Best Practices for a Robust Cleaning Validation

Transcription

1 Best Practices for a Robust Cleaning Validation Beth Kroeger Technical Services Specialist STERIS Life Sciences & Amanda Deal Senior Technical Services Associate STERIS Life Sciences 1/

2 Agenda Variables impacting the cleaning process Risk identification Equipment Residue evaluation Limits Best Practices for a successful cleaning validation Performing a bench-scale cleaning process development 2/

3 Parameters and attributes of cleaning processes Critical Performance Parameters (CPPs) Process times (includes Dirty-hold) Turbulence or flow Cleaning agent concentration Temperature Critical Quality Attributes Visual Inspection Analytical residue limits (HPLC or TOC) Microbial (Bioburden/Endotoxin) Conductivity/pH 3/

4 Cleaning parameters Why parameters important? Defining cleaning system Modeling lab study Selecting worst case 4/

5 Cleaning Chemistry Process parameters Cleaning also depends on cleaning conditions Water Quality Which glass is cleaner? 5/

6 Cleaning Chemistry Process parameters Cleaning also depends on cleaning conditions Individual Performing Cleaning (especially in manual cleaning) 6/

7 Manual Cleaning Process Variation Variation sources Different persons Different motivation Different physical strength Day shift, 2 nd shift, 3 rd shift, weekend differences On and on.. 7/

8 Cleaning Chemistry Cleaning also depends on cleaning conditions Surface being cleaned 8/

9 Cleaning Chemistry Cleaning also depends on cleaning conditions Nature of Soil 9/

10 Cleaning Chemistry Cleaning also depends on cleaning conditions Nature of Soil Slip agents: commonly used in the manufacture of plastics Insoluble in water and most buffers. Tend to float at air/liquid interface Deposit on side wall of tanks at the meniscus levels 10/

11 Identifying Risk in Cleaning Validation Need to know and understand: Process flow Equipment Process soils Components of cleaning Where to start? 11/

12 Performing a risk assessment Risk based Scientific Rationale are needed for the following: Equipment Grouping Soil Selection Product Grouping Sampling method selection Sampling site selection Limit selection & calculation Analytical Approach 12/

13 Equipment Grouping Criteria Must be same type Size Same equipment of different sizes Example: 50L, 100L, 300L, 500L and 1000L tanks Same materials of construction Configuration Equivalent geometries/design risks Must have same cleaning process Cleaning agent TACT Frequency of cleaning 13/

14 Equipment Grouping Criteria Must have same manufacturing process/soil characteristics Role/position in process Campaign length/dirty hold time Alternatives -- Validate separately largest /smallest sizes Validate together testing extremes 14/

15 Types of Soils Potential Residues for consideration: API (Drug substance) Excipients / Colorants / Dyes / Fragrances / Flavors Preservatives Degradants / Impurities Starting materials / Processing aids Mother liquors / Solvents Lubricants / antifoams - silicates Bioburden Mycoplasma / Prions / Viral particles Endotoxin 15/

16 How do we choose? Which materials represent the greatest risk to the next process? High potency; high toxicity; allergenic Creates condition that is unacceptable to consumer (e.g. off-color, abnormal fragrance, particulates) Hardest to clean / remove. Is there justification to look for one residue as a worst case when compared to other selected residues? Cleanability Toxicity Solubility 16/

17 Product Grouping Criteria Build soil categories Determine Worse Case 17/

18 Risk Ranking Specific Soils Parameter Product Difficulty to clean - lab study or subjective Toxicity LD 50 (oral rat) Risk Level 0 Very easy to clean water effective 2500 mg/kg Risk Level 1 Risk Level 2 Risk Level 3 Risk Level 4 Risk Level 5 Easy to clean and highly mobile in liquid state > 2500 mg/kg and 1250 mg/kg Moderately easy to clean some viscosity issues >1250 mg/kg and 500 mg/kg Moderately hard to clean Thicker products, some insoluble ingredients >500 mg/kg and 250 mg/kg Difficult to clean oily substance, builder or excipient >250 mg/kg and 25 mg/kg Very Difficult to clean such as denatured protein, dyes, titanium dioxide 25 mg/kg Solubility - g/100ml of water Very soluble 100% in water Freely Soluble 99.9 % in water Soluble 99% in water Slightly Soluble >10% but <90% in water Very Slightly Soluble < 10% in water Practically Insoluble < 0.01% in water 18/

