 Recording is on. All right. Hello. We're going to do an overview of the program that we've been working on on computational biology. We've mostly been focusing on quantitative high-throughput screening for neurofibromatosis type 1, but we've really come to find that our algorithm is generalizable to other cancers and other types of analysis looking at high throughput screening data so a little bit of background on us we are a collective of an all volunteer collective of different groups i'm coming from the moco makers group which is a maker space in montgomery county maryland we're also part of the dmv peachy dish umbrella which from the Moco Makers Group, which is a makerspace in Montgomery County, Maryland. We're also part of the DMV Peachy Dish Umbrella, which is a nonprofit that is fostering biology and technology and entrepreneurship in the DMV area. Now our team is pretty large. We have over 40 people on our roster at this point. They come and they go, but our core is about five or six people. But over the course of this competition, we've had a lot of people help us out. NIH professionals, NIST professionals, PhDs, people from industry, people from healthcare, machine learning experts, computer scientists, just really a who's who of expertise. We've had bioinformaticians and all kinds of people. So we historically had entered a competition for the Children's Tumor Foundation and we ended up winning two out of three of the challenges that were in that competition. And one of them was an incubation prize that we continued our research into. And that was the one on drug discovery for neurocarbomatosis type 1. So we've continued that work. We ended up publishing in December of 2023. So it's a good culmination of all the efforts up until that point. Now on the other side of that, we're looking at the algorithm that we published. We want it to be better understood in the industry and by scientists, more awareness, but also generalizing our work to other cancers and other aspects, even within NF1. There's a lot more we can do with it than we had just on our original data set and with our original premise. So what we're going to cover during this slide deck and during these presentations is where we are and where we've been. So a lot of where we've been is this initial development of S-indices. We'll get into what that means coming up but basically these are ranking criteria for ranking potential compounds for treatment and our original publication was around something called delta s we are now also looking at a variation of that called S prime. So our current status is we're looking at the S prime indices. We're trying to look at that and generalize it. So how can it be applied to other cancer cell lines? You know, generalizing to lots of other types of cancers and diseases as well. And then long range, we have some stuff coming up as far as combinational drug evaluations, rare disease drug evaluations, and neural network modeling. So let's look at our background for the syndrome. And then Paul, if you ever want to speak up, go for it. Just feel free to interrupt me at any time. Neurofibromatosis is a rare genetic disorder. It's defined by some kind of mutation in the NF1 gene. A little bit on the notation, because this is really important for the industry. When you're talking about the NF1 gene, you're going to italicize it. And when you're talking about the disease, you have it bold like this. That's really important because the terms are reused a lot. So having really clear standards for which you're referring to which, especially in literature, is very important. But we'll try and follow that convention here as well. So it is a gene with a protein product called neurofibromin. You can consider it a tumor suppressor. It's part of a complex that does affect cell growth. And when there's a disorder with it, one of the effects is the creation of many benign tumors. So it is autosomal dominant, which means that if you have one copy of it in your two pairs on the chromosome, the dominant one will have an overwhelming effect in the phenotype. And most NF1 patients are born with one mutated gene copy per cell. So what tends to happen is that most NF1 patients are plus slash minus for the gene, meaning that they have one wild type and then one mutated version. The reason you don't see minus minus very often, it tends to be fatal in the embryonic state. So a lot of times those people that are born minus minus don't make it to adulthood. But in any case, most people are plus minus with the disorder. But in any case, most people are plus minus with the disorder. And you will see minus minus crop up later in our slides related to cancers. You can have minus minus in your cells if it's in a tumor state or there's some additional mutation that's happened. But for the most part, you can think of most patients as heterozygous meaning they're plus slash minus it is a multi-system disorder which we'll get into coming up as far as some of the symptoms but it does have other than the physical parts it also has some cognitive effects learning disabilities adhd common complaints are pain and decreased quality of life. And it predisposes patients to developing tumors. Now the tumor that we focus on is plexiform neurofibromas. We'll get into that coming up. But these tend to be tumors of the myelin sheath that are on the inside of the body. Another very common one are cutaneous neurofibromas, which are the same thing, tumors of the myelin sheath, but they tend to be more surface, towards the surface of the skin. And occasionally those become malignant, and when they do, they're gonna be referred to as MPNST, malignant peripheral nerve sheath tumors. So again, it's a rare disease. It affects one in 250, sorry, 2,500. This means that from the FDA's perspective, it would qualify for an orphan drug status, which means that nobody's really researching it. So what you have to do to be approved is a little bit lower. But it's fair to say also that a lot of the