U.S. patent application number 13/316354 was filed with the patent office on 2012-06-07 for genetic data analysis and database tools.
This patent application is currently assigned to GENELEX, INC. Invention is credited to Howard Coleman, Jessica Oesterheld, Robert Patterson.
Application Number | 20120143622 13/316354 |
Document ID | / |
Family ID | 40524043 |
Filed Date | 2012-06-07 |
United States Patent
Application |
20120143622 |
Kind Code |
A1 |
Oesterheld; Jessica ; et
al. |
June 7, 2012 |
Genetic Data Analysis and Database Tools
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.
Inventors: |
Oesterheld; Jessica; (Bath,
ME) ; Patterson; Robert; (Lexington, MA) ;
Coleman; Howard; (Seattle, WA) |
Assignee: |
GENELEX, INC
Seattle
WA
|
Family ID: |
40524043 |
Appl. No.: |
13/316354 |
Filed: |
December 9, 2011 |
Related U.S. Patent Documents
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Application
Number |
Filing Date |
Patent Number |
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12031327 |
Feb 14, 2008 |
8099298 |
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13316354 |
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61026724 |
Feb 6, 2008 |
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60901528 |
Feb 14, 2007 |
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Current U.S.
Class: |
705/2 |
Current CPC
Class: |
G16B 50/00 20190201;
G06Q 30/04 20130101; G16H 70/40 20180101; G06Q 10/10 20130101; G16B
20/00 20190201; G16C 20/30 20190201; G16C 20/50 20190201; G16C
20/90 20190201; G16H 20/10 20180101 |
Class at
Publication: |
705/2 |
International
Class: |
G06Q 50/22 20120101
G06Q050/22 |
Claims
1-11. (canceled)
12. A method for preventing an adverse drug event, which comprises:
a) entering a patient identifier on a graphical user interface; b)
entering, retrieving or modifying a patient treatment regimen,
wherein said patient treatment regimen comprises a list of any
drugs, substances, genotypes, phenotypes, or clinical factors to be
associated with said patient identifier; c) identifying a
victim-culprit interaction that defines a first interaction pair on
said list, wherein said first interaction pair comprises a first
victim and a first culprit which interact by at least one metabolic
or transport pathway to produce a change in activity of said
victim, and predicting a level of severity of the victim-culprit
interaction according to an algorithm for calculating a change in
patient exposure to said first victim; and, d) graphically
displaying a warning of the victim-culprit interaction and level of
severity thereof, if any, on said graphical user interface so that
said patient treatment regimen may be modified if needed to prevent
an adverse drug event.
13. The method of claim 12, wherein said adverse drug event is
associated with a drug:drug interaction and said culprit is a drug
from said list.
14. The method of claim 12, wherein said adverse drug event is
associated with a drug:substance interaction and said culprit is a
substance from said list.
15. The method of claim 12, wherein said adverse drug event is
associated with a drug:genotype or drug:phenotype interaction and
said culprit is a genotype or a phenotype, and wherein said
genotype or phenotype is determined from genetic testing and is
associated with said patient identifier.
16. The method of claim 12, wherein said adverse drug event is
associated with a substance:factor interaction and said culprit is
a clinical factor from said list.
17. The method of claim 12, wherein said change in patient exposure
is calculated by multiplying an intensity factor (INTX) associated
with the intensity of the interaction between said first culprit
and said first victim times a fraction (R.sub.1/1-n) indicative of
the fraction of the metabolism or transport modulated by said first
culprit.
18. The method of claim 12, which further comprises predicting from
said list a plurality of victim-culprit interactions that define a
plurality of interaction pairs, wherein each said interaction pair
comprises a victim and a culprit which interact by at least one
metabolic or transport pathway to produce a change in patient
exposure to the victim, wherein said prediction is made by an
algorithm for calculating a change in patient exposure to an
individual victim by summing the changes for all interaction pairs
involving said individual victim, and further comprising
graphically displaying a warning or warnings for the predicted
victim-culprit interaction or interactions and level of severity
thereof on said graphical user interface so that said patient
treatment regimen may be modified if needed to prevent an adverse
drug event.
19. The method of claim 18, wherein the change in patient exposure
for any one victim having a plurality of interaction pairs
associated with a plurality of culprits acting thereon is obtained
by multiplying an intensity factor (INTX) associated with the
intensity of the interaction between said any one victim and each
culprit times a fraction (R.sub.1/1-n) of the metabolism or
transport of the any one victim modulated by each culprit, and then
summing the changes for said plurality of interaction pairs.
20. The method of claim 18, wherein said plurality of
victim-culprit interactions include drug:drug interaction,
drug:substance interaction, drug:gene interaction, drug:phenotype
interaction, substance:factor interaction, or a combination
thereof
21. The method of claim 18, wherein said plurality of
victim-culprit interactions include culprits selected from food
substance, herbal substance, over the counter medication,
recreational drug use, alcohol use, history of smoking, obesity,
prescription drug use, genetic polymorphism, genetic sequence,
phenotype, pregnancy, gender and age, or a combination thereof.
22. The method of claim 18, wherein said plurality of
victim-culprit interactions are selected from inhibition,
inhibition of metabolism, inhibition of transport, induction,
induction of metabolism, induction of transport, or a combination
thereof.
23. The method of claim 19, wherein said plurality of
victim-culprit interactions include culprits involving a Cytochrome
P450-mediated reaction, a Phase I reaction, a Phase II reaction, an
oxidation, a reduction, a hydrolysis reaction, a cyclization
reaction, a decyclization reaction, a conjugation reaction, a
transporter uptake, an organ specific transporter uptake, a
transporter efflux, an elimination reaction, or a combination
thereof.
24. The method of claim 23, wherein the fraction (R.sub.1/1-n) of
metabolism or transport by each of a plurality of metabolic or
transport pathways acting on each victim is the fraction of
metabolic or transport throughput occurring by each of said
plurality of metabolic pathways divided by the total metabolic or
transport throughput occurring by all parallel pathways
(R.sub.1-n).
25. The method claim 12, wherein said method is implemented on a
computerized network, said network comprising a computer or server
having a first software engine in digital communication with said
graphical user interface, at least one database, and
computer-executable instructions for implementing said predictive
algorithm when performing operations on said list.
26. The method of claim 18, wherein said method is implemented on a
computerized network, said network comprising a computer or server
having a first software engine in digital communication with said
graphical user interface, at least one database, and
computer-executable instructions for implementing said predictive
algorithm when performing operations on said list.
27. The method of claim 12, further comprising a) for any
interaction pair for which said change in exposure for said first
victim exceeds a threshold level associated with a potential
adverse drug event, identifying a therapeutic class associated with
said first victim and a therapeutic class associated with said
first culprit; b) identifying at least one alternative member for
each said therapeutic class and calculating a change in patient
exposure thereto; and, c) if said alternative member does not cause
a change in patient exposure exceeding a threshold level associated
with a potential adverse drug event, displaying the alternative
member, whereby said alternative member may be substituted in said
proposed patient treatment regimen by a prescriber so that an
adverse drug event is prevented.
28. The method of claim 27, comprising identifying said at least
one alternative member of said therapeutic class from a
reimbursement eligibility list or formulary list associated with
said patient identifier.
29. The method of claim 18, further comprising a) for any drug or
substance on said list, identifying a class membership, wherein
said class membership is defined by a side effect associated with
the class; b) for any class membership identified therein,
identifying any plurality of drugs or substances from the list
sharing said class membership in common and forming a sublist
thereof; and, c) displaying said sublist of factors sharing said
class membership in common, with a note advising that said side
effect can be additive.
30. The method of claim 12, further comprising a) for any first
drug member on said list which is a member of a therapeutic class
but does not meet reimbursement eligibility requirements,
identifying a substitute member of said therapeutic class having
reimbursement eligibility; b) if the substitute member having
reimbursement eligibility does not cause a change in patient
exposure exceeding a threshold level associated with a potential
adverse drug event, then displaying the substitute member.
31. A method for warning of a potential adverse drug event
according to level of severity, which comprises: a) entering a
patient identifier on a graphical user interface; b) entering,
retrieving or modifying a patient treatment regimen, wherein said
patient treatment regimen comprises a list of any drugs,
substances, genotypes, phenotypes, or clinical factors associated
with said patient identifier; c) identifying a victim-culprit
interaction that defines a first interaction pair from said list,
wherein said first interaction pair comprises one victim and one
culprit which interact to produce a change in activity of the
victim of the pair; d) predicting a level of severity of the
victim-culprit interaction according to an algorithm for
calculating a change in patient exposure to the victim; e)
searching said database for any record of a clinical report of a
documented interaction between said victim and said culprit and
ranking said documented interaction according to level of severity;
and, f) for any interaction pair having either a predicted level of
severity or a documented level of severity that is clinically
significant on said graphical user interface, graphically
displaying a warning indicating said level of severity; g) updating
said warning if said list is modified; and further wherein said
method is implemented on a computerized network, said network
comprising a computer or server having a first software engine in
digital communication with said graphical user interface, at least
one database, and computer-executable instructions for implementing
said method when performing operations on said list.
32. The method of claim 31, wherein said change in patient exposure
is calculated by multiplying an intensity factor (INTX) associated
with the intensity of the interaction between said first culprit
and said first victim times a fraction (R.sub.1/1-n) indicative of
the fraction of the metabolism or transport modulated by said first
culprit.
33. The method of claim 31, wherein said method is repeated for all
interaction pairs on said list, whereby a plurality of warnings may
be caused to be displayed.
34. The method of claim 33, wherein the change in patient exposure
for any one victim having a plurality of interaction pairs
associated with a plurality of culprits acting thereon is obtained
by multiplying an intensity factor (INTX) associated with the
intensity of the interaction between said any one victim and each
culprit times a fraction (R.sub.1/1-n) of the metabolism or
transport of the any one victim modulated by each culprit, and then
summing the changes for said plurality of interaction pairs.
35. The method of claim 33, which further comprises: for each
interaction pair, searching said database for any note or notes of
clinical relevance and displaying said note or notes according to a
hierarchical listing of potential importance.
36. The method of claim 35, comprising displaying any note or notes
for an interaction pair associated with an additive side effect or
drug:drug interaction not directly linked to a genetic
polymorphism.
37. The method of claim 36, wherein said interaction pair comprises
a drug pair causing QT/QTc prolongation or slowing the heart
rate.
38. The method of claim 35, wherein said warnings and notes are
displayed according to the following priority ranking: i) a major
interaction documented in a clinical report; ii) a minor
interaction documented in a clinical report; iii) a major
interaction predicted by said algorithm; and, iv) a lesser
interaction predicted by said algorithm.
39. A method for analyzing an adverse drug reaction having a
pharmacogenetic basis, which comprises: a) entering a patient
identifier and a patient treatment regimen on a graphical user
interface, said patient treatment regimen comprising a first
interaction pair, wherein said first interaction pair is defined by
a victim and a culprit which interact to produce a change in
metabolism of said victim by at least one metabolic pathway; b)
entering a postulated phenotype associated with an adverse reaction
involving said interaction pair; c) calculating a level of severity
of the interaction therebetween according to a predictive algorithm
for calculating a change point score (CP) for said victim, wherein
said predictive algorithm is implemented on a computerized network,
said network comprising a first software engine, at least one
database, and computer-executable instructions for implementing
said algorithm when provided with said at least one said
interaction pair, said network having a digital connection to said
user interface; d) displaying said predicted level of severity on
said graphical user interface; and, e) if said predicted
interaction and predicted level of severity of the interaction
correlates with the clinical features of an adverse drug reaction
having a pharmacogenetic basis, then ordering a genetic test that
is confirmatory for the postulated phenotype.
40. The method of claim 39, which further comprises displaying a
hyperlink for ordering said confirmatory genetic test from a
preferred supplier of genetic testing.
Description
CROSS-REFERENCE TO RELATED APPLICATIONS
[0001] This application claims priority to U.S. patent application
Ser. No. 12/031,327, now U.S. Pat. No.______, filed 14 Feb. 2008,
which claims priority to provisional patent application Ser. No.
61/026,724, filed 6 Feb. 2008 and to provisional patent application
Ser. No. 60/901,528, filed 14 Feb. 2007, from which priority is
claimed under 35 USC 119(e); said priority patent documents are
hereby incorporated in full by reference.
BACKGROUND
[0002] Traditionally, physicians have been expected to retain in
memory knowledge relating to potential adverse drug reactions,
pharmacology, and pharmacogenetics, or to have access to such
information from published (generally hard-copy)
reports--information that is not accessible from a single source
and which is increasingly complex. More recently, some classes of
information, for example labeling warnings published in the PDR,
have become accessible through wireless and PDA devices. There is
increasing interest in expanding the availability of this kind of
information at the point of care.
