Mitigating Consumer Risk When Manufacturing Under Verification for Drug Shortages

Transcription

1 Mitigating Consumer Risk When Manufacturing Under Verification for Drug Shortages Presented By Kathy Eley, Principal Consultant and Hector Rivera, Senior Engineer Hyde Engineering + Consulting

2 Presentation Overview Cleaning Validation vs. Cleaning Verification When to use Cleaning Verification/Pros and Cons Case Study Background Life Cycle Approach to Cleaning Validation and Cleaning Verification Establishing a Cleaning Verification Program based on Guidance Documents Stage 1: Cleaning Process Design Stage Stage 2: Cleaning Process Qualification Stage Stage 3: Continued Cleaning Process Verification Stage Conclusions and Summary

3 Cleaning Validation vs. Cleaning Verification Cleaning Validation = a process for determining the effectiveness and consistency of a cleaning process for defined products and equipment. Cleaning Verification = one-time process for determining the effectiveness of a specific cleaning event.

4 Why Cleaning Verification? A product is only manufactured once a year or for the one-time manufacture of a clinical trial material To justify the release of piece equipment that has expired its dirty hold time. Where a consistent cleaning process is difficult to achieve and therefore not possible to validate affectively. In order to remediate a company s cleaning and maintenance shortfalls in an effort to return to the production of a drug in short supply.

5 Pros and Cons to Cleaning Verification Pros Allows for some flexibility in the initial stages for improvements and cycle development May provide enough data to make statistically sound conclusions. Allows manufacturing products in absence of a sound Cleaning Validation program. Helps identify process gaps and measures the effectiveness of the corrective action Cons On-going complex process Increases operational costs Operating at risk due to length of time required to receive Bioburden results.

6 Case Study Background Only one licensed manufacturing facility for the manufacture of drug product. Compliance and sterility issues at the forces operations to be suspended. A market shortage of our client s widelyused drug product was created

7 Establishing a Cleaning Verification Program Stage 1 Cleaning Process Design laboratory-scale studies performed using design of experiments and the data analyzed to understand the cleaning process Stage 2 Cleaning Process Qualification Confirmation of Cleaning efficacy and consistency Stage 3 Continued Cleaning Process Verification ongoing monitoring activities to ensure that the cleaning process operates within the established process design space statistical data is analyzed to assess drift and make corrections prior to failures.

8 Stage 1: Cleaning Process Design Design Space Studies Design of experiments approach to build a multivariate response surface model. Determine the Visual Residue Limit (VRL) for a defined viewing distance, viewing angle, and light intensity. Develop a Total Organic Carbon (TOC) Surface Swab Sampling Method.

9 Stage 1: Cleaning Process Design Initial monitoring of the current cleaning cycles effectiveness The analysis approach used for all four cleaning method was conducted using a statistical analysis software as follows: Define and identify special and common causes Calculate descriptive statistics Graphical representation Side-by-side Boxplot

10 Stage 1: Cleaning Process Design Establish the approach to identify special causes. Upon confirmation of a special cause for a specific sample set, all results associated to that sample set were not considered. Display results showing the measured variable, the production lot number, the within-lot mean, within-lot standard deviations and range, and data set size (N). Graphical representation of the cleaning methods using the mean and standard deviation. Control chart supplemented with Box-and-whisker plots Cleaning processes were out of statistical control

11 Stage 1: Cleaning Process Design Approved Cleaning Verification Protocol Cleaning Procedures Acceptance Limits/MAC Calculations Discrepancy Handling Quality Review and Equipment Release Important Lessons

12 Stage 2: Cleaning Process Qualification Cleaning Verification Process Overview Operators Request a CV Number Samplers Issue CV Number Operators execute the cleaning and fill out GFRM Forms to document the cleaning. These forms are reviewed by a supervisor and submitted to QA for review Samplers perform sampling and fill out sample submission forms Samples are delivered to QC groups QC departments analyze TOC, Conductivity and Endotoxin samples and submit provisional release sample results and sample submission forms to QA QA Reviews GFRMs, sample submission forms, sample results and any discrepancies.

