U.S. patent application number 13/764374 was filed with the patent office on 2013-08-22 for automation of ingredient-specific particle sizing employing raman chemical imaging.
This patent application is currently assigned to Chemimage Corporation. The applicant listed for this patent is Chemimage Corporation. Invention is credited to Michael Fuhrman, Oksana Klueva, Oksana Olkhovyk, Ryan Priore.
Application Number | 20130218480 13/764374 |
Document ID | / |
Family ID | 42319660 |
Filed Date | 2013-08-22 |
United States Patent
Application |
20130218480 |
Kind Code |
A1 |
Fuhrman; Michael ; et
al. |
August 22, 2013 |
Automation of Ingredient-Specific Particle Sizing Employing Raman
Chemical Imaging
Abstract
A system and method for determining at least one geometric
property of a particle in a sample. A sample is irradiated to
thereby generate Raman scattered photons. These photons are
collected to generate a Raman chemical image. A first threshold is
applied wherein the first threshold is such that all particles in
the sample are detected. A particle in the sample is selected and a
second threshold is applied so that at least one geometric property
of the selected particle can be determined. At least one spectrum
representative of the selected particle is analyzed to determine
whether or not it is a particle of interest. The step of
determining a second threshold may be iterative and automated via
software so that candidate second thresholds are applied until a
satisfactory result is achieved.
Inventors: |
Fuhrman; Michael;
(Pittsburgh, PA) ; Priore; Ryan; (Wexford, PA)
; Olkhovyk; Oksana; (Pittsburgh, PA) ; Klueva;
Oksana; (Pittsburgh, PA) |
|
Applicant: |
Name |
City |
State |
Country |
Type |
Chemimage Corporation; |
|
|
US |
|
|
Assignee: |
Chemimage Corporation
Pittsburgh
PA
|
Family ID: |
42319660 |
Appl. No.: |
13/764374 |
Filed: |
February 11, 2013 |
Related U.S. Patent Documents
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Application
Number |
Filing Date |
Patent Number |
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12684495 |
Jan 8, 2010 |
8374801 |
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13764374 |
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61143562 |
Jan 9, 2009 |
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Current U.S.
Class: |
702/29 |
Current CPC
Class: |
G01J 3/027 20130101;
G01N 21/65 20130101; G01J 3/02 20130101; G01J 3/44 20130101; G01N
15/0227 20130101 |
Class at
Publication: |
702/29 |
International
Class: |
G01N 15/02 20060101
G01N015/02 |
Claims
1. A method comprising: (a) irradiating a sample comprising at
least one unknown particle to thereby produce Raman scattered
photons; (b) collecting said Raman scattered photons to thereby
generate a Raman chemical image representative of said sample; (c)
applying a first threshold to said Raman chemical image wherein
said first threshold is such that all particles in said sample are
detected; (d) selecting one of said particles detected as a result
of applying said first threshold; (e) applying a second threshold
to said Raman chemical image to thereby determine at least one
geometric property of said selected particle, wherein said second
threshold is unique to said selected particle such that said at
least one geometric property can be determined; (f) analyzing at
least one spectrum representative of said selected particle to
thereby classify the selected particle as at least one of: a
particle of interest and not a particle of interest.
2. The method of claim 1 wherein said analyzing comprises comparing
at least one spectrum representative of said selected particle to
at least one reference spectrum representative of a particle of
interest.
3. The method of claim 2 further comprising the steps of: (g) if
said comparing results in a match between said spectrum
representative of said selected particle and said reference
spectrum representative of said particle of interest, identifying
the selected particle as a particle of interest; and (h) if said
comparing does not result in a match between said spectrum
representative of said selected particle and said reference
spectrum representative of said particle of interest, identifying
the selected particle as not a particle of interest.
4. The method of claim 1 further comprising repeating steps (d)-(f)
for at least one other unknown particle present in said sample.
5. The method of claim 1 wherein said at least one geometric
property comprises the size of said selected particle.
6. The method of claim 1 wherein said at least one geometric
property of said selected particle is selected from the group
consisting of: an area, a perimeter, a feret diameter, a maximum
chord length, a shape factor, an aspect ratio, and combinations
thereof.
7. The method of claim 1 wherein said at least one geometric
property of said selected particle is characteristic of particle
size distribution.
8. The method of claim 1 further comprising fusing said Raman
chemical image with a brightfield image representative of said
sample to thereby generate a fused image representative of said
sample.
9. The method of claim 8 further comprising analyzing said fused
image to thereby determine at least one geometric property of at
least one unknown particle in said sample.