[0003] A basic problem with all such information, however, is the
need for computer systems, databases, networks, and software tools
to display and bring to the foreground the information most
relevant to the issue at hand, a problem that requires extensive
software development. In short, it is no longer sufficient to
merely publish information in the form of a text book or a
reference manual. But because of the difficulty in obtaining
reimbursement for the costs of the software services and databases,
progress toward sustainable software innovation and deployment has
been disappointing. Thus, there is a need in the medical arts for
business models to support computerized implementation of systems
designed to store and process metabolic, pharmacologic, and
pharmacogenetic data (herein "metabolomic data"), to interpret that
data in the context of patient-specific factors such as age,
pregnancy, smoking and use of alcohol (herein "clinical factors",
or "patient characteristics"), to make available that data at the
point of care, prioritized by relevance, and to provide integrated
reimbursement tools for the costs of the equipment, database
updates and maintenance. Needed are business models that support
implementations of these computerized tools.
[0004] Mental Health Connections (Lexington, Mass.) was an early
entrant into computerized medical bioinformatics services. Their
GeneMedRx service, introduced in 1995, was initially based on
computerized tables for looking up drug interactions as a function
of induction or inhibition of the cytochrome P450s involved in
their metabolism. In 2006, in partnership with Genelex (Seattle
Wash.) testing was begun on systems for interpretation of drug-drug
and drug-gene interactions within the framework of a patient's
overall medication regimen. In recent versions, GeneMedRx has grown
as a database and now recognizes transporter and conjugation-linked
as well as cytochrome P450-linked drug interactions. The drug
interaction service now also includes a novel predictive algorithm.
These efforts have provided valuable lessons in the need for
improved business models to successfully commercialize various
aspects of bioinformatics.
[0005] Marchand (USPA 2006/0289019) describes computer systems for
optimizing medical treatment based on pharmacogenetic testing and
Pareto modelling. But the disclosure is silent as to how to pay for
these systems. Pickar (USPA 2003/0104453) describes computer
systems for minimizing adverse drug events but is silent with
respect to means to recover the costs. Hoffman (USPA 2004/0197813
and USPA 2004/0199333) describes a method for determining whether
an atypical response to drug therapy is attributable to an error in
metabolism but again does not describe a business method. Early
work describing the application of computers to pharmacogenetics is
described in a 1999 paper by Evans and Relling (Science
286:487-491), a 2000 paper by Ichikawa (Internal Medicine
39:523-24), and in a patent application that same year by Reinhoff
(U.S. 2002/0049772). Reinhoff describes a computer implementation
of a program on a networked computer for analyzing polymorphisms in
human populations and using this information to, for example,
"gauge drug responses", but these citations again do not address
reimbursement concerns.
[0006] Although Gill-Garrison in U.S. Pat. No. 7,054,758 describes
computer preparation and delivery of genetic reports that include
"personalized dietary advice", the report service as commercialized
(Sciona, Boulder Colo.) is limited to direct marketing to consumers
by the testing laboratory under `fee-for-service` arrangements and
does not contemplate methods for billing such as `pass-through`
reimbursement models or wholesale services to contract laboratories
or clinics. Nor does the service quantitate or extrapolate the
effects of impacting substances or factors (as practiced and
defined here) on the pharmacokinetics of drug metabolism, for
preparation of reports relying instead on a simple look-up table or
tables to correlate "advice" with "risk factor" and genetic
polymorphism.
[0007] Holden (USPA 2004/0088191) addresses the issue of secure
access to genetic test results over a network and the use of
passwords to share genetic test data with third parties such as
physicians. Dodds (USPA 2003/0135096) again recognizes the security
issues, but sees that secure access can be linked to payment
authorization in a simple fee-for-service model with on-line
authorization of credit card purchases. Issued patent U.S. Pat. No.
7,054,755 also proposes prior art financial service means,
specifically means to purchase genetic testing kits electronically,
in what is basically a shopping cart model such as might have been
assembled from the teachings of U.S. Pat. No. 5,960,411, the
"one-click" patent to Amazon.com, and related arts.
[0008] However, an invitation to the customer to pay directly for
preventative medical care, for example genetic testing, has not
been generally appealing or successful. More typically, customers
will habitually defer the costs of preventative medicine. Thus,
whereas Larder in U.S. Pat. No. 7,058,616 states, "The main
challenge in genotyping is the interpretation of the results" (Col
9, lines 27-28), to the contrary we have found that the main
challenge is supporting the costs of the required servers,
databases, networks and programming. A particularly preferred
model, as disclosed here, eliminates the need for mental processes
in operation of the system. Genetic testing services are thus still
in need of improved business models built on automated systems,
business models capable of generating sufficient revenue to support
their development and implementation at the point of care.
SUMMARY
[0009] Significant efficiencies in patient care are anticipated
from computerization of medical and genetic data related to drug
metabolism, herein "metabolomics". Metabolomics includes not only
drug-drug and drug-allele interactions, but drug interactions
precipitated by foods, over-the-counter medicines, herbal
preparations, or clinical factors such as age, pregnancy, smoking,
alcohol use, liver disease, and so forth.
[0010] As an example of potential medical benefits and cost
savings, consider the potential savings and reduced mortality and
morbidity by preventing adverse drug reactions (ADRs) to prescribed
drugs. According to the FDA, it is estimated that "there are more
than 2,216,000 serious ADRs in hospitalized patients, causing over
106,000 deaths annually. If true, then ADRs are the 4th leading
cause of death--ahead of pulmonary disease, diabetes, AIDS,
pneumonia, accidents, and automobile deaths." In another study,
"The total cost for patients with an ADR increased an average of
$2401/patient (19.86% increase), . . . . Extrapolating this finding
to the entire Medicare population resulted in $516,034,829 in costs
associated with ADRs" (Bond C A et al. 2006. Adverse Drug Reactions
in United States Hospitals. Pharmacotherapy 26:601-608). Also to be
considered are the cost of treatment failures resulting from
ADRs.
[0011] ADRs have many causes, and one of the most important and
hardest to predict, but also most preventable cause, is
interactions between drugs, herbals, foods (generally,
"substances") and individual genotype. Another important class of
these interactions are drug-drug interactions (DDIs).
[0012] In our invention, predictive algorithms are provided that
can prevent many ADRs by issuing warnings on a graphical user
interface at the point of care before the prescription is written.
Graphical user interfaces for querying hierarchical databases are
gateways for transforming raw data into customized reports or
"views" relevant in real time to weighing the risks and benefits of
therapeutic options. Each graphical user interface (GUI) also
serves as a "paypoint" for automated management of
reimbursement.
[0013] The novel PK predictive algorithms disclosed here have been
found to be surprisingly effective in predicting drug interactions
of the types associated with ADRs, and hence contributes to their
prevention. Our PK predictive algorithms provide a way to make
quantitative predictions of metabolism-based interactions among
substances for which there are metabolic data but not clinical
studies. The absence of clinical studies is a serious issue; as
there are thousands of drugs and other substances the paired
interaction of which have not been studied. Rarely, clinical
studies report on simultaneous interactions among multiple
substances. Far more data is available on the metabolism of drugs
and substances, mostly in the form of pharmacokinetic (PK) data,
and it is this information that is used to make drug interaction
predictions by the algorithms of the present invention.
[0014] The algorithms can also runs a comparative subroutine, in
which known clinical studies of drug and substance interactions are
tabulated so that the quantitative predictions of the algorithm can
be compared against published results, thus validating the
performance of the algorithms. The supporting databases are
frequently updated to extend the scope and power of the PK
predictive algorithms.
[0015] One such PK predictive algorithm described here is a
multifactorial algorithm capable of predicting drug-drug,
drug-substance, drug-gene, substance-gene, drug-clinical factor,
substance-clinical factor, and multiple complex interactions, many
of which have been associated with adverse drug interactions. While
in the text there are frequent references to `drug-drug` and
`drug-gene` interactions, these should be interpreted broadly to
include drug-factor, substance-factor, gene-factor, and clinical
factor-factor (or "patient characteristic"-factor) interactions.
The predictive algorithms have been shown to be capable of
processing superimposed interactions among multiple factors.
[0016] Tools and methods for production, processing and delivery of
metabolomic and pharmacogenetic interpretive information are also
disclosed, comprising a digital, computer-implemented system having
algorithms and databases for storing, collating, accessing,
cross-referencing, and interpreting genetic and pharmacologic data,
and a network or networks whereby contracting client laboratory
providers of genetic testing services, and other customers, can
access the core host servers. The Host System includes interactive
software engines (the "Medical Metabolomics Engine" and the "Lab
Report Engine") that support an improved business model for genetic
testing, test interpretation, test reporting, and assist in
prevention of ADRs.
[0017] The Host System is configured for preparation of two kinds
of reports: The first report type (Type I) is used by laboratories
(with access to the Host System under contract) to report genetic
test data to their customers. It provides a formatted test report
containing patient's genotype, diagnosis of the resulting
phenotype, and drug-gene interaction information--detailed lists of
drugs for which drug metabolism is impacted by the phenotype, for
example. It is generated by the Host System's Lab Report Engine,
but optionally may be formatted with the logo and look of the
reporting contract laboratory. The second report type (Type II) is
generated, for example, when the patient or an authorized user of
report Type I logs onto the central platform directly and enters
added confidential medical information such as drugs currently
taken, herbal usage, certain foods in the diet, and clinical or
"patient characteristic" factors such as smoking or pregnancy. The
Host System includes highly interactive GUIs with tools to select
and display views of the most contextually relevant analysis of
drug-drug and drug-gene metabolic interactions based on
patient-specific information inputted by the user, who may be the
patient or a health care provider. The Type II report is generated
on the fly in response to the patient entries, and is a fully
interactive webpage with multilevel displays, including for
example: ranked warnings on possible drug or herbal interactions
specific to the patient's drug regime or proposed prescription use,
suggestions for alternative drugs in the same therapeutic class,
annotations with links to the medical literature, recommendations
for added genetic testing, and so forth.
[0018] Both the Type I and Type II interaction reports thus use a
PK predictive algorithm. The Type I report includes a drug-gene
interaction report for drugs selected by the host system. The Type
II report can include drug-drug interaction reports, where the
drugs are selected by the user based on current medications. The
Type II report is thus a personalized tool for use in managing
medications. The predictive algorithms used in the two report types
are thus modified for the purpose to which they are employed, and
can be modified further for use with other interacting factors.
[0019] The two report types are presented on different GUIs. In the
current embodiment, the Type I report is presented by either of two
GUIs built into the Lab Report Engine. The Type II report is
presented by a GUI specific for the Medical Metabolomics Engine, as
will be explained further.
[0020] Both Type I and Type II report interfaces are accessible in
real time on any network, including the world wide web, including
wireless telephones and PDAs, or on an intranet or wireless
intranet network. "Informational transaction" or "data exchange"
events can be captured for billing purposes as by credit card,
subscription, direct billing, online debiting, or third party
billing. In this way, a long-sought need is at last met for a
system that provides flexible billing tools in support of covering
the costs of the information technology support required for
widespread genetic testing and use of pharmacogenetic interpretive
services.
[0021] There are differences in how the two report types are
reimbursed. The Type I report, containing a genetic test result and
diagnosis of a phenotype, is typically generated by the Host System
at the request of an outside laboratory accessing the Lab Report
Engine under subscription or contract, and can be billed by the
laboratory to a third party payer such as an insurance company
under "current procedural terminology (CPT codes) codes", or other
reimbursement codes, from which the costs of maintaining and
updating the core servers and databases can be paid to the Host
System operator. These fees can also be paid by credit card
directly by the patient receiving the report but this has been
shown not to be a preferred method. Automated fees for the Type II
report are established for different market segments, including
free trial access, monthly or yearly subscription access,
"pay-per-ping" access, wholesale access, and in a preferred
embodiment, third party `pass through` billing by use of the
appropriate reimbursement codes (such as when the service is used
by physicians during office visits), and so forth. Use of pass
through billing, which frees the patient from the cost of the
service, also frees the physician or health care provider to make
greater use of the service.
[0022] The Type I report is automatically updated by the predictive
algorithm each time it is accessed on-line. In an improved
reimbursement model, the Type I report contains interactive links
and security access codes so that the recipient can access the Type
II report service, thus enabling the Host Operator to convert
client laboratory customers to direct-service customers.
[0023] A "sponsored-use hyperlink" embedded in a Type I report in
the above method has the property that when securely accessed by
entering the patient's identifiers and access code from a remote
terminal, such as at a doctor's office or a home, a Type II report
is created that includes updated metabolomic content from Host
System databases and offers a series of interactive options. The
Type II report is presented by a GUI dedicated for this purpose.
