Genetic data analysis and database tools

• US 8,676,608 B2
• Filed: 10/08/2012
• Issued: 03/18/2014
• Est. Priority Date: 02/14/2007
• Status: Active Grant

• Current Assignee: Youscript Technologies LLC
• Original Assignee: Genelex Corporation
• Inventors: Oesterheld, Jessica, Patterson, Robert D, Coleman, Howard C
• Primary Examiner(s): Sereboff, Neal
• Assistant Examiner(s): Reyes, Reginald R
• Application Number:
  US13/647,246
• Publication Number:
  US 20130066649A1
• Time in Patent Office: 526 Days
• Field of Search: 705/2
• US Class Current: 705/3
• CPC Class Codes: G06Q 10/10 Office automation; Time management
• CPC Class Codes: G06Q 30/04 Billing or invoicing
• CPC Class Codes: G06Q 50/22 Social work or social welfare, e.g. community support activities or counselling services
• 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 50/00 ICT programming tools or database systems specially adapted for bioinformatics
• CPC Class Codes: G16B 50/30 Data warehousing; Computing architectures
• CPC Class Codes: G16C 20/30 Prediction of properties of chemical compounds, compositions or mixtures
• CPC Class Codes: G16C 20/50 Molecular design, e.g. of drugs
• CPC Class Codes: G16C 20/90 Programming languages; Computing architectures; Database systems; Data warehousing
• CPC Class Codes: G16H 20/10 relating to drugs or medications, e.g. for ensuring correct administration to patients
• CPC Class Codes: G16H 70/40 relating to drugs, e.g. their side effects or intended usage

Abstract

A computerized tool and method for delivery of pharmacogenetic and pharmacological information, comprising a core system having algorithms and databases for storing, collating, accessing, cross-referencing, and interpreting genetic and pharmacologic data, with a graphical user interface for a client network of providers of laboratory genetic testing services to access the core services under contract. The system includes “paypoints” in support of improved business models. Included are mechanisms for ‘pass through’ third party and insurance reimbursement for interpretive reports, insurance reimbursement for on-line access to pharmacogenetic information at the point of care, tools for market segmentation, and a conversion tool for capturing new subscribers. Also disclosed are tools and predictive algorithms for preventing drug-drug and drug-gene adverse drug reactions.

Claims

1. A method for warning of an adverse drug reaction or substance-factor interaction in a patient, which comprises:

a) providing a computing machine, said computing machine in electronic communication with at least one computer-readable database and at least one user interface, wherein said computing machine is programmed to perform a predictive calculation, said calculation comprising;

i) selecting culprits from a list of patient-specific factors stored in said at least one database, said culprits comprising prescription drug usage, substance usage, personal characteristic, phenotype, genotype, clinical factors, or combinations thereof, where a culprit is a factor having the property of being an inhibitor or an inducer, said culprits each having an intensity index INTX, where intensity index INTX indicates relative strength of inhibition or induction by each culprit;

ii) selecting a sublist of victims from the list of patient specific factors, where a victim is a factor having the property of being a metabolic substrate of one or more metabolic routes;

iii) identifying metabolic mutes associated with said sublist of victims;

iv) identifying interaction pairs associated with said metabolic routes, each interaction pair defining a victim and a culprit having a known metabolic interaction on at least one metabolic route of the metabolic routes;

v) for each victim of each interaction pair;

calculating a CP score by multiplying the intensity index INTX associated with the culprit times a metabolic throughput proportion calculated as the metabolic throughput of each said metabolic route divided by a sum of the throughput of all metabolic pathways acting on the victim in parallel;

vi) summing the CP scores for each victim and for each interacting pair, and tabulating the sums Σ

CP;

vii) from the sums Σ

CP, calculating a predicted percent change AUC for each victim of each interacting pair; and

,

b) displaying a report on said at least one user interface, said report including a patient identifier, and if said percent change AUC for an interaction pair exceeds a threshold level, providing a warning of a potential patient-specific adverse reaction.

2. The method of claim 1, further comprising writing or modifying a prescription based on said report.

3. The method of claim 1, wherein said list of patient-specific factors are enterable into a graphical user interface in digital communication with said at least one database of said computing machine and said report is displayed on said graphical user interface or on a network compatible device in digital communication with said computing machine.

4. The method of claim 1, wherein said computing machine is digitally linked to a computerized network, said computerized network comprising a database having electronic medical records.

5. The method of claim 1, comprising entering a change to or an addition of a drug to said list of patient-specific factors and displaying an updated report.

6. The method of claim 5, wherein said updated report includes an updated warning if said change to or an addition of a drug to said list of patient factors results in a percent change AUC that exceeds a threshold level.

7. The method of claim 5, further comprising writing or modifying a prescription based on said updated report.

8. The method of claim 1, wherein said metabolic interaction comprises a drug-gene interaction, a drug-drug interaction, or a combination thereof.

9. The method of claim 1, wherein said patient factors comprise one or more drugs in a regimen, and said report is displayed to a physician prescribing said regimen or to a patient receiving said regimen.

10. The method of claim 1, wherein said patient factors comprise one or more patient phenotypes or genotypes.

11. The method of claim 1, wherein said calculation comprises:

a) for each said interaction pair, identifying any record(s) accessible to said computing machine of published reports of interactions between said victim and said culprit of said interaction pair;

b) if such said record(s) are identified, displaying in said report at least one note or warning abstracted from said record(s); and

c) even if no such said record(s) are identified, but said predicted percent change AUC exceeds a threshold value;

reporting a warning.

12. The method of claim 1, wherein said calculations comprises:

a) for a prescription drug or substance included in said patient factors, identifying a class membership, wherein said class membership is defined by a side effect produced by all members of the class;

b) for any class membership identified therein, identifying any subgroup of factors from the list having said class membership in common; and

,

c) reporting said subgroup of factors having said class membership in common, with a note advising that said side effect(s) can be additive.

13. The method of claim 1, wherein said calculations comprise:

a) for a potential interaction pair for which said percent change AUC exceeds a threshold value, identifying a therapeutic class associated with said victim of said interaction pair;

b) identifying an alternate member of said therapeutic class and calculating an alternate percent change AUC for the alternate member; and

,

c) reporting the alternate member as a therapeutic alternative if the alternate percent change AUC does not exceed said threshold value.

14. The method of claim 1, wherein said calculations comprise:

a) for a potential interaction pair for which said percent change AUC exceeds a threshold value, identifying a therapeutic class associated with said culprit of said interaction pair;

b) identifying an alternative member of said therapeutic class associated with an alternate metabolic route and calculating an alternate percent change AUC for the alternate member; and

,

c) reporting the alternative member as a therapeutic alternative if the alternate percent change AUC does not exceed said threshold value.

15. The method of claim 1, wherein said intensity index INTX is associated with an inhibition constant K_i.

16. The method of claim 1, wherein said intensity index INTX is associated with a metabolic phenotype.