13 Stage 2: Cleaning Process Qualification Cleaning Verification Process Overview If all provisional release criteria are met, QA provisionally releases the equipment for use in manufacturing. QC departments analyze samples for Bioburden and API QC submits final release sample results and remaining sample submission forms to QA QA Reviews the entire package again, including the final release criteria. If all release criteria are met, QA will final release the equipment.

14 Cleaning Verification Sampling Plan Type of Sample Ver 6.0 TOC 55 CONDUCTIVITY 107 ENDOTOXIN 84 BIOBURDEN 84 UV-Vis(Rinse) 16 UV-Vis(Swab) 54 Total number of samples 400 Cleaning Verification Protocol Wash Method Washes per Lot Samples per Lot TOC CONDUCTIVITY ENDOTOXIN BIOBURDEN UV-Vis Rinse Rinse Rinse WFI Control Pitcher Control Rinse WFI Control Pitcher Control Rinse Swab Swab Control TANKS (Sprayball) X X2 10X TANKS (Manual) 5 PARTS WASHERS(Load Only) X X2 4X2 4X X2 25X2 PW Individual Parts X X2 1X MANUALS X Total 55X X2 39X2 5X Avg of 400 Samples per Lot.

15 Vessel Sprayball/Eductor Cleaning Process Eductor/Sprayball apparatus and HWFI to siphon prebatched cleaning agents through the chemical sprayball into the vessel. educator/sprayball apparatus is inserted into the vessels Initial HWFI Rinse Pre-batched cleaning agent is siphoned through the sprayball/eductor, mixes with the incoming HWFI Intermediate HWFI Rinse The process is repeated with a second cleaning agent. For the final rinse, a dedicated sprayball replaces the sprayball/educator apparatus and a HWFI is performed

16 Vessel Sprayball/Eductor Discrepancies Less than 1 discrepancy per manufacturing run 80% provisional release failures and required a rewash of the vessel. Visual inspection failures accounted for 42% of the total number of vessel discrepancies. Operator Error made up 22%.

17 Vessel Manual Cleaning Process Manual cleaning procedure The cleaning agents are batched with HWFI and added to a pressurized sprayer. The first cleaning agent is applied with the sprayer and the vessel is wiped down with a lint free cloth. Following an intermediate rinse, the process is repeated with a second cleaning agent. A final HWFI rinse is performed using a hose attached to the HWFI drop.

18 Vessel Manual Cleaning Discrepancies Less than 1 discrepancy per manufacturing run 82% provisional release failures and required a rewash of the vessel. Operator Error made up 59% of the discrepancies. Conductivity Failures made up 18% of the discrepancies.

19 Parts Washer Cleaning Process Immersion style washers. General instructions for the loads: Parts should not be placed on top of each other with product contact surfaces touching. parts should not shadow each other. parts must be loaded so that they are completely submerged during wash and rinse steps. Specific instructions for some parts: when loading the filter housings, the large opening must face the drain. dip tubes are connected loosely, without gaskets, to the parts washer sanitary port. Final rinse samples were collected by performing a burst rinse at the end of the cleaning cycle Rinse samples and swab samples (if applicable) for three individual worst case parts in the load.

20 Parts Washer Cleaning Discrepancies 4 discrepancies per manufacturing run. 88% provisional release criteria failures which led to rewashes of the loads in question. Operator error accounted for 28% of the discrepancies. 20% of the discrepancies were due to conductivity failures.

21 Manual Cleaning Process Cleaning agents are made either in a sprayer or in an appropriate sized wash tub. The parts to be cleaned are either soaked in the tub for a specified time or they are sprayed with the solution in the sprayer. The temperature of the cleaning solutions is monitored and must remain above a specified temperature. Intermediate HWFI rinse. Cleaning is repeated with a second cleaning agent. HWFI final rinse.

