Let's Get Personal

Science, Policy, and Ethics in Personalized Medicine

Realizing the Promise of Pharmacogeomics

On Friday, the Secretary's Advisory Committee on Genetics, Health, and Society (SACGHS), an advisory body for the Secretary of Health and Human Services (HHS), released its draft report Realizing the Promise of PGx: Challenges and Opportunities for public comment. I want to talk about my impressions of their findings and recommendations. I'm going to constrain myself to the Executive Summary and the Introduction (with the occasional stop into the main text for more context), mainly because I haven't had time to thoroughly read the report's hefty 100 pages.

To begin with, I want to mention one caveat. This report focuses (like the title says) on pharmacogenomics (for brevity I'll use their abbreviation, PGx). This is distinct from personalized medicine, both because personalized medicine is broader (it incorporates a number of facets other than a patient's response to a specific drug) and because PGx is broader (there are some important basic science problems that can be addressed by pharmacogenomic research that, while tangentially related to medicine, are not directly clinically relevant. There is significant overlap, however, and many of the problems and challenges of PGx also apply to personalized medicine more broadly.

The report makes recommendations in fifteen areas. I'm going to focus on just a few of these and talk about their recommendations for

The recommendations suggest that the FDA needs to provide guidance for companies intending to develop drugs and associated diagnostic tools to assess the drugs efficacy for a specific person. In particular it says they need to address the review process for the case where the drug is subject to FDA review but the diagnostic test is not. I have a simpler question: should there ever BE a case where a diagnostic test intended to identify people who will respond to a specific drug? This is a case where I'm not sure how far the regulatory oversight of the FDA extends, and what precisely is covered or not, but one important step in both making PGx effective and ensuring public confidence is extensive and exhaustive validation by an objective body. For drugs and diagnostics this means FDA review. The report also recommends providing incentives to the private sector for developing PGx technologies. To a limited extent I think this is an excellent idea, but it has to be executed properly. Being first into a market is potentially expensive, yes, but there are definite benefits to it. I think that providing some financial incentives is a reasonable way to encourage investment, and I think that expedited FDA review (another suggestion) is an excellent idea. The last suggested way of encouraging investment (and I understand that these are simply ideas for discussion and not concrete recommendations) is increasing intellectual property rights of these early investors. This is a very bad idea, and one that is at odds with another goal - equitable and widespread access to PGx technology. Private industry will be an important driver of the field, but the financial rewards they stand to reap should be enough. Strengthening IP protection will only serve to limit access due to cost.

Analytic validity, clinical validity, clinical utility, and cost-effectiveness are the foundations that clinical practice modifications are based on. Unless a new test or technique sufficiently demonstrates these traits, no physician is going to adopt it. The report recommends that HHS work to assess these for PGx applications and develop ways to improve it, such as better datasets and improvements to study methodologies, as well as quantifying the differing levels of evidence required for different uses of PGx technology. More importantly, pharmaceutical manufacturers should publish the results of studies on the clinical validity and utility of PGx, even (I would say especially) non-significant or negative results) or make the data available to be studied by others. I think a better approach may be to require drug makers to report these results to the FDA as part of the approval and surveillance process. They will still want to publish positive results in peer-review journals, and I think that's a fine thing, but the results from all of their studies should be available to other researchers in some other form.

Data sharing is a potential goldmine for researchers. Right now obtaining datasets can quite difficult, both mechanically (because of their size and format) and politically (because they are well-protected even by government-funded researchers). The report recommends that HHS identify the obstacles to data sharing and encourage companies and academic institutions to participate. It is also important for future research to develop ways to share and use patient data, and again, the report suggests that HHS work in coordination with other agencies and programs to ensure the interoperability of the various electronic health records systems in use and in development.

Of course if this work stays in the basic research phase indefinitely, it doesn't do a lot of good. The report recommends that HHS help to catalog and and disseminate applications of PGx technology, work with professional and licensing organizations to improve physician education, publish systematic reviews of PGx and its applications as they become available to help inform usage guidelines, and ensure that package inserts and labels on both drugs and PGx tests contain all available PGx information. This is especially important, because over 70% of current drugs have some PGx information available about them, but almost none contain this on their labels.

Last is health information technology. HHS needs to both encourage the growth of health IT experts as well as the inclusion of PGx in to current and future electronic health records (EHR). The report recommends working with Office of the National Coordinator for Health Information Technology (did you know there was such a thing?) and other agencies to ensure that both EHR and clinical decision support tools take into account currently available PGx information. Also, for the current time (when EHR are not universal), HHS should develop way for physicians to retrieve and utilize PGx information.

Overall, I think the report strikes the right tone - hopeful for the future applications but realistic both of the current state and the challenges that face the field. A number of the recommendations the report makes will also directly benefit personalized medicine broadly as well. My sense is that this report won't change very extensively before becoming finalized, and when it is, it will be an important roadmap within HHS and the NIH specifically as to what PGx projects should have priority. Anyone who is working in the field should read this both to get a sense for how the wind is blowing as well as for the chance to have some impact through your comments on the direction of PGx in the next decade.

