Prescription decision support system and method using comprehensive multiplex drug monitoring

• US 10,262,112 B2
• Filed: 08/14/2015
• Issued: 04/16/2019
• Est. Priority Date: 02/04/2013
• Status: Active Grant

• Current Assignee: Phenomics Health Inc.
• Original Assignee: Precera Bioscience, Inc. (Phenomics Health Inc.)
• Inventors: Ryan, Timothy P.
• Primary Examiner(s): Augustine, Victoria P
• Assistant Examiner(s): Newton, Chad A
• Application Number:
  US14/827,036
• Publication Number:
  US 20150371003A1
• Time in Patent Office: 1,341 Days
• CPC Class Codes: G06Q 10/10 Office automation; Time management
• CPC Class Codes: G16H 20/10 relating to drugs or medications, e.g. for ensuring correct administration to patients
• CPC Class Codes: G16H 40/67 for remote operation
• CPC Class Codes: G16H 70/40 relating to drugs, e.g. their side effects or intended usage

Abstract

A decision support system and method for optimizing medication dosages includes comprehensive patient databases correlating medications and exposure values across data models having pharmacokinetic and pharmacodynamic dimensions. The system determines distinctions between expected and detected medications and exposure values for a patient, and further distinguishes between non-compliant patients from compliant patients having high metabolizing characteristics with respect to certain expected but undetected medications. The system links to and reconciles the patient medical record accordingly. Graphical user interfaces display recommended dosages and projected exposure ranges for the expected and detected medications. In an embodiment, the interface presents certain alternative treatment options determined as being available with respect to the expected but undetected medications. The user in an embodiment may manipulate or select respective nominal dosage values or alternative treatment options, wherein projected exposure values are dynamically calculated and displayed for each of the medications.

Claims

1. A decision support method for optimizing medication dosages, the method comprising:

measuring for each of a comprehensive plurality of medications from respective body fluid samples for each of a plurality of patients, and identifying actual concomitant medication levels for any one or more actual medications associated with the patients as a subset of the comprehensive plurality of medications;

iteratively developing proprietary drug interaction data in data storage over time, said drug interaction data based only on the identified concomitant medication levels and historical patient data for the respective patients comprising administered dosages of the one or more associated medications;

identifying one or more expected medications and desired exposure values for a first patient of the plurality of patients from at least an associated patient medical record;

determining one or more actual medications and exposure values for the patient from at least multiplex assay results associated with the patient;

with respect to each of one or more distinctions between the expected and actual medications and exposure values, implementing analyses of patient genotype, and of medications and metabolites as determined from the assay results, to distinguish between a non-compliance state for the patient and a compliance state for the patient further in view of high metabolizing characteristics as masking actual exposure values for an associated medication;

predicting potential medication levels corresponding to potential dosages for each identified expected medication and actual medication associated with the first patient based on at least the drug interaction data and the identified concomitant medication levels relative to the corresponding administered dosages;

generating a recommended dosage for each of the one or more identified expected medications and actual medications based on a desired outcome and the respective predicted medication level;

graphically generating a user interactive interface on a display unit comprising the recommended dosage values for each of the one or more identified expected medications and actual medications, and associated projected exposure ranges for each of the displayed medications;

enabling user manipulation of respective dosage values for any one or more of the identified expected and actual medications; and

dynamically calculating and displaying in real time projected exposure values for each of the one or more identified actual and expected medications based on the one or more user manipulated dosage values and on the drug interaction data.

2. The decision support method of claim 1, wherein exposure values are determined for each of the one or more identified expected medications, each of the one or more determined actual medications, and associated exposure ranges, based at least in part on machine learning extrapolation from the comprehensive patient database.

3. The decision support method of claim 2, wherein medication duplicity is determined for a plurality of identified expected medications.

4. The decision support method of claim 2, wherein non-compliance for an expected medication is determined upon failing to detect both parent medication and metabolic breakdown products, further wherein both of said parent medication and metabolic breakdown products are determined as detectable using associated pharmacokinetic properties.

5. The decision support method of claim 2, wherein upon detecting parent medication or metabolic breakdown products only outside of associated pharmacokinetic properties for an expected medication, further triggering presentation of one or more alternative treatment options with respect to the expected medication for display on the interactive user interface.

6. The decision support method of claim 5, further comprising:

enabling user selection of one or more alternative treatment options for any of the expected medications; and

dynamically calculating and displaying projected exposure values for each of the one or more medications based on the selected one or more alternative treatment options.

