Daniel Y. Peng, Ph.D. - PQRI

1 1 Using Control Charts to evaluate Process Variability Daniel Y. Peng, Quality Assessment Lead Office of Process and Facility (OPF) OPQ/CDER/FDA PQRI 2015 Annual Meeting North Bethesda, Maryland October 5, 2015 Walter Andrew Shewhart (1891-1967) A physicist, engineer and statistician Father of statistical quality control Statistical method from the viewpoint of quality control (1939) Creator of PDSA (Plan, Do, Study and Act) cycle Creator of control chart Originator of the Chance and Assignable variation concept 2 Uncontrolled variation is the enemy of quality 3 Dr. W. Edwards Deming (1900-1993) 4 Sources of Variation Variation exists in all processes. Variation can be categorized as either: Chance or Common causes of variation Inherent to a system, random, always present and hence predictable within statistical limits Eliminate inherent variability (noise) is difficult Assignable or Special causes of variation Exterior to a system, non-random, not always present (intermittent) can cause changes in the output level, such as a spike, shift, drift, or non-random distribution of the output.

2 Are usually easier to be detected, controlled or eliminated 5 Control Chart Definition: a graphical display of a product quality characteristic that has been measured or computed periodically from a process at a defined frequency Every control chart consists of: A set of data A central line (CL) (mean) Two statistical process control limits (UCL and LCL) (Is the process Stable?) Upper and Lower Specification Limits (USL and LSL) Patient s need ( Safety and Efficacy) (Is the process Capable?) Quality attribute (unit) Sample # 30 40 50 60 USL LSL CL UCL LCL Potential Applications To proactively monitor and trend a process To detect the presence of special cause variation To identify continual improvement opportunities To maintain the process in a state of statistical control Using science and risk-based approach Take

3 Action in a timely manner 6 Key Considerations for Constructing a Control Chart 7 Choice of Product Quality Characteristics Critical Quality Attributes (CQA) A physical, chemical, biological or microbiological property or characteristic of an output material including finished drug product that should be within an appropriate limit, range, or distribution to ensure the desired product quality (ICH Q8) Identification of CQA: primarily based upon the severity of harm to the patient (safety and efficacy) Critical (input) material attributes and critical process parameters (CMAs/CPPs) Other relevant process characteristics that can assist in process monitoring and controlling 8 9 Types of Control Chart Variable Control Chart Characteristics that can be measured (continuous numeric data) Assay, Dissolution, % of The average and variability charts are usually prepared and analyzed in pairs Average Range chart (Xbar-R chart, subgroup size 2-10) Average Standard Deviation chart (Xbar-S chart, subgroup size >10) Individual Moving Range chart (I-MR chart, n=1)

4 Attribute Control Chart Characteristics that have discrete values and can be counted, % defective, # of failed batches in a month p chart / np Chart: for fraction of occurrence of an event- Binominal distribution % of unsuccessful batch at a facility every month c chart / nc Chart: for counts of occurrence in a defined time or space increment -Poisson distribution number of particulate matter in an injection vial Other types of control chart: cumulative sum control chart (CUSUM) exponentially weighted moving average control charts (EWMA) 10 Subgroup Size and Sampling Frequency Subgroup: the observations sampled at a particular time point Subgroup Size and Sampling Frequency (N x K) The number of observations in each subgroup: 1 n the objective of the monitoring (detect large or small shift) how quickly the output responds to upsets consequences of not reacting promptly to a process upset time and cost of an observation Rational Subgroup: Minimize the variation of observations within a subgroup Maximize variation between subgroups Statistical Process Control Limits UCL and LCL: the thresholds at which the process output is considered statistically unlikely typically, 3 SD (Shewhart limits) Rationale: to balance the two risks: Failing to signal the presence of a special cause when one occurs; False alarm of an out-of-control signal when the process is actually in a state of statistical control 11 How out-of-control points are identified?

5 Rule any point falls outside UCL/LCL Other Rules certain nonrandom patterns of the plotted data Use it judiciously Risk of false alarm 12 8 Western Electric Rules Over-Reaction vs. No-Reaction 13 Procedures should describe how trending and calculations are to be performed and should guard against overreaction to individual events as well as against failure to detect unintended process variability (2011 FDA Process Validation Guidance) Control chart and process capability analysis often go hand-in -hand Illustrative Examples 14 15 Within Batch Variability Example ER coated beads, mixed with extra-granular cushioning excipients and compressed into tablets Compression: ~ 5h, sample frequency: every 8-10 min (total 33 subgroups), subgroup size= 6 1625160215391515145214291356133813091233 10544540353025 TimeSample Mean__X= Range_R= Chart of Disso@240minNot Stable & Not Capable 16 Between Batch Variability Example 25232119171513119753110210098 Batch Mean__X= Range_R= (%)1041021009896 LSLUSLLS L96US L104 Specifications1051029996W ithinOv * Capability Analysis of Tablet Assay (first 25 batches, subgroup size =3)Xbar ChartR ChartRun ChartCapability HistogramNormal Prob PlotAD : , P : PlotData source: Chopra, V.

6 , Bairagi, M., Trivedi, P., et al., A case study: application of statistical process control tool for determining process capability and sigma level, PDA J Pharm Sci and Tech, 66 (2), 2012, pp. 98-115 USP: 90-110 Cpk: Stable & Capable 17 Between Batch Variability Example Tablet content uniformity (AV) of last 30 commercial batches of Tablet X manufactured by Firm Y (subgroup size =1, I-MR chart) Value_X= L= L= Range__MR= L= L=055524946434037343128642 Batch tD p*C P tD p*P pm*P P v erall111 Process Capabi lity Analysis of Tabl et X Content Uniformity (AV)I ChartMoving Range ChartLast 30 ObservationsCapability HistogramNormal Prob PlotAD: , P: PlotNot Stable but Capable 18 Site Performance Monitoring Example onthP r opor tion_P = C L= L=025201510565432M onthC umulativ e Unsuccess RateU pper C D efectiv er C pper C P M D ef:43726 Low er C I:27917U pper C I.

7 64891P rocess er C ( confidence)S ummary S tats30252020100T otal B atch M anufactur ed/M onth% Unsuccess Unsuccess RateFr equencyTarBinomial Process Capability Analysis of Unsuccess BatchP C har tTests performed w ith unequal sample size sC umulativ e Unsuccess RateUnsuccess RateH istogr am Binomial process capability index: % of unsuccessful batch /month at Site A (# of lots attempted: 20-30/month) Stable but Not Capable Paradigm Shift Culture of Quality Manufacturers take full responsibility for quality of their products Focus on meeting patients expectations Regulators expectations considered minimal approach Strive for continual improvement Management and organizational commitment to prioritizing quality Each person in organization understands and embraces their role in quality 19 20 Summary Brief introduction of control chart: history, definition, types Key considerations for constructing a control chart: Choice of drug product quality characteristics Subgroup size and sampling frequency Statistical process control limits (UCL and LCL) Illustrative examples for process monitoring and control.

8 Within batch variability Between batch variability Site performance monitoring Control Chart can be a valuable tool to: Proactively monitor and trend a process Detect the presence of special cause variation Identify continual improvement opportunities Maintain the process in a state of statistical control 21 Acknowledgements Dr. Christine Moore Dr. Naiqi Ya Dr. Ubrani Venkataram