pharmaceutical industry is not looking at this because they don't see this as a large market. So it's something that does affect a large number of people, but not necessarily large enough to move the needle from a commercialization perspective. to move the needle from a commercialization perspective. So the actual mutation itself can be as little as a point mutation. There's literally one base pair that can flip, creating a different amino acid, and that can basically cause all of the effect of the disorder if it's in the right location. But it can also be a set of mutations. So when we have patients that all have NF1 but some are responsive to diseases and others are not and they have different states, one of the things involved there is that the mutation may not be in the same location, right? You might have a defective in a really critical region or it might be you have a few in some somewhat moderately important locations but not the critical region you know there's different ways of looking at that what ultimately deactivates it and those do matter because it could be that your protein is partially effective at its original function, but there's some nuance there. It is a very large protein. This is 320 kilodalton. It's a way of saying the size of proteins, and it's generally a pretty large one compared to a lot of other ones in the body. It contains 2,818 amino acids. It contains 2,818 amino acids. So this represents the main symptoms. The ones that we're really focusing on really are the tumors. It's one of the hallmarks of the disease, which are the, these are cutaneous neurofibromas. So these are the surface ones. We're looking at the plexiform in our original paper, which are interior to the body, but still just as prevalent all over. And these can be very painful. Most of these are benign tumors, but they can still be life-threatening, even if they're benign. So one of the big issues is, you know, if they grow and they're impinging on an important tissue, they can kill that tissue and that can be very, that can be fatal. They are also very painful. So a lot of the the the the disfigurement it can also have other effects so there are cognitive effects as well learning disabilities headache seizures macroencephaly oversized head so sometimes what the families are complaining about is that they need support, right? The families have trouble raising children with NF1 sometimes because they have learning disabilities, cognitive issues, as well as the physical things. But one of the points of this slide is that this is a whole body disease. So there are scoliosis, which is curved spine, leech nodules, which are little specks in the iris, optical gliomas, chronic constipation, diarrhea. And then these are hallmark symptoms. These are cafe au lait. These are little dark spots that form on the skin, but you can also have crackling in the creases of the skin. So a lot of times I like to say that the neurofibromin protein, it plays an important role in connective tissues and one of the things that it's associated with is microtubules. And microtubules are an important building block protein in axons as they're forming. Axons being parts of the nerve that are growing out and connecting to other nerves. And if those aren't being regulated properly, then you have problems with cell growth of the neurons. And that kind of logically relates to learning disabilities. But some of the other things with the high blood pressure, you know, there are blood-brain vessel defects. So the lining of the vessels of blood in the brain, that's also an important factor. vessels of blood in the brain that's also an important factor and then of the myelin myelin is a really important one to understand because it's a lot of these are myelin cell tumors myelin is a cell type that wraps around the axons of neurons and normally it's insulating it so that the signal going down the axon which which is this long finger-like projection from the neuron, that electrical signal going down the nerve, it actually travels faster when it's wrapped by the myelin. So it's normally important for the central nervous system, but it also has a role in the peripheral nervous system. also has a role in the peripheral nervous system. And it is what, it's the tumor type that is, it's the cell type that is part of the plexiform and cutaneous neurofibromas that we're looking at. But there are, again, there are a number of cell types and it is important to think of this as a whole body disease. For our continued research, we'll start to hyper-focus on the cancers, but please don't forget that it's not just cancers that are affecting. It is the whole body, and it is very important to think about what are the other pathways involved in the cells, and what are the other cell types involved in this disorder all right so when we look at NF1 type 1 by age first of all there there is a second type NF type 2 it has different it has different proteins involved but there is some overlap in some of the downstream proteins. So we are focusing on NF type 1. And again, that's the NF1 gene is probably the one that's mutated for that. In the embryonic version, you could have developmental disorders coming up out of this so the there's improper formation of dendrites in the hippocampus hippocampus is related to storing memories and neurite growth and growth cone formation problems so these are again about how neurons are growing and at the very tip of the part where it's growing, you have deformation in some of these proteins. So it's just not able to grow properly. That would, of course, logically relate to some of these cognitive disorders that we do see coming out of it, learning disorders, ADHD, cognitive impairments. And again, the families are looking for support and awareness around these developmental issues again if it's if you're minus minus it's probably fatal so a lot of this is again that heterozygous in childhood most often you are diagnosed with nf1 by age five that's because that's