and how?
Establish responsibilities for processTrainingContinuous improvement
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Challenges and Opportunities Associated with RTRn Regulatory challenges (global harmonization)n Risk Management – better understanding is
necessaryn Resources
Initial capital commitment is needed for PATPersonnel with diverse background necessary for successful PAT implementationCulture/mindset challenges (proactive versus reactive quality)Impact to QP/Q release person (understand control strategy, RM approach, quality systems, etc for RTR environment)
n Quality Systems DevelopmentWill need quality systems to be based on risk management principles (e.g. Need systems in place for PAT equipment failure)Robust change control systems needed
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Benefits from RTR, QbD, PAT
n RTR/QbD can lead to lower manufacturing costs (faster cycle times, fewer rejects, reduced QC resources, and greater yields)
n Demonstration of Process/Product Knowledge can lead to RTR and other examples of regulatory flexibility (e.g. fewer post-approval supplements)
n Use of PAT/QbD can facilitate Technology Development and Transfer (TD&T) process
Understanding of process/product makes TD&T easierContinuous Quality Verification (ASTM Standard Guide E2537-08)--not today’s 3 batch validation
n Even higher level of product quality for our patients
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Acknowledgements
Steve SimmonsChunsheng Cai
Carlos Conde-ReyesPlinio Delos-Santos
Parimal DesaiJoseph DevitoLori Henning
Nirdosh JagotaShailesh Singh
Merlin UtterT.G. Venkateshwaran
Dominic Ventura