10. The method of claim 1 wherein said second threshold comprises a
fraction of the peak intensity of the Raman spectrum corresponding
to said selected particle.
11. The method of claim 10 wherein said fraction comprises one
half.
12. The method of claim 1 wherein said second threshold comprises
the peak intensity of at least one Raman spectrum corresponding to
at least one edge of the selected particle.
13. The method of claim 1 wherein said second threshold is
determined by averaging the peak intensities of two or more Raman
spectra corresponding to at least one edge of said selected
particle.
14. The method of claim 1 wherein said second threshold is
determining by a method comprising: (a) applying a candidate second
threshold to said Raman chemical image; (b) assessing the
effectiveness of said candidate second threshold; and (i) if, based
on said assessment, said candidate second threshold is effective,
identifying said candidate second threshold as a second threshold
unique to said selected particle such that said at least one
geometric property can be determined, and (ii) if, based on said
assessment, said candidate second threshold is not effective,
repeating steps (a)-(b) for at least one other candidate second
threshold.
15. The method of claim 14 wherein a candidate second threshold is
determined to be effective when the intensity of the selected
particle is five standard deviations above the average
background.
16. The method of claim 1 further comprising applying a chemometric
technique to said Raman chemical image.
17. The method of claim 16 wherein said chemometric technique is
selected from the group consisting of: cluster analysis, principal
component analysis (PCA), Cosine Correlation Analysis (CCA),
Euclidian distance analysis (EDA), multivariate curve resolution
(MCR), band t. entropy method (BTEM), Mahalanobis distance (MD),
adaptive subspace detector (ASD), multivariate curve resolution
(MCR), and combinations thereof.
18. The method of claim 1 wherein said method is automated via
software.
19. The method of claim 14 wherein said second threshold
determination method is automated via software.
20. A method comprising: (a) irradiating a sample comprising at
least one unknown particle of interest to thereby produce
interacted photons wherein said interacted photons are selected
from the group consisting of: Raman scattered by said sample,
reflected by said sample, emitted by said sample, absorbed by said
sample, and combinations thereof; (b) collecting said interacted
photons to thereby generate a chemical image representative of said
sample; (c) applying a first threshold to said chemical image
wherein said first threshold is such that all particles in said
sample are detected; (d) selecting one of said particles detected
as a result of applying said first threshold; (e) applying a second
threshold to said chemical image to thereby determine at least one
geometric property of said selected particle, wherein said second
threshold is unique to said selected particle such that said at
least one geometric property can be determined; (f) analyzing at
least one spectrum representative of said selected particle to
thereby classify the selected particle as at least one of: a
particle of interest and not a particle of interest.
Description
RELATED APPLICATIONS
[0001] This application is a continuation to pending U.S. patent
application Ser. No. 12/684,495, entitled "Automation of
Ingredient-Specific Particle Sizing Employing Raman Chemical
Imaging," filed on Jan. 8, 2010, which claims priority pursuant to
35 U.S.C. .sctn.119(e) to U.S. Provisional Application No.
61/143,562, entitled "Automation of Ingredient-Specific Particle
Sizing Employing Raman Chemical Imaging", filed on Jan. 9,
2009.
FIELD OF INVENTION
[0002] The invention relates generally to the use of Raman
spectroscopic methods, including Raman chemical imaging and Raman
spectroscopy for analyzing particles present in a sample. The
invention relates more specifically to the use of these methods to
determine at least one geometric property of particles present in a
sample. Examples of geometric properties the present invention may
be used to determine include, but are not limited to, particle
size, morphology, and spatial distribution.
BACKGROUND
[0003] Surfaces form the interface between different physical and
chemical entities, and the physical and chemical processes that
occur at surfaces often control the bulk behavior of materials. For
example, the rate of dissolution of drug particles in a biological
fluid (e.g., stomach, intestinal, bronchial, or alveolar fluid in a
human) can strongly influence the rate of uptake of the drug into
an animal. Differences in particle size distribution between two
otherwise identical compositions of the same drug can lead to
significant differences in the pharmacological properties of the
two compositions. Further by way of example, the surface area of a
solid chemical catalyst can strongly influence the number and
density of sites available for catalyzing a chemical reaction,
greatly influencing the properties of the catalyst during the
reaction. For these and other reasons, manufacturers often try to
closely control particle size and shape. Associations between and
among particles can also affect the pharmacological properties of
substances in the particles, such as the ability of a substance to
dissolve or become active in a biological system.
