U.S. patent application number 10/235922 was filed with the patent office on 2004-12-16 for apparatus and method for high-throughput preparation and spectroscopic classification and characterization of compositions.
Invention is credited to Almarsson, Orn, Cima, Michael J., Gonzalez-Zugasti, Javier P., Johnson, Alasdair Y., Lemmo, Anthony V., Levinson, Douglas, McNulty, Christopher.
Application Number | 20040252299 10/235922 |
Document ID | / |
Family ID | 27499825 |
Filed Date | 2004-12-16 |
United States Patent
Application |
20040252299 |
Kind Code |
A9 |
Lemmo, Anthony V. ; et
al. |
December 16, 2004 |
Apparatus and method for high-throughput preparation and
spectroscopic classification and characterization of
compositions
Abstract
Systems and methods are described that allow the high-throughput
preparation, processing, and study of arrays of samples, each of
which comprises at least one compound. Particular embodiments of
the invention allow a large number of experiments to be performed
in parallel on samples that comprised of one or more compounds on
the milligram or microgram quantities of compounds. Other
embodiments of the invention encompass methods and devices for the
rapid screening of the results of such experiments, as well as
methods and devices for rapidly determining whether or not
similarities exist among groups of samples in an array. Particular
embodiments of the invention encompass methods and devices for the
high-throughput preparation of different forms of compounds (e.g.,
different crystalline forms), for the discovery of new forms of old
compounds, and for the discovery of new methods of producing such
forms. Embodiments of the invention also allow for the
high-throughput determination of how specific compounds or forms of
compounds behave when exposed to other chemicals or environmental
conditions.
Inventors: |
Lemmo, Anthony V.; (Sudbury,
MA) ; Gonzalez-Zugasti, Javier P.; (N. Billerica,
MA) ; Cima, Michael J.; (Winchester, MA) ;
Levinson, Douglas; (Sherborn, MA) ; Johnson, Alasdair
Y.; (Newburyport, MA) ; Almarsson, Orn;
(Shewsbury, MA) ; McNulty, Christopher;
(Minneapolis, MN) |
Correspondence
Address: |
SALIWANCHIK LLOYD & SALIWANCHIK
A PROFESSIONAL ASSOCIATION
PO BOX 142950
GAINESVILLE
FL
32614-2950
US
|
Prior
Publication: |
|
Document Identifier |
Publication Date |
|
US 0123057 A1 |
July 3, 2003 |
|
|
Family ID: |
27499825 |
Appl. No.: |
10/235922 |
Filed: |
September 6, 2002 |
Related U.S. Patent Documents
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Application
Number |
Filing Date |
Patent Number |
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10235922 |
Sep 6, 2002 |
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10103983 |
Mar 22, 2002 |
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10103983 |
Mar 22, 2002 |
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09756092 |
Jan 8, 2001 |
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10103983 |
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09994585 |
Nov 27, 2001 |
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09994585 |
Nov 27, 2001 |
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09756092 |
Jan 8, 2001 |
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09994585 |
Nov 27, 2001 |
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PCT/US01/00531 |
Jan 8, 2001 |
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10103983 |
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PCT/US01/44818 |
Nov 28, 2001 |
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10235922 |
Sep 6, 2002 |
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09756092 |
Jan 8, 2001 |
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10235922 |
Sep 6, 2002 |
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09994585 |
Nov 27, 2001 |
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10235922 |
Sep 6, 2002 |
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10142812 |
May 10, 2002 |
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10235922 |
Sep 6, 2002 |
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10103983 |
Mar 22, 2002 |
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60318152 |
Sep 7, 2001 |
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60318157 |
Sep 7, 2001 |
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60318138 |
Sep 7, 2001 |
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60221539 |
Jul 28, 2000 |
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60196821 |
Apr 13, 2000 |
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60175047 |
Jan 7, 2000 |
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60175047 |
Jan 7, 2000 |
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60196821 |
Apr 13, 2000 |
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60221539 |
Jul 28, 2000 |
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60253629 |
Nov 28, 2000 |
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60290320 |
May 11, 2001 |
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Current U.S.
Class: |
356/301 |
Current CPC
Class: |
B01J 19/0046 20130101;
G01N 21/3563 20130101; B01J 2219/00691 20130101; B01L 3/5082
20130101; G01N 21/253 20130101; G01N 2035/042 20130101; C30B 29/58
20130101; G01N 2035/0425 20130101; B01J 2219/00587 20130101; C30B
7/00 20130101; B01J 2219/00547 20130101; B01J 2219/00495 20130101;
B01J 2219/00756 20130101; B01L 7/52 20130101; B01J 2219/00689
20130101; G01N 35/028 20130101; C40B 70/00 20130101; B01J
2219/00274 20130101; C40B 60/14 20130101; G01N 2021/651 20130101;
B01L 2300/0851 20130101; B01L 2300/1805 20130101; B01J 2219/00659
20130101; G01N 21/35 20130101; B01L 2300/0829 20130101; B01J
2219/0031 20130101; G01N 2035/041 20130101; G01N 21/65 20130101;
B01J 2219/00335 20130101; B01J 2219/00344 20130101; B01J 2219/00283
20130101; G01J 3/28 20130101; G01N 35/0099 20130101 |
Class at
Publication: |
356/301 |
International
Class: |
G01J 003/44; G01N
021/65 |
Claims
What is claimed is:
1. A method of screening an array of samples which comprises
obtaining a Raman spectrum of each sample and determining which, if
any, of the spectra share a spectral feature.
2. The method of claim 1 wherein the spectral feature is unique to
a particular form of a compound-of-interest.
3. A method of screening an array of samples for the presence of a
particular form of a compound-of-interest, which comprises
obtaining a Raman spectrum of each sample.
4. A method of screening an array of samples for the absence of a
particular form of a compound-of-interest, which comprises
obtaining a Raman spectrum of each sample.
5. The method of claim 1, 2, or 3 wherein the particular form is a
solid form.
6. The method of claim 5 wherein the solid form is a crystalline or
amorphous form.
7. The method of claim 1, 2, or 3 wherein the particular form is a
hydrated form.
8. A system for detecting similarities among a plurality of
samples, which comprises: a) a device for obtaining a spectrum for
each sample; and b) a computer configured to analyze each of the
spectra and to generate a plurality of bins, wherein each bin
corresponds to samples sharing at least one spectral feature.
9. The system of claim 8 wherein the device is an infrared
spectrometer, near infrared spectrometer, NMR spectrometer, X-ray
diffractometer, neutron diffractometer, light microscope, electron
microscope, second harmonic generator, circular dichroism
spectrometer, linear dichroism spectrometer, differential scanning
calorimeter, thermal gravimetric analyzer, or melting point
analyzer.
10. The system of claim 8 wherein the device is a Raman
spectrometer.
11. The system of claim 8 wherein the computer is further
configured to generate a binary spectral representation for a
spectrum that reflects the presence or absence of a spectral
feature.
12. The system of claim 8 wherein the computer is configured to
mutually compare a plurality of spectra and generate a hierarchical
clustering dendrogram.
13. The system of claim 8 wherein the computer is configured to
cluster the plurality of spectra.
14. The system of claim 13 wherein the computer is configured to
cluster the plurality of spectra in accordance with iterative
k-means clustering.
15. The system of claim 8 wherein the computer is configured to
cluster the plurality of spectra such that if a majority of spectra
obtained from a single sample are assigned to a particular bin,
then all spectra from that sample are assigned to that bin.
16. The system of claim 8 wherein the computer is configured to
assign newly obtained spectra to at least one of the plurality of
bins.
17. The system of claim 8 wherein the computer is configured to
modify, in response to an analysis of newly obtained spectra, at
least one of the plurality of bins.
18. The system of claim 8 wherein the computer is configured to
add, in response to an analysis of newly obtained spectra, at least
one bin to the plurality of bins.
19. The system of claim 8 wherein the computer is configured to
generate a similarity matrix representing the similarity between at
least two of the plurality of samples.
20. The system of claim 19 wherein the computer is further
configured to sort the samples such that they are arranged to
reflect their similarity.
21. The system of claim 19 wherein the computer is further
configured to sort the similarity matrix such that a diagonal in
the matrix represents samples exhibiting the greatest
similarity.
22. A method of detecting similarities among a plurality of
samples, which comprises: a) collecting a spectrum for each of the
plurality of samples; b) calculating a similarity metric between
the spectrum of one sample and that of at least one other of the
plurality; c) clustering, based on the similarity metric, the
spectra into bins, each bin containing similar spectra; and d)
presenting the clustered spectra with similar spectra located close
to each other.
23. The method of claim 22 wherein the spectra are preprocessed
after they are collected.
24. The method of claim 23 wherein the positions of one or more
spectral peaks in the preprocessed spectra are used to generate
real value vectors.
25. The method of claim 24 wherein binary spectra are generated
from the vectors.
26. The method of claim 22 wherein the spectra are Raman
spectra.
27. A method of analyzing a plurality of samples, which comprises:
a) analyzing the samples with a spectrometer to produce spectral
data; b) under processor control, identifying similarities between
the spectra; and c) grouping the spectra into bins of
similarity.
28. The method of claim 27 wherein the spectrometer is a Raman
spectrometer.
29. A database containing a plurality of spectral samples organized
into a plurality of bins, the bins corresponding to a hierarchical
organization of the plurality of spectral samples based on
pair-wise similarity scores calculated in accordance with a
similarity metric.
30. The database of claim 29 wherein the similarity metric is a
Tanimoto coefficient, Tversky index, Euclidean distance, or Hamming
distance.
Description
[0001] This application claims priority to U.S. provisional
application Nos. 60/318,152, 60/318,157, and 60/318,138, each of
which was filed on Sep. 7, 2001, and each of which is incorporated
herein in its entirety.
1. FIELD OF THE INVENTION
[0002] This invention generally relates to devices, systems, and
methods for conducting and evaluating multiple small-scale
experiments. Particular embodiments of the invention encompass
methods and devices for the high-throughput preparation and study
of a variety of compounds, compositions, and forms of compounds and
compositions.
2. BACKGROUND OF THE INVENTION
[0003] In recent years, chemical discovery has seen an explosion of
new science, such as genomics, proteomic and bioinformatics, as
well as high-throughput technologies for identifying and/or
creating new compounds or chemical entities, such as combinational
chemistry. Such technologies allow the researcher to rapidly
synthesize and/or identify large numbers of compounds. At the same
time, these technologies have led to the development of more
compounds that are larger, greasier and more hydrophobic, and thus
more challenging to develop into products.
[0004] Conducting large numbers of experiments results in the need
to inspect or otherwise analyze hundreds or thousands of samples,
e.g., for the presence of the desired result. And, a large number
of the pre-selected samples require continuing analysis. The
resulting voluminous data must then be processed effectively and
efficiently, e.g., within a reasonable amount of time.
[0005] The physical form of a compound, particularly that of an
active pharmaceutical ingredient (API), plays a role in a number of
areas. For example, in order to be developed into a drug, a
compound must be able to be delivered to the patient via some
suitable device or formulation, and it must also pass criteria in
several categories, such as safety, metabolic profile,
pharmacokinetics, cost and reliability of synthetic process,
stability, and bioavailability.
[0006] High-throughput technologies, when possible, enable the
discovery of various physical forms of a compound, some of which
may be particularly useful as pharmaceuticals, for formulating
pharmaceuticals, intermediates for manufacturing drugs, foods, food
additives and the like. (See, e.g., International Application Nos.
WO00/59627, WO01/09391, and WO01/51919). Such technologies can
result in extraordinary numbers of experiments being conducted very
rapidly thereby creating large amounts of data and results that
must be reviewed and analyzed by the scientist in order to identify
a desired form of the compound. For example, in order to discover
various solid forms of a compound, often thousands of experiments,
using many different conditions, solvents, additives, pH, thermal
cycles, and the like must be conducted. Dozens or even hundreds of
the forms must be analyzed before a desired form of the compound
can be identified and chosen for further development as a potential
product.
[0007] Some devices for facilitating large numbers of experiments
simultaneously are known. In addition, there are systems consisting
of blocks with multiple wells for performing reactions for
different applications such as combinatorial chemistry. Examples of
such system include the TITAN.TM. Reactor Clamp and TITAN.TM. PTFE
MicroPlates (both available from Radleys, Shire Hill, Saffron
Walden, Essex CBII 3AZ, United Kingdom). A multiple-well tray for
crystallization reactions is described in U.S. Pat. No. 6,039,804.
There also exist systems of block, tubes, and seals, such as the
Radleys TITAN.TM. Glass Micro Reactor Tube System and the WebSeal
System (available from Radleys, Shire Hill, Saffron Walden, Essex
CBII 3AZ, United Kingdom). Many tubes or vials of different
geometries also exist, including many with crimp, threaded, or
snap-on caps.
[0008] Spectroscopic techniques such as infrared (IR) and Raman
spectroscopy are useful for detecting changes in structure and/or
order. In addition, techniques such as Nuclear Magnetic Resonance
(NMR), Differential Scanning Calorimetry, ultra-violet (UV)
spectroscopy, circular dichroism (CD), linear dichroism (LD), and
X-ray diffraction are powerful techniques. However, each of these
techniques must be coupled with data analysis and handling
techniques to enable data collection and processing of hundred or
thousands of samples. All these techniques are not easily adaptable
for high-throughput analysis of structural information and order.
Indeed, high-throughput analysis still remains a challenge due to
the high degree of automation desired in both physical sample
handling and in analysis of the collected data. These and many
other difficulties are overcome by the system and methods disclosed
herein. The invention disclosed herein further extends the reach of
high-throughput analysis with a high degree of sensitivity and
specificity. Moreover, the disclosed techniques also efficiently
use limited test material quantities to enable effective screening
at a low cost. 3. SUMMARY OF THE INVENTION
[0009] This invention is directed, in part, to methods and systems
for determining conditions that when applied to a particular
compound or composition provide a particular result (e.g., a
compound or composition having particular chemical and/or physical
properties). The invention is further directed to methods and
systems for the generation, synthesis, and/or identification of
various forms of a compound or composition, such as, but not
limited to, polymorphs, salts, hydrates, solvates, desolvates, and
amorphous forms. The invention is also directed to methods and
systems for the generation, synthesis, and/or identification of
various forms of solids such as, but not limited to, crystal habit
and particle size distribution.
[0010] The invention encompasses a complete system for planning and
conducting high-throughput experiments, e.g., experiments on one or
more arrays of samples. The system encompasses apparatuses and
methods that can be used to prepare and process samples,
apparatuses and methods that can be used to inspect, process, and
screen samples, apparatuses and methods that can be used to collect
spectroscopic and other data from one or more of the samples, and
apparatuses and methods that can be used to process, interpret, and
analyze the data. The system includes robotics, computers, spectral
techniques, and various mechanical devices, each designed to
conduct high-throughput experiments on large or preferably small
amounts of material, including materials on the milligram and
microgram scales.