when you start to show some of these other symptoms cafe ole spots the benign tumors and some of the other diagnosable traits and the needs when you're at this age your social social issues around disfigurement there's the pain there can be the mortality issues going from childhood into adulthood typically you're going to have the benign tumors and 15% of those will go from a benign state to a malignant state. That is absolutely a life-threatening condition if you have a malignant tumor. So the treatments are going to change very much. Now we need life-saving cancer treatments. One thing to keep in mind with all of the compounds that we have coming up, they're all essentially chemotherapies. We're talking about something that is killing cancer, and those are toxins. So it's very challenging for us to have somebody on a toxin for a very long period of time. If it's a life-threatening situation, if you have malignant cancer, we're going to give you the toxins that are bad for you because they're worse for the tumor cells. When we talk about people that don't have that and they just have the benign tumors, their options are more limited. The doctor isn't going to give you such a strong toxin. And so that's a known problem in the NF1 field. What are we doing for people with benign tumors? Most often they're doing a surgery, but that's not always an option. Sometimes you have tumors that are really near critical organs and you just can't do a surgery near there and they don't have many options. We'll kind of get into that coming up. So let's take a step back and talk about the clinical trials process. I'm going to hand it over to Paul. Paul, if you could kind of do a little summary of what does the pharmaceutical investigational process look like? Okay. The clinical trial format arises out of an extensive series of preclinical investigations that look at all aspects of the manufacturing as well as the drug action. So those are all wrapped up into a preclinical evaluation of the drug as it moves forward. All of the preclinical studies and clinical trials themselves ultimately are regulated by the FDA, and it's the FDA, the Food and Drug Administration, that is involved in the approval process to allow the drug on the market in the U.S. on the market in the US. The intent of these clinical trials is to show safety and efficacy. That remains from the onset in the earliest phases all the way through the late stages and commercialization. The preclinical investigations usually and typically will involve in vitro studies. That also includes high throughput screening. And we'll be talking more about that later. In silico studies, animal studies, there's a heavy focus on animal studies that try to mimic the human disease. Those all lead to a phase one study, which is a very limited study in humans, and it's primarily designed to establish safety. Phase two studies is a limited study looking primarily at safety, but also efficacy, looking primarily at safety but also efficacy, whether the drug actually works or not. But it is very limited in the number of patients that it looks at. Phase three are large studies involving thousands of patients and is involved in looking and establishing the proof of safety and efficacy. and establishing the proof of safety and efficacy. Phase four is marketing. But even after marketing, when a drug enters the market, it is followed for anywhere from five to 10 years to ensure that its use is both safe and effective. And it's important to note that clinical trial costs can approach nearly three quarters of a billion dollars depending on the drug that is involved. So it's a huge amount of money that's involved in clinical trials and drug development for each one that comes to market. And underlying this all is the initial drug screening that is intended to assure that only good candidates, drug candidates, are actually brought through the entire process and leading up to clinical trials. And this is especially important in underfunded areas like rare diseases, one of which is the NF1 syndrome. Matthew? Matthew Kuehnert Thank you. So we won't go into this too much. We just wanted to give you a survey of what drugs are available in NF1. And the answer is there's really only one down here, selumetadib, which is approved for pediatric patients with interoperable tumors. So at this time, it's not approved for adults and it's only for pediatric patients that loose tumors can't be dealt with with surgery it's a very very narrow set of people we in our data we have reason to believe some like so you mention that isn't even the best compound we think we see things that are more effective or potentially more effective. So, you know, it's just, it's not the ideal compound. One of the things that also happens is that you can become resistant to this drug. So it stops working over time. And that can be a very serious challenge for some people. But there are in various phases other compounds folks are looking at. And we are hoping as an industry that more solutions are presented to the patients. So I wanted to go and dig into how does neurofibromin work. And remembering that it has multiple functions in multiple cell types we're really going to focus on the the archetypal role in myelin which is causing the the the cancers but it's going to have variations of this and different pathways for other cell types but But in something like myelin, we're going to look at a receptor that's receiving growth factor. Normally when the growth factor comes in, it's going to turn on this Ras protein here, and it's going to go from a GDP state, guanidine diphosphate, to a Ras GTP, guanidine triphosphate. And in this triphosphate state, it's going to have activation of these downstream pathways. And all of these downstream pathways end up resulting in cell growth, cell proliferation, and survival. The role of NF1 essentially is to help with deactivating that activated GTP. So you can imagine if you don't have NF1, then you have this activation