I always use this term like we stand on the shoulder of giants, right? It's not like individualized medicine just sprang out of nowhere. The reason that Mielec can exist is because there has been years, not decades of work by other researchers throughout the world. Hey there. I'm Luca Fuzarbassini. I'm a PhD student in computational biology at EPFL in Switzerland. And you're listening to a biotech feature list. The biotech feature list aims to foster deep understanding and discussion about exciting hot topics in biotech. But I want to say from the beginning that it is by no means rigorous in teaching the subject. And for the sake of outreach, sometimes we need generalizations that of course simplify the reality of a science behind what we're discussing. But I can say that my guests and I do our best to be clear and to go in that. You can imagine to be out with me and my expert guest for our friendly conversation to get a general understanding and more curiosity, having fun as much as I've had recording this podcast. This podcast has no sponsors and any reference is not meant to support any commercial activity. This podcast is a solo effort. So if you wish to support me, I'd be grateful if you followed the biotech feature list on Spotify, Apple podcast, YouTube, Instagram or your top podcasting platform and share it with your friends. With that said, I am excited to move on to today's conversation at biotech features. This week we discuss interventional genomics. As the topic is huge, I thought to first discuss in 10 minutes some notions before interviewing our guest, Winston Yan. So to make sure that we are on the same page and do the most of our conversation, interventional genomics was five years old recently. And I must say to begin with, but most of what I'm about to tell you comes from my notes of a presentation that Winston Yan beautifully gave. So let's talk from the beginning. Everything started with no scene or son. No scene or son is a drug that was approved in 2016 for the treatment of a spinal muscular atrophy, at the ability to think monogenic disease. And the treatment is an azo or anti-sensorygonucleotide, which is the divert through spinal tab. So an injection for your spine and it was shown to bio distribute well through the central nervous system. No sooner than is what enabled the development of Milos and which is the first drug for single patient. It was approved in 2017 after an incredible journey guided by the U lab in Boston, which in 10 months led from the discovery of a mutated gene in Milos genome to therapy. And initially the therapy really helped Milos, but luckily it was too late for her. And later her mom Julia said that Milos and showed that this is all in deep possible. So rare diseases are actually not sorry. Are if you take some of them, we are actually quite widespread. And it is often understated how much they do impact not only children's but also their families. And also only 5% of her disease have FDA approved treatments. So versus you're just for common guidelines for your genetic disease and indeed they share something strong, which is the genetic base of most of them. And typically they only depend on a single or a few wrong letters in your genome. So very some possibility to unify at least partly the treatment for this disease. And for sure we must say first that it is crucial to get early diagnostics and in this area that we discuss in another episode we are getting better day by day. And sequencing at birth is an option that is becoming more feasible and accepted. It has lots of benefits that we discuss. And also prenatal testing is something that can help. But then our mutations and complex mutations may still happen. And in vitro fertilization is something that maybe not every people want to do and has complications and limitations too. So back to our disease we were saying that it is crucial to find common principle to treat. And of course treatment is different from cure treatment is making a life better. The most that we can with current procedures. So ultimately every genetic disease is different. So we should find kind of a balance between common principles and the importance of remembering how every genetic disease has its own place. And the wonderful metaphor but with the deliver to us that rare disease is becoming much like surgery where there are common principles but in the end each intervention is unique. And now the landscaping rare disease is quite heterogeneous and only a few people can afford it. So typically there are parents building infrastructure for startups that raise funds for research. And what we need is structure, a systematization of a field. And this includes proper regulatory framework. So this is more and more complicated as number of one treatments really cannot be supported by classical treated versus placebo clinical trials. Let's step back from the science. And the milestones that enable the international genomics to rise. First is ASOS or anti-SNO oligonucrotitis which I was mentioning before. And they are great point in favor is that they distribute very well through the central nervous system and many of them are not debilitating single gene rare diseases indeed to affect the central nervous system. And the second is of course CRISPR-anterated tools which are versatile and also in CRISPR worlds and clinical trials are now finally underway. Then I want to spend some words about the applicability and the organization but is needed to bring the science to bedside. So first financial sustainability is key in a place where market in some time is low for pharma. And here we need highly trained and expert personnel who are experienced for previous trials will build step-by-step knowledge on the issues and the people and the expertise which is needed. And the second crucial thing to solve for applying science to bedside is the developing of great small batch clinical grade manufacturing for genomic medicines and delivery vectors. So now how does all this relate to people's lives? First informed consent is very important given the risk benefit balance in a field