[0011] In particular, this invention encompasses methods and
devices for the high-throughput preparation, processing, screening,
and/or analyzing of samples. Particular methods of the invention
utilize arrays of samples, each of which comprises the compound or
composition of interest (referred to herein as the
"compound-of-interest") in optional contact with one or more
solvents or excipients. In specific embodiments of the invention,
each sample is held in a container that can be manipulated
separately from other samples in the array.
[0012] One embodiment of the invention encompasses a high
throughput system for evaluating experiments, which comprises: a) a
plurality of containers, each of which contains a
compound-of-interest and optionally one or more additional
compounds; b) a block containing an array of holes for receiving
said containers; and c) an imaging device capable of producing
images of the samples while in the containers.
[0013] Another embodiment of the invention encompasses a method of
evaluating experiments which comprises: a) providing a system for
evaluating experiments comprising: i) a plurality of containers,
each of which contains a compound-of-interest and optionally one or
more additional compounds; ii) a block containing an array of holes
for receiving the containers; and iii) an imaging device; b)
positioning the block near the imaging device; c) producing images
of the contents of each of the containers; d) analyzing the images
for the presence of a desired experimental result; and e)
identifying containers with the desired experimental result.
[0014] Another embodiment of the invention encompasses an automated
high throughput method for screening for solid forms of a
compound-of-interest, wherein the compound-of-interest is a
biologically active small organic molecule, which comprises: a)
preparing an array of samples, each of which comprises the
compound-of-interest and optionally one or more additional
compounds; b) processing the array so as to generate solid forms;
c) prescreening the array for solid formation using a digital
imaging camera; d) identifying samples with solid formations for
further analysis; e) rearranging and reprocessing samples with
solids, and optionally repeating steps (b) to (d).
[0015] Another embodiment of the invention encompasses a
high-throughput system for processing samples and screening for
solid forms of a compound-of-interest, which comprises: a)
removable containers, each of which contains a compound-of-interest
and optionally one or more additional compounds; b) a block made of
a thermally conductive material having an array of holes, each hole
having a top and a bottom, the top having an opening for receiving
the removable container and the bottom having an access hole; c) a
thermal processing system for heating and cooling multiple blocks
simultaneously; and d) an imaging device (e.g., a camera,
preferably a digital video camera) for detecting solid forms.
[0016] Another embodiment of the invention encompasses a method for
the high-throughput processing and screening of samples, which
comprises: a) providing a system for processing a sample
comprising: i) removable containers; ii) a block having an array of
holes, each hole having a top and a bottom, the top having an
opening for receiving a container and the bottom having an access
hole; b) placing the containers in the holes; c) dispensing a
controlled amount of a compound-of-interest and optionally one or
more additional compounds in each container to provide an array of
samples; d) processing the array; and e) screening the samples for
the presence or absence of solid forms using an imaging device.
[0017] Another embodiment of the invention encompasses a method of
screening an array of samples which comprises obtaining a Raman
spectrum of each sample and determining which, if any, of the
spectra share a spectral feature.
[0018] Another embodiment of the invention encompasses a method of
screening an array of samples for the presence of a particular form
of a compound-of-interest, which comprises obtaining a Raman
spectrum of each sample.
[0019] Another embodiment of the invention encompasses a method of
screening an array of samples for the absence of a particular form
of a compound-of-interest, which comprises obtaining a Raman
spectrum of each sample.
[0020] Another embodiment of the invention encompasses a system for
detecting similarities among a plurality of samples, which
comprises: a) a device for obtaining a spectrum for each sample;
and b) a computer configured to analyze each of the spectra and to
generate a plurality of bins, wherein each bin corresponds to
samples sharing at least one spectral feature.
[0021] Another embodiment of the invention encompasses a method of
detecting similarities among a plurality of samples, which
comprises: a) collecting a spectrum for each of the plurality of
samples; b) calculating a similarity metric between the spectrum of
one sample and that of at least one other of the plurality; c)
clustering, based on the similarity metric, the spectra into bins,
each bin containing similar spectra; and d) presenting the
clustered spectra with similar spectra located close to each
other.
[0022] Another embodiment of the invention encompasses a method of
analyzing a plurality of samples, which comprises: a) analyzing the
samples with a spectrometer to produce spectral data; b) under
processor control, identifying similarities between the spectra;
and c) grouping the spectra into bins of similarity.
[0023] Another embodiment of the invention encompasses a database
containing a plurality of spectral samples organized into a
plurality of bins, the bins corresponding to a hierarchical
organization of the plurality of spectral samples based on
pair-wise similarity scores calculated in accordance with a
similarity metric.
3.1. BRIEF DESCRIPTION OF THE DRAWINGS
[0024] Features and advantages of the invention can be understood
with reference to the attached figures. The drawings are not
necessarily to scale, emphasis instead being placed upon
illustrating the principles of the invention.
[0025] FIG. 1 is a schematic diagram of steps associated with a
specific embodiment of the invention, wherein tubes are filled with
a compound-of-interest and optional other compounds, processed, and
inspected.
[0026] FIGS. 2A and 2B are views of a tube in its open and capped
configurations, respectively.
[0027] FIGS. 3A and 3B are top and bottom perspective views of a
block, respectively, and FIG. 3C is a top perspective view of the
block filled with capped tubes.
[0028] FIG. 4A is a drawing of a temperature-controlled shelf
assembly, or "hotel," loaded with twelve blocks.
[0029] FIG. 4B is a drawing of a shelf equipped for use with a
heating/cooling loop (e.g., using water, ethylene glycol, or
another solvent) shown as dotted lines.
[0030] FIG. 5 shows a drawing of a thermal cycling system top-level
assembly, including an environmental enclosure. In this
arrangement, 18 hotels in a semi-circular pattern around a robotic
arm are shown.
[0031] FIG. 6 shows a flowchart depicting an example of the logic
for addressing solid form generation using the vision station
approach.
[0032] FIG. 7A is a schematic diagram of a specific vision
station.
[0033] FIGS. 7B and 7C are drawings of a vision station in
operation, showing a side view (FIG. 7B) and a perspective view
(FIG. 7C) of the block wherein a lifting mechanism lifts an entire
row of vials at the same time. The light source can be positioned
on top of the samples (e.g., normal to the CCD camera) or opposite
the samples.
[0034] FIG. 8 is a drawing that shows the difference between
samples with no birefringence and samples with birefringence.
[0035] FIG. 9 is a drawing showing the scattering and diffusion of
laser light pointed at a single tube and consecutive tubes.
[0036] FIG. 10 is a drawing that shows the effect of illuminating
of a tube containing a colloidal suspension (a) compared to a tube
containing pure water (b). In FIG. 10, the laser light is at an
angle that is not normal to the camera lens.
[0037] FIG. 11 is a flow diagram depicting the use of the vision
station to detect birefringence in crystalline solid forms, or to
differentiate crystalline versus amorphous solid forms by the
observance of birefringence.
[0038] FIG. 12 is a flow diagram depicting the use of the vision
station for laser light interrogation of samples. Shown are
diagrams of nano-suspension compared to true solutions.
[0039] FIG. 13A is a perspective diagram of the Raman system.
[0040] FIG. 13B is a diagram showing a block of tubes being moved
inside an enclosure.
[0041] FIG. 13C is a diagram showing the lifting mechanism
elevating a tube to be gripped by the tube gripper.
[0042] FIG. 13D is a diagram showing the tube gripper and tube
having moved in the vertical direction.
[0043] FIG. 13E is a diagram showing the tube gripper and tube
having rotated.
[0044] FIG. 13F is an enlarged diagram showing the tube gripper and
tube having moved in the horizontal direction to bring the tube
closer to the microscope objective.
[0045] FIG. 13G is an enlarged diagram showing the tube gripper and
tube having been lowered to a position near the tube holder.
[0046] FIG. 13H is an enlarged diagram showing the tube gripper
having loaded the tube into the tube holder.
[0047] FIG. 13I is an enlarged diagram showing the tube gripper
having retracted after loading the tube into the tube holder.
[0048] FIG. 13J is an enlarged diagram showing a tube rotator
engaging the tube.
[0049] FIG. 13K is an enlarged diagram showing the tube being moved
under the microscope objective.
[0050] FIG. 14A is a perspective view of a tube and microscope
objective. The long axis of the tube is preferably at a 90 degree
angle to the axis of the objective.
[0051] FIG. 14B is diagram of a tube and microscope objective
indicating the available axes of motion for the tube.
[0052] FIG. 14C is a closer view of a tube with a crystal inside
that is in an out-of-focus position with respect to the microscope
objective. This figure also indicates the available axes of motion
for the tube.
[0053] FIG. 14D is a detailed view of a tube with a solid, such as
a crystal, inside that is located toward the narrow end of the tube
and on the bottom surface with respect to the microscope
objective.
[0054] FIG. 14E is a detailed view of a tube with a solid, such as
a crystal, inside that is being moved in the horizontal direction
to bring it closer to the in-focus position beneath the microscope
objective.
[0055] FIG. 14F is a detailed view of a tube with a solid, such as
a crystal inside that is being rotated to bring it closer to the
in-focus position beneath the microscope objective.
[0056] FIG. 14G is a detailed view of a tube with a solid, such as
a crystal, inside that is being moved in the vertical direction to
bring it to the in-focus position beneath the microscope
objective.
[0057] FIG. 15 is a flowchart depicting six stages of a
computational binning process of one embodiment of the invention,
and one optional stage in such a process.
[0058] FIG. 16A is a graph showing Raman intensity plotted as a
function of Raman shift (cm.sup.-1) for an empty glass vial.
[0059] FIG. 16B is a graph showing Raman intensity plotted as a
function of Raman shift (cm.sup.-1) for a fluorescent sample.
[0060] FIG. 16C is comparative graph showing Raman intensity
plotted as a function of Raman shift (cm.sup.-1) for the
pre-filtered sample of FIG. 17B compared to the corresponding
filtered spectra after the fluorescence has been removed.
[0061] FIG. 17A is a screen shot showing the output from the
binning software captured during the binning procedure for the
flufenamic acid sample set.
[0062] FIG. 17B is a screen shot showing the output from the
binning software captured during the binning procedure for the
theophylline sample set.
[0063] FIG. 18 illustrates an implementation of the binning
procedure.
[0064] FIG. 19A is a comparative graph of X-ray powder diffraction
patterns for Form I and Form III of flufenamic acid.
[0065] FIG. 19B is a comparative graph of X-ray powder diffraction
patterns for anhydrous and monohydrate forms of theophylline.
[0066] FIG. 20A is a comparative graph of DSC thermograms for Form
I and Form III of flufenamic acid where heat flow (W/g) is plotted
as a function of temperature (.degree. C.).
[0067] FIG. 20B is a comparative graph of DSC thermograms for
anhydrous and monohydrate forms of theophylline where heat flow
(W/g) is plotted as a function of temperature (.degree. C.).
[0068] FIG. 21 is a graph showing thermograms obtained for the
anhydrous and hydrous forms of theophylline. An inset graph shows
an enlargement of the same thermograms between 35.degree. C. and
150.degree. C.
[0069] FIG. 22A is a graph showing Raman intensity (arbitrary
units) plotted as a function of Raman shift (cm.sup.-1) for Form I
and Form III of flufenamic acid.
[0070] FIG. 22B is a graph showing the Raman intensity (arbitrary
units) plotted as a function of Raman shift (cm.sup.-1) for
anhydrous and monohydrate forms of theophylline.
[0071] FIG. 23A is the output after clustering illustrating sorted
cluster diagrams for the flufenamic acid sample set.
[0072] FIG. 23B is the output after clustering illustrating sorted
cluster diagrams for the theophylline sample set.
[0073] FIG. 24A illustrates X-ray crystal diffraction spectra
corresponding to the anhydrate and the hydrate forms of
Theophylline.
[0074] FIG. 24B illustrates the binning of Raman Spectra
corresponding to Hydrate distinctly from Anhydrate form of
Theophylline.
3.2. DEFINITIONS
[0075] As used herein and unless otherwise indicated, the term
"array," when used to refer to a plurality of objects (e.g.,
samples), means a plurality of objects that are organized
physically or indexed in some manner (e.g., with a physical map or
within the memory of a computer) that allows the ready tracking and
identification of specific members of the plurality. Typical arrays
of samples comprise at least 6, 12, 24, 94, 96, 380, 384, 1530, or
1536 samples.
[0076] As used herein and unless otherwise indicated, the term
"compound-of-interest" refers to the substance, compound, molecule,
or chemical studied, formulated, or otherwise manipulated using
methods or devices of the invention. Examples of
compounds-of-interest include, but are not limited to,
pharmaceuticals, veterinary compounds, dietary supplements,
alternative medicines, nutraceuticals, sensory compounds,
agrochemicals, the active components of consumer products, and the
active components of industrial formulations. A preferred
compound-of-interest is the active component of a pharmaceutical,
also referred to as the active pharmaceutical ingredient (API).
Specific APIs are suitable for administration to humans. Specific
APIs are small organic molecules that are not polypeptides,
proteins, oligonucleotides, nucleic acids, or other macromolecules.
Small organic molecules include, but are not limited to, molecules
with molecular weights of less than about 1000, 750, or 500
grams/mol.
[0077] As used herein and unless otherwise indicated, the term
"controlled amount" refers to an amount of a compound that is
weighed, aliquotted, or otherwise dispensed in a manner that
attempts to control the amount of the compound. Preferably, a
controlled amount of a compound differs from a predetermined amount
by less than about 10, 5, or 1 percent of the predetermined amount.
For example, if one were to dispense, handle, or otherwise use 100
.mu.g of a compound-of-interest, a controlled amount of that
compound-of-interest would preferably weight from about 90 .mu.g to
about 110 .mu.g, from about 95 .mu.g to about 105 .mu.g, or from
about 99 .mu.g to about 101 .mu.g.
[0078] As used herein and unless otherwise indicated, the term
"form" refers to the physical form of a compound or composition.
Examples of forms include solid and liquid. Examples of forms of
solids, or "solid forms," include, but are not limited to, salts,
solvates (e.g., hydrates), desolvates, clathrates, amorphous and
crystalline forms, polymorphs, crystal habits (e.g., needles,
plates, particles, and rhomboids), crystal color, crystal size,
crystal size distribution, co-crystals, and complexes.
[0079] As used herein and unless otherwise indicated, the term
"pharmaceutical" refers to a substance, compound, or composition
that has a therapeutic, disease or condition preventive, disease or
condition management, diagnostic, or prophylactic effect when
administered to an animal or human, and includes prescription and
over-the-counter pharmaceuticals. Examples of pharmaceuticals
include, but are not limited to, macromolecules, oligonucleotides,
oligonucleotide conjugates, polynucleotides, polynucleotide
conjugates, proteins, peptides, peptidomimetics, polysaccharides,
hormones, steroids, nucleotides, nucleosides, amino acids, small
molecules, vaccines, contrasting agents, and the like.
