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BahradSokhansanjFirstPaper 18 - 06 Mar 2012 - Main.BahradSokhansanj
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Free Medicine | | | 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, favored by incumbent interests, is "personalized medicine." The idea is to match drug choice and dosage to an individual's DNA sequence and information from real-time measurement of biochemical processes. Personalized medicine will be based on the current legal regime of data exclusivity and patents on both drugs and sequence information. Pharmaceutical companies would retain exclusive rights over how to diagnose and treat patients based on their own DNA sequence and sensor data. To get there, drug patents or exclusive rights to market drugs will be extended, by allowing the extension of monopoly rights on both chemical isoforms and new applications of drugs, as well as patents on diagnostic methods. Within this context, open source drug discovery” can exist on the margins, where pharmaceutical companies share data and license molecules for humanitarian -- and unprofitable -- problems, like tuberculosis. | >
> | The future of medicine requires absorbing the revolution as a whole: rethinking the integration of information and health. One vision, favored by incumbent interests, is "personalized medicine." The idea is to match drug choice and dosage to an individual's DNA sequence and information from real-time measurement of biochemical processes. Personalized medicine will be based on the current legal regime of data exclusivity and patents on both drugs and sequence information. Pharmaceutical companies would retain exclusive rights over how to diagnose and treat patients based on their own DNA sequence and sensor data. To get there, drug patents or exclusive rights to market drugs will be extended, by allowing the extension of monopoly rights on both chemical isoforms and new applications of drugs, as well as patents on diagnostic methods based on restricting the use of otherwise freely available genetic information. In this context, open source drug discovery can exist on the margins of strictly "humanitarian" -- i.e. unprofitable -- projects, like tuberculosis. | | | The alternative is Free Medicine. This is exemplified by the emerging use of social networks for conducting genome-wide association studies at one end of a new pipeline, and clinical trials at the other end. These developments will facilitate distributed innovation in networks of the doctors and patients. Indeed, 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. | |
<
< | The power of free medicine is that it works on health as a process, rather than just focusing on products, like drugs. So, it will lead not to just better health care, but to better health. The problem is that free medicine isn’t rooted to products that can be tied to exclusive rights, whether through patents or patent-like legal schemes. But, this is actually the power of free medicine, which is that it does not rely just on particular treatment products -- drugs -- but on a range of ways of treating patients, including natural products, changes to diet, and a world of research that has been neglected because it can’t be incentivized by the award of exclusive rights. | >
> | The power of free medicine is that it works on health as a process, rather than just focusing on products, like drugs. So, it will lead not to just better health care, but to better health. Unlike personalized medicine, free medicine won't just be about products that can be tied to exclusive rights, whether through patents or patent-like legal schemes. The IP regime puts such an emphasis on research that can lead to the award of exclusive rights, which neglects a wide range of ways of treating patients, including natural products, changes to diet, and modifying the built environment --a world that would open up with the renaissance of free medicine. | | | | |
<
< | -- BahradSokhansanj - 12 Jan 2011 | >
> | -- BahradSokhansanj - 5 Mar 2012 | | | |
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BahradSokhansanjFirstPaper 17 - 16 Jan 2012 - Main.BahradSokhansanj
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Free Medicine
Second Draft -- Ready for Review | |
<
< | 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: | >
> | Prescription drug spending in the U.S. is projected to nearly double by 2020. At the same time, drug development is becoming even more expensive and less productive. Today's drug R&D methodology is based on the development of chemicals that target short-term, acute symptoms and infection. But, most of these 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. Medicine in the future is being defined now through a comprehensive revolution 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. | | | 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, favored by incumbent interests, is "personalized medicine." The idea is to match treatments to an individual's DNA sequence and information from real-time measurement of biochemical processes. Personalized medicine perpetuates the current legal regime of data exclusivity and patents. Pharmaceutical companies will retain exclusive rights over how to diagnose and treat patients based on their own DNA sequence and sensor data. Drug patents or exclusive rights to market drugs will be extended, by allowing the extension of monopoly rights on chemical isoforms or repurposed drugs (i.e. where new treatment targets or ways of coupling with diagnostic methods are found). Within this context, open source drug discovery” can exist on the margins, where pharmaceutical companies share data and license molecules for humanitarian -- and unprofitable -- problems, like tuberculosis. | >
> | The future of medicine requires absorbing the revolution as a whole: rethinking the integration of information and health. One vision, favored by incumbent interests, is "personalized medicine." The idea is to match drug choice and dosage to an individual's DNA sequence and information from real-time measurement of biochemical processes. Personalized medicine will be based on the current legal regime of data exclusivity and patents on both drugs and sequence information. Pharmaceutical companies would retain exclusive rights over how to diagnose and treat patients based on their own DNA sequence and sensor data. To get there, drug patents or exclusive rights to market drugs will be extended, by allowing the extension of monopoly rights on both chemical isoforms and new applications of drugs, as well as patents on diagnostic methods. Within this context, open source drug discovery” can exist on the margins, where pharmaceutical companies share data and license molecules for humanitarian -- and unprofitable -- problems, like tuberculosis. | | | The alternative is Free Medicine. This is exemplified by the emerging use of social networks for conducting genome-wide association studies at one end of a new pipeline, and clinical trials at the other end. These developments will facilitate distributed innovation in networks of the doctors and patients. Indeed, 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. |
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BahradSokhansanjFirstPaper 16 - 14 Jan 2012 - Main.BahradSokhansanj
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Free Medicine | |
<
< | Second Draft -- still under revision | >
> | Second Draft -- Ready for Review | | | 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: | | | 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 future of medicine requires absorbing the revolution as a whole: rethinking the integration of information and health. One vision, favored by incumbent interests, is "personalized medicine." The idea is to match treatments to an individual's DNA sequence and information from real-time measurement of biochemical processes. Personalized medicine perpetuates the current legal regime of data exclusivity and patents. Pharmaceutical companies will retain exclusive rights over how to diagnose and treat patients based on their own DNA sequence and sensor data. Drug patents or exclusive rights to market drugs will be extended, by allowing the extension of monopoly rights on chemical isoforms or repurposed drugs (i.e. where new treatment targets or ways of coupling with diagnostic methods are found). Within this context, open source drug discovery” can exist on the margins, where pharmaceutical companies share data and license molecules for humanitarian -- and unprofitable -- problems, like tuberculosis. | | | | |
<
< | 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. | >
> | The alternative is Free Medicine. This is exemplified by the emerging use of social networks for conducting genome-wide association studies at one end of a new pipeline, and clinical trials at the other end. These developments will facilitate distributed innovation in networks of the doctors and patients. Indeed, 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. | | | | |
>
> | The power of free medicine is that it works on health as a process, rather than just focusing on products, like drugs. So, it will lead not to just better health care, but to better health. The problem is that free medicine isn’t rooted to products that can be tied to exclusive rights, whether through patents or patent-like legal schemes. But, this is actually the power of free medicine, which is that it does not rely just on particular treatment products -- drugs -- but on a range of ways of treating patients, including natural products, changes to diet, and a world of research that has been neglected because it can’t be incentivized by the award of exclusive rights. | | | -- BahradSokhansanj - 12 Jan 2011 |
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BahradSokhansanjFirstPaper 15 - 12 Jan 2012 - Main.BahradSokhansanj
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<
< | | | | 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. The closed regime based on patents and exclusivity is failing.
