Continuous Process Verification Risk Based … 10...Continuous Process Verification –Risk Based Monitoring Sanjay Sharma. Sr. General Manager & Head Technology Transfer 2 • Variations

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Continuous Process Verification – Risk Based Monitoring

Sanjay Sharma.Sr. General Manager & Head Technology Transfer

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• Variations and Process Robustness

• Risk Based Approach in Improving Process Robustness

• Process Analytical Technology in Manufacturing

Agenda

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– Understand sources of variation

– Robustness Evaluation – Detect presence & degree of variation

– Understand Impact of variation on product quality

– Control variation



Agenda

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Types of Variation

• Every process has variation. The sources of process variation can be divided into two categories: Natural or Common cause Unnatural or Special cause

Continuous Improvement

Variation

Process

Common cause of variation(natural or expected variation)

Special cause of variation(unexpected variation )

Common Causes are those that are inherent to the process and generally are not controllable by process operators

Special Causes of variation include unusual events that the operator, when properly alerted, can usually remove or adjust.

Baking a loaf of bread

The oven's thermostat allows the temperature to drift up and down slightly.

Changing the oven's temperature or opening the oven door during baking can cause the temperature to fluctuate needlessly.

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Sources of Variation

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Measurement variation- Gauge R & R

Gage Repeatability•It is the variation in measurements obtained when one operator uses the same gage several times for measuring the identical characteristics of the same sample or part.

Gage Reproducibility•It is the variation in the average of measurements made by different operators using the same gage when measuring identical characteristics of the same sample or part.

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What is a gage R&R study?

•A gage R&R study helps you investigate: Whether your measurement system variability is small compared with the process variability.

•How much variability in the measurement system is caused by differences between Analysts/operator.

•Whether your measurement system is capable of discriminating between different parts.

For example, several operators measure the thickness of tablets to ensure that they meet specifications. A gage R&R study indicates whether the inspectors are consistent in their measurements of the same part (repeatability) and whether the variation between inspectors is consistent (reproducibility).

Measurement variation- Gauge R & R

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History of Control Charts

The control chart was invented by Walter Andrew Shewhart (Father of Statistical Quality Control) while working for Bell Labs in 1920s.

Control charts also known as Shewhart charts or process behavior charts.

Process/Product variation- Control Charts

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Components-- Set of data (CQA, CMA, CPP)- A central line (mean) - CL- Two statistical process control limits (UCL &

LCL) : whether process is stable?- Upper & Lower specification limit (USL & LSL) :

whether process is capable?

Control Chart- It’s a graphical display of a product quality characteristic that has been measured or computed periodically from a process at a defined frequency.

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Potential Applications-- To proactively monitor and trend a process- To detect the presence of special cause

variation- To identify continual improvement

opportunities- To maintain the process in the state of

statistical control

Control Chart- It’s a graphical display of a product quality characteristic that has been measured or computed periodically from a process at a defined frequency.

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Types of Control charts:

Variable Control chart- Characteristics which can be measured (continuous numeric values) e.g- Assay, UOD, etc- The average and variability chart are usually prepared & analyzed in pairs

Average – Range chart (Xbar- R chart, subgroup size 2 – 10) Average – Standard Deviation chart (Xbar- S chart, subgroup size >10) Individual Moving Range chart (I-MR chart, n=1)

Attribute Control chart- Characteristics that have discrete values and can be counted, e.g- %defect tabs, #of failed batches/M, etc- P-chart, np chart, U-chart, C-chart belongs to this category

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Xbar- R chart (subgroup size 2 – 10)

The X-bar chart shows how the mean or average of a process changes over time.

The R chart shows how the range of the subgroups changes over time.

Interpreting the chart:Always look at the Range chart first.

The control limits on the X-bar chart are derived from the average range, so if the Range chart is out of control, then the control limits on the X-bar chart are meaningless.

After reviewing the Range chart, look for out of control points on the X-bar Chart

If there are any point beyond control limits, then the special causes must be eliminated.

Brainstorm and conduct Designed Experiments to find those process elements that contribute to changes in process location.

If the process in control :

Look for obviously non-random behavior.

Turn on the Run Tests, which apply statistical tests for trends to the plotted points.

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Xbar- R chart (subgroup size 2 – 10)

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• Process capability is the ability of the process to meet the design specifications for a service or product.

• Nominal value is a target for design specifications.

• Tolerance is an allowance above or below the nominal value.

Process/Product variation- Process Capability

Centering –The Process Is On Target

Spread – Reduce The Variation

LSL USL

DefectsDefects

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16


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Process/Product variation- Process Performance

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Process/Product variation- Process Capability vs Performance

•Process Capability is the variation the process would exhibit if only common cause variation were present:

•Process Performance is the total variation experienced by the customer; includes common cause, and special cause variation:

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A Short-term Capability study covers a relatively short period of time (days, weeks) generally

consisting of 30 to 50 data points. The actual number depends on the subject under study.

Is The Process

In Control ?

Is It Producing

Defects ?

35302520151050

39

34

29

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Observation Number

Indiv

idua

l V

alu

e

I Chart for C1

X=30.60

3.0SL=37.36

-3.0SL=23.84

CP & CPK

Measure Short-term Capability


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A long-term capability study covers a relatively long period of time (weeks, months) generally

consisting of 100-200 data points. Again, the actual amount depends on the subject under study.