[0080] As used herein and unless otherwise indicated, the term
"sample" refers to an isolated amount of a compound or composition.
A typical sample comprises a controlled amount of a
compound-of-interest, and may also contain one or more excipients,
solvents, additives (e.g., stabilizers and antioxidants), or other
compounds or materials (e.g., materials that facilitate crystal
growth). Specific samples comprise a compound-of-interest in an
amount less than about 100 mg, 25 mg, 1 mg, 500 .mu.g, 250 .mu.g,
100 .mu.g, 50 .mu.g, 25 .mu.g, 10 .mu.g, 5 .mu.g, 2.5 .mu.g, 1
.mu.g, or 0.5 .mu.g.
4. DETAILED DESCRIPTION OF THE INVENTION
[0081] This invention is based, in part, on the discovery of
various methods and devices that can facilitate the rapid and
efficient preparation and analysis of compounds, compositions, and
various forms of compounds and compositions. For the sake of
convenience, methods and devices of the invention may be described
with reference to four general aspects of it, which are referred to
herein as "Sample Containment and Preparation," "Sample Handling
and Processing," "Sample Imaging," and "Spectroscopic Data
Collection and Analysis."Each of these aspects encompasses novel
embodiments of the invention which can be used alone or
together.
[0082] For example, a specific method comprises the preparation of
an array of samples, each of which is held in a sealed container;
exposing the samples to a condition, such as heat or cold, for a
particular amount of time; imaging the samples to determine, for
example, whether they produced or contain a solid or liquid; and
collecting and analyzing spectroscopic data obtained from one or
more of the samples. FIG. 1 provides a general illustration of this
method and a system that can be used to implement it. Briefly, one
or more tubes 50 are placed in an array of wells in a thermally
conductive block 60. Next, chemical ingredients 52 for each
experiment are dispensed into tubes 50. Optionally, a cap 54,
possibly including a seal 64, is placed on tube 50 to prevent
leakage, evaporation, or contamination of the contents. The tubes
are then optionally processed using a controlled thermal cycling
system 56. Following inspection of the contents of tubes 50 (e.g.,
using automated imaging equipment), experimental specimens or
samples of interest are identified. The samples of interest are
then optionally separated from other samples for further analysis
or processing.
[0083] The other embodiments of the invention can be understood
from the more detailed discussions of its Sample Containment and
Preparation, Sample Handling and Processing, Sample Imaging, and
Spectroscopic Data Collection and Analysis aspects provided
below.
[0084] 4.1. Sample Containment and Preparation
[0085] 4.1.1. Tubes and Blocks
[0086] High throughput preparation and analysis of samples is aided
by the assembly of arrays of samples, each of which can be the same
or different from other samples in the array. In specific
embodiments of this invention, arrays of samples are prepared in
removable containers (e.g., vials or tubes), which fit in holes,
wells, or depressions in a holder, or what is referred to herein as
a "block." This system is referred to herein as "tubes and blocks"
or "tubes in blocks."
[0087] A wide variety of containers known to those skilled in the
art can be used to hold the individual samples in an array. Because
preferred embodiments of the invention are directed to the
high-throughput preparation, processing, and/or testing of samples
that contain relatively small amounts of the compound-of-interest,
preferred containers are sufficiently small so that many of them
can be fit into a block. Preferred containers are also optically
transparent or translucent to allow visual inspection of their
contents, are chemically inert (e.g., will not chemically react
with the compounds they contain), and can withstand physical
conditions (e.g., thermal processing) to which it will be exposed.
Specific containers are made of glass or polypropylene. Preferred
containers can be sealed or closed. For example, a septum that can
be pierced by a needle or other device that can add fluids to the
container or remove fluids from the container is used in a
preferred embodiment. Containers may also be closed with a closure
(e.g., a cap or top) that allows light to pass through into the
container to illuminate its contents. Moreover, the closure may be
used to imprint or otherwise provide an identifier to a single tube
or a sub-block. Such an identifier may be in addition to or in lieu
of an identifier associated with each block.
[0088] FIG. 2 provides a view of a tube container in its open and
capped configurations. The specific closure 54 shown in the figure
can be crimped, is made of aluminum or some other suitable
material, and incorporates a polymer septum 55.
[0089] The blocks that hold the containers preferably allow for the
automated removal and insertion of the containers. For example, a
particular block has holes with top openings large enough to
accommodate containers and smaller bottom openings that allow the
containers to be pushed out of their holes with a rod or pin. Such
blocks can be used with particular systems of the invention that
comprise a lifter mechanism capable of protruding through the
access hole in the blocks to elevate one or more containers until
at least partially removed from the block. Preferably, the block is
thermally conductive and is made from metal (e.g., copper, steel,
or aluminum), although other materials, such as plastic, may also
be used. As shown in FIGS. 3A-3C, a specific block 60 is made of
aluminum, and has 96 holes 61 into which containers will fit. Block
60 provides thermal transfer between the tubes and a controlled
means for temperature regulation. Block 60 incorporates a set of
bottom access holes 63 that provide for physical and optical access
to each individual vial. Optionally, block 60 has one or more
indents 65 on two opposing sides of block 60 to provide means for
preventing slipping or dropping of the block 60 when automated
handling, such as by a robotic arm, is used to move block 60.
[0090] The geometry, size, and materials from which a block is made
can be readily adapted for use with particular containers,
processing conditions, and sample and block handling devices. For
example, the holes in a block may be counter-bored, counter-sunk,
stepped, tapered, or more complex-shaped to fit different tube and
seal shapes, although in FIG. 3 they are simply shown as
illustrative straight through holes.
[0091] The tube and block system has several distinct advantages
over alternative ways of performing parallel experiments. First,
the use of individual containers, instead of using a plate, format
allows for the individual handling of each sample, or experiment,
in an array. This makes it possible to re-array containers to
separate those that show desired properties from the rest, in order
to perform further processing or analysis of only some of the
experiments. In addition, for experiment samples or products that
can exhibit different properties depending on orientation (e.g.,
samples that contain crystals), the containers can be precisely
oriented with respect to an analysis instrument, such as a Raman
spectrometer or X-ray diffractometer.
[0092] The invention also encompasses the use of various tube
materials, including the use of different types of glass such as
amber glass vials, which can protect their contents from
degradation due to exposure to light during processing. Optical
inspection of the contents of each vial is possible by illuminating
the samples with light source having a wavelength able to penetrate
the vial walls, and using a detector (camera) for imaging light at
that wavelength.
[0093] Second, a translucent, transparent, semitransparent, or
clear container allows for optical inspection of the experiment
from multiple angles, generally perpendicular to the axis of the
container, but also from underneath through optional access holes
in the block. Also, the use of such containers, including but not
limited to, glass or clear polypropylene tubes, allows for optical
inspection methods such as machine vision or microscopy. In
addition, clear plastic tubes, or tubes fabricated from quartz or
any other optically transparent, translucent, or clear material can
be used. Chromacol Ltd. (2 Little Mundells, Wellwyn Garden City,
Herts AL7 1EW, UNITED KINGDOM) offers many examples of the variety
of available vial shapes and materials. The ability to visually
inspect each experiment in an array, from all angles, allows
analysis of the contents, such as solids or precipitates, in a
number of ways, including without limitation, estimating size,
color, shape, orientation and location in the container.
[0094] Third, because containers can be of any shape, the tubes and
blocks system enables the testing of a wide range of experimental
volumes. By selecting the shape of the containers, small volume
experiments (e.g., about 2 .mu.l) are still clearly visible in the
narrow, preferably conical, tip of the preferred container, while
larger volume experiments (greater than 100 .mu.l) may also be
tested due to the larger diameter top section of the tubes. Also,
the container geometry in the preferred embodiment permits the use
of a tightly sealing cap. An airtight seal isolates the contents of
the experiments from the environment and prevents evaporation,
leakage or contamination of, or changes to, the components in the
containers.
[0095] Fourth, many containers can be capped. The use of a cap with
an integral translucent frit or septum allows for the ability to
probe, add, or remove components to/from the experiment, as well as
the ability to illuminate the container's contents through the
septum. This lighting of the samples in the containers though the
septa can be accomplished through the use of light sources such as
fiber optic light guides or light-emitting diodes (LEDs).
[0096] Fifth, the use of thermally conductive blocks allows for
quick heat transfer between a heating or cooling source and the
containers, as well as a large thermal mass to maintain the
containers at the desired temperature when temporarily not in
contact with a heat/cooling source. As noted previously, many
metals, plastics, and a variety of other materials can used to
build the blocks. Although aluminum does not exhibit the best
thermal conductivity or heat capacity, it is preferred in view of
additional considerations such as weight, cost,
corrosion-resistance, and ease of manufacturing.
[0097] Sixth, the chosen geometry of the block offers certain
advantages. For example, the access holes at the bottom of each
container hole allows for physical access to the containers, so
that they can be partially or fully removed from the block for
inspection or rearranging purposes. In addition, the holes also
provide a window for optical inspection of the containers from the
underside of the block that can be used alone or in conjunction
with top lighting of the containers, e.g., through translucent
septa, to image the experiments in the containers in a block.
[0098] 4.1.2. Sample Preparation
[0099] The composition of a particular sample in an array will
depend on the use to which the particular method or device of the
invention is put. For example, if an array is used to provide
crystalline forms of a compound-of-interest, each sample might
contain one or more solvents or solvent mixtures in addition to the
compound-of-interest (which could be evaporated) or to which other
solvents (e.g., antisolvents, reagents that affect pH, counterion
concentration, or the ionic character of the solvent) or materials
(e.g., nucleation promoters) could be added during the processing
of the samples. The specific composition of each sample in an array
might be the same (to allow redundancy) or different (to allow the
simultaneous testing of numerous crystallization conditions).
However, the invention also encompasses the use of arrays to
attempt the crystalizations of compounds-of-interest from melts, in
which case the samples might only contain solid
compound-of-interest.
[0100] In another example, the array is used to determine various
characteristics of a compound-of-interest, or how they change when
exposed to particular conditions (e.g., those described below in
the Sample Handling and Processing section). Examples of
characteristics include, but are not limited to, form, chemical
composition, solubility, physical and/or chemical stability, and
hygroscopicity.
[0101] Whatever the purpose to which an embodiment of this
invention is put, each container (apart from any containers used as
controls, or blanks), will comprise a controlled amount of the
compound-of-interest and, optionally, one or more additional
compounds (e.g., solvents, excipients, or nucleation agents). The
containers may also contain a stirbar or other device to facilitate
stirring, uniform heating, or anything else that is deemed
necessary for the partcular use to which the invention is being
put. All of these materials are preferably added to containers in
an automated fashion. For example, compounds-of-interest and
solvents can be deposited into the vials in a variety of ways,
ranging from hand-pipetting to automated liquid and/or solid
dispensing. Dispensing of chemicals into the vials is preferably
accomplished with an automated reagent dispensing apparatus, such
as Cartesian Technologies' PreSys model (available from Cartesian
Technologies Inc., 17851 Sky Park Circle, Suite C, Irvine, Calif.
92614, USA), and multiple-channel liquid dispensers, such as those
available from Tecan Group Ltd. (Tecan Group Ltd., Seestrasse 103,
8708 Mnnendorf, SWITZERLAND). Other models and brands of liquid
dispensers can also be used. Solid compounds and compositions can
also be dispensed by hand or by automated means known in the art.
For example, a solution comprising a compound-of-interest can be
dispensed into sample containers, after which the solvent can be
removed to provide a controlled amount of the compound-of-interest
(e.g., in a milligram or microgram quantity).
[0102] After samples have been prepared, the containers that hold
them are preferably sealed to prevent leakage, contamination, and
evaporation (unless otherwise desired), as well as to prevent
outside factors (e.g., humidity changes) from affecting the
samples. Preferred containers are vials which can be sealed using
crimpable metal caps or compliant gaskets, such as a silicon frits
or septa. Other means of sealing containers include, but are not
limited to, wax plugs, threaded caps, caps that snap over the vial
opening, and compression or adhesive seals. Preferred septa allow
for the illumination of the contents of a container from the top,
and also allow for the addition or withdrawal of materials or
components to/from the tube. The capping or sealing of the
containers is preferably accomplished using an automated means,
such as a Wheaton Crimpmaster Crimping Station (Wheaton Science
Products, 1501 No. 10.sup.th Street, Millville, N.J. 08332, USA)
pneumatically powered crimper. Alternatively, hand powered crimper
tools (also Wheaton Science Products) may be used.
[0103] The invention encompasses the labeling of either the vial
itself or the crimped seal cap that allows the ready identification
of individual samples. Both crimp caps and glass vials may be
labeled, for instance, through laser and inkjet marking, by using
human-readable, alphanumeric codes, as well as using
machine-readable codes such as DataMatrix 2-D codes. Such codes may
advantageously be scanned and tracked with optical readers.
Similarly, other types of barcodes and marking technologies may be
used without limitations.
[0104] 4.2 Sample Handling and Processing
[0105] Particular embodiments of the invention encompass exposing
the samples in an array to one or more conditions such as, but not
limited to, pH, ion concentration, solvent, temperature, and light
for a particular amount of time. A typical condition is
temperature, and one embodiment of the invention encompasses a
thermal cycling system capable of processing many blocks
simultaneously. This system comprises one or more shelves,
preferably thermally conductive, onto which blocks can be placed,
and heating and/or cooling means such as, but not limited to,
chillers, baths (e.g., water), dry baths, hot plates,
temperature-controlled rooms, ovens, thermoelectric devices, such
as devices employing Peltier-effect cooling and/or joule-heating,
and environmental chambers. The temperature of the samples can be
controlled by heating or cooling the thermally conductive
shelves.
[0106] The thermal cycling system can be used to simply incubate an
array of samples at a specific temperature for a particular time
(isothermal incubation), or can be used to cycle their
temperatures, e.g., to vary their temperature as a function of
time. When employed, thermal processing comprises varying the
temperature of the contents of each vial in a controlled cycle,
usually a heating period followed by a cooling period. Heat
transfer through the blocks that hold the arrays of containers
changes the temperature of the containers. Thus, when thermal
processing is used to process the samples, the blocks used should
allow heat transfer between a heating/cooling source (e.g.,
thermally controlled shelves) and the sample containers (e.g.,
vials).
[0107] FIGS. 4A-4B illustrate a specific example of a thermal
cycling system, which comprises temperature-controlled shelf
assemblies 56, which are also referred to herein as "hotels." A
number of hotels can be arranged to a number of different blocks.