Here, the patient may enter personal information such as current
prescription drug usage, relevant clinical factors such as
pregnancy, age, history of smoking, and so forth. Displayed in the
resulting Type II report are detailed DDI ("drug-drug interaction")
and ADR warnings specific to the patient's personal drug regime and
personal genetic data at that moment in time. In other words, the
Type II report, given a phenotype and a personal drug regimen, can
predict both drug-drug and drug-gene interactions of possible
immediate concern to the user. A user may modify the drug regimen
to remove the culprit drug or drugs responsible for the warning and
generate an updated report. In this way it is possible to check a
prescription for potential DDIs or ADRs before it is written. This
report is updated on the fly whenever the patient or anyone the
patient authorizes to access the records (e.g. a physician)
accesses the Host System if the core databases have been updated
with relevant new clinical information or if there is a change in
the patient's medical regimen or status. The services provided by
this GUI are billable in several ways: as a "pay-per-ping" fee to
the patient or to the physician, as a subscription service to the
patient or to the physician, as a free trial, wholesale to a
clinic, or to third parties through the mechanism of a CPT code or
other reimbursement code arranged for third party billing, and so
forth. This innovative service also will function in a single-payer
insurance model.
[0024] Whereas the initial laboratory report (Type I) can be billed
by the client laboratory to the end use customer (e.g. patient or
physician) or to insurance, the second report (Type II) is
configured as a direct transaction with the Host Server, and thus
results in fees directly payable by the customer to the operator of
the Host System. By including in the Type I report a link to the
Type II user interface, the first report thus generates what is
essentially a business referral to the Host System. This is
beneficial to all parties because it allows the patient or health
care provider to better manage ADRs, and encourages use of genetic
testing. The Type II user interface may also display
recommendations for further genetic testing services if indicated,
or links to related services such as paternity testing services,
and thus becomes a central hub in a network for accessing a broad
range of medical and genetic services or information.
BRIEF DESCRIPTION OF THE DRAWINGS
[0025] The teachings of the present invention can be readily
understood by considering the following detailed description in
conjunction with the accompanying drawings, in which:
[0026] FIG. 1 is a schematic of a computerized apparatus for
storing, collating, accessing, crossreferencing and interpreting
metabolomic data, with multiple GUIs capable of supporting a
segmented business model.
[0027] FIG. 2 is a flow diagram illustrating the operation of
Paypoint 1.
[0028] FIG. 3 is a flow diagram illustrating the operation of
Paypoint 2.
[0029] FIG. 4 is a flow diagram illustrating the operation of
Paypoints 3, 4 and 5.
[0030] FIG. 5 is a flow diagram outlining the major steps of a PK
predictive algorithm and showing subroutines A and B.
[0031] FIG. 6 is a detail showing the steps of subroutine A.
[0032] FIG. 7 is a detail showing the steps of subroutine B.
[0033] FIG. 8 is an example of a table used by a computer algorithm
for making calculations of the effect of interacting factors on the
AUC of a drug.
[0034] FIG. 9 is an example of an experimental Type I lab report
containing a "sponsored-user" hyperlink.
[0035] FIG. 10 is a detail of an interactive webpage for entering
personal information and current drug regimen.
[0036] FIG. 11 is a detail of an interactive webpage containing a
command interface for preparing a Type II report.
[0037] FIG. 12 is a detail of an interactive Type II report showing
a DDI in a 2D6 intermediate metabolizer.
[0038] FIG. 13 is a detail of an interactive screen demonstrating a
computerized tool for use of market segmentation in a business plan
for vending genetic information and interpretation services.
DETAILED DESCRIPTION
[0039] Although the following detailed description contains
specific details for the purposes of illustration, one of ordinary
skill in the art will appreciate that many variations and
alterations to the following details are within the scope of the
invention as claimed. Accordingly, the exemplary embodiments of the
invention described below are set forth without any loss of
generality to, and without imposing limitations upon, the claimed
invention.
[0040] Adverse Drug Reaction (ADR): as used here, describes a
response to a drug or substance which is noxious and unintended,
and which occurs at doses normally used in man for prophylaxis,
diagnosis, or therapy of disease, or for modification of a
physiological function. ADRs have many causes and one the most
important and hardest for clinicians to predict, but often most
preventable cause, is interactions among drugs, herbals, foods and
genetic factors. At the molecular level, a comprehensive treatment
includes both Phase 1 and Phase 2 metabolic systems, including
conjugation enzymes (such as uridine
diphosphglucuronylsyltransferases and sulfotransferases),
transporters (such as ABC and SLCO), as well as the known
cytochrome P450 oxidative enzymes.
[0041] Phase I reactions can occur by oxidation, reduction,
hydrolysis, cyclization, and decyclization reactions. Oxidation
involves the enzymatic addition of oxygen or removal of hydrogen,
carried out by mixed function oxidases, often in the liver. These
oxidative reactions typically involve a cytochrome P450
haemoprotein, NADPH and oxygen. If the metabolites of phase I
reactions are sufficiently polar, they may be readily excreted at
this point. However, many phase I products are not eliminated
rapidly and undergo a subsequent "Phase II" reaction in which an
endogenous substrate combines with the newly incorporated
functional group to form a highly polar conjugate.
[0042] Phase II reactions (e.g., glucuronidation, sulfonation,
glutathionyl-conjugation or amino acid conjugation) speed clearance
by increasing polarity, and involve conjugation at functional
groups formed in Phase I metabolism.
[0043] Information about genetic loci responsible for polymorphisms
of Phase I and Phase II enzymes and associated transporters is thus
of significance in making predictions about potential ADRs.
Drug-drug; substance-substance; drug-gene; substance-gene, and more
generally "substance-factor" interactions must be considered in
assessing possible ADRs.
[0044] Drug-Drug Interactions (DDI): include interactions between
pairs of drugs or substances and among multiple drugs or substances
that result in changes in the pharmacokinetic parameters of one of
the interacting drugs or substances. Drug-drug interactions (DDIs)
are not ADRs but many ADRs are caused by DDIs. We note that some
drug interactions are beneficial because the inhibition of
metabolism of a drug can increase the patient's exposure to it and
increases the therapeutic benefit. Among such interactions, the
cytochrome P450 system, which plays a major role in metabolizing
drugs and other potentially toxic substances, is believed
responsible for about 70% of DDIs.
[0045] Drug: is a substance used as a medicine.
[0046] Prodrug: is a drug which is not therapeutically active until
it undergoes metabolism in the body.
[0047] Bioactive: a substance with biological activity. Bioactive
is a broad term encompassing drugs, foods, herbals, and so forth.
More specific examples not named elsewhere include lipids,
surfactants, retinoids and flavonoids. Also included are
metabolites of foodstuffs, drugs and substances.
[0048] Food: a substance ingested for flavor or nutrition.
[0049] Herbal: a plant or plant derived substance used for its
therapeutic properties.
[0050] Substance: a drug, bioactive, excipient, herbal, food, or
other chemical. As used herein, a drug-drug interaction can refer
to a substance-substance interaction. More generally, a
substance-factor interaction refers to a substance-substance,
substance-gene, or substance-clinical factor interaction.
[0051] Factor: includes substances, bioactives, phenotypes, and
"patient characteristics", also termed "clinical factors", which
may cause or be subject to an interaction. Importantly, in the
algorithms, factors are specific to and associated with a patient
identifier. Drug class membership (below) is also a factor.
[0052] "Clinical factor" or "patient characteristic": as used here,
includes any patient-specific characteristic, classification or
status, such as pregnancy, age, race, history of smoking or alcohol
use, liver pathology, kidney pathology, cholecystectomy, colostomy,
diabetes, lifestyle, and so forth, that can cause or exacerbate a
substance interaction, DDI, or ADR. Clinical factors are generally
drawn from the clinical history. The list of factors is expansible
within the table structure of a database and can be used in an
algorithm to predict potential DDIs and ADRs, as taught here.
[0053] Drug "class membership": as used here is also a "factor" in
the context of the PK predictive algorithm and is directed to a
"synthetic" class or group comprised of drugs with a common side
effect. Many SSRI's for example share common side effects. Certain
other drugs commonly cause QT/QTc prolongation, slowing the heart
rate, such as risperidone and haloperidol. Simultaneous
administration of two such drugs can lead to additive side effects,
a DDI that is not directly linked to a genetic polymorphism
(although it would be exacerbated by one), but is picked up and
displayed by the Type II PK predictive algorithm.
[0054] "Medical metabolomics", or "metabolomics" as used here,
includes elements of pharmacogenetics, pharmacology, naturopathy,
biochemistry, physiology and medicine. We limit the scope of the
information, strictly for the sake of relevance, to the patterns of
metabolism of drugs and other bioactives (jointly "substances") and
their interaction with metabolic enzymes and transporters and with
each other in the complex environment of the human body. Of
particular interest is the question of the effect of these
interactions on the pharmacokinetics, efficacy and safety of a
particular drug in a particular patient. Thus metabolomics includes
the study of ADRs.
[0055] Bioinformatics: Bioinformatics derives knowledge from
computer analysis of biological data. These data can consist of the
information stored in the genetic code, but also include
experimental results from various sources, patient statistics, and
scientific literature. Research in bioinformatics methods and
apparatuses is ongoing, and includes algorithm development for
storage, retrieval, and analysis of the data.
[0056] Pharmacogenetics: Refers to the evaluation of individual
genetic variation in relation to the delivery, safety, and
effectiveness of drugs. Knowledge of individual genotypes and
phenotypes makes it possible to customize drug delivery regimens
for specific patients so as to avoid ADRs and maximize the benefits
of drug therapies. Additionally, pharmacogenetics encompasses the
study of the differences among individuals with respect to
gene-linked responses to a drug. Relevant laboratory workups
include "genotyping" or "genetic testing" by methods such as array
hybridization, PCR-direct sequencing, PCR-linked electrophoretic
restriction fragment polymorphism, or PCR-linked allele specific
primer extension. Samples include buccal swabs and blood collected
by venipuncture or lancet. Genetic markers of interest in
pharmacogenetics include polymorphisms at selected allelic loci,
particularly SNPs and deletions, often referred to in the
literature as "haplotypes" or "star data". More recently, through
direct sequencing of whole human genomes, unpaired chromosomal
fragments (i.e. unpaired alleles) have been discovered in which an
individual is mono-allelic, and these too may also have a bearing
on health and disease. Relevant pharmacogenetic information
includes the testing data, sample and testing protocols, and
annotations of the primers or sequence data used for identification
of a genotype from, all generally recognized as necessary
components of a genetic testing report. Interpretation of
pharmacogenetic data commonly requires categorization of the
genotype into one or several phenotype classifications, for example
slow or poor metabolizer, intermediate metabolizer, normal, and
ultra-fast metabolizing forms, as well as various classifications
based on the organ in which the gene is expressed.
[0057] AUC: also termed "area under the curve", is a measure of the
amount of a drug or substance the body is exposed to. It reflects
both the time-course and concentration of a drug or substance in
bodily fluids.
[0058] Inhibitor: generally a ligand interacting with an enzyme,
transport, conjugation, allosteric or other binding site and
resulting in reduced throughput of the substrate. Inhibition at the
level of gene expression is also contemplated by this term. In the
PK prediction algorithm, published Ki values are used to estimate
the interaction intensity index IP. When a Ki is unknown, a default
value is assigned. If the impacting factor responsible for reduced
throughput is a phenotype or clinical factor, an index value is
assigned based on intensity ratings extracted from the research
literature.
[0059] Inducer: generally a ligand causing increased expression of
a gene responsible for synthesis of an enzyme or transporter.
Inducer-specific experimental and clinical ratings of induction
intensity are assigned an index value INTX comparable to the points
assigned for inhibition but of negative sign. The intensity of
inducer effects can be estimated, for example, by Neil's rules of
thumb. If the impacting factor responsible for increased throughput
is a phenotype or clinical factor, an index value is assigned based
on intensity ratings in the research literature.
[0060] Interaction pair: refers to substances or factors that
interact by increasing or decreasing the metabolism of one member
of the pair via one or more metabolic pathways. One of the members
of the pair is a substrate of the pathway or pathways and is called
the "victim", the other substance or factor is called the "culprit"
and can be: i) an inducer, ii) an inhibitor, iii) a genetic
polymorphism of a gene encoding a protein of the pathway, or iv)
other clinical factor such as pregnancy or age. Inducers and
inhibitors are not limited to prescription drugs, but may include
clinical factors such as exposure to tobacco smoke or environmental
chemicals. While victims and substrates interact via metabolic
routes, it should be understood that the interaction may involve
oxidative enzymes, conjugative enzymes, or transporters, of which
cytochrome P450, glucuronyl transferase, and P-glycoprotein are
illustrative examples, and that metabolism may be influenced by
factors such as age and pregnancy, as well as genetic polymorphism,
allosteric and competitive inhibition, and induction of gene
expression. Thus "interaction pair" is not limited to drug-drug
interaction.
[0061] "Victim": is a term of art referring to a member of a drug
interaction pair for which metabolic throughput is impacted,
resulting in a change in AUC, i.e. a change in peak or cumulative
exposure.