Is The Process

In Control ?

Is It Producing

Defects ?

100500

50

40

30

20

Observation Number

Indiv

idua

l V

alu

e

I Chart for C3

X=33.80

3.0SL=47.12

-3.0SL=20.49

Short term Capability

PP & PPK

Measure Short-term Capability


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A Further Look at

Capability

Compare the estimates of the process deviations from the short-term and

long-term data

What is the difference between the short-term and the

long-term data?

What implication does this have in doing capability

studies?

Descriptive Statistics

Variable N Mean Std. Dev

short term 30 30.6 2.23

long term 180 33.8 4.44


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Capability vs.

Performance

5040302010

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13

12

11

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Index

CO

2-S

hrt

CO2 Levels for 55 Time PointsDays Sales Outstanding for 55 Days

DS

OProcess Performance:

Total Variation

including shifts and

drifts

(Pp & Ppk)

Capability: Only

random or short

term variability

(Cp & Cpk)


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An unstable process is characterized by change. Each time a set of samples is selected and histogram drawn, the average has shifted. The variation may also change. The process is unpredictable and difficult to control.

A stable process on the other hand, is one characterized by a lack of change hour after hour looks the same. It is not that all variation has been eliminated. The variation consistently falls within certain well-defined limits.

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It is a stable process whose remaining variation fitssafely between the sp. Limits. it consistently producesquality products hour after hour, day after day. If theprocess is stable and remaining variation does notsafely fit between the sp. Limits, the process is said tobe not capable.

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• Six Sigma is a comprehensive and flexible system for achieving, sustaining, and maximizing business success by minimizing defects and variability in processes.

• It relies heavily on the principles and tools of TQM.

• It is driven by a close understanding of customer needs; the disciplined use of facts, data, and statistical analysis; and diligent attention to managing, improving, and reinventing business processes.

Sigma (ST)

% Good (LT)

DPMO* (LT)

6 99.9997% 3.4

5 99.98% 233

4.5 99.87% 1340

4 99.38% 6,210

3 93% 66,807

2 69% 308,537

1 31% 690,000

Process/Product variation- Control (Six Sigma)

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1. Define Determine the current process characteristics critical to customer satisfaction and identify any gaps.

2. Measure Quantify the work the process does that affects the gap.

3. Analyze Use data on measures to perform process analysis.

4. Improve Modify or redesign existing methods to meet the new performance objectives.

5. Control Monitor the process to make sure high performance levels are maintained.

Process/Product variation- Six Sigma Improvement Model

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Use SPC to Maintain Current

Process

Collect & Interpret Data

Select Measures

Define Process

IsProcessCapable

?

Improve Process

Capability

IsProcessStable

?

Investigate & Fix Special

Causes

No

Yes

No

Yes

Is Process Capable?

Purpose: Determine the adequacy of the process with respect to customer /management needs.


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Effects of reducing variability on Process capability

Lowerspecification

Mean

Upperspecification

Nominal valueSix sigma

Four sigma

Two sigma


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– Update of FDA’s draft guidance on submission of Quality metrics data – Nov’16

– Establish criticality in product life cycle , Creation of CTQ document

– Product score card & use of quality trending in assisting continual improvement

– Quality Risk management

– Process for applying science & Risk based approach to an existing product (Legacy Products)


Agenda

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Quality Metrics that FDA Intends to Calculate

• Lot Acceptance rate (LAR)– Indicator of manufacturing process performance

• Product Quality compliance rate(PQCR) – Indicator of Patient / customer feedback

• Invalidated Out of Specification (OOS) rate(IOOSR) – Indicator of Laboratory operations

• Annual Product Review (APR) or Product Quality Review (PQR) on Time rate – removed from the revised draft version

FDA intends to calculate the following quality metrics for each product and establishment, where applicable:

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No. of Accepted Lots in a timeframeLAR = --------------------------------------------------------------------------------

No. of Lots started by the same firm in the same timeframe

e.g. There are 97 lots accepted out of 100 lots per Annum, then The Lot Acceptance rate would be

97 / 100 = 0.97.

Lot Acceptance Rate (LAR)

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Product Quality Compliance Rate (PQCR)

Product Quality Complaint Rate = the number of product quality complaints received for the product divided by the total number of dosage units distributed in the current reporting timeframe

e.g. There are 5 lots having product complaints out of 100 dosage units distributed for the same product per Annum, then the Product Quality Compliance rate would be

5 / 100 = 0.05.

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Invalidated Out of Specification Rate(IOOSR)

Invalidated Out-of-Specification (OOS) Rate = the number of OOS test results for Lot release and long term stability testing invalidated by the establishment due to an aberration of the measurement process divided by the total number of lot release and long term stability OOS test results in the current reporting timeframe

e.g. There are 10 OOS out of which 5 OOS are due to analytical testing error whereas there are 100 test release data done per Annum, then the Invalidated OOS rate would be

(5 / 10) / 100 = 0.005.