The principles of free software map cleanly onto drug development because drugs are really information products in two ways. First, the molecular structure of a drug contains information about how to modify the drug's target, which is a specific molecular structure based on information contained in DNA. Second, the value of a drug is the information provided by credible, government-sanctioned clinical trials, which show the safety and efficacy of the drug. Open source principles can promote free drug development in both respects. 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.
While promising, this open source vision merely replicates today's drug R&D methodology. It is well suited for the treatment of simple or acute problems, such as short-term symptoms like acute pain, or most infectious diseases. Many 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. Ensuring exclusivity is consequently less important, and thus fewer barriers to free drugs.
The real battle is going to be over what Big Pharma sees as the future of medicine. We are now going through the first stage of a comprehensive revolution, built on three big shifts. | >
> | 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 better and cheaper 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. | >
> | 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. | | | | |
<
< | Big Pharma has responded by considering and applying these transformations separately in the context of conventional drug development. This approach has failed, because there is so much information for a drug to interface with, and the information varies between individuals. So, they made statins, like Lipitor, that have at best an uncertain impact, despite their ubiquity and cost. They made drugs like Vioxx, designed to be more specific than its predecessor, but which through that specificity somehow causes more serious side effects. Moreover, 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. This is not eagerly hyped "personalized medicine," which is about specifying the population for which a drug is prescribed. Rather, the 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.
Since the necessary technologies are constantly getting better and cheaper, the hardest work will be educational, cultural, and political. The alternative is a less effective and more expensive -- ultimately crueler -- system relying on monopolizing DNA sequences, tissue from patients, naturally occurring molecules, and treatment algorithms. Instead, Free Medicine takes advantage of sharing and distributed invention. And, it works on health as a process, rather than just making drugs as products. Free Medicine will lead not to just better health care, but to better health.
This essay's great
virtue is that it thinks long, gaining the advantage of depth of
field, seeing medicine evolving over decades. The essay's great
difficulty is that it thinks long, accumulating predictive error over
time, necessarily becoming "a trajectory" rather than "the
trajectory" of the endeavors it forecasts. What happens in the
meantime is also crucial, because of path-dependence.
I think this is a pretty good guess about the direction of travel in
the absence of significant societal distortions from the Big Pharma
parties whose involvement you mention but do not analyze. They want
the vested-rights system from which they benefit to exist long
enough, and to accrue enough apparent normative authority that they
can commandeer the "biological engineering" industry that your
hypothesized science brings into existence. They want it for
themselves on a long-duration individual-monopolies basis secured
through the patent system, such as they benefit from now in the
closing period of this phase of the industry. Their patent-intensive
approach, as we can see around us, allows significant distortion of
the societal research investment, comprehensively replacing socially
useful research by patentable research (a different and in no way
congruent category), with significant second-order effects on the way
medicine is practised and the way the information health care accrues
is distributed and used.
You also do not take full account of the immense social changes, in
health care particularly, that will be brought about by omnipresent
intelligent sensor networks. You refer to the streams of genetic and
other biological information available. When the environment is
comprehensively measured everywhere, inside our bodies and outside,
by billions of inexpensive network-attached sensors, the process of
maintaining health and preventing injury is modified as fundamentally
as the 20th century modified it, first with the automobile and then
with the seat belt and the air bag.
Comments | >
> | 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. | | | | |
<
< | I do have more to say about vaccines than I have discussed here. | >
> | 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. | | | | |
<
< | http://www.forbes.com/sites/matthewherper/2011/11/10/what-bill-gates-says-about-drug-companies-2/ | | | | |
<
< | Left unsaid in this article is that vaccines often have production issues that are different from that of most drugs. So,some of the most valuable information is in how you make the vaccine as opposed to what is in it. This can also be the case for biologics as well. | >
> | -- BahradSokhansanj - 12 Jan 2011 | | | | |
<
< | -- BahradSokhansanj - 15 Nov 2011
| >
> | | | |
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BahradSokhansanjFirstPaper 14 - 27 Nov 2011 - Main.EbenMoglen
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<
< | [Please feel free to comment, criticize, edit, etc. Thanks!] | | | Free Medicine | | | Since the necessary technologies are constantly getting better and cheaper, the hardest work will be educational, cultural, and political. The alternative is a less effective and more expensive -- ultimately crueler -- system relying on monopolizing DNA sequences, tissue from patients, naturally occurring molecules, and treatment algorithms. Instead, Free Medicine takes advantage of sharing and distributed invention. And, it works on health as a process, rather than just making drugs as products. Free Medicine will lead not to just better health care, but to better health. | |
>
> | This essay's great
virtue is that it thinks long, gaining the advantage of depth of
field, seeing medicine evolving over decades. The essay's great
difficulty is that it thinks long, accumulating predictive error over
time, necessarily becoming "a trajectory" rather than "the
trajectory" of the endeavors it forecasts. What happens in the
meantime is also crucial, because of path-dependence.
I think this is a pretty good guess about the direction of travel in
the absence of significant societal distortions from the Big Pharma
parties whose involvement you mention but do not analyze. They want
the vested-rights system from which they benefit to exist long
enough, and to accrue enough apparent normative authority that they
can commandeer the "biological engineering" industry that your
hypothesized science brings into existence. They want it for
themselves on a long-duration individual-monopolies basis secured
through the patent system, such as they benefit from now in the
closing period of this phase of the industry. Their patent-intensive
approach, as we can see around us, allows significant distortion of
the societal research investment, comprehensively replacing socially
useful research by patentable research (a different and in no way
congruent category), with significant second-order effects on the way
medicine is practised and the way the information health care accrues
is distributed and used.