FIG. 5 provides an illustration of one example of a thermal cycling
system, which comprises 18 hotels 56 in a semi-circular arrangement
around a robotic arm 55. In a preferred embodiment, a hotel
comprises twelve shelves 58 arranged in a vertical fashion as shown
in FIG. 4A, held in place with supporting members and incorporating
locating features for securing the assembly in the desired
position. In a preferred embodiment, each individual shelf 58
contains an internal loop through which a liquid, such as water, is
circulated to control the shelf temperature. The loops are piped to
a bath, e.g., a water bath acting as the cooling/heating source.
Finally, the thermal cycling system can optionally include an
environmental-control enclosure 57 that regulates the humidity
and/or ambient temperature of the air surrounding the blocks,
preventing condensation on the containers and other components. One
embodiment of an environmental control system 57 is shown in FIG.
5. Alternatively, the thermal cycling system can be located in an
environmentally-controlled room.
[0108] In a specific embodiment of the invention, different water
baths (which may also employ various other fluids for conducting
heat or cold to the samples) allow for the processing of multiple
blocks at different temperatures. The blocks are located in hotels
that are connected to the baths, the temperatures of which are
computer controlled. In this embodiment, computers also record the
heating/cooling time and temperature for each assembly of shelves,
or "hotel." Because each block contains a plurality of sample
containers, each of which is identifiable by is location in the
block and/or the use of a bar code or other identifier, the
conditions to which each sample in a given hotel is exposed is
recorded and tracked by computer.
[0109] The processing of samples or arrays of samples can involve
more than simply subjecting the samples to a particular temperature
or range of temperatures. For example, the samples can be exposed
to other environmental conditions, such as humidity, using an
environment-controled room. As shown in FIG. 5, environment control
is achieved in one embodiment of the invention using an enclosure
57 that surrounds the shelf assemblies 56, and is connected to a
supply of air that has been treated to provide the desired humidity
level inside the enclosure.
[0110] Samples in an array can be processed in any number of ways.
For example, samples in an array can all be subjected to the same
temperature for the same amount of time, or can be processed
individually using, for example, robotic techniques. For example, a
solvent or antisolvent can be added to just one or a few of the
containers held in a block with the aid of automated dispensing
devices and robotic arms, such as that shown in FIG. 5.
[0111] Samples can also be subjected to a combination of different
processes. For example, in what is referred to as a "mixed-mode"
crystallization process, more than one processing mode is applied
to samples in an array either serially or in parallel. For
instance, thermal processing (described above), followed by
anti-solvent addition to the container(s) and/or partial or
complete evaporation of the volatile contents of the container(s)
can be used to facilitate crystallization of a
compound-of-interest. Here, the term "anti-solvent" refers to a
solvent in which the compound to be crystallized has very low
solubility. An evaporation process entails allowing the sample
solvent systems to evaporate and may involve flowing a dry, inert
gas over the samples and/or heating the samples to an extent and
for a time sufficient to effect concentration of the
compound-of-interest in the sample. In a specific example of
mixed-mode processing, a thermal process is followed by an
evaporative step in which the sample vessels are opened (uncrimped)
and dry nitrogen is blown over the surface of the samples to
promote evaporation of the solvent to an extent and for a time
sufficient to allow crystallization. In another example of
mixed-mode processing, a thermal process is followed by addition of
an anti-solvent to the sample vessels in an amount sufficient to
allow crystallization. In still another example of mixed-mode
processing, a thermal process is performed on duplicate sets of
sample formulations followed by an evaporative step on one set and
anti-solvent addition to the other set. A mixed-mode
crystallization process may conclude with an incubation step, where
the samples are incubated at a temperature and for a time
sufficient to allow crystallization. Any combination of individual
process steps (e.g., thermal, anti-solvent addition, and
evaporation) may be used in serially or sample arrays may be split
to allow different process modes to be used in parallel.
[0112] Visual inspection of the samples is preferably done at least
once during their processing (i.e., their exposure to one or more
chemical or environmental conditions). Such inspection can occur at
any time before, during, or after the processing of the samples,
and is preferably done using automated means. For example, a
robotic arm 55 as shown in FIG. 5 can be used to removed the blocks
60 from the shelves 58 and transfer them to an imaging, or vision
station, such as that which is described below in Section 4.3 and
elsewhere herein. Depending on the result of the imaging, the block
can then be replaced onto a shelf in the thermal cycling system, or
its containers can be separated, rearranged into new blocks, or
removed entirely for more detailed (e.g., spectroscopic) analysis.
As mentioned above, the location and processing history of each
sample is preferably tracked and recorded, so that it can be
located, analyzed, and reproduced at any time. Because each
container can be imaged separately from others in the array to
which it belongs, this invention allows the rapid identification of
samples that can be further processed or removed for detailed
analysis even when such samples are just a few of hundreds or even
thousands of samples being processed.
[0113] In one embodiment of the invention, the processing of one or
more samples in an array is stopped at a specific time using what
is referred to herein as a "quenching station." It is at such a
station that the condition(s) to which a sample is exposed are
removed. For example, if the condition to which a
compound-of-interest has been exposed involves contact with a
particular solvent, the samples can be quenched by extracting any
fluid component that remains in each container. This can be
accomplished by puncturing the seal of the container, or tube, with
a needle that can extract the liquid from the tube and provide a
relief path through which air can flow into the tube, so as not to
create a vacuum. In addition, samples can be air-dried after
removal of the liquids in a vial by using a similar needle assembly
to punch through the septum and inject dry air into the vial for a
specific amount of time. The dry air (or other gases) removes
remaining liquids from the sample through evaporation, and vents
them outside the vial. As with sample preparation, quenching can be
automated, and can be triggered by a human operator or by
computer.
[0114] 4.3. Sample Imaging
[0115] A result of conducting a large number of small scale
experiments using various processing methods creates the need to
interrogate or inspect each of the samples in the containers for
the presence (or absence) of solid forms or other products of
interest. Although visual inspection can be done manually,
preferred embodiments of the invention utilize what is referred to
herein as a "vision station," which is an automated system that
allows for the rapid and efficient imaging and screening of
samples. Preferred vision stations are designed for the analysis of
samples contained in tubes and blocks-type arrangements, as
discussed above in Section 4.1 and elsewhere herein.
[0116] In one embodiment of the invention, the vision station
comprises a device for capturing an image of small particles, such
as a microscope/camera system with a highly magnifying lens to
capture images of small (down to sub-micron) particles onto a CCD
such as the Canty Particle Size Vertical Imaging Microscope (J M
Canty Inc., Buffalo, N.Y. USA). Another example is the published
report from December 2000 on image analysis of protein crystals: An
optical system for studying the effects of microgravity on protein
crystallization, Alexander McPherson et al., application note from
American Biotechnology Laboratory, December 2000 issue, which is
incorporated herein in its entirety by reference.
[0117] Depending on the use to which the invention is put, sample
imaging can be used to determine the presence of a solid form in a
sample or container. Alternatively, the absence of solids can be
also be detected. Consequently, vision stations of the invention
can be used to determine the stability of liquid formulations
(e.g., drug formulations for intravenous administration to
patients) and the stability of a formulation in a simulated body
(e.g., gastric) fluid.
[0118] Samples can be imaged at any time after their preparation.
Consequently, imaging information can be used to determine whether
or not a sample should be processed, how it should be processed,
and whether or not it should be subjected to more detailed, (e.g.,
spectroscopic) analysis.
[0119] A typical vision station of the invention comprises a light
source and a camera. A suitable camera can be any unit capable of
yielding photographic images of the contents of containers, e.g.,
the presence or absence of solids or solid forms, but is preferably
capable of digital capture. In a preferred embodiment of this
invention, a charge coupled device (CCD) camera provides adequate
sensitivity, but other digital capture devices may also be used.
The light source is selected based on the types of containers being
used and the design of the experiment. Examples of light sources
include, but are not limited to, visible light, laser light of
varying wavelengths, monochromatic laser, plane-polarized, or
circularly polarized light. In an example embodiment, the light
source is white light from one or more tungsten lamps. Depending on
the mode of application of the vision station, light can be brought
in from the top of the array, the bottom, or from the side. Blocks
containing removable containers allow improved access by light to
the sample due to the ability to elevate the containers from the
block, either by hand, or using an automated means.
[0120] In one embodiment, the vision station system is adapted for
use with the tubes and blocks system. In this embodiment, the
vision station system comprises a camera, a light source, and,
optionally, a mechanism to elevate containers (e.g., tubes) from a
block, thus presenting the containers to the camera. The mechanism
to elevate containers from a block can lift containers out of a
block individually, or in groups, including without limitation,
lifting all the containers in one or more rows or columns of a
block at the same time, and preferably, lifting all the containers
in one row or column at the same time. Additionally, the system can
employ software to capture, store, and analyze images and digitally
flag or select tubes containing contents of interest, e.g., solid
forms, in a series of images. Furthermore, the vision station
system may optionally comprise a database for warehousing of the
results and collation of information on the identity, composition
and history of samples in order to allow further detailed analysis
of the combined data.
[0121] Ultimately, the vision station system enables the automatic
selection of specific samples (or containers containing samples)
from an array based on their appearance. Advantages of the vision
station system include, but are not limited to, speed of
acquisition coupled with the details of the solid form, such as
gross crystal habit, color, form, and location of solids (e.g.,
crystals) in a container. Such information about where solid
formation occurs (such as where a crystal nucleates, e.g., at the
air-liquid interface or in the bulk solution), and shape of the
crystals or precipitate is useful in studying and controlling
crystallization. The vision station also provides many automation
opportunities (both in hardware as well as in software analysis of
images) and the ability to capture a variety of data regarding the
detailed physical form of the compound-of-interest (e.g., its
crystallinity, amorphous character, physical stability, and size
range information). In terms of speed, embodiments of the vision
station system can observe 96 sample tubes in less than one minute,
and the image capture is rapid (on the order of 30 milliseconds
with current digital camera technology).
[0122] In a specific embodiment of the invention, the vision
station system can accept different arrays or blocks of containers
for analysis in rapid succession. Using the vision station system,
the information obtained can include: (1) detection of solids based
on illumination (e.g., white light) and image capture; (2)
observation of birefringence (backlit crystalline samples seen with
the help of cross-polarized light); (3) observation of
nano-particle presence (using laser beams at various angles to the
camera lens); and (4) temporal information (nucleation kinetics and
kinetic stability of colloidal suspensions toward growth and phase
separation are two examples). In addition, automated exemplary and
example machine vision algorithms further enhance the utility of
the system by obviating the need for a user to manually select
tubes that are of interest.
[0123] In another specific embodiment, the vision station system is
adapted to process blocks that contain about 96 containers in an
arrangement of 8 rows of 12 columns. FIG. 6 illustrates the logic
used in one embodiment to address solid form generation using the
vision station approach. The embodiment in the flowchart 80
involves tubes in blocks, but the process can be adapted for use
with other container systems. This embodiment comprises a vision
station analysis 82, which includes optical inspection of vials
holding samples. If no solids are detected 84 in the vial, a
determination is made whether or not that particular reaction is of
further interest 88. If it is not of further interest, the
experiment may be stopped 92 for that particular vial. If a sample
in a vial is still of interest, it may be returned to the block 94
and sent back for further processing, such as in the thermal
cycling system. The process may then be repeated as to that vial.
Alternatively, it may be removed without further processing. If the
experiment is designed to detect solids, vials that contain solids
are sent to a re-arraying process 86 whereby multiple vials with
solids present are grouped together in the same output block. At
every step, the address of each vial is tracked and updated if
necessary. Such tracking can be done using various methods known to
the skilled artisan, including without limitation using bar codes.
Optionally, the entire output block can then be sent for detailed
(e.g., spectral) analysis 90. The output block is preferably
entirely filled, but it need not be.
[0124] FIG. 7A shows a schematic diagram 100 of a vision station. A
block 60 containing an array of vials 50 is placed before an
imaging device 104. While it is recognized that a camera can be
oriented underneath the block so as to be capable of viewing the
contents of the vials through the access holes in the bottom of the
block, the preferred embodiment contemplates raising the vials at
least partially out of the blocks into view of the imaging device.
Thus, FIGS. 7B and 7C are drawings of a vision station in
operation, showing a side view 111 (FIG. 7B) and a perspective view
113 (FIG. 7C) of the block 60, wherein a lifting mechanism 115
lifts an entire row of vials 50 at the same time. Alternatively,
vials 50 can be lifted one by one.
[0125] As shown in FIGS. 7A-7C, vial 50 is illuminated by a light
102 that can be placed in a variety of locations to light up
different portions of the vial 50, depending on where in the vial
50 illumination is desired. The level of illumination is determined
by inspecting the resulting images for the desired contrast and is
controlled by the operator adjusting the level or voltage until the
desired contrast is obtained. Alternatively, the level of
illumination can be automatically adjusted using appropriate
sensors and/or algorithms. The resulting illumination provides
sufficient contrast for an image or picture to be captured. Various
software 108 can be used to capture the image, such as Component
Works IMAQ Vision (National Instruments). In a preferred
embodiment, the image capture software is integrated into a custom
VB software. The camera 104 then takes a picture of the vial 50.
The picture is then stored on a computer. A hardware card, such as
National Instruments Image Capture Card, model number PCI-1422, is
used to capture the image in conjunction with image capture
software 108. A custom software application then displays the
picture of the vial and the vial can then be designated as
containing various results, such as, but not limited to, solids,
lack of solids, sediment, phase separation. The pictures and the
vial 50 designations can then be stored in a database 110. The
process is repeated for all of the vials 50 in a block 60, and for
all the blocks in a given experiment run or design. The vials can
be all processed at one time, or it can be done intermittently.
[0126] A preferred embodiment of the vision station system
comprises a camera 104, preferably a CCD camera, for example, a CCD
camera manufactured by Roper Scientific (model MegaPlus ES:1.0)
(now Redlake MASD, Inc., 11633 Sorrento Valley Road, San Diego,
Calif. 92121 USA) with an 9.times.9 mm image array with a total of
1008.times.1008 pixels. Another suitable source of imaging cameras
is Spectral Instruments, Inc., Tucson Ariz. that provides a CCD
camera that can be cooled to -50.degree. C. Alternatively, image
plate technology based on CMOS can be used for obtaining images,
but CCD is the preferred capture mechanism.
[0127] In one implementation, an area of the width of roughly 72 mm
is observed when 8 tubes (a row at a time) are pushed out of a
block for vision analysis, although, for instance, tubes may be
viewed in groups of fewer than 8 such as single tubes or two tubes
per captured image. This observed area leads to a pixel resolution
of about 70 microns/pixel. A resolution range from about 5 to 1000
microns is useful in the many embodiments of this invention, since
most organic crystalline materials in a powdery state range in
particle size from a few microns to hundreds of microns. Single
crystals are often a few hundred microns on the shortest edges,
while on the other hand extreme colloidal particles, such as
titania (TiO.sub.2) and silica (SiO.sub.2) can be stably prepared
in the nanometer size range.