19 Example (Dietary Supplements) Oral Dosage Form Calcium 7 Solubility (active) Practically insoluble Potency - RDA (mg) Toxicity Oral LD50 (mg/kg) Cleanability in Alkaline Detergent Total RPN (S P T C) Chromium 4 Not specified Iron 3 Soluble Magnesium 5 Slightly soluble Potassium 3 soluble 3 Not specified Selenium 7 Insoluble Zinc 3 Soluble /

20 Grouping for a Contract Manufacturing Facility Facility may not know next product Want to avoid frequent re-validation of cleaning processes. How? Develop cleaning matrix for products currently produced in facility Identify worst case soil, and validate the cleaning process When new products are introduced into facility, add it to matrix and determine if re-validation is required. During original cleaning validation, adjust limits to be low enough that the potential for re-validation is reduced (based on historical manufacturing trends) 22/

21 Sampling Sampling locations should be selected based on: Hard to clean locations Locations that might disproportionately contribute residue to the next product Materials of construction or surface finishes with an affinity for the soil The role in the process that is likely to lead to build-up or difficult to remove soils May use forced ranking to ID Worst case swab locations /

22 Example of Forced Ranking - Bioreactor Sampling Location Critical Site: Potential large contaminant area Hot Spot (historically hard to clean) Affinity to MOC or Surface Finish Role in process likely to lead to difficult residue Cleanability of location/ coverage and access Sidewall Ranking Bottom Outlet Valve Dome Lid Instrument Port Sampling Port Agitator = Low Risk 3 = Moderate Risk 5 = High Risk 24/

23 Sources of Guidance for Limits US FDA, Guide to Inspections of Validation of Cleaning Processes (1993) Pharmaceutical Inspection Convention (PIC), Recommendations on Cleaning Validation (2001) Canadian HPFB, Cleaning Validation Guidelines (2001) EC Guide to Good Manufacturing Practice Annex 15 (Paragraph 36) (2006) & GMP Part II (formerly Appendix18) (2005) WHO Technical Report No. 937: WHO Supplementary Guidelines on GMP (Annex 4): Validation (2006) European Medicines Agency EMA/CHMP/CVMP/ SWP/169430/2012. Guideline on setting health based exposure limits for use in risk identification November 2014 (effective June 2015) 25/

24 Interesting Agreement. All guidelines agree that they won t set limits Yet 4 of the 5 guidelines go on to list examples of limits that are then commonly employed and that are frequently cited as requirements 26/

25 Background Limits timeline: Fourman, G., and Mullin, M., Determining Cleaning Validation Acceptance Limits for Pharmaceutical Manufacturing Operations, Pharmaceutical Technology, April 1993 ISPE Baseline Guide: Risk-Based Manufacture of Pharmaceutical Products (Risk-MaPP), September, FDA Guidance: Guide to Inspections Validation of Cleaning Processes, July1993 EMA: Guideline on setting health based exposure limits for use in risk identification in the manufacture of different medicinal products in shared facilities, November, /

26 Sources of Guidance for Limits European Medicines Agency EMA/CHMP/CVMP/ SWP/169430/2012. Guideline on setting health based exposure limits for use in risk identification November 2014 (effective June 2015) 28/

27 Residue Limits Fourmen and Mullen 1 approach for active: Most stringent of dose calculation and 10 ppm (in next product) AND Visually clean Typical visual limits is 1 4 µg/cm 2 Spiking studies should determine the concentration at which most active ingredients are visible, 1 Fourman, Gary L., and Michael V. Mullen, Determining Cleaning Validation Acceptance Limits for Pharmaceutical Manufacturing Operations, Pharmaceutical Technology, Vol.17, No. 4, April, pages 54-60, /

28 Residue Limits Possible uses of limit Daily amount allowed (ADI or ADE) (µg or mg) Concentration in next product (µg/ml or µg/g) Absolute amount in manufacturing vessel/train (MAC or MACO maximum allowable carryover) (mg) Amount per surface area (µg/cm 2 ) Amount per swab (µg) Concentration in swab extract solution (µg/ml) Concentration in rinse solution (µg/ml) 30/

29 Residue Limits In Layman s Terms. Need to determine how much of the product we just cleaned will be administered to each patient taking the next product. But in order to make this number useful. Need to determine how much that will represent in the next batch and translate that number to a value in terms of how much that might represent on the surface to measure. Need the residual amount to be safe, add safety factor. 31/

