detailed explanation of tools of process validation

1. Biostatistical tools of
process validation

2. Index
1. Introduction.
2. Objectives.
3. Process validation decision.
4. Conduct of validation.
5. Types of process validation.
6. Validation protocol.
7. Bio Statistical tools of process validation.
8. Advantages of process validation.
9. Conclusion.

3. Introduction:
Process Validation:
According to U.S FDA “Process validation is establishing documented
evidence which provides a high degree of assurance that a specific
process will consistently produce a product meeting its
predetermined specification and quality characteristics”.
Validation is a key process for a effective quality assurance.
Quality assurance is heart and soul of quality control.
QA=QC+GMP

4. Objectives:
•To form basis for written procedures for production and process
control which are designated to assure that the drug products have
the identity, strength, quality and purity are maintained or
represented to possess.
•To determine that process consistently performs as intended by
repeatedly running the system on its intended schedules and
reporting all relevant information and data.
•Results must demonstrate that the process meets pre-determined
specifications under normal condition.

5. •With the emerge of process validation concept, the conventional
quality control procedures for finished product testing includes the
following steps:
Establishment of specification and performance characteristic.
Selection of appropriate methodology, equipment and
instrumentation to assure that testing of product meets
specification.
Qualification of a process facility and its equipments.
Qualification and validation of manufacturing process through
appropriate means.
Revalidation when there is a significant change in either the
product or its manufacturing procedure.

6. Process validation:
A
Is process
output
verifiable
B
Is verification
sufficient
C
Verify and
control the
process
yes yes
no no
D
validate
E
Redesign
product/process

7. Process which should be validated
•Sterilization process.
•Clean room ambient conditions.
•Aseptic filling process.
•Sterile packaging sealing process.
•Lyophilization process.
•Heat treating process.
•Plastic injection molding process.

8. Conduct of validation
The best approach in carrying out the process validation assignment
is to establish a chemistry, manufacturing and control (CMC)
coordination committee at specific manufacturing plant site.
conduct of validation team could include representative from or
personnel with expertise in:
•Quality assurance.
•Engineering.
•Pharmaceutical manufacturing.
•Formulation development (usually a laboratory function).
•Process development (usually a pilot plant function).
•Quality control.
•API operations.
•Regulatory affairs.

9. Types of process validation
Prospective validation:
•This is performed for all new equipment, products and process.
•It is a proactive approach of documenting the design, specification
and performances before the system is operational.
•This is the most defendable type of validation.
Concurrent validation:
•This is performed in two instances i.e., for existing equipments,
verification of proper installation along with specific operational test
is done.
•In case of existing, in frequently made product, data is gathered
from at least three successful trials.

10. Retrospective validation:
•This is established documented evidence that the process has
performed satisfactorily and consistently overtime, based on review
and analysis of historical data.
Retrospective validation generally not accepted because…
•The issue to be addressed here are changes to equipments, process,
specification and other relevant changes in the past.
•Lack of validation protocols usually indicates a lack of documentation,
and often data is reported as only pass or fail which does not permit
statistical analysis which only can be performed on numerical data.

11. Revalidation:
•Consider periodic revalidation where cumulative minor changes to
process and raw material may eventually affect process.
•Sterilization processes typically are revalidated periodically (once a
year or as needed)
Some reasons for revalidation..
•Change in process that may effect the quality or validation status.
•Negative trend in quality indicators.
•Change in the product design that affects the process.
•Process is moved with in facility or transferred from one facility to
another.
•Change in the application of the process.

12. Design qualification: Documented verification of the design
of equipments and manufacturing facilities.
Installation qualification: Documented verification that
equipment is installed and operating properly.
Operational qualification: Documented verification of
equipment or system performance in the target operating range.
Process performance qualification: Documented
operation that equipment or system operates as expected under
routine production conditions. The operation is reproducible, reliable
and in a state of control.

13. Validation protocol
Detailed protocols for performing validations are essential to
ensure that the process is adequately validated. Process validation
protocols should include the following elements:
•Identification of the process to be validated.
•Identification of device(s) to be manufactured using this process.
•Objective and measurable criteria for a successful validation.
•Length and duration of the validation.
•Shifts, operators, equipment to be used in the process.
•Identification of utilities for the process equipment and quality of
the utilities.
•Identification of operators and required operator qualification.
•Complete description of the process.

14. •Relevant specifications that relate to the product, components,
manufacturing materials, etc.
•Any special controls or conditions to be placed on preceding processes
during the validation.
•Process parameters to be monitored, and methods for controlling and
monitoring.
•Product characteristics to be monitored and method for monitoring.
• Any subjective criteria used to evaluate the product.
• Definition of what constitutes non-conformance for both measurable
and subjective criteria.
•Statistical methods for data collection and analysis.
•Consideration of maintenance and repairs of manufacturing equipment.
• Criteria for revalidation.

