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Using Optical Microscopy as an Orthogonal Method to Characterize the Gray Zone between Sub‐Visible...
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Transcript of Using Optical Microscopy as an Orthogonal Method to Characterize the Gray Zone between Sub‐Visible...
Using Optical Microscopy as an Orthogonal Method to Characterize the Gray Zone
between Sub‐Visible Particle Counting and Visual Inspection for Injectables
Mark BerdovichDirector of Sales / Marketing for
Micro Measurement Laboratories, Inc.
Agenda• Particle counting / detection requirements for parenterals• The Visual Inspection “Gray Zone” is wider than 50 – 200 µm• The larger the container gets
– The wider the Gray Zone gets (eg. > 200 µm), and simultaneously…– The less‐sensitive the preferred LO method is to larger particles
• Particle Size Distribution or “Contamination Profile” analysis• (Very) basic discussion on particle counting statistics• Case study – LO and MM testing on 520 ultra‐clean 2 L bottles• Power Law Fit of data to determine Contamination Profile• Summary discussion on findings / suggestions for future work
Regs for Particle Detection in Injections• From USP <1>
– All articles intended for parenteral administration….prepared in such a manner as to exclude particulate and other foreign matter, and that..
– Each final container must be inspected for the presence of visible particulates.
• From USP <788>– Test for sub‐visible particulates (≥ 10 & ≥ 25 µm), preferably by Light
Obscuration (LO) instrument “Method 1”– Alternative Microscopic “Method 2” when LO technology is limited
• From USP <790>– Generally agreed‐upon LOD for visible detection is 50 µm– Routine, reliable detection (i.e. ≥ 70%) not achieved until ≥ 200 µm
The Risk…and Mitigating It• As parenteral container size grows large, the POD for visible
particulates (i.e. ≥ 50 µm) goes down. • The maximum particle size resolved by (the preferred) Light
Obscuration method for sub‐visible particles is ≥ 25 µm.• Additionally, sampling limitations prevent the LO method
from being sensitive to small populations of large particles.• This leaves a bigger‐than‐realized Gray Zone, especially for
larger containers, that can’t be adequately characterized by LO and visual inspection alone.
• Microscopic “Method 2” as an orthogonal method provides useful information to help better characterize the Gray Zone.
A “Typical” Sub‐Visible Particle Count Test• Four runs x 5 mL each; discard first run• Average data from remaining 3 runs = 15 mL
• If SVP, regulatory limits are satisfied and lot passes• But of what value to process life‐cycle management is this? • What if we turned on more ‘informational’ size channels?
≥ 10 µm ≥ 25 µmAvg. Count 50 8Std Dev 7.1 2.8% RSD 14.1 36.1
≥ 2 µm ≥ 5 µm ≥ 10 µm ≥ 15 µm ≥ 25 µm ≥ 50 µmAvg. Count 2170 318 50 19 8 2Std Dev 46.6 17.8 7.1 4.4 2.8 1.4% RSD 2.1 5.6 14.1 22.9 36.1 70.7
Track Products’ “Contamination Profile”
0.000
0.001
0.010
0.100
1.0001 10 100 1000
Long
‐Term Average Particles/mL
Particle Size (um)
Power Law ExpressionProduct A Avg Count = 3.176*(Size)‐1.98
Process B Avg Count = 1.789*(Size)‐1.34
Particle Counting is a Poisson Process
• Counting a number of particles (more generally, independently‐occurring events) within a specified interval obeys a Poisson Distribution – i.e. expresses probability of X events/interval– Expected Value (X) is long‐term average count over many measurements– Standard Deviation is Square Root of Expected Value (average)
• Two Very Important Insights– As more particles are counted within each interval, the statistical
variation of the measurement decreases – i.e. better signal‐to‐noise– A longer sampling interval (whether time or volume) will also improve
the natural statistical variation of the average measurement
…Back to that “Typical” Sub‐Visible Test
• For SVP and/or LVP, same minimum volume: 4 x 5 mL runs• Discard data from first portion, average 3 remaining (15 mL)• %RSD values indicate big swings in counts for large particles
from natural statistical variation; AND….additionally…• This wide variation in pooled volume (SVP / LVP) with no
change in requirements for volume sampled has an effect…
≥ 2 µm ≥ 5 µm ≥ 10 µm ≥ 15 µm ≥ 25 µm ≥ 50 µmAvg. Count 2170 318 50 19 8 2Std Dev 46.6 17.8 7.1 4.4 2.8 1.4% RSD 2.1 5.6 14.1 22.9 36.1 70.7
Volume Sampled vs. Solution Pool Volume
Number Units Pooled
Unit Volume (mL)
Solution Pool(mL)
Volume Sampled (mL)
% Sampled
SVP 10 2 20 15 75%SVP 10 20 200 15 8%SVP 1 50 50 15 30%LVP 1 500 500 15 3%LVP 1 1,000 1,000 15 2%LVP 1 2,000 2,000 15 1%
The Problem Statement• The case‐study process was using an ultra‐clean plastic bottle
for storage of manufactured injectable drug product (2 Liter)• Each lot of bottles filled were already verified (with very low
counts) to meet the USP <788> LO sub‐visible particle limits :– NMT 25 counts/mL ≥ 10 µm– NMT 3 counts/mL ≥ 25 µm
• However, once the drug product was added to each of the bottles, a much higher‐than‐expected percentage of the filled bottles were rejected during visual inspection.