30 Nature Term How much of the product we just cleaned (Product A) May be expressed as one of the following: Toxicity or LD50 (with appropriate safety factor) Minimum Therapeutic Dosage Allergenic Level Minimum pharmacological effect level NOEL (No Observable Effect Level) Most Conservative Approach 32/

31 Daily amount allowed Product A Assume tablet Product A: 50 mg active per tablet, 1 tablet per dose, 2-4 doses/day Minimum daily dose of active is: 50mg/tablet x 1 tablet/dose x 2 doses/day = 100 mg/day Daily amount allowed: Acceptable Daily Intake (ADI) Acceptable Daily Exposure (ADE) Based on Safety/toxicity evaluation (Or) of a minimum daily dose of an active. 33/

32 Safety Factor Term We want the amount of residual soil to be safe, therefore may add a safety factor Safety factor is any convenient number, usually a factor of 10 (e.g. 100, 1000, 10000) Safety factor is optional in some cases (not optional when using terms such as LD 50 ) The greater the safety factor, the larger the reduction in the limit 34/

33 Safety Factor Term (Continued) Common practice is to apply safety factors uniformly within a plant Topical Products: 10 to 100* Oral Dosage Products: 100 to 1000* Parenteral/Opthalmic Products: 1,000 to 10,000 Research/Investigational Products: 10,000 to 100,000 *Note: Significant rationale must be given if safety factor is less than the industry-standard 1,000 35/

34 Dose Term Amount that will be administered to each patient taking the next product (Product B) The amount of the next product that may be administered Always most conservative to over-estimate this term Does not depend on the Nature or Level of active in Product B. Product B is the Maximum daily dose Typically mass or volume 36/

35 Concentration in next product - Product B Assume Product B: 1,000 mg tablets, 1 tablet per dose, 2-4 doses per day Maximum daily dose of Product B is: 1,000 mg/tablet x 1 tablet/dose x 4 doses/day = 4000 mg/day or 4 gm/day Concentration in next product: (100 mg/day)(0.001 safety factor) = mg/g 4 gm/day NOTE: (25 µg/g) > 10 ppm (10 µg/g) 37/

36 Batch Term How much of the soil will be present in the next batch? May be expressed as batch size (L or kg) or in the number of doses (1,000,000 tablets for example) Most conservative to work with smallest possible batch size (worst case) (Larger batch sizes will dilute your residue which is safer) 38/

37 MAC Absolute amount in manufacturing vessels For tablet example: concentration limit of active in A in product B is 10.0 µg/g. Batch size is 200 kg 10.0 µg/g x 200 kg x 1,000 g/kg = 2,000,000 µg 39/

38 Size Term How much of the soil may remain on the surface? Size of the equipment May represent full shared or maximum surface area of an equipment train Conservative approach is to over-estimate surface area of shared equipment 40/

39 Limit per surface area Calculated by dividing absolute amount limit by shared surface area Example: Absolute amount = 2,000,000 µg Surface area is 450,000 cm 2 Limit per surface is: 2,000,000 µg/ 450,000 cm 2 = 4.4 µg/ cm 2 41/

40 Putting the Terms Together (Completed) Nature Batch Size x Dose (Next Product) (Next Product) 1 Safety Factor Where: Nature = Pharmacology of the active ingredient from the product just completed Batch = Batch size or volume or number of units of the next product Size = Surface area of shared or maximum equipment train Dose = Amount (total dose weight) to be given per patient (daily or per regime of the next product) Safety Factor = Optional or variable term depending on other considerations in the limit 42/

41 Limit per swab Amount per swab depends on Limit per surface (µg/cm 2 ) Swabbed area (cm 2 ) Example: Limit per surface = 4.4 (µg/cm 2 ) Swabbed area = 25 cm µg/cm 2 x 25 cm 2 = 110 µg (active Product A) 43/

42 Limit in swab extract Concentration in swab extract depends on Limit per surface (µg/cm 2 ) Swabbed area (cm 2 ) Amount of solvent for extraction (g) Example: Extracted in 20 g of solvent 4.4 µg/cm 2 x 25 cm 2 = 5.5 µg/g 20 g 44/