turning this thing on and nothing to turn it off. So it's like a car with the accelerator all the way down, and then the brake pedal is is broken you just have nothing to turn off the activation state so it's very important uh to have nf1 this does form a complex this is a little over simplification but this should give you the flavor of what we're talking about i go into a little bit more detail here um so what we have are pictures showing a kind of structural perspective. This thing at the bottom is the whole neurofiberman protein. You'll see that again, it's a very large protein, as we said. And then we have a few separate proteins up here. The brown and orange is the receptor. There's these other things that interact with the receptor, the spread 2, and then you have the RASP GTP over here, which is normally kind of floating independently, but it can form a complex with this whole thing. So you have the NF1 and the receptor and these other things. And again, its purpose is to cleave one of the phosphate groups. We're going to end up calling that a GTPase. But the main function of it is to remove one of the phosphate groups. Thinking of this GTP, it's very similar if you're familiar to ATP, which is the main energy source inside the cell. You have glucose in the blood and it's broken down into ATP. ATP is adenosine triphosphate. And what happens normally when you have movement inside the cell, you're normally converting an ATP to an ADP. It's very similar to this when you when you do it with ATP you're releasing energy and that energy is generally deforming the the proteins doing something by changing the shape of the protein so that's very similar to what we have going on here except the emphasis with the the GTP is again as a signaling cascade, which we'll get into a little bit more detail. So just to go through some other points, the NF1 gene product is neurofibermin. It's a multifunctional protein, and it's expressed in most tissues of the cell. It's a large protein. functional protein and it's expressed in most tissues of the cell. It's a large protein. It has two isoforms, which means that two structures that are similar but different in terms of their actual structure. And those come together to form a dimer. A dimer is two proteins put together into a complex. And it is typically associated with the inner surface of the plasma membrane so again the inside of the cell wall again we're embedding ourselves in the cell wall up here almost always for most signaling proteins and it associates with many protein binding partners we focus in on some of them, but there's more than what we're covering. And it is an upstream activator of other types of proteins, including cytosolic kinases. Cytosol is the interstitial fluid. No, it's the fluid inside the cell. And kinases are enzymes that are normally activating or deactivating something. And we're going to throw some terms out at you. This is just so you start to make recognition of them. Like, oh, I've seen that before. You'll see these very commonly in cancers. So we're not expecting you to know what these are to remember them. But we want to start introducing them so that when you see them come up in pathways and in the cell signaling, you start to associate them with cancers and with cell growth and proliferation. So we have R RAS gtp ace activating protein which regulates several signaling pathways Rath me ke RK p i3k a KT and mtor row lmi k-AMP, PKA, those are important ones. And that's all to say, again, that we have upstream and downstream signaling, and that what we're trying to do is work with proteins that have a cascade of downstream effects, and those downstream effects relate to cell growth. So we kind of go into that a little bit here. This is actually a more comprehensive version of the first graphic, but here we have the neurofiber min. It interacts with the RAS protein. So when we talk about NF1, NF2, the one thing they all have in common is that they're called RASopathies. RASopathies is a class of disorders that somehow relate to the RAS signaling cascade going wrong. So in the case of NF1, what's going wrong is that we're not able to turn off this Ras-GTP, which is the activated state. We're not able to deactivate it to the Ras-GTP state, DP, sorry, D for diphosphate. Now, in its activated state, again, there's the RAF, there's the MEK and the ERK, there's the AKT and the mTOR that are relating to cell growth. I know that the neurofibromatosis type 2 has some stuff over here, and this is the cell nucleus. I won't go into that too much, but generally there's going to be these receptors. Over here, we have an epithelial growth factor receptor I think this one is a receptor tyrosine kinase different types of receptors and different types of things in the microtumor environment which is just a fancy way of saying the the the the all of the interstitial fluid around the cells that have the tumors. There's growth factors and things that are hitting the cell surface that are promoting that growth. And that's turning things on that turn things on that all relate to tumor formation. So we're going to kind of continue from there. Before I go too much deeper, I'm going to take a pause and Hassan, do you have any questions at least high level? We're not going to ask you about the pathways exactly, but do you have any question conceptually about why NF1 is involved in tumor formation? Well I Well, I don't have a background in biology. My question is, so the analysis that we will be making through the Python development, is it to come up with a general procedure of assessing drugs, or is it like working on a specific proposed drug to treat this disease? It's more general than not. What it is, is it's compound agnostic. It works no matter what the compound is. So when we look at high throughput screening screening which we'll get into coming up uh i think uh very shortly uh it's it's about looking at we're going to throw a thousand compounds at it and of those thousands which one