[0004] Numerous methods of analyzing particle sizes and
distributions of particle sizes are known in the art, including at
least optical and electron microscopy, laser diffraction, physical
size exclusion, dynamic light scattering, polarized light
scattering, mass spectrometric, sedimentation, focused beam
backscattered light reflectance, impedance, radiofrequency
migration, Doppler scattering, and other analytical techniques.
Each of these techniques has a variety of limitations that preclude
its use in certain situations. However, all of these techniques
share a critical limitation that prevent effective use of the
techniques for a wide variety of samples for which particle
analysis would be valuable--namely, none of the prior art
techniques is able to distinguish two particles that differ only in
chemical composition. Put another way, a first particle having
substantially the same size, shape, and weight as a second particle
cannot be distinguished from the second particle in these methods.
One method for using Raman spectroscopic methods for component
particle analysis is described in U.S. Pat. No. 7,379,179 to Nelson
et al., entitled "Raman Spectroscopic Methods for Component
Particle Analysis", which is hereby incorporated by reference in
its entirety.
[0005] In addition to distinguishing particles based on chemical
composition, it is also useful to determine particle size and
particle size distribution ("PSD"). Particle sizing of Active
Pharmaceutical Ingredients ("API") and Excipients of Interest
implemented using image analysis must be accurate because of the
requirements of customers and the Food and Drug Administration
("FDA"). The FDA acknowledges a critical path opportunity for the
development of methodologies for accurate and precise drug particle
size measurements in suspension products, thereby minimizing the
requirement for in vivo testing.
[0006] Batch comparison testing is an important part of product
quality studies and is necessary in studying bioavailability ("BA")
and/or establishing bioequivalence ("BE") for products including,
but not limited to, nasal sprays. It is recommended by the FDA that
in the BA and BE submission that PSD data is submitted for both new
drugs ("NDAs") and abbreviated new drug applications ("ANDAs") for
spray and aerosol formulations. Data must be presented prior to and
post actuation since this information closely relates to the drug
efficacy based on the dissolution rate of the particles. Such
information can help establish the potential influence of the
device on de-agglomeration.
[0007] Optical microscopy is currently the recommended method of
assessing and reporting drug and aggregated drug PSD. However, such
methodology is subjective and cannot be used with a high degree of
confidence for formulated suspensions where drug particle sizing
can be easily misjudged due to the presence of insoluble
excipients.
[0008] Currently, no validated method exists for characterizing API
particle size distribution in nasal aerosols and sprays dispite the
request of such data for BE testing for NDAs and ANDAs. A
qualitative and semi-quantitative estimation of drug and aggregated
drug PSD is recommended based on optical microscopy, but insoluble
suspending agents found in nasal spray formulations typically
complicate the ingredient-specific particle size ("ISPS")
determination. Therefore, there exists a need for an accurate and
reliable system and method for performing such analysis on particle
samples.
SUMMARY OF THE INVENTION
[0009] The present disclosure provides for a system and method for
determining geometric properties of particles in a sample using
Raman spectroscopic methods, including Raman chemical imaging and
Raman spectroscopy. The invention disclosed herein overcomes the
limitations of the prior art by implementing an individual particle
based approach to particle analysis, thereby improving the dynamic
range of particle analysis (increase the range of particles that
can be detected). Such an approach is advantageous because it
provides for more accurate detection and determination of the
number of particles present in a sample and their sizes.
[0010] Raman chemical imaging is a versatile technique that is well
suited to the analysis of complex heterogeneous materials. In a
typical Raman chemical imaging experiment, a specimen is
illuminated with monochromatic light, and the Raman scattered light
is filtered by an imaging spectrometer which passes only a single
wavelength range. The Raman scattered light may then be used to
form an image of the specimen. A spectrum is generated
corresponding to millions of spatial locations at the sample
surface by tuning an imaging spectrometer over a range of
wavelengths and collecting images intermittently. Changing the
selected passband (wavelength) of the imaging spectrometer to
another appropriate wavelength causes a different material to
become visible.