43 Leveraging sampling Increase concentration by decreasing volume for extraction. If extracted into 20 g solvent: 110 µg / 20 g = 5.5 µg/g If extracted into 10 g solvent: 110 µg / 10 g = 11 µg/g If extracted into 5 g solvent: 110 µg / 5 g = 22 µg/g 45/

44 Leveraging sampling Increase concentration by increasing swabbed area: If 25 cm 2 is extracted into 20 g solvent: 110 µg / 20 g = 5.5 µg/g If 100 cm 2 is extracted into 20 g solvent: 440 µg / 20 g = 22 µg/g If 100 cm 2 is extracted into 5 g solvent: 440 µg / 5 g = 88 µg/g 46/

45 Limits for Cleaning Agents No therapeutic index for cleaning agents Commonly, only information available is LD 50 LD 50 specific to animal model (e.g. rat) and route of administration (e.g. oral, IV) First calculate either Acceptable Daily Intake (ADI) or No Observed Effect Level (NOEL): ADI = LD 50 (mg/kg) body weight (kg) NOEL = LD 50 (mg/kg) ( ) x 70 kg 1 The ADI or NOEL would take the nature place in equation 1 Doursman and Stara, J. Regulatory Toxicology and Pharmacology, 3, , /

46 Things to Avoid in Setting Limits Limits based on analytical assay Limits based on compendial water specs Limit unrelated to target residue Limits selected arbitrarily No documentation of rationale or risk ranking for how selected 48/

47 Recent Approaches ISPE Risk MaPP Risk based approach for determining the ADE (acceptable daily exposure) Typically established by trained toxicologist Focuses limit on how the carryover might cause harm Would be used in place of 1/1000th therapeutic dose approach 49/

48 Calculating the Safety Threshold Value (STV) (ISPE) Batch Size ADE STV Maximum DailyDose Rather than: Minimum Therapeutic Dose Batch Size Maximum Daily Dose 1 Safety Factor MACO NOTE: Safety factor is already included in the ADE as uncertainty factors 50/

49 Calculation of ADE Value ADE(mg/day) NOAEL UF BW Where: ADE = Acceptable daily exposure NOAEL = No observed adverse effect level BW = Body weight UF c = Composite uncertainty factor MF = Modifying factor (professional judgment) PK = Pharmacokinetic adjustments (route to route adjustments) C MF PK 51/

50 Calculation of ADE Value Per ISPE Baseline Guide: Risk-Based MaPP ADE should not be seen as a limit Use as a reference point for determining level of risk Establish Process Control Limits based on PD Studies Like limits set for temp/humidity/alert, etc Tighter inner control limits than OOS limits (MOR s & PAR s) ADE limit alone may not be acceptable as carryover, though considered safe Flavor, smell, product quality, etc. Default to visually clean 52/

51 New Approach: EMA Guidance Why? Some felt that traditional method was not riskbased Use of 1/1000 th daily dose of active seen by some to be arbitrary Some questioned the use of 10 ppm as the default value as arbitrary and possibly not reflective of potential risks Current approach could lead to either overly prescriptive limits or inadequate limits Desire to update the approach to validation requires a toxicological assessment of product residues. 53/

52 PDE Value Key part of this assessment is the PDE Permitted daily exposure (PDE) value represents dose that is unlikely to cause an adverse effect if an individual is exposed at this dose every day for a lifetime Not new approach; used in ICH Q3(R4), Guideline for Residual Solvents PDE determination carried out substance-specific on basis of all available toxicological and pharmacological data 54/

53 Uncertainty factors Used to account for various uncertainties and to allow extrapolation to a reliable and robust no-effect level F1: (value 2-12) to account for extrapolation between species F2: (factor of 10) variability between species F3: (factor of 10) to account for repeat dose studies of short duration F4: (value 1-10) applied in case of severe toxicity F5: (factor up to 10) applied if no-effect level was not established. 55/

54 Case Study: CIP 100 detergent The traditional method LD50 BW MBS MAC MF1 MF2 LDD 860 mg/kg 60 kg 10 kg MAC 800 mg 100,000 Where:» LD50 is 860 mg/kg (CIP 100 detergent)» BW is body weight (60 kg)» MBS = Minimum batch size (10 kg)» LDD = Largest daily dose (800 mg) 6,450 mg 56/

55 Case Study: CIP 100 detergent EMA Method: NOAEL = 120mg/kg/day F1 = 10 F2 = 10 F3 = 3 F4 = 1 F5 = 3 As determined by toxicologist PDE (mg/day) 120 mg/kg/day 60 kg mg/day 57/