where the risk is high now and it will be high for some time. So the risk is acceptable and justified only when the cost of doing nothing is comparatively high. Then we should decide who assumes the risk and we still have a sentence in this presentation that the microbe scalpel generally does not assume the procedural risk, whereas pharma typically assumes a significant responsibility for drug safety. So this is a place full of risks and unknowns and this is for sure to be discussed first with patients or at least with families. But then what happens after a patient undergoes genomic surgery? The follow-up on the target efficiency and the off-target effects for genomic medicines is crucial and the difficulties that you want to follow up with is but you really don't have a comparison of how the child of a person would do without the treatment. So the idea is to kind of follow the treatment phase of a patient and see if a worsening is a little better or if a worsening is still strong in that case when medicine maybe is not really helping or hopefully if there is some reversal of initial condition at least partly. So of course this will be complemented by serial sampling of cells for whole genome sequencing and biomoharchar to establish correlations and this will help the field of vertices in general to establish methods to see how a patient is doing after medicine is delivered. So this is also a community effort to establish common guidelines in order to develop appropriate markers. And we should always remember to that, early skills acceptable, only when the benefit for a patient is predicted to be substantial and highly likely. So we must remember that we are not developing drugs on our patients. I want to also mention another great concept that we stand delivered which is the opening doors concept. So we were mentioning in the beginning of episode that Piraza was creating the intellectual space for millions and two of course. So it demonstrated that ventrothical, so through your spine, injection of azos is safe and can work. And now something happening in the CRISPR field is VEX vivo editing of the hematopoietic stem cell. And clinical trial is opening with RABO and RABO lignos with particle delivery. And if this is approved, then a cicosa will enable to target other blood diseases. So each step we do in various disease field is a step that opens doors for more and more diseases. Now I also want to mention a couple of concepts from Arabian title, the therapies for rare diseases.
therapeutic modalities, progress and challenges ahead. As I was mentioning this field is very young and full of risks, geographical and desperate young patients, high diversity also inside a single area because you know a genius big and mutations can occur at multiple places in the gene and this can result in different problems and phenotypes and this is a field where we need knowledge foundations so something that will be crucial also to follow up on patients after genomic intervention as we were mentioning is digital biomarkers online platforms and patient or caregiver reported outcomes so biomarkers are defined as something quantitative to measure about a biological process and they must be well defined and indeed precisely measurable with well described procedures and they also can measure something related to a pathological process or response to an intervention and specifically digital biomarkers are enabled by random home devices, ingestable pills, subcutaneous devices and everything in the long run will be connected to your smartphone or some processing system that will inform your doctors to monitor the disease progression, response to treatment, unexpected toxicities and in the long run also to increase the general understanding of diseases by pulling all these data together. Couple of examples would be the electrocardiogram recorded for a one-year long for some heart diseases or inertial sensors to monitor for marks constraint and another thing that will be crucial is patient reports for instance are parents keeping track of a number of seizures per day in epileptic patients and this all delineates the importance of monitoring after the genomic procedures and if this is indeed an area of active research where post treatment follow-up must be objective, quantitative and useful to really do what's useful for the patient. Let's now move on to today's conversation with Wistanean. Hi everybody, today I have a great pleasure to host Wistanean to discuss interventional genomics. Wistane defines he himself as a genome engineer and startup founder motivated by bringing genome editing therapies to treating patients with serious genetic disease. Wistane, can you introduce us to your story, beliefs and goals briefly? Yeah so my story, first of all thank you, Luca, for having me. It's really a pleasure to be one of the inaugural episodes on your podcast. For me, I think my story is complicated enough where it might be helpful to actually walk backwards from where I am right now. I just finished my MD PhD at Harvard Medical School and it was a bit of a scenic journey as my friends say. I actually started my MD PhD program 11 years ago, finished my PhD during the peak of this CRISPR craze if you will, doing it at the Broad Institute in Fung Zhang's lab, working on therapeutic uses of CRISPR Cas9. And then for me, afterwards instead of returning back to medical school, I had this opportunity to start a biotech company. And so for about three and a half years I was building Arbor Biotechnologies from what they call from zero to one where we grew from just myself and my co-founder to 50 people. And then afterwards, I had the chance to really through the privilege of my mentors to go back to medical school and do the clinical training. And that's where I just saw this opportunity in front of me of individualized medicines. Like how do we build to treat all those ultra rare diseases that have no other treatments right now, that long tail where you have like five, ten patients that do not have any commercially available treatments and will not in the future. So that's