Process controls involved in the manufacturing process of pharmaceutical dosage forms And statistical quality control charts and its application application in process control Presented by, Y.Maheshwara prasad M pharmacy (Pharmaceutics) 1st semester Care college of pharmacy,warangal pharmacy,warangal
Contents: • Introduction to process controls involved in the manufacturing of pharmaceutical dosage forms • Statistical process control • Control charts • Control charts for attributes • Control charts for variables • Control chart patterns • Applications in pharmacuticals
Contents: • Introduction to process controls involved in the manufacturing of pharmaceutical dosage forms • Statistical process control • Control charts • Control charts for attributes • Control charts for variables • Control chart patterns • Applications in pharmacuticals
Process controls involved in the manufacturing process of pharmaceutical dosage forms • The in-process checking during manufacturing plays an important role in the auditing of the quality of the product at various stages of production. duties of the control inspector consisting of checking, enforcing and reviewing procedures and suggesting the change for upgrading the procedures when necessary The aim of in process quality control system is to monitor all the features of a product that may affect its quality and to prevent errors during processing
process controls involved in the manufacturing process of parental • Checking the bulk solution before filling for drug content,pH,color,clarity, content,pH,color,clarity, and completeness of solutions • Checking the filled volume of liquids or the filled weight of sterile powders powders for injection injection in the final containers containers at predetermined intervals during filling • Testing for leakage leakage of flame flame sealed sealed ampoules • Subjecting the product to physical examination • Examining the sterility sterility indicator indicator placed in various various areas of the sterilizer for each sterilization operation
Process controls involved in the manufacturing process of solid dosage forms • Determining the drug content of the formulation • Checking the weight variation for tablets and capsules at pre determined intervals during manufacturing • Checking the disintegration and dissolution time ,hardness and friability at least during the beginning, middle, and end of production or at prescribed intervals during manufacturing
Process controls involved in the manufacturing of the semisolid dosage forms • Checking for the uniformity and homogeneity of drug content prior to the filling operations • Determining the particle size of the preparation when appropriate • Checking the appearance ,viscosity, specific gravity ,sediment volume and other physical parameters at prescribed intervals • Testing for filling weight during the filling operation • Testing for leakage on the finished jars or tubes
Introduction: • The term statistical means collecting the data,tabulating and summarizing using prescribed statistical tools for purpose of analysis and reporting. • SQC is important for improving the quality. • Identifies any decline in quality during initial stages of production and taking immediate corrective steps instead of identifying defectives after the damage has been done. • One of the methods used for identifying defects is “SAMPLING”. • Sampling always shows defects as well as 100%inspection.
Statistical quality control: •
Statistical quality control (SQC) is the term
used to describe the set of statistical tools used by quality professionals. Statistical quality control can be divided into three broad categories: 1. Descriptive statistics are used to describe quality characteristics and relationships. • Included are statistics such as the mean, standard deviation, the range,and a measure of the distribution of data.
• 2. Statistical process control (SPC) involves inspecting a random sample of the output from a process and deciding whether the process is producing products with characteristics that fall within a predetermined range. SPC answers the question of whether the process is functioning properly or not. • 3. Acceptance sampling is the process of randomly inspecting a sample of goods and deciding whether to accept the entire lot based on the results. Acceptance sampling determines whether a batch of goods should be accepted or rejected.
General terms used in statistical analysis: • Mean: A statistic that measures the central tendency of a set of data. (average) • Range: The difference between the largest and smallest observations in a set of data. • Standard deviation: A statistic that measures the amount of data dispersion around the mean.
Causes of variation: • Common causes: Random causes that cannot be identified. • Assignable causes: Causes that can be identified and eliminated.
Normal Distribution: “shewhart chart”
95% 99.74% -3
-2
-1
=0
1
2
3
Process Control Chart Out of control Upper control limit Process average Lower control limit
1
2
3
4
5
6
Sample number
7
8
9
10
A Process Is in Control If …
1. … no sample points outside limits 2. … most points near process average 3. … about equal number of points above and below centerline 4. … points appear randomly distributed
Types of Control Charts: • Control charts are one of the most commonly used tools in statistical process control. • They can be used to measure any characteristic of a product, such as the weight of a cereal box, the number of chocolates in a box, or the volume of bottled water. • The different characteristics that can be measured by control charts can be divided into two groups: variables and attributes.
