METHOD AND SYSTEM FOR THE ANALYSIS AND ASSOCIATION OF PATIENT-SPECIFIC AND POPULATION-BASED GENOMIC DATA WITH DRUG SAFETY ADVERSE EVENT DATA

• US 20090076847A1
• Filed: 11/14/2008
• Published: 03/19/2009
• Est. Priority Date: 08/29/2001
• Status: Abandoned Application

• Current Assignee: Druglogic Incorporated
• Original Assignee: Druglogic Incorporated
• Inventors: GOGOLAK, Victor
• Application Number:
  US12/271,224
• Publication Number:
  US 20090076847A1
• Time in Patent Office: Days
• US Class Current: 705/2
• CPC Class Codes: G06Q 10/0637 Strategic management or analysis, e.g. setting a goal or target of an organisation; Planning actions based on goals; Analysis or evaluation of effectiveness of goals
• 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 20/40 Population genetics; Linkage disequilibrium
• 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 50/00 ICT programming tools or database systems specially adapted for bioinformatics
• CPC Class Codes: G16H 10/20 for electronic clinical trials or questionnaires
• CPC Class Codes: G16H 50/70 for mining of medical data, e.g. analysing previous cases of other patients
• CPC Class Codes: G16H 70/40 relating to drugs, e.g. their side effects or intended usage
• CPC Class Codes: Y10S 707/99942 Manipulating data structure, e.g. compression, compaction, compilation
• CPC Class Codes: Y10S 707/99945 Object-oriented database structure processing

Abstract

A method for assessing and analyzing one or more drugs, adverse effects and associated risks, and patient characteristics resulting from the use of at least drug of interest is disclosed. The method comprises the steps of selecting one or more cases for analysis, said cases describing the behavior between at least one drug of interest and a patient genotype; profiling statistically derived values from multiple cases related to the safety of the at least one drug, wherein at least one filter is employed for deriving said values; at least one data mining engine; and an output device for displaying the analytic results from the data mining engine. A system for performing the method is likewise disclosed.

Claims

1. A method for assessing and analyzing one or more drugs, adverse effects and associated risks, and patient demographics resulting from the use of at least drug of interest, comprising the steps of:

(a) collecting data from a plurality of sources comprising drug information, adverse effects relating to drugs and patient demographics;

(b) generating a relational database for relating drug information, adverse effects and patient demographics.

(c) selecting at least one case for analysis, the at least one case describing the behavior between at least one drug of interest and a patient genotype;

(d) profiling statistically derived values from multiple cases related to the safety of the at least one drug, wherein at least one filter is employed for deriving the values;

(e) submitting the values to at least one data mining engine; and

(f) displaying the analytic results from the data mining engine through an output device.

2. The method of claim 1, wherein analyzing using a data mining engine comprises correlating or proportionally comparing any two data types, wherein the data types are drug, adverse effects, or patient demographics.

3. The method of claim 1, wherein the data is cleaned, wherein cleaning comprises removal of noise, spell checking, and removal of redundant entries.

4. The method of claim 1, wherein the relational database comprises stored data which is mapped to tokens, wherein a token comprises a standardized search term.

5. The method of claim 4 wherein a token is selected from the group of reference sources consisting of MEDRA, WHO Drug Directories, FDA Orange Book, COSTART, NDCD, GPRD and WHOART.

6. The method of claim 1, wherein steps (a)-(f) are performed in a network environment.

7. The method of claim 1, wherein the demographics are age, sex, weight, diet, reactions, environment, illness, dosage, genotype, outcome, report source or concomitant drugs.

8. The method of claim 1, wherein the data mining engine is a correlator, a proportional analysis engine, or a comparator.

9. A system for assessing and analyzing one or more drugs, adverse effects and associated risks, and patient demographics resulting from the use of at least one drug of interest, comprising:

(a) a selector for selecting one or more cases for analysis, the cases describing the behavior between the at least one drug of interest and a patient genotype;

(b) a profiler profiling statistically derived values from multiple cases related to the safety of the at least one drug, wherein at least one filter is employed for deriving said values;

(c) at least one data mining engine for submitting the values to; and

(d) an output device for displaying the analytic results from the data mining engine.

10. The method of claim 9, wherein analyzing using a data mining engine comprises correlating or proportionally comparing any two data types, wherein the data types are drug, adverse effects, or patient demographics.

11. The method of claim 9, wherein the data is cleaned, wherein cleaning comprises removal of noise, spell checking, and removal of redundant entries.

12. The method of claim 9, wherein the relational database comprises stored data which is mapped to tokens, wherein a token comprises a standardized search term.

13. The method of claim 9 wherein a token is selected from the group of reference sources consisting of MEDRA, WHO Drug Directories, FDA Orange Book, COSTART, NDCD, GPRD and WHOART.

14. The method of claim 9, wherein steps (a)-(d) are performed in a network environment.

15. The method of claim 9, wherein the demographics are age, sex, weight, diet, reactions, environment, illness, dosage, genotype, outcome, report source or concomitant drugs.

16. The method of claim 9, wherein the data mining engine is a correlator, a proportional analysis engine, or a comparator.