QA QC

Research Methodology

QA/QC1Quality ManagementQA-GMP-QCMr. Dipak D. Gadade, Dept. of Pharmaceutics,Shri Bhagwan College of Pharmacy,Auranagabad

What is Quality ?Fitness for use

Freedom from defects

Degree of excellence

Customer/Buyers satisfaction

Compliance with specified/official requirements

QA/QC2

What is Quality ?USFDAA measure of a products or services ability to satisfy the custmors stated or implied needsSwiss Standard Association:-The degree to which the product characteristics conform to the requirement placed upon that product including reliability, maintainability and safety.

QA/QC3

What is Quality ?Quality of a pharmaceutical product is measured by its fitness for intended use. Safety and efficacy are part of quality & not separable from Quality.

QA/QC4

Relation Between QM, QA, QC & GMPQA/QC5

What is Quality Management?WHO Definition : The aspect of management functions that determines and implements the quality policyResponsible quality of pharm. productProduct must comply with basic requirementsIdentity, Strength/ potency, PurityBioavailabity and Biopharmaceutical parametersBasic Elements of QMQuality system infrastructure & systematic actionsQA/QC6

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/QC7

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/QC8

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 its quality

QA/QC9

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/QC10

What is difference?Q.A.Systematic actions necessary to provide adequate confidence that a product will satisfy the requirements for qualityQA is ORGNIZATION basedResponsible 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/QC11

Total Quality ControlProcess 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/QC12

Sources of Quality Variation and its Control Quality Control

VariationThere 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 variationWithin-piece variationOne portion of surface is rougher than another portion.Apiece-to-piece variationVariation among pieces produced at the same time.Time-to-time variationService given early would be different from that given later in the day.

Sources of variationEquipmentTool wear, machine vibration, MaterialRaw material qualityEnvironmentTemperature, pressure, humadityOperatorOperator performs- physical & emotional

Control of Quality VariationRaw Materials Q.A. monographIn process Quality ControlQ.A. before startupEnvironmental and microbiologic control, sanitationMWFPRaw materialsMfg. equipment

QA/QC17

Control of Quality VariationIn process Quality ControlQ.A. at startupRaw materials processingCompoundingLabels ControlFinished product control

QA/QC18

Statistical Quality ControlMonitoring quality by application of statistical methods in all stages of productionQA/QC19

Control Chart ViewpointVariation due to Common or chance causes Assignable causesControl chart may be used to discover assignable causes

Control chart functionsControl charts are powerful aids to understanding the performance of a process over time.

PROCESS

InputOutputWhats causing variability?

Control charts identify variationChance causes - common causeinherent to the process or random and not controllableif only common cause present, the process is considered stable or in controlAssignable causes - special causevariation due to outside influencesif present, the process is out of control

Control charts help us learn more about processesSeparate common and special causes of variationDetermine whether a process is in a state of statistical control or out-of-controlEstimate the process parameters (mean, variation) and assess the performance of a process or its capability

Control charts to monitor processesTo monitor output, we use a control chartwe check things like the mean, range, standard deviationTo monitor a process, we typically use two control chartsmean (or some other central tendency measure)variation (typically using range or standard deviation)

Types of DataVariable dataProduct characteristic that can be measuredLength, size, weight, height, time, velocity

Attribute dataProduct characteristic evaluated with a discrete choiceGood/bad, yes/no

Control chart for variablesVariables are the measurable characteristics of a product or service.Measurement data is taken and arrayed on charts.

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 chartsThe 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 componentsCenterlineshows where the process average is centered or the central tendency of the dataUpper control limit (UCL) and Lower control limit (LCL) describes the process spread

The Control Chart MethodX 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

NominalUCLLCL

Sample numberVariations

22/03/2011Statistics31

30

And finally, this chart shows a process with two points actually outside the control limits, an easy indicator to detect but not the only one.

These rules, there are a few more, are commonly referred to as the Western Electric Rules and can be found in any advanced quality reference.

How to develop a control chart?

Define the problemUse other quality tools to help determine the general problem thats occurring and the process thats suspected of causing it.

Select a quality characteristic to be measuredIdentify a characteristic to study - for example, angle of repose or any other variable affecting performance.