how I, you know, through my own reading and research, made my way to Tim, who is clearly a leader here and onto the end of one collaborative. And it's been this amazing experience so far working with them to build this framework for the best practices of how do we make individualized medicines and define the future for these ultra rare diseases. Yeah, that's so inspiring and we're looking forward to hear more of how your story develops in the next few years. So can you tell us more about what is N1CN, how did it start? Yeah, so, you know, I would say that the history of the N1C was really a brainchild of and N1C being the N1 collaborative. It's really something that Julia Vitorello and Tim you were imagining even from the early days when they were thinking about me listen. So they thought that there needed to be the centralized hub that provided this glue for the academic efforts and you could establish the best practices that can then be distributed to the rest of the practitioners in this field. It's a really new field. The N1 individualized treatments have a lot of heterogeneity and in order to actually build what's safe for patients and what's best for, you know, that's our best shot at actually helping them with their disease, you needed to have the centralized hub of information. And then the other part of it is that in order to interact with external stakeholders that are key to this like the FDA, the NIH disease organizations, you wanted a single place to interact with them most efficiently so you don't have all the redundancies and the kind of duplicated efforts that sometimes it would just be efforts that are not helpful to this whole effort. So I think, you know, what we are trying to provide is this centralized intellectual framework and the best practice of the field all done in the collaborative transparent manner. Yeah, so that's great. So how did you join all these and what were your main ideas when you started doing this? Yeah, so the N1 collaborative actually started before I joined it. The first meeting was in June of 2021. Tim, he gathered together clinicians and researchers from other academic medical centers both in the US and the Europe so that, you know, it would be international from the beginning. He pulled in folks from nonprofits in the ultra rare disease space like NLORM and other industry groups plus, you know, folks and government just everyone who is anyone in this field. And then since then there have been regular meetings where the N1C has tried to self-organize to provide both resources and best practices for everyone who is working on these. The, you know, for me, I actually was introduced to the N1 collaborative through Tim where I, let's see, he was just the leader in the field that people knew and as soon as I started asking around about how do you make CRISPR into a tool for individualized medicines, they were like, oh, you should talk to Tim. So for me, I emailed Tim, probably incessantly about how do we actually bring CRISPR into individualized medicines. You know, it would be really great if with gene editing you could just swap out a targeting mechanism or, you know, the targeting sequence and it can make the genetic correction for a different disease easily. And I shared all this with Tim, he absolutely agreed with this vision and says that ASOs were important but likely the starting point and we will need other tools like CRISPR in the future. Yeah, that's great and maybe we can spend a couple of minutes now here. Why CRISPR not now but as does now and CRISPR in the future? Well, I think that the big thing is that there's a, you know, ASOs have a very distinguished history of development and there's so much that we've learned of how to make them safely and effectively. The reason that mealisand could exist is because there has been years, not decades of work by other researchers throughout the world and companies like Ionis pushing for the safety and the science of medicines like newsonerson, right? And so CRISPR is still early in that space. I truly believe that we will have the CRISPR equivalence to newsonerson that then unlock different organ systems and, you know, like cell types for future and of one medicines. Yeah, I remember this opening doors concept of a dimension then your presentation to our lab meeting and this was really, you know, a very clear metaphor with all what's happening out there. Yeah, exactly. And, you know, I think there's benefits to CRISPR. For example, if you a lot of disorders with this clear genetic correction, many of us in the world we walk around with the wild type of that, right? So we should know that if we can correct certain cell types, the specific mutation to that wild type in certain cells, in certain organs at a time where it's still beneficial, that seems like a hypothesis that should reduce the burden of disease. Meanwhile, with RNAs, sometimes, you know, you're upregulating a little bit, you're changing a spice configuration. Like that is not as clear of a therapeutic hypothesis. So I think there's a simple elegance to genome editing that could be powerful. The challenge is that it is a more permanent edit, right? You want to superb show our best species good? Exactly. And so I think that's why there's a lot of, I mean, it's good in many ways too because that means that you could have a durable therapy that's life long, but the same
