There are four major types of high-throughput measurements that are commonly performed: genomic SNP analysis (i.e., the large-scale genotyping of single nucleotide polymorphisms), transcriptomic measurements (i.e., the measurement of all gene expression values in a cell or tissue type simultaneously), proteomic measurements (i.e., the identification of all proteins present in a cell or tissue type), and metabolomic measurements (i.e., the identification and quantification of all metabolites present in a cell or tissue type). Each of these four is distinct and offers a different perspective on the processes underlying disease initiation and progression as well as on ways of predicting, preventing, or treating disease.

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

• Development and Co-development of PGx Products
• Analytic Validity, Clinical Validity, Clinical Utility, and Cost-Effectiveness
• Data Sharing and Database Interoperability
• Use of PGx Technologies in Clinical Practice
• Health Information Technology

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.

Drug response has two separate components, each of which can be studied in pharmacogenomic terms. The first component of drug response is pharmacokinetics, or the way the body metabolizes a drug. This can be crudely estimated now with some simple genotyping. Polymorphisms in he genes CYP2C19 and CY2D6 are known to affect the rates of metabolisms of many drugs. By modifying the effective concentration of medications, these polymorphisms can either decrease the drug's effectiveness or increase the risk of toxic side effects. The second component is pharmacodynamics, which is how the drug acts to treat the specific condition. Complex diseases from cancer to hypertension are heterogeneous both in their symptoms and in their response to drugs, and some of this variability is due to genetic factors. The underlying molecular cause of the disease, then, can be used to decide which drug is best suited for which patient.

Becuase these issues are so much more difficult, I'm going to limit myself to three different classes of issues:

1. Protecting patient privacy
2. Protecting patient autonomy
3. Allowing access to personalized medicine

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

But R.B. Laughlin and David Pines write

So the triumph of the reductionism of the Greeks is a pyrrhic victory: We have succeeded in reducing all of ordinary physical behavior to a simple, correct Theory of Everything only to discover that it has revealed exactly nothing about many things of great importance.¹

1. Identifying genetic polymorphisms that have an impact on disease
2. Predicting what diseases a person is at risk for
3. Developing drugs that are more effective and have fewer adverse effects

It's easy to lose sight of how far medicine has come in the past 100 years. We take for granted that most diseases are able to be treated if not cured, and we dedicate significant resources to medical research. Modern chemistry has led to hundreds of drugs that have saved countless lives. For all that, medicine can still be a crude endeavor.

Sort of.

The blog is now actually an experiment of sorts. I'm waist-deep in writing my thesis, which is a risk prediction system that is able to sit at the heart of a personalized medicine system. It's fascinating work and I'm learning incredible amounts both about the mechanism of making a prediction and about what extra steps are necessary to make an algorithm clinically relevant and doctor friendly.