Choose a subgroup size to be sampledChoose homogeneous subgroupsHomogeneous 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 dataGenerally, 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 centerlineThe 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 chartThe 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

Determining an alternative value for the standard deviation

Determine trial control limits - R chartThe 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 Tablet thickness (mm)Sample 12345 X R15.025.014.944.994.96 4.98 0.0825.015.035.074.954.96 5.00 0.1234.995.004.934.924.99 4.97 0.0845.034.915.014.984.89 4.96 0.1454.954.925.035.055.01 4.99 0.1364.975.065.064.965.03 5.01 0.1075.055.015.104.964.99 5.02 0.1485.095.105.004.995.08 5.05 0.1195.145.104.995.085.09 5.08 0.15 105.014.985.085.074.99 5.03 0.10 50.09 1.15

22/03/2011Statistics41Example4.3

CalculationFrom Table above:Sigma X-bar = 50.09Sigma R = 1.15m = 10Thus;X-Double bar = 50.09/10 = 5.009 mmR-bar = 1.15/10 = 0.115 mmNote: The control limits are only preliminary with 10 samples.It is desirable to have at least 25 samples.

Trial control limitUCLx-bar = X-D bar + A2 R-bar = 5.009 + (0.577)(0.115) = 5.075 mmLCLx-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 mmLCLR = D3R-bar = (0)(0.115) = 0 cm

3-Sigma Control Chart FactorsSample size X-chart R-chartnA2D3D421.8803.2731.0202.5740.7302.2850.5802.1160.4802.0070.420.081.9280.370.141.86

X-bar Chart

R Chart

Run Chart

Another Example of X-bar & R chart

SubgroupX1X2X3X4X-barUCL-X-barX-DbarLCL-X-barRUCL-RR-barLCL-R16.356.46.326.376.366.476.416.350.080.200.0876026.466.376.366.416.46.476.416.350.10.200.0876036.346.46.346.366.366.476.416.350.060.200.0876046.696.646.686.596.656.476.416.350.10.200.0876056.386.346.446.46.396.476.416.350.10.200.0876066.426.416.436.346.46.476.416.350.090.200.0876076.446.416.416.466.436.476.416.350.050.200.0876086.336.416.386.366.376.476.416.350.080.200.0876096.486.446.476.456.466.476.416.350.040.200.08760106.476.436.366.426.426.476.416.350.110.200.08760116.386.416.396.386.396.476.416.350.030.200.08760126.376.376.416.376.386.476.416.350.040.200.08760136.46.386.476.356.46.476.416.350.120.200.08760146.386.396.456.426.416.476.416.350.070.200.08760156.56.426.436.456.456.476.416.350.080.200.08760166.336.356.296.396.346.476.416.350.10.200.08760176.416.46.296.346.366.476.416.350.120.200.08760186.386.446.286.586.426.476.416.350.30.200.08760196.356.416.376.386.386.476.416.350.060.200.08760206.566.556.456.486.516.476.416.350.110.200.08760216.386.46.456.376.46.476.416.350.080.200.08760226.396.426.356.46.396.476.416.350.070.200.08760236.426.396.396.366.396.476.416.350.060.200.08760246.436.366.356.386.386.476.416.350.080.200.08760256.396.386.436.446.416.476.416.350.060.200.08760

Given Data (Table 5.2)

CalculationFrom Table 5.2:Sigma X-bar = 160.25Sigma R = 2.19m = 25Thus;X-double bar = 160.25/29 = 6.41 mmR-bar = 2.19/25 = 0.0876 mm

Trial control limitUCLx-bar = X-double bar + A2R-bar = 6.41 + (0.729)(0.0876) = 6.47 mmLCLx-bar = X-double bar - A2R-bar = 6.41 (0.729)(0.0876) = 6.35 mm

UCLR = D4R-bar = (2.282)(0.0876) = 0.20 mmLCLR = D3R-bar = (0)(0.0876) = 0 mm

X-bar Chart

R Chart

Revised CL & Control LimitsCalculation 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 LimitsNew value:

Using standard value, CL & 3 control limit obtained using formula:

From Table B:A = 1.500 for a subgroup size of 4,d2 = 2.059, D1 = 0, and D2 = 4.698Calculation results:

Trial Control Limits & Revised Control Limit

UCL = 6.46CL = 6.40LCL = 6.34

LCL = 0CL = 0.08UCL = 0.18Revised control limits

Revise the chartsIn 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 chartsInterpret the original chartsIsolate the causesTake corrective actionRevise the chartOnly remove points for which you can determine an assignable cause

Process in ControlWhen 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.

The NormalDistribution

-3 -2 -1 +1 +2 +3Mean

68.26%95.44%99.74%

= Standard deviation

LSLUSL-3+3

CL


20

And at +/- 3 sigma, the most common choice of confidence/control limits in quality control application, the area is 99.97%.