The Genetic Information Nondiscrimination Act of 2007

I'm going to deviate a little from the planned topic for today. A bill I've mentioned before, the Genetic Information Nondiscrimination Act, has been in the news recently, and will hopefully pass within the next few weeks. I have a ton of respect for Congresswoman Slaughter (she represents Rochester, NY, where I went to college, and was a big supporter of RIT), the bills primary sponsor in the House, and she has real science bona fides, with a degree in microbiology and masters degree in Public Health, but how good is this bill?

I want to spend a little bit of time dissecting it (not parsing phrase-for-phrase, but rather pulling out important points), and trying to assess its potential impact. Most of the press this bill has received has been positive (if uncritical, but what do I expect from the mainstream media on science?), but I'm always uneasy when I see a very diverse group of people supporting something. If all of these people like it, how can it possibly be doing much of anything? At the same time, at least some health insurers are opposed, and that gives me some visceral, if not intellectual, confirmation that the bill on the right track.

The bill begins with five findings that motivate the bill and inform its purpose:

  1. Genetic tests and research can lead to disease prevention but could be used for discriminatory practices
  2. Government's track record with genetics is spotty, we need to prevent a repeat of the practice of forced sterilization
  3. Genetic markers could lead to stigmatization of groups in which particular dieases are most common
  4. At least some workplace discrimination has already occurred because of genetic tests (Norman-Bloodsaw v. Lawrence Berkeley Laboratory)
  5. Federal law doesn't adequately address the potential for discrimination.
These five findings lay out a pretty compelling argument for some type of legislation - it gives solid examples of the potential consequences of this information, offers a concrete instance of one of the examples happening, and says that the current laws can't fully combat the potential problems. If you don't take issue with any of these facts, then the logical conclusion is a strengthening of current nondiscrimination legislation.

That's precisely what this bill proposes. First, the bill amends Employee Retirement Income Security Act of 1974 (29 U.S.C. 1182) to prevent requirements for genetic tests before enrollment and to prevent modifying the premiums in a group health plan because of information about a plan member or the family of a plan member. At the same time, it also ensures that a person isn't denied access to genetic tests because of this, that a health care provider can discuss or suggest genetic tests with a patient as part of their care, and that a doctor cannot force someone to take a genetic test. The bill proceeds to extend the same protections to both group insurance plans not affiliated with employment, with individual health plans, and to medicare supplemental coverage. Finally the bill places genetic information as protected health information under HIPAA.

The next section deals with employment discrimination. It prevents an employer from refusing to hire or firing an employee because of the results of a genetic test, and it prevents them from segregating employees based on genetic tests in such a way that is harmful to their employment opportunities. Moreover, an employer can't request, require, or purchase genetic information about an employee or a family member of an employee. There are a few exceptions to this, but they typically require informed consent from the employee and no individually identifying data being received by the employer. These regulations are similarly applied to employment agencies, labor unions, and training programs. The bill explicitly excludes discrimination based on genetic information as a cause of action for a federal civil rights case, but establishes a committee to meet in 6 years to reconsider that decision. Lastly, the bill says that medical information about an existing disease, even diseases with a genetic basis, is not considered genetic information.

After giving the bill a fairly thorough reading, I'm satisfied with it, it goes a very long way to preventing health insurance and employment discrimination because of genetic information. The specific penalties for employment discrimination are somewhat opaque to me, but the minimum damages for health insurance discrimination ($2,500 normally or $15,000 in some special cases) seem reasonable to me. I also like the fact that it explicitly defines some medical information as not genetic information even though that medical condition may have a genetic basis. A line is definitely needed to separate genetic and non genetic information, because without that, almost any trait or characteristic can be claimed to have a genetic underpinning. An employee with a bad attitude, who starts fights and is generally a pain in the ass, is someone you'd want to fire. But what if they claim that their attitude is genetically based (and there is certainly evidence that some factors that make up temperament are genetic)? Without that line, an argument could be made that the firing is based on genetic information and therefore illegal. I'm not suggesting that this scenario is likely, but I do think that by creating the distinction the bill prevent some potentially abusive claims.

So I think I've answered my own question. Despite (or maybe because of?) the wide array of people backing this bill, it does have a positive impact and really does accomplish something. The bill is a very good step forward, and hopefully will have the added side effect of recruiting subjects for genetic research a little easier. This next week I should be back to Monday/Thursday blogging; there aren't any deadlines looming to trip me up. On Monday I'll talk about the Secretary’s Advisory Committee on Genetics, Health, and Society draft report Realizing the Promise of Pharmacogenomics: Opportunities and Challenges

What are the Policy Issues in Personalized Medicine?

As with any significant undertaking, the challenges facing personalized medicine are not limited to the science behind it. A large number of public policy challenges exist that must be addressed before personalized medicine can become a reality. Each of these challenges must be dealt with not by a single person or group, but by all of the stakeholders that are affected by it. Who are the stakeholders? That seems like an easier question than it actually is, but in general, the stakeholders are physicians, health care organizations like hospitals and health networks, private insurance providers, public insurance providers such as medicare and medicaid, pharmaceutical companies, state governments, the federal government, and, of course, patients. Not all of these are affected by each issue, but solutions will only be possible when the affected stakeholders work together.