[0062] "Culprit": is a term of art referring to a member of a
drug-drug or drug-factor interaction pair responsible for a change
in AUC, i.e. in peak or cumulative exposure, of a "victim". Because
clinical factors can also impact drug exposure, the term "culprit"
is used in a broad sense, conveying a list not only of drugs and
substances, but also of clinical factors and patient
characteristics that can impact a victim drug or substance.
Culprits may be inhibitors or inducers.
[0063] Intensity: refers to the degree of interaction between two
substances or factors. In subroutine A of the PK predictive
algorithm, the intensity of interaction is quantitated by "INTX".
Intensity indices are drawn from literature values and include
indices of induction and inhibition.
[0064] Proportion: refers to the relative fraction R.sub.1/1-n of a
drug or substance's metabolism directed through a particular
metabolic pathway where more than one pathway operates in
parallel.
[0065] Paypoint: an automated tool for flagging, configuring and
routing information about a data exchange between the Host System
and a user, and initiating an automated financial transaction on a
billing server that generates a debit on an account.
[0066] Graphical user interface (GUI): a combination of one or more
visual, acoustic and tactile means for engaging a computer,
commonly based on a mixture of graphics and text that is used to
query a database. GUIs include tools such as keyboards and mouse
pointers for entering information in a computer.
[0067] Genotype: As used here refers to a genetic marker or
"allele"--one of several possible hereditable DNA sequences
characterizing one genetic locus of an individual. It pertains to a
specific gene, but "genotype" may also be used to describe a
collection of genotypes for each of a set of genes of an
individual.
[0068] Phenotype: By contrast, phenotype refers to the
manifestation of expressed genetic information, and thus indicates
not only a particular protein or set of proteins of an organism or
tissue, but also variants in the way protein expression or activity
responds to environmental factors.
[0069] The following are representative genetic testing data. These
include genetic loci that are known to be important in drug
metabolism. Also relevant are the disclosures of U.S. Pat. No.
7,054,758, assigned to Sciona Ltd, and US Patent Application
2006/0289019, assigned to IPPM Holding SA, hereby incorporated in
full by reference. Also relevant are the disclosures of
Tomalik-Scharte (Tomalik-Sharte, D et al. 2008, The clinical role
of genetic polymorphisms in drug-metabolizing enzymes.
Pharmacogenetics J 8:4-15) hereby incorporated in full by
reference.
[0070] CYP2D6 (cytochrome P450 2D6) is the best studied of the DMEs
and acts on one-fourth of all prescription drugs, including the
selective serotonin reuptake inhibitors (SSRI), tricylic
antidepressants (TCA), beta blockers such as Inderal and the Type
1A antiarrhythmics. Approximately 10% of the population has a slow
acting form of this enzyme and 7% a super-fast acting form.
Thirty-five percent are carriers of a non-functional 2D6 allele,
especially elevating the risk of ADRs when these individuals are
taking multiple drugs. Drugs that CYP2D6 metabolizes include
Prozac, Zoloft, Paxil, Effexor, hydrocodone, amitriptyline,
Claritin, cyclobenzaprine, Haldol, metoprolol, Rythmol, Tagamet,
tamoxifen, and the over-the-counter diphenylhydramine drugs,
Allegra, Dytuss, and Tusstat. CYP2D6 is responsible for activating
the pro-drug codeine into its active form and the drug is therefore
relatively inactive in CYP2D6 slow metabolizers.
[0071] CYP2C9 (cytochrome P450 2C9) is the primary route of
metabolism for Coumadin (warfarin). Approximately 35% of the
population are carriers of at least one allele for the
slow-metabolizing form of CYP2C9 and may be treatable with 50% or
less of the dose at which normal metabolizers are treated. Other
drugs metabolized by CYP2C9 include Amaryl, isoniazid, ibuprofen,
amitriptyline, Dilantin, Hyzaar, THC (tetrahydro-cannabinol),
naproxen, and Viagra.
[0072] CYP2C19 (cytochrome P450 2C19) is associated with the
metabolism of carisoprodol, diazepam, Dilantin, and Prevacid.
[0073] CYP1A2 (cytochrome P450 1A2) is associated with the
metabolism of amitriptyline, olanzapine, haloperidol, duloxetine,
propranolol, theophylline, caffeine, diazepam, chlordiazepoxide,
estrogens, tamoxifen, and cyclobenzaprine.
[0074] NAT2 (N-acetyltransferase 2) is a second-step DME that acts
on isoniazid, procainamide, and Azulfidine. The frequency of the
NAT2 "slow acetylator" in various worldwide populations ranges from
10% to more than 90%.
[0075] VKOR vitamin K 2,3-epoxide reductase. Factor V Leiden and
Factor II (Thrombin) are related to the 2C9/VKOR package in that
the individual's genotype at this locus is a factor in predicting
clotting risk.
[0076] Other genetic loci of known interest include C734A4, C734A5,
C734A7, MTHFR genotype, methionine tetrahydrofolate reductase,
homocysteine metabolism, TPMT poor metabolizer, UGT1A1,
glucuronosyl transferase (active in metabolism of Labetalol,
Morphine and Naloxone), S-methyltransferase, Factor II Thrombin,
Celiac Disease Panel, Factor V, obesity-associated genetic loci,
and ABCB1-P-glycoprotein. Some of these are experimental, some of
proven impact on health care decisions. While the application is
not limited narrowly to pharmacogenetic data, and may comprise
genetic and metabolomic data more generally, the core GeneMedRx
application server and programming is currently configured with a
pharmacogenetic and pharmacological database as a preferred
embodiment.
[0077] The issues involved in interpreting genetic test reports and
potential drug interactions are by no means simple. Some cytochrome
P450s are expressed in multiple tissues (e.g., CYP3A4 has
intestinal and hepatic sub-routes). A drug may be metabolized by
one or both of the sub-routes. Drugs may also inhibit one sub-route
preferentially. Selective entry into the brain is also controlled
by independently expressed drug portals and metabolic enzymes of
the blood brain barrier.
[0078] The particular tissue-specific sub-route by which a drug is
metabolized is often not known because the data were collected
before sub-routes were recognized. In the current algorithm,
subroute information is utilized in predicting interactions;
however, if no subroute information is available, then one
embodiment of the algorithm makes a conservative assumption that
the drug is a substrate or inhibitor of all subroutes.
[0079] Given the complexity of the interactions between genotype,
drug indications and other factors in delivery of personalized
medicine, it should now be clear that a computerized tool of the
kind disclosed here is essential for managing the required and
associated medical information.
[0080] Turning now to the figures, FIG. 1 is an overview of the
host software engines and servers 20, which are typically under
control of a single operating entity, the Host System operator, who
is responsible for constructing and maintaining the servers,
databases, software and network interfaces. The Host System
operator is reimbursed as shown in the figure, which includes five
paypoints where financial transactions may be initiated and
configured. These are paypoints 1, 2, 3, 4 and 5 as shown, and are
directed to multiple market segments. Also shown are customer types
6 (full-use subscribers), 7 (fee-for-service users), 8 (conversion
subscribers), 9 (sponsored users), 10 (contract laboratories),
although these terms should be construed sensu lato and are not
narrowly limited. For example, full-use subscribers may include
wholesale users, and sponsored users may include promotional users.
Certain customer types are interconvertible or overlapping, as will
be described below.
[0081] Between horizontal lines 27 and 28, the host software is
responsible for initiating the financial transactions and includes
all Host System-compatible interfaces. Below horizontal line 28,
outside software vendors may supply the required software, servers
and user interfaces. Laboratory Billing System 25 and 3d Party
Payer System 26 may also be supplied and operated by outside
parties. The titleblock "3rd party payer" refers generically to
private insurance carriers, granting agencies, government agencies
and the like, where the financial transaction is indirect and might
not involve the host software or servers directly. Although
depicted once, the client laboratory block 25 and 3d Party Payer
block 26 are indicative of a plurality of such entities.
[0082] Computing equipment of the Host System 20 comprises the
Metabolomics Engine 21, datapipe (arrow) 29, the Genetic and
Pharmacologic Database Editor module 17, Administrative server 18,
datapipes 31, 32, 34, 35, 36, 41 and 51, and GUIs 30, 40 and 50,
each of which will be explained subsequently in more detail.
Datapipes are arrows indicating the flow of data in the system.
Provision of hardware for computerized implementation of the system
falls within conventional skills.
[0083] Integration of Host System functions may rely on hardwired
interconnections or on networked interconnectability. Network
accessibility 12 is indicated in FIG. 1 and may comprise internet,
intranet, wireless networking, and so forth. Web servers, wireless
protocols, and GUIs suitable for connectivity of these sorts are
well known in the art. Servers 17, 18, 25 and 26 may be remote
servers and wirelessly connected to the Host System, which may be a
single, integrated whole or can be distributed over multiple
locations. Secure access, digital certificates, and encrypted web
pages are known in the art. Recent computer security innovations
such as reCaptcha.TM., an open source project of Carnegie Mellon
University, are also useful in implementing secure access.
[0084] The Metabolomics engine 21 comprises databases and logic
modules programmed with software algorithms. While the following
description includes particulars, it should be understood that the
number of databases, the number of servers, and the location of
data storage functions and logic modules, and so forth, may be
modified by those skilled in the art while still consistent with
the spirit and teachings of the invention. Database 22, encoded on
a computer readable medium, is termed the Genetic and Pharmacologic
Database (or "Pharmacogenomics database") and database 23, encoded
on a computer readable medium, is the Administrative Records &
Clinical Records Database, also termed simply the "Administrative
Database". The Administrative Editor 18 handles business records,
which are stored in Administrative Database 23, and related
backoffice functions such as insurance claims processing, and
validation and error tracing, and includes a user interface for
authorized personnel. Database 23 is also the site for secure
storage of patient medical records. Genetic and Pharmacologic
Database Editor 17 is used to update biological, pharmacological,
and pharmacogenetic look-up tables in the Pharmacogenomics database
22, and includes a user interface generally restricted to clinical
specialists responsible for researching, maintaining and updating
data extracted from current medical literature. The records entered
in database 22 can include for example inhibition constants (Ki),
inducers, metabolic pathways, sub-pathways, organ-specific
pathways, interaction intensities (INTX), metabolic enzymes,
correlations between markers for genetic polymorphisms (such as
SNPs) and phenotypes, extensive references and annotations from the
medical literature, related hyperlinks, drug generic and brand
names, drugs, prodrugs, herbals, excipients, metabolite
identifiers, drug interaction classifiers, drug metabolic routes,
drug metabolic route weightings (R.sub.1/1-n), uptake transporters,
uptake transporter-substance interactions, organ-specific
transporters, herbal interactions with drugs, patient
characteristics, patient characteristic interactions with drugs and
herbals, phenotype interactions with drugs and herbals, phenotype
interactions with patient characteristic factors, drug therapeutic
classes, therapeutic substitutes, composition of pharmaceutical
mixtures, clinical status factors, drug label warnings, warnings
from the medical literature, clinical trials, and cross-references,
for example, and may be expanded to include new tabulations of data
as deemed useful. Phenotypes include for example, "poor
metabolizer", "normal metabolizer", "intermediate metabolizer",
"ultra-metabolizer", "reduced heterozygous expressor", and so
forth. Metabolic enzymes include for example cytochrome P450
enzymes CYP1A2, CYP2C19, CYP2C9, CYPNAT2 and CYP3A4 concerned with
drug metabolism and p-glycoprotein transporters concerned with drug
uptake and elimination. Among the algorithms programmed in the
Metabolomics engine 21 are one or more PK predictive algorithms,
which calculate the impact of phenotypic interaction, drug
interaction, and clinical factor interaction on the AUC of
pharmaceutically active compounds and metabolites, and makes
predictive warnings if an adverse drug interaction is possible.
Also included in the Metabolomics engine is a genotype-phenotype
translator. These algorithms will be discussed in more detail in a
subsequent section.
[0085] Lab Report Engine 24 includes programming for data entry
functions, for interfacing with the databases of the Metabolomics
Engine 21, and for assembling and transmitting laboratory reports.
The contract laboratory GUI 30 is used for data entry and for
controlling the production of Type I lab reports by the Host
System. These reports, shown here as transmitted to user interfaces
30 and 40 via a network connection, contain laboratory-proprietary
formatting and information such as a logo and contact information,
patient information, and also genetic testing data. An algorithm in
the Medical Metabolomics Engine 21 interprets or "correlates" the
genetic test result with a phenotype and the Lab Report Engine
incorporates the phenotype data into the Type I lab report, also
storing a copy of the patient's record in Clinical Records database
23. Optionally included in the report is predictive content
highlighting interactions of the reported phenotype with selected
drugs where there is a likely interaction. The report takes into
account that the patient may have multiple abnormal phenotypes.