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How to Report Quality Data to FDA

• Quality Metrics data to be submitted Annually (data segregated in the report on a quarterly basis)

• FDA expects to begin the data analysis when the portal is closed and then publish initial findings and the quality metric reporters list on the FDA Web site

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How to Report Quality Data to FDA

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How FDA intends to use Quality Metrics

FDA intends to use data from the quality metrics reporting program to focus the use of FDA resources on the areas of highest risk to public health (e.g., risk-based inspection scheduling). Specifically,

• establish a signal detection program as one factor in identifying establishments and products that may pose significant risk to consumers;

• identify situations in which there may be a risk for drug supply disruption; • improve the effectiveness of establishment inspections; and • improve FDA’s evaluation of drug manufacturing and control operations.

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Flow for the Critical to Quality (CTQ) template

List of CQA’s

• Provides a list of Critical Quality Attributes along with the type and criticality level for the attributes for both Finished Product and Intermediates

Impact of CPP/CMA on CQA

•Evaluates impact of CMA of API on CQAs (High/Medium/Low)

•Evaluates impact of CPP of both Intermediates and Finished Product on CQAs (High/Medium/Low)

Deep dive of impact of

CMA on CQA & CS

•Provides justification for criticality of CMA for CQA

CPP & Control Strategy

•Evaluates in detail impact of CPPs and their control strategy value

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List of CQAs

Level 1A : Critical Quality Attributes - Finished Product (Coated Tablet)

S. No. AttributeType of attribute

Safety/Quality/EfficacyCriticality level of the attribute

1 Description Quality Medium2 Odor Quality Low3 Identification Quality Medium4 Assay (%w/w) Efficacy High

5 Content uniformity by UOD Efficacy High

6 Dissolution profile (%) Efficacy High

7 Related Substances limit (%w/w) Safety High

8 Anti-oxidant potency (%w/w) Quality Medium

9 Water content (%w/w) Quality High

10 Residual Solvents (%w/w) Safety Medium

11 Solid state nature of API in product Quality High

12 Microbial enumeration Safety Medium

Note: In similar lines CQA for intermediates will also be listed

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Impact of CMA on CQAs

Level 2A: Evaluation of impact of CMA of API & RM on CQA

S.No.Assay

(%w/w)

Related Substances

(%w/w)

Content uniformity by

UOD

Dissolution profile (%)

Water content (%w/w)

Solid state nature of API

in product

1 API Low Medium Low Low Low High2 Copovidone (Kollidon VA64) Low Medium Low Low Low Low

3 Butylated HydroxyToluene Low Low Low Low Low Low

4 Butylated Hydroxy Anisole Low Low Low Low Low Low

6 Methanol Low Low Low Low Low Low

7Polyethylene Oxide (PEO WSR303) Low Low Low High Low Low

8 Polyethylene Glycol (PEG 8000) Low Low Low Low Low Low

9 Hypromellose (HPMC K100 LVCR) Low Low Low High Low Low

10 Magnesium Stearate Low Low Low Low Low Low

11 Hypromellose (HPMC E5 LV) Low Low Low Low Low Low

12 Polyethylene Glycol NF Low Low Low Low Low Low

13 Isopropyl Alcohol Low Low Low Low Low Low

14 Opadry 200 Yellow (for 50 mg ) Low Low Low Low Low Low

15 Opadry 200 Brown (for 25 mg) Low Low Low Low Low Low

16 Purified Water Low Low Low Low Low Low

CQACMA

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Impact of CPP on CQAsLevel 2B: Evaluation of impact of CPP on CQA

Finished Product CQA

S.No.Assay

(%w/w)

Related Substances

(%w/w)

Content uniformity

by UOD

Dissolution profile (%)


Solid state nature of

API in product

1 Sifting Low Low Low Low Low Low

2 Drug-binder solution preparation Low Low Low Low Low Low

3 Dry mixing Low Low Low Low Low Low4 Fluid bed granulation Low Low Low High Low High6 Granules drying Low Low Low Low Low Low7 Milling Low Low Low Low Low Low8 Blending (Pre-lubrication) Low Low Low Low Low Low9 Blending (Lubrication) Low Low Low Low Low Low

10 Compression Low Low Medium High Low Low

11Seal & Film Coating solution preparation

Low Low Low Low Low Low

12 Seal coating Low Low Low Low Low Medium13 Film coating Low Low Low Low Low High14 Seal & Film coating drying Low Low Low Low Low Low

CPP

CQA

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Deep dive of impact of CMA on CQA & CSLevel 2A: Evaluation of impact of CMA on CQA

S.No.Specificat

ionAssay

(%w/w)

Related Substances (%w/w)

Content

uniformity by

UOD

Dissolution

profile (%)


Solid state

nature of API in

product

Justification for criticality (only for High/Medium)

1Water content (API)

NMT 4.0% w/w

Low Medium Low Low Low High

(I) API is hygroscopic in nature and is prone to hydrolytic degradtion as evident from the API forced degradation study and this may impact the related substance of the product. Hence the risk is rated as Medium.

(II) Impact of water content of active on retaining the input polymorphic form of API is considered as high, because API is hygroscopic in nature and polymorphic conversion may take place due to change in the water content of API. However API water content will be controlled through API specification. Hence the risk is High.