56 Case Study: CIP 100 detergent EMA Method (Continued): Safe threshold value (STV) Upper limit for statistical analysis used to determine the process capability (Cpk) and cleaning validation limits STV PDE MBS LDD STV 8 mg/day 10 kg 800 mg x mg 1kg 100,000 mg/day 58/

57 Residue Level Process Capability and Limits The STV should be used with the process capability of the cleaning process to establish the alert and action levels: Process Capability (Cpk) STV A Area of knowledge Action Limit Alert Limit Proven Acceptable Range (PAR) Process Control Limit (MOR) Individual Data Points 59/

58 Best Practices for successful Cleaning Validation A Manufacturing perspective.. A successful Cleaning Validation program isn t successful if it doesn t work in Manufacturing! 60/

59 Best Practices for successful Cleaning Validation Don t work in a vacuum. Involve every group that will be involved with Validation Quality Assurance QC Chemistry and Micro Manufacturing: Usually will execute the PQ. (Most likely be pulling samples and will disassemble equipment). Process Engineers: (Fix equipment after bullet point #2). Facilities and Engineer: May have to re-engineer and involved in facility effluent. EH&S: 61/

60 Best Practices for successful Cleaning Validation Come to the manufacturing area and look at the equipment, not just the P&ID.. Look for possible problems areas BEFORE you start. Corners, crevices, deadlegs, valves, seals, Sample locations Consider where can contamination occur? Where can problems be fixed by re-engineering? What portions of the process can be cleaned in place? Do sections need to be removed for cleaning? COP or use of jumpers/manifolds? 62/

61 Best Practices for successful Cleaning Validation Work with Process Development, Manufacturing and Technical Services to establish Design Space. MOR: Manufacturing Operating Ranges PAR: Proven Acceptable Ranges. The information and knowledge gained from pharmaceutical development studies and manufacturing experience provide scientific understanding to support the establishment of the design space, specifications and manufacturing controls 63/

62 Best Practices for successful Cleaning Validation Design space: Defined in ICH Q8 as The multi-dimensional combination and interaction of input variables (e.g. material attributes) and process parameters that have been demonstrated to provide an assurance of quality. MOR Area of Control ph 7.0 ± 0.5 PAR Area of Success ph 7.0 ± 1.0 Area of Knowledge ph 7.0 ± /

63 Best Practices for successful Cleaning Validation Clean hold times and Dirty hold times are acceptable: Work with Manufacturing to see what s optimal and what s achievable. May be schedule driven. (not able to tie up for Validation) Dirty hold times >24 hours. 3 days optimal. Take time out past 24 hours and set validated time-limit back to a whole day. Don t make Manufacturing have to calculate delta time. 66/

64 Most Frequent form 483 observations FY Top 10 FDA form 483 observations (21 CFR 211) for FY (d) 160(b) (a) 67(b) 113(b) 165(a) 67(a) 68(a) 110(a) Information from FDA website: 67/

65 Cleaning Process Problems Cleaning procedure documentation Should be equivalent to manufacturing process documentation Details (exact requirements) Times Personnel accountability 68/

66 Cleaning procedure exercise 69/

67 Best Practices for successful Cleaning Validation TWO WORDS: Experimental Protocol (or significant pilot work) Confirms lab performance of cleaning agents Tests critical process parameters Tests engineering design and controls Determines rinse conditions and acceptance levels (both active and cleaning agents) to ensure they are achievable Test hold times Optimize conditions ID sampling locations 70/

68 Best Practices for successful Cleaning Validation Clear and concise procedures: ID all pieces of equipment: use checklists, tables or pictures for clarity. (tools may be equipment specific and not have universal names). 71/

69 Best Practices for successful Cleaning Validation Clear and concise procedures: 72/

70 Best Practices for successful Cleaning Validation Clearly define all sample requirements: Step taken In what? Volume Storage conditions How to label 73/

71 Information included in SOP Piece of equipment or system Frequency of visual inspections Department responsible for conducting the visual inspection HOW to visually inspect Is extra light used? Scope? Camera?? Follow-up actions defined 74/

72 And Finally.. Perform a Lab-Scale cleaning study Optimize cleaning parameters using beaker/coupon study Soiling is expensive large scale May not be feasible due to availability of equipment Quantitative measurement of residue removal easier at small scale Laundry mat approach: Run multiple conditions at the same time. 75/