Each time it is accessed, this report is newly created by the
system (using the Type I predictive algorithm) and reflect the most
current patient genetic information and metabolic information in
the databases. Access to the report is typically password
protected. The lab report also may contain a live, sponsored-use
hyperlink 43 (*) which will be discussed in more detail in the
context of the paypoints.
[0086] Returning to a discussion of the paypoints and the multiple
business models described in FIG. 1, we turn first to paypoints 3,
4 and 5. These financial transaction initiation points are linked
to data transfers indicated by datapipe arrows 32, 31 and 33
respectively. The Host System operator is paid (Paypoint 4) by
contracting laboratory 10, typically as part of a subscription for
access to the Host System, for a service comprising the delivery of
an enhanced laboratory report to the end user at user interfaces 30
and 40, or optionally by displaying the webpage report through a
webserver on the laboratory's server 25. Note that the user is a
customer for genetic testing services by the contracting laboratory
and the contracting laboratory is in turn a customer of the Host
System operator. The enhanced genetic test report includes a
determination of a phenotype, where the phenotype is determined by
a computerized interpretation of the raw genetic test data entered
by the laboratory. The interpretation is made by the Lab Report
Engine 24 using genetic and bioinformatic records on the
Pharmacogenomics database 22. The Host System provides subscription
services to multiple contract laboratories, but each test report is
customized with the logo and contact information of the particular
laboratory providing the testing, with such files and formatting as
are required by the report-generating algorithm being stored in
Administrative database 23.
[0087] The contracting laboratory is responsible for providing the
results of a genetic test on a sample submitted by a patient,
health care provider, or other party. To initiate a report of the
test result, contracting laboratory 10 is provided with GUI 30 for
accessing the Host System and enters patient identifier data and
genetic testing result data into the Host System through datapipe
arrow 31. This data is stored in the secure Clinical Records
Database 23. Lab Report Engine 24 builds the report and transmits
it to user interface 40, as represented by datapipe arrow 32. GUI
30 can also be used to print out a paper copy of the report for
mailing to the customer or for reviewing and archiving the content.
In another embodiment, the user can obtain this report by logging
on to the laboratory server 25 and requesting it, the background
operations of the Host System being seamlessly integrated into the
foreground operations of the laboratory server.
[0088] The interpretation made by the Metabolomics Engine between
the genetic test result and a metabolic phenotype is a service that
can be billed as an interpretive or diagnostic laboratory service
under a recognized CPT code ("Current Procedural Terminology code")
or equivalent authority when performed under the supervision of a
pathologist or recognized medical practitioner associated with the
contracting laboratory. The Type I report also includes a detailed
table showing the commonly prescribed drugs available where the
user resides that interact significantly with the phenotype. This
service is also billable as part of the Lab Report and
reimbursement for access to the host system resources and
predictive algorithm is passed on to the Host System operator.
[0089] In one preferred embodiment of the business model, upon
delivery of the enhanced "interpretive" report to user interfaces
30 and 40, the laboratory server sets a flag that in turn results
in an invoice being sent to a third party payer 26 (Paypoint 5),
such as an insurance carrier, as indicated by datapipe arrow 33.
All necessary information that the insurance company typically
needs to process a claim will be included with the invoice. In this
model, Paypoint 5 is external to the operation of the Host System,
but is an inducement to use the Host System resources and serves as
a supplemental profit source for the contracting laboratory.
Contract genetic testing laboratories offering the enhanced power
of the Metabolomics Engine have a marketable advantage because of
greatly enhanced information they can report to their customers,
information that would be very costly for any single laboratory to
assemble, maintain, and deliver, even if the software had been
commercially available. Insurance carriers offering coverage for
genetic testing services, which can directly reduce medical costs
by avoidance of adverse drug reactions, also have a marketable
advantage. Use of existing CPT codes for automated interpretive
services as a tool for billing for enhanced genetic test reports is
a novel solution to a longstanding and unmet need in the industry.
Prior art models include shopping cart-type fee-for-service billing
and subscription billing. Here however, the billable event that
drives the model is the fee for professional interpretation of the
phenotype or the fee for professional predictive interpretation of
potential drug-drug or drug-gene interactions, and by automating
the billing, a broad range of pharmacogenetic interpretive services
can be supported. Although the billable service accrues to the
contract laboratory, and is paid by a third party payer, the
revenue drives the subscription fees collected by the Host System
operator to maintain the system.
[0090] Note that the end consumer is a customer of the contract
laboratory and may be a patient or a health care professional, such
as a physician. Patients in many states are authorized to order
laboratory testing services in propria persona. Thus payment for
interpretive services may be made in two ways, as distinguished by
Paypoints 3 and 5. Either the contract laboratory is paid at
Paypoint 3 directly by the customer for delivering the genetic test
report or by an insurer indirectly at Paypoint 5. Serendipitously,
this model allows health care providers, who wish to order genetic
testing, to `pass through` the costs of that testing and
professional interpretation to insurance carriers (Paypoint 5). In
this preferred model, the patient, health care provider, or end-use
customer are not parties to the resulting financial transactions
and are termed, "sponsored users" (9), who can access the report at
interface 40. The laboratory may thus offer the enhanced report
service at interface 40 to the patient or to an authorized health
care provider at no charge under this model, a surprising and
unanticipated solution to the problem of reimbursement for genetic
testing services.
[0091] In the preferred arrangement, where the payer is a
third-party and the invoice is submitted with a recognized CPT
code, the market for genetic testing services is shown to increase
over the direct fee-for-service model, and increased use results in
reductions in overall costs of health care delivery and increased
efficiencies. The result is a virtuous cycle. The rising cost of
health care delivery, which includes an important component
representing the increasing frequency and severity of adverse drug
reactions and related complications and litigation, has been well
established. Access to pharmacogenetics at the point of care is
needed to help bring this escalating cost under control, but has
been impeded by difficulties in discovering models and formulae for
reimbursement. The problem of more widely providing genetic testing
is solved by the reimbursement mechanisms described here--a
third-party private insurance payer or a single-payer system is
invoiced for those costs under accepted medical billing codes and
accounting practices, allowing the health care provider to pass
through those fees and access the data without an intervening
fee-for-service, shopping cart, or subscriber transaction.
[0092] Paypoint 1 provides a parallel or alternative reimbursement
pathway and illustrates a second aspect of the invention whereby
real time, point of care access to bioinformatics can be funded. A
full-service subscriber 6 interfaces with the Medical Metabolomics
Engine 21 through Paypoint 1, as indicated by datapipe arrow 51 and
GUI 50. Paypoint 1 can be configured for subscription access for
full service customers 6, for example with annual or semi-annual
dues, but also as a "pay-per-ping" fee for access. Paypoint 1 may
also be marketed and priced for wholesale users, for example
medical clinics with multiple patients and broadband access routed
directly into each examination room. In this model, the patient
uses passwords or access codes to control access, but a healthcare
provider or physician can with the patient's consent, for a limited
time, view the patient's genetic information on interactive user
interface 50. This interface differs from interface 40 and 30 in
that it allows the user to enter and model the pharmacogenetic
consequences of various prescription and patient factors on a
secure linkage, among other options and services, and to store the
prescription data on-line. Full service user access includes guided
assistance in evaluating the clinical effect of genetic
polymorphisms, aid in assessing the impact of patient
characteristics and factors such as pregnancy, alcohol,
recreational drug use and smoking in the context of the patient's
genetic makeup, predictive warnings about probable drug
interactions not reported in the medical literature (based on the
novel PK predictive algorithm), contextually specific assistance in
choosing alternate drugs in a therapeutic category, annotated
in-depth literature citations and hyperlinks, and ready reference
to labeling, indications, package warnings, toxicology, and
chemical information about drugs, herbals, pharmaceutical
formulations, and mixtures, all in the context of current
information about the patient's prescription regimen. The
interactive Type II report capacity is novel in that it is
generated "on the fly" (by the predictive algorithm) whenever
accessed and thus increases the range of its predictions whenever
PK or factor data is added to the database. The pharmacogenetic
database is updated regularly by Editor function 17 so that the
Type II report will always contain timely research findings. The
Type II report and will flag any newly discovered or predicted
interactions of immediate relevance to the particular patient's
care. That is, an interactive report accessed in late September
will likely contain new information not available in early June; an
interactive report accessed while the patient is receiving one drug
will contain unique information not included if the patient is
switched to another drug; an interactive report accessed after the
results of a genetic test are entered will contain a whole range of
new information not available before the test result was entered,
and so forth. Thus the report is a living, dynamic view of the most
relevant patient-specific pharmacogenetic information at any given
time and is accessible at the point-of-care by those with wireless
devices or with an internet connection. This underlines the
importance of designing reimbursement into the system.
[0093] Another novel feature of GUI 50 is access to a PK predictive
algorithm in the Medical Metabolomics Engine 21. The PK predictive
algorithms, unlike prior art efforts to present drug interaction
data, are designed to identify drug-drug interactions, including
interactions among three or more drugs, and to display the
clinically significant interactions. The display integrates
interaction studies from the clinical literature and the
predictions of the PK predictive algorithm. The PK predictive
algorithm also is designed to handle multiple polymorphism
interactions, so that the significance of multiple alleles and
multiple phenotypes is fully reflected in the predictions. As a
novel and unexpected solution to a longstanding problem, this
algorithm is effective even in the absence of clinical reports of a
specific interaction, although when both a prediction and clinical
study are available, priority is given to the medical literature in
the choice of warnings displayed. The calculation is a mixed
semi-quantitative and empirical estimate as explained below.
[0094] Also included are algorithms for adjusting dosage during
changes in medication that factor in genetic polymorphisms, and
hyperlinks for access to on-line information such as PDR and PubMed
citations. The program will suggest specific therapeutic
alternatives in a drug class when requested. The recommended
alternatives are chosen by the program so as to avoid the potential
DDI detected by the predictive algorithm.
[0095] The patient also can have the option of entering insurance
information at Paypoint 1. A CPT code corresponding to the
requested access level, is paired with insurer identifiers entered
by the patient and contractual terms stored in the Administrative
database 23. CPT codes are the most common currently used service
descriptors generally accepted by insurers. These
mutually-understood reimbursement code data are represented here by
dotted arrow 52 between the Host System operator and one or more
insurer servers 26. Thus, Paypoint 1 can be configured to permit
direct invoicing from the Host System operator to an insurer, again
a form of `pass through` invoicing that allows the health care
provider to order Type II pharmacogenetic interpretive services
payable by the insurer. Under this model, the Host System operator
provides interactive access and interpretive services to an
authorized health care provider at no charge, or limited co-pay, to
the patient or end user.
[0096] The host server 20 in this embodiment contains an insurance
submodule, insurance information stored in the administrative
database 23, and algorithms to detect reimbursable events at
Paypoint 1 in GUI 50 and to process insurance claims. Certain
insurance claims must be authorized in advance. All necessary
information that an insurance company typically needs to process a
claim will be included in a request for authorization to permit a
service. The decision by the insurance company will determine how
the Metabolomics Engine will process a transaction at Paypoint 1.
The decision-making process is optionally represented by
bidirectional arrow 52. If the insurance company authorizes the
service, the system will proceed to offer the authorized service,
for example access to a Type II report function or to a genotype
interpretive function. If the insurance company denies
authorization, however, the user will be held at Paypoint 1 pending
selection of another option, for example an option to email a
customer representative.
[0097] Once insurance authorization has been completed, the system
processes the user's query. The insurance submodule, in conjunction
with algorithms associated with Paypoint 1, will detect
reimbursable services and assign the appropriate reimbursement
codes, in conjunction with a billing server such as server 18. A
preferred reimbursement code is a CPT code. The CPT code assigned
will correspond to a generally approved fee schedule for
professional interpretation of a genetic test result.
[0098] In another embodiment, users gaining first access to the
Metabolomics Engine in the course of purchasing genetic testing
services from a contract laboratory are converted to direct Host
System customers. In this new model, "sponsored user" 9, viewing
the lab report through user interface 40, is provided with a
sponsored-use hyperlink or URL for access to user interface 50. The
sponsored-use hyperlink (*), indicated by arrow 43, when accessed
with a password and access code, opens up datapipe 41, which
includes a selectable level of interactive access to the
Metabolomics Engine at 44 under control of Paypoint 2. In this way,
the contract laboratory customer is now directly accessing the
Metabolomics Engine at GUI 50, which offers the user an opportunity
to enter personal medical information and view multiple subpages
with active links to in-depth information related specifically and
contextually to the patient history. With this incentive, the
customer can chose to continue as a sponsored user, for example for
a trial period, or can convert to a subscription use (ie, as a
"conversion subscriber" 8 or to a fee-for-access user 7), directly
paying the Host System operator for the interactive access
(Paypoint 2). The system can also offer links for ordering other
genetic testing services from the referring lab, for example.