[0128] The vision station can be used to identify amorphous, as
well as crystalline, solids. The amorphous form can be of
significant interest with regard to certain compounds-of-interest,
such as, but not limited to, increased solubility relative to
crystalline forms. Generally, amorphous forms of a given compound
are thermodynamically unstable compared with crystalline forms, but
can be rendered kinetically stable toward physical form change,
e.g., as a glass. Amorphous particles are typically irregular in
size, and the material lacks the property of optical birefringence.
This is defined as the ability of most crystalline materials to
interact with polarized light by changing the direction of the
polarization as it passes through the crystals. Plane-polarized
light is generally rotated upon traveling through a crystalline
material. If the light is subsequently sent through an analyzing
filter (this is another plane polarized filter where the
polarization direction is 90 degrees perpendicular to the first
filter) at a right angle to the plane-polarizing filter on the
light source, the rotated light escapes the analyzer. Therefore
true crystals appear as bright spots on a dark background.
Conversely, amorphous disordered materials generally do not rotate
plane-polarized light such that minimal light (equal to background)
escapes the filter resulting in a dark image. It may be
advantageous to look for the presence or absence of crystallinity
in this way, and by comparison of birefringence image with the
plain image rather than simply looking for the presence of
solids.
[0129] The lighting used to capture white light images of elevated
tubes is flexible, in that it can be brought in (a) from the top of
the tubes (if the top is either open or any seal is transparent),
and (b) from the side of the tubes, behind the camera. The latter
is referred to as backlighting and this approach is required when
one wants to capture birefringence information. In principle, the
light can be brought in at a number of angles, but the preferred
orientations are either vertical or horizontal. The lighting can be
provided by fiber optics (for example, NT39-366 from Edmund
Industrial Optics,101 East Gloucester Pike, Barrington, N.J.
08007), although white light strips (for example, Stocker Yale,
Imagelite brand) can also be used. Various polarizing filters can
be obtained from a number of commercial sources, e.g., polarizing
filters, such as NT45-669, are available from Edmund Industrial
Optics.
[0130] FIG. 8 illustrates an embodiment of the vision station
adapted for the detection of birefringence. On the left side, a
pair of tubes with water 112 and another pair of tubes with varying
amounts of glycine crystals 114 are shown with backlighting without
a polarizing filter on the camera lens. On the right are the same
samples 116 and 118 with a polarizer in place. With use of color
images, one can capture polychromism (i.e., multi-color crystals)
information from the experiment with a suitable camera, or simply
run the analysis with black and white images and look for bright
pixels. In addition, a quarter-wave retarder filter can be used to
confirm the presence of crystals by causing a color shift when the
filter is applied.
[0131] FIG. 9 shows the use of a combination of white light and
laser scattering. A laser beam 124 (which can be of any color, such
as red, green, or blue) can be brought into proximity with a tube
or vial 50. Single tube analyses are typically preferred, due to
some scattering and diffusion of the laser beam in cases where one
attempts to send the beam 124 through several tubes consecutively
(FIG. 9B). The laser beam, which can be generated by any number of
laser devices such as with a He--Ne Class II laser pointer at <1
mW power, will interact with sub-micron particles inside the tubes
and the radiation is scattered, resulting in a contiguous trail of
laser light through the tube. If no significant colloidal component
is present (the sample is a true solution) no such trail of laser
light will be observed in the image. Using this application, the
vision station system with the laser beam can be used to obtain
kinetic information regarding colloidal stability (i.e., how long
it takes a suspension to settle or ripen to microcrystals),
solution physical stability (how stable is a solution toward
nucleation), or phase segregation.
[0132] Another embodiment of the invention utilizes laser light at
an angle different from 90 degrees (e.g., at a 45 degree angle)
relative to the camera lens. This is shown in the example of FIG.
10, where the image 126 in panel (a) clearly shows a contiguous
path of laser light due to the presence of the colloids. In
contrast, the image 128 in panel (b) shows a single point of
scatter on the right side of the tube (where the laser beam hits
the tube). This effect is due to partial scattering of the laser
light by the glass, and becomes more pronounced in the image as the
angle between the camera lens and the laser beam is decreased.
[0133] FIGS. 11 and 12 show flow charts for the logic used in
specific vision station systems for the detection of birefringence
130 and laser light interrogation 132, respectively. The funnel
widths roughly represent the number of samples at a given stage of
the experimental workflow. The charts illustrate how a vision
station system can facilitate analysis of crystallinity or lack
thereof in a set of solid forms (FIG. 11), and also allows analysis
of nano-particulate and true solutions along with the stability of
each (FIG. 12).
[0134] In a preferred embodiment, the analysis of images obtained
by the vision station is automated. For example, software (e.g.,
National Instruments IMAQ VISION software) is employed in image
acquisition and analysis. When image analysis is performed
manually, an operator flags the samples that satisfy the criteria
used in the particular experiment (e.g., which ones contain a
solid) using a software interface. Such software can perform a
variety of function, such as, but not limited to, automated capture
and storage of images, creating and storing logic for each sample
(e.g., which ones contain solid, was a sample in solution at the
start of the experiment), and ultimately containing algorithms for
time-based measurements as well as automated isolation of
containers that satisfy given criteria. Such software can also
inform the user which samples are of interest, and facilitates the
re-array of hit tubes from the source block into a destination
block for further off-line processing or characterization.
Preferred software provides an actual image of vials that allows a
user to observe and manually select vials of interest for further
processing.
[0135] In another specific embodiment of the invention, the vision
station system further comprises a means of determining the optimal
laser light configuration relative to the tubes for interrogation
of colloidal suspensions (e.g., as to the size of the particles
they contain). In another embodiment, the vision station system
comprises a means of optimizing the capture of birefringence
information, including the investigation using a quarter wave plate
and other filters in concert with plane or other polarizers to
ensure that light scattering is not interfering with image analysis
and interpretation.
[0136] In a specific embodiment of the invention, once a number of
blocks have been processed through the vision station system, there
will be one or more output blocks holding vials containing solids.
Optionally, in a preferred embodiment, these blocks are then
processed further (e.g., moved to a quenching station) as described
above in Section 4.2 and elsewhere herein.
[0137] 4.4. Spectroscopic Data Collection and Analysis
[0138] In a typical embodiment of the invention, one or more
samples in an array are analyzed using spectroscopic techniques. In
preferred embodiments of the invention, the sample(s) that are
analyzed have been screened or selected (e.g., using the methods or
devices described above in Section 4.3) from an original array of
samples. For example, the vision station can be used to identify
samples that contain solids, and the contents of those samples are
then analyzed further using spectroscopic techniques.
[0139] The specific analysis done will depend on the purpose to
which a particular embodiment of the invention is put. For example,
if the invention is used to prepare solid forms of
compound-of-interest, the solids that have been identified in
samples can be analyzed to determine their chemical and physical
form, e.g., whether they are salts or solvates (e.g., hydrates) of
the compound-of-interest, whether or not they are crystalline, and,
if they are crystalline, the nature of their crystal form (e.g.,
their crystal structures). Spectroscopic analysis can also be used
to determine if any of the compounds in a sample (e.g., the
compound-of-interest) decomposed or reacted with other compounds in
that sample.
[0140] Spectroscopic techniques can also be used to identify
samples that share one or more characteristics. For example, if a
solid compound-of-interest can exist in more than one solid form,
and each of a plurality of samples comprises solid
compound-of-interest, it may be desirable to identify which samples
contain compound-of-interest of which form. The grouping of samples
as a function of a particular characteristic (e.g., a spectral
characteristic unique to a particular solid form) is referred to
herein as "binning." Such binning provides a means of avoiding
unnecessary duplication of further experiments. For example, if a
group of samples are binned based on a particular spectral
characteristic which corresponds to a previously unknown solid form
of the compound-of-interest, further analysis of that solid form
need not require a detailed analysis of each sample in the
group.
[0141] Examples of spectroscopic techniques that can be used to bin
or analyze samples are numerous, and will be readily apparent to
those skilled in the art. Some specific examples include, but are
not limited to, optical absorption (e.g. UV, visible, or IR
absorption), optical emission (e.g., fluorescence or
phosphorescence), Raman spectroscopy (including resonance Raman
spectroscopy), nuclear magnetic resonance spectroscopy (e.g.,
single and multi-dimensional .sup.1H and .sup.13C), X-ray
diffraction (e.g., powder X-ray diffraction), neutron diffraction,
and mass spectroscopy. For the sake of convenience, other methods
of analysis are encompassed by the term "spectroscopic technique,"
as it is used herein, include, but are not limited to, microscopy
(e.g., light and electron microscopy), second harmonic generation,
circular dichroism, linear dichroism, differential scanning
calorimetry (DSC), thermal gravimetric analysis (TGS), and melting
point. Preferred embodiments of the invention utilize Raman
spectroscopy.
[0142] 4.4.1. Raman Spectroscopy
[0143] The use of Raman spectroscopy for the high-throughput
screening and/or analysis of multiple samples is believed to be
novel, particularly in view of the relatively low intensity of
Raman scattering as compared to other spectroscopic techniques.
When coupled with the devices and techniques disclosed herein,
however, Raman spectroscopy has been found to be particularly
useful in the high-throughput screening and analysis of
samples.
[0144] The Raman spectrum of a compound can provide information
both about its chemical nature as well as its physical state. For
example, Raman spectra can provide information about intra- and
inter-molecular interactions, inclusions, salts forms, crystalline
forms, and hydration states (or solvation states) of samples to
identify suitable or desirable samples, or to classify a large
number of samples. With regard to the hydration states of
molecules, methods and devices of this invention, particularly the
binning methods discussed in more detail below, allow their
determination in situ.
[0145] Raman spectroscopy can also be used in this invention to
examine kinetics of changes in the hydration-state of a sample or
compound-of-interest. Moreover, the ability of Raman spectroscopy
to distinguish, in certain situations, forms with different
hydration states is comparable to X-ray diffraction, thus promising
specificity and sensitivity. The lack of a strong Raman signal from
water, a common solvent or component in preparations allows
collection of Raman data in-situ in a manner relevant to many
applications.
[0146] This invention also encompasses the use of Raman
spectroscopy to determine the amount of a compound-of-interest that
is dissolved in a particular sample. Advantageously, it has been
discovered that for many compounds-of-interest and solvents, a
correlation between the amount of compound-of-interest dissolved in
a liquid sample and certain characteristics of its Raman spectrum
can be obtained using one solvent, yet can be applied to the
high-throughput analysis of samples prepared using a variety of
other solvents.
[0147] These and other aspects of the invention are made possible
by the utilization of several devices and methods described herein,
which overcome problems inherent to Raman spectroscopy that would
otherwise limit its usefulness as a high-throughput analytical
technique. Examples of such problems include, but are not limited
to, weak signals, background (e.g., solvent) emissions, and signals
due to other solids or liquids in a sample, as well as the sample
container itself.
[0148] Improvements in reproducibly obtaining Raman spectra for
samples of interest include rapid and sensitive spectra acquisition
and rejection of background noise. The strength of Raman emissions
is improved by the use of lasers to excite the target substance.
Use of a carefully selected wavelength also results in resonance
Raman spectra. Sample preparation techniques resulting in adsorbing
of a target to a surface further increase Raman signals, although
such preparation is not always possible or desirable in the case of
in-situ data collection. Since the strength of the Raman signal can
vary depending on many factors, it is important to use on-line data
analysis in order to determine when a sufficient quality and
quantity of data have been collected to meet the goals of the
measurement (e.g. a prescribed signal-to-noise threshold). Of
course, optical amplifiers further improve sensitivity and
specificity. Each of these techniques or process steps may be used
alone or in combination.
[0149] Filtering techniques encompassed by the invention that can
be used to reject noise include but are not limited to temporal,
spatial, and frequency domain filtering. Spatial filtering requires
collecting emissions from a small area to reject noise from
surrounding sources. Such confocal techniques, for instance with
the target in the focus of an objective and/or using a pinhole
arrangement, allow scanning of a target to reduce unwanted noise
due to emissions from the material surrounding the target area.
[0150] The invention also encompasses temporal filtering, which
rejects or accepts signals received in a particular time window. In
the case of Raman spectra, temporal filtering relies on the
different times taken for emission of Raman spectra and the
background fluorescence spectra. Notably, Raman emissions, although
weak, can be detected much earlier than fluorescence following
excitation. Furthermore, fluorescent radiation continues over a
significantly longer period, thus making possible selection of time
windows for collecting Raman signal with a higher S/N ratio than
otherwise. An example of such filtering is provided by Matsousek et
al. in "Fluorescence suppression in resonance Raman spectroscopy
using a high-performance Picosecond Kerr Gate," in J. Raman
Spectroscopy, vol. 32, pages 983-988 (2001). The Kerr gate realized
by Matousek et al. exhibits a response time of about 4 picoseconds,
thus allowing collection of Raman emissions during a window of 4
picoseconds following an exciting laser pulse. This example should
be regarded as illustrative and not limiting as to temporal
considerations in collecting and filtering spectra in possible
embodiments since other gates, including virtual gating techniques
are also intended to be within the scope of the claimed invention.
Such filtering techniques, which can be used separately or
together, can be augmented with mathematical filtering, e.g.,
convolution with the characteristic shape of a Raman line to
further reduce the noise and reject unwanted frequencies and
emissions.
[0151] In another aspect, the invention encompasses the use of
polarized excitation and detection. Raman scattering emissions are
sensitive to the orientation of the polarization of the exciting
light relative to the molecules being examined. If the exciting
light (typically from a laser) is polarized and the molecules in a
crystal have fixed orientations, the Raman signal varies as a
function of the orientation of the crystal. This property, while
useful for detecting and evaluating crystalline samples, presents
challenges in collecting representative Raman spectra due to the
change in the amplitude of individual lines. The use of spectral
binning, which is discussed elsewhere herein in more detail, can be
used to overcome such challenges. Following the collection of a
plurality of spectra that are, optionally, preprocessed to remove
contaminating signals, as described more fully below, it is
possible to identify peaks in each of the spectra. Optionally, from
these identified peaks of the spectra it is possible to generate,
for instance, a peak height or binary spectra reflecting the peak
positions. The use of binary spectra reduces the computational
overhead in binning and otherwise interpreting the data while
taking into account variations due to orientation and the like.
Filtered raw spectra, peak height spectra generated from identified
peaks of filtered raw spectra, or binary spectra may be used to
calculate similarity scores using any suitable metric, and the
similarity scores allow binning of the spectra in accordance with
various clustering techniques.