A control chart for variables: • A control chart for variables is used to monitor characteristics that can be measured and have a continuum of values, such as height, weight, or volume. • EG: Syrup solution bottling operation is an example of a variable measure, since the amount of syrup solution in the bottles is measured and can take on a number of different values. • Other examples are the weight of a bag of paracetamol powder, the temperature of a Hot air oven, or the diameter of plastic tubing.
A control chart for attributes: • A control chart for attributes, on the other hand, is used to monitor characteristics that have discrete values and can be counted. Often they can be evaluated with a simple yes or no decision. • Examples include color, taste, or smell. • The monitoring of attributes usually takes less time than that of variables because a variable needs to be measured. • An attribute requires only a single decision, such as yes or no, good or bad, acceptable or unacceptable • e.g., the apple is good or rotten, the meat is good or stale, or counting the number of defects e.g., the number of broken cookies in the box, the number of dents in the car, the number of barnacles on the bottom of a boat.
A control chart for variables: • Two of the most commonly used control charts for variables monitor both the central tendency of the data (the mean) and the variability of the data (either the standard deviation or the range). • Mean (x-Bar) Charts • Range (R) Charts
Mean (x-Bar) Charts: • A mean control chart is often referred to as an x-bar chart. It is used to monitor changes in the mean of a process. • This chart serves mainly in validation. • Changes in the process can be detected by these charts. • Accuracy may also be monitored to some extent.
Construction of x-bar Chart: =+ z σ UCL = x z = x
x 1
x x
=- z σ LCL = x
x x
+ x 2 + ... x n n
= Where, x = average of sample means. n z =standard normal variable (2 for 95.44% confidence, 3 for 99.74%confidence). = n = sample size. x = /√n, σ =population (process) SD.
x-bar Chart Example: Standard Deviation Known (cont.)
x-bar Chart Example: Standard Deviation Known (cont.)
x-bar Chart Example: Standard Deviation Unknown = R UCL = x x + A A22R
LCL = = x x - A A22R R
Where, AA2 == factor(dependant factor(dependant on on sample sample size), size), R R= = average average range range of of samples, samples, x = average average of of sample sample means. means. x =
Control Limits
x-bar Chart Example: Standard Deviation Unknown OBSERVATIONS (RING DIAMETER, CM) SAMPLE k 1 2 3 4 5 6 7 8 9 10
1
2
3
4
5
x
R
5.02 5.01 4.99 5.03 4.95 4.97 5.05 5.09 5.14 5.01
5.01 5.03 5.00 4.91 4.92 5.06 5.01 5.10 5.10 4.98
4.94 5.07 4.93 5.01 5.03 5.06 5.10 5.00 4.99 5.08
4.99 4.95 4.92 4.98 5.05 4.96 4.96 4.99 5.08 5.07
4.96 4.96 4.99 4.89 5.01 5.03 4.99 5.08 5.09 4.99
4.98 5.00 4.97 4.96 4.99 5.01 5.02 5.05 5.08 5.03 50.09
0.08 0.12 0.08 0.14 0.13 0.10 0.14 0.11 0.15 0.10 1.15
x-bar Chart Example: Standard Deviation Unknown (cont.) ∑R
R= 0.115k = x =
∑x
k
=
=
1.15 10
=
50.09 = 5.01 cm 10
=
UCL = x + A2R = 5.01 + (0.58)(0.115) = 5.08 = LCL = x - A2R = 5.01 - (0.58)(0.115) = 4.94 Retrieve Factor Value A2
5.10 – 5.08 –
UCL = 5.08
5.06 – 5.04 – n a e M
x- bar Chart Example (cont.)
=
5.02 –
x = 5.01
5.00 – 4.98 – 4.96 – LCL = 4.94
4.94 – 4.92 – | 1
| 2
| 3
| | | | 4 5 6 7 Sample number
| 8
| 9
| 10
Range (R) chart: •
Range (R) chart a control chart that monitors changes in the dispersion or variability of process. Whereas x-bar charts measure shift in the central tendency of the process.