2

Limit of Peroxides (Copovidone)

NMT 0.40% w/w

Low Medium Low Low Low Low

The drug substance is prone to oxidation. Peroxide content may trigger oxidation of drug substance which expedite the impurity generation due to oxidation. The impurities likely have to impact on safety of product. Hence, the risk is medium.

3

Viscosity (HPMC K100 LVCR)

NLT 80 & NMT 120

mPas

Low Low Low High Low Low

The viscosity hypromellose depends on polymer parameters like molecular weight, hydrophilicy etc. Drug release through polymer matrix is inversely proportional to viscosity of hypromellose. The viscosity of selected grade of polymer is 80-120 mPas. The lot of hypromellose used for development trials had viscosity of 98 mPas and 102 mPas. The viscosity of hypromellose towards extremity of specification (towards lower side and higher side of the specification) will have impact on drug through polymer matrix. Hence, it is rated as High.

CQA

CMA

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CPP and Control StrategyLevel 4: Detailed evaluation of impact of critical process parameter & its Control strategy value

Unit Operation Parameter Value (Lab scale) UOM

Fluid bed granulation

Spray rate 36 (15-60) g/min

Airflow 100 (80-140) CFM

% inlet RH 5 -55 %

Atomization pressure 1 (0.8-1.2) bar

Granules dryingInlet temperature 25-45 ˚C

EXhaust temperature 25-30 ˚C

Milling Milling speed 1200 (1200 -1700) RPM

Blending (Lubrication) Blending time 5 min.

Compression

Precompression force 10% of MCF kN

Main compression force (MCF) 20-Oct kN

Turret speed 20-40 RPM

Seal coatingSpray rate 2-15 g/min

Atomization pressure 1-2 bar

Film coating

Spray rate 1-5 g/min

Atomization pressure 2-3 bar

Inlet temperature 30-65 ˚C

EXhaust temperature 35-45 ˚C

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• Capable of being deployed for all products and compare products• Same basis of evaluation• Capture all key patient centric parameters• Serve as a basis for taking preventive action – Lead indicator• Quantified and based on statistically appropriate concepts

Product Scorecard Concept & Key Attributes

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For Qualitative data

Upper limit = 𝑈𝐿 =𝑣1∗𝐹

𝑣2+𝑣1∗𝐹

Wherev1 = 2(x+1)v2 = 2(n-x)x = number of nonconformancen = number of batchesF = lower α/2 point of F with v1 and v2 degrees of freedomNote: when x=0 or x=n, calculate the one-sided confidence interval.

Converted to individual score based on the 95% upper confidence limits on percent non-conformance using (1-UL)*100

For Quantitative data following robustness score is used for calculation

Ppk Range Score Range

Ppk <= 1 0 – 25

1 < Ppk <= 1.33 26 – 50

1.33 < Ppk <= 1.67 51 – 75

Ppk > 1.67 75 – 100


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Process Engineering Update

Use Non parametric PpK to compute an overall Product score

Method:Continuous data: Non parametric calculation of PpK using medians and percentiles Correction Factor assign scoreDiscrete data: Calculate failure rate UL of Non-conformance assign score

• Independent of normality assumption, batch size

• Sensitive to variability & closeness to specs


Ppk Range Score Range

Ppk <= 1 0 – 25

1 < Ppk <= 1.33 26 – 50

1.33 < Ppk <= 1.67 51 – 75

Ppk > 1.67 75 – 100

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Process Engineering Update

Product Scorecard – Online Tracking

85

87

89

91

93

95

97

0

0.5

1

1.5

2

2.5

3

3.5

4

4.5

5

Water by KF

0

1

2

3

4

5

6

7

8

9

Assay UOD

Product Score

Score – CQA ( High)

Score – CQA ( Medium)

The process is behaving normally

& within limits as indicated by

the product score and the CQAs,

the advantage of this

methodology is capturing the

performance at a ‘batch level’

and thus capturing information

on an almost real time basis and

thus enabling the organization to

take pre-emptive/ corrective

action wherever necessary

The process though has not

produced any OOS, even an out

of trend/ extreme observation is

magnified and highlighted to the

management for taking

appropriate corrective action.

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ICH Q9’s Risk management Process

The study of risk management began after

World War II. Risk management has long been associated with the use of

market insurance to protect individuals and companies

from various losses associated with accidents.

Risk Management

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DescriptionPotential Risk of generating OOS

Justification Action

Blending YesIf not correctly performed, the uniformity of content of tablets could be modified

Risk Analysis

The potential problems that have emerged from the previous step could be analysed by means of the fault tree analysis

(FTA) or a similar logical analysis. The correlation and applicability of each occurrence with the process and process-

related factors, such as the facilities and procedure system, are then evaluated by process knowledge. Examples of such

tabular output are reported in Table I below:

Risk Management

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Score Evaluation Description

1 None No effect on the drug’s pharmacological properties or in its administering system

2 Low Changes only to drug’s administering system

3 Moderate Reduction of drug benefits

4 High Pharmacological properties of the drug are voided

5 Maximum Negative effects of the drug on the patient

Table II (Severity)

Severity (S) – Evaluation of Errors in manufacturing and the severity of the impact of these deficiencies

on the patient

Risk Management

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1 CertainThese are double checks (activities performed by one operator & simultaneously verified by a second operator) and stop controls (technological &/or analytical).

