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    "text": " Hi, everybody. My name is Andrei Teguideira. I'm the co-founder of a company called Fleming Protocol, which is in essence a data economy where patients, scientists, hackers, and industry come together to create biomedical innovation. Today I'm going to talk about patient DAOs and the future of patient care in the decentralised world. And we believe very strongly that patient DAOs will be the future of this because they are ultimately structures which put the patient at the core of the biomedical research process, making them true partners and also giving them the ability to own their data in a more privacy-preserving way. So just a quick definition as to what a patient DAO is. So it's in essence a decentralised patient-led community which collaborates with scientists and hackers and industry to tackle patient needs in a more meaningful way. And this involves several elements, but these are three of the key ones. So they collaborate together to discover projects and initiatives based on the curated patient need. They also fund these projects based on either funds from the patient DAO or through partners of the patient DAO. And finally, there's going to be an underlying data economy which helps to accelerate the research and product delivery of whatever's funded. Some of you are probably wondering why patient DAOs need to exist. And to illustrate the point, I'm going to go through three examples throughout history where patients and their needs and perspectives haven't been properly considered. And one of those examples would be the story of Henri S. Lex, who some of you may know died from cervical cancer in the 1950s. But her cell actually demonstrated some unique properties which sparked the curiosity of scientists and it was shared without her or her family's consent. And what this ultimately resulted in is significant breakthroughs in a variety of fields. It also led to hundreds of thousands of research papers being published. It also led to a lot of products and cures for patients and ultimately improved the profits of Big Pharma. So whilst this did inadvertently lead to a lot of scientific discoveries, there are some ethical concerns of this story which we'll get into in a moment. Another example is the example of the AIDS movement of the 1980s, where another patient-led movement, which was a collaboration with scientists as well to help find a cure for AIDS, but also to get funding for early stage research. And all this was done in the face of complacency of the FDA and the government at the time. But ultimately this was a positive movement in the sense that cures were found, a lot of scientific progress was made, and there were some long-lasting changes in the FDA, which is more patient-centric now. For example, they have more representation of patients in their boards, and they also have paths to get cures to reach the market and patients more quickly. And finally, we've got the example of the Open Artificial Pancreas movement, which took place in the 2010s. In essence, this was a story where a group of diabetes patients weren't happy with the offer of the medical industry at the time in terms of devices to manage their blood glucose levels. But what this community did in response to this was collaborate together online in an open-source community, alongside scientists and hackers to create this new automated insulin delivery system. The result of this is that they ended up creating a really great innovation which improved the quality of life and their patient outcomes. And if you fast forward to now, you actually find that the industry, particularly Medtronic, for example, have actually kind of mimicked this innovation and they're now profiting from that. But this innovation wasn't initiated by industry, it was initiated by patients, which again just shows the power of patient collaboration alongside scientists. So what are the commonalities amongst these three examples? Well, you find that when you bring together patient communities and their data plus motivated scientists and hackers, it leads to substantial progress in terms of the understanding about disease, but also innovation, but also how biomedical research is regulated by the likes of the FDA, for example. But whilst this is so, it wasn't entirely perfect in some of these examples. So if you take the story of Henrietta Lacks, we find that her information was taken without her or her family's consent. And this obviously led to a lot of benefits for industry and scientists' careers. So there is an ethical concern there around having permissioned access to information. And then if you take, for example, the example of the AIDS movement, we found that there was a conflict within the movement. So, for example, the more activist side of the movement versus the more science and such policy side of the movement, which led to a lot of infighting, which overall impacted the overall movement. But the good thing about the Web3 and the whole patient data infrastructure is that it provides mechanisms to help manage those imperfections. So, for example, the Web3 infrastructure allows for people to take more ownership and control over their data. And in this case, we can have patients who can aggregate their information into a single place and allow for permissioned access to people who want to access it. So they'll be able to easily track who is doing that, but also they could potentially earn from that in terms of charging for that access. And then with DAOs, we also have governance around that, where, in