[0099] Importantly, the reports available at this level of service
through GUI 50 include: interpretive Type II reports of contextual
interpretation related to personalized information entered by the
patient or end consumer and stored on the database, such as
information about current prescription regimen, history of smoking,
alcohol, and use of herbals. In contrast, the earlier-described
Type I lab reports accessed through user interface 30 and 40 do not
permit entry of patient-specific medical information related to
treatment, drugs taken, or patient characteristics. The services
offered can be endowed with multilevel permissions with
corresponding costs (by configuring Paypoint 2), up to and
including the full service benefits discussed in regard to Paypoint
1 above. Customers who convert in this manner become increasingly
sophisticated in the use of pharmacogenetics in managing their
medical care. By accessing GUI 50 while consulting with a
physician, for example, the possible patient-specific risks of a
new drug can be evaluated in the context of the patient's existing
drug regime and genetic makeup before the prescription is written.
Interactive access at 44 is thus seen as a natural step in
conversion of the customer to full access at datapipe 51, whereby
the customer becomes a direct customer of the Host System operator
and accesses the system through Paypoint 1.
[0100] In another embodiment, when drugs are being prescribed that
are subject to substantial genetic variability in metabolism, the
algorithms of the Metabolomics Engine will advise the user of
potential risks and suggest specific genetic tests.
[0101] The sponsored-user and conversion subscriber portal is
secured by methods known in the art, using passwords, access codes
and digital certificates, for example. The medical databases are
encrypted.
[0102] FIG. 2 is a flow diagram illustrating the operation of
Paypoint 1 of FIG. 1. As implemented on a computer system, for
example the Host System of FIG. 1, an end user such as a physician,
other health care professional or patient, accesses Host System 20
through GUI 50. The user enters a patient identifier and a password
or other access information to gain access to a medical record
stored on database 23.
[0103] The user then enters a list of a plurality of factors to be
associated with the patient identifier on an interactive webpage,
where the factors are selected from the group consisting of
prescription drug usage(s), bioactive substance usage(s), and
clinical factor(s). Metabolic phenotype information is also a
factor, but is generally entered by a clinical laboratory under the
supervision of a pathologist and not generally accessible to
editing by the patient or end user. The genetic test data
(genotype) entered by the laboratory is translated into a phenotype
by the Medical Metabolomics Engine (21) and stored in the Clinical
Records database. This establishes the patient's current drug and
substance regimen and any significant clinical factors. On command
of the user, the view is then updated with a prediction assessing
the biocompatibility of the patient-specific data entered (i.e. a
Type II report). Also displayed are warning(s) of any predicted
bioincompatibility between the listed factors. The algorithm
considers not only drug-gene interactions but also drug-drug
interactions and drug-clinical factor interactions.
[0104] Typically, a predictive algorithm of the type disclosed
herein, the Type II PK predictive algorithm explained in FIG. 5, is
used to make the prediction. The service is flagged as a
pharmacogenetic interpretive service for automated billing. At
Paypoint 1, the system flags the operation in step 4 and
automatically generates an invoice to a third party payer (step 4)
or to a customer.
[0105] At Paypoint 1, access is a billable service, and the user
has multiple choices in selecting a reimbursement method. In one
option, the user can enter a financial instrument at a paypoint
associated with said second graphical user interface. Of particular
interest is a business model in which payment is received from a
third party payer for the end user's access to the system. For
example an insurance payer 26 may be billed by the Host System
directly. One aspect of this is indicated in FIG. 1 by arrow 52,
whereby the backoffice administration of the Host System operator
has prearranged contractual understandings and a table of accepted
billing codes that are used to monetize the value of the specific
characteristics of the Type II report functions utilized by the
user.
[0106] FIG. 3 is a flow diagram illustrating the operation of
Paypoint 2 of FIG. 1. Paypoint 2 is more complex but also involves
GUI 50. A major advantage of this automated business method is the
ability to convert a contract client's customer to a direct
customer of the Host System, while providing win-win value to the
client laboratory. As implemented on a computer system, a client
laboratory in the business of providing genetic testing services
accesses the Host System 20 at first GUI (GUI 30). The client
typically has a contract or subscription agreement to access the
Host System. In step 1, the client (typically a laboratory
technician) enters a patient identifier and a genetic test result
(typically a genotype or "star data") associated with the patient
and stores that information in Clinical Records database 23. The
system merges records associated with a single patient identifier.
The client then enters a command to the host server that generates
a test report (Type I) using host system resources (step 2); the
test result includes a phenotypic interpretation of the
genotype(s), a list of drugs for which the drug's metabolism is
likely to be adversely impacted by the phenotype (i.e. potential
drug-gene interactions), a hyperlink to a second GUI (GUI 50), and
a password or access codes whereby the customer (such as a patient,
physician) can access the Host System (20) directly. Typically, in
step 3, the client transmits the report to the customer 9,
optionally via a Host System webserver. Encryption is commonly used
to secure the data during transmission and storage of
passwords.
[0107] Operation of the PK predictive algorithm used in preparation
of Type I reports is explained in more detail in FIG. 4.
[0108] In step 4, when the customer 9 accesses the Host System
directly at GUI 50, and enters the patient identifier and password,
the Host System provides an interactive webpage (the opening screen
for a Type II report). This screen is illustrated in FIG. 10. The
user has the option of entering a plurality of clinical factors
associated with the patient identifier, for example prescription
drug(s) and bioactive substance(s) taken, and other clinical
factor(s) (i.e. "patient characteristics") such as age, pregnancy,
smoking, and so forth. Customers 9 may have restrictions on levels
of access. In one embodiment the customer is not authorized to
delete or add phenotypes. This prevents the user from giving
unauthorized access to guest users. After entering the list of
drugs and other factors, the patient can give his or her healthcare
providers access to the complete patient record from any computer
or PDA with internet access. The medical record is stored on the
Host System server and requires a password or other access code for
authorized access.
[0109] A Type II PK predictive algorithm (discussed below, FIGS.
5-7) is then run. For any metabolic pathway associated with a drug
or substance on the list entered by the user, the system identifies
any drug-drug, drug-bioactive, drug-clinical factor (or
"drug-patient characteristic"), drug-phenotype and
substance-phenotype potential "interaction pair(s)", and identifies
the impacted substrate (the "victim"). One drug (the "culprit"),
for example, will inhibit the metabolism of another, resulting in
greater exposure of the patient to the victim drug. An overdose can
occur if the drugs are co-administered. The algorithm then
calculates a change in the AUC of the impacted substrate and
annotates a table with this information (see FIG. 6, subroutine A,
and FIG. 8). The table may also contain hierarchically selected
warnings drawn from the literature or based on the PK algorithm
result (FIG. 7, subroutine B). This information is assembled into a
webpage (a Type II report) at GUI 50 and presented to the customer
9. The exchange is flagged at Paypoint 2 and the information about
the transaction is forwarded to a billing server.
[0110] Paypoint 2 also offers the user the ability to configure the
transaction. In one model for reimbursement, the customer is
offered several methods of paying for the data ranging from
subscription and "pay-per-ping" with a credit card to wholesale
bulk access at a dedicated GUI. A preferred reimbursement model
permits the customer (often either the clinic or a physician) to
`pass through` the cost of the interpretive access and consultation
to a third party insurer. In this case, an interactive screen
associated with Paypoint 2 allows the user to enter insurance
information. Customers of type 9 may convert to customers of type
8, 7 or 6 by this method.
[0111] Note that in this model the customer is essentially handed
off from the client laboratory to the Host System operator. Given
the synergies of the model, the client laboratory also benefits by
this arrangement.
[0112] FIG. 4 is a flow diagram illustrating the production of a
Type I report and the operation of Paypoints 3, 4 and 5 of FIG. 1.
In this embodiment, a "pass-through" cost model for the client
laboratory is developed.
[0113] The client laboratory accesses the Host System 20 and enters
a patient identifier, laboratory identifier, and a genetic test
result in a database. A command from the client causes the Lab
Report Engine 24, operating with the Metabolomics Engine 21, to
produce a Type I laboratory report formatted with the client's
logo. The test report includes a phenotypic interpretation of the
genetic test result and a list of drugs likely to be associated
with a Change % AUC based on the phenotype. This is a drug-gene
interaction report. The report is then securely transmitted to the
consumer, either directly by the Host System at GUI 40 or by the
laboratory server. The laboratory flags the transmission (step 3)
as a billable service either at Paypoint 5, submitting an invoice
for reimbursement to a third party payer for the phenotypic
interpretation service, or at Paypoint 3, submitting an invoice to
the customer. In the preferred method, (step 4) the subscribing
laboratory recovers the costs of accessing host system resources by
generating a "pass-through" billing based on a reimbursement code,
such as a CPT code, corresponding to a generally approved fee for a
diagnostic pathology fee, which according to a preferred embodiment
of the method, is paid by the patient's insurance. The client
laboratory pays the Host System operator for access to the host
system.
[0114] The steps of the PK predictive algorithm in FIG. 4
illustrate the use of phenotype data to calculate CP for each drug.
In step B, drugs are selected for the sublist from the database
(22) by therapeutic class, Medicare Part D reimbursement
eligibility, regulatory approval specific to the jurisdiction,
frequency of prescription usage data, and so forth. The list is
unbundled by adding components of drug mixtures, prodrugs,
enantiomers and metabolites. Drug-gene interaction pairs are then
identified. Literature concerning the phenotype is searched to
determine the intensity INTX of inhibition or induction associated
with the impacted metabolic route. Parallel alternate metabolic
pathways are also evaluated for FRACTION R.sub.1/1-n (metabolic
throughput on the affected route R divided by total metabolic
throughput by all parallel pathways R.sub.1-n). The equation for CP
"change points" can then be solved:
CP=INTX*(R.sub.1/1-n),
[0115] This calculation is performed only if the patient phenotype
is abnormal. For each potential victim drug, the net affect of all
phenotypes is calculated by summing CP:
.SIGMA.CP=(CP.sub.R1+CP.sub.R2+ . . . CP.sub.Rn)
[0116] The calculations are stored in a summary table of results.
The value .SIGMA.CP is converted to a Change % AUC using a look up
table such as shown in FIG. 8. If the change in CP is less than a
threshold level and there are no clinical warnings in the notes in
the database, then the drug is deleted from the sublist. This
process is repeated for all drugs and the drugs remaining on the
sublist are tabulated for presentation in the Type I lab report as
shown for example in FIG. 9. The Type I lab report typically
consists of a patient identifier, laboratory identifier, a
phenotypic interpretation of the genetic test result(s), and the
predicted drug-gene interactions from the sublist, also showing
predicted Change % AUC (up or down). This report can be formatted
so that it appears consistent with the look of other documents or
webpages of the client laboratory.
[0117] FIG. 5 is a flow diagram outlining the major operations of a
PK predictive algorithm used in the preparation of Type II reports
of the examples. In step 6, any potential interaction pairs that
can be associated with a drug interaction are identified. These
include drug-drug, drug-bioactive, drug-clinical factor,
drug-phenotype, and substance:phenotype interactions.
[0118] In step 1, a factors list is entered and tabulated. This
list consists of drugs, bioactives, factors taken from the clinical
history, and genotypes or phenotypes. In step 2, the list is then
factored or "unbundled" by converting any drug mixtures to their
individual drug components, identifying prodrugs, replacing racemic
substances which have relevant enantiomers with the enantiomers
(for example warfarin has r and s isomers with markedly differing
metabolism and bioactivity), and adding to the table any
pharmacologically active metabolites. A class membership may also
be identified. Typically the genotype is already in the clinical
records database and the translation to phenotype has already been
made, but it may be done so in step 3 if not already completed.
[0119] In step 4, inhibitors and inducers on the list are
identified. Inhibitors and inducers may act on more than one
metabolic route. An intensity index expressing the degree of
induction or inhibition of each metabolic route is also tabulated.
Victim substances are then identified in step 5 and associated with
the metabolic routes. These operations are performed by accessing a
list of the metabolic pathways for each substance, and then
ascertaining all inducers and inhibitors of those pathways
contained in the factors list. The resulting table contains all
"interaction pairs" relevant for each metabolic pathway. Each
interaction pair includes one victim and one culprit substance or
factor (step 6).
[0120] The computer then makes, in step 7, quantitative interaction
calculations for each interaction pair:
CP=INTX*(R.sub.1/1-n),
where CP'' is the "change point" score for each metabolic route R
of each drug identified as the victim in the interaction pairs
table, INTX is an intensity of interaction index derived from
clinical and laboratory studies of individual substances and genes
(or factors), R.sub.1/1-n denotes R.sub.1(R.sub.1+R.sub.2 . . .
Rn), where Rn refers to one of the set of parallel metabolic routes
taken by the substance and R.sub.1/1-n is the proportion of
metabolism that flows through pathway R.sub.1, and so on. This
quantifies the relative change in AUC of the victim substance
resulting from one interaction on one metabolic pathway. The change
point score CP can be positive or negative, representing the
opposing effects of induction and inhibition.