15.  They are many method and bio statistical tools can be used in
process validation include:
• Capability studies
• Control charts.
• Designed experiments.
• Tolerance analysis.
• Robust design method.
• Failure mode and effect of analysis.
• Sampling plan.

16. Capability study
Capability study measures the ability of the process to measure, to
consistently meet the specifications.
It is appropriate for measurable characteristics where
nonconformities are due to variation and off-target conditions.
Several capability indices are used to measure how well the
histogram fits within the specification limits.
One index called Cp is used to evaluate the variation. Another index
Cpk is used to evaluate the centering of the process.
Together these two indices are used to decide whether the process
meets its requirements.
While capability studies evaluate the ability of a process to
consistently produce good product, these studies do little to help
achieve such processes.

17. Process capability is expressed as a ratio of specifications/process
variability.
Cp=USL-LSL/6σ
CPu=USL-µ/3σ
CPL=µ-LSL/3σ
USL-upper specification limit
LSL-lower specification limit
µ-sample mean
σ-estimated variability of a process

18. Control charts
Capability study is used to determine whether a process is stable
and capable. It involves collecting samples over a period of time.
The average and standard deviation of each time period is
estimated and these estimates plotted in the form of a control chart.
The control chart is a graph used to study how a process changes
over time. Data are plotted in time order.
A control chart always has a central line for the average, an upper
line for the upper control limit and a lower line for the lower control
limit. These lines are determined from historical data.
Control charts help to identify key input variables causing the
process to shift and aid in the reduction of the variation.
Control charts are also used as part of a capability study to
demonstrate that the process is stable or consistent.

19. The control limits represent the maximum amount that the average
or range should vary if the process does not change.
 A point outside the control limits indicates that the process has
changed.

21. ----------------------------------------
UPL
LCL
MEAN
0 30
min
60
min
90
min
120
min
160
min
280
285
290
295
300
305
310
Tablet weight variation
w
e
i
g
h
t
time

22. Different type of control charts are used based on the data available
 Two types of data-
1. Continuous data
2. Discrete data
 Continuous data: Information can be measured on a scale, can have
almost any numeric value and can meaning fully subdivided.
 Discrete data: Takes only one particular value. They may be infinite
number of those values and the values cannot be subdivided.
Continuous data Discrete data
X-MR/I-MR CHART C CHART
X BAR-R CHART U CHART
X BAR-S CHART np chart

24. Continuous chart:
I-MR CHART-
•An I-MR chart plots individual observations (I chart) and moving ranges
(MR chart) over time for variables data.
•Use this combination chart to monitor process center and variation
when it is difficult or impossible to group measurements into subgroups.
•This occurs when measurements are expensive, production volume is
low, or products have a long cycle time.
•When data are collected as individual observations, you cannot calculate
the standard deviation for each subgroup.
•The moving range is an alternative way to calculate process variation by
computing the ranges of two or more consecutive observations.

25. Example of I-MR CHART:
•A hospital administrator wants to determine whether the time to
perform outpatient hernia surgery is stable.
•Because the data are not collected in subgroups, he uses an I-MR
chart to monitor the mean and variation of the surgery times.
•The points vary randomly around the center line and are within the
control limits. No trends or patterns are present.
•The amount of time to perform hernia surgery and the variation in
times are stable.

26. XBAR-R CHART:
•An Xbar-R chart plots the process mean (Xbar chart) and process
range (R chart) over time for variables data in subgroups. This
combination control chart is widely used to examine the stability of
processes in many industries.
•For example, you can use Xbar-R charts to monitor the process mean
and variation for subgroups of part lengths, call times, or hospital
patients' blood pressure over time.
•The Xbar chart and the R chart are displayed together because you
should interpret both charts to determine whether your process is
stable. Examine the R chart first because the process variation must be
in control to correctly interpret the Xbar chart. The control limits of
the Xbar chart are calculated considering both process spread and
center.

27. •If the R chart is out of control, then the control limits on the Xbar chart may
be inaccurate and may falsely indicate an out-of-control condition or fail to
detect one.
•You can use the Xbar-R chart when your subgroup size is 8 or less. Use the
Xbar-S chart when your subgroup size is 9 or more.
Example of XBAR-R CHART:
•A plastics manufacturer wants to determine whether the production
process for a new product is in control. Analysts sample 5 units every
hour for 20 hours and assess the strength of the plastic.
•The points vary randomly around the center line and are within the
control limits for both charts. No trends or patterns are present. The
strength of the plastic product is stable across the 20 subgroups.