essence, there's more of a collective element around how decisions are made and reputation systems can be built to help weed out any potential bad actors which aren't aligned with or are undermining the overall objective of the DAO. But because these new DAO infrastructures are actually web-based, it will actually be able to garner more support. So, obviously, back in the day, the internet wasn't much of a thing, but nowadays we've got these internet-native structures where anyone in the world can join these and help to advance the movement's cause if they align with that, but also it'll be easier to generate funds to help fund the DAO's mission, for example. So these are two additional features which really help to enhance what was so great about the movements of the past. Once it's all well and good, acknowledging the importance of patient movements and patient DAOs, we also need to consider the paradigm which has influenced scientific inquiry over the past hundred or so years. So there was a time when, in the 1800s and early 1900s, where science was more open and there was a lot of hobbyists and very curious people who were very keen on the idea of sharing their thoughts and discoveries, and this led to substantial progress and contributions towards the airplane industry, for example. But if you fast forward to now, you find that things are much more centralised and you find that large entities and regulators are controlling the vast majority of activity that ends up impacting the patient, for example. And this has come with some benefits, but of course, this has led to a lot of stifling in terms of more openness and collaboration and is part of the reason which has inspired such movements, such as the ones I've mentioned in previous slides. What a key driver was of this change in paradigm was the fundamental belief in these three points, that such important research in the biomedical space can only be done in large, established organisations, in essence, only restricting it to large institutions, such as the ones I've just mentioned. And in addition to that, also that only cutting edge and the latest technology can only make these breakthroughs. And finally, that only large clinical trials are needed to validate a new product. And all of this obviously increases the price of discovery and innovation, which ultimately leads to barriers to entry, which are too high for a lot of small firms, but also patient groups and also biohacking groups and open scientists. And a result of this shift in a more centralised manner of doing biomedical research is that a lot of the research which has been done doesn't actually translate to a end product for society or for patients. So if you look at the early stage research setting, we find that there's a huge translation problem in terms of translating early academic discoveries into products for patients. And it's often known as the valley of death in the context of biomedical research. And if you look at the pharma and biotech side of things, you also find that there's challenges there as well. So, for example, 90% of R&D efforts by industry ends up failing. And that's despite there being an exponential increase in the R&D costs since the 1960s and also in the face of many technological advances, whether that be in computational biology, genomics or also machine learning. So there's clearly an issue across the kind of early stage academic setting and plus the pharma and biotech setting. And there's an incentive for everybody to change. But ultimately, patients are the ones who are the most incentivised to make this change because they're the ones that ultimately bear the brunt of this failure. So in terms of where we go next, I think Thomas Kuhn, in his book, has some really interesting quotes in the book. And one of them was, in both political and scientific development, the sense of malfunction that can lead to crisis is prerequisite to revolution. And it's fair to say that what we've seen on the previous slides in terms of the pharma and academic performance of translating research has been a failure for patients. And there's a new movement of decentralised science, which is taking place. It's really picking up steam over the past few years. And that is, in essence, going to be the revolution in the face of that malfunction. But ultimately, to make sure that this revolution is driven to the place where it needs to be, patients need to be at the core of this new movement. And this is where patient DAOs will come in, because ultimately, with the patient perspective guiding the intellectual capability of the new movement, but also the capital which is flowing into that movement, will lead to more translatable and impactful research and products for patients. So in future, patient DAOs are going to be the key collaborative, which will have the primary aim of curing disease. And it will do this through three key factors. So it will have the patient perspective to guide decentralised science to more translatable research. It will also have the capacity to pull resources toward these patient needs, which are identified. And finally, this new decentralised infrastructure, alongside technological advancements in other fields, will allow for this new infrastructure to experiment with more novel methods to speed up biomedical research. So just on the first point, in terms of guiding decentralised science, in essence, patient DAOs can pull the data together and share this in a way that aggregates all of this information, so that users can spot trends in that data. And in addition to this, they