INDIVIDUAL AND COHORT PHARMACOLOGICAL PHENOTYPE PREDICTION PLATFORM

• US 20200135337A1
• Filed: 12/23/2019
• Published: 04/30/2020
• Est. Priority Date: 05/12/2017
• Status: Active Grant

• Current Assignee: Board of Regents of the University of Michigan (University of Michigan)
• Original Assignee: Board of Regents of the University of Michigan (University of Michigan)
• Inventors: Athey, Brian D., Allyn-Feuer, Ari, Higgins, Gerald A., Burns, James S., Kalinin, Alexandr, Pauls, Brian, Ade, Alex, Reamaroon, Narathip
• Granted Patent: US 10,867,702 B2
• Time in Patent Office: Days
• CPC Class Codes: A61K 31/37 Coumarins, e.g. psoralen
• CPC Class Codes: G06N 3/08 Learning methods
• 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 30/00 ICT specially adapted for sequence analysis involving nucleotides or amino acids
• 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 40/30 Unsupervised data analysis
• CPC Class Codes: G16H 10/60 for patient-specific data, e.g. for electronic patient records
• CPC Class Codes: G16H 50/20 for computer-aided diagnosis, e.g. based on medical expert systems
• CPC Class Codes: G16H 50/30 for calculating health indices; for individual health risk assessment
• CPC Class Codes: G16H 50/70 for mining of medical data, e.g. analysing previous cases of other patients

Abstract

For patients who exhibit or may exhibit primary or comorbid disease, pharmacological phenotypes may be predicted through the collection of panomic data over a period of time. A machine learning engine may generate a statistical model based on training data from training patients to predict pharmacological phenotypes, including drug response and dosing, drug adverse events, disease and comorbid disease risk, drug-gene, drug-drug, and polypharmacy interactions. Then the model may be applied to data for new patients to predict their pharmacological phenotypes, and enable decision making in clinical and research contexts, including drug selection and dosage, changes in drug regimens, polypharmacy optimization, monitoring, etc., to benefit from additional predictive power, resulting in adverse event and substance abuse avoidance, improved drug response, better patient outcomes, lower treatment costs, public health benefits, and increases in the effectiveness of research in pharmacology and other biomedical fields.

Claims

1. A computer-implemented method for identifying lithium phenotypes, the method executed by one or more processors programmed to perform the method, the method comprising:

identifying a plurality of SNPs correlated with lithium phenotypes;

comparing the plurality of SNPs to a database of SNPs to identify additional SNPs that are linked to the plurality of SNPs, wherein the plurality of SNPs and additional SNPs are included in a set of permissive candidate variants;

performing, by one or more processors, a bioinformatics analysis to filter the set of permissive candidate variants into a subset of intermediate candidate variants based on at least one of;

regulatory function, variant dependence, a presence of target gene relationships for the permissive candidate variants, or whether the permissive candidate variants are non-synonymous coding variants with a minor allele frequency; and

performing, by the one or more processors, a pathway analysis to filter the subset of intermediate candidate variants to generate a lithium response pathway associated with SNPs which are causally related to the lithium phenotypes.

2. The method of claim 1, wherein performing a bioinformatics analysis to filter the set of permissive candidate variants into a subset of intermediate candidate variants includes:

evaluating, by the one or more processors, genomic regions around the set of permissive candidate variants for regulatory function.

3. The method of claim 2, wherein genomic regions around the set of permissive candidate variants are evaluated for regulatory function to at least one of:

(1) determine whether sequence contexts for the set of permissive candidate variants influence the regulatory function, or (2) determine target genes for the set of permissive candidate variants.

4. The method of claim 3, wherein target genes for the set of permissive candidate variants are determined using machine learning techniques.

5. The method of claim 1, wherein performing a bioinformatics analysis to filter the set of permissive candidate variants into a subset of intermediate candidate variants includes:

scoring, by the one or more processors, each of the permissive candidate variants based on at least one of;

regulatory function, variant dependence, a presence of target gene relationships for the permissive candidate variants, or whether the permissive candidate variants are non-synonymous coding variants with a minor allele frequency; and

assigning, by the one or more processors, one or more of the permissive candidate variants that are scored above a threshold score to the subset of intermediate candidate variants.

6. The method of claim 1, further comprising:

obtaining a biological sample of a patient;

comparing the biological sample to the SNPs in the lithium response pathway which are causally related to the lithium phenotypes; and

providing, by the one or more processors, an indication of one or more lithium phenotypes for the patient for display to a health care provider based on the comparison.

7. A computing device for identifying lithium phenotypes, the computing device comprising:

a communication network,

one or more processors; and

a non-transitory computer-readable memory coupled to the one or more processors and storing thereon instructions that, when executed by the one or more processors, cause the computing device to;

identify a plurality of single nucleotide polymorphisms (SNPs) correlated with lithium phenotypes;

compare the plurality of SNPs to a database of SNPs to identify additional SNPs that are linked to the plurality of SNPs, wherein the plurality of SNPs and additional SNPs are included in a set of permissive candidate variants;

perform a bioinformatics analysis to filter the set of permissive candidate variants into a subset of intermediate candidate variants based on at least one of;

regulatory function, variant dependence, a presence of target gene relationships for the permissive candidate variants, or whether the permissive candidate variants are non-synonymous coding variants with a minor allele frequency;

perform a network analysis to filter the subset of intermediate candidate variants to generate a lithium response pathway associated with SNPs which are causally related to the lithium phenotypes.

