Quality ManagementQA-GMP-QC : QA/QC 1 Quality ManagementQA-GMP-QC Mr. Dipak D. Gadade,
Dept. of Pharmaceutics,
Shri Bhagwan College of Pharmacy,
Auranagabad
What is Quality ? : What is Quality ? Fitness for use
Freedom from defects
Degree of excellence
Customer/Buyer’s satisfaction
Compliance with specified/official requirements QA/QC 2
What is Quality ? : What is Quality ? USFDA
A measure of a product’s or service’s ability to satisfy the custmor’s stated or implied needs
Swiss Standard Association:-
The degree to which the product characteristics conform to the requirement placed upon that product including reliability, maintainability and safety. QA/QC 3
What is Quality ? : What is Quality ? Quality of a pharmaceutical product is measured by it’s fitness for intended use. Safety and efficacy are part of quality & not separable from Quality. QA/QC 4
Relation Between QM, QA, QC & GMP : Relation Between QM, QA, QC & GMP QA/QC 5
What is Quality Management? : What is Quality Management? WHO Definition :
The aspect of management functions that determines and implements the ‘quality policy’
Responsible quality of pharm. product
Product must comply with basic requirements
Identity, Strength/ potency, Purity
Bioavailabity and Biopharmaceutical parameters
Basic Elements of QM
Quality system infrastructure & systematic actions QA/QC 6
What is Quality Assurance? : What is Quality Assurance? WHO definition :
It is a wide-ranging concept covering all matters that individually or collectively influence the quality of a product. It is the totality of the arrangements made with the object of ensuring that pharm. products are of quality required for their intended use. QA/QC 7
What is GMP? : What is GMP? GMP is that part of QA which ensures that products are consistently produced and controlled to the quality standards appropriate to their intended use and as required by the Marketing Authorization or product specification. QA/QC 8
What is Quality Control? : What is Quality Control? Is that part of GMP concerned with sampling, specification & testing, documentation & release procedures which ensure that the necessary & relevant tests are performed & the product is released for use only after ascertaining it’s quality QA/QC 9
What is difference? : What is difference? Q.A. sum total of organized arrangements made with the object of ensuring that product will be of the Quality required by their intended use. Q.C. concerned with sampling,specifications, testing and with in the organization, documentation and release procedures which ensure that the necessary and relevant tests are carried out QA/QC 10
What is difference? : What is difference? Q.A. Systematic actions necessary to provide adequate confidence that a product will satisfy the requirements for quality
QA is ORGNIZATION based
Responsible for assuring adopted quality policies Q.C. Operational laboratory techniques and activities used to fulfill the requirement of Quality
QC is lab based
Responsible for day to day quality within org. QA/QC 11
Total Quality Control : Total Quality Control Process of striving to produce a perfect product by a series of measures requiring an organized effort to prevent or eliminate errors at every stage in production QA/QC 12
Sources of Quality Variation and its Control : Sources of Quality Variation and its Control Quality Control
Variation : Variation There is no two natural items in any category are the same.
Variation may be quite large or very small.
If variation very small, it may appear that items are identical, but precision instruments will show differences.
Categories of variation : Categories of variation Within-piece variation
One portion of surface is rougher than another portion.
Apiece-to-piece variation
Variation among pieces produced at the same time.
Time-to-time variation
Service given early would be different from that given later in the day.
Sources of variation : Sources of variation Equipment
Tool wear, machine vibration, …
Material
Raw material quality
Environment
Temperature, pressure, humadity
Operator
Operator performs- physical & emotional
Control of Quality Variation : Control of Quality Variation Raw Materials
Q.A. monograph
In process Quality Control
Q.A. before startup
Environmental and microbiologic control, sanitation
MWFP
Raw materials
Mfg. equipment QA/QC 17
Control of Quality Variation : Control of Quality Variation In process Quality Control
Q.A. at startup
Raw materials processing
Compounding
Labels Control
Finished product control QA/QC 18
Statistical Quality Control : Statistical Quality Control Monitoring quality by application of statistical methods in all stages of production QA/QC 19
Slide 20: Control Chart Viewpoint Variation due to
Common or chance causes
Assignable causes
Control chart may be used to discover “assignable causes”
Control chart functions : Control chart functions Control charts are powerful aids to understanding the performance of a process over time. PROCESS Input Output What’s causing variability?
Control charts identify variation : Control charts identify variation Chance causes - “common cause”
inherent to the process or random and not controllable
if only common cause present, the process is considered stable or “in control”
Assignable causes - “special cause”
variation due to outside influences
if present, the process is “out of control”
Control charts help us learn more about processes : Control charts help us learn more about processes Separate common and special causes of variation
Determine whether a process is in a state of statistical control or out-of-control
Estimate the process parameters (mean, variation) and assess the performance of a process or its capability
Control charts to monitor processes : Control charts to monitor processes To monitor output, we use a control chart
we check things like the mean, range, standard deviation
To monitor a process, we typically use two control charts
mean (or some other central tendency measure)
variation (typically using range or standard deviation)
Types of Data : Types of Data Variable data
Product characteristic that can be measured
Length, size, weight, height, time, velocity
Attribute data
Product characteristic evaluated with a discrete choice
Good/bad, yes/no
Control chart for variables : Control chart for variables Variables are the measurable characteristics of a product or service.
