Quality by Design – Facilitating Real Time Release (RTR) · 2012-11-27 · FDA PAT GUIDANCE,...

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,

2

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

4

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

Science

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,

http://www.investopedia.com/terms/s/stochasticoscillator.asp

http://www.investopedia.com/terms/c/correlation.asp

Monte Carlo Simulations –Contour Plots for Potential Scenarios

93

94

95

96

97

98

99

100

101

102

103

104

105

106

107W

Tmea

n

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


minimum

5.65305.65305.24095.01154.55703.92243.26242.54892.03631.83691.8369


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


minimum

0.112310.112310.085910.077040.066710.057740.049490.043580.034660.033760.03376


0.05884210.01335790.00123490.06128810.0563962

117

Moments

Last_SpcDist

Distributions Dose=50, Stage_By_Time_Num=1


Specification.

0.1.


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


Portion.

0.10310.1031

Percent.

1030.93381030.9338

PPM.

4.5814.581

Sigma Quality

Z BenchZ LSLZ USL

Benchmark Z..

3.081

Index


Capability Analysis

Last_SpcDist


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


minimum

107.33107.33105.84104.17102.78101.0499.2797.8696.2395.8595.85


100.933182.45229820.2167546101.3621

100.50426128

Moments

Rel_DVS_Ave


Mean API Concentration 101%

Final blendingRSD


Specification.

10.499.


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


Portion.

0.00000.0000

Percent.

0.00000.0000

PPM.

9.0979.097

Sigma Quality

Z BenchZ LSLZ USL

Benchmark Z..

7.598

Index


Capability Analysis

DVS_RSD_12


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


minimum

6.59066.59065.55674.68013.99543.42552.85202.38681.65741.46991.4699


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


minimum

0.374120.374120.095740.076900.068440.058940.051330.044690.039040.036810.03681


0.06252890.03098720.0027940.06806

0.0569979123

Moments

Last_SpcDist



Specification.

0.1.


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


Portion.

11.328411.3284

Percent.

113284.34113284.34

PPM.

2.7092.709

Sigma Quality

Z BenchZ LSLZ USL

Benchmark Z..

1.209

Index


Capability Analysis

Last_SpcDist


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%)


Specification95

105100


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


Portion0.00000.00000.0000

Percent0.00090.21410.2150

PPM7.5076.5566.555

Sigma Quality

Z BenchZ LSLZ USL

Benchmark Z5.0556.0075.056

Index


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


minimum

102.94102.94102.28101.44101.00100.4699.9399.0798.5098.1298.12


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


minimum

105.07103.81102.79101.90101.15100.4099.7599.0498.4197.9897.40


100.462381.10431940.0212408100.50403100.42073

2703

Moments

Mean(Rel.DVS%)

Distributions


Specification92.5

107.5100


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


Portion0.00000.00000.0000

Percent0.00000.00010.0001

PPM8.7107.8737.872

Sigma Quality

Z BenchZ LSLZ USL

Benchmark Z6.3727.2106.373

Index


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


minimum

103.76103.11101.92101.22100.1399.6099.1898.8298.4598.1597.55


99.7765970.91054470.017513799.81093899.742255

2703

Moments

Mean(Rel.HPMC%)

Distributions


Specification92.5

107.5100


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


Portion0.00000.00000.0000

Percent0.00000.00000.0000

PPM9.4929.4419.492

Sigma Quality

Z BenchZ LSLZ USL

Benchmark Z7.9927.9918.482

Index


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


minimum

108.61105.15103.81102.53101.47100.3699.3698.4897.5896.7294.77


100.454081.59546480.0098345100.47336100.4348

26319

Moments

Rel.DVS%

Distributions


Specification90

110100


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


Portion0.00000.00000.0000

Percent0.00000.00110.0011

PPM8.0527.4837.479

Sigma Quality

Z BenchZ LSLZ USL

Benchmark Z5.9796.5525.983

Index


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


minimum

105.89103.32102.31101.20100.2599.6299.1098.6598.1997.8096.75


99.7761351.02458960.006315699.78851499.763756

26319

Moments

Rel.HPMC%

Distributions


Specification90

110100


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


Portion0.00000.00000.0000

Percent0.00000.00000.0000

PPM9.4419.4419.441

Sigma Quality

Z BenchZ LSLZ USL

Benchmark Z7.9419.5429.978

Index


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

Quality by Design – Facilitating Real Time Release (RTR) · 2012-11-27 · FDA PAT GUIDANCE,...

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Transcript of Quality by Design – Facilitating Real Time Release (RTR) · 2012-11-27 · FDA PAT GUIDANCE,...