You also do not take full account of the immense social changes, in
health care particularly, that will be brought about by omnipresent
intelligent sensor networks. You refer to the streams of genetic and
other biological information available. When the environment is
comprehensively measured everywhere, inside our bodies and outside,
by billions of inexpensive network-attached sensors, the process of
maintaining health and preventing injury is modified as fundamentally
as the 20th century modified it, first with the automobile and then
with the seat belt and the air bag.
| | |
Comments |
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|
BahradSokhansanjFirstPaper 13 - 15 Nov 2011 - Main.BahradSokhansanj
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| META TOPICPARENT | name="FirstPaper" |
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[Please feel free to comment, criticize, edit, etc. Thanks!] | | | 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 better and cheaper 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, a 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. | >
> | 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. This approach has failed, because there is so much information for a drug to interface with, and the information varies between individuals. So, they made statins, like Lipitor, that have at best an uncertain impact, despite their ubiquity and cost. They made drugs like Vioxx, designed to be more specific than its predecessor, but which through that specificity somehow causes more serious side effects. Moreover, drug pipelines are running dry, as most projects fail despite the use of expensive new technologies in the development process. | | | Since the necessary technologies are constantly getting better and cheaper, the hardest work will be educational, cultural, and political. The alternative is a less effective and more expensive -- ultimately crueler -- system relying on monopolizing DNA sequences, tissue from patients, naturally occurring molecules, and treatment algorithms. Instead, Free Medicine takes advantage of sharing and distributed invention. And, it works on health as a process, rather than just making drugs as products. Free Medicine will lead not to just better health care, but to better health. | |
>
> |
Comments
I do have more to say about vaccines than I have discussed here.
http://www.forbes.com/sites/matthewherper/2011/11/10/what-bill-gates-says-about-drug-companies-2/
Left unsaid in this article is that vaccines often have production issues that are different from that of most drugs. So,some of the most valuable information is in how you make the vaccine as opposed to what is in it. This can also be the case for biologics as well.
-- BahradSokhansanj - 15 Nov 2011
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BahradSokhansanjFirstPaper 12 - 07 Nov 2011 - Main.BahradSokhansanj
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| META TOPICPARENT | name="FirstPaper" |
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[Please feel free to comment, criticize, edit, etc. Thanks!] | | | The principles of free software map cleanly onto drug development because drugs are really information products in two ways. First, the molecular structure of a drug contains information about how to modify the drug's target, which is a specific molecular structure based on information contained in DNA. Second, the value of a drug is the information provided by credible, government-sanctioned clinical trials, which show the safety and efficacy of the drug. Open source principles can promote free drug development in both respects. 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. | |
<
< | While promising, this open source vision merely replicates today's drug R&D methodology. It is well suited for the treatment of simple or acute problems, such as short-term symptoms like acute pain, or most infectious diseases. Many 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. Ensuring exclusivity is consequently less important. There are thus fewer obstacles to free drugs. | >
> | While promising, this open source vision merely replicates today's drug R&D methodology. It is well suited for the treatment of simple or acute problems, such as short-term symptoms like acute pain, or most infectious diseases. Many 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. Ensuring exclusivity is consequently less important, and thus fewer barriers to free drugs. | | |
The real battle is going to be over what Big Pharma sees as the future of medicine. We are now going through the first stage of a comprehensive revolution, built on three big shifts. | | | The future of medicine requires absorbing the revolution as a whole: rethinking the integration of information and health. This is not eagerly hyped "personalized medicine," which is about specifying the population for which a drug is prescribed. Rather, the 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. | |
<
< | Since the necessary technologies are constantly getting better and cheaper, the hardest work will be educational, cultural, and political. The alternative is a less effective and more expensive -- ultimately crueler -- system relying on patents for DNA sequences, tissue from patients, naturally occurring molecules, and treatment algorithms. Free Medicine takes advantage of sharing and distributed invention. And, it focuses on health as a process rather than on drugs as products. Free Medicine will lead not to just better health care, but to better health. | >
> | Since the necessary technologies are constantly getting better and cheaper, the hardest work will be educational, cultural, and political. The alternative is a less effective and more expensive -- ultimately crueler -- system relying on monopolizing DNA sequences, tissue from patients, naturally occurring molecules, and treatment algorithms. Instead, Free Medicine takes advantage of sharing and distributed invention. And, it works on health as a process, rather than just making drugs as products. Free Medicine will lead not to just better health care, but to better health. | | |
| META FILEATTACHMENT | attachment="Nature_1991_Paradigm_shift_in_Biology.pdf" attr="h" comment="" date="1319718531" name="Nature_1991_Paradigm_shift_in_Biology.pdf" path="Nature_1991_Paradigm_shift_in_Biology.pdf" size="142040" stream="Nature_1991_Paradigm_shift_in_Biology.pdf" user="Main.BahradSokhansanj" version="1" |
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| META FILEATTACHMENT | attachment="72FordhamLRev477_pharma_product_innovation_patents_Eisenberg_2003.pdf" attr="h" comment="" date="1319718937" name="72FordhamLRev477_pharma_product_innovation_patents_Eisenberg_2003.pdf" path="72FordhamLRev477 pharma product innovation patents Eisenberg 2003.pdf" size="992453" stream="72FordhamLRev477 pharma product innovation patents Eisenberg 2003.pdf" user="Main.BahradSokhansanj" version="1" |
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BahradSokhansanjFirstPaper 11 - 07 Nov 2011 - Main.BahradSokhansanj
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| META TOPICPARENT | name="FirstPaper" |
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[Please feel free to comment, criticize, edit, etc. Thanks!] | | | The real battle is going to be over what Big Pharma sees as the future of medicine. We are now going through the first stage of a comprehensive revolution, built 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, and a substantial part of molecular biology is no longer a trial-and-error, basic discovery-oriented science. | >
> | 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 better and cheaper 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, like diabetes, asthma, cardiovascular disease, and COPD, long-term infections like HIV/AIDS and hepatitis, and related long-term diseases like cancer. These are also not really "diseases"; in the way we understand infectious diseases -- they do not have a discrete causative moment, particular group of symptoms, specific range of outcomes, and most importantly of all, a definable "cure." Chronic diseases involve a complex of molecular pathways, and disease etiology and progression vary highly between individuals, which means that all of the information described in the previous paragraph is highly significant. | >
> | 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, a 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. This approach has failed, because there is so much information for a drug to interface with, and the information varies between individuals. So, they have made statins, like Lipitor, that have at best an uncertain impact, despite their ubiquity and cost. They make drugs like Vioxx, designed to be more specific than its predecessor, but which through that specificity somehow causes more serious side effects. Moreover, drug pipelines are running dry, as most projects fail despite the use of expensive new technologies in the development process. | >
