Drug discovery methods

• US 10,006,148 B2
• Filed: 07/28/2014
• Issued: 06/26/2018
• Est. Priority Date: 02/04/2002
• Status: Active Grant

• Current Assignee: Qiagen Redwood City, Inc. (Qiagen NV)
• Original Assignee: Qiagen Redwood City, Inc. (Qiagen NV)
• Inventors: Chen, Richard O., Cho, Raymond J., Felciano, Ramon M., Holley, Bret, Patel, Viresh, Richards, Daniel R., Selvarajan, Sushma, Steward, Keith, Schneider, Sara Tanenbaum
• Primary Examiner(s): Clow, Lori A.
• Application Number:
  US14/444,973
• Publication Number:
  US 20150024951A1
• Time in Patent Office: 1,429 Days
• Field of Search: None
• CPC Class Codes: G16B 25/00 ICT specially adapted for hybridisation; ICT specially adapted for gene or protein expression
• CPC Class Codes: G16B 25/10 Gene or protein expression profiling; Expression-ratio estimation or normalisation
• CPC Class Codes: G16B 30/00 ICT specially adapted for sequence analysis involving nucleotides or amino acids
• CPC Class Codes: G16B 35/00 ICT specially adapted for in silico combinatorial libraries of nucleic acids, proteins or peptides
• CPC Class Codes: G16B 35/20 Screening of libraries
• CPC Class Codes: G16B 5/00 ICT specially adapted for modelling or simulations in systems biology, e.g. gene-regulatory networks, protein interaction networks or metabolic networks
• CPC Class Codes: G16B 5/30 Dynamic-time models
• CPC Class Codes: G16C 20/60 In silico combinatorial chemistry
• CPC Class Codes: G16C 20/64 Screening of libraries

Abstract

Methods for identifying disease-related pathways that can be used to identify drug discovery targets, to identify new uses for known drugs, to identify markers for drug response, and related purposes.

Claims

1. A method for evaluating user-supplied genomics data, comprising:

receiving a set of genes, selected from user-supplied genomics data, that are used to generate a library of profiles;

computing a triangles score for each gene in the selected set by computing a count of three-neighbor loops including the gene, the loops defined by edge connections stored in a structured database, wherein the structured database is structured according to predetermined, causal relationships among genes and/or gene products;

generating a plurality of seed sets based on the computed triangles scores, each seed set having a unique subset of the set of genes, wherein each profile, from the profile library, is a seed set, from the plurality of seed sets, that is converted into a subnetwork having additional genes selected from the structured database according to one or more criteria;

identifying one or more profiles, from the profile library, including respective subsets of data that overlap at least a portion of the user-supplied genomics data;

determining, for each such overlapped profile, whether the overlap with the user-supplied genomics data is statistically significant; and

presenting a list of the one or more overlapped profiles determined to have a statistically significant overlap,

wherein each of the receiving, computing, generating, identifying, determining, and presenting is performed by a processing system.

2. The method of claim 1, wherein the set of genes is selected according to one or more attributes of the user-supplied genomics data.

3. The method of claim 1, wherein the selected set of genes is a user-supplied set of differentially expressed genes from the user-supplied genomics data.

4. The method of claim 1, further comprising:

converting findings from a knowledge base into the structured database, wherein the findings include data, from public and/or proprietary sources, used to build the knowledge base, and wherein the structured database is a graph data structure that associates concepts, from the structured database, that are related to each other as specified by the findings; and

querying the graph data structure to compute the triangles scores used in generating the profiles from the profile library.

5. The method of claim 1, wherein the determining whether the overlap is statistically significant comprises:

computing a probability of the overlap as a function of information contained in the structured database.

6. The method of claim 1, wherein the selected set of genes is differential gene expression data, the method further comprising:

identifying a new use for a known therapy wherein the gene expression data relates to a pathway affected by the known therapy;

prioritizing candidate development compounds for further development wherein the gene expression data relates to the target of one or more candidate development compounds and giving higher priority to development compounds on the basis of whether or not they are likely to result in an undesirable effect based on their involvement in other biological pathways as embodied in the profile; and

identifying disease-related pathways wherein the disease is a side effect of drug therapy, wherein the gene expression data relates to the target affected by the drug therapy and the alternative pathways that are also affected by the drug or the drug discovery target and that result in an undesirable phenotype are embodied in the profile.

7. The method of claim 1, wherein the user-supplied genomics data includes differential gene expression data relating to a particular disease state, the method further comprising:

validating whether the differential gene expression data are genotypic markers for the disease state according to whether a database-asserted biological association related to the disease state, which is shared among a plurality of overlapped profiles, is statistically significant.

8. The method of claim 1, further comprising computing a statistical significance for a biological association in the one or more statistically significant profiles.

9. The method of claim 1, wherein generating the profile library comprises:

for each profile generated, selecting a node for a profile based on the number of similar findings in the structured database that link the node to a neighboring node.

10. The method of claim 1, further comprising:

displaying information related to the one or more statistically significant profiles and genomics data using a graphical user interface (GUI).

11. The method of claim 1, further comprising:

annotating the one or more statistically significant profiles with biological associations asserted by the structured database including one or more of a cellular process, molecular process, organismal process, and disease process.

12. The method of claim 11, further comprising:

displaying the biological association using one of a graphical user interface (GUI) and a report.

13. The method of claim 11, wherein the annotating uses classification information found in an ontology.

14. The method of claim 1, wherein the determining whether the overlap of an overlapped profile is statistically significant further comprises:

testing a null hypothesis over a discrete probability distribution, the distribution being a function of the database size, profile sizes, the user-supplied genomics data size, and expression values.

15. The method of claim 1, wherein the generating comprises:

generating a plurality of profile libraries, wherein each profile library corresponds to a different one of a plurality of profile generation criterions.

16. The method of claim 1, further comprising:

converting findings from a literature-based representation of a knowledge base into a biology-based representation of the structured database.

17. The method of claim 1, wherein a three-neighbor loop is a set of three genes, from the structured database, that are related to each other.

18. The method of claim 1, the generating the plurality of seed sets comprising:

sorting the set of genes by decreasing triangles scores; and

generating the plurality of seed set based on the sorted set of genes.

19. The method of claim 1, wherein the one or more criteria for a seed set includes one or more of a minimum number of connections between an additional gene and genes in the seed set, a specificity for interactions of the additional gene with the genes in the seed set, and a level of differential regulation of the additional gene.