29. XBAR-S CHART:
•An Xbar-S chart plots the process mean (Xbar chart) and process
standard deviation (S chart) over time for variables data in subgroups.
This combination control chart is widely used to examine the stability
of processes in many industries.
•For example, you can use Xbar-S charts to examine the process mean
and variation for subgroups of part lengths, call times, or hospital
patients' blood pressure over time.
•The Xbar chart and the S chart are displayed together because you
should interpret both charts to determine whether your process is
stable. Examine the S chart first because the process variation must be
in control to correctly interpret the Xbar chart. The control limits of
the Xbar chart are calculated considering both process spread and
center.

30. •. If the S chart is out of control, then the control limits on the Xbar chart
may be inaccurate and may falsely indicate an out-of-control condition or fail
to detect one.
•Use the Xbar-S chart when your subgroup size is 9 or more. You can use the
Xbar-R chart when your subgroup size is 8 or less.
Example of XBAR-S CHART
•A paint manufacturer wants to assess the stability of the process used
to fill paint cans. Analysts collect subgroups of 10 cans every hour and use
an Xbar-S chart to monitor the mean and variation of the filled paint
cans.

31. •The points vary randomly around the center line and are within the
control limits. No trends or patterns are present. The variability in
the fill weight is stable across the 30 subgroups.
Discrete chart:
C CHART:
• C charts are used to chart the total number of defects in a sample
when the sample size is constant. You can inspect for one type of
defect such as dead pixels and also inspect for several defects
together such as dead pixels, stuck pixels, scratches, and blurry spots.
An LCD screen may have 2 or 3 dead pixels, yet still be acceptable
Example of C CHART:
•An LCD manufacturer wants to monitor defects on 17-inch LCD
screens. Technicians record the number of dead pixels for each
subgroup of 10 screens per hour.

32. •They use a C chart to monitor the number of dead pixels.
•On average, technicians find 10 dead pixels in each sample. Sample 17
is out of control. The technicians should try to identify any special
causes that may have contributed to the unusually high number of dead
pixels.

33. U CHART:
•A U chart plots the number of defects (also called nonconformities)
per unit. It is possible for a unit to have one or more defects but still
be acceptable in function and performance.
•For example, you can use a U chart to monitor the following:
•The number of tears and pulls per 50 running feet of carpet
•The number of dead pixels per foot of LCD screen
•You can inspect for one type of defect such as dead pixels. You can
also inspect for several defects together such as dead pixels, stuck
pixels, scratches, and blurry spots. An LCD screen may have 2 or 3
dead pixels, yet still be acceptable.

34. Example of U CHART:
•An LCD manufacturer wants to monitor defects on 17-inch LCD
screens. Technicians record the number of dead pixels for each
screen. Each subgroup has a different number of screens. They use a
U chart to monitor the average number of dead pixels per screen.
•Because of the unequal subgroup sizes, the control limits vary. On
average, technicians find about 1 dead pixel on each screen. Subgroup
17 is out of control. The technicians should try to identify any special
causes that may have contributed to the unusually high number of dead
pixels.

35. NP CHART:
•The NP chart plots the number of nonconforming units (also called
defectives). For example, you can use an NP chart to monitor the
following:
1.The number of flights that depart late
2.The number of bicycle tires that are flat
3.The number of printed logos that are smudged
•While a unit may have many quality characteristics that can be
evaluated, it is always considered as either conforming or
nonconforming.

37. When controlling ongoing processes by finding and correcting
problems as they occur.
When predicting the expected range of outcomes from a process.
When determining whether a process is stable (in statistical
control).
When analyzing patterns of process variation from special causes
(non-routine events) or common causes (built into the process).
When determining whether your quality improvement project should
aim to prevent specific problems or to make fundamental changes to
the process.
When to use a control chart

38. Designed experiments
DOE begins with determining the objectives of an experiment and
selecting the process factors for the study.
An Experimental Design is the laying out of a detailed
experimental plan in advance of doing the experiment.
An experimental design is a series of statistically sufficient
qualification trials that are planned in a specific arrangement and
include all processing variables that can possibly affect the expected
outcome of the process under investigation.
In the case of a full factorial design, n the number of factors or
process variables, each at two levels, i.e., the upper (+) and lower (−)
control limits.
Such a design is known as a 2n factorial.

39. The term designed experiment is a general term that encompasses
screening experiments, response surface studies, and analysis of
variance.
In general, a designed experiment involves purposely changing one or
more inputs and measuring resulting effect on one or more outputs.