Measurement data is taken and arrayed on charts.
Control charts for variables : Control charts for variables X-bar chart
In this chart the sample means are plotted in order to control the mean value of a variable (e.g., Fill vol. of liquid , hardness of tablet, etc.).
R chart
In this chart, the sample ranges are plotted in order to control the variability of a variable.
S chart
In this chart, the sample standard deviations are plotted in order to control the variability of a variable.
S2 chart
In this chart, the sample variances are plotted in order to control the variability of a variable.
X-bar and R charts : X-bar and R charts The X- bar chart is developed from the average of each subgroup data.
used to detect changes in the mean between subgroups.
The R- chart is developed from the ranges of each subgroup data
used to detect changes in variation within subgroups
Control chart components : Control chart components Centerline
shows where the process average is centered or the central tendency of the data
Upper control limit (UCL) and Lower control limit (LCL)
describes the process spread
The Control Chart Method : The Control Chart Method X bar Control Chart:
UCL = XDmean + A2 x Rmean
LCL = XDmean - A2 x Rmean
CL = XDmean
R Control Chart:
UCL = D4 x Rmean
LCL = D3 x Rmean
CL = Rmean
Control Chart Examples : Control Chart Examples Nominal UCL LCL Sample number Variations
How to develop a control chart? : How to develop a control chart?
Define the problem : Define the problem Use other quality tools to help determine the general problem that’s occurring and the process that’s suspected of causing it.
Select a quality characteristic to be measured
Identify a characteristic to study - for example, angle of repose or any other variable affecting performance.
Choose a subgroup size to be sampled : Choose a subgroup size to be sampled Choose homogeneous subgroups
Homogeneous subgroups are produced under the same conditions, by the same machine, the same operator, the same mold, at approximately the same time.
Try to maximize chance to detect differences between subgroups, while minimizing chance for difference with a group.
Collect the data : Collect the data Generally, collect 20-25 subgroups (100 total samples) before calculating the control limits.
Each time a subgroup of sample size n is taken, an average is calculated for the subgroup and plotted on the control chart.
Determine trial centerline : Determine trial centerline The centerline should be the population mean,
Since it is unknown, we use X Double bar, or the grand average of the subgroup averages.
Determine trial control limits - Xbar chart : Determine trial control limits - Xbar chart The normal curve displays the distribution of the sample averages.
A control chart is a time-dependent pictorial representation of a normal curve.
Processes that are considered under control will have 99.73% of their graphed averages fall within 3.
UCL & LCL calculation : UCL & LCL calculation
Determining an alternative value for the standard deviation : Determining an alternative value for the standard deviation
Determine trial control limits - R chart : Determine trial control limits - R chart The range chart shows the spread or dispersion of the individual samples within the subgroup.
If the product shows a wide spread, then the individuals within the subgroup are not similar to each other.
Equal averages can be deceiving.
Calculated similar to x-bar charts;
Use D3 and D4
Example: Control Charts for Variable Data : Example: Control Charts for Variable Data Tablet thickness (mm)
Sample 1 2 3 4 5 X R
1 5.02 5.01 4.94 4.99 4.96 4.98 0.08
2 5.01 5.03 5.07 4.95 4.96 5.00 0.12
3 4.99 5.00 4.93 4.92 4.99 4.97 0.08
4 5.03 4.91 5.01 4.98 4.89 4.96 0.14
5 4.95 4.92 5.03 5.05 5.01 4.99 0.13
6 4.97 5.06 5.06 4.96 5.03 5.01 0.10
7 5.05 5.01 5.10 4.96 4.99 5.02 0.14
8 5.09 5.10 5.00 4.99 5.08 5.05 0.11
9 5.14 5.10 4.99 5.08 5.09 5.08 0.15
10 5.01 4.98 5.08 5.07 4.99 5.03 0.10
50.09 1.15
Calculation : Calculation From Table above:
Sigma X-bar = 50.09
Sigma R = 1.15
m = 10
Thus;
X-Double bar = 50.09/10 = 5.009 mm
R-bar = 1.15/10 = 0.115 mm Note: The control limits are only preliminary with 10 samples.
It is desirable to have at least 25 samples.