time because of it you can't take back the treatment so to speak right you can't just stop giving someone a pill or an ASO or even like you know a biologic in the same way so I think there's a lot that we need to think carefully about so that we can have the best safety and efficacy combined. Yeah and there's a lot of great research going on in this field especially also for delivering these technologies to the right set types at the right moment and we've enough quantity and that's maybe a topic for another podcast episode so maybe now we can switch to the second big question of today's episode so can you tell us about the objective outcome measures? Can you provide us with some examples in the field and what kind of measures will be developed in the next few years and what would be at best one measuring clinical trial where you can't compare control versus treatment. So yeah. Yeah so there's a those are all extremely good questions there's frankly a lot to unpack there within the all of your questions have sub answers to them but I'll try my best to at least start from the the broadest sense and then go into some of the examples and details. So the first is that we need to lay out why do we need clinical outcome measures right? I think there's often an instinct when you see when you're seeing a patient with severe need or just you know a really tragic diagnosis like why can't we do anything to help them right? The reality is that when we do an intervention on a patient especially one that is untested we need to make sure that it's not only safe but we have an expectation of benefit. So then it gets the the question of how do you measure that benefit? How do you rigorously say yes this person received an intervention has ended up in a better place than before versus we just tried something on them you know without any rigorous measurements. So of course you'd like to say that there's some effects that are so obvious like someone who couldn't walk can walk again or they couldn't see now can see again or with some of the early like newson nursing results where they were just like amazing and you know you didn't have to really have rigorous clinical outcome measures they still did but oftentimes there's more subtle right? So in the nor in the traditional clinical trial sense you have these randomized clinical trials that are the gold standard because they can tease out the subtle differences of whether a treatment worked or not. So here now we think about individualized medicine right? What is your case and what is your control? You don't really have one right? So that is a place where we need to think about how do we do it? Well you know you can start by saying let's measure somebody's individual baseline right? You start with their baseline while you're trying to make the drug in the laboratory and that way you can maybe differentiate from whether there's a delta from their baseline but then how do you differentiate that from the natural history of the disease right? Sometimes when you're measuring seizures as kids their brains develop and they you know the neural connections form and there's pruning that happens all that biology you actually naturally get fewer seizures. So yeah also because of so much heterogeneity and so few cases. Yeah exactly so I think what you know this is just a sneak peek at some of the challenges I didn't even talk about bringing the families right? Sometimes what clinicians think of as the most beneficial clinical outcome measure may actually differ from what the family feels is most important right? Again going to the example of seizures a clinician might say I want to reduce the burden of anti-apileptic drugs right? because they sometimes have their toxicity but a family might say the seizures are well controlled on these drugs I want them to be able I want my child to be able to communicate with me so that they can become more independent and needs that's value enough yeah exactly so I'm not saying that either viewpoint is correct or incorrect there's just these nuances you have to consider at the end of one level. Yeah so how can you also track not only a efficacy about safety in parallel with efficacy and yeah I guess here it's crucial to collect aggregated data to know what to look for possible toxicity because first if you want to check something you have to know what you're looking for or at least have some broad concept that you can really then decide what to look for for each class of individualized medicine so first days of vancrete spread whatever what do you think this will entail? Yeah again I think we're actively in the process of developing this and my my first thought would be that we again back to that concept of standing on the shoulders of giants right these medicines we are we lowered the risk to any potential patient receiving this new drug by changing as few parameters as possible from the older medic or not the the prior medication and so what that means is for example anti-sensaligos into inter inter-interethical space with new synerson we keep the chemistry is the same we keep the delivery method the same all of the protocols are kept in the same way so that we understand like you said what are some of our expectations of any clinical outcomes that are you know not ideal at the same time I think there is this aspect of collecting maybe more data that we need in the beginning simply because we want to capture as much as possible and just make sure that we're not missing anything it's always a balance in clinical medicine of saying how do you kind of be targeted with your approaches to collect you know intervenable data versus how do you not miss something and there's a question of cost as well so all these are questions that I think we are in the process of building out the protocols for in the best practices so what criteria do you look for in deciding the right outcome measures that's a that's a very great question actually a topic that we are actively working on so as you can imagine in individualized medicines you don't have the benefit of averaging right so in this case if there's noise in your biomarker or your outcome measure high individual variability that can disguise the signal that you're actually looking for so it's important that we have something that can meaningfully have a you know variability that's well controlled on top of that you want to be able to see a change so you want a biomarker that is sensitive to a change on the end of one level as well as sensitive change in a reasonable time frame that you're trying to to measure this clinical trial right so for example if you're trying to say have someone maybe regain mobility that's something that might take longer to measure potentially over the course of years rather than over the course of months for initial clinical