[0121] In the next calculation, step 8, the CPs for all metabolic
routes R for each interaction pair are summed:
.SIGMA.CP=(CP.sub.R1+CP.sub.R2+ . . . CP.sub.Rn)
and the calculations are stored in a summary table of results.
[0122] For each drug or substance, the change in AUC can be
complex, resulting from multiple interactions. In a preferred
embodiment, for each interaction pair, a .SIGMA.CP score is
calculated that quantitates one particular interaction, and the
.SIGMA.CP scores of all the interactions are then summed across all
pairs for a common victim to determine the net change in drug blood
level and clearance for the victim of multiple interactions.
[0123] Step 9 converts raw change values to Change % AUC values for
each victim substance (See FIG. 8. FIG. 8 is representative of the
look-up process whereby .SIGMA.CP is converted to a percent change
in blood level). When the results are compared with published
clinical studies from the literature, this composite method is
surprisingly effective at making accurate predictions. We have
found that these results correlate well with literature studies
where available. The PK predictive algorithm's accuracy can be
tested by comparing the AUC changes it predicts for pairs of
interacting drugs with literature reports of clinical studies of
the same pairs.
[0124] In step 10, the algorithm may comprise a subroutine for
collating literature-derived reports related to each interaction
pair identified. Relevant clinical notes in the database
bibliographical records are called up and attached to the main
results table. Literature clinical study notes germane to the
substance class or "class membership" are also identified if
desired. The algorithm creates a list of the clinical studies and
their associated scientific confidence ratings. In instances where
research confirms the absence of interaction, an appropriate note
presents this information.
[0125] The result is a prediction of the effect of the interactions
on the victim substance AUC, its blood level and clearance time, as
reflected in the change in its pharmacokinetics as a result of the
second impacting "culprit" substance or factor. The prediction is
made even if supporting clinical studies are not available.
[0126] In subroutine A (FIG. 6) the impact of each metabolic route
Rn on the victim substrate is quantified as the product of the
interaction intensity INTX with the fractional metabolism of the
victim by the metabolic route over the total metabolism by all
parallel metabolic routes R.sub.1/1-n.
[0127] FIG. 7 is a detail showing the steps of subroutine B.
Subroutine B compares the prediction of an interaction with
literature citations stored in the databases. The warnings are
ranked by severity and the most significant warning is
displayed.
[0128] The warnings are displayed according to the following rules.
After tabulating all warnings according to priority from highest to
lowest, the highest priority warning on the list is displayed on
the Type II interaction report.
[0129] A. Major interaction warnings based on a clinical study
[0130] B. Reported interaction based on a clinical study
[0131] C. Major interaction warning predicted by the PK predictive
algorithm
[0132] D. Reported lesser interaction based on the PK predictive
algorithm
[0133] FIG. 8 shows a table used in the PK predictive algorithm to
convert .SIGMA.CP to a percent change in AUC. Showing are ranges of
point scores corresponding to .SIGMA.CP calculations (81, column
1), an interpretation of the predicted qualitative effect (82,
column 2), a change index used to build column 2, and a predicted %
change in AUC (84, column 4). Note that the Change % AUC can be up
or down (plus or minus sign).
[0134] Returning to FIG. 5, in Step 11 the Host System builds a
webpage that reports the Type II predictive analysis back to the
user. The user may in turn modify the input by selecting an
alternate drug not linked to an interaction (and potential ADR) and
run the analysis again. All clinical data is stored in the
system.
[0135] A refinement in the PK predictive algorithm of FIG. 5 is as
follows. Class memberships of the victim and culprit drugs are
identified. The database is then searched for other members of the
same class membership. The commonality of these class memberships
is a shared side effect. If found, the two drugs or substances of
the class are annotated in the report. This is done because side
effects can be additive if the two drugs are co-administered. Thus
the algorithm can also detect DDIs even when a genetic interaction
is not relevant.
[0136] Interestingly, the patient can share this service with the
physician, or vice versa. During an office visit, patient and
physician can model and discuss alternative therapies and use the
system to explain any unexpected adverse reactions when the patient
tries a medication, ordering genetic testing if necessary. The
system will update the Type II report each time it is accessed.
[0137] Prior art reports do not contain a list of drugs sorted by
drug class that have been shown by predictive algorithm or review
of the clinical literature to interact with the listed phenotype.
Instead there is a black-box warning, "Do not alter the dosage
amount or schedule of any drug you are taking without first
consulting your doctor or pharmacists." This warning is necessary
given the risk of DDI and ADR in this patient phenotype, but is of
little value at the point of care in prescribing safely. There is
thus a need for improvement over the prior art, a need met by the
algorithms of the current invention.
[0138] Avoidance of a major ADR is a medical cost savings, but
requires the `pass through` reimbursement functions of the system
to be generally accessible to physicians. In this system, the user
is again interfacing directly with the Metabolomics Engine at GUI
50 and may be preferred "full-subscription" customers 6,
"conversion" subscribers 8, "trial use" or "sponsored use" users 9,
or "fee-for-service" customers 7, for example. Paypoint 2 is
provided with a means for flexibly selecting a suitable payment
option, including insurance options for pass-through of costs, thus
ensuring that the drug interaction warning and supplemental
information is made available where and when needed.
[0139] In a preferred embodiment, this advanced level of
interactive information is made accessible to the end user through
a hyperlink or access code appended to a Type I Lab Report such as
is generated by the Lab Report Engine at GUI 30 or 40. The
hyperlink is a link to interactive GUI 50 with more detail on 1A2
Hyperinduction related to any pair of drugs (or drug:herbal pairs,
etc.), and a user can proactively enter their own prescription
information to determine whether there is a contraindication before
prescribing or taking them.
[0140] FIG. 9 is a view of a sample Type I lab report 170 with
sponsored-use hyperlink 173. The report includes an interpretation
of a phenotype 171 associated with the below-named genotype. In
this example a 2D6 poor metabolizer is associated with a CYP2D6
*3/*4 genotype, as determined by genetic testing. At the bottom of
the report, an extended list 172 (here truncated) of drugs of
predicted interactions with the named phenotype is given, allowing
the end user to identify any potential concerns for follow up.
[0141] Also provided is a hyperlink 173 for more personalized
information. In this embodiment, the hyperlink corresponds
"sponsored user" hyperlink of the type shown in FIG. 1, element 43
(*) and is linked to GUI 50 at Paypoint 2. At GUI 50, various
structured types of payment for access are available, but by
providing a trial or sponsored service at Paypoint 2, genetic
testing customers 9 are converted to use of the Host System. In a
preferred business model, a "sponsored user" hyperlink 43 takes the
consumer, whether a physician, patient, or lay caregiver, to an
enhanced graphic user interface (GUI 50) with improved paypoint
capability (Paypoint 2). With hyperlinks of this sort, a trial
period is offered so that customers discovering the enhanced
services of GUI 50 through the sponsored user hyperlink access will
be encouraged to try out the service and convert to a direct
financial relationship with the Host System operator, opting for
the full or layered service of Paypoint 1. Payment options can
comprise a fee-for-service, subscription, trial, discount,
wholesale, or other relationship whereby the user accesses the
tools of GUI 50. In a preferred embodiment, Paypoint 1 offers a
`pass through` feature convenient for medical health care
professionals who need access to pharmacogenetic testing data, such
as when writing prescriptions, but who would not choose to pay for
those services when undertaken on a patient's behalf. The patient
can, for example, access the service while meeting with a health
care provider, to ensure that any prescriptions written is likely
to be compatible with other patient factors already entered in the
system, and optionally, bill the on-line consultation services to
an insurer.
[0142] The predicted drug-drug interactions have been reassorted by
therapeutic class and when accessed, the list is much more
comprehensive, spanning 5 pages (not shown). The report states,
"These genetype-based drug metabolism tables are generated from the
GeneMedRx drug interaction computer program, which is based on a
compilation of information found in the medical literature and
interpreted by the use of a computer algorithm. The tables are to
be used as a tool to provide decision support, consultation and
advisory input to clinical care by medical professionals." Contrast
this with the black box warning above.
[0143] FIG. 10 is a representative view of a interactive screen 180
titled "Drug-Drug and Gene-Drug Interactions" and begins with a
note to "start here" (181). This is provided as an example of a
portal to the Type II GUI. The end user (a patient or patient's
representative) begins by entering a patient regimen at window 182.
The selections can be entered by selecting factors from the list
shown in window 187. The regimen consists of prescription drugs
being taken and other patient factors as may be configured by the
system operator. Notes regarding any relevant clinical history can
also be entered in window 184. Information about the patient's
phenotype is displayed at 184. The phenotype is generally entered
by a contract laboratory providing genetic testing services and is
stored on the Host System without need for re-entry by the patient.
Sublists (e.g. "herbals", bullet 185) are provided for entry of
other factors, such as herbals, over-the-counter drugs, and
foodstuffs known to interact in drug metabolism. There is also an
option 186 to make referrals. Once this information is entered, we
turn to FIG. 11 for the next step--step three.
[0144] FIG. 11 shows a "check interactions" button 190 displayed on
the GUI 50 interactive website. Also shown is a convenience
function 191 for ordering genetic testing services. In one
embodiment, this ordering function is a referral of the user back
to the laboratory that provided the genetic test result which in
turn brought the user to GUI 50. It may also include links to other
genetic testing services, such as paternity services.
[0145] FIG. 12 is an "interaction report" (i.e. a Type II report)
generated by GUI 50. The report 200 is sent to the user in response
to a command to check interactions as shown in FIG. 11. This Type
II functionality is characteristic of GUI 50. The report describes
a major interaction 201 (known in the clinical literature) between
tamoxifen and paroxetine, also predicted by the PK predictive
algorithm, for a 2D6 intermediate metabolizer phenotype 202. The
interaction between tamoxifen and paroxetine is both a drug-drug
interaction, paroxetine the victim and tamoxifen the culprit, and
also a drug-gene interaction; both drugs are impacted by the 2D6
intermediate metabolizer phenotype. Added information about the
mechanism and notes to hyperlinks for in-depth information and
self-directed search are also provided. Note that the user can
click on a hyperlink 203 to return to and enter or edit the list of
drugs substances (and other factors) that are part of the patient's
current treatment regimen, perhaps selecting an alternate drug.
[0146] FIG. 13 is a webpage 210 used at Paypoint 1 or Paypoint 2 to
configure reimbursement options. The user is asked for more
information which corresponds to a market segment. By selecting the
appropriate bullet from the list 211 (bracket), the user is
directed to follow-on pages with the appropriate functionality.
Data is entered that allows the system to process financial
transactions covering reimbursement for the system's data exchanges
with the user. A patient who selects bullet 212, for example, is
offered additional choices of credit card or entry of insurance
information, and the credit card or insurance is then verified for
authorization to conduct the transaction. Various trial
subscriptions 213 are also optional, both for medical professionals
and for patients. After completion of the financial information,
the user is then directed to a start page for selection of
permissible tasks. Not all users have equal access to core and
extended functionality. Users who have a sponsored subscription
will be directed to a webpage to enter the appropriate passwords or
access codes before being granted access to system functions.
[0147] In one aspect, the invention is directed at a Type II
predictive algorithm and apparatus or method for performing the
operations of the predictive algorithm. The invention comprises a
method or apparatus for predicting a substance-factor interaction,
including drug-drug and drug-gene interactions, and comprises steps
for [0148] a) providing a graphical user interface, a host system,
and a database, wherein said graphical user interface is configured
for: [0149] i) accessing a patient record in said database, said
patient record comprising a patient identifier and a first patient
phenotype; [0150] ii) entering one or more factors into a list
associated with said patient identifier, wherein said one or more
factors are selected from the group consisting of prescription
drug, substance, and personal characteristic; [0151] b) providing a
predictive algorithm implemented on said host system, said
algorithm having instructions for performing operations on said
database, said patient record and said associated list, wherein
said operations comprise: [0152] i) unbundling the list, thereby
forming an unbundled list; [0153] ii) determining each factor on
the unbundled list that is an inhibitor or an inducer; and
assigning an intensity index INTX to each said inhibitor and
inducer; [0154] iii) selecting from the unbundled list a sublist of
victims, where a victim is a factor having the property of being a
metabolic substrate of one or more metabolic routes Rn; [0155] iv)
identifying each metabolic route associated with said sublist of
victims; [0156] v) identifying each interaction pair associated
with said each metabolic route, each interaction pair consisting of
a victim and a culprit; [0157] vi) for each victim of said each
interaction pair; calculating a CP score by multiplying an
intensity index INDX associated with the culprit times a metabolic
throughput proportion R1/1-n, where R1/1-n is calculated as the
metabolic throughput of said each metabolic route Rn divided by a
sum of the throughput of all metabolic pathways acting on the
victim in parallel; [0158] vii) summing the CP scores for each
victim and for each interacting pair, and tabulating the sums
.SIGMA.CP; [0159] viii) computing a change percent AUC for each
victim and for each interacting pair; [0160] ix) displaying a Type
II report tabulating patient identifier, patient phenotype, factors
entered in said list, and change % AUC for each victim; and, [0161]
x) flagging the Type II report as a billable service.
