The promise of personalized medicine is one that is fundamentally rooted in science. It's based, at least partly, on the belief that drives all science: knowing more (relevant) information about a process can lead to a deeper understanding of how that process works. Much science, however, (and particularly molecular biology) has followed a fundamentally reductionist paradigm. Each part of a system is studied in isolation, and the information it provides is considered additive to the information provided by a separate piece of the system.
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.1Laughlin & Pines are talking about the Theory of Everything in physics, but the principle holds. Human biology is inordinately complex, with variables working not in isolation but in concert. To individualize medical care requires a deeper understanding of that biology, and that is no small order. I'm going to cover three major problems in this post, which is by no means an exhaustive list of the scientific issues facing personalized medicine. Rather, its a subset of issues that are very interesting to me and present significant hurdles to the field. These are
The first problem is how single nucleotide polymorphisms (SNPs) affect a person's risk of disease or how they influence a complex phenotype. Some of these polymorphisms have an obvious impact. They change either an amino acid or an exon splice site, leading to a different protein product. Others might affect a promoter binding sites or have no discernible change whatsoever. Genetic association studies, the typical tool for identifying SNPs with an impact of a disease or quantitative phenotype, have run against a roadblock in recent years; the findings of many promising studies have not been replicated when the studies were repeated in independent samples. The reasons for this lack of reproducibility are numerous, ranging from spurious initial findings to poor choices for replication samples. Although a number of potential solutions, from cross-validation to requiring all association studies to include an independent validation sample, have been proposed, the field still lacks a consistent, realistic, and tractable method for assessing which associations are "real" (i.e., have real predictive power or mechanistic insight and are worth devoting further resources to) and which are either artifacts of the data or are exclusive to the population being studied.
The next problem is predicting a person's risk of developing a disease. Biology and disease etiology are very complex, and attempting to create a mathematical model of who will develop a disease is impractical. At the same time, being able to estimate a person's risk of a disease is an important goal; it allows people who will benefit from early interventions to be identified, and it can both improve people's health and lower the long-term cost of a person's care. Right now, the only way to predict a person's risk of a disease is to have a medium to large study population that measures the outcome of interest and develop a prediction scheme from that. It's laborious, and if there isn't a study that looks at your outcome, there's no way to create one. Worse, it's difficult to know how well a risk prediction scheme will work when applied to a new patient, especially if that patient is very different from the people used to construct the scheme. These systems are built are imperative to personalized medicine (and, not coincidentally, the subject of my dissertation research, but that's a topic for another time - we're laying out problems, not solutions today), and ways to make them more accurate and more broadly applicable must be found.
Last is a group of problems that fall into their own own category: pharmacogenomics. Creating drugs that have fewer adverse side effects and are more effective has long been a goal of researchers and pharmaceutical companies. The genomics revolution means that more information than ever before can be integrated into this process. But to design more effective drugs requires two very difficult things: a deeper understanding of the mechanisms that underlie different disease subtypes and a more thorough understanding of how drugs act. As an exercise, flip through a drug reference book like the Physician's Desk Reference. The precise mechanism of many (if not most) recent blockbuster drugs is unknown. Doctors understand that some drugs work better for some people than for others, but there is no systematic way to tell who will respond. Targeting drugs to specific disease subtypes should help alleviate that problem. But as an extra layer of consternation, there is significant variability not only in how people respond to a drug, but in what dose they need and what side effects they suffer. This is because a number of factors influence drug metabolism. Polymorphisms in the 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. Despite the fact that the CYP subunits responsible for processing many drugs are known and the effect of polymorphisms in these subunits is fairly well studied, it is still not typical clinical practice to base drug dosage on this information. Hopefully increased accuracy will change this.
This list of problems just begins to scratch the surface, but it should provide a good sense of the scale of the scientific issues that face personalized medicine. None of the problems are easy, and none of them will be solved overnight. It's not even clear right now what the best solution to some of these problems looks like. In fact, the answer to some of these problems depends on the answer to some of the policy and ethical issues facing personalized medicine. On Monday I'll begin to describe what those policy issues are.
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