trial I would say the last thing is just thinking through what is most relevant to that individual's disease history and trajectory look at I think you mentioned that there's each individual maybe a different points of severity of a disease right so maybe there's some outcome measures that are relevant no longer to a particular individual because they progress beyond that or they're in a particularly severe form of the disease so anchoring it back in that individual's own disease and their presentation is really important so those are just a few of the considerations that we think about when we're designing those outcome measures for the end of one population and these are the case by case I guess analysis because every time it's different as you were saying every patient is different and the patient is a different trajectory point in disease so yeah and I think at the beginning that will be the case where we try to be very thoughtful about this but the whole point of the end of one collaborative is that we can develop these frameworks and best practices so that at no longer at some point it doesn't become this bespoke thing every single time but you really start developing you know essentially a pathway that you take a given patient through even though they are unique they have a unique mutation their own presentation you know again back to that analogy of surgery there's protocols and ways that you evaluate a given surgical candidate even if the individual anatomy their disease history their you know their lives have been different yeah it's a beauty of scaling science from single anecdotes to making something systematic and accessible to most of the people who need this yeah there's this I mean we had this discussion lately where the plural of anecdote is not data and I think that's important thing that we have to keep in mind that just by piecing these together we don't suddenly become you know we don't try to make conclusions that are beyond what they can say but I think they can be very informative and guiding our practice sure let's start with wisdom thank you so yeah you were mentioning a biomarker and I'd love to hear more about what you think about biomarkers molecular biomarkers digital biomarkers and how to visit patients who are a few and widespread the rubber words in this field probably best to break up the the molecular biomarkers and the digital ones right so I think molecular biomarkers are something that traditionally in clinical trials people look for as a more just sensitive measure of exactly the biological pathway that you are addressing it may not be a clinical endpoint like you know being able to have speech again or you know reducing your ataxia burden but for us it truly is important to have molecular markers they can tell us whether or not a particular ASO is actually doing what we think it's doing at the
on target. They may not exist. They may still need to be developed. Again, that's work in progress from the groups. I would say that the digital biomarkers are an interesting new area that has been potentiated by COVID, right? Because so many of these clinical trials that required patients to come on site every couple of months, you actually see a neurologist. If you think about it, that's not very many data points for a given clinical trial, right? Especially when you're talking about an end of one trial where you're just seeing there could be significant day-to-day variability of these interactions. We need something that perhaps via video or via even just apps that people can download onto their home iPads for working with their children or the patients who have these diseases. I think that could be an incredible way of having more granular time points. Then I also think about how can we learn from the consumer electronics industry, right? Instead of using your Fitbit or technologies like that, these activities trackers just for counting your steps or your calorie burn. If they can be really used in a manner to track abnormal movements or just seeing that capturing seizures in a more rigorous way at home versus just using a notebook, those are all things that could benefit the quality of the data that's being gathered, as well as reduce the burden that the caretakers have to record every single thing all the time. I'm super curious about what will happen in this field in the next few years also for these parts. That's super beautiful. So yeah, now maybe we can switch topic a bit and let's talk about how to make the most of collaboration. So, end-of-one collaborative emphasizes the importance of data sharing. What data needs to be gathered and how will standards and databases be established? I mean, what is the fundamental rationale behind what your team is working on? What kind of data do you need to generate the most and what technological achievements advancements need to be soon developed? I think if we're talking about the fundamental rationale or like why do we care so much about the data sharing, there's this idea that I have in mind that it really is a privilege that some of these patients are donating not only their time but their bodies towards not only helping themselves, but ultimately they know they're at the forefront of medical technologies. They're donating themselves really towards advancing the science. Yeah, knowing that they may not be the ones to fully benefit from this. So, if there's a way that if we capture all the data that we can and use it to power up the next end-of-one studies, which could be happening just like months from now, right? If we could do that in an efficient, rigorous, and like widely distributed way, that seems like it would fit both the gratitude that we have as well as the best practice for how do we move this field forward. So, and I think this is opposed to a traditional clinical trial model where maybe there's a farmer company that's sponsoring it and they would aggregate and keep all the data internally, make sure that it's all, you know, I guess it's all up to snuff or at least it's like controlled before it's released. There's good reasons for that too and