2 HighThese are double checks (step performed by one operator and simultaneously verified by a second operator).

3 MediumThere is a verification at the end of the activities performed by a second operator and a release analysis

4 Low There are final release analysis

5 None No subsequent analysis.

Table III (Detectability)

Detectability (D) – Presence of double checks or analysis.

Risk Management

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1 None Simple phase, brief, manual, and/or automatic

2 Low Simple phase, long, manual, and/or automatic

3 Medium Difficult phase, brief, automatic

4 High Difficult phase, (brief, manual) or (Long, automatic)

5 Certain Difficult phase, long, manual

Table IV (Probability)

Probability (P) of the duration and difficulty of the operation

Risk Management

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1 None The phase impacts only external appearances.

2 Low The phase impacts qualitative & quantitative characteristics, with tested/ verified parameters.

3 MediumThe phase impacts qualitative & quantitative characteristics, with no tested/ verified parameters, but there are two subsequent steps that are able to compensate potential mistakes in that phase.

4 HighThe phase impacts qualitative & quantitative characteristics, with no tested/ verified parameters, but there is one subsequent step that is able to compensate potential mistakes in that phase.

5 CertainThe phase impacts qualitative & quantitative characteristics, with no tested/ verified parameters, and there are no subsequent steps that are able to compensate potential mistakes in that phase.

Table V (Impacted Phase)

Impacted Phase (F) - Each phase is analyzed for its own features and position during the manufacturing process.

Risk Management

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Stage Extent of error S P Control D Impacted Phase F RPN

Blending

Blending (PreMix)

Mixing not Proper

3 1Double Check

2Phase impacts on qualitative & quantitative characteristics, with tested/ verified parameters.

3 18

Documentationerror

1 2Double Check

2 No Impact on finished product. 1 4

Table VI (Risk priority number (RPN) evaluation)

Risk Management

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Legacy Products

Challenges

- May not have CMA / CPP defined - May not have been developed on QbD platform- Retrieval of development data may be a challenge

Way Forward

Protocol for conducting the study needs to be prepared which should encompass the following:

Establish Proactive approach. Use Existing data to predict product performance and draw conclusions. Apply standard methodology, based on science and statistics Perform Holistic evaluation. Contribute to Process sustainability. Culture of Continuous Improvement. Utilization of current data review mechanisms e.g. APQR

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Legacy Products – Steps involved

1• Product Prioritization

2• CTQ Identification (CQA,CPP, CMA)

• Analysis Methodology (Protocol based)

3• Database creation

4 • Data collection

5• Statistical evaluation

6• Recommendation Implementation

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Legacy Products

(*) Currently based on Setting up the criteria for selection of product for revalidation: e.g.• Cpk < 1• Repeat OOS in any C of A parameter• Yield < BPR specification

1• Product Prioritization

Priority Old/ Legacy Products

1 Relatively High Risk (*)

2 Product underwent Major change

3 Number of batches / Annum

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Legacy Products

Critical Quality Attribute Criticality Rating Rationale & Comments

Assay High Over Dose- Side effects, Under dose- Lack of efficacy

UOD High Variability in Plasma levels- Side effects or poor response

Dissolution High Bioavailability



- Criticality/ Risk Assessment to be performed for Raw / In process Material attribute, Process Parameter that may impact patient w.r.t.

• Safety• Efficacy• Quality

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Legacy Products

Quality AttributeRisk

Content Uniformity Dissolution

Patient Risk Level

Unit Operations Risk

Material Risk

Operations Practices Risk

Residual Overall Risk



CU – Based on analysis, no additional monitoring is required post PPQ

Dissolution- Based on analysis, further evaluation is needed.

Additional sampling and testing for dissolution should be included for period of time until control and capability established.

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Legacy Products

• To perform such study, it is necessary to consider all the data available, and this is performed by accurately checking the registration dossier, the subsequent notifications, all production documents (batch records), and all quality documents (annual periodic reviews).

Following documents can be evaluated for data collection: COA’s APQR BPR Stability Data Summary of Incident, Deviation & OOS. Summary of yield, etc.

3• Database creation

4• Data collection

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Legacy Products

5• Statistical evaluation

Thresholds for statistical assessment tools to be identified (e.g. Ppk < “x” or exceeding statistically derived control

limits) which may trigger enhanced monitoring of additional inputs (e.g. raw materials, storage conditions) which may

impact the attributes/ parameter.

Assay CU Disintegration Dissolution Impurities

0.9 2.66 2.16 2.34 5.09

In this example, assay data has a lower Ppk and the process has a statistical

control limit (+/- 3 sigma) which lies slightly below the product specification.

The lower Ppk index value indicates lower long term capability based on an

overall estimate of standard deviation (all variation estimates) and suggests a

need to reduce variability and center the process.

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Legacy Products

Pre-determined criteria could be set, such as a defined confidence interval of process capability (e.g. Cpk > 1, with 90% confidence) which when achieved would drive a review to decide if and what testing could be reduced to the routine monitoring level, and which elements of the control strategy could be considered for change.