worked and again we know that what's going wrong is that we're not able to turn off the RAS signaling. The RAS signaling up here is on, NF1 is broken, so we're not able to deactivate it. So a lot of the kinds of things, like, for example, that selumetinib, the one approved, is an MEK inhibitor. So this MEK, which is downstream from this signaling up here, if we're able to inhibit it, then it's kind of like applying a break. We have less MEK, and therefore we have less cell growth, proliferation, and survival. So even though NF1 is broken, if we give you this MEK inhibitor, that helps to address the tumor formation. But basically, we're looking at all kinds of compounds and seeing just which ones work and then trying to answer after we've identified that they work, kind of figure out why did they work? What part of this signaling pathway did they affect so that we know how they did what they did? Does that make sense or do you have any questions? Yeah, that makes sense. Okay. Yeah, we'll get into the high throughput screening in just a few slides. So what we're gonna go through right now is a little bit more of a deep dive into what we're working on or what we had worked on historically, which is this initial development of S indices. So just to clear up some confusion, S and S prime are different. They're very similar in approach, but they are different algorithms. So what we published historically are S and delta S, which we'll get into right now. But then just keep in mind that when we get to S prime, it's slightly different. There's a different calculation involved. So again, some of the history, this came out of the Children's Tumor Foundation Hackathon. It was all about high throughput screening data. That's the only type of data that we are looking at right now. And the original data set was all about plexiform neurofibromas, which are, again, benign tumors. We're not talking about malignant tumors at this point in the evaluation. We're just talking about very specifically plexiform neurofibromas and which are the compounds that we are finding that address this type of tumor. And the tumors that were sampled were all the NF1 minus minus for the most part so that's just a a side note so our goal was to establish a robust algorithm that identified new or repurposed drug candidates for the preclinical evaluation. So we know that it's very expensive to go to clinical trials and there isn't much money in the rare diseases. It's very challenging to throw money around in this space. Money does exist, thankfully, through some of these foundations, but it's very challenging to do these kinds of studies. So generally, before you do a more expensive study, whether it's an animal study, or, you know, if you can get to the human studies, great. But it is very expensive. And you want to kind of know ahead of time, if it's going to be successful and that's where really good and nuanced analyses that you can do for cheap uh cheaper relatively speaking are really important so high throughput screening data anything anything you can do in situ um or in in silico is again with using algorithmic modeling anything you can do to kind of clear up and and remove the false positives as early as possible that's what you want to do it's also helpful for identifying the mechanism of action so that's in biology that's the way of saying how does it do what it does oh it you know for example the selumetinib we talked about it do what it does oh it you know for example the cell you met in it we talked about how how does it help with cancer well it inhibits this part of the pathway right here it inhibits as any K so that's what it does its role is to reduce cell growth and proliferation by going lower in this signaling cascade and inhibiting that point so for for that compound, selumetinib, its mechanism of action is MEK inhibitor. But finding that out for these new compounds can be very, very helpful. Again, we want to have a biological justification. Why do we think this compound is effective? Step one is, okay, we found that it seems to do well in our screening of the cells, but why? Can we answer that? So ultimately, all of this is supposed to inform our clinical response forecast. So can we look ahead and project how likely this compound will have a good effect in clinical situations. Clinical, again, are clinical trials which involve humans. So by the time that we're involving humans, do we have a strong reason to believe that it will have a good effect? It is known that high-throughput screening data has a poor association with clinical outcomes. It's not a one-to-one all the time. It's a problem. So you can get promising molecules out of it, but it's not a magical algorithm that always works. So anything we can do above and beyond a classic high-throughput screening analysis to also support why these compounds are going to work in humans. It's all going back to the point of being more effective with the money, more effective with how we're guiding future assessments. I'm going to take a pause there, Paul. Is there any flavoring you want to give that or any kind of additional caveats? No, I think you're doing well. You just go ahead, go right ahead. Okay. All right. So a little bit more on the project background. So high throughput screening tests in cell lines over multiple drug concentrations so I think what we're trying to say here is not worded quite well but I think what we're trying to say here is we are testing multiple drug multiple compound concentrations and once we have once we're for those multiple drug concentrations we are evaluating a cell response so let me just change that up a little bit so I've heard but screening so it tests compounds at a variety of concentrations and evaluates cell responses and those can be viability or reaction the one that we're looking at for ours is viability which is how many of the cells survived. Again, think of this as chemotherapies. We're trying to kill the cells and how many of them died or survived. The