[0152] 4.4.2. Data Collection
[0153] Spectroscopic data can be obtained for one or more samples
by manually removing the containers that contain them from the
block holding them, and presenting the containers to the particular
analytical device being used (e.g., Raman spectrometer).
Preferably, a mechanical system (such as an automated robotic arm)
is used to select, or "cherry-pick," particular containers (e.g.,
those identified as satisfying certain criteria by the vision
station) from the block(s) that contain them.
[0154] In a specific embodiment of the invention used to detect
and/or characterize solid forms of compounds-of-interest, a
container is presented to a Raman spectrometer, and is imaged down
the centerline at predetermined x, y positions. At each x, y
position, two predetermined z positions are selected in order to
focus imaging on the upper and lower inside face of the container
(e.g., the upper and lower inside glass faces of a glass tube).
Preferably, at least one position is used to focus imaging. This
image acquisition step is repeated for different angles of rotation
of the container until the entire inside surface of the container
(e.g., glass tube) is imaged. After each image capture, an analysis
is performed to determine where the "areas of interest" in a
container are, where "areas of interest" can include solids or
solid forms (e.g., crystals), and in some instances, any remaining
droplets of solution or solvent.
[0155] A vision algorithm designed to automatically detect areas of
interest (e.g., solid forms) in a container carries out the
following: 1) locates or recognizes the presence or absence of a
container; 2) locates the meniscus, if any, of the sample in a
container; and 3) searches the area between the meniscus and the
bottom of the container for particles, solids, solid forms, or
other areas of interest.
[0156] After identifying areas of interest in a container, the
Raman stage is moved to the center of the excitation source (e.g.,
laser) on to each of areas of interest in a container, and the
Raman detection apparatus is focused using manual or automated
means.
[0157] In one embodiment, auto-focusing of the Raman spectrometer
can be performed. One way in which auto-focusing can be performed
is by taking a series of Raman spectra at various z positions (to
change the focus), for each x, y position representing an area of
interest in a container. The one with the "best" Raman signal is
marked, wherein the "best" Raman signal is defined by predetermined
criteria, including, for example, by filtering each spectrum for a
location and taking the maximum peak other than the normal peak
associated with the effects of the container (e.g., glass tube).
The resulting series of "best" Raman spectra for various areas of
interest in a container can then be sorted based on similarities,
and clustered into bins with spectra from other containers in an
experiment. Automated focusing of a Raman spectrometer can result
in a series of "best" Raman spectra for various areas of interest.
These spectra can be sorted to distinguish droplets of solution or
solvent from solids and clustered with data (spectra) from the
other containers in the experiment.
[0158] When multiple spectra are obtained, one or more of the
following can be also done: (1) find the one "best" spectra of a
set of spectra for an area of interest or a solid form, with best
being defined in a predefined way, including without limitation,
highest peak signal, highest average signal, best S/N ratio, most
peaks, and the like; (2) construct an average spectrum of all the
spectra for an area of interest or a solid form, and use this
spectrum in further processing; (3) construct an "agglomerated
spectrum" that contains the highest peak of the set for every peak
window, wherein a peak window is defined as a region in which peaks
are considered to be the same; and/or (4) keep all of the spectra
and perform downstream analysis on all of the spectra.
[0159] In processing (e.g., sorting and clustering) spectral data,
the knowledge that several spectra come from each sample can used
to score the clustering results, or the labeled spectra can be used
to influence the clustering run. For example, a k-means clustering
run can be altered in the following manner: for each step of the
k-means run, cluster assigmnents are made in the traditional sense,
such that each point is assigned to the cluster with the nearest
centroid, resulting in precluster assignments that are not the
final assignments for the step; the precluster assignments for all
points coming from the area of interest or solid form are then
compared, and the most popular cluster assignment is assigned to
all of the points in the group as the final assignment; and new
centroids are determined from these final cluster assignments.
[0160] 4.4.3. Data Analysis
[0161] In particular embodiments of the invention, spectroscopic
data is processed using what is referred to herein as a "spectra
binning system," which allows the rapid analysis and identification
of samples in an array by creating, for example, a family or
similarity map. Preferred embodiments of the spectra binning system
comprise a hardware-based instrumentation platform and a
software-based suite of algorithms. The computer software is used
to analyze, identify and categorize groups of samples having
similar physical forms, thus identifying a group from which the
operator, or scientist, can then select a few samples for further
analysis. This selection can be performed independently by the
scientist or using an automated means, such as software designed to
automatically select samples of interest. Although, many
applications made possible by the spectral binning system will be
apparent to those skilled in the art, preferred systems of this
invention is used to identify and characterize samples or
compounds-of-interest. Particular binning and analytical methods
useful in the invention are disclosed in U.S. patent application
no. 10/142,812, filed May 10, 2002, the entirety of which is
incorporated herein by reference.
[0162] The spectral binning system is generally used in this
invention to detect similarities in the properties of a plurality
of samples by observing their binning behavior. Thus, the number of
forms of a substance can be estimated by binning spectra. The
plurality of samples are examined with a device for generating a
corresponding spectrum of acceptable quality, i.e., sufficient S/N
ratio. Spectral peaks or other features are next identified to
obtain a binary fingerprint. Advantageously, the spectra are
compared pairwise in accordance with a metric to generate a
similarity score. Other comparisons that use more than two spectra
concurrently are also acceptable, although possibly complex.
[0163] One or more clustering techniques can be used to generate
bins that are preferably well defined, although this is not an
absolute requirement since it is acceptable to generate a reduced
list of candidate forms for a given substance as an estimate of the
heterogeneity of the substance's structure. Advantageously, the
generation of bins facilitates the ready evaluation of structure
heterogeneity among samples. For instance, frequency, frequency
shift, amplitude, and other similar measurements based on Raman
spectra are often limited by the lack of suitable standards.
However, the number of bins generated from evaluation of Raman
spectra obtained by sampling a substance of interest is a measure
that does not directly depend on having a good standard.
[0164] The invention also encompasses the use of hierarchical
clustering to represent the data in the form of a similarity matrix
having similar spectra/samples listed close together. Such a
similarity matrix may be sorted to generate similarity regions
along a diagonal. The resulting sorted similarity matrix may be
used as a basis for setting the number of clusters for k-means
clustering or other clustering techniques based on a specified
number of clusters such as Gaussian Mixture Modelling.
[0165] Advantageously, although the clusters are actually in higher
dimensional space, they can be projected into 2 or 3 dimensional
space and visualized. Therefore, the binning procedure allows for
both steady state and kinetic evaluation of states (e.g., hydration
states, crystalline states, and other states, or forms, that can
vary over time). This method is well suited for such measurements
since individual Raman spectra can be collected rapidly (e.g., in a
few seconds). Preferably, the turn-around time for generating a
spectrum and assigning the spectrum to a bin is less than about two
minutes, one minute, ten seconds, or one second. Moreover, limited
real time processing is often possible if an acquired spectrum is
to be assigned to existing bins, or, in a preferred embodiment of
the invention, a library of binned spectra is updated with newly
acquired spectra. In a preferred embodiment, newly acquired spectra
from a single sample may all be binned into a single bin based on a
majority of them being more related to the single bin in accordance
with a metric, such as those discussed below and elsewhere
herein.
[0166] Once the spectra from all of the samples to be analyzed have
been collected, they are processed by a series of algorithms. These
algorithms facilitate the binning of sample spectra according to
one or more spectral features. Examples of such features include,
but are not limited to, the locations of peaks, peak shoulders,
peak heights, and peak areas. In a preferred embodiment, the
spectral binning process bins spectra based on the locations of
their scattering peaks and peak shoulders, expressed as wavelength
or Raman shift (cm.sup.-1).
[0167] In the spectra binning system, the collected spectra can be
binned using the raw or filtered spectra, peak height spectra
generated using peaks selected from the raw or filtered spectra,
and binary spectra generated using the raw or filtered spectra.
[0168] FIG. 15 represents the computational process applied by a
specific embodiment of the spectra binning system. As shown in the
flow chart 270, the process can be divided into preprocessing 271,
peak finding 275, similarity matrix calculation 281, spectral
clustering 283, and visualization 285 stages An optional binary
spectra generation stage 279 can also be used. Each of these
stages, which are applicable to the analysis of data obtained using
a variety of spectroscopic techniques. For the sake of convenience,
however, each is discussed in more detail below in reference to
Raman spectroscopy.
[0169] 4.4.3.1. Preprocessing
[0170] In specific embodiments of the invention, preprocessing is
used to eliminate artifacts of the Raman spectra that are not
caused by Raman scattering. Preprocessing can also be used to make
the Raman scattering peaks as sharp as possible. Raman spectra
often contain large fluorescence peaks spread over a broad spectral
range and much smaller, narrower peaks caused by scattering from
containers (e.g., glass) and instrument noise. Several different
filtering techniques can be used to eliminate such noise including,
but not limited to, Fourier filtering, wavelet filtering, matched
filtering, and averaging. A preferred method uses a matched filter
approach, wherein the filter kernel is a zero-mean, symmetric
product of sinusoids matched approximately to an average Raman peak
width. The specific form of the matched filter is given by the
following equation: 1 k [ n ] = sin ( 3 n N - 1 ) sin ( n N - 1
)
[0171] where N is the length of the kernel. Preferably, the matched
filter equation includes a normalization term: 2 k [ n ] = - 4 N -
1 sin ( 3 n N - 1 ) sin ( n N - 1 )
[0172] The normalization factor ensures that the magnitude of the
"passed" peaks in a filtered signal are about the same as the
magnitude of the original peaks, and that all peaks point in the
right direction. In one embodiment, filtered points having a value
less than zero are automatically set to equal zero.
[0173] In a specific embodiment of the invention, the bandwidth of
the main kernel peak is set to be equal to or slightly smaller than
the bandwidth of an average Raman peak. When matched filters of
this type are viewed in the Fourier domain, they perform as
bandpass filters, almost completely attenuating low and high
frequency spectral components. Furthermore, with the bandwidth of
the filter kernel chosen to be equal to or slightly smaller than
the average Raman peak bandwidth, this filter detects peaks that
are very close to each other. A raw, unfiltered spectrum will often
display two close peaks as a main peak with a "shoulder" on one of
its sides. After a matched filtering step, though, the shoulder
will often be distinguished as a separate peak. This separation is
useful for the selection, or finding, of peaks used for
binning.
[0174] An example of the effect of such filtering means is provided
in FIG. 16. Specifically, FIG. 16A shows Raman intensity plotted as
a function of Raman shift (cm.sup.-1) for an empty glass vial. The
resulting waveform shows the pattern of absorbance present. FIG.
16B shows a Raman intensity of a fluorescent sample as a function
of Raman shift. FIG. 16C shows the same pre-filtered plot as that
of FIG. 16B, but also shows the corresponding filtered spectra
after the fluorescence has been removed.
[0175] 4.4.3.2. Peak Finding
[0176] The process of finding peaks in a spectrum is an essential
aspect of many spectral processing techniques, so there are many
commercially available programs for performing this task. The many
variations of peak finding algorithms can be found in the
literature. An example of a simple algorithm is to find the
zero-crossings of the first derivative of a smoothed or unsmoothed
spectrum, and then to select the concave down zero-crossings that
meets certain height and separation criteria. In a specific
embodiment, the peak finding function available in the software
provided with the Almega dispersive Raman spectrometer (Thermo
Nicolet, OMNIC software) is used. This function allows the
threshold and sensitivity values to be set by the user. The
threshold sets the lowest peak height that will be counted as a
peak, and the sensitivity controls how far apart each peak must be
to count as a separate peak.
[0177] In an optional step, once the peaks have been found for all
of the spectra, binary spectral representations are created for all
of the initial spectra. These binary spectra are essentially
vectors of ones and zeros. Each zero represents the absence of a
peak feature and each one represents the presence of a peak
feature. A peak feature is simply a peak that occurs within a
certain spectral range, usually a few wave numbers. The vectors for
all of the spectra are the same length and corresponding elements
of these vectors correspond to peak features occurring at nearly
the same locations in the spectra.
[0178] In order to create these binary spectra, peaks are clustered
into ranges of peak features. The process used to perform this peak
clustering is a modified form of a 1-dimensional iterative k-means
clustering algorithm. The process begins with the peaks picked from
a single spectrum. These peak positions are used to define the
centers of the spectral bins, peak feature bins, for the creation
of the binary spectra. A spectral bins cover a range of wave
numbers that may be specified by the operator (in one embodiment,
the default is five wave numbers). The rest of the spectra are then
iteratively added to the peak feature representation. At each step,
any peak that fits into a pre-existing peak feature bin is added to
that bin. For any peak that does not fit into a bin, a new bin is
created. The centers of the bins are not permitted to move
resulting in overlapping peak feature ranges. Then, the centers of
all of the ranges are re-calculated, optionally with a modified
range of wave numbers, and the peak feature bins are re-defined
relative to the new centers. This process can leave some peaks
outside of an existing peak feature range. In this case, a new
range is created for these peaks. This process creates a matrix
with each row of the matrix corresponding to a binary spectrum
specified in terms of the bins in which its peaks fall. An example
of such a matrix for five spectra is given below in TABLE 1.
1 TABLE 1 Peak Position 270 350 390 430 510 Spectrum 1 1 1 0 1 1
Spectrum 2 1 0 0 1 1 Spectrum 3 1 1 0 0 0 Spectrum 4 0 1 1 1 0
Spectrum 5 1 1 0 1 1
[0179] In this matrix, Spectrum 1, for example, has a peak in each
of the bins corresponding to wave numbers 270, 350, 430 and 510,
but does not have a peak in the bin associated with wave number
390. This optional step of binary spectra generation has several
benefits over "spectrum to spectrum" or peak height spectra
comparisons, which include, but are not limited to, yielding data
on differences between spectra that is useful in refining spectra
collection and peak-finding algorithms, and reducing or removing
orientation-dependent peak amplitudes from the spectra.
[0180] From either the spectra themselves, the peak height spectra,
or from binary spectra such as those generated using the process
described above, similarity between all of the spectra in the
matrix can be calculated. This similarity measure is utilized to
identify and create cluster boundaries. Illustrative, but not
limiting, similarity measures include Hamming or Euclidean
distance, or non-metric similarity indices such a the Tversky
similarity index (or its derivatives such as the Tanimoto or Dice
coefficients) or functions thereof.
[0181] In order to describe a spectra-to-spectra similarity matrix,
the following notation can used: N.sub.mn=number of peak values in
a first spectrum falling in the same peak feature bin in a second
spectrum; N.sub.m=number of peak values in the first spectrum; and
N.sub.n=number of peak values in the second spectrum. Similarity
can then be calculated using various methods, e.g., 3 Tanimoto ( m
, n ) = N m n N m + N n - N m n
[0182] The matrices shown in FIGS. 17A and 17B were generated using
this foregoing method.