6• Recommendation Implementation

CQA HMP Rationale Sampling Acceptance CriteriaTarget min Cpk

# of batches

Rationale

Appearance NoRoutine sampling & inspection uses AQL acceptance sampling

Routine Meets release criteria NA NA NA

Dissolution Yes

Complex multivariate relationship requires more data to assess robustness

60 tabs/ batch using stratified sampling

Mean >85%; RSD 8.5 max MAs for API particle size, Mg stearate SA & crushing force & lubrication time monitored

> 1.0

As needed to assess/ achieve Cpk

*Material Attribute, *Control Strategy, *Process capability

Note – acceptance criteria follows ASTM E2709 [8] using 90% confidence interval.

AQL- Limiting quality level

HMP- Heightened Monitoring Plan

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– Role of PAT in process understanding and process controls

– PAT as enabler in product robustness

– Real-life case studies

Agenda

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Presented by Janet Woodcock, CDER FDA at International symposium in Cambridge, MA, May’14

What FDA has to say?

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Presented by Janet Woodcock, CDER FDA at International symposium in Cambridge, MA, May’14

What FDA has to say?

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PAT FRAMEWORK

FTO

DESIGN FOR

QUALITYSENSORS

PROCESS

CONTROLSDATA ANALYSIS

DOE, FMEA

Univariate, Multivariate, Real time

Soft- Temp, CFMDirect- NIR, Raman

Feedback, Feedforward

Its just not about NIRs

“PAT is considered to be a system for designing, analysing and controlling manufacturing through timeline measurements (i.e., during processing of critical quality and performance attributes of raw and in-process materials and processes

with the goal of ensuring final product quality)”

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PAT in Product Lifecycle Management

FTO

Process Qualificatio

n

Continuous Process

Verification

Product Design

PAT can be employed during the entire Product Life cycle for varying benefits

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FTO

Process Qualificat

ion

Continuous Process

Verification

Product Design

New Products- PAT utilized throughout development & Scale up.

Better understanding of Impact of CPP,CMA on the CQA


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FTO

Process Qualificat

ion

Continuous Process

Verification

Product Design

Commercial Products- PAT utilized for Analyzing CQA’s & Monitoring CPP

Step wise approach, first improve quality & then efficiency


69

FTO

Process Qualificat

ion

Continuous Process

Verification

Product Design

Existing Marketed Robust Products-PAT utilized to improve efficiency.

Mechanism to keep ensuring product is in state of control

State of Control


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FTO

GRANULATION

GRAN

ULA

TIO

N M

ECH

AN

ISM

S

Granulating Mechanisms

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FTO

Primary ParticlesBinder Capillary Bridges Granule

Inputs Outputs

1. Process Parameters (e.g. speed, time) 1. Particle/ Granule size distribution

2. Formulation Properties (e.g. Viscosity, wettability, solubility)

2. Porosity & Bulk Density

3. Flow characteristic

3. Design Parameter (Geometry, blade type, etc.)

4. Content Uniformity

5. Dissolution

Wet Granulation

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FTO

Ability to accommodate expected range of variability in input materials and processes

Allow process flexibility with the end in mind (Real time data evaluation) Real time data with feed back / feed forward ability to adjust the processes Rapid QbD development with minimal experiment Robust scale up/ tech transfer

Process trending over time / batches to identify subtle changes

Net reduction in failure rates.

Desired future state

ProcessVariable

input

Robust andAdjustable

Process

Fixedoutput

Real time moni-toring and control

Facilitated e.g.by PAT / DS

Next Gen Wet Granulation Drivers

73

FTO

Outputs INLINE ATLINE SIMULATION

1. Particle/ Granule size distribution Eyecon, FBRM

DEMPBM

2. Porosity & Bulk Density Torque, DFF FT4

3. Flow characteristic FT4

4. Content Uniformity NIR

5. Dissolution Torque, DFF FT4

Wet Granulation

74

FTO

Need to implement QbD paradigm through modelling

Extensive & Expensive laboratory testing leading to high costs & time during Product Design.

Powder / Granule flow is poorly understood due to lack of robust scientific tools

Need of Fundamental or Mechanistic understanding of process from the first principles.

Relating Process Parameters, equipment geometry and material properties to quality attributes.

Discrete Element Modelling – Need?

75

FTO

Newton’s 2nd law for each particle along with contact force model is solved explicitly with time.

Input Parameters:- Mechanical properties: Density, shear modulus, Poisson ratio. Interaction properties: Coefficient of restitution, Static friction and rolling friction.

Output Parameters:- The “position and velocity” of each individual particle, particle-particle and particle-

wall “contact forces” at any instant during the process can be obtained

mi g

Fij

gmFdt

dvm i

N

j

iji

i 1

Discrete Element Modelling – How?

76

FTO

• DEM doesn’t inherently simulate the sub granulation process such as agglomeration and break-up.

• Empirical models or coupling with population balance modeling is needed.

Barrasso, D. and Ramachandran, R., 2015. Multi-scale modeling of granulation processes: bi-directional coupling of PBM with DEM via collision frequencies. Chemical Engineering Research and Design, 93, pp.304-317.

Discrete Element Modelling – Coupled PBM - DEM

77

FTO

• Images of particles colored by liquid fraction

Barrasso, D. and Ramachandran, R., 2015. Multi-scale modeling of granulation processes: bi-directional coupling of PBM with DEM via collision frequencies. Chemical Engineering Research and Design, 93, pp.304-317.