> | Big Pharma has responded by considering and applying these transformations separately in the context of conventional drug development. This approach has failed, because there is so much information for a drug to interface with, and the information varies between individuals. So, they made statins, like Lipitor, that have at best an uncertain impact, despite their ubiquity and cost. They made drugs like Vioxx, designed to be more specific than its predecessor, but which through that specificity somehow causes more serious side effects. Moreover, 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. This is not eagerly hyped "personalized medicine," which is about specifying the population for which a drug is prescribed. Rather, the 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. | >
> | The future of medicine requires absorbing the revolution as a whole: rethinking the integration of information and health. This is not eagerly hyped "personalized medicine," which is about specifying the population for which a drug is prescribed. Rather, the 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. | | | | |
<
< | What will be required for this vision of Free Medicine to succeed? The main challenge is likely to be developing social networks through education and the spread of communication tools. But, as this challenge is solved, the infrastructure will exist to defeat the application of patents to genetic sequences, tissue banks, cell lines, and naturally occurring biomolecules and biochemical reactions. Free Medicine will not only reduce drug spending. Free Medicine will also lead to better medicine, and thus reduce the costs of both medical care and poor health. | >
> | Since the necessary technologies are constantly getting better and cheaper, the hardest work will be educational, cultural, and political. The alternative is a less effective and more expensive -- ultimately crueler -- system relying on patents for DNA sequences, tissue from patients, naturally occurring molecules, and treatment algorithms. Free Medicine takes advantage of sharing and distributed invention. And, it focuses on health as a process rather than on drugs as products. Free Medicine will lead not to just better health care, but to better health. | | |
| META FILEATTACHMENT | attachment="Nature_1991_Paradigm_shift_in_Biology.pdf" attr="h" comment="" date="1319718531" name="Nature_1991_Paradigm_shift_in_Biology.pdf" path="Nature_1991_Paradigm_shift_in_Biology.pdf" size="142040" stream="Nature_1991_Paradigm_shift_in_Biology.pdf" user="Main.BahradSokhansanj" version="1" |
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BahradSokhansanjFirstPaper 10 - 04 Nov 2011 - Main.BahradSokhansanj
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| META TOPICPARENT | name="FirstPaper" |
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[Please feel free to comment, criticize, edit, etc. Thanks!]
Free Medicine | |
<
< | 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. The present regime dominated by IP and data exclusivity is failing. | >
> | 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. The closed regime based on patents and exclusivity is failing. | | | | |
<
< | The principles of free software map cleanly onto drug development because drugs are really information products. The molecular structure of a drug -- the sequence and spatial configuration of its components -- contains information about how to modify the drug's target, itself a specific molecular structure based on information contained in DNA. In another sense, the value of a drug is the information provided for by credible, government-sanctioned clinical trials, which show the safety and efficacy of the drug (see Eisenberg). Open source principles can promote free drug development in both respects. 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 the clinical trials globally and sharing the data freely can mitigate their expense. Such an "open source" drug discovery network has been launched for tuberculosis drug development. | >
> | The principles of free software map cleanly onto drug development because drugs are really information products in two ways. First, the molecular structure of a drug contains information about how to modify the drug's target, which is a specific molecular structure based on information contained in DNA. Second, the value of a drug is the information provided by credible, government-sanctioned clinical trials, which show the safety and efficacy of the drug. Open source principles can promote free drug development in both respects. 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. | | | | |
<
< | But, this open source vision merely replicates current drug development. Conventional drugs are well suited for the treatment of simple or acute problems, such as short-term symptoms like acute pain, or most infectious diseases. Pharmaceutical companies are generally not interested in obtaining IP or data exclusivity for these kinds of drugs. Many problems are already solved by generics, and even needed new infectious disease treatments are not profitable. So, Big Pharma does this kind of drug development to solve other disease problems, but it can only lead to flawed solutions. | >
> | While promising, this open source vision merely replicates today's drug R&D methodology. It is well suited for the treatment of simple or acute problems, such as short-term symptoms like acute pain, or most infectious diseases. Many 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. Ensuring exclusivity is consequently less important. There are thus fewer obstacles to free drugs. | | | | |
<
< | This is because we are now going through the first stage of a comprehensive revolution in medicine. The revolution is the product of three big changes. | >
> | The real battle is going to be over what Big Pharma sees as the future of medicine. We are now going through the first stage of a comprehensive revolution, built 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, and a substantial part of molecular biology is no longer a trial-and-error, basic discovery-oriented science. | >
> | 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, and a substantial part of molecular biology is no longer a trial-and-error, basic discovery-oriented science. | | | | |
<
< | Second, medicine is being transformed by the fundamental information and communications revolution that is changing the world as a whole. 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 better and cheaper 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. | >
> | 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 better and cheaper 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, like diabetes, asthma, cardiovascular disease, and COPD, long-term infections like HIV/AIDS and hepatitis, and related long-term diseases like cancer. These are also not really "diseases"; in the way we understand infectious diseases -- they do not have a discrete causative moment, particular group of symptoms, specific range of outcomes, and most importantly of all, a definable "cure." Chronic diseases involve a complex of molecular pathways, and disease etiology and progression vary highly between individuals, which means that all of the information described in the previous paragraph is highly significant. | |
<
< | It should be clear why conventional drugs, as Big Pharma wants to develop, are only partially successful. There is a massive amount of information for a drug to interface with, and the information varies between individuals. So, we have statins, like Lipitor, that have at best an uncertain impact, despite their ubiquity and cost. We have drugs like Vioxx, which is apparently more specific than its predecessor, but which through that specificity somehow causes more serious side effects. | >
> | Big Pharma has responded by considering and applying these transformations separately in the context of conventional drug development. This approach has failed, because there is so much information for a drug to interface with, and the information varies between individuals. So, they have made statins, like Lipitor, that have at best an uncertain impact, despite their ubiquity and cost. They make drugs like Vioxx, designed to be more specific than its predecessor, but which through that specificity somehow causes more serious side effects. Moreover, 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 rethinking the relationship between information and health. This does not mean the "personalized medicine" that just narrows the population for which a drug is prescribed. Rather, the way forward is exemplified by successful first steps towards the use of social networks for conducting genome-wide association studies at one end of a new Free Medicine pipeline, and clinical trials at the other end of the pipeline, 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.