can also share their perspective in terms of any unmet needs that they have, or just expressing their dissatisfaction in terms of current treatment options and devices available. And all of this information will be shared in a privacy-preserving way, and they'll be able to grant access to whoever they please, and also have permissioned access to make sure they really keep on top of that. And then this information in aggregate will help to inform scientists and hackers' hypotheses and analysis in terms of looking at the literature that's available, and also looking at data trends and how that can inform future directions. And over a period of time, this will result in a research and development project proposal, which has then been validated by the patient perspective and data, making it more likely to translate to a real-world device and treatment. And if you look at academics such as Andrew Lowe, they've also recently released some papers on incorporating the patient's perspective and preferences via Bayesian decision analysis. And this can be applied to a variety of scenarios, whether that be clinical trials or options to explore. Then once a valid R&D proposal has been created by the community, the community can then pull resources to help fund that R&D project to make it become a reality. So DAOs typically have a treasury which is voted on in terms of how it's used by the members of that DAO. And in this case, a patient DAO can just fund the general operations of the R&D project. They can also use the funds to seek out lab services to help assist the community with delivering that project. And also you can use funds to help with the production of the device or the therapeutics in question. But it goes beyond just funds as well. So patients can pull the data into one place as a community to help inform analysis related to the R&D project. So, for example, to find a valid target to investigate or to validate compounds worthy of further investigation. Patient data can be used to inform that and data could be, for example, data from the devices. It could be genomics data, could be sample data, bio samples, for example, to help with this process. And finally, the patient DAO can be the infrastructure which can be very ideal in terms of experimenting with new methods to speed up biomedical research. So, for example, scientists and hackers, which were involved with the delivery of a project to cure disease, would then, if it got to the right point, could design trials to make that a reality. And patients who are involved in the community can also use that information and data to enroll into clinical trials and also a study design to make it increase the likelihood of success. And when it comes to trial execution, then decentralization is definitely an ideal infrastructure to look at this, because ever since the pandemic, we've been seeing an increase in the usage of decentralized clinical trials, which are done remotely and patients are submitting their data closer to home or having complex procedures more closer to home, which is increasing the success and also reducing the cost of clinical trials. And also the idea of doing an N of 1 clinical trial over a longer period of time is also a method which is worth exploring, and the patient DAO is in a very good place to explore that. Whilst the idea of a patient DAO is definitely the ideal way to guide decentralized science efforts going forward, we also need to acknowledge that there are some practicalities that the patient DAO infrastructure would need to overcome. So, for example, traditionally, it's been known that the clinical trials cost a lot of money, and to do so in a distributed way and where there's no institutional funds, or there may not be, for example, how does this community get a product to market? And the answer is that, you know, as I mentioned earlier, patients can pull together resources to fund this trial. They can also, because of the decentralized nature of the trial, do more efficient ways of doing the trial. So, for example, decentralized clinical trials and also N of 1 trials, which can potentially impact and reduce the cost in a positive way. And also, because we're trying to facilitate a community of being able to self-experiment, if a patient wants to take that risk, if the patient is willing to take that risk and test the new therapy or device, which they are wanting to do of their own accord, then that will also have implications in terms of reducing the cost. Another point is also the appetite for risk. So the biomedical field is also known for its risks in terms of, you know, getting a new therapy to market and testing it to make sure it's safe. And a way that we get around this is to make sure that the patients who are involved in the process in a more intensive way, for example, if they are taking part of clinical trials, is to make sure that they have true informed consent for the risk that they're taking. And you find that a lot of patients, especially depending on the severity of the mentality, are definitely willing to take this risk in order to improve their lives and ability to manage their disease. And like I also mentioned earlier, there are methods of quantifying and factoring in patient preferences when it comes to deciding how research is conducted, such as Andrew Lowe's method of Bayesian statistics and helping to inform research direction. There's a point around ethics. So I know there are a few people out there who think that the idea of doing citizen science holds its risks, especially in light of technological advances, for