I think it's appropriate for the types of development that they're doing, but with these very heterogeneous end-of-one trials, it feels like there's a special need for something that is open and collaborative. So, then that gets back to your question of what types of data do you want to collect, right? It's just complicated, I guess. Yeah, and I think at the highest level, you can collect both pre-clinical data as well as clinical data. What I mean by pre-clinical data is say, you know, with ASO, you're looking at what are the principles to target a particular mutation or gene and you don't want labs necessarily to reinvent the wheel if there is a very similar, you know, approach that's already been tried and it didn't work. So, I think that's one rationale of just learning from others positive data, but also to negative data, which is really important. I think the second thing from clinical data is we think a lot about how can we provide a way that allows, you know, investigators to access, like to almost answer questions in the very lonely space of end-of-one trials of saying, oh wait, I wonder if what I'm seeing in this particular patient is actually a class effect of a particular drug or something that's like a safety issue that I have to address in my patient right now. And just having this database be readily accessible to the investigators at their fingertips really, that could help be this resource for them as they move forward the individualized medicines. I think this is also very interesting on the side of how technical implement all these, I mean, the ideas are great and we want to make them most of them by really creating the infrastructure at the technological level that can support these rights. Yeah, and this is a non-trivial task, right, because first of all, all of the academic data centers might have their own standards or how the data is collected, right. So, this is what we call a federated data model where there's some control at the individualized centers, but also there needs to be some centralization, otherwise you lose, you're going to have a bunch of, you know, decentralized databases. I think the other thing is that how do you anonymize it, right? Sure. And of one, by its very definition, I think it's, we're going to get to a point where someone's genome is a unique identifier of oneself, right. But as much as possible, if you can play by the standards of, you know, anonymity, making sure that the patient's information is protected, I mean, those are also challenges that we need to think through for very, very unique individuals. Sure, but it's very interesting. And, yeah, also related to this, it's important to consider that the mutation in a patient that may lead to a disease is also dependent on the whole genomic background that a patient has. So, you really want to know the most you can to make the most for a patient, but at the same time, want to make sure that their data is protected enough for whatever reason, right. Yeah, actually, that point is a great lead into your next question of like, how do you select a patient, right? Because you're right. Some genomic mutations actually do have lots of regulation by other factors within their genome. And, frankly, there's aspects that are still biologically unknown and we need to further study. So, anyways, all the aspects. Yeah, thank you, wisdom. Yeah, maybe first before John Pinging, patient selection criteria, it would be great to share what you think, but columns of data that need to be collected in order to get the most from each NO1 trial are good to have. And, yeah, I know you're working progress on this. So, maybe it's just a few words about thank you, Riz. Right, so, this is the, you know, when I think about columns, it's like, if you have a giant spreadsheet with the rows being your individualized N of one trials, your columns would be what features of the data do you want to collect. And, of course, there's a, there's a, we talked about before, there's a cost benefit to, you could collect all the data in the world, but sometimes collecting data without looking for something is you get incidental findings. Other thing is that you could end up making a trial incredibly onerous for the patient and the investigators if you have to mark down every single thing. So, I think that's a balance that we're going to have to figure out as we continue. And, it's going to look different now than maybe 30 years down the road. Yeah, that's insightful. Yeah, we were already jumping to my next question, but it would be if you can describe for us what is patient selection and why is it a meaningful topic right now when the field of individualized medicines is just at its birth. And, whatever may include your own criteria for patient selection, how will this change to your mind as we feel the progress is in the next few years? I think when, when you consider drug development, right, so much of what you do with developing a completely new modality of treatment is about balancing the, the risk with what you can do for a given patient who has severe disease, right. And, I think it's that risk for ward spectrum that is constantly being evaluated every single day at the FDA. When there's not a lot known about a particular intervention, especially something like gene editing that could potentially, I mean, it will affect their entire body going forward. I should rephrase that, it's not their entire body, but it will be a permanent change to given cells going forward. All of that is unknown. So, in the beginning, I think it's unlikely that you will, you know, do it for, again, being careful to choose my word here. I think it's really important that you choose the indications that have very severe debilitating life threatening outcomes, where if you do this intervention, you can really expect to make a difference in their quality of life, right. So, this is where we can, we can really break out the risk and the benefits carefully, where if we look at risk, as I said earlier, we minimize risk by changing as a few pieces of an existing drug as possible. and then we move on.