What will be required for this vision of Free Medicine to succeed? The main challenge is likely to be developing social networks through education and the spread of communication tools. But, as this challenge is solved, the infrastructure will exist to defeat the application of patents to genetic sequences, tissue banks, cell lines, and naturally occurring biomolecules and biochemical reactions. Ultimately, Free Medicine will not only reduce drug spending. It will also lead to better medicine, and thus reduce the costs of both medical care and poor health. | >
> | The future of medicine requires absorbing the revolution as a whole: rethinking the integration of information and health. This is not eagerly hyped "personalized medicine," which is about specifying the population for which a drug is prescribed. Rather, the 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. | | | | |
>
> | What will be required for this vision of Free Medicine to succeed? The main challenge is likely to be developing social networks through education and the spread of communication tools. But, as this challenge is solved, the infrastructure will exist to defeat the application of patents to genetic sequences, tissue banks, cell lines, and naturally occurring biomolecules and biochemical reactions. Free Medicine will not only reduce drug spending. Free Medicine will also lead to better medicine, and thus reduce the costs of both medical care and poor health. | | |
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BahradSokhansanjFirstPaper 9 - 01 Nov 2011 - Main.BahradSokhansanj
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| META TOPICPARENT | name="FirstPaper" |
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>
> | [Please feel free to comment, criticize, edit, etc. Thanks!] | | | Free Medicine
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. The present regime dominated by IP and data exclusivity is failing. | |
<
< | The principles of free software map cleanly onto drug development because drugs are really information products. The molecular structure of a drug -- the sequence and spatial configuration of its components -- contains information about how to modify the drug's target, itself a specific molecular structure based on information contained in DNA. In another sense, the value of a drug is the information provided for by credible, government-sanctioned clinical trials, which show the safety and efficacy of the drug (see Eisenberg). Open source principles can promote free drug development in both respects. 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 the clinical trials globally and sharing the data freely can mitigate their expense. Such an"open source" drug discovery network has been launched for tuberculosis drug development. | >
> | The principles of free software map cleanly onto drug development because drugs are really information products. The molecular structure of a drug -- the sequence and spatial configuration of its components -- contains information about how to modify the drug's target, itself a specific molecular structure based on information contained in DNA. In another sense, the value of a drug is the information provided for by credible, government-sanctioned clinical trials, which show the safety and efficacy of the drug (see Eisenberg). Open source principles can promote free drug development in both respects. 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 the clinical trials globally and sharing the data freely can mitigate their expense. Such an "open source" drug discovery network has been launched for tuberculosis drug development. | | | | |
<
< | But, this open source version merely replicates current drug development. Conventional drugs are well suited for the treatment of simple or acute problems, such as short-term symptoms like acute pain, or most infectious diseases. Pharmaceutical companies are generally not interested in obtaining IP or data exclusivity for these kinds of drugs. Many problems are already solved by generics, and even needed new infectious disease treatments are not profitable. So, Big Pharma does this kind of drug development to solve other disease problems, but it can only lead to flawed solutions. | >
> | But, this open source vision merely replicates current drug development. Conventional drugs are well suited for the treatment of simple or acute problems, such as short-term symptoms like acute pain, or most infectious diseases. Pharmaceutical companies are generally not interested in obtaining IP or data exclusivity for these kinds of drugs. Many problems are already solved by generics, and even needed new infectious disease treatments are not profitable. So, Big Pharma does this kind of drug development to solve other disease problems, but it can only lead to flawed solutions. | | | | |
<
< | This is because wee are now going through the first stage of a genuine revolution in medicine. This revolution is the product of three big changes. | >
> | This is because we are now going through the first stage of a comprehensive revolution in medicine. The revolution is the product of three big changes. | | |
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, and a substantial part of molecular biology is no longer a trial-and-error, basic discovery-oriented science. | | | Second, medicine is being transformed by the fundamental information and communications revolution that is changing the world as a whole. 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 better and cheaper 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 are changing. Medicine's future is dealing with chronic diseases, like diabetes, asthma, cardiovascular disease, and COPD, long-term infections like HIV/AIDS and hepatitis, and related long-term diseases like cancer. These are also not really "diseases"; in the way we understand infectious diseases -- they do not have a discrete causative moment, particular group of symptoms, specific range of outcomes, and most importantly of all, a definable "cure." Chronic diseases involve a complex of molecular pathways, and disease etiology and progression vary highly between individuals, which means that all of the information described in the previous paragraph is highly significant. | >
> | Third, the basic nature of the diseases that are important in medicine is changing. Medicine's future is dealing with chronic diseases, like diabetes, asthma, cardiovascular disease, and COPD, long-term infections like HIV/AIDS and hepatitis, and related long-term diseases like cancer. These are also not really "diseases"; in the way we understand infectious diseases -- they do not have a discrete causative moment, particular group of symptoms, specific range of outcomes, and most importantly of all, a definable "cure." Chronic diseases involve a complex of molecular pathways, and disease etiology and progression vary highly between individuals, which means that all of the information described in the previous paragraph is highly significant. | | | | |
<
< | It should be clear why conventional drugs, as Big Pharma wants to develop, are only partially successful. There is a massive amount of information for a drug to interface with, and the information varies between individuals. So we have statins, like Lipitor, that have at best an uncertain impact, despite their ubiquity and cost. We have drugs like Vioxx that are apparently more specific than its predecessors, but which through that specificity somehow causes more serious side effects. | >
> | It should be clear why conventional drugs, as Big Pharma wants to develop, are only partially successful. There is a massive amount of information for a drug to interface with, and the information varies between individuals. So, we have statins, like Lipitor, that have at best an uncertain impact, despite their ubiquity and cost. We have drugs like Vioxx, which is apparently more specific than its predecessor, but which through that specificity somehow causes more serious side effects. | | | | |
<
< | The future of medicine requires rethinking the relationship between information and health. Not a "ersonalized medicine," that is just about focusing the population for which to prescribe a drug. Rather, , the way forward is exemplified by successful first steps towards the use of social networks for conducting genome-wide association studies at one end of a new Free Medicine pipeline, and clinical trials at the other end of the pipeline, 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 where we can measure individual variability, it makes sense to return to a more distributed and flexible form of medical development. | >
> | The future of medicine requires rethinking the relationship between information and health. This does not mean the "personalized medicine" that just narrows the population for which a drug is prescribed. Rather, the way forward is exemplified by successful first steps towards the use of social networks for conducting genome-wide association studies at one end of a new Free Medicine pipeline, and clinical trials at the other end of the pipeline, 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. | | |
