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Purdue’s ‘Mock Patient’ Software Platform May Arm Pharmacists With Genetic Info

As direct-to-consumer genetic testing gains traction, researchers at Purdue University have developed a data-management platform to help pharmacists make dosage recommendations to physicians based on patients’ genetic profiles. The system could also be used abroad, where pharmacists play a greater role in determining prescription dosage than they do in the U.S.
Michael Kane, an assistant professor in the department of computer and information technology and lead genomic scientist at Purdue’s Bindley Bioscience Center, has led development of the database and software platform, called PGRx.
Kane told BioInform that he plans to begin beta-testing the platform in a pharmacology program at Ohio Northern University this spring, and later at Purdue. Longer term, he envisions the system being of interest to pharmacists, who might use it to ensure that a patient’s genotype will not affect how a prescribed drug is. However, he is not sure when they might actually use the system, he said.
“Next summer we plan to extend the educational use of the system to host a workshop for continuing education for pharmacists who are interested in pharmacogenetics training,” said Kane. Yet “ I don't see the PGRx system being used in the pharmacy anytime soon” --- at least not into next year --- “so I don't know when it will be used as an operational component of healthcare.”
He added that it’s tough to provide additional details about commercialization since many activities are either confidential or subject to uncertainty.
Also, in the US, the pharmacist’s role is more limited than in some other countries – a reason, Kane said, why the system might first launch overseas.
“In some countries such as England, Scotland and Wales, pharmacists have prescription power for some medications under certain conditions,” Kane said. “This capability better positions the pharmacists in these countries to use pharmacogenetics tools at the point of drug dispensing, providing that they would have the authority to simply alter the dosing regimen if it was required to diminish the risk of an adverse drug response.”
Further, if Kane’s future commercialized vision of the software takes off in the States as well, “the pharmacist will respond to warnings derived from patient-specific genomic information in a similar manner to uncovering a drug-drug interaction. In this scenario, a gene-drug interaction will prompt the dispensing pharmacist to immediately contact the prescribing physician and recommend an alternate dosing regimen or an alternative drug altogether.”
He said, though,that the “initial rational for the system is education, and we are [simply] exploring a production version to support pharmacist-patient interactions.”
According to Kane, adoption of the system by pharmacists is dependent upon other factors. “Importantly, the utility of this system is dependent upon low-cost genomic screening of known SNPs in the gene that encode these enzymes for patients, which is a new concept to healthcare in general,” he said.
Mock Patient Polymorphisms
PGRx is based on a database of 30 “mock patients” that includes information on known polymorphisms that could affect drug metabolism. This population “represents an exhaustive collection of known SNPs in major drug-metabolism enzymes that have been shown in vivo to confer altered drug metabolism phenotypes in humans,” Kane said.
"We used the major phase-1 oxidative enzymes involved in human drug metabolism (CYP1A2, 2B6, 2C19, 2C9, 2D6, 2E1, 3A4,5,7) and incorporated the SNPs that have been shown to have an effect on these enzymes in vivo, not just in in vitro models,” he added.
He said these effects include SNPs in promoter regions that alter the gene's inducibility and/or expression, splicing defects, and altered protein sequences in the enzymes. “This totals about 30 known SNPs that have been proven phenotypic effects on drug metabolism.”
With PGRx, “if you type in a patient’s name, which would eventually represent a real patient, one has a drop-down list to pick the patients, the drug they’ve been prescribed and [knowledge about whether the patient] harbors a polymorphism that makes them less likely to metabolize that drug,” Kane said.
He said the mock patient population is not intended to represent a specific population demographic or genetic pool. “It is simply intended to exhaustively represent all clinically relevant SNPs in phase-1 oxidative enzymes in humans.”
In addition, Kane said, the database includes “all approved drugs that are known to be substrates to the major P450 metabolic enzymes,” including the anticoagulant warfarin, for which the manufacturers were asked by the US Food and Drug Administration in August to explain how certain genetic variations may influence metabolism and affect response.

“If you type in a patient’s name, which would eventually represent a real patient, one has a drop-down list to pick the patients, the drug they’ve been prescribed and [knowledge about whether the patient] harbors a polymorphism that makes them less likely to metabolize that drug.”

While the link between genetic variants and metabolic response to warfarin and other drugs is well established, Kane said that physicians and other healthcare providers do not have ready access to this information.  
This disconnect has prompted stakeholders to begin using the knowledge themselves in an effort to improve outcomes and profits. For instance, last December pharmacy benefits manager giant Medco and the Mayo Clinic said they planned to study the clinical and economic value of incorporating genetic testing into warfarin therapy as a way to ease payor anxiety about using such information to make drug-coverage and reimbursement decisions [see 12/6/2006 issue of BioInform sister publication Pharmacogenomics Reporter].
Three months earlier, molecular diagnostic company Clinical Data signed a deal with PharmaCare Management Services, another large national pharmacy benefits manager, to use its testing service to improve how warfarin and another commonly prescribed drug is prescribed [see Pharmacogenomics Reporter, 9/20/2006].
The goal of both projects is to help gauge whether certain drugs will trigger an adverse event that’s dangerous to the patient and costly to the payor client.
These kinds of alliances are designed to eventually lower the cost of genetic testing, which will in turn become more common.
Kane is Able?
The system has also caught the attention of DNA-testing firm Genelex, which is considering how it could possibly work with the Purdue research team. Genelex CEO Howard Coleman told BioInform his company is interested in, but not committed to, the Purdue scheme.
“We are simply exploring the possibility of a relationship with them,” said Coleman. Kane’s proposal “doesn’t go into a lot of depth; it’s a view from 3,000 feet. It seems like it could be viable, but there are a lot of people working on similar sorts of things.”
Genelex has its own software platform, called GeneMedRx, and Coleman said they are always keen to integrate it with other complementary platforms and says that in this instance, at least, “it appears we’ve already solved part of the puzzle that they are working on.”
“There’s no sense in people having to reinvent the wheel,” Coleman told BioInform, “and there are so many elements to a comprehensive … pharmacogenomics database and interpretation software that it makes sense to incorporate software from other sources.”
Kane’s vision for PGRx as a pharmacy-support tool depends on several factors, including widespread adoption of electronic medical records that contain patients’ genetic information.
Coleman said he believes widespread adoption of EMRs is at an “inflection point” right now and that adoption may be common in two years. He admitted, though, that the model for life science technology might not necessarily mirror the adoption rate of other industries.
Indeed, in 2001, Norman Beaulieu, former editor of IEEE’s Transactions on Communications, noted that “technology typically must first be proven experimentally, justified economically, and then become standardized and economically affordable before it penetrates the consumer market.”
According to Kane, “at a much higher level, the adoption of an [electronic health record] system requires a beneficial value proposition to the consumer and the healthcare industry. The system we have developed represents a viable application of clinical genomic data for decreasing the risk of adverse drug responses, and therefore will contribute to the eventual adoption of an EHR model in healthcare.”
PGRx is built with standard web service tools and has been designed for three different database back ends — MySQL, Oracle, and MS Access.
Kane said that “educational components” of the system will be available online in six to eight weeks but declined to discuss further details of the development timeline.

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