[0011] The Raman chemical image is comprised of multiple images,
each captured at a different wavelength. Contrast is generated in
the images based on the relative amounts of Raman scatter or other
optical phenomena, such as luminescence, that is generated by
different species located throughout the sample. Since a spectrum
is generated for each pixel location, chemometric analysis tools
can be applied to the image data to extract pertinent information
otherwise missed by ordinary univariate measures. The information
contained within this multi-wavelength image cube is transformed
into a single image plane for image analysis. Any method known in
the art may be used to obtain the single plane image. In one
embodiment, this may be achieved by extracting an image plane
corresponding to a spectral peak of interest. Another method that
may be used in another embodiment, which enhances signal-to-noise,
is to sum the intensities of the spectral planes which are unique
to particles of interest and from this subtract the average of
background planes. Still another method that may be used, in
another embodiment, is to perform a multivariate analysis to
extract a small set of image(s) with high information content for
further image processing. Examples of multivariate analysis include
cluster analysis, principal component analysis (PCA), Cosine
Correlation Analysis (CCA), Euclidian distance analysis (EDA),
multivariate curve resolution (MCR), band t. entropy method (BTEM),
Mahalanobis distance (MD), adaptive subspace detector (ASD),
multivariate curve resolution (MCR), combinations thereof and
others known in the art.
[0012] A spatial resolving power of approximately 250 nm may be
useful for Raman chemical imaging using visible laser wavelengths.
This is almost two orders of magnitude better than infrared imaging
which is typically limited to 20 microns due to diffraction. In
addition, image definition (based on the total number of imaging
pixels) can be very high for Raman chemical imaging because of the
use of high pixel density detectors (often one million plus
detector elements).
[0013] The invention disclosed herein is advantageous over the
prior art in several ways. For example, the systems and methods of
the present disclosure improve the accuracy of particle size
measurements by addressing at least three sources of error in
particle size measurements including: (1) the non-uniform
excitation illumination across the field of view of each image, (2)
the dependency of Raman emission from individual particles on their
size, morphology, and individual chemistry, and (3) that the
physical process of image capture is subject to degradation by
noise.
[0014] The prior art includes a method known as field flattening to
compensate for non-uniform illumination. Prior art uses methods
known as baseline correction and spectral normalization to
implement field flattening. Other image analysis methods, include
the use of an image of uniform field, morphological filters,
frequency domain filters, and polynomial functions can be used to
improve field flattening. Improvement of field flattening may allow
a particle to be visible above background noise, and can be
segmented and labeled as an object for further analysis.
[0015] The prior art sets a threshold level above background noise.
This threshold is set so that the sizes of the particles detected
in the Raman chemical image match the appearance of the sizes of
the particles in the corresponding brightfield image. Particles
with intensities above this threshold are detected as particles and
particle sizes are determined from the detected pixels comprising
the particles.
[0016] At least two problems are apparent from this process when
the results are validated: (1) failure to detect all particles in a
sample, and (2) failure to accurately size particles detected.
Utilization of a global threshold alone may not be sufficient for
accurate detection and size determination of particles. This is
because particles with low Raman signals will not be detected and
can be missed visually by a human performing validation. Particles
may have low Raman signals either because they were situated in
regions where the excitation illumination was low compared with the
center of the field of view or because they were simply low
emission particles.
[0017] Lowering the threshold intensity, in an attempt to detect
more particles, may result in inaccurate sizing of particles. So,
while some particles are correctly sized, the sizes of many
particles may be too large or too small. This is because a global
threshold will not be the optima threshold for every particle in a
sample. This may also result in groups of smaller particles being
identified as one larger particle. Reprocessing the image within
the neighborhood of each detected particle to recompute the size
may show that nearby particles were found to affect the
automatically computed local threshold and affect the particle
size.
[0018] The invention of the present disclosure addresses these
issues by considering the particle detection step and the particle
sizing step as two separate processes. This ensures more accurate
particle sizing and is API specific. First, a low global threshold
is set to guarantee the detection of all particles. Because of the
noise in the Raman spectra, individual pixels which do not
correspond to particles of interest may be inadvertently detected.
The size of each detected particle is then determined using a
threshold unique to each particle detected by applying the global
threshold.
[0019] Since particle chemistry is just as important as particle
size, the present disclosure also provides for a validation step
wherein the chemical spectra of each particle is evaluated after
the particle has been sized. This step is necessary because the
first step of detecting potential particles is subject to noise and
therefore the potential for interference exits. After each particle
is sized its spectrum is evaluated. This may be achieved by
verifying that a spectrum has been obtained, that the shape and
appearance of the spectra is characteristic of a particle of
interest, or comparing the spectrum to a reference spectrum of a
particle type of interest to determine whether or not there is a
match (i.e., API or excipient). Particles that do not share the
spectrum of the particle of interest are rejected as not a particle
of interest.
BRIEF DESCRIPTION OF THE DRAWINGS
[0020] FIG. 1 is a flow chart diagram representing one embodiment
of the present disclosure.
[0021] FIG. 2 is a flow chart diagram representing one embodiment
of the present disclosure.