What will be required for this vision of Free Medicine to succeed? The main challenge is likely to be developing social networks through education and the spread of communication tools. But, as this challenge is solved, the infrastructure will exist to defeat the application of patents to genetic sequences, tissue banks, cell lines, and naturally occurring biomolecules and biochemical reactions. Ultimately, Free Medicine will not only reduce drug spending. It will also lead to better medicine, and thus reduce the costs of both medical care and poor health. | |
<
< | [Please feel free to comment, criticize, edit, etc. Thanks!] | >
> | | | |
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BahradSokhansanjFirstPaper 8 - 31 Oct 2011 - Main.BahradSokhansanj
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| META TOPICPARENT | name="FirstPaper" |
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| |
<
< |
An Open Future for Medicine
| >
> | Free Medicine | | | | |
<
< | 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. Could we break down the present regime dominated by IP and data exclusivity? | >
> | 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. The present regime dominated by IP and data exclusivity is failing. | | | | |
<
< | The principles of free software map cleanly onto drug development because drugs are really “information products.” The molecular structure of a drug -- the sequence and spatial configuration of its components -- contains information about how to modify the drug’s target, itself a specific molecular structure based on information contained in DNA. In another sense, the value of a drug is the information provided for by credible, government-sanctioned clinical trials, which show the safety and efficacy of the drug (see Eisenberg). Open source principles can promote free drug development in both respects. 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 the clinical trials globally and sharing the data freely can mitigate their expense. Such an“open source drug discovery” network has been launched for tuberculosis drug development. | >
> | The principles of free software map cleanly onto drug development because drugs are really information products. The molecular structure of a drug -- the sequence and spatial configuration of its components -- contains information about how to modify the drug's target, itself a specific molecular structure based on information contained in DNA. In another sense, the value of a drug is the information provided for by credible, government-sanctioned clinical trials, which show the safety and efficacy of the drug (see Eisenberg). Open source principles can promote free drug development in both respects. 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 the clinical trials globally and sharing the data freely can mitigate their expense. Such an"open source" drug discovery network has been launched for tuberculosis drug development. | | | | |
<
< | But, this vision of open source drug development replicates current drug development. Conventional drugs are well suited for the treatment of simple or acute problems, such as short-term symptoms like acute pain, or most infectious diseases. Pharmaceutical companies are generally not interested in obtaining IP or data exclusivity for these kinds of drugs. Many problems are already solved by generics, and even needed new infectious disease treatments are not profitable. So, Big Pharma does this kind of drug development to solve other disease problems, but it can only lead to flawed solutions. | >
> | But, this open source version merely replicates current drug development. Conventional drugs are well suited for the treatment of simple or acute problems, such as short-term symptoms like acute pain, or most infectious diseases. Pharmaceutical companies are generally not interested in obtaining IP or data exclusivity for these kinds of drugs. Many problems are already solved by generics, and even needed new infectious disease treatments are not profitable. So, Big Pharma does this kind of drug development to solve other disease problems, but it can only lead to flawed solutions. | | | | |
<
< | We are now going through the first stage of a genuine revolution in medicine, which is really the product of three big changes. | >
> | This is because wee are now going through the first stage of a genuine revolution in medicine. This revolution is the product of three big changes. | | | | |
<
< | First, biology, has transformed so much that it would be completely unrecognizable to someone familiar with its practice in 1980s. 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 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 than it was before. There is now even the possibility of tinkering available to simply equipped garage laboratories and the consequent emergence of “DIY biology” and “biohacking.” To be sure, kit-based biology is sloppy and requires substantial tuning. But, the trend is clear, and a substantial part of molecular biology is no longer a trial-and-error, basic discovery-oriented science. | >
> | 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, and a substantial part of molecular biology is no longer a trial-and-error, basic discovery-oriented science. | | | | |
<
< | Second, medicine is being transformed by the fundamental information and communications revolution that is changing the world as a whole. Human biology is much more of an information-oriented science, and that information is being pumped into 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 colonize our bodies. And, the technologies to actually measure all of this information are becoming better and cheaper 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. | >
> | Second, medicine is being transformed by the fundamental information and communications revolution that is changing the world as a whole. 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 better and cheaper 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 are changing. Medicine’s future is dealing with chronic diseases, like diabetes, asthma, cardiovascular disease, and COPD, long-term infections like HIV/AIDS and hepatitis, and related long-term diseases like cancer. These are also not really “diseases” in the way we understand infectious diseases -- they do not have a discrete causative moment, particular group of symptoms, specific range of outcomes, and most importantly of all, a definable “cure.” Chronic diseases involve a complex of molecular pathways, and disease etiology and progression vary highly between individuals, which means that all of the information described in the previous paragraph is highly significant. | >
> | Third, the basic nature of the diseases that are important in medicine are changing. Medicine's future is dealing with chronic diseases, like diabetes, asthma, cardiovascular disease, and COPD, long-term infections like HIV/AIDS and hepatitis, and related long-term diseases like cancer. These are also not really "diseases"; in the way we understand infectious diseases -- they do not have a discrete causative moment, particular group of symptoms, specific range of outcomes, and most importantly of all, a definable "cure." Chronic diseases involve a complex of molecular pathways, and disease etiology and progression vary highly between individuals, which means that all of the information described in the previous paragraph is highly significant. | | |
It should be clear why conventional drugs, as Big Pharma wants to develop, are only partially successful. There is a massive amount of information for a drug to interface with, and the information varies between individuals. So we have statins, like Lipitor, that have at best an uncertain impact, despite their ubiquity and cost. We have drugs like Vioxx that are apparently more specific than its predecessors, but which through that specificity somehow causes more serious side effects. | |
<
< | The future of medicine requires rethinking the relationship between information and health. This is not “personalized medicine,” which is a vision that just narrows the population to which a drug applies. Rather, it is a rethinking of medicine from the bottom-up, and it can be done in a way that leads to Free Medicine. The way forward is exemplified by successful first steps towards the use of social networks for conducting genome-wide association studies at one end of the Free Medicine pipeline, and clinical trials at the other end of the pipeline. In the middle, will be the biological “engineering” effort described in the first paragraph above, which are promoted by open source tool development. | >
> | The future of medicine requires rethinking the relationship between information and health. Not a "ersonalized medicine," that is just about focusing the population for which to prescribe a drug. Rather, , the way forward is exemplified by successful first steps towards the use of social networks for conducting genome-wide association studies at one end of a new Free Medicine pipeline, and clinical trials at the other end of the pipeline, 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 where we can measure individual variability, it makes sense to return to a more distributed and flexible form of medical development. | | | | |