7. The decision support method of claim 1, wherein the user interactive interface for an exposure range associated with each of the one or more medications comprises a horizontally oriented display including a first value to a second value defining the projected exposure range,

further wherein the enabled user manipulation comprises engaging and horizontally sliding an icon corresponding to a respective dosage value for one of the medications along the respective display.

8. The decision support method of claim 1, wherein the displayed projected exposure values for the one or more medications comprise recommended dosages for each of the respective one or more medications based on a drug interaction output and a measured effectiveness with respect to historical patient data.

9. A server system comprising:

a processor,

data storage comprising

historical patient data for a first patient, comprising one or more expected chemical entities and desired exposure values,

general chemical entity effectiveness data for each of a first plurality of chemical entities, corresponding to different historical outcomes resulting from variable medication levels in patients administered equivalent dosages of a given chemical entity, and

drug interaction data iteratively developed over time from previously integrated data comprising medication levels and corresponding administered dosages, said drug interaction data corresponding to detected interactions for each of the first plurality of chemical entities with respect to one or more concomitantly administered chemical entities, said interactions detected only from the previously integrated data,

a computer-readable medium having program instructions residing thereon, the processor configured to execute the program instructions and to direct the performance of operations further comprising;

measuring each of the first plurality of chemical entities in a multiplex format from a body fluid sample for the first patient and identifying a second plurality of actual chemical entities associated with the first patient as a subset of the first plurality of chemical entities;

identifying actual exposure values for each of the second plurality of measured chemical entities relative to respective target ranges, and relative to corresponding administered dosages retrievable from the historical patient data;

with respect to each of any one or more distinctions between the expected and actual chemical entities and exposure values, implementing analyses of patient genotype, and of medications and metabolites as determined from assay results, to distinguish between a non-compliance state for the patient and a compliance state for the patient further in view of high metabolizing characteristics as masking actual exposure values for an associated chemical entity;

iteratively integrating the identified exposure values and the corresponding administered dosages from the historical patient data to the drug interaction data in the data storage;

predicting exposure values for the first patient corresponding to potential dosages for each of the second plurality of chemical entities based on at least the drug interaction data and the identified exposure values relative to the corresponding administered dosages;

generating a recommended dosage for each of the second plurality of chemical entities based on a desired outcome and the respective predicted exposure value;

generating a user interface accessible via a display unit for a user computing device, the user interface comprising identified exposure values, the respective target ranges, and user-selectable visual elements corresponding to the respective recommended dosages for each of the second plurality of chemical entities associated with the patient;

in response to user selection and manipulation of a visual element for a first chemical entity from a first exposure value to a second exposure value, dynamically recalculating and displaying in real time one or more of the identified exposure value, target range and recommended dosage for each of the second plurality of chemical entities associated with the patient, based at least in part on the drug interaction data in the data storage and the identified or manipulated exposure values for each of the second plurality of chemical entities associated with the patient.

10. The server system of claim 9, wherein concomitant exposure values are used as covariates in generating recommended dosages for each of the second plurality of corresponding chemical entities.

11. The server system of claim 9, wherein non-compliance for an expected chemical entity is determined upon failing to detect both parent chemical entity and metabolic breakdown products, further wherein said chemical entity and metabolic breakdown products are determined as detectable using associated pharmacokinetic properties.

12. The server system of claim 11, wherein upon detecting parent medication or metabolic breakdown products only outside of associated pharmacokinetic properties for an expected chemical entity, further triggering presentation of one or more alternative treatment options with respect to the expected chemical entity for display on the interactive user interface.

13. The server system of claim 9, further comprising:

enabling user selection of one or more alternative treatment options for any of the expected chemical entities; and

dynamically calculating and displaying projected exposure values for each of the one or more chemical entities based on the selected one or more alternative treatment options.

14. The server system of claim 9, wherein the user interactive interface for an exposure range associated with each of the one or more chemical entities comprises a horizontally oriented display including a first value to a second value defining the projected exposure range,

further wherein the enabled user manipulation comprises engaging and horizontally sliding an icon corresponding to a respective dosage value for one of the chemical entities along the respective display.

15. The server system of claim 9, wherein the displayed projected exposure values for the one or more chemical entities comprise recommended dosages for each of the respective one or more chemical entities based on a drug interaction output and a measured effectiveness with respect to historical patient data.