[0022] FIG. 3 is a flow chart diagram representing one embodiment
of the present disclosure.
[0023] FIG. 4 is a schematic of an exemplary system that may be
used to achieve the methods of the present disclosure.
[0024] FIGS. 5A and 5B illustrate further explanation of the
methods of the present disclosure.
[0025] FIG. 6A represents a brightfield reflectance optical image
of a Rhinocort Aqua.RTM. droplet.
[0026] FIG. 6B represents a Raman image of budesonide particles
after global processing corresponding to FIG. 6A.
[0027] FIG. 6C illustrates the PSD of the Rhinocort Aqua API,
budesonide.
[0028] FIG. 7A represents a single field of view of budesonide
particles in a Rhinocort Aqua.RTM. droplet.
[0029] FIG. 7B represents a global processed Raman chemical
image.
[0030] FIG. 7C represents a local processed Raman chemical
image.
[0031] FIG. 7D illustrates the representative Raman spectra of
particles in the sample.
[0032] FIGS. 8 and 9 illustrate brightfield images of Batch 1 and
Batch 2 samples, respectively.
[0033] FIG. 10 represents Raman dispersive spectra of Rhinocort
Aqua.RTM. formulation components.
[0034] FIG. 11 illustrates a brightfield reflectance/processed
Raman fusion image of a single field of view of budesonide
particles in a Rhinocort Aqua.RTM. droplet and associated
spectra.
[0035] FIG. 11B represents a Raman chemical image at 1657 cm.sup.-1
of a single field of view of budesonide particles in a Rhinocort
Aqua.RTM. droplet.
[0036] FIG. 11C represents normalized Raman spectra of the
identified budesonide particles in a Rhinocort Aqua.RTM.
droplet.
[0037] FIGS. 12 and 13 illustrate Brightfield reflectance/Raman
fusion images for Batch 1 and Batch 2 samples, respectively.
[0038] FIG. 14 represents a budesonide particle size distribution
histogram and summary table of particle size distribution.
DETAILED DESCRIPTION
[0039] The present disclosure provides for a system and method for
analyzing particles in a sample. The method disclosed herein is
useful for determining geometric properties of particles in a
sample. The method also holds potential for evaluating other
attributes of particles in a sample during particle analysis.
[0040] In one embodiment, illustrated by FIG. 1, the method 100
comprises irradiating a sample comprising at least one unknown
particle to thereby produce Raman scattered photons in step 110. In
step 120, said Raman scattered photons are collected to thereby
generate a Raman chemical image representative of said sample. In
step 130, a first threshold is applied to said Raman chemical image
wherein said first threshold is such that all particles in said
sample are detected. One particle of the particles detected as a
result of applying the first threshold is selected in step 140. In
step 150, a second threshold is applied to said Raman chemical
image to thereby determine at least one geometric property of said
selected particle, wherein said second threshold is unique to said
selected particle such that said at least one geometric property
can be determined. At least one spectrum representative of said
selected particle is analyzed in step 160 to thereby classify the
selected particle as at least one of: a particle of interest and
not a particle of interest.
[0041] In one embodiment, the sample is irradiated using wide-field
illumination. In another embodiment, the sample is irradiated with
substantially monochromatic light. In one embodiment, the
determination of geometric properties of particles in the sample is
achieved using a RCI hypercube. In such an embodiment, the
intensity within the spectral peak is integrated at each pixel to
create a working image with a higher signal-to-noise ratio than the
peak intensity plane alone. In one embodiment, this can also be
used as a method of base-line correction. The resulting working
image depicts potential API particles as bright regions.
[0042] In one embodiment, the global threshold may be such that it
is just above the background noise level. In such an embodiment,
the background noise level is estimated and a global threshold
barely above the background is implemented. In another embodiment,
the global threshold may be some order of standard deviations of
the noise above the average background intensity. In another
embodiment, the global threshold may comprise three standard
deviations of the noise above the average background intensity.
Although a global threshold may ensure that all particles in a
sample are detected (although with inaccurate sizes), there is also
the possibility that some noise will be detected. The second
threshold and validation steps account for this.
[0043] In one embodiment, the second threshold is determined by:
individually processing the edges and brightness of each detected
particle. The edges may be detected by computing the gradient of
the working image to find the pixels where the intensity changes
most rapidly. The pixels corresponding to the steepest edges can be
identified and the average intensity of the edge pixels computed.