• How could this be? Each lot was ‘approved’ by LO…and the numbers weren’t anywhere near the limits.
Experiment Set‐Up• 2,000 mL plastic bottles for injectable drug product storage• Each test lot = 130 bottles & 4 lots were included in the plan• Each lot was broken into 13 groups of 10 bottles each• Each bottle was filled with 500 mL (25% of volume) with 0.2 µm
filtered DI‐H2O and inverted 25 x in 10 sec; 3 min rest time• Light Obscuration tests were individual bottles sampled by 5
runs of 20 mL each, no data were discarded in the calculations• The remaining 500 – (5 x 20) = 400 mL from each of the 10
bottles from each group was pooled and filtered through a single 0.8 µm pore size filter (~4,000 mL thru each membrane)
Data Reduction Technique – Power Law Fits• Plot concentration (particles/mL or /Liter or /gram or…) vs.
particle size (µm) on a log‐log axis.• Fit a Power Law trend line through the data.
– Requires special treatment of 0 counts/mL results– Note / record the value of the y‐intercept (C Value) and the exponent
(P Value); note the quality of fit (R‐squared) to the model – good data?
• Express these empirically‐derived values as a Power‐Law expression of this type:
∗
Technique – Power Law Fits (cont.)• Performing this analysis over many lots of product or runs of a
particular process, the potential ability arises to trend overall expected shape of the PSD (i.e. Contamination Profile) using one parameter with statistical confidence
• This means time saved during investigations; where do I start?– A big increase in slope (i.e. a bias to small particles) – too much Si oil?– A big decrease in slope (i.e. a bias to big particles) – filter PM needed? – If the slope remains very similar from lot to lot, but the entire PSD is
shifted up or down by the same proportion – less CR particle load?– With enough data over time, its possible to have the ability to predict
problems as they begin to unfold, which eliminates unplanned down‐time.
0.000
0.001
0.010
0.100
1.000
10.000
1 10 100
Average Cu
mulative Particles/mL
Particle Size (µm)
Figure 1: LO Trendline Summary ‐ 4 Plastic Bottle Lots (130 /Lot)
Lot P‐Value R2 Value36496 ‐2.18 0.99433769 ‐1.94 0.98136732 ‐2.69 0.91836866 ‐2.33 0.972
Light Obscuration
0.0001
0.0010
0.0100
0.1000
1.00001 10 100 1000
Average Cu
mulative Particles/mL
Particle Size (µm)
Figure 2: Microscopic Membrane Trendline Summary ‐ 4 Lots
Lot P‐Value R2 Value36496 ‐1.59 0.98033769 ‐1.34 0.95836732 ‐1.55 0.98136866 ‐1.46 0.967
Microscopic Membrane
0.000
0.001
0.010
0.100
1.000
10.000
1 10 100
Average Cu
mulative Particles/mL
Particle Size (µm)
Figure 3: Corrected LO Trendline Summary ‐ Removed 0 Point
Lot P‐Value R2 Value36496 ‐2.18 0.99433769 ‐1.94 0.98136732 ‐2.02 0.99636866 ‐2.33 0.972
Light Obscuration (Corrected)
Table 2: Summary of P‐Values, All Data
Lot P‐Value R2 Value Lot P‐Value R2 Value36496 ‐2.18 0.994 36496 ‐1.59 0.98033769 ‐1.94 0.981 33769 ‐1.34 0.95836732 ‐2.02 0.996 36732 ‐1.55 0.98136866 ‐2.33 0.972 36866 ‐1.46 0.967Avg. ‐2.12 Avg. ‐1.49%RSD 8.2% %RSD 7.5%
Light Obscuration (Corrected) Microscopic Membrane
N/A N/A
y = 6.895x‐2.188R² = 0.9975
y = 1.788x‐1.465R² = 0.9728
0.000
0.001
0.010
0.100
1.000
10.000
1 10 100 1000
Cumulative Particles/mL
Particle Size (µm)
Figure 4: Grand Avg PSD Profiles: LO vs MM, 2 L Bottles
GrandAvg Light Obscuration
GrandAvg Microscopic Method
Table 3: Predicted Conc. @ Size vs. Unit Vol.≥ 10 µm ≥ 25 µm ≥ 50 µm ≥ 100 µm ≥ 200 µm ≥ 300 µm ≥ 500 µm
LO 0.04486 0.00603 0.00132 0.00029 0.00006 0.00003 0.00001MM 0.06059 0.01575 0.00569 0.00205 0.00074 0.00041 0.00019
Δ (MM‐LO)(# / Cont.) 31.5 19.4 8.7 3.5 1.4 0.8 0.4
