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BahradSokhansanjFirstPaper 9 - 01 Nov 2011 - Main.BahradSokhansanj
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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
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> | 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. | |
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> | 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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