MedWorm.com provides a medical RSS filtering service. Over 5000 RSS medical sources are combined and output via different filters. This feed contains the latest items from the 'Let's Get Personal' source. Sat, 16 Aug 2008 14:44:31 +0100 http://www.reagank.com/2007/03/what_are_omics_technologies.php I want to get back to considering some ideas to build infrastructure, but I need to take one other detour first. I've used the terms "high-thoughput" and "omics" quite a bit, but what, exactly do they mean? Simply, high-throughput refers to just that, a technology in which a large (or even exhaustive) number of measurements that can be taken in a fairly short time period. "Ome" and "omics" are suffixes that are derived from genome (the whole collection of a person's DNA, as coined by Hans Winkler, as a combinaion of "gene" and "chromosome"1) and genomics (the study of the genome). Scientists like to append to these to any large-scale system (or really, just about anything complex), such as the collection of proteins in a cell or tissue (the proteome), the collection of metabolites (the metabolome), and the collection of RNA that's been transcribed from genes (the transcriptome). High-throughput analysis is essential considering data at the "omic" level, that is to say considering all DNA sequences, gene expression levels, or proteins at once (or, to be slightly more precise, a significant subset of them). Without the ability to rapidly and accurately measure tens and hundreds of thousands of data points in a short period of time, there is no way to perform analyses at this level. 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. (Source: Let's Get Personal) Let's Get Personal blogs http://www.medworm.com/rss/comments.php?id=512405 Sat, 31 Mar 2007 02:39:07 +0100 512405 http://www.reagank.com/2007/03/realizing_the_promise_of_pharm.php 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 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 (Source: Let's Get Personal) Let's Get Personal blogs http://www.medworm.com/rss/comments.php?id=502768 Tue, 27 Mar 2007 01:12:18 +0100 502768 http://www.reagank.com/2007/03/the_genetic_information_nondis.php 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. (Source: Let's Get Personal) Let's Get Personal blogs http://www.medworm.com/rss/comments.php?id=497585 Sat, 24 Mar 2007 02:38:05 +0100 497585 http://www.reagank.com/2007/03/what_is_pharmacogenomics.php In my post defining "personalized medicine" I mentioned trying to tailor a person's drug treatment to get the best possible effect. Using a person's genetic makeup to choose the optimal drug treatment is called pharmacogenomics. Put another way, this is the study of the way a person's genome influences the effect of drug treatments. Drug response is a very complex phenotype that's influenced by both genetic and environmental factors, but high-throughput technologies such as gene expression microarrays and SNP genotyping arrays allow these genetic factors to be considered on a scale never before possible. 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. (Source: Let's Get Personal) Let's Get Personal blogs http://www.medworm.com/rss/comments.php?id=485731 Mon, 19 Mar 2007 23:32:38 +0100 485731 http://www.reagank.com/2007/03/what_are_the_ethical_issues_in.php I've discussed some of the scientific and policy challenges that surround personalized medicine, but I've left for last a much harder task: defining the ethical issues. From my perspective, policy and science problems share at least one important common feature - even if no one can agree on the optimal solution, it's possible to propose a solution, consider it's appropriateness in a reasonably objective fashion (hopefully using some pre-determined metric), and make adjustments based on its performance. Ethical issues are a bit trickier. I would guess that most people are willing to agree on what (most) the issues are, but that's about as far as things go. Trying to decide on appropriate solutions is much harder, because it's essentially a balancing act, and success looks less like an objective criteria and more like alienating the fewest people possible. Becuase these issues are so much more difficult, I'm going to limit myself to three different classes of issues: Protecting patient privacy Protecting patient autonomy Allowing access to personalized medicine (Source: Let's Get Personal) Let's Get Personal blogs http://www.medworm.com/rss/comments.php?id=485732 Fri, 16 Mar 2007 17:28:24 +0100 485732 http://www.reagank.com/2007/03/what_are_the_policy_issues_in.php 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 Getting access to data for secondary uses Preventing healthcare discrimination Genetics education for healthcare providers Genetics education for patients Creating infrastructure Funding (Source: Let's Get Personal) Let's Get Personal blogs http://www.medworm.com/rss/comments.php?id=485733 Mon, 12 Mar 2007 19:16:33 +0100 485733 http://www.reagank.com/2007/03/what_are_the_scientific_issues.php 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.1 Laughlin & 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 Identifying genetic polymorphisms that have an impact on disease Predicting what diseases a person is at risk for Developing drugs that are more effective and have fewer adverse effects (Source: Let's Get Personal) Let's Get Personal blogs http://www.medworm.com/rss/comments.php?id=485734 Thu, 08 Mar 2007 18:39:01 +0100 485734 http://www.reagank.com/2007/03/what_is_personalized_medicine.php If I want to talk about personalized medicine (and I do), I have to begin by saying what I mean by it. (As a side note, I'll use the term individualized medicine interchangeably. Occasionally, people will use them to slightly different effect, but for my purposes, they're the same thing.) And what I mean is pretty simple - the combining of all different types of data (clinical, environmental, and genetic) to predict what diseases a person is at risk for and to identify medical treatments that will work for that specific person. 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. (Source: Let's Get Personal) Let's Get Personal blogs http://www.medworm.com/rss/comments.php?id=485735 Mon, 05 Mar 2007 19:27:32 +0100 485735 http://www.reagank.com/2007/03/welcome_lets_get_personal.php After a long time of thinking about it (and a long time spent procrastinating), I've decided to resurrect the blog. So I've slapped on a new coat of paint, added a few new gew-gaws, and I'm off to the races. 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. (Source: Let's Get Personal) Let's Get Personal blogs http://www.medworm.com/rss/comments.php?id=485736 Thu, 01 Mar 2007 18:11:39 +0100 485736