maximize benefit by trying to understand as much of the science as we can to say this particular genetic fix in this given organ in this time period of a patient's life should have the benefit that we want. Yeah, you were saying, but we do science for the patients and not on the patients. Exactly. That's absolutely right. This is a key thing of, we will learn certainly from this and that's a crucial part of using the scientific methods, but that's not that is absolutely not what we anyone wants to do of just doing's, you know, kind of uncalibrated and not rigorous experiments without the expectation of actually benefiting our patients. So, yeah, what do you think of the family has to do with all these development process and the willingness of patients? Yeah, I think I've, you know, in the earlier answer, like so there's a, on the clinician and investigator side, there's the patient selection criteria, which is thinking about severity of disease, you know, the timing of when their disease is discovered, whether we can actually be in the, you know, able to make an intervention that can help them. I think from the family side, it's also about being very transparent and honest about the risks, right? There will be families just like we, there's a bell curve of, you know, of a given disease, there's patients who are more severe and less severe. There's also a bell curve within that bell curve of families who are more willing to say, please, let's go for an intervention versus saying, I'd rather wait and kind of see what happens versus just doing the treatment. And I think this is where it gets back to that concept of, you know, it has to be that informed consent. The family has to know the risks and the benefits clearly, they have to be well educated and that is one of the key learnings that we can take away from, you know, trials of old as well as the surgical community. Yeah, I'm super curious about this. Is there some specific difference in, and of one trials in informed consent compared to classical trials or any other disease outside, where are these fields? That would be outside of my realm of specialty. I think we are trying to develop those in the N1C for the entire field, but folks like Tim and people on the team like Tori, Ashley, they have so much of the experience of actually doing that. So I would defer to their answer. Yeah, that's great. I will. So, yeah, I think you also have a great metaphor that you still on from team, maybe about surgery and genomic medicine. Can you describe this for us to finish our great episode? Yeah, so this, I mean, like so many of my learnings, this really comes from not only folks like Tim, but I should say all of what I've described before is just from my interactions with these master clinicians and investigators throughout the N1C. So Tim has this great analogy to transplant surgery, right, of saying when you really think about the first successful, like kidney transplantation, right, or any organ transplantation, these are incredibly complex, complicated medical procedures that are not just like interoperatively challenging, but they have so many things about the resourcing, the psychosocial support, the patient selection that now has just become routine because it works, right? So the first successful kidney transplant was done on identical twins at the Brigham, and that's a very rare patient population that you're selecting, right? You're literally finding identical twins so you can move the kidney from one to the other without immune rejection. This is the type of Goldilocks or like, you know, very unique patient selection that's often needed in the beginning to minimize risk and to say we can really find the ideal candidates for this. But then as you start doing transplants more broadly, right, you saw that this worked, you have to think about immune rejection, you have to think about, you know, what are the ways of prioritizing people who are candidates and what, how do we rank them for a limited resource of organs? And I feel like this analogy is so apt because this is the same set of challenges that we'll have to face to make end of one individualized treatments with these new drugs, programmable medicines that we have coming down the pike and accepted part of the medical practice just like transplant surgery. Yeah, but it was also great. Thank you so much, we've done an Ireland joint talking to you and yeah, I hope you're also going to like our listeners will also enjoy this beautiful insights from Winston Ant. Be looking forward to see what happens in this field as the yards go by and I'm sure it will be a lot and for now I'm just looking forward to interviewing with the next time when things will be moving forward, right? Thanks, Luke, I appreciate you taking the time to ask me the questions. You've just listened to a Balutac futurist, a podcast by Luca Fuzarbassini. This is the first series and the new episode is out every Monday. Please consider subscribing and rating the podcast on Spotify, Apple Podcasts, YouTube, Instagram or your top podcasting platform. And if you liked this episode, consider sharing it with your friends as a growth of new podcasts relies on word of mouth. If you have any suggestions, don't hesitate to reach out to me on Instagram or Gmail at the Balutac
[email protected]. You can find the full AI generated transcript of this episode on my website, lookofuzarbassini.com. I also post the links to the main papers referenced in this episode, which you can find here in the description too. Thanks for listening to a Balutac futurist. I'm looking forward to talking with you and I will.
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