1. QA/QC 1 Quality ManagementQA-GMP-QC Mr. Dipak D. Gadade, Dept. of Pharmaceutics, Shri Bhagwan College of Pharmacy, Auranagabad

3. Freedom from defects

4. Degree of excellence

5. Customer/Buyer’s satisfaction

6. Compliance with specified/official requirements QA/QC 2

9. Relation Between QM, QA, QC & GMP QA/QC 5

10. 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

11. 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

12. 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

13. 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

14. 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

15. 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

16. 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

17. Sources of Quality Variation and its Control Quality Control

18. 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.

19. 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.

20. Sources of variation Equipment Tool wear, machine vibration, … Material Raw material quality Environment Temperature, pressure, humadity Operator Operator performs- physical & emotional

21. 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

22. Control of Quality Variation In process Quality Control Q.A. at startup Raw materials processing Compounding Labels Control Finished product control QA/QC 18

23. Statistical Quality Control Monitoring quality by application of statistical methods in all stages of production QA/QC 19

25. Common or chance causes

26. Assignable causes

28. 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”

29. 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

30. 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)

31. 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

32. Control chart for variables Variables are the measurablecharacteristics of a product or service. Measurement data is taken and arrayed on charts.

33. 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.

34. 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

35. 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

36. 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

37. Control Chart Examples UCL Nominal Variations LCL Sample number

38. How to develop a control chart?

39. 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.

40. 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.

41. 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.

42. 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.

43. 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.

44. UCL & LCL calculation

45. Determining an alternative value for the standard deviation

46. 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 D3and D4

47. 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

48. 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.

49. 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

50. 3-Sigma Control Chart Factors Sample size X-chart R-chart nA2D3D4 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

51. X-bar Chart

52. R Chart

53. Run Chart

54. Another Example of X-bar & R chart

55. Given Data (Table 5.2)

56. 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

57. 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

58. X-bar Chart

59. R Chart

60. 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

61. New Control Limits New value: Using standard value, CL & 3 control limit obtained using formula:

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

63. Trial Control Limits & Revised Control Limit Revised control limits UCL = 6.46 CL = 6.40 LCL = 6.34 UCL = 0.18 CL = 0.08 LCL = 0

64. 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.

65. 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

66. 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.

67. LSL USL Mean -3 -2 -1 +1 +2 +3 68.26% 95.44% 99.74% -3 +3 CL The Normal Distribution  = Standard deviation

69. What is the probability that the sample means will lie outside 3-sigma limits?

71. Assignable Causes (a) Mean Average Grams

72. Assignable Causes Average (b) Spread Grams

73. Assignable Causes Average (c) Shape Grams

74. Control Charts Assignable causes likely UCL Nominal LCL 1 2 3 Samples

75. Control Chart Examples UCL Nominal Variations LCL Sample number

77. As we improve the process, the spread of the data will continue to decrease.

79. 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.

80. 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

81. 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. Mean -3 -2 -1 +1 +2 +3 68.26% 95.44% 99.74%

83. The points appear to float back and forth across the centerline.

84. The points are balanced on both sides of the centerline.

85. No points beyond the control limits.