The presence of both visible and subvisible particulate matter can pose risks during the whole biologic drug development, packaging, and delivery process.
Numerous sources of potential particulate contamination exist. Inherent particles, much like protein aggregates, originate from the formulation itself. Considerable contamination risks may also stem from intrinsic sources such as filter fibers or metal fragments from processing equipment, or even glass chips from primary packaging.
Extrinsic sources such as clothing or hair fibers also pose a risk of contamination, with other potential contaminants including organic, inorganic, protein, and metal particulates.
Hound combines automated microscopy, Raman spectroscopy, and Laser-Induced Breakdown Spectroscopy (LIBS) in a single instrument to identify both visible and subvisible particles across a diverse array of chemical compositions (Figure 1).
Figure 1. Hound counts and identifies the composition of visible and sub-visible particles with both automated and manual modes. Hound uses Raman (532 nm and 785 nm) and LIBS to identify the composition of particles, helping users track down the particle source.
Hound is capable of fully automating the identification of thousands of particles, or it can be used manually to rapidly identify a handful of particles. Hound can also use automated microscopy to quickly count and size every particle in a sample.
Raman spectroscopy offers users the ability to chemically fingerprint organic, inorganic and protein particles, LIBS can be used to conduct elemental analysis, identifying metals and inorganics.
The identification of particles is done by matching their elemental and chemical spectra with integrated LIBS and Raman reference databases. Alongside these built-in databases, it is also possible to add custom reference spectra, allowing identification to be tailored to a specific process.
In the study outlined here, LIBS was employed to identify the elemental composition of five separate inorganic contaminants: a bottle cap fragment, a metal crimp cap fragment, copper wire, a syringe needle fragment, and a glass shard. Additionally, a custom reference database of items found in the test laboratory was used to identify the exact source of the metal cap fragment.
Visible particles were created by shattering or cutting materials frequently found in a laboratory. Metal particles were obtained from four distinct sources: a bottle cap, a common syringe needle found in the lab, a metal crimp cap from Wheaton Industries and copper jumper wire. A glass particle was sourced from lead-barium glass shards.
Each sample was prepared by placing the particle on a nitrocellulose membrane glued to an aluminum mesh backing to establish an adhesive round. The adhesive round was completely dried before particle identification took place.
The adhesive round included five particles, all of which were automatically analyzed using LIBS. Next, the adhesive round was placed on the Hound instrument and the whole sample area was imaged with a 10x scanning objective.
The majority of the particles present were identified via a single LIBS measurement. Particles that possessed an outer coating (in this case, copper wire) necessitated the use of repeat measurements at the same spot – this allowed the device to burn through the outer coating before collecting a measurement that enabled particle identification.
LIBS match criteria
Spectra from each particle were evaluated with the built-in LIBS reference database, enabling identification. A match rank between the sample itself and the reference spectra was calculated by multiplying the Pearson correlation by 1000. In this study, a match rank >700 (out of 1000) was understood to be a high-quality match.
After the metal crimp cap particle had been identified, its spectrum was evaluated using a custom reference database that contained a user-created reference spectrum for the exact Wheaton Industries crimp cap used. The metal crimp cap particle’s spectrum was then re-analyzed and compared to this custom reference database in Hound Analysis.
The 10x objective (Figure 2) was used to create a mosaic image of the filter area, before identification via LIBS took place. Hound identifies a particle’s composition by comparing the spectrum acquired during analysis to spectra in its reference database until it finds a match (Table 1).
Figure 2. Mosaic image captured by automatically scanning an adhesive round at 10x. The five particles analyzed can clearly be seen. A: Syringe needle fragment. B: Crimp cap particle. C: Bottle cap fragment. D: Piece of copper wire. E: Glass shard. LIBS spectra were acquired for each particle and compared to the Hound built-in reference database.
Table 1. Each of the five particles was identified with LIBS on Hound at high match ranks. A match rank >700 (out of 1000) is considered a high-quality match.
||Low alloy steel
|Copper jumper wire
As can be seen, the syringe needle fragment was identified as stainless steel, possessing a matching rank of 953 when compared against the reference (Figure 3A). Meanwhile, the bottle cap particle was identified as being low alloy steel with a matching rank of 860 in comparison with the reference (Figure 3B).
Two repeated LIBS measurements were necessary before the composition of the copper wire could be identified. The initial measurement provided an unidentified result, but this was able to burn through the wire’s outer coating.
The second measurement completed in this same location identified the particle’s composition as copper, with the copper wire being successfully identified as copper with a rank of 983 (Figure 3C).
Finally, the glass shard was successfully identified as lead-barium glass with a rank of 950 (Figure 3D). Because this glass shard was already known as lead-barium glass, the accuracy of the analysis was therefore confirmed.
Figure 3. A: The LIBS spectrum (green) of a syringe needle fragment matched to a stainless-steel reference (blue) with a rank of 953. B: The LIBS spectrum of a bottle cap fragment (green) matched to a low alloy steel reference (blue) with a rank of 860. C: The LIBS spectrum of a copper jumper wire (green) matched to a copper reference (blue) with a rank of 983. D: The LIBS spectrum of a lead-barium glass shard (green) matched to a lead-barium glass reference (blue) with a rank of 950. All reference spectra are in the Hound built-in reference database.
The Hound was able to identify the metal crimp cap particle as aluminum with just one measurement, with the spectrum acquired resulting in a high match to the reference database spectrum for aluminum, confirming a rank of 993 (Figure 4A).
Additionally, the metal crimp cap’s spectrum was re-analyzed before being compared to a custom reference database which contained the LIBS spectrum for the Wheaton Industries crimp cap used as the source of the particle. The metal crimp cap was found to match the Wheaton crimp cap reference spectrum, returning a rank of 992 out of 1000 (Figure 4B).
Figure 4. A: The LIBS spectrum of a metal crimp cap particle (green) matched to Hound’s built-in reference spectrum for aluminum (blue) with a rank of 993. B: The spectrum of a metal crimp cap particle (green) compared to the custom mean reference spectrum for the brand of aluminum crimp cap used in this study (blue) in the custom reference database. The metal crimp cap was matched to the Wheaton crimp cap custom reference with a rank of 992. The expanded area between 375 nm and 425 nm shows a distinct peak at 403 nm for the Wheaton crimp cap.
Hound was able to successfully identify all five elemental particles’ compositions with LIBS. Users can acquire a particle’s elemental identification, allowing them to narrow down the particle source using an integrated reference database.
With an expandable custom reference database, the operator can create a highly specific reference spectra of any materials used within their process or laboratory. This powerful functionality enables users to correctly identify the exact source of a particle, should contamination be detected in the future.
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