[0162] The invention is adapted for predicting interactions between
a plurality of substance-factors where there are a plurality of
victims. In one Type II method, the sublist of victims comprises a
plurality of victims. In another Type II method, the patient record
comprises a plurality of patient phenotypes. In another Type II
method, the substance-factor interaction comprises a drug-gene
interaction, a drug-drug interaction, or a combination thereof.
[0163] Type II methods also can include provision for identifying
and displaying literature notes and warnings. The algorithms
further comprise a subroutine, said subroutine having instructions
for performing operations on said database, said patient record and
said associated list, wherein said operations comprise: [0164] i)
accessing the database and identifying notes or warnings compiled
from published reports of an interaction between said first victim
and said culprit; [0165] ii) if no notes or warnings compiled,
reporting said change percent AUC identified with said first victim
and said culprit; and, [0166] iii) if notes or warnings compiled,
reporting said notes or warnings identified with said first victim
and said culprit.
[0167] Type II methods can also include provision for assessing
class membership, wherein the operations further comprise: [0168]
i) for any prescription drug or substance on said list, accessing
the database and identifying a class membership, wherein said class
membership is defined by a side effect produced by all members of
the class; [0169] ii) for any class membership identified herein,
accessing the database and identifying any factors from the list
having said class membership in common; and, [0170] iii) reporting
said factors in common, with a note advising that said side effect
can be additive.
[0171] Type II methods include tools for making alternate drug
selections. The operations further comprise: [0172] i) for any
potential interaction pair for which said change percent AUC
exceeds a threshold value, accessing said database and identifying
a therapeutic class associated with said first victim; [0173] ii)
identifying an alternate member of said therapeutic class and
calculating an alternate percent change AUC for the alternate
member; [0174] iii) reporting the alternate member in a listing of
interactive selection of alternates if the alternate percent change
AUC does not exceed a threshold value.
[0175] The methods and apparatus also include provision for
generating a Type I lab report, which will predict a drug-gene
interaction when given a phenotype and a list of drugs. The method
or apparatus comprises a graphical user interface, an host system,
and a database, wherein said graphical user interface is configured
for: [0176] i) entering a patient record in said database, said
record comprising a patient identifier and a first patient
phenotype; [0177] ii) providing a predictive algorithm implemented
on said host system, said algorithm having instructions for
performing operations on said database, said patient record and
said associated list, wherein said operations comprise: [0178] iii)
accessing a list of drugs on a database, said drugs comprising
prescription drugs and substances, and unbundling the list, thereby
compiling an unbundled list; [0179] iv) determining an inhibition
or an induction of at least one metabolic route Rn associated with
said first phenotype; and assigning an intensity factor INTX to
said inhibition or induction; [0180] v) selecting from the
unbundled list a sublist of victims, where a victim is a member of
said unbundled list having the property of being a metabolic
substrate of said metabolic route Rn associated with said first
phenotype; [0181] vi) for each victim in said sublist, calculating
a CP score by multiplying an intensity index INDX associated said
inhibition or induction of said at least one metabolic route Rn
associated with said first phenotype times a metabolic throughput
proportion R1/1-n, where R1/1-n is calculated as the metabolic
throughput of said metabolic route Rn divided by a sum of the
throughput of all metabolic pathways acting on the victim in
parallel; [0182] vii) from the CP score of the preceding step,
computing a change percent AUC; [0183] viii) discarding any drugs
in the sublist if the change % AUC is below a threshold value;
thereby forming a summary table; [0184] ix) displaying a Type I
report tabulating patient identifier, patient phenotype, laboratory
identifier, and change % AUC for each victim in said summary table;
and, [0185] x) flagging the Type I report as a billable
service.
[0186] Type I methods are also adapted to predicting a drug-gene
interaction for a plurality of patient phenotypes, and said
operations comprise calculating a .SIGMA.CP score for each victim,
where said .SIGMA.CP score is the sum of the CP scores over said
plurality of phenotypes, and computing change % AUC for each victim
from the .SIGMA.CP score in said summary table.
[0187] The methods are also adapted as business methods. In one
embodiment the invention is a business method for obtaining
reimbursement for pharmacogenetic interpretive services, which
comprises: [0188] a) implementing a billing server configured for
detecting a flag associated with a service of claim 1; [0189] b)
invoicing a payer associated with the patient identifier, and
optionally, said payer is a third party payer and said billing
server comprises an insurance submodule.
[0190] In another embodiment the invention is a business method for
obtaining reimbursement for pharmacogenetic interpretive services,
which comprises: [0191] a) implementing a billing server configured
for detecting a flag associated with a service of claim 7; [0192]
b) invoicing a payer associated with the patient identifier, and
optionally, said payer is a third party payer and said billing
server comprises an insurance submodule.
[0193] The methods are adapted for operation at Paypoint 1.
Conceived is a business method, as implemented on a computerized
host system, for obtaining automated third-party reimbursement by
providing pharmacogenetic interpretive services for preventing a
possible adverse drug reaction, comprising the steps of: [0194] a)
providing a first user with a means for accessing a host system and
a means for entering a patient record comprising a patient
identifier of a patient and a genotype associated with said patient
identifier, and thereupon [0195] b) on command of said first user,
translating said genotype into a phenotype and entering said
phenotype in said patient record; [0196] c) providing a second user
with a means for entering a plurality of factors into the patient
record, wherein the factors are from the group consisting of
prescription drug(s) prescribed, substance(s) used, clinical
factor(s); [0197] d) upon command of said second user, computing a
change % AUC for any interacting pairs of factors entered,
computing a prediction warning of a potential bioincompatibility
between said interacting pairs, wherein said prediction is made by
a PK predictive algorithm, and displaying a report; and, [0198] e)
flagging the prediction as a billable service; and wherein the
method is further characterized in that reimbursement is made
according to a prearranged fee schedule between an operator of the
host system and a third party payer contracted by said patient to
pay for said billable service.
[0199] The methods are also adopted for operation at Paypoint 3, 4
and 5. Conceived is a business method, as implemented on a
computerized host system, for obtaining automated third-party
reimbursement by providing pharmacogenetic interpretive services
for preventing a possible adverse drug reaction, comprising the
steps of: [0200] a) providing a first user with a means for
accessing a host system and a means for entering a patient record
comprising a patient identifier of a patient and a genotype
associated with said patient identifier; and thereupon [0201] b) on
command of said first user, translating said genotype into a
phenotype and entering said phenotype in said patient record;
[0202] c) upon command of said first user, selecting a list of
drugs, computing a change % AUC for any interacting pairs of
factors entered, computing a prediction warning of a potential
interaction between said drug and phenotype, wherein said
prediction is made by a PK predictive algorithm; and preparing a
Type I lab report comprising a pharmacogenetic interpretive
service; [0203] d) upon command of said first user; transmitting
said report to a customer, wherein said customer is a customer of
said first user and flagging said transaction to a billing server
operated by said first user; [0204] e) receiving a reimbursement
from said first user for access to said host system; wherein the
method is further characterized in that the billing server operated
by said first user automatically bills for said pharmacogenetic
interpretive service. Optionally, the billing server is further
characterized in that said billing server automatically bills a
third party payer contracted by said patient to pay for said
pharmacogenetic interpretive service.
[0205] Customer conversion methods are also conceived. In one
embodiment, we conceive a business apparatus for obtaining
automated reimbursement for pharmacogenetic interpretive services,
said apparatus comprising a computerized host system operated by a
host system operator and having a means for data storage, a means
for data processing, a means for networking, a first graphical user
interface for access to the host system by a first user, a second
graphical interface for access to the host system by a second user,
wherein said apparatus is configured with means for: [0206] a)
under control of said first user, entering and storing a laboratory
identifier, patient identifier and a genetic test result comprising
a patient genotype in a patient record on said first graphical user
interface of said host system, said first user being a laboratory
with a client relationship with said host system operator; [0207]
b) on command of said first user, performing a phenotypic
interpretation of said genotype entering said phenotype in said
patient record on said host system; [0208] c) on command of said
first user, using a first predictive algorithm resident in said
host system to prepare a predictive drug-gene interaction report (a
Type 1 report); [0209] d) under control of said host system
operator, appending a hyperlink to said predictive drug-gene
interaction report, said hyperlink having the property of linking
to said second graphical user interface; [0210] e) on command of
said first user, transmitting said predictive drug-gene interaction
report with appended hyperlink to said second user, said second
user being a patient or a responsible medical care provider; [0211]
f) under control of said second user, opening said second graphical
user interface when said second user accesses said appended
hyperlink; [0212] g) under control of said second user, editing
said patient record to add a list of factors to be associated with
said patient identifier, wherein said factors are selected from the
group consisting of prescription drug, substance, and clinical
factor; [0213] h) on command of said second user, using a second
predictive algorithm resident in said host system to prepare a
predictive drug-drug and drug-gene interactive report (a Type II
report), flagging said predictive drug-drug and drug-gene
interactive report as a pharmacogenetic interpretive service, and
displaying said Type II report to said second user on said second
graphical interface; [0214] i) in response to said flag, billing
said second user for said pharmacogenetic interpretive service,
wherein said second user has preselected a payment method by
entering a financial instrument at a paypoint associated with said
second graphical user interface.
[0215] Optionally, said paypoint (typically paypoint 2) is
configured for entering a financial instrument to be used as
payment for said pharmacogenetic interpretive service. The
financial instrument may be selected from insurance information,
credit card information, on-line debit information, sponsored use
access code, trial use access code, or subscription
information.
[0216] In another embodiment of the conversion methods, conceived
is a business method, as implemented on a computer host system, for
obtaining automated third-party reimbursement by providing
professional interpretation of a genetic testing result, comprising
the steps of: [0217] a) As a service to a client, said client
having a customer, said client having provided a genetic test
service for a patient on request of said customer, [0218] 1)
providing said client with a means for accessing a host system (20)
at a first graphical user interface (30) and a means for entering a
customer identifier and a genetic test result associated with the
customer, and storing that information in a database on the host
system; [0219] 2) on command of the client, generating a test
report for the client; the test result including i) a phenotypic
interpretation of the genetic test result associated with that
customer, ii) a list of drugs likely to be associated with an
adverse drug reaction when administered to said patient, iii) a
"sponsored user" hyperlink to a second graphical user interface,
and iv) a password; [0220] 3) on command of the client, securely
transmitting the test report to the customer, said transmission
constituting a service billable by the client to a third party
payer; [0221] b) when the customer accesses the "sponsored user"
hyperlink and enters the password at a paypoint (2), [0222] 1)
providing the customer with direct access to an interactive webpage
on the second graphical user interface (50) and allowing the
customer to enter a plurality of clinical factors to be associated
with the patient identifier and patient phenotype, including
clinical factors selected from the group consisting of prescription
drug(s) prescribed, bioactive substance(s) used, and clinical
history factor(s), [0223] 2) securely updating the interactive
webpage with a prediction evaluating the biocompatibility between
the clinical factors associated with the patient identifier and
with the patient phenotype; [0224] 3) displaying a warning of any
predicted bioincompatibility; and, [0225] 4) flagging the access as
a billable service, and receiving reimbursement for the direct
access to the system, and further receiving reimbursement from the
client for access to the system.
[0226] In this latter conversion model, paypoint 2 is configured
with a menu for choosing a method of payment selected from a) free
trial period with authorization code, b) credit card payment for
access, c) debit card information; d) entry of insurance
information and on-line authorization from the insurer, e)
subscription payment for access, f) sponsored use with
authorization code, and g) wholesale group contract payment for
access.
[0227] Unless the context requires otherwise, throughout the
specification and claims which follow, the word "comprise" and
variations thereof, such as, "comprises" and "comprising" are to be
construed in an open, inclusive sense, that is, as "including, but
not limited to".
[0228] Reference throughout this specification to "one embodiment"
or "an embodiment" means that a particular feature, structure or
characteristic described in connection with the embodiment is
included in at least one embodiment of the present invention. Thus,
the appearances of the phrases "in one embodiment" or "in an
embodiment" in various places throughout this specification are not
necessarily all referring to the same embodiment. Furthermore, the
particular features, structures, or characteristics may be combined
in any suitable manner in one or more embodiments.
[0229] While the above is a description of the preferred
embodiments of the present invention, it is possible to use various
alternatives, modifications and equivalents. Therefore, the scope
of the present invention should be determined not with reference to
the above description but should, instead, be determined with
reference to the appended claims, along with their full scope of
equivalents.
* * * * *