• The method for developing and using R-charts is the same as that for x-bar charts. • The center line of the control chart is the average range, and the upper and lower control limits are computed as follows:
R- Chart UCL = D4R R =
LCL = D3R ∑R
k
where R = range of each sample k = number of samples
R-Chart Example OBSERVATIONS (RING DIAMETER, CM) SAMPLE k 1 2 3 4 5 6 7 8 9 10
1
2
3
4
5
x
R
5.02 5.01 4.99 5.03 4.95 4.97 5.05 5.09 5.14 5.01
5.01 5.03 5.00 4.91 4.92 5.06 5.01 5.10 5.10 4.98
4.94 5.07 4.93 5.01 5.03 5.06 5.10 5.00 4.99 5.08
4.99 4.95 4.92 4.98 5.05 4.96 4.96 4.99 5.08 5.07
4.96 4.96 4.99 4.89 5.01 5.03 4.99 5.08 5.09 4.99
4.98 5.00 4.97 4.96 4.99 5.01 5.02 5.05 5.08 5.03 50.09
0.08 0.12 0.08 0.14 0.13 0.10 0.14 0.11 0.15 0.10 1.15
R-Chart Example (cont.)
UCL = D 4R = 2.11(0.115) = 0.243 LCL = D 3R = 0(0.115) = 0 Retrieve Factor Values D3 and D4
R-Chart Example (cont.) 0.28 – 0.24 –
UCL = 0.243
0.20 – e g n a R
0.16 –
R = 0.115
0.12 – 0.08 – 0.04 – 0–
LCL = 0 | | | 1 2 3
| | | | 4 5 6 7 Sample number
| 8
| 9
| 10
Control Charts for Attributes
p-chart
uses portion defective in a sample
c-chart
uses number of defective items in a sample
P-charts: • P-charts are used to measure the proportion of items in a sample that are defective. Examples are the proportion of broken vials in a batch . • P-charts are appropriate when both the number of defectives measured and the size of the total sample can be counted. • The center line is computed as the average proportion defective in the population, . This is obtained by taking a number of samples of observations at random and computing the average value of p across all samples.
p-Chart UCL = p + z LCL = p - z
p p
= number of standard deviations from process average = sample proportion p defective; an estimate of process average = standard deviation of sample p proportion z
p
=
p(1 - p) n
Construction of p-Chart SAMPLE
1 2 3 : : 20
NUMBER OF DEFECTIVES
PROPORTION DEFECTIVE
6 0 4 : : 18 200
.06 .00 .04 : : .18
20 samples of 100 pairs of jeans
Construction of p-Chart (cont.) total defectives = 200 / 20(100) = 0.10 p= total sample observations UCL = p + z
p(1 - p) n
= 0.10 + 3
0.10(1 - 0.10) 100
UCL = 0.190 LCL = p - z LCL = 0.010
p(1 - p) n
= 0.10 - 3
0.10(1 - 0.10) 100
0.20 UCL = 0.190
0.18 0.16 0.14 e vi t c ef e d n oi tr o p or P
0.12 0.10
p = 0.10
0.08 0.06 0.04 0.02
LCL = 0.010 2
4
6
8 10 12 Sample number
14
16
18
20
C-charts: •
C-charts are used to monitor the number of
defects per unit. • Examples are the number of recalled products in an industry in a month, and the number of bacteria in a milliliter of water. • Note that the types of units of measurement we are considering are a period of time, or a volume of liquid. • The average number of defects, is the center line of the control chart. The upper and lower control limits are computed as follows:
c-Chart
UCL = c + z LCL = c - z
c c
c
= c
where c = number of defects per sample
c-Chart (cont.) Number of defects in 15 sample rooms NUMBER OF SAMPLE DEFECTS
1 2 3
12 8 16
: :
: :
15
15 190
190 = 12.67 c = 15 UCL = c + z c = 12.67 + 3 12.67 = 23.35 LCL = c - z c = 12.67 - 3 12.67 = 1.99
24 UCL = 23.35 21
cChart (cont.)
ts c ef e d f o r e b m u N
18
c = 12.67
15 12 9 6 LCL = 1.99
3
2
4
6 8 10 Sample number
12
14
16
Contol chart for capsule weight data
Application to pharmaceuticals Drug potency Tablet or capsule inprocess characteristics Powder chcharacteristics like mean particle size Microbial count Drug content application (nasal spray ) Fill weight and fill volume Liquid charecteristics like viscosity and refractive index Consumer complaints and industrial safety measurements
Conclusion • statistical process control improve the quality of the processes • Statistical process control provides a statistical approach for evaluating process • When statistical process controls implemented benefits can be derived through a reduced cost of manufacture, improved quality ,reduced trouble shooting crises
