Free Medicine

Second Draft -- still under revision

Prescription drug spending in the U.S. is projected to nearly double by 2020. At the same time, new drug development is becoming even more expensive and less productive. What pharmaceutical companies are doing now is failing. Today's drug R&D methodology is well suited for the treatment of simple or acute problems, such as short-term symptoms like acute pain, or most infectious diseases. But, any problems have already solved by generics, and even where needed, profitability of infectious disease treatment is limited by the ability of Third World patients or governments to pay. As Big Pharma recognizes, a comprehensive revolution in medicine has begun, based on three big shifts:

First, the underlying science, biology, has transformed. The most outwardly visible manifestation of this is the Human Genome Project, and the re-orientation of biology towards the primacy of DNA sequence and understanding the cell as a complex system of molecular interaction. But, the changes are broader than that. The techniques of molecular biology have changed to become more modular, kit-based, and reliant on instrumentation and software applications than it was before. There is now even the possibility of both computational and wetlab tinkering available outside the traditional laboratories, leading to the emergence of "DIY biology" and "biohacking." To be sure, kit-based biology is sloppy and requires substantial tuning, and computational methods are still limited. But, the trend is clear. An already substantial and growing part of molecular biology is no longer trial-and-error, basic discovery-oriented science.

Second, the broader information and communications revolution is fundamentally reshaping medicine. Twenty years ago, the molecular understanding of human health was limited to a few metabolites and protein levels, like glucose concentration and insulin levels. Now we are aware of the billions of bases in the human DNA sequence, and of the importance of the dynamic expression levels of 30,000 genes, hundreds of thousands of proteins, thousands of small molecule -- and even beyond that, the identities and dynamic function of the bacteria that live within us. And, the technologies to actually measure all of this information are becoming more precise, more comprehensive, more portable, less expensive, and faster at an exponential rate. In addition to providing the means for storing and analyzing massive data sets for individuals, the global information revolution means that all of these data can be recorded and compared with data from other people in other conditions.

Third, the basic nature of the diseases that are important in medicine is changing. Medicine's future is dealing with chronic diseases that are a function of heredity, lifestyle, and environment: diabetes, asthma, cardiovascular disease, COPD, long-term infections like HIV/AIDS and hepatitis, and cancer. These are not really "diseases" in the way we understand an infectious disease like flu. Chronic diseases have no discrete causative moment, particular group of symptoms, specific range of outcomes, and most importantly of all, definable "cure." Chronic diseases involve a complex of molecular pathways, and disease etiology and progression vary highly between individuals. So, all that information about genetics and complex molecular dynamics of cells matters for prognostication, prevention, and treatment.

Big Pharma has responded by considering and applying these transformations separately in the context of conventional drug development. The problem is that there is simply too much information for a drug to interface with -- the nature of the molecular structures and complex chemical reactions that are reprogrammed by the drug to change the function of cells and organs to affect the course of disease. Moreover, this information varies between individuals. The result is that the state-of-the-art includes drugs like statins, like Lipitor, which have at best an uncertain impact, despite their ubiquity and cost. Alternatively, there have been unequivocal failures like Vioxx -- which was designed to be more specific than its predecessor, but which through that specificity somehow causes more serious side effects. Overall, drug pipelines are running dry, as most projects fail despite the use of expensive new technologies in the development process.

The future of medicine requires absorbing the revolution as a whole: rethinking the integration of information and health. One vision, is "personalized medicine," which is about matching treatments to an individual's DNA sequence, as is possible today, and information from emerging technologies that will allow the real-time measurements by microscopic sensors. The interim is data exclusivity and patents, and the future is to extent data exclusivity and patents to diagnostic algorithms, repurposing of drugs that have lost patents, etc. The problem is that exclusivity as the driver of innovation only incentivizes this kind of research -- which focuses on particular kinds of drugs, rather than looking at holistic intervention, which doesn't see drugs in the context of health generally or with other kinds of non-drug treatment.

The alternative way forward is Free Medicine, exemplified by successful first steps towards the use of social networks for conducting genome-wide association studies at one end of a new pipeline, and clinical trials at the other end, with biological "tinkering" in the middle. These developments will facilitate distributed innovation (or peer production) driven from the doctor-patient level. This is not itself new in medicine. Innovation once emerged from case studies rather than mass trials. In an era when we can measure individual variability, it makes sense to return to a more distributed and flexible form of medical development. Networks of scientists with access to collaborative computational tools, like molecular libraries and free data sharing, can rapidly and effectively search for and refine potential targets and leads. Then, distributing clinical trials globally and freely sharing data reduces their expense. Such an "open source" drug discovery network has started for tuberculosis drug development. Free medicine works on health as a process, rather than just making drugs as products. So, it will lead not to just better health care, but to better health.

-- BahradSokhansanj - 12 Jan 2011