22 Manual Cleaning Discrepancies 1 discrepancy per manufacturing run. 76% provisional release failures which led to rewashes 38% Conductivity failure discrepancies 31% Operator Error discrepancies

23 Stage 3: Continued Cleaning Process Verification Stage Continuous monitoring and evaluating for validation suitability: The statistical analysis approach used for all four cleaning methods was conducted in terms of conductivity and TOC to evaluate the cleaning process stability and process performance. The process stability and performance were evaluated by completing the following steps for each parameter: Preliminary Data Evaluation Verify data distribution Verify process stability Determine process performance

24 Preliminary Data Evaluation Tool/ Parameter Mean Median Standard Deviation Minimum, Maximum Time series plot Tool/Parameter Definition The sum of all the observations divided by the number of observations. The midpoint of a ranked order set of data. Measure of dispersion, or how spread out the data is from the mean. It is calculated by taking the positive square root of the variance. Maximum refers to the highest value in a data set and the minimum refers to the lowest value in a data set. Time series plots display observations on the y-axis against equally spaced time intervals on the x-axis. Use to Used to determine central tendency Used to determine central tendency Standard deviation can be for estimating the overall variation of a process. It is used to calculate the range or spread of a set of data Evaluate patterns and behavior in data over time.

25 Verify Data Distribution Verify data distribution to identify the distribution of a data in order to select appropriate analysis tools and interpreting their results. Completed using the distribution identification tool available in the statistical software version. A widely used alternative when the data is not normally distributed is to use transformations

26 Verify Process Stability Verify process stability by plotting the raw data against a set of control limits A single result per sample identifier was reported for the each parameter, consequently, Individual/ Moving Range (I-MR) charts were used The I-Chart requires the data to follow normal distribution. A process parameter was considered to be in statistical control if the last thirty data points were within the control limits

27 Determine Process Performance Determine process performance to measure the ability of a process to meet specification regardless of the state of statistical control Ppk is an index that measures the ability of a process to meet specification for one sided specifications. A minimum of thirty (30) data points are necessary to perform the Ppk analysis. If the data is normally distributed the process performance indexes are calculated assuming Normal distribution. When the normal distribution fitting is not valid, another distribution fitting or transformation is explored. If the data do not fit any other distribution or transformation available, a non-parametric capability index is calculated.

28 Vessel Sprayball/Eductor Statistical Analysis Vessel Eductor/Sprayball Cleaning Method - Conductivity Process Performance Process Capability of Conductivity Raw Data Tanks with Spray Ball Process Data USL 6.49 Sample Mean Sample N 103 S td ev(within) StDev(O verall) USL Within Overall P otential (Within) C apability C PU 5.57 C pk 5.57 O verall C apability PPU 3.61 Ppk O bserved Performance PPM < LSL * PPM > USL 0.00 PPM Total 0.00 Exp. Within Performance PPM < LSL * PPM > USL 0.00 PPM Total 0.00 Exp. O verall Performance PPM < LSL * PPM > USL 0.00 PPM Total 0.00

29 Vessel Sprayball/Eductor Statistical Analysis Vessel Eductor/Sprayball Cleaning Method - TOC Process Performance Process Capability of TOC-Johnson Process Data LSL * Target * USL S ample M ean Sample N 103 StDev(Overall) USL Overall Capability Pp * PPL * PPU 2.69 Ppk 2.69 Cpm * Observed Performance PPM < LSL * PPM > USL 0.00 PPM Total 0.00 Exp. Overall Performance PPM < LSL * PPM > USL 0.00 PPM Total 0.00

30 Vessel Manual Cleaning Statistical Analysis Vessel Manual Cleaning - Conductivity Process Performance Process Capability of Conductivity Raw Data Tanks Manual Wash Process Data USL 6.49 Sample Mean Sample N 57 StDev(Within) StDev(O v erall) USL Within Overall Potential (Within) C apability CPU 4.63 Cpk 4.63 Overall Capability PPU 3.58 Ppk O bserv ed Performance PPM < LSL * PPM > USL 0.00 PPM Total 0.00 Exp. Within Performance PPM < LSL * PPM > USL 0.00 PPM Total 0.00 Exp. O v erall Performance PPM < LSL * PPM > USL 0.00 PPM Total 0.00

