Apparatus and method for high-throughput preparation and spectroscopic classification and characterization of compositions

• US 20030123057A1
• Filed: 09/06/2002
• Published: 07/03/2003
• Est. Priority Date: 01/07/2000
• Status: Active Grant

• Current Assignee: Alasdair Y. Johnson, Anthony V. Lemmo, Christopher McNulty, Douglas Levinson, Javier P. Gonzalez-Zugasti, Michael J. Cima, Orn Almarsson
• Original Assignee: TransForm Pharmaceuticals, Inc. (Johnson & Johnson)
• Inventors: Cima, Michael J., Almarsson, Orn, Lemmo, Anthony V., McNulty, Christopher, Levinson, Douglas, Gonzalez-Zugasti, Javier P., Johnson, Alasdair Y.
• Granted Patent: US 6,977,723 B2
• Time in Patent Office: Days
• US Class Current: 356/301
• CPC Class Codes: B01J 19/0046 Sequential or parallel reactions, e.g. for the synthesis of polypeptides or polynucleotides; Apparatus and devices for combinatorial chemistry or for making molecular arrays synthesis methods per se C40B50/00
• CPC Class Codes: B01J 2219/00274 Sequential or parallel reactions; Apparatus and devices for combinatorial chemistry or for making arrays; Chemical library technology
• CPC Class Codes: B01J 2219/00283 Reactor vessels with top opening
• CPC Class Codes: B01J 2219/0031 the racks or blocks being mounted in stacked arrangements
• CPC Class Codes: B01J 2219/00335 Septa
• CPC Class Codes: B01J 2219/00344 Caps
• CPC Class Codes: B01J 2219/00495 Means for heating or cooling the reaction vessels
• CPC Class Codes: B01J 2219/00547 Bar codes
• CPC Class Codes: B01J 2219/00587 High throughput processes
• CPC Class Codes: B01J 2219/00659 Two-dimensional arrays
• CPC Class Codes: B01J 2219/00689 using computers
• CPC Class Codes: B01J 2219/00691 using robots
• CPC Class Codes: B01J 2219/00756 Compositions, e.g. coatings, crystals, formulations
• CPC Class Codes: B01L 2300/0829 Multi-well plates; Microtitration plates
• CPC Class Codes: B01L 2300/0851 Bottom walls
• CPC Class Codes: B01L 2300/1805 Conductive heating, heat from thermostatted solids is conducted to receptacles, e.g. heating plates, blocks
• CPC Class Codes: B01L 3/5082 Test tubes per se
• CPC Class Codes: B01L 7/52 with provision for submitting samples to a predetermined sequence of different temperatures, e.g. for treating nucleic acid samples amplification or hybridisation processes per se C12Q1/68; controlling sequential reactions for synthesis B01J19/0046
• CPC Class Codes: C30B 29/58 Macromolecular compounds
• CPC Class Codes: C30B 7/00 Single-crystal growth from solutions using solvents which are liquid at normal temperature, e.g. aqueous solutions from molten solvents C30B9/00; by normal or gradient freezing C30B11/00; under a protective fluid C30B27/00
• CPC Class Codes: C40B 60/14 : for creating libraries
• CPC Class Codes: C40B 70/00 : Tags or labels specially adapted for combinatorial chemistry or libraries, e.g. fluorescent tags or bar codes
• CPC Class Codes: G01J 3/28 : Investigating the spectrum using colour filters G01J3/51
• CPC Class Codes: G01N 2021/651 : Cuvettes therefore
• CPC Class Codes: G01N 2035/041 : lifting items out of a rack for access
• CPC Class Codes: G01N 2035/042 : moved independently, e.g. by fork manipulator
• CPC Class Codes: G01N 2035/0425 : Stacks, magazines or elevators for plates
• CPC Class Codes: G01N 21/253 : for batch operation, i.e. multisample apparatus analytical automats G01N35/00
• CPC Class Codes: G01N 21/35 : using infrared light G01N21/39 takes precedence
• CPC Class Codes: G01N 21/3563 : for analysing solids; Preparation of samples therefor
• CPC Class Codes: G01N 21/65 : Raman scattering
• CPC Class Codes: G01N 35/0099 : comprising robots or similar manipulators robots per se B25J
• CPC Class Codes: G01N 35/028 : having reaction cells in the form of microtitration plates

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.

Claims

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.

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.

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.

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.