[0183] For similarity matrices based on binary spectra, the
following notation can be used: a=number of 1's in a first spectrum
that are zeros in a second spectrum; b=number of 1s in a second
spectrum that are zeros in the first spectrum; and c=number of 1s
in the first spectrum that are ones in the second spectrum. These
values can be calculated in the following manners:
[0184] Hamming distance: d=a+b
[0185] Euclidean distance: d={square root}{square root over (a+b)}
4 Tversky index : t = c a + b + c
[0186] Some of these metrics are related. For instance, the
Tanimoto coefficient is equal to the Tversky index with .alpha. and
.beta. equal to 1. The Dice coefficient is equal to the Tversky
index with .alpha. and .beta. equal to 0.5. In a preferred
embodiment, 1--Tanimoto coefficient is used as the (dis)similarity
measure. It should be noted that additional metrics, including
metrics based on other metrics, may be used in alternative
embodiments of the invention.
[0187] Once a particular way of measuring the similarity of sample
spectra has been selected, the similarity of the spectra is
determined. This determination typically results in a symmetric
similarity matrix with each element (i,j) of the matrix
representing the similarity between spectra i and j.
[0188] Using the similarity matrix or the binary spectra matrix,
several different clustering methods can be employed to assign
spectra into bins. Hierarchical clustering, k-means clustering,
Gaussian mixture model clustering, and self-organizing map (SOM)
based clustering are just some of the methods that can be used.
These and other methods are well described in the literature. See
Kohonen, T., "Self-organizing Maps", Springer Series in Information
Sciences, Vol. 30, Springer, Berlin, Heidelberg, New York, 3.sup.rd
Extended Edition (2001); Duda, R., Hart, P., and Stork, D.,
"Pattern Classification", John Wiley & Sons, 2.sup.nd Edition
(November 2000); and Kaufman, L., Rowseeaww, "Finding Groups in
Data", John Wiley & Sons, (1990). In a preferred embodiment,
hierarchical clustering is used as a first-pass method of data
analysis.
[0189] Using the information from the hierarchical clustering run,
k-means clustering is then performed with user-defined cluster
numbers and initial centroid positions. In another embodiment, the
number of clusters can be automatically selected in order to
minimize some metric, such as the sum-of-squared error or the trace
or determinant of the within cluster scatter matrix. See Duda, R.,
Hart, P., and Stork, D., "Pattern Classification", John Wiley &
Sons, 2.sup.nd Edition (November 2000).
[0190] Hierarchical clustering produces a dendrogram-sorted list of
spectra, so that similar spectra are very close to each other. This
dendrogram-sorted list can be used to present the similarity matrix
in a coded manner, wherein similarity indicia are used for each
similarity region, including without limitation different symbols
(such as cross-hatching), shades of color, or different colors. In
a specific embodiment, the coded similarity matrix is presented in
a color-coded manner, with regions of high similarity in hot colors
and regions of low similarity in cool colors. Using such a
visualization, many clusters become apparent as hot-colored square
regions of similarity along the matrix diagonal. These square
regions represent the high degree of similarity between all of the
spectral (i,j) pairs in those regions. However, it should be noted
that the failure of the coded similarity matrix to present a
diagonal form is to be expected with some types of samples,
although the matrix is still useful in representing more complex
similarity relationships. Furthermore, in some cases there can be
similarity regions along more than one possible diagonal that
correspond to different rearrangements. Such rearrangements result
in off-diagonal similarity square regions becoming part of the
diagonal similarity square regions.
[0191] Along with the matrix representation of the cluster data, it
is also useful to show where all of the spectra and the cluster
boundaries lie in a dimensionally reduced space (usually
2-dimensions). There are several ways to perform this
dimensionality reduction. In a preferred embodiment, a linear
projection is made of a binary spectra matrix onto its first two
principal components. Alternatively, the chosen similarity matrix
could be used in order to create a map of the data using
multidimensional scaling.
[0192] FIG. 18 illustrates an implementation of the binning
procedure. At step 1800 the filter spectra are obtained. In one
branch of the possible procedure, the peaks are located and
corresponding binary spectra constructed in step 1805. The binary
spectra are used to create the similarity matrix during step 1810.
Next, hierarchical clustering results from sorting the similarity
matrix to place similar spectra close to each other during step
1815. This matrix is suitable for visualization during step 1830.
In one of the many alternative ways of processing the raw spectra
of step 1800, the peaks are located and instead of binary vectors
of step 2405, peak height vectors are generated during step 1820.
Control can flow to step 1810 for the construction of a similarity
matrix or directly point based clustering may be performed during
step 1825 followed by visualization of the results in step 1830.
Other alternative embodiments include control flowing from step
1805 following generation of binary vectors to point based
clustering in step 1825 and then onto visualization in step
1830.
[0193] An example Raman binning application is written in Visual
Basic (VB). This VB program allows a user to select a group of
spectra and set processing parameters. Preprocessing is performed
within the VB application and then the filtered spectra are sent to
OMNIC for peak finding through the Macros/Pro DDE communication
layer provided by OMNIC. Once peaks are found, binary spectrum and
distance matrix generation is performed in the main VB application.
Then, the distance matrix is sent to MATLAB through a socket
communication layer. Using a program such as MATLAB, clusters are
generated and visualizations are created. These visualizations are
made available to the main VB application through a web server. The
resulting visualization allows for the easy identification of
groups of samples that all have similar physical structure.
5. EXAMPLES
[0194] Some specific, non-limiting examples of particular features
of the invention are provided below.
5.1. Example 1
Raman Data Acquisition System
[0195] An automated robotic mechanism has been constructed and
integrated with a microscope to facilitate selecting the sample
containers (e.g., tubes) from the blocks and positioning the
containers under the microscope objective for spectral acquisition.
The spectral data collection system comprises a dispersive Raman
microscope (Almega dispersive Raman by Thermo Nicolet, 5225 Verona
Road, Madison, Wis. 53711, USA), which is a research grade
dispersive Raman instrument, combining a confocal Raman microscope
and a versatile macro sampling Raman spectrometer. The highly
automatable and versatile system offers multiple laser options
under software automation, for optimized sensitivity, spatial
resolution, and confocal operation. The Almega dispersive Raman
spectrometer is capable of housing up to two lasers. Selection of
the lasers and control of laser power is accomplished through
software. In addition, the appropriate Rayleigh rejection filters,
apertures and gratings are automatically selected when the laser
excitation wavelength is changed. The high-resolution setting
provides better than 2 cm.sup.-1 resolution for all laser
wavelengths. The spectral range of operation for CCD based
detection is 400-1050 nm, allowing collection of Raman spectra over
the full range for laser wavelengths. The example system is
equipped with a 785 nm laser, a 256 k.times.1024 k CCD detector,
and a NTSC video camera to monitor samples in the microscope with a
spectral range, when using the 785 nm laser, of 100-3200
cm.sup.-1.
[0196] FIG. 13A shows a schematic of an entire automated spectra
collection in Raman system 150. Material handling automation has
been designed around the microscope to allow automated sample
handling in and out of Raman system 150. Operationally, block 60
containing samples, in tubes 50, is placed into block nest 25.
Block nest 25 is attached to XY stage 27 (Parker) to allow
individual addressing of tubes within the block. XY stage 27 is
attached to linear actuator 16 (Parker) that moves in the X
direction. When block 60 is placed in block nest 25, a sensor
(commercially available from Keyence) is activated, causing linear
actuator 16 to move block 60 into light-tight enclosure 18
surrounding the Raman microscope, shown in FIG. 13B. Once inside
enclosure 18, bar code reader 22 (commercially available from
Keyence) reads the bar code on the sample block in order to track
the contents of block 60. Referring to FIG. 13C, lift mechanism 24
presents individual tube 50 to tube gripper 26 by pushing tube 50
up through the access hole in the bottom of block 60. In FIG. 13D,
tube gripper 26 and tube 50 are raised vertically from block 60.
Tube gripper 26 is attached to linear actuator 32 and rotary
actuator 30. FIG. 13E shows rotary actuator 30 rotating 90 degrees
counter-clockwise to position tube 50 in a horizontal direction.
Tube gripper 26 is then ready to travel horizontally along linear
actuator 32 to move tube 50 near tube holder 36 in input stage 40
of microscope 20 as shown in FIG. 13F. FIG. 13G shows tube gripper
26 lowering tube 50 to tube holder 36. Tube 50 is then placed in
tube holder 36, as shown in FIG. 13H, and tube gripper 26 is
retracted vertically, as shown in FIG. 13I. Tube rotator 46 then
engages tube 50 in tube holder 36, shown in FIG. 13J. Finally, in
FIG. 13K, microscope input stage 40, under computer control, then
actuates tube holder 36 under objective 38 for Raman analysis.
[0197] FIGS. 14A-G shows a procedure for focusing the Raman
spectrometer on a solid form inside tube 50. The solid form is
typically found attached to an inside surface of the tube.
Therefore, the Raman spectrometer is preset to first look at that
position and depth. This focusing also reduces the noise due to out
of focus fluorescent emissions. Although confocal techniques are
not used in this example implementation, in alternative embodiments
of the invention they provide greater reduction in the noise since
only the radiation through a pinhole is used at any time with
integration over time to reconstruct the entire image. Naturally,
data collection is over longer periods of time.
[0198] Returning to the described embodiment, it becomes necessary
to properly position the tube beneath objective 38 of the
microscope so that the solid form is at the right depth. As shown
in FIG. 14B, this is accomplished by moving the entire microscope
stage (not shown) supporting the tube holder (tube holder 36, as
shown in FIG. 14K) in the X, Y and Z directions, as indicated by
arrows 152, as well as rotating tube 50, as shown by arrow 154, to
present the solid form at the depth expected by the spectrometer.
In a preferred embodiment, tube 50 has the geometry as shown in
FIG. 2, where the top half of tube 50 is cylindrical, but the
bottom half of tube 50 is tapered. If the solid form is located in
the tapered portion of the tube, then moving the tube in the XY
plane to position it under the objective will also result in
changing the Z-height of the sample with respect to the
spectrometer, thus the need for controlling the Z-height of the
stage as well.
[0199] FIG. 14C shows a detailed perspective view of solid 160
inside tube 50 in an out-of-focus position and indicates the
available axes of motion 152 and 154. FIG. 14D shows a solid 160
that is out of focus because it is attached to the inside bottom
(with respect to objective 38 located above) wall of tube 50 and is
also located closer to the end of tube 50 than where the objective
is focused. FIG. 14E shows how the microscope (not shown) stage is
moved in the horizontal direction to bring solid 160 closer to the
focal position. FIG. 14F then shows how tube 50 is rotated 154 to
bring solid 160 closer to the focal position. However, after this
rotational movement, solid 160 is now closer to the focal point and
needs to be lowered. FIG. 14G shows how tube 50 is moved in
vertical direction 152 to complete the process of bringing solid
160 into focus just below the inner surface of tube 50.
[0200] Using spectral signal intensity feedback from the Raman CCD,
focal distance is "auto-focused" by computer controlling the XY
position and the Z-height between the tube and the microscope
objective. This auto-focus capability allows for the automated
collection of Raman spectra once the tube is in place under the
objective. Additionally, the NTSC video camera on the Raman allows
for video capture and frame grabbing of the sample as it is being
analyzed. This feature further allows for a spatial "history" to be
created whereby the exact location of laser on the tube can be
associated with a specific Raman spectrum. In order to implement
the previously mentioned auto-focus capability, the tube holder has
a computer controlled, motorized rotation axis. This controllable
rotation allows the system, again under feedback control, to rotate
the tube under the microscope objective in order to scan the entire
inside surface of the tube.
[0201] When this feature is used, it is often not quite as
important to pre-align the samples in the tube so that the sample
is in the field of view as discussed above. Moreover, this feature
allows for rotation during collection of a Raman spectrum. This is
important to minimize so-called orientation effects that are
sometimes observed in Raman spectra from anisotropic crystalline
samples. Orientation effects exist when a sample has two or more
unequivocal crystallographic "faces" that can be targeted by the
laser source. Depending on the analyzed face, different spectra are
generated, although the sample is physically unchanged. These
different spectra might cause one to draw the conclusion that two
or more different samples were present.
[0202] Once the sample in the tube is analyzed, the tube gripper
removes the tube from the tube holder and returns it to the
original location in the tube block followed by the XY stage
indexing to the next tube to be analyzed.
5.2. Example 2
Data Collection and Binning
[0203] The effectiveness of binning was demonstrated using two test
sets that included the Raman spectra of a polymorphic material and
a material with two hydration states. First, the authenticity of
the samples was validated. Next, Raman spectra for each sample
under varying acquisition conditions were collected. The spectra
were then filtered and binned using the previously described
algorithms and method. Finally, the results were cross-checked by
comparison of the known sample identification to the bin/cluster
assignment. Each of these steps is outlined below.
[0204] Authentic polymorphic forms (polymorphs) and
anhydrate/hydrate forms for a given material each exhibit a unique
x-ray powder diffraction pattern and melting transition. Such
criteria were deemed sufficient evidence to verify authenticity of
each sample. Representatives of each of the forms of sample sets 1
and 2 were therefore characterized using x-ray diffraction (XRD)
and differential scanning calorimetry (DSC), generating x-ray
powder diffraction patterns and thermal transition data to
determine sample uniqueness. Aliquots of samples from set 2 were
further characterized using thermo-gravimetric analysis (TGA) to
confirm the hydration state (i.e., water content) of the
samples.
[0205] 5.2.1. Materials and Experimental Methods
[0206] Two test sets were used to demonstrate the binning procedure
for Raman spectra. Set 1 had two polymorphic forms of Flufenamic
acid (2-[[3-(Trifluoromethyl)phenyl]-amino]benzoic acid), and set 2
had the anhydrate and monohydrate of theophylline
(3,7-Dihydro-1,3-dimethyl-1-H-p- urine-2,6-dione). Anhydrous
theophylline was obtained from Fluka Biochemica (Lot & Filling
Code 403967/1 13700). The monohydrate was prepared by suspending
4.0 g of anhydrous theophylline in 20 ml of methyl alcohol. While
stirring, 20 ml of de-ionized water was added to the suspension and
the as-diluted suspension was warmed to approximately 40.degree. C.
to promote conversion to the hydrated form. The resulting
suspension was continuously stirred and allowed to cool to
25.degree. C. under ambient conditions. An aliquot of the
suspension was collected by filtration after 6 hours and allowed to
air dry. The solid obtained was characterized as described below to
verify its hydration state.