This average intensity can then be used as the second threshold. In
one embodiment, these steps can be performed for each particle
detected in the sample. In another embodiment, the second threshold
comprises a fraction of the peak intensity of the selected particle
above the background intensity. In another embodiment, this
fraction may comprise one half. Whatever method is used to
determine the second threshold, it will be a threshold unique to
the selected particle so that at least one geometric property can
be accurately determined.
[0044] In one embodiment, the invention disclosed herein may be
automated. This may be achieved via software. In one embodiment,
the determination of a second threshold method may be iterative,
meaning that the software will continue to apply one or more
different particle specific thresholds ("candidate second
thresholds") to a selected particle until a satisfactory result is
achieved. A result is satisfactory when the results can be trusted.
In one embodiment, this is measured using Rose's Criterion wherein
object intensity is five standard deviations above the average
background. The software then repeats this method, detecting and
measuring the size of each particle until all of the individual
particles present in the sample are detected and measured. This
adaptive embodiment may provide for a feedback loop in which
information received from the application of a second threshold is
evaluated to determine whether or not is it satisfactory. If the
result is satisfactory, then this threshold may be applied to
assess the particle. If the result is not satisfactory, then a
different second threshold is applied and evaluated to determine if
a satisfactory result is reached. This feedback loop can continue
until the satisfactory result is reached.
[0045] In one embodiment, the method may be adaptive in that the
processing takes place in each local region while continuously
adjusting threshold levels until a satisfactory result is achieved.
Such adaptive processing may be useful for the situation where a
region thought to contain one particle is found to actually contain
one or more particle. The adaptive processing may iteratively
continue such that if more than one particle is detected a unique
and improved threshold is determined for each subsequently detected
particle.
[0046] It is further contemplated by the present disclosure that
the system and method disclosed herein may hold the potential for
parallel processing. In such an embodiment, one or more systems may
be configured in such a way that allows for more than one particle
to be processed simultaneously. This may be achieved through a
computer network or other configuration.
[0047] Said second threshold is such that at least one geometric
property of the selected particle can be determined. This geometric
property can be any property that may be of interest in particle
analysis. In one embodiment, the geometric property is
characteristic of the size of the particle. In another embodiment,
the geometric property is characteristic of the particle size
distribution. In yet another embodiment, the geometric property can
be selected from the group consisting of: an area, a perimeter, a
feret diameter, a maximum chord length, a shape factor, an aspect
ratio of the particle, other geometric properties known in the art
and combinations thereof.
[0048] At least one spectrum of said selected particle is analyzed
in the validation step. This validation can be achieved in several
ways. Implementing this validation step holds potential for
reducing the number of false positives (i.e., the number of
particles thought to be a particle of interest that are not
actually a particle of interest), making assessment of the sample
more accurate. In one embodiment, said validation may comprise
confirming that a spectrum is in fact obtained from the selected
particle. In another embodiment, said validation may comprise
confirming the attributes of the spectrum obtained from the
selected particle. This may mean that the spectrum looks like one
would expect a spectrum is characteristic of the particle of
interest (i.e., does it have a proper shape). In yet another
embodiment, the validation step may comprise comparing at least one
spectrum obtained from the selected particle to at least one
reference spectrum. This reference spectrum may comprise the
particle of interest or an excipient or other substance in the
sample. This reference spectrum may be one of many reference
spectra in a reference database which can be searched depending on
the particular particle of interest, excipient, or other substance.
The database may comprise more than one reference spectra of one
particular particle. It may also comprise two or more reference
spectra corresponding to two or more different particles of
interest, excipient, or other substance.
[0049] FIG. 2 illustrates one embodiment of the present disclosure
in which a reference spectrum is used in the validation step. The
method 200 comprises illuminating a sample comprising at least one
unknown particle to thereby produce Raman scattered photons in step
210. These photons are collected in step 220 to thereby generate a
Raman chemical image representative of said sample. In step 230, a
first threshold is applied to said Raman chemical image wherein
said first threshold is such that all particles in said sample are
detected. One of said particles detected as a result of applying
said first threshold is selected in step 240. In step 250 a second
threshold is applied to said Raman chemical image to thereby
determine at least one geometric property of said selected
particle, wherein said second threshold is unique to said selected
particle such that that said at least one geometric property can be
determined. In step 260, at least one spectrum representative of
said selected particle is compared to a reference spectrum
representative of a particle of interest. This comparison is
performed to determine whether or not there is a match between the
spectrum representative of the selected particle and the reference
spectrum representative of the particle of interest 270. If there
is a match, the selected particle is identified as a particle of
interest 280. If there is not a match, the selected particle is
rejected as not a particle of interest 290.