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 atNormal Distribution Review

Process Out of ControlThe 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.

Assignable Causes

(a) MeanGrams

Average


7

The new distribution will look as shown in this slide.

Assignable Causes(b) Spread

Grams

Average


9

The new distribution has a much greater spread (higher standard deviation).

Assignable Causes(c) Shape

Grams

Average


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A skewed (non-normal) distribution will result in a different pattern of variability.

Control ChartsUCLNominalLCLAssignable causes likely

1 2 3Samples


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However, the third sample plots outside the original distribution, indicating the likely presence of an assignable cause.

Control Chart Examples

NominalUCLLCL

Sample numberVariations


30

And finally, this chart shows a process with two points actually outside the control limits, an easy indicator to detect but not the only one.

These rules, there are a few more, are commonly referred to as the Western Electric Rules and can be found in any advanced quality reference.

Achieve the purposeOur 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

Examine the processA 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 processBlaming people for problems that they cannot controlSpending time and money looking for problems that do not existSpending time and money on unnecessary process adjustmentsTaking action where no action is warrantedAsking for worker-related improvements when process improvements are needed first

Process variationWhen a system is subject to only chance causes of variation, 99.74% of the measurements will fall within 6 standard deviationsIf 1000 subgroups are measured, 997 will fall within the six sigma limits.

-3 -2 -1 +1 +2 +3Mean

68.26%95.44%99.74%

Chart zonesBased 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.

QA/QC75 Quality should be built into product and testing alone can not relied on to ensure product quality

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Chart84.9854.974.964.995.015.025.055.085.03

LCLCLUCLSubgroupX bar

Sheet114.982534.9744.9654.9965.0175.0285.0595.08105.0310.0820.1230.0840.1450.1360.170.1480.1190.15100.1

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LCLCLUCLSubgroupRange

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Chart50.080.120.080.140.130.10.140.110.150.1


Sheet114.982534.9744.9654.9965.0175.0285.0595.08105.0310.0820.1230.0840.1450.1360.170.1480.1190.15100.1

Sheet1


Sheet2


Sheet3

Chart20.080.10.060.10.10.090.050.080.040.110.030.040.120.070.080.10.120.30.060.110.080.070.060.080.06

Subgroup numberRange, R

Sheet116.3626.436.3646.6556.3966.476.4386.3796.46106.42116.39126.38136.4146.41156.45166.34176.36186.42196.38206.51216.4226.39236.39246.38256.4110.0820.130.0640.150.160.0970.0580.0890.04100.11110.03120.04130.12140.07150.08160.1170.12180.3190.06200.11210.08220.07230.06240.08250.06

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Subgroup numberMean, X-bar

Sheet2


Sheet3

Chart16.366.46.366.656.396.46.436.376.466.426.396.386.46.416.456.346.366.426.386.516.46.396.396.386.41


Sheet116.3626.436.3646.6556.3966.476.4386.3796.46106.42116.39126.38136.4146.41156.45166.34176.36186.42196.38206.51216.4226.39236.39246.38256.41

Sheet1


Sheet2

Sheet3

Chart16.366.46.366.656.396.4

SubgroupMean, X-bar

Sheet116.3626.436.3646.6556.3966.476.4386.3796.46106.42116.3916.36126.3826.4136.436.36146.4146.65156.4556.39166.3466.4176.367186.428196.389206.5110216.411226.3912236.3913246.3814256.411516171810.081920.12030.062140.12250.12360.092470.052580.0890.04100.11110.03120.04130.12140.07150.0810.08160.120.1170.1230.06180.340.1190.0650.1200.1160.09210.087220.078230.069240.0810250.06111213141516171819202122232425

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SubgroupMean, X-bar

SubgroupRange, R

Chart20.080.10.060.10.10.09

SubgroupRange, R

Sheet116.3626.436.3646.6556.3966.476.4386.3796.46106.42116.3916.36126.3826.4136.436.36146.4146.65156.4556.39166.3466.4176.367186.428196.389206.5110216.411226.3912236.3913246.3814256.411516171810.081920.12030.062140.12250.12360.092470.052580.0890.04100.11110.03120.04130.12140.07150.0810.08160.120.1170.1230.06180.340.1190.0650.1200.1160.09210.087220.078230.069240.0810250.06111213141516171819202122232425

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SubgroupMean, X-bar

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