LO 0.04486 0.00603 0.00132 0.00029 0.00006 0.00003 0.00001MM 0.06059 0.01575 0.00569 0.00205 0.00074 0.00041 0.00019
Δ (MM‐LO)(# / Cont.) 78.6 48.6 21.8 8.8 3.4 1.9 0.9
LO 0.04486 0.00603 0.00132 0.00029 0.00006 0.00003 0.00001MM 0.06059 0.01575 0.00569 0.00205 0.00074 0.00041 0.00019
Δ (MM‐LO)(# / Cont.) 157.3 97.2 43.7 17.6 6.8 3.8 1.8
LO 0.04486 0.00603 0.00132 0.00029 0.00006 0.00003 0.00001MM 0.06059 0.01575 0.00569 0.00205 0.00074 0.00041 0.00019
Δ (MM‐LO)(# / Cont.) 314.5 194.5 87.3 35.3 13.6 7.6 3.7
2 Liter
5 Liter
10 Liter
20 Liter
Summary of Key Findings• With sufficient data to work with, empirically‐derived Power
Law Expressions can be created to effectively model the expected PSD from a particular parenteral product or process
• Power Law Expressions from Figure 4 (Avg. of 520 units/4 lots)
– Light Obscuration: . 6.948 ∗ .
– Microscopic Method: . 1.788 ∗ .
• As expected MM model predicts more large particles then LO– Low sampled volume of LO method (20%) a factor (vs. 80% for MM)– Impact minimized as container size gets very small (b/c clean bottles!)– As container size grows large, difference can mean a lot!
Credits / References
1. USP Visual Inspection of Injections <1790>, USP 41‐PF 1, 2015.2. Dunham, A. Visible Particulate Matter Detection in LVIs. Paper presented at the PDA Annual
Meeting, Colorado Springs, CO, April 2008.3. Xu, Mindi, Wange, Hwa‐Chi, “Minimum Sampling Time/Volume for Liquid‐borne Particle
Counters and Monitors.” Proceeding of the 43rd Annual Technical Meeting of the Institute of Environmental Sciences, pp. 20‐25. Los Angeles (1997).
4. Walpole, Ronald E., Myers, Raymond H., “Poisson Distribution and the Poisson Process.” Probability and Statistics for Engineers and Scientists, pp. 131‐133. London (1989).
5. Knollenberg, R.G., Veal, D.L., “Optical Particle Monitors, Counters, and Spectrometers: Performance Characterization, Comparison and Use.” Proceedings of the 37th Annual Technical Meeting of the Institute of Environmental Sciences, pp. 750‐771. San Diego (1991.)
6. Mitchell, J., “Statistical Analysis of Particle Instruments for Liquid‐borne Particles: Understanding the Impact of Size Sensitivity and Sample Volume.” Proceedings of the 44thAnnual Technical Meeting of the Institute of Environmental Sciences. Phoenix (1998.)
Suggestions / Further Study• It would seem that an ultra‐clean bottle production process
can reasonably be monitored in this way; what else? How does this apply to active drug product?
• Assuming additional studies proved this technique useful on ADP, could more general models be developed for products that are filtered vs. not filtered during filling, or other criteria?
• Would this be a helpful analysis for particle data from biologic products? – Probably not without a lot of frustration….– Sufficiently predictable behavior and subsequent particle results from
protein samples may be too tall a barrier for this kind of analysis.
Back‐Up / Extra Material• All MM data from case study was sized according to
Equivalent Spherical Diameter (ESD)– This is contrary to USP <788> recommendations for fibers (long‐dim)– But, more accurate comparison btwn. the LO data and the MM data
• Sizing long aspect ratio particles (fibers, rods..) by the ESD method tends to result in an under‐sizing vs. longest dim.
• In this case‐study, this sizing error effect can be seen in the ≥ 100 and 200 µm MM data – note the shape of the orange, yellow, and gray curves in Figure 5.
• Re‐sizing by longest‐dimension method would likely decrease count at 100 µm, but increase at 200 µm – would be better fit