Testing of Medicinal Drugs and Drug Combinations

• US 20170193157A1
• Filed: 12/30/2015
• Published: 07/06/2017
• Est. Priority Date: 12/30/2015
• Status: Abandoned Application

• Current Assignee: Microsoft Technology Licensing LLC (Microsoft Corporation)
• Original Assignee: Microsoft Technology Licensing LLC (Microsoft Corporation)
• Inventors: Poon, Hoifung, Mayhew, Stephen William, Wang, Sheng, Yih, Wen-tau, Quirk, Christopher B., Toutanova, Kristina
• Application Number:
  US14/985,023
• Publication Number:
  US 20170193157A1
• Time in Patent Office: Days
• CPC Class Codes: G16B 20/00 ICT specially adapted for functional genomics or proteomics, e.g. genotype-phenotype associations
• CPC Class Codes: G16B 20/20 Allele or variant detection, e.g. single nucleotide polymorphism [SNP] detection
• 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 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 40/00 ICT specially adapted for biostatistics; ICT specially adapted for bioinformatics-related machine learning or data mining, e.g. knowledge discovery or pattern finding
• CPC Class Codes: G16B 40/20 Supervised data analysis
• 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 5/20 Probabilistic models
• CPC Class Codes: G16C 20/60 In silico combinatorial chemistry

Abstract

Drug combinations offer promising treatment for some conditions such as cancer. However, the large number of available drug combinations makes it impractical to try all possible combinations. Machine-learning techniques described in this disclosure train a classification algorithm. Once trained, the classification algorithm uses genomic data from a specific patient to perform in silico tests of drugs and drug combinations against the genomic data to determine which therapies are likely to be effective for treating a condition of the specific patient.

Claims

1. A computing device for in silico testing of drugs comprising:

a processing unit;

a memory;

a genomics module configured to receive genomics information of a patient from an external source and provide the genomics information to a classification module;

a drug selection module configured to identify a plurality of drugs to the classification module for testing to determine if one or more drugs from the plurality of drugs that have more than a threshold probability of affecting at least one physical parameter associated with a condition in the patient; and

the classification module, wherein the classification module is configured to identify one or more drugs from the plurality of drugs that have more than the threshold probability of affecting the at least one physical parameter associated with the condition in the patient based at least in part on the genomics information of the patient.

2. The computing device of claim 1, wherein the plurality of drugs are selected automatically based at least in part on the condition of the patient.

3. The computing device of claim 1, wherein the genomics module obtains genomics information from a cell of the patient exhibiting an effect of the condition.

4. The computing device of claim 3, wherein the genomics module obtains the genomics information at least partially from a gene expression profile of the patient.

5. The computing device of claim 1, wherein the classification module uses a classification model learned from a combination of a gene network, functional effects of at least one drug from the plurality of drugs on a previously-treated patient, and genomics information from the previously-treated patient.

6. The computing device of claim 5, wherein the classification model includes logistic regression.

7. The computing device of claim 1, wherein the plurality of drugs are represented as a plurality of drug targets, individual ones of the plurality of drug targets indicating a target and a disassociation constant between the drug and the target.

8. A method of selecting drugs to administer to a patient, the method comprising:

receiving an indication of a condition of the patient;

receiving genomics information of the patient;

receiving a selection of drugs for in silico testing;

providing the condition, the genomics information, and the selection of drugs to a classifier trained with supervised learning to perform the in silico testing; and

receiving, from the classifier, identification of one or more drug treatments from the selection of drugs.

9. The method of claim 8, wherein the genomics information comprises a gene expression profile of a cell from the patient.

10. The method of claim 8, classifying, by a linear classifier, drugs from the selection of drugs as effective or ineffective for affecting at least one physical parameter associated with a condition in a patient.

11. The method of claim 8, further comprising training the classifier at least in part by a gene network in which individual genes in the network are represented as n-dimensional vectors.

12. The method of claim 8, wherein receiving the one or more drug treatments comprises:

receiving, from the classifier, a plurality of drug targets; and

mapping the plurality of drug targets to the one or more drug treatments.

13. The method of claim 8, wherein the one or more drug treatments comprises at least two drug treatments ordered by a probability of affecting the condition of the patient.

14. The method of claim 8, further comprising:

obtaining a cell from the patient, the cell exhibiting the condition;

growing the cell in a cell culture;

measuring mRNA expression levels of the cell culture; and

generating at least part of the genomics information from the mRNA expression levels.

15. The method of claim 8, further comprising:

obtaining a cell from the patient, the cell exhibiting the condition;

growing the cell in a cell culture;

separately applying at least two of the drug treatments to the cell culture;

identifying a subset of the drug treatments that affect the cell culture; and

administering at least one drug treatment that is identified as affecting the cell culture to the patient.

16. A method of identifying a downstream effect of a drug on a gene that is not a direct target of the drug, the method comprising:

identifying a set of gene descriptors that identify a first gene, a second gene, a type of influence between the first gene and the second gene, and a direction of the influence;

generating a gene network that includes the first gene, the second gene, and a plurality of other genes, individual genes in the gene network represented by nodes and relationships between the individual genes represented by edges;

representing information contained in the gene descriptors as a plurality of n-dimensional real vectors;

propagating an effect of the drug on a target gene through the edges of the gene network from the target gene to the gene that is not a direct target of the drug; and

determining a probability of the drug influencing the gene that is not the direct target of the drug.

17. The method of claim 16, wherein the identifying comprises identifying the set of gene descriptors at least in part from natural language processing of scientific literature.

18. The method of claim 16, wherein the determining comprises determining the probability of the drug influencing the gene that is not the direct target of the drug by iteratively simulating a random walk process through the gene network.

19. The method of claim 16, further comprising optimizing scores of the set of gene descriptors such that gene descriptors that are in the gene network have higher scores than gene descriptors that are outside the gene network.

20. The method of claim 16, further comprising grouping a set of genes in a target cluster based at least in part on a shortest path in the gene network between the set of genes.