31 Vessel Manual Cleaning Statistical Analysis Vessel Manual Cleaning - TOC Process Performance Process Capability of TOC-Johnson Tanks Manual Wash Process Data USL Sample Mean Sample N 57 StDev(Within) StDev(O v erall) USL Within Overall Potential (Within) C apability CPU 3.15 Cpk 3.15 Overall Capability PPU 3.06 Ppk O bserv ed Performance PPM < LSL * PPM > USL 0.00 PPM Total 0.00 Exp. Within Performance PPM < LSL * PPM > USL 0.00 PPM Total 0.00 Exp. O v erall Performance PPM < LSL * PPM > USL 0.00 PPM Total 0.00

32 Parts Washer Cleaning Statistical Analysis Part Washer#4 Conductivity Process Performance Process Capability of Conductivity Raw Data Johnson Transformation with SU Distribution Type * Asinh( ( X ) / ) Process Data USL 6.49 Sample Mean Sample N 206 StDev(O v erall) A fter Transformation USL* Sample Mean* StDev(O v erall)* transformed data USL* Overall Capability PPU 0.69 Ppk O bserv ed Performance PPM < LSL * PPM > USL PPM Total Exp. O v erall Performance PPM < LSL* * PPM > USL* PPM Total

33 Parts Washer Cleaning Statistical Analysis Part Washer#4 TOC Process Performance Process Capability of TOC Raw Data Johnson Transformation with SU Distribution Type * Asinh( ( X ) / ) Process Data USL Sample Mean Sample N 206 StDev(O v erall) A fter Transformation USL* Sample Mean* StDev(O v erall)* transformed data USL* Overall Capability PPU 0.88 Ppk O bserv ed Performance PPM < LSL * PPM > USL PPM Total Exp. O v erall Performance PPM < LSL* * PPM > USL* PPM Total

34 Parts Washer Cleaning Statistical Analysis Part Washer#5 Conductivity Process Performance Process Capability of Conductivity Raw Data Process Data USL 6.49 Sample Mean Sample N 33 StDev(Within) StDev(O v erall) USL Within Overall Potential (Within) C apability CPU 5.03 Cpk 5.03 Overall Capability PPU 4.39 Ppk O bserv ed Performance PPM < LSL * PPM > USL 0.00 PPM Total 0.00 Exp. Within Performance PPM < LSL * PPM > USL 0.00 PPM Total 0.00 Exp. O v erall Performance PPM < LSL * PPM > USL 0.00 PPM Total 0.00

35 Parts Washer Cleaning Statistical Analysis Part Washer#5 TOC Process Performance Process Capability of TOC Raw Data Johnson Transformation with SU Distribution Type * Asinh( ( X ) / ) Process Data USL Sample Mean Sample N 33 StDev(O v erall) A fter Transformation USL* Sample Mean* StDev(O v erall)* transformed data USL* Overall Capability PPU 0.75 Ppk O bserv ed Performance PPM < LSL * PPM > USL PPM Total Exp. O v erall Performance PPM < LSL* * PPM > USL* PPM Total

36 Manual Cleaning Statistical Analysis Equipment Manual Wash Cleaning Method - TOC Process Performance Process Capability of TOC-Johnson Equipment Manual Wash Process Data USL S ample M ean Sample N 328 StDev(Overall) USL Overall Capability PPU 3.03 Ppk Observed Performance PPM < LSL * PPM > USL 0.00 PPM Total 0.00 Exp. Overall Performance PPM < LSL * PPM > USL 0.00 PPM Total 0.00

37 Vessel Cleaning Process Conclusions Too many operator error discrepancies. A more automated cleaning system is necessary assure consistent cleaning when we to move to validation. A portable CIP system capable of cleaning all vessel sizes is being analyzed.

38 Parts Washer Cleaning Conclusions Execute cycle development studies in order to determine the worst case loads for our equipment. Validate worst case loads for the cleaning cycle that we are currently using. Beef up the PM program on the Parts Washers.

39 Manual Cleaning Conclusions Improve the cleaning process Increase the final rinse times Replace manually cleaned parts with disposable parts where possible Scoops Pails Drip Pans Funnels

40 Summary Continual Cleaning Process Verification Collect ample data for statistical analysis Recognize drift in our cleaning process and improve them before a failure occurs Identify trends that could improve the cleaning processes Demonstrate that the cleaning process is continually in a validate state.