[0050] In another embodiment, illustrated by FIG. 3, the method 300
comprises irradiating a sample comprising at least one unknown
particle of interest to thereby produce interacted photons in step
310. In one embodiment, these interacted photons are selected from
the group consisting of: photons scattered by said sample, photons
reflected by said sample, photons absorbed by said sample, photons
emitted by said sample, and combinations thereof. In step 320, the
photons are collected to thereby generate a chemical image
representative of the sample. In step 330, a first threshold is
applied to the spectroscopic image wherein said first threshold is
such that all particles in said sample are detected. One of said
particles detected as a result of the first threshold is selected
in step 340. A second threshold is applied in step 350 to thereby
determine at least one geometric property of the selected particle
wherein the second threshold is unique to said second threshold
such that a geometric property can be determined. In step 360 at
least one spectrum representative of the selected particle is
analyzed to thereby classify the selected particle as at least one
of: a particle of interest or not a particle of interest.
[0051] In one embodiment, the method may further comprise repeating
the steps enumerated herein for one other unknown particle present
in said sample. The steps may also be repeated for each unknown
particle detected in said sample.
[0052] In another embodiment, the method disclosed herein may
further comprise fusing a Raman chemical image of a sample with a
bright field image of said sample to thereby generate a fused
image. This fused image can then be analyzed to determine at least
one geometric property of at least one unknown particle in a
sample.
[0053] FIG. 4 is a schematic representation of one system that may
be used to perform the method of the present disclosure.
[0054] FIGS. 5A and 5B are provided to further illustrate the
advantages of the present invention, implementing a
particle-specific analysis method. FIGS. 6A-6C illustrate global
processing of brightfield and Raman chemical images of a Rhinocort
Aqua.RTM. droplet, yielding a total of 313 particles with a maximum
chord of 3.5.+-.3.1 .mu.m. Due to secondary scattering and the
reliance upon spectral normalization to flat-field the chemical
image, medium to large particles are typically oversized while
small particles are sometimes lost. FIG. 6A represents a
brightfield reflectance optical image of a Rhinocort Aqua.RTM.
droplet, FIG. 6B corresponds to a Raman image of the budesonide
particles after global processing, and FIG. 6C represents the PSD
of the a Rhinocort Aqua.RTM. API, budesonide.
[0055] FIGS. 7A-7D illustrate a comparison of global and local
processing of a Rhinocort Aqua.RTM. droplet. FIG. 7A represents a
brightfield reflectance optical image, FIG. 7B represents a global
processed Raman chemical image, FIG. 7C represents a local
processed Raman chemical image, and FIG. 7D represents the Raman
spectra of the locally processed particles.
[0056] It is further contemplated by the present disclosure that
the system and method provided for herein may implement other
spectroscopic and/or imaging modalities including but not limited
to: fluorescence, infrared (including short wave infrared, near
infrared, mid infrared, and far infrared), ultraviolet, visible,
others known in the art, and combinations thereof.
[0057] It is also contemplated by the present disclosure that the
system and method disclosed may be applied to other fields
including but not limited to threat detection, anatomic pathology,
and forensics.
[0058] The present disclosure may be embodied in other specific
forms without departing from the spirit or essential attributes of
the disclosure. Accordingly, reference should be made to the
appended claims, rather than the foregoing specification, as
indicating the scope of the disclosure. Although the foregoing
description is directed to the embodiments of the disclosure, it is
noted that other variations and modification will be apparent to
those skilled in the art, and may be made without departing from
the spirit of the disclosure.
EXAMPLES
[0059] Two different batches of nasal spray suspension (Rhinocort
Aqua.RTM.) containing an insoluble corticosteroid AP (budesonide)
and multiple excipients were analyzed to characterize the
budesonide particle size distribution in the samples. Approximately
1000 particles of the API were counted for each batch using
wide-field RCI (Falcon II.TM. ChemImage, Corporation, Pittsburgh,
Pa.). The chemical identity of the budesonide particles was
confirmed for each pixel against a Raman spectral library. Particle
size information was obtained for each identified particle using an
automated image processing and analysis algorithm. The ISPS
determined from the RCI was compared to a complementary brightfield
optical image. Statistical analysis of the total drug PSD for each
batch based on a Kol-Smirnov goodness-of-fit hypothesis test was
calculated to compare to the batches.