8. The computing device of claim 7, wherein to perform a bioinformatics analysis to filter the set of permissive candidate variants into a subset of intermediate candidate variants, the instructions cause the computing device to:

evaluate genomic regions around the set of permissive candidate variants for regulatory function.

9. The computing device of claim 8, wherein genomic regions around the set of permissive candidate variants are evaluated for regulatory function to at least one of:

(1) determine whether sequence contexts for the set of permissive candidate variants influence the regulatory function, or (2) determine target genes for the set of permissive candidate variants.

10. The computing device of claim 9, wherein target genes for the set of permissive candidate variants are determined using machine learning techniques.

11. The computing device of claim 7, wherein to perform a bioinformatics analysis to filter the set of permissive candidate variants into a subset of intermediate candidate variants, the instructions cause the computing device to:

score each of the permissive candidate variants based on at least one of;

regulatory function, variant dependence, a presence of target gene relationships for the permissive candidate variants, or whether the permissive candidate variants are non-synonymous coding variants with a minor allele frequency; and

assign one or more of the permissive candidate variants that are scored above a threshold score to the subset of intermediate candidate variants.

12. The computing device of claim 7, wherein the instructions further cause the computing device to:

obtain a biological sample of a patient;

compare the biological sample to the SNPs in the lithium response pathway which are causally related to the lithium phenotypes; and

provide an indication of one or more lithium phenotypes for the patient for display to a health care provider based on the comparison.

13. A method for treating a patient comprising:

obtaining a biological sample of a patient;

comparing or having compared the biological sample to a set of single nucleotide polymorphisms (SNPs) within a lithium response pathway having a plurality of SNPs each of which are causally related to lithium phenotypes;

determining a dosage of lithium for administering to the patient based on the comparison; and

administering the determined dosage of lithium to the patient.

14. The method of claim 13, wherein the one or more SNPs in the lithium response pathway are identified by:

identifying a plurality of SNPs correlated with lithium phenotypes;

comparing the plurality of SNPs to a database of SNPs to identify additional SNPs that are linked to the plurality of SNPs, wherein the plurality of SNPs and additional SNPs are included in a set of permissive candidate variants;

performing a bioinformatics analysis to filter the set of permissive candidate variants into a subset of intermediate candidate variants based on at least one of;

regulatory function, variant dependence, a presence of target gene relationships for the permissive candidate variants, or whether the permissive candidate variants are non-synonymous coding variants with a minor allele frequency; and

performing a pathway analysis to filter the subset of intermediate candidate variants into candidate variants of SNPs, genes associated with the SNPs, or pathways associated with the SNPs which are causally related to the lithium phenotypes.

15. The method of claim 13, wherein the lithium phenotypes include at least one of:

a lithium efficacy phenotype, an adverse lithium response phenotype, or a lithium dose phenotype.

16. The method of claim 13, further comprising:

comparing or having compared SNPs in the biological sample to the set of SNPs within the lithium response pathway; and

assigning scores to the SNPs in the biological sample that match with the set of SNPs within the lithium response pathway; and

combining the assigned scores to determine the dosage for administering to the patient.

17. The method of claim 13, wherein the lithium response pathway includes one or more of:

ankyrin 3 (ANK3) gene, aryl hydrocarbon receptor nuclear translocator like (ARNTL) gene, calcium voltage-gated channel auxiliary subunit gamma 2 (CACNG2) gene, calcium voltage-gated channel subunit alpha1 C (CACNA1C) gene, cyclin dependent kinase inhibitor 1A (CDKN1A) gene, cAMP responsive element binding protein 1 (CREB1) gene, glutamate inotropic receptor AMPA type subunit 1 (GRIA2) gene, glycogen synthase kinase 3 beta (GSK3B) gene, nuclear receptor subfamily 1, group D, member 1 (NR1D1) gene, solute carrier family 1 member 2 (SLC1A2) gene, 5-hydroxytryptamine receptor 1A (HTR1A) gene, and TRAF2 and NCK interacting kinase (TNIK) gene.

18. The method of claim 17, wherein SNPs in the lithium response pathway include one or more of:

rs2185502, rs10821792, rs1938540, rs3808943, rs61847646, rs75314561, rs61846516, rs10994397, rs10994318, rs61847579, rs12412727, rs10994308, rs4948418, rs4948412, rs4948413, rs4948416, rs10821745, rs10994336, rs10994360, rs9633532, rs1938526, rs10994322, rs10994321 included in the ANK3 gene;

rs10766075, rs7938308, rs10832017, rs4603287, rs7934154, rs12361893, rs4414197, rs4757140, rs4757141, rs61882122, rs11022755, rs11022754, rs1481892, rs1481891, rs4353253, rs4756764, rs2403662, rs4237700, rs10832018, rs12290622, rs7928655, rs34148132, rs4146388, rs4146387, rs7949336, rs4757139, rs7107287, rs1351525 included in the ARNTL gene;

rs2284017, rs2284016 included in the CACNG2 gene;

rs2007044, rs1016388 included in the CACNA1C gene;

rs3176336, rs3176333, rs3176334, rs3176320, rs4135240, rs2395655, rs733590 included in the CDKN1A gene;

rs10932201 included in the CREB1 gene;

rs78957301 included in the GRIA2 gene;

rs334558 included in the GSK3B gene;

rs2314339 included in the NR1D1 gene;

rs3794088, rs3794087, rs4354668, rs12418812, rs1923294, rs5791047, rs111885243, rs752949, rs16927292 included in the SLC1A2 gene;

rs6449693, rs878567 included in the HTR1A gene; and

rs7372276 included in the TNIK gene.