73 Why Perform a Lab-Scale Study Run Cleaner Temperature Concentration 1 Cleaner A Ambient 1% v/v 2 Cleaner B Ambient 1% v/v 3 Cleaner C Ambient 1% v/v 15 min 30 min 45 min 60 min 4 Cleaner A Ambient 5% v/v 5 Cleaner B Ambient 5% v/v 6 Cleaner C Ambient 5% v/v 7 Cleaner A 45 C 1% v/v 8 Cleaner B 45 C 1% v/v 9 Cleaner C 45 C 1% v/v 10 Cleaner A 45 C 5% v/v 11 Cleaner B 45 C 5% v/v 12 Cleaner C 45 C 5% v/v 13 Cleaner A 60 C 1% v/v 14 Cleaner B 60 C 1% v/v 15 Cleaner C 60 C 1% v/v 16 Cleaner A 60 C 5% v/v 76/

74 Perform a Lab-Scale cleaning study Parameters: TACT Criteria Visually clean Water Break-free Gravimetric Assessment Acceptance criteria: ± grams Scale accuracy ± grams Coupon blank weight, amount of residue spiked on coupons. Amount of residue remaining after cleaning assessment 77/

75 Where to start: Cleaning Agent Selection Alkaline Cleaners Organic acids Tableting excipients Proteins/Fermentation residues Oils/Waxes/Fats Grease Polysaccharides Acidic Cleaners Particulates Alkaline Salts: Bicarbonates, carbonates Metal Oxides Hard water scale 78/

76 Performing a Lab-Scale Cleaning Study Coat coupon with 1-2 gms of soil Emulate process conditions and dirty hold time 79/

77 Performing a Lab-Scale Cleaning Study Use experimental design to vary parameters for optimization Start with agitated immersion: Calibrated digital stirplate Vary detergent concentration and temperature Check cleaning progress at specific time intervals. Concentration Temperature C Time 1% % % % % % Concentration Temperature 80/

78 Acceptance Criteria Soiled Coupon Visual Failure Water Break Free Failure 81/

79 Advantages of Lab-Scale Cleaning Study Fails TOC/HPLC Passes Acceptance Criteria 82 82/

80 Advantages of Lab-Scale Cleaning Study Residue build-up over time 83/

81 Advantages of Lab-Scale Cleaning Study Same coupon, different angle. Residue visible in first angle, not in second. Same coupon, different angle. Residue visible on both angles, 15 coating and cleaning cycles. 84/

82 Advantages of Lab-Scale Cleaning Study Same coupon, different angle. Both coupons wet Same coupon, different angle. Both coupons dry 85/

83 Best Practices for a Robust Cleaning Validation References & Additional Reading: Fourman, G.L. and Mullen, M.V., Determining Cleaning Validation Limits for Pharmaceutical Manufacturing Operations, Pharmaceutical Technology 17(4), (1993). FDA, 1993, Guide to inspections of validation of cleaning processes. Rockville, MD, USA: Food and Drug Administration, Office of Regulatory Affairs. Forsyth, R., O Neill, J.C. and Hartman J.L. (2007) Materials of construction based on recovery data for cleaning validation. Pharmaceutical Technology, Oct., pp LeBlanc, D.A. (2000) Validated Cleaning Technologies for Pharmaceutical Manufacturing. USA: Interpharm Press. Verghese, G. and Lopolito, P. (2007) Process Analytical Technology and Cleaning. Contamination Control, Fall 2007, pp LeBlanc, Destin A. et al., Cleaning Technology for Pharmaceutical Manufacturing Pharmaceutical Technology 17:7, (1993). Points to Consider for Cleaning Validation. PDA Technical Report No. 29. PDA. Bethesda, MD.March 30, Forsyth, RJ. et al., Correlation of Visible-Residue Limits with Swab Results for leaning Validation. Pharmaceutical Technology 30 (11), (2006). LeBlanc, Destin A., Issues in Setting Limits for Actives in Bulk Biotech Manufacture. Journal of Validation Technology 15 (1), (2009). Pluta, P (editor). Cleaning and Cleaning Validation Volume I. PDA Books (2009). ISBN Troy, F., Hold Time Studies: A Lost Parameter for Cleaning Validation. Journal of Validation Technology 13 (3) (2007). Thank you for your attention! beth_kroeger@steris.com 86/