As with the scientific issues, in no way is my listing complete, nor is the discussion about the problems. Rather, I want to give a sense for how broad the policy issues are and who they affect. The main issues I want to describe are

  1. Getting access to data for secondary uses
  2. Preventing healthcare discrimination
  3. Genetics education for healthcare providers
  4. Genetics education for patients
  5. Creating infrastructure
  6. Funding

Risk prediction is an important part of personalized medicine, but it requires a fairly large sample of individuals on which to base the prediction. Moreover, the individuals used should be fairly similar to the patient (or patients) whose disease risk we want to estimate. One of the most promising ways of doing this is by using data collected by hospitals. This is called a "secondary use" of health data since it is not being used for its original purpose - making a diagnosis about the patient from whom it was collected. There are other potential secondary uses also, including using this data for pharmacogenomic drug development - another important goal of personalized medicine. These secondary uses are not currently possible, and policies must be developed to allow this data sharing in such a way that protects a patient's confidentiality and their right to opt-out of these uses.

Genetic information has the potential to reveal a large amount about a person's risk of developing a disease in the future. Insurance companies are likely to want to use this information to set policy rates and limits or even potentially to deny coverage altogether. Even if insurance companies have no immediate plans to do this, fear of it may prevent people from having this information collected. One policy solution has already been proposed, a legislative initiative called the "Genetic Information Nondiscrimination Act". The US Senate passed this act in both 2003 and 2005, but it was never brought out of subcommittee in the House of Representatives. The bill has been re-proposed in both the House and Senate this year, but its future is still unclear. Legislation of this type is extremely important, and must be undertaken at a national level. Hopefully mounting public pressure will force the House to consider and pass a bill to prevent this discrimination.

As of now, even if the scientific issues I've discussed before were solved, many physicians would not understand how to use this information. Doctors who don't specialize in genetic conditions are exposed to genetics mainly during medical school, and often only at a very basic level. Although many understand the impact of genes on simple Mendelian diseases, they are unfamiliar with the role genetics plays in complex disease and the way that genetic information can assist diagnosing and treating patients. Increased education, both during medical school and through continuing education programs, is the answer to this problem, but fitting that into an already overcrowded curriculum is no small task. As insurance companies, hospital systems, and patients realize the benefits of personalized medicine, though, doctors will catch up out of necessity. Being proactive with policy solutions should make that process both quicker and easier.

Physicians aren't alone in not understanding genetics. Patients have an even more limited knowledge of genetics, with much of their information coming from popular media. And let's be honest, if all a person knows about genetics they learned from watching Heroes or CSI, then making informed decisions about how to use genetics in medical care is going to be difficult. They will have unrealistic expectations about what can be done to correct genetic problems ("Isn't there some sort of gene therapy for this?") and exaggerated fears about what might be done with this information ("I don't want my DNA in some database where the government can get it!"). Bringing expectations in line with reality and talking about the reasonable risks of gathering this information then must be accomplished through public education policies. The exact forms of these campaigns could vary, but drawing on the successes of cancer awareness and safer sex awareness programs should inform the process.

Bringing personalized medicine to the public is a large computational challenge. Databases must be created to store the information, computing power to actually perform risk prediction calculations must be purchased, deployed, and maintained, and access to high-throughput technologies (genome-wide genotyping, transcriptomic profiling, proteomic profiling, and metabolomic profiling) must be increased. What is the best way to accomplish this? Are private doctor's offices ever going to have the resources to build and maintain a cluster of servers? And will their patient population be large enough to use for risk prediction? High-throughput screening for now is limited to research hospitals, but just as most doctor's offices use external labs to perform clinical tests, external companies will likely fill this role for them.

Of course, none of these problems will be solved cheaply. Educating physicians and the public require some cost, but are not terribly onerous, and the government and professional groups are likely to step into these tasks, because they have some expertise in providing this education. The infrastructure, on the other hand, is far more expensive. Some of the cost will likely be covered by private industry as companies spring up to do high-thoughput screening or provide application hosting for doctor's offices, but that cost will then be passed to doctors, insurance companies, and inevitably to patients. Policy solutions must be created to ensure that health care consumers (i.e., patients) don't get stuck with a disproportionate amount of the bill, and that these infrastructure concerns aren't used to hike insurance rates unjustifiably.

So there is not shortage of policy issues that have to be addressed to make personalized medicine a reality, but some solutions have already been proposed. In some cases, the problems are similar to other problems that have been extensively studied. By taking advantage of these, we can make sure that these challenges don't prove to be long-term roadblocks. Next up I'll talk about some of the ethical issues in personalized medicine. I have my very first dissertation committee meeting on Thursday, so I may be a day late, but I'll do my best to get the post finished on time.

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Reagan Kelly is a PhD student at University of Michigan studying bioinformatics. His thesis is focused on risk prediction algorithms for personalized medicine systems, and he is also interested in the policy and societal implications of individualized healthcare.You can read his CV for more information about him. If you would like to contact him, please send an email to reagank -at- reagank.com

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