<
< | What will be required for Free Medicine to succeed? The main challenge is likely to be developing social networks through education and the spread of communication tools. But, as this challenge is solved, the infrastructure will exist to defeat the application of patents to genetic sequences, tissue banks, cell lines, and naturally occurring biomolecules and biochemical reactions. Ultimately, Free Medicine will not only reduce drug spending. It will also lead to better medicine, and thus reduce the costs of both medical care and poor health. | >
> | What will be required for this vision of Free Medicine to succeed? The main challenge is likely to be developing social networks through education and the spread of communication tools. But, as this challenge is solved, the infrastructure will exist to defeat the application of patents to genetic sequences, tissue banks, cell lines, and naturally occurring biomolecules and biochemical reactions. Ultimately, Free Medicine will not only reduce drug spending. It will also lead to better medicine, and thus reduce the costs of both medical care and poor health. | | | [Please feel free to comment, criticize, edit, etc. Thanks!] |
|
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BahradSokhansanjFirstPaper 7 - 31 Oct 2011 - Main.BahradSokhansanj
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| META TOPICPARENT | name="FirstPaper" |
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| |
<
< | | >
> |
An Open Future for Medicine
| | | | |
<
< | being edited (not ready for review) | | | | |
<
< | Who Will Own The Future of Medicine? | >
> | 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. Could we break down the present regime dominated by IP and data exclusivity? | | | | |
<
< | Consider this article from 1991. Nature 1991 Paradigm Shift in Biology | | | | |
<
< | Drugs are just information products
Eisenberg article has written about the observations that drugs are information products whose only value is information about efficacy and safety in FDA clinical studies (so she extends that to say that this is the avenue for ensuring exclusivity). | >
> | The principles of free software map cleanly onto drug development because drugs are really “information products.” The molecular structure of a drug -- the sequence and spatial configuration of its components -- contains information about how to modify the drug’s target, itself a specific molecular structure based on information contained in DNA. In another sense, the value of a drug is the information provided for by credible, government-sanctioned clinical trials, which show the safety and efficacy of the drug (see Eisenberg). Open source principles can promote free drug development in both respects. 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 the clinical trials globally and sharing the data freely can mitigate their expense. Such an“open source drug discovery” network has been launched for tuberculosis drug development. | | | | |
<
< | Next-generation medical intervention will be information products in more ways, because they will be tied to personal genetic information. | | | | |
<
< | I don't really care about treatment of acute conditions -- while there will be some personalization, things like treatment of infectious diseases will still likely be a mass approach, though there may be more refined diagnostic procedures, which are significant (who will control them? this can just be collected at the hospital level). Indeed, drug companies don't care about them. All the money is in chronic diseases, and/or making acute into chronic, or "preventive" treatment, which is basically just chronic but pre-symptomatic. | >
> | But, this vision of open source drug development replicates current drug development. Conventional drugs are well suited for the treatment of simple or acute problems, such as short-term symptoms like acute pain, or most infectious diseases. Pharmaceutical companies are generally not interested in obtaining IP or data exclusivity for these kinds of drugs. Many problems are already solved by generics, and even needed new infectious disease treatments are not profitable. So, Big Pharma does this kind of drug development to solve other disease problems, but it can only lead to flawed solutions. | | | | |
<
< | Importance of chronic disease globally | | | | |
<
< | Prescription drug spending is a lot of money, but not all, and indeed what I'm talking about in terms of the future of medicine is a lot broader than drugs, because it also includes diagnostics and other kinds of physician "interventions" (and things like less ICU time) here is Kaiser's projected costs: Drug spending that is in 2009 approximately $250B more than doubling by 2020, driven by faster growth after a period of slower growth due to drugs coming off patents but with drugs having more exclusivity | >
> | We are now going through the first stage of a genuine revolution in medicine, which is really the product of three big changes. | | | | |
<
< | Innovations happening now that are "open source" that I guess I have to talk about: | | | | |
<
< | OpenNotebookScience, spearheaded by Jean-Claude Bradley at Drexel. | >
> | First, biology, has transformed so much that it would be completely unrecognizable to someone familiar with its practice in 1980s. 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 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 than it was before. There is now even the possibility of tinkering available to simply equipped garage laboratories and the consequent emergence of “DIY biology” and “biohacking.” To be sure, kit-based biology is sloppy and requires substantial tuning. But, the trend is clear, and a substantial part of molecular biology is no longer a trial-and-error, basic discovery-oriented science. | | | | |
<
< | web-based platforms for collaborative drug discovery, like this product | | | | |
<
< | the Open Source Drug Discovery project, which is working on TB (notably infectious disease) | >
> | Second, medicine is being transformed by the fundamental information and communications revolution that is changing the world as a whole. Human biology is much more of an information-oriented science, and that information is being pumped into 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 colonize our bodies. And, the technologies to actually measure all of this information are becoming better and cheaper 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. | | | | |
<
< | drug costs have been estimated to be $802 million, though this is variable and is also subject to a lot of assumptions and it includes opportunity cost of capital, which is actually the majority of that figure ($574M). The people who came up with $802 came up with a similar $600-1B range for biotech products | | | | |
<
< | What is a drug? Well, traditionally you go with the "Lipinski Rule of 5," based on certain physical properties, such as size and key chemical properties that make it compatible with metabolism. Of course, not every drug satisfies this rule (e.g. lithium carbonate)... also, it's sort of weird that biologics are treated differently than small molecule drugs when they are basically the same thing (though of course they are developed differently with different IP ramifications) | >
> | Third, the basic nature of the diseases that are important in medicine are changing. Medicine’s future is dealing with chronic diseases, like diabetes, asthma, cardiovascular disease, and COPD, long-term infections like HIV/AIDS and hepatitis, and related long-term diseases like cancer. These are also not really “diseases” in the way we understand infectious diseases -- they do not have a discrete causative moment, particular group of symptoms, specific range of outcomes, and most importantly of all, a definable “cure.” Chronic diseases involve a complex of molecular pathways, and disease etiology and progression vary highly between individuals, which means that all of the information described in the previous paragraph is highly significant. | | | | |
<
< | Also, biohacking and "garage biology" or DIY biology: OpenWetWare (closed access Nature article) or the Wired article I can also have a link to DIYBio, even New York City has Genspace | | | | |
<
< | Also protein folding at home using passive grid computing. Aaron Chan's essay talks about some chintzy protein folding game stuff that isn't really all that interesting but has a "cool factor" that appeals to computer type people. | >
> | It should be clear why conventional drugs, as Big Pharma wants to develop, are only partially successful. There is a massive amount of information for a drug to interface with, and the information varies between individuals. So we have statins, like Lipitor, that have at best an uncertain impact, despite their ubiquity and cost. We have drugs like Vioxx that are apparently more specific than its predecessors, but which through that specificity somehow causes more serious side effects. | | | | |
<