[0060] Two different lots of a Rhinocort Aqua.RTM. nasal spray (32
mcg budesonide, AstraZeneca, Wilmington, Del.) with different
expiration dates were acquired. Samples were prepared by shaking,
priming (eight actuations each) and spraying in an upright position
onto an inverted aluminum-coated glass microscope slide positioned
approximately 15 cm above the spray nozzle. The microscope slides
were then immediately turned upright and the nasal suspension
droplets were allowed to dry. Actuated samples were analyzed to
include actuation device influence as opposed to bulk samples.
Sixteen (16) droplets varying in size and shape were randomly
selected on the microscope slide for each batch (FIGS. 8 and 9).
Optical microscopy and RCI were used to measure the budesonide PSD
in each droplet, and the drug PSD data was assembled to yield a
representative PSD for budesonide API for each batch. All data was
collected using a FALCON II.TM. Wide-Field Raman Chemical Imaging
System (ChemImage Corporation, Pittsburgh, Pa.) with 532 nm laser
excitation (FIG. 4). Brightfield reflectance and Raman chemical
images were collected over the necessary number of fields of view
yielding a sampling area sufficient to image each individual
droplet without significant oversampling. The spectral range for
the RCI measurements was chosen to include a characteristic C.dbd.C
feature at 1657 cm-1, that can be used to discriminate budesonide
from all other excipients in this particular formulation (FIG. 10).
Imaging data was analyzed using ChemImage Xpert.TM. software
package (Version 2.3.1, ChemImage Corporation, Pittsburgh, Pa.)
yielding both the Raman/brightfield fusion images as well as the
budesonide particle statistics.
[0061] Brightfield Raman chemical fusion images of representative
field of view selected for ISPS analysis is shown in FIG. 11, along
with corresponding spectra. Automated particle sizing was performed
using the RCI hypercube. The intensity within the spectral peak was
integrated at each pixel to create a working image with a higher
signal-to-noise ratio than the peak intensity plane alone. This
also served as a method of baseline correction. The resultant
working image showed potential API particles as bright regions on a
dark background (FIG. 5B). A particle detection threshold equal to
three standard deviations of the noise above the average background
intensity was applied to the working image to detect objects for
size measurement and verification of the particle spectral
signatures (FIG. 5C).
[0062] Sizing the objects of interest was performed by individually
processing the edges and brightness of each object. Edges of each
object were detected by computing the gradient of the working image
to find the pixels where the intensity changes most rapidly. A
small copy of each detected object was cropped from the working
image, and pixels located at the steepest edges of the objects
within this cropped region were identified. The average intensity
of the edge pixels was computed and used as a threshold within the
cropped region. Neighboring objects which were originally grouped
into large masses by the global threshold there were separated into
individual objects. A unique threshold based on the intensity at
object edges was iteratively determined for each object. Standard
image analysis routines were then used to compute the sizes and
shapes of detected objects. The spectral shape of each object was
verified after detection and sizing. A "shape" constraint was
imposed on the average spectrum of an object so that it must have a
continuously rising leading edge, and a continuously falling
trailing edge, i.e., it must look like a peak to a human observer.
A brightness constraint determined whether or not a particle
counted, meaning the particle had sufficient contrast to be
recognizable above background noise. Rose's Criterion was used to
make this determination wherein object intensity should be five
standard deviations above the average background.
[0063] Brightfield/Raman chemical fusion images were obtained for
all droplets analyzed (FIGS. 12 and 13). The PSD based on maximum
chord, the longest distance across the particle, was statistically
evaluated for D10, D50, D90 and standard deviation for each batch
(FIG. 14). Table 1 shows a good agreement of the metric values for
drug PSD between two batches. For a normal distribution, the Taylor
approach may compare two populations based on a mean and standard
deviation using a defined confidence interval. However, the
achieved particle size distribution is not normal. A two-sample
Kolmogrov-Smirnov goodness-of-fit hypothesis test was performed on
this data set where the null hypothesis was accepted at the 95%
confidence level meaning that the drug PSD populations are the same
for these two batches.
[0064] ISPS based on wide-field RCI coupled with brightfield
optical imaging demonstrated potential as a method for accurate
particle size analysis and shape characterization. This approach
can directly benefit batch release testing as well as the
bioequivalence requirements for NDA and ANDA for corticosteroids in
aqueous nasal spray suspension formulations. Automated data
acquisition and image processing is shown to produce objective
accurate drug particle sizes for comparison across multiple batches
of a nasal spray suspension with sufficient representative sampling
required for product quality assessment. High-fidelity, wide-field
Raman chemical imaging with superior spectral and spatial
resolution can also show advantages in identification of
agglomerates and particle association.
* * * * *