[0207] All x-ray powder diffraction patterns were obtained using
the D/Max Rapid X-ray Diffractometer (Rigaku/MSC, The Woodlands,
Tex., U.S.A.), which uses as its control software RINT Rapid
Control Software, Rigaku Rapid/XRD, version 1.0.0 (@1999 Rigaku
Co.), equipped with a copper source (Cu/K 1.5406), manual x-y stage
and 0.3 mm collimator. Samples were loaded in to 0.3 mm quartz
capillary tubes supplied by Charles Supper Company by tapping the
open end of the capillary into a bed of the powdered sample. The
loaded capillary was mounted in a holder that was placed into the
x-y stage. Diffractograms were acquired under ambient conditions at
a power setting of 46 kV at 40 mA in transmission mode, while
oscillating about the omega-axis from 0-5 degrees at 1 degree/s and
spinning about the phi-axis at 2 degrees/s. Exposure times were 30
minutes unless otherwise specified. The diffractograms obtained
were integrated over 2-theta from 2-60 degrees and chi (1 segment)
from 0-40 degrees at a step size of 0.02 degrees using the cyllnt
utility in the RINT Rapid display software version 1.18 provided by
Rigaku with the instrument. No normalization or omega, chi or phi
offsets were used for the integration.
[0208] 5.2.2. Results and Discussion
[0209] The resultant X-ray powder patterns, plotted as intensity
(arbitrary units) as a function of 2-theta (degrees), are shown in
FIGS. 19A and 19B for the flufenamic acid 176 and theophylline 178
samples, respectively. Comparison of the x-ray powder patterns
within each set clearly shows unique reflections (e.g., shifted
peaks) in each pattern, indicating structural differences between
the samples within each set and hence, validating the authenticity
of the samples. Note, comparable x-ray powder patterns for the
anhydrous and monohydrate forms of theophylline have been reported
by Zhu et al. (International Journal of Pharmaceutics, 135:151-160
(1996))).
[0210] Further confirmation of the authenticity of the test sets
was provided by DSC thermal analysis. An aliquot of each sample was
weighed into an aluminum sample pan obtained from TA Instruments
(pan number 90078.609, lid number 900779.901). Pans containing
flufenamic acid samples were crimped closed, whereas pans
containing theophylline samples were fit pressed to avoid pressure
build up due to potential water vaporization. Sample pans were
loaded into the apparatus and thermograms were obtained by
individually heating the samples at a rate of 10.degree. C./min
from 20.degree. C. to 350.degree. C. using an empty crimped
aluminum pan as a reference.
[0211] The DSC thermograms for the flufenamic acid 178 and
theophylline 180 sample sets are shown in FIGS. 20A and 20B,
respectively, where heat flow (W/g) is plotted as a function of
temperature (.degree. C.). The melt transition (peak temperature)
of flufenamic acid samples was observed at 134.4.degree. C. for
Form I showing it to be pure Form I, whereas Form III exhibited a
melt at 126.1.degree. C., followed by re-crystallization and
another melt at 133.9.degree. C. indicating the conversion of Form
III (melting point=126.1.degree. C.) to Form I upon heating. The
DSC thermogram for anhydrous theophylline shows a single sharp
endotherm at 273.1.degree. C. corresponding to the melting
transition of the sample. The DSC curve for the hydrated sample
exhibits two endotherms, the first occurring at a peak temperature
of approximately 77.4.degree. C. where dehydration of the sample is
expected. This is followed by an endotherm at 273.1.degree. C.,
where the anhydrous form melts.
[0212] Thermo-gravimetric analysis (TGA) was performed on samples
from set 2 to verify water content. An aliquot of each sample was
transferred into a platinum sample holder obtained from TA
Instruments (#952019.9061) and loaded in to the apparatus.
Thermograms were obtained by individually heating the samples at
10.degree. C./min from 25.degree. C. to 300.degree. C. under
flowing dry nitrogen (balance purge 40 ml/min; sample purge 60
ml/min).
[0213] The thermograms obtained for the anhydrous and hydrous forms
of theophylline are shown in FIG. 21, where the weight change (%)
is plotted 182 as a function of temperature (.degree. C.). As
illustrated in FIG. 21, the hydrated sample undergoes a two-step
weight loss. The first weight loss 184 of 9.2% begins at
approximately 25.degree. C. and continues until approximately
70.degree. C. This weight change is associated with loss of loosely
bound water from the hydrate structure and corresponds to a water
mole fraction of 0.50, indicating the sample is a monohydrate of
theophylline. For comparison, the theoretical water content for the
monohydrate of theophylline is 9.09%. The small deviation in the
measured sample is attributed to surface absorbed water, typically
ranging from 0.0-0.3%. At approximately 172.degree. C., the second
weight loss 186 indicative of decomposition of the compound is
observed. Note, the anhydrous theophylline sample exhibits only one
weight loss 187 corresponding to decomposition beginning at
approximately 172.degree. C.
[0214] 5.2.3. Reference Raman Spectra
[0215] For reference, Raman spectra were collected for each of the
samples in sets 1 and 2. An aliquot of the sample was transferred
to a glass slide that was positioned in the sample chamber. The
measurement was made using the Almega.TM. Dispersive Raman system
fitted with a 785 nm laser source. The sample was manually brought
into focus using the microscope portion of the apparatus with a
10.times. power objective, thus directing the laser onto the
surface of the powdered sample atop a glass slide. The spectra were
acquired using the parameters outlined in the following table:
2TABLE 2 Raman spectral acquisition parameters Parameter Setting
Used Exposure time (s) 2.0 Number of exposures 10 Laser source
wavelength (nm) 785 Laser power (%) 100 Aperture shape pin hole
Aperture size (.mu.m) 100 Spectral range 105-4252 Grating position
single Temperature at acquisition (.degree. C.) 24.0
[0216] The unfiltered Raman spectra generated for each sample are
shown in FIGS. 22A and 22B, where the Raman intensity (arbitrary
units) is plotted as a function of Raman shift (cm.sup.-1). Note
that the appearance or disappearance of peaks and/or shifts in peak
position between the samples within a set was observed. For
example, the spectra shown in FIG. 22A for flufenamic acid
polymorphs of sample set 1 show a doublet 190 centered around 450
cm.sup.-1 for form I and a singlet 192 for form III at that
position, as well as significant shifting of the three peaks 193 in
the 1150-1250 cm.sup.-1 range. Such peak appearance/disappearance
and/or shifts in peak position indicate a unique crystal packing
configuration, thus differentiating the forms and showing that the
Raman spectra can be used as a unique signature for a given
form.
[0217] 5.2.4. Filtering of Raman Spectra
[0218] Evaluation of the filtering and binning algorithms was
carried out by acquiring at least 20 spectra for each of the
samples from sets 1 and 2, filtering the spectra to remove
background signals, and binning the spectra. To collect the Raman
spectra, an aliquot of each sample was transferred onto a glass
slide or into a glass vial. Measurements were made by directing the
laser onto the surface of the powdered sample atop the glass slide
(theophylline) or through the glass vial (flufenamic acid). The
acquisition parameters used are provided in TABLE 2 with the
exception that half of the spectra collected for each polymorph of
set 1 (flufenamic acid) were collected using the 50.times.
microscope objective rather than the 10.times. objective. The
sampling location was varied either by moving the glass slide or
rotating the glass vial.
[0219] All spectra were filtered to remove background signals,
including glass contributions and sample fluorescence. This is
particularly important as large background signal or fluorescence
limit the ability to accurately pick and assign peak positions in
the subsequent steps of the binning process. Such background
contributions to the Raman spectra are shown in FIGS. 16A and 16B
for representative glass and fluorescent samples, respectively.
Spectra from all samples of test sets 1 and 2 were filtered using a
matched filter of feature size 25. An example of the original and
filtered spectra for a fluorescent sample is shown in FIG. 16C.
[0220] 5.2.5. Binning of Raman Spectra
[0221] Filtered spectra were binned using the algorithm described
above under the peak picking and binning parameters given in TABLE
3 and screen shots showing the output from the binning software
captured during the binning procedure are provided in FIGS. 17A and
17B for the flufenamic acid and theophylline sample sets,
respectively.
3TABLE 3 Peak picking and binning parameters used. Parameter
Setting Used for Binning QC Parameters Peak Height Threshold 1000
Region for noise test (cm.sup.-1) 0-10000 RMS noise threshold 10000
Automatically eliminate failed spectra yes Region of Interest
Include (cm.sup.-1) 120-1800 Peak Pick Parameters Peak Pick
Sensitivity 99 Peak Pick Threshold 100 Peak Comparison Parameters
Peak Window (cm.sup.-1) 3 Analysis Parameters Number of clusters
2
[0222] The sorted cluster diagrams 194 and 196 showing the output
for each sample set are illustrated in FIGS. 23A and 23B and the
corresponding cluster assignments for each spectral file are
provided in TABLES 4 and 5, respectively.
4TABLE 4 Cluster assignments for each spectral file for flufenamic
acid sample set. Cluster Original Sorted File Name Number Number
Number Filtered flufenamic I 10x 1 1 1 Filtered flufenamic I 10x
10.SPA 1 2 5 Filtered flufenamic I 10x 2.SPA 1 3 6 Filtered
flufenamic I 10x 3.SPA 1 4 9 Filtered flufenamic I 10x 4.SPA 1 5 4
Filtered flufenamic I 10x 5.SPA 1 6 7 Filtered flufenamic I 10x
6.SPA 1 7 8 Filtered flufenamic I 10x 7.SPA 1 8 15 Filtered
flufenamic I 10x 8.SPA 1 9 2 Filtered flufenamic I 10x 9.SPA 1 10 3
Filtered flufenamic I 50x 1.SPA 1 11 16 Filtered flufenamic I 50x
10.SPA 1 12 11 Filtered flufenamic I 50x 2.SPA 1 13 17 Filtered
flufenamic I 50x 3.SPA 1 14 18 Filtered flufenamic I 50x 4.SPA 1 15
20 Filtered flufenamic I 50x 5.SPA 1 16 12 Filtered flufenamic I
50x 6.SPA 1 17 19 Filtered flufenamic I 50x 7.SPA 1 18 13 Filtered
flufenamic I 50x 8.SPA 1 19 14 Filtered flufenamic I 50x 9.SPA 1 20
10 Filtered flufenamic III 10x 1.SPA 2 21 21 Filtered flufenamic
III 10x 2 22 28 10.SPA Filtered flufenamic III 10x 2 23 29 11.SPA
Filtered flufenamic III 10x 2.SPA 2 24 26 Filtered flufenamic III
10x 3.SPA 2 25 22 Filtered flufenamic III 10x 4.SPA 2 26 23
Filtered flufenamic III 10x 5.SPA 2 27 31 Filtered flufenamic III
10x 6.SPA 2 28 30 Filtered flufenamic III 10x 7.SPA 2 29 27
Filtered flufenamic III 10x 8.SPA 2 30 24 Filtered flufenamic III
10x 9.SPA 2 31 25 Filtered flufenamic III 50x 1.SPA 2 32 33
Filtered flufenamic III 50x 2 33 34 10.SPA Filtered flufenamic III
50x 2.SPA 2 34 36 Filtered flufenamic III 50x 3.SPA 2 35 35
Filtered flufenamic III 50x 4.SPA 2 36 32 Filtered flufenamic III
50x 5.SPA 2 37 37 Filtered flufenamic III 50x 7.SPA 2 38 39
Filtered flufenamic III 50x 8'.SPA 2 39 38 Filtered flufenamic III
50x 9.SPA 2 40 40
[0223]
5TABLE 5 Cluster assignments for each spectral file for
theophylline sample set. Cluster Original Sorted File Name Number
Number Number Filtered Theophylline Hydrate1.SPA 1 1 1 Filtered
Theophylline Hydrate10.SPA 1 2 14 Filtered Theophylline
Hydrate11.SPA 1 3 7 Filtered Theophylline Hydrate12.SPA 1 4 8
Filtered Theophylline Hydrate13.SPA 1 5 9 Filtered Theophylline
Hydrate14.SPA 1 6 15 Filtered Theophylline Hydrate15.SPA 1 7 10
Filtered Theophylline Hydrate16.SPA 1 8 11 Filtered Theophylline
Hydrate17.SPA 1 9 16 Filtered Theophylline Hydrate18.SPA 1 10 12
Filtered Theophylline Hydrate19.SPA 1 11 17 Filtered Theophylline
Hydrate2.SPA 1 12 19 Filtered Theophylline Hydrate20.SPA 1 13 2
Filtered Theophylline Hydrate3.SPA 1 14 3 Filtered Theophylline
Hydrate4.SPA 1 15 4 Filtered Theophylline Hydrate5.SPA 1 16 18
Filtered Theophylline Hydrate6.SPA 1 17 13 Filtered Theophylline
Hydrate7.SPA 1 18 5 Filtered Theophylline Hydrate8.SPA 1 19 6
Filtered Theophylline Hydrate9.SPA 1 20 20 Filtered
Theophylline1.SPA 2 21 21 Filtered Theophylline10.SPA 2 22 27
Filtered Theophylline11.SPA 2 23 33 Filtered Theophylline12.SPA 2
24 28 Filtered Theophylline13.SPA 2 25 34 Filtered
Theophylline14.SPA 2 26 29 Filtered Theophylline15.SPA 2 27 30
Filtered Theophylline16.SPA 2 28 22 Filtered Theophylline17.SPA 2
29 31 Filtered Theophylline18.SPA 2 30 23 Filtered
Theophylline19.SPA 2 31 40 Filtered Theophylline2.SPA 2 32 36
Filtered Theophylline20.SPA 2 33 24 Filtered Theophylline3.SPA 2 34
38 Filtered Theophylline4.SPA 2 35 25 Filtered Theophylline5.SPA 2
36 26 Filtered Theophylline6.SPA 2 37 39 Filtered Theophylline7.SPA
2 38 37 Filtered Theophylline8.SPA 2 39 32 Filtered
Theophylline9.SPA 2 40 35
[0224] In each sample set, two distinct clusters are observed
represented by sorted spectra numbers 1-20 and 21-40 that
correspond to the file names and sample identifications provided in
TABLES 4 and 5. In comparing the cluster assignments to the sample
identification (by file number), 100% binning accuracy is observed
for each test set. For example, all form I samples are binned in
cluster 1 and all form III samples are binned together in cluster 2
for flufenamic acid test set 1.
[0225] While the invention has been described in connection with
what is presently considered to be the practical and preferred
embodiments, the invention is not limited to the disclosed
embodiments. In particular, it will be clear to those skilled in
the art that this invention may be embodied in other specific
forms, structures, and arrangements, and with other elements, and
components, without departing from the spirit or essential
characteristics thereof. One skilled in the art will appreciate
that the invention may be used with many modifications of
structure, arrangement, and components and otherwise, used in the
practice of the invention, which are particularly adapted to
specific environments and operative requirements without departing
from the principles of this invention. The presently disclosed
embodiments are therefore to be considered in all respects as
illustrative and not restrictive, the scope of the invention being
indicated by the appended claims.
* * * * *