< | Community sourcing / social networking for patient experiences: PatientsLikeMe which has had some preliminary success in ALS being faster than traditional studies with similar results | | | | |
<
< | Then we have something with genomics research by people sequencing their own genomes -- the most prominent effort in this regard is 23andWe, which has already published some results of genome-wide studies that appear to check out, including work on Parkinson's | >
> | The future of medicine requires rethinking the relationship between information and health. This is not “personalized medicine,” which is a vision that just narrows the population to which a drug applies. Rather, it is a rethinking of medicine from the bottom-up, and it can be done in a way that leads to Free Medicine. The way forward is exemplified by successful first steps towards the use of social networks for conducting genome-wide association studies at one end of the Free Medicine pipeline, and clinical trials at the other end of the pipeline. In the middle, will be the biological “engineering” effort described in the first paragraph above, which are promoted by open source tool development. | | | | |
<
< | -- BahradSokhansanj - 25 Oct 2011 | >
> | What will be required for Free Medicine to succeed? The main challenge is likely to be developing social networks through education and the spread of communication tools. But, as this challenge is solved, the infrastructure will exist to defeat the application of patents to genetic sequences, tissue banks, cell lines, and naturally occurring biomolecules and biochemical reactions. Ultimately, Free Medicine will not only reduce drug spending. It will also lead to better medicine, and thus reduce the costs of both medical care and poor health. | | | | |
<
< |
| >
> | [Please feel free to comment, criticize, edit, etc. Thanks!] | | |
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BahradSokhansanjFirstPaper 5 - 28 Oct 2011 - Main.BahradSokhansanj
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| | | I don't really care about treatment of acute conditions -- while there will be some personalization, things like treatment of infectious diseases will still likely be a mass approach, though there may be more refined diagnostic procedures, which are significant (who will control them? this can just be collected at the hospital level). Indeed, drug companies don't care about them. All the money is in chronic diseases, and/or making acute into chronic, or "preventive" treatment, which is basically just chronic but pre-symptomatic. | |
>
> | Importance of chronic disease globally | | | Prescription drug spending is a lot of money, but not all, and indeed what I'm talking about in terms of the future of medicine is a lot broader than drugs, because it also includes diagnostics and other kinds of physician "interventions" (and things like less ICU time) here is Kaiser's projected costs: Drug spending that is in 2009 approximately $250B more than doubling by 2020, driven by faster growth after a period of slower growth due to drugs coming off patents but with drugs having more exclusivity
Innovations happening now that are "open source" that I guess I have to talk about: |
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BahradSokhansanjFirstPaper 3 - 27 Oct 2011 - Main.BahradSokhansanj
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| META TOPICPARENT | name="FirstPaper" |
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<
< | It is strongly recommended that you include your outline in the body of your essay by using the outline as section titles. The headings below are there to remind you how section and subsection titles are formatted. | | | Who Will Own The Future of Medicine? |
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BahradSokhansanjFirstPaper 2 - 27 Oct 2011 - Main.BahradSokhansanj
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| META TOPICPARENT | name="FirstPaper" |
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It is strongly recommended that you include your outline in the body of your essay by using the outline as section titles. The headings below are there to remind you how section and subsection titles are formatted.
Who Will Own The Future of Medicine? | |
>
> | Consider this article from 1991. Nature 1991 Paradigm Shift in Biology | | | Drugs are just information products | |
<
< | Rebecca Eisenberg has written about the observations that drugs are information products whose only value is information about efficacy and safety in FDA clinical studies (so she extends that to say that this is the avenue for ensuring exclusivity). | >
> | Eisenberg article has written about the observations that drugs are information products whose only value is information about efficacy and safety in FDA clinical studies (so she extends that to say that this is the avenue for ensuring exclusivity). | | | Next-generation medical intervention will be information products in more ways, because they will be tied to personal genetic information. | | | Then we have something with genomics research by people sequencing their own genomes -- the most prominent effort in this regard is 23andWe, which has already published some results of genome-wide studies that appear to check out, including work on Parkinson's
-- BahradSokhansanj - 25 Oct 2011 | |
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BahradSokhansanjFirstPaper 1 - 25 Oct 2011 - Main.BahradSokhansanj
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It is strongly recommended that you include your outline in the body of your essay by using the outline as section titles. The headings below are there to remind you how section and subsection titles are formatted.
Who Will Own The Future of Medicine?
Drugs are just information products
Rebecca Eisenberg has written about the observations that drugs are information products whose only value is information about efficacy and safety in FDA clinical studies (so she extends that to say that this is the avenue for ensuring exclusivity).
Next-generation medical intervention will be information products in more ways, because they will be tied to personal genetic information.
I don't really care about treatment of acute conditions -- while there will be some personalization, things like treatment of infectious diseases will still likely be a mass approach, though there may be more refined diagnostic procedures, which are significant (who will control them? this can just be collected at the hospital level). Indeed, drug companies don't care about them. All the money is in chronic diseases, and/or making acute into chronic, or "preventive" treatment, which is basically just chronic but pre-symptomatic.
Prescription drug spending is a lot of money, but not all, and indeed what I'm talking about in terms of the future of medicine is a lot broader than drugs, because it also includes diagnostics and other kinds of physician "interventions" (and things like less ICU time) here is Kaiser's projected costs: Drug spending that is in 2009 approximately $250B more than doubling by 2020, driven by faster growth after a period of slower growth due to drugs coming off patents but with drugs having more exclusivity
Innovations happening now that are "open source" that I guess I have to talk about:
OpenNotebookScience, spearheaded by Jean-Claude Bradley at Drexel.
web-based platforms for collaborative drug discovery, like this product
the Open Source Drug Discovery project, which is working on TB (notably infectious disease)
drug costs have been estimated to be $802 million, though this is variable and is also subject to a lot of assumptions and it includes opportunity cost of capital, which is actually the majority of that figure ($574M). The people who came up with $802 came up with a similar $600-1B range for biotech products
What is a drug? Well, traditionally you go with the "Lipinski Rule of 5," based on certain physical properties, such as size and key chemical properties that make it compatible with metabolism. Of course, not every drug satisfies this rule (e.g. lithium carbonate)... also, it's sort of weird that biologics are treated differently than small molecule drugs when they are basically the same thing (though of course they are developed differently with different IP ramifications)
Also, biohacking and "garage biology" or DIY biology: OpenWetWare (closed access Nature article) or the Wired article I can also have a link to DIYBio, even New York City has Genspace
Also protein folding at home using passive grid computing. Aaron Chan's essay talks about some chintzy protein folding game stuff that isn't really all that interesting but has a "cool factor" that appeals to computer type people.
Community sourcing / social networking for patient experiences: PatientsLikeMe which has had some preliminary success in ALS being faster than traditional studies with similar results
Then we have something with genomics research by people sequencing their own genomes -- the most prominent effort in this regard is 23andWe, which has already published some results of genome-wide studies that appear to check out, including work on Parkinson's
-- BahradSokhansanj - 25 Oct 2011 |
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