40. Tolerance analysis
Tolerance analysis is the general term for activities related to the
study of potential accumulated variation in mechanical parts and
assemblies.
Tolerance analysis can be used to establish operating windows or
control schemes that ensure the output consistently conforms to
requirements.
Using tolerance analysis, operating windows can be set for the
inputs that ensure the outputs will conform to requirements.
 Performing a tolerance analysis requires an equation describing the
effects of the inputs on the output.
If such an equation is not available, a response surface study can be
performed to obtain one.

41. To help ensure manufacturability, tolerances for the inputs should
initially be based on the plants and suppliers ability to control them.
 Capability studies can be used to estimate the ranges that the
inputs currently vary over.
 If this does not result in an acceptable range for the output, the
tolerance of at least one input must be tightened.
 However, tightening a tolerance beyond the current capability of
the plant or supplier requires that improvements be made or that a
new plant or supplier be selected.
Before tightening any tolerances, robust design methods should
be considered.

42. Robust design method
Robust design methods refers collectively to the different methods
of selecting optimal targets for the inputs.
 Generally, when one thinks of reducing variation, tightening
tolerances comes to mind.
However, as demonstrated by Taguchi, variation can also be reduced
by the careful selection of targets.
 When nonlinear relationships exist between the inputs and the
outputs, one can select targets for the inputs that make the outputs
less sensitive to the inputs.
 The result is that while the inputs continue to vary, less of this
variation is transmitted to the output.
 The result is that the output varies less.

43. Reducing variation by adjusting targets is called robust design.
 In robust design the objective is to select targets for the inputs
that result in on-target performance with minimum variation.
 Several methods of obtaining robust designs exist including robust
tolerance analysis, dual response approach and Taguchi methods.

44. Failure mode & effective analysis
An FMEA is systematic analysis of the potential failure modes.
It includes the identification of possible failure modes,
determination of the potential causes and consequences and an
analysis of the associated risk.
It also includes a record of corrective actions or controls
implemented resulting in a detailed control plan.
 FMEAs can be performed on both the product and the process.
Typically an FMEA is performed at the component level, starting
with potential failures and then tracing up to the consequences. This
is a bottom up approach.
 A variation is a Fault Tree Analysis, which starts with possible
consequences and traces down to the potential causes.
This is the top down approach.

45. An FMEA tends to be more detailed and better at identifying
potential problems.
 However, a fault tree analysis can be performed earlier in the
design process before the design has been resolved down to
individual components.

46. Sampling plan
Sampling plan takes a sample of product and uses this sample to
make an accept or reject decision.
Acceptance sampling plans are commonly used in manufacturing to
decide whether to accept (release) or to reject (hold) lots of product.
 However, they can also be used during validation to accept (pass) or
to reject (fail) the process.
 Following the acceptance by a sampling plan, one can make a
confidence statement such as: “With 95% confidence, the defect rate
is below 1% defective.”

49. Operating characteristics curve (OC) measures the performance of
an acceptance sampling plan.
The OC curve plots the probability of accepting the lot against the
lot of defective.

50. Advantages of process validation
Quality:
•Customer satisfaction: Non-conforming products can lead to lost
customers.
•Customer mandated: Provision for securing good business.
Product liability: Conformance to product specification must be
maintained.
Understanding equipment, system, process:
•Process improvement, technology transfer, related product
validation, rapid failure investigation, increased employee awareness.
Regulatory requirement:
•Successful inspection.
•Approved products.

51. Cost reduction:
•Increased efficiency, shortening lead-time resulting in lowering
inventories.
•Fewer rejects and reworks.
• longer equipment life by operating the equipment as per
manufacturer’s specification and establishment of cost effective
and preventive maintenance schedule.
•Possible reduced testing of raw materials, bulk formulations and
finished products.

52. Conclusion
Quality of the end product can be ensured by carefully designing
and validating the process as well as process control.
A successful process validation lowers the need for intensive end-
product and in-process testing.
Validation is carried out by drafting a comprehensive protocol
prepared by careful and accurate collection of data that gives a
clear picture of the fact.
Validation also ensures that all the important process variables
are monitored, documented and the data obtained is analyzed
thoroughly to establish the variability process parameters for
individual runs.

53. References
Pharmaceutical process validation, 3rd edition, revised, expanded
by ROBERT A.NASH and ALFRED H.WACHTER.
The theory and practice of industrial pharmacy, LEON LACHMAN.
http://www.sipa.gov.tw/bioweb/file/law_down1-6.pdf.
http://asq.org/learn-about-quality/data-collection-analysis-
tools/overview/control-chart.html
http://www.slideshare.net/Hardik_Mistry/process-validation.

54. Thank you