Trial control limit : Trial control limit UCLx-bar = X-D bar + A2 R-bar
= 5.009 + (0.577)(0.115)
= 5.075 mm
LCLx-bar = X-D bar - A2 R-bar
= 5.009 - (0.577)(0.115) = 4.943 mm
UCLR=D4R-bar=(2.114)(0.115)=0.243 mm
LCLR = D3R-bar = (0)(0.115) = 0 cm
Slide 44: 3-Sigma Control Chart Factors Sample size X-chart R-chart
n A2 D3 D4
2 1.88 0 3.27
3 1.02 0 2.57
4 0.73 0 2.28
5 0.58 0 2.11
6 0.48 0 2.00
7 0.42 0.08 1.92
8 0.37 0.14 1.86
X-bar Chart : X-bar Chart
R Chart : R Chart
Run Chart : Run Chart
Slide 48: Another Example of X-bar & R chart
Slide 49: Given Data (Table 5.2)
Calculation : Calculation From Table 5.2:
Sigma X-bar = 160.25
Sigma R = 2.19
m = 25
Thus;
X-double bar = 160.25/29 = 6.41 mm
R-bar = 2.19/25 = 0.0876 mm
Trial control limit : Trial control limit UCLx-bar = X-double bar + A2R-bar = 6.41 + (0.729)(0.0876) = 6.47 mm
LCLx-bar = X-double bar - A2R-bar = 6.41 – (0.729)(0.0876) = 6.35 mm
UCLR = D4R-bar = (2.282)(0.0876) = 0.20 mm
LCLR = D3R-bar = (0)(0.0876) = 0 mm
X-bar Chart : X-bar Chart
R Chart : R Chart
Revised CL & Control Limits : Revised CL & Control Limits Calculation based on discarding subgroup 4 & 20 (X-bar chart) and subgroup 18 for R chart:
= (160.25 - 6.65 - 6.51)/(25-2)
= 6.40 mm
= (2.19 - 0.30)/25 - 1
= 0.079 = 0.08 mm
New Control Limits : New Control Limits New value:
Using standard value, CL & 3 control limit obtained using formula:
Slide 56: From Table B:
A = 1.500 for a subgroup size of 4,
d2 = 2.059, D1 = 0, and D2 = 4.698
Calculation results:
Slide 57: Trial Control Limits & Revised Control Limit UCL = 6.46 CL = 6.40 LCL = 6.34 LCL = 0 CL = 0.08 UCL = 0.18 Revised control limits
Revise the charts : Revise the charts In certain cases, control limits are revised because:
out-of-control points were included in the calculation of the control limits.
the process is in-control but the within subgroup variation significantly improves.
Revising the charts : Revising the charts Interpret the original charts
Isolate the causes
Take corrective action
Revise the chart
Only remove points for which you can determine an assignable cause
Process in Control : Process in Control When a process is in control, there occurs a natural pattern of variation.
Natural pattern has:
About 34% of the plotted point in an imaginary band between 1s on both side CL.
About 13.5% in an imaginary band between 1s and 2s on both side CL.
About 2.5% of the plotted point in an imaginary band between 2s and 3s on both side CL.
Slide 61: The Normal
Distribution = Standard deviation
Slide 62: Define the 3-sigma limits for sample means as follows:
What is the probability that the sample means will lie outside 3-sigma limits?
Note that the 3-sigma limits for sample means are different from natural tolerances which are at Normal Distribution Review
Process Out of Control : Process Out of Control The term out of control is a change in the process due to an assignable cause.
When a point (subgroup value) falls outside its control limits, the process is out of control.
Slide 64: Assignable Causes (a) Mean Grams Average
Slide 65: Assignable Causes (b) Spread Grams Average
Slide 66: Assignable Causes (c) Shape Grams Average
Slide 67: Control Charts UCL Nominal LCL Assignable causes likely 1 2 3
Samples
Control Chart Examples : Control Chart Examples Nominal UCL LCL Sample number Variations
Achieve the purpose : Achieve the purpose Our goal is to decrease the variation inherent in a process over time.
As we improve the process, the spread of the data will continue to decrease.
Quality improves!!
Improvement : Improvement
Examine the process : Examine the process A process is considered to be stable and in a state of control, or under control, when the performance of the process falls within the statistically calculated control limits and exhibits only chance, or common causes.
Consequences of misinterpreting the process : Consequences of misinterpreting the process Blaming people for problems that they cannot control
Spending time and money looking for problems that do not exist
Spending time and money on unnecessary process adjustments
Taking action where no action is warranted
Asking for worker-related improvements when process improvements are needed first
Process variation : Process variation When a system is subject to only chance causes of variation, 99.74% of the measurements will fall within 6 standard deviations
If 1000 subgroups are measured, 997 will fall within the six sigma limits.
Chart zones : Chart zones Based on our knowledge of the normal curve, a control chart exhibits a state of control when:
Two thirds of all points are near the center value.
The points appear to float back and forth across the centerline.
The points are balanced on both sides of the centerline.
No points beyond the control limits.
No patterns or trends.
Slide 75: QA/QC 75 Quality should be built into product and testing alone can not relied on to ensure product quality