Discrete Element Modelling – Coupled PBM - DEM

78

FTO

Flow in screw feeder Milling

Granulation Die filling

Barrasso, D. and Ramachandran, R., 2016. Discrete Element Modeling of Solid Dosage Manufacturing Processes. Process Simulation and Data Modeling in Solid Oral Drug

Development and Manufacture, pp.105-131.

Discrete Element Modelling – Applications

79

FTO

Powder flow characterisation using FT4 rheometer and

angle of repose

Assessment of

processibility and

flowability from flow

properties

Developing material calibration

data for Discrete Element

Modelling

Avoiding caking

segregation in

hoppers, die fillingHopper Mixing Granulation

Tablet

coating

Achieve better process understanding and product quality,

identifying the relation between CPPs and CQAs

Measured parameters- Torque- Force- Height

FT4

Discrete Element Modelling – Work flow

80

Discrete Element Modelling – Case Study 1 (Hopper design using flow properties)

FTO

Objectives

– Understand flow properties of uniformly blended material

– Provide recommendations for better hopper designs, material flow type based

on the measured material properties to avoid segregation

Approach

– Mass flow hopper reduces the segregation tendency.

– Shear and bulk properties measured using FT4 powder rheometer.

– Jenike design procedure was followed to determine hopper angle and outlet

diameter.

Results

– Successfully predicted the important parameters like outlet diameter, maximum

cone angle, flow type, probability of arching.

81

FTO

Objectives

– To understand the mixing of different sized particles (material A and

material B) at different fill levels and rpms.

– To suggest optimum operating parameters.

Approach

– Angle of repose (AoR) of both the materials were measured.

– Material model for DEM was developed using the AoR measurements with

scaled particle sizes (factor of 80-90).

– Blender is filled with 1200 g of material A and 245 g of material B.

– Simulation is conducted at 30 rpm.

Discrete Element Modelling – Case Study 2 (Simulation of Bin blending)

82

FTO

• The blending in the hopper is reached steady state after 10 sec.

• Regions of unmixed material B is observed at the near walls at hopper transition.

• Segregation index was found be 1.05.

• To further improve the blend homogeneity, it was suggested to reduce the fill level and rpm.

Simulated blending process with fill level 53 % and 30 rpm


83

FTO

Simulated blending process with fill

level 53 % and 30 rpm

Segregation index of 2

implies no mixing

Segregation index of 1

implies randomly mixed

state


84

Wet Granulation – end point

FTO

What is the end point?

• Target particle size mean

• Target particle size distribution

• Target granule viscosity

• Target granule density

Principle of equifinality

• Determining the end point, and then reproducibly arriving at that same end point as

equipment size and model changes are encountered, has been a continual challenge

85

Torque Sensor – Assembly

FTO

Torque

Sensor

Impeller

Shaft

Cross sectional view

0

2

4

6

8

10

12

1140 1240 1340 1440 1540 1640

Torq

ue

(N

m)

Time (s)

Torque profiles - 15 L High Shear Mixer

NT-001 (Reference batch) NT-003 (Decreased binder addition time) NT-004 (Increased binder addition time)

86

FTO

• Can be used to find the minimum liquid volume required for granulation

• As an output parameter in the DOE and arrive at a torque value for the right end point

• Advantageous in case of change in raw material properties - reproduce torque value

• Can be used as a scale up tool – reproduce calculated torque during scale up

Torque Sensor – Applications

87

Drag Force Flow Sensor (DFF) – Assembly

FTO

• Hollow cylindrical pillar with two optical strain gauges

• Based on the linear movement of the hollow cylindrical pillar by a distance xo in the direction of wet mass

flowing with velocity, v, and mass, m

• Composite response for wet mass densification, tackiness, and particle growth

• Force profiles as a granule approaches, collides and breaks down near the sensor

Approach Collison Breakage

88

FTO

DFF Sensor reports are also reflected in the FT4 measurements for BFE

Drag Force Flow Sensor (DFF) – Assembly

89

Near Infra Red (NIR) – Case study 1 (Moisture content prediction in drying)

FTO

• Moisture content can be determined with in-line Near Infrared Spectrometer in fluid bed drying.

• Preparation of calibration set by varying moisture concentration.

• PLS model should be developed by correlating NIR spectral absorbance of calibration samples with

the reference values from laboratory.

• Chemometric knowledge required to build regression model.

Second derivative spectra shows the linearity for different moisture samples.

90

FTO

• Endpoint can be seen in real time by projecting the validation batch spectrum on the PCA model developed with endpoint spectrum of calibration batches..

• PLS model predicts the moisture content in real time.

Near Infra Red (NIR) – Case study 1 (Moisture content prediction in drying)

91

Mass Spectometry (MS) – Assembly

FTO

MS - main components

• Ion source

– To convert neutral gas molecules and atoms to positive ions

• Mass filter

– To separate ions produced in the ion source based on m/z ratio.

• Detection system

– To measure the electric signal generated by ions

92

FTO

Objective:

To monitor the drying profile of methanol and to find out drying end point in real time.

• Fluid bed dryer exhaust gas analyzed by Mass Spectrometer.