example, home crisper kits, which some deem is quite controversial. But yeah, I think definitely to kind of overcome this, as I mentioned earlier, DAOs do have governance, which needs to be designed in a way that the community is on board with, but also in a way that ensures that risks are mitigated. So for example, we could factor in a open source IRB into the process to make sure that the research conducted is ethical and is in line with patient needs, and also just making sure that there's mechanisms in place to ensure that patients who are involved have proper true patient consent, and if so, they have the right to conduct and be a part of trials if they're so pleased. But we also, on the other hand, have to consider that there's ethical considerations of not doing things in a more distributed way or also not allowing patients to take risks and also to experiment with new technologies, which can be quite impactful. So for example, as you saw in some of the other examples of the open artificial pancreas system movement, and also with the AIDS movement, there's a lot of patients who were taking risks and experimenting with their own bodies, and ultimately that led to a lot of benefit for them and the broader patient community. So that also needs to be considered as well in the conversation of ethics, and this is something that we're really trying to push and guide in the right direction. And if you look at some examples, some very recent examples of self-experimentation and decentralized science, as I mentioned, the open artificial pancreas system, which has been in the last decade and has really informed automated insulin delivery innovation, and to the point where industry is now adopting that technology and profiting from that. Well, we also got examples of the RADVAC group, which at the beginning of the COVID-19 pandemic decided to create an open source vaccine, which was discovered in a few months. And yeah, there's a lot of promise and benefits there. They were able to make a lot of scientific progress in a short space of time. And I believe that they've recently received some additional funding to progress this even further, because there are some broader societal benefits of doing this open source research, but also allowing people to self-experiment with innovations like this. And we also need to acknowledge that there's been a long history of self-experimentation in the scientific space. And it's led to a lot of breakthroughs and really profound levels of understanding based on those self-experimentations, as you'll see on the screen here. So, again, I think if we're able to facilitate the idea of self-experimentation in a way that's safe and in the sense that we have the right governance, but also making sure that technology is used in the right way and the people that are using them are informed about that in the right way, then I think this level of benefit to society can be exponentially increased if we're able to democratise that process. So with all this in mind, what's the call to action of my presentation today? It's for biohackers and industry to collaborate with patient data, like Filament Protocol, but the others that are increasingly becoming more prevalent in the world. Because we believe very strongly that if you bring together the patient's perspective, the experience and expertise of scientists and hackers who have a very creative way of conducting science and doing so in a way that's more patient centric and speeds up the process of getting treatments and devices to people. And finally, combining all of that with the industry who have a great ability to manufacture and scale innovations in a way that allows for broader access to society, if obviously priced in the right way and whatnot. And this will ultimately benefit all of the parties involved, because the patients will be able to have more treatments translated to them to help manage their and even cure the disease. Scientists and hackers will be able to have involvement in the process and see their creativity scale in a way that has a broader impact for society. And finally, industry will be able to benefit from this as well, because more products are reaching the market. And because patient DAOs like ours are looking to build a profit sharing mechanism, all of the people involved with developing the cures and devices will be able to profit from the eventual output if there is a output commercialized. So there's a huge incentive all around, not just in terms of money, but also in terms of improving outcomes for all parties involved. So that brings me to the end of my presentation. I thought a fitting way to end would be to finish with a quote from one of our collaborators, who's a part of the diabetes community, Ben West. He's one of the founders of the Night Scout and OpenAPS movement, which was fundamental to driving the diabetes movement that I've been discussing throughout this presentation. He said, let us consider how we can unify our demands for trustworthy, high fidelity therapy that, in adapting to our lifestyle, allows us to return to lives we want to pursue with the people and activities we love. Let us make the benefits we discover accessible to as many as possible. And I think that should, in essence, capture the essence of the scientific inquiry and decentralized science movement going forward. And patient DAOs ultimately need to be at the core of this to help drive this alongside citizen sciences, biohackers and industry. So I hope you join us on that journey. Thank you."
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