• Methanol solvent was sprayed on the mixture of Lactose and MCC in Fluid bed Top spray

granulator.

mass to charge ratio of 31 was used for the drying monitoring of Methanol.

Mass Spectometry (MS) – Case study 1 (Drying Monitoring for Non Aqueous gran)

93

FTO

0

2

4

6

8

10

12

14

16

0 500 1000 1500 2000 2500 3000 3500 4000

(31

/40

)*1

00

0

Time, sec

31/40*1000

ATPMS-PBO-NT-003 ATPMS-PBO-NT-009 ATPMS-PBO-NT-010

0

100

200

300

400

500

600

700

800

900

0 10 20 30 40 50 60 70

Met

han

ol

con

ten

t, p

pm

Time, min

Methanol content by GC Analysis

ATPMS-PBO-NT-003

ATPMS-PBO-NT-009

ATPMS-PBO-NT-010

• Drying endpoint reached after the (31/40)*1000 ratio value

reaches below 3 which is happening after 2300 seconds of

drying.

• There is no variation in the ion current after 2300 seconds at

those particular process parameters and the same is confirmed

by the Methanol content by Head Space GC analysis.


94

FTO

0

5

10

15

20

25

0 500 1000 1500 2000 2500 3000 3500 4000

(31

/40

)*1

00

0

Time, sec

31/40*1000ATPMS-PBO-NT-011

The validation batch shows that drying endpoint was reached after 40

minutes. The same was confirmed by Methanol content analysis by GC.

Validation


95

Eyecon – Particle size analyzer using direct imaging

FTO

• Particle sizing and characterising system

• Non contact system based on direct imaging

• Size range of 50-3000 µm can be measured

• D10, D25, D50, D75, D90,Mean, Median and average aspect ratio can be determined

• Applications of the technology include FBP granulation and coating, extrusion and

spheronization, milling and twin screw granulation processes

96

Eyecon – Case study 1 (Granule growth in Top Spray granulation)

FTO

97

Near Infra Red (NIR) – Case study 1 (Blend uniformity to avoid sampling bias)

FTO

• The ideal mix has a homogeneous distribution of all the components throughout the blender.

• The homogeneity of a blend, in the traditional pharmaceutical sense, addresses only the distribution uniformity

of the active drug substance while assuming that the excipients are also evenly distributed, which may not be

the case.

• Active, Lactose and Maize starch as shown in the table were used for blending study. Blending was carried out

in 1250L blender.

• Moving block standard deviation was calculated with block size of 5 spectra to find out the blending end point.

Unit Composition of Capsules

Composition % w/w

Active 75.76

Lactose

Monohydrate8

Maize Starch 8

Talc 8

Total 100.00

98

FTO

• Blend samples out of spec but UOD values are very narrow

• API is crystalline and has needle shaped particles inducing sampling bias

• NIR used as an alternative to sampling to ensure uniformity of the blend

Problem Statement

Blend uniformity

S.No. Blender SS Bin

1 102.8 100.4

2 105.4 98.7

3 97.6 101

4 101.0 100.7

5 102.3 98.9

6 101.3 103.9

7 112.7 100.7

8 88.1 101.6

9 100.0 111.4

10 99.5 123.4

Avg 101.1 104.3

%RSD 6.1 6.8

UOD of capsules

S.No. 300 mg

1 100.8

2 99.1

3 99.5

4 100

5 99.9

6 101.2

7 100.4

8 99.5

9 100.8

10 99.8

Avg 100.1

AV 1.6

112.7

88.1

111.4

123.4

85

90

95

100

105

110

115

120

125

1 2 3 4 5 6 7 8 9 10 11 12

Blender Bin

UOD


99

FTO

Bin

Blender Type OGB

Capaacity(L) 1250

Speed(RPM) 4

Prelubrication

Time(min)40

Lubrication Time(min) 9

Batch conditions

0

0.001

0.002

0.003

0.004

0.005

0.006

0.007

0.008

1 7

13

19

25

31

37

43

49

55

61

67

73

79

85

91

97

10

3

10

9

11

5

12

1

12

7

13

3

13

9

14

5

15

1

15

7

Po

ole

d M

BSD

No. of rotations

Pooled MBSD

0

0.002

0.004

0.006

0.008

0.01

0.012

0.014

0.016

1 5 9

13

17

21

25

29

33

37

41

45

49

53

57

61

65

69

73

77

81

85

89

93

97

10

1

10

5

10

9

11

3

11

7

12

1

12

5

12

9

13

3

13

7

14

1

14

5

14

9

15

3

15

7

Stan

dar

d d

evi

atio

n

No. rotations

API@1688nm

UOD results

Mean 99.4

min 104

max 102.3

AV 4.4


100

Where are we heading to – Continuous Manufacturing (Future state in Pharma)

FTO

Why Continuous Process?

• Exploring the design space of a production process will be in hours/ days not months

• There will be no scale up work between development and manufacturing

• Plants will produce “batches” of any size in order to meet market demand

• Quality Assurance of products is continuous, and in real time

• Quality is Assured without relying on end product testing, but process control

• There will less product quality

101

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Continuous Process Verification Risk Based … 10...Continuous Process Verification –Risk Based Monitoring Sanjay Sharma. Sr. General Manager & Head Technology Transfer 2 • Variations