responses that you get when you actually plot the activity level that you're measuring versus concentration uh they're going to look like a sigmoid curve which is an s shape uh and they use a similar uh regression mode in analysis so all all sigmoid uh data uh are use some kind of uh analysis which we'll get into coming up uh so different cell lines uh in the uh different cell lines can be used some of the cell lines are the benign tumor cell lines in our original data set we also had non-tumor cell lines, and that ended up being very useful because, just to give you a preview of what we'll have coming up, we're going to look at a difference between what the compound did in normal cell lines versus what it did in the tumor cell lines. All right, so a little bit about high throughput screening. It is a large operation. Normally you're going to use robots, something like this on the left, and you have these multi-plate wells. And so you're going to have, you know, thousands and thousands of these wells. And each well is going to get a copy of the original cell genes, sorry, of the original cell line. And these original cell lines, by the way, are clones. So you take a sample of somebody's DNA, sorry, you take a sample of somebody's tissue. And then you actually you need to mutate it so that you can make the clone. So this is where some of the problems starts for this style of analysis. You have to actually immortalize the cells, which means that you mutate them so that they do propagate forever. And then you also add another protein in there, a genetic sequence for a protein that also helps it glow in certain lighting. So a fluorescence, basically. And that's going to help you downstream when you're trying to measure it. So what's going to happen eventually is you're going to give it the compound and then you're going to shine light on it. So what's going to happen eventually is you're going to give it the compound and then you're going to shine light on it and you're going to the cells that have that mutation for the luminescence are going to glow and you're going to measure how much the cells are glowing. And the intensity of the light is going to tell you how many cells there are. So you're actually essentially able to count or at least come up with a metric that relates to the number of cells after giving a compound. So if there is more light, if there's more luminance, there are more cells. If there's less light, there's less luminance. And that's the fundamental activity that we're measuring. when we give these compounds at different concentrations, we're measuring after that how much, how many of the cells survived based on how much lighting there was. Are there any questions on that, Hassan? This was actually like the example that clarified it the most you basically like assume what the results are going to be based on other experiments that have already been done my understanding that right um i don't think i quite understood the last part um basically what you're doing is there are already data based on experiments that have been already done, right? That's correct. Yeah, so you use them to predict the results before actually doing the experiment. That's right. So the data set that we're starting with was the outcome of this setup. We did not run the robotics. The people that ran the robotics released their data, and we used that data to do our analysis. And that's very common for what we're looking at. We're mostly focusing after the data has been generated and doing the computational parts of it after that. But yes, somebody somewhere actually physically did this rigging, and that was not us. That was prior researchers. All right. and that was not us that was prior researchers all right so um when you do these high throughput screens again you get you get data and and the data is this at the bottom this is a dose response curve and you have the concentration of something that you're adding and then you have the activity and as i mentioned they tend to be this sigmoid shape. You can kind of look at different cell lines or different variations and kind of do a comparison. This model that's the sigmoid, its technical term is a four parameter regression model and we'll get into that coming up. But when you turn your data points at these different concentrations into the sigmoid, that's what you're doing. You're running a four parameter regression model on it. All right. So the data that we started with was many, many compounds. We're careful with our wording between drugs and compounds. Drugs generally has an emphasis of already being approved by the FDA for something. So a lot of these are experimental. They may not be approved for anything. In which case, a more general term for us is compounds. So a lot of these are experimental. They may not be approved for anything. In which case, a more general term for us is compounds. So we have these large set of compounds. Some of them may become drugs at some point. Some may be drugs that are approved for something else. So that's actually a really good situation for us. If we can find things that have been approved for other diseases that we can reuse here, or the term is repurpose, if we can find things that have been approved for other diseases that we can reuse here or work the term is repurpose if you can repurpose those drugs that's great because we know that it's safe in humans and it may be able to help this this patient population it's still a challenge from a economic modeling perspective but you know one problem at a time. We'd rather find scientifically what are the best compounds for this treatment. We have a mix of plexiform neurofibroma tumor cell lines, and then we have a set of non-tumor cell lines. And in our, strictly speaking, in our data, we had 11 concentrations. And for each of those concentrations, one reading of what the activity level was for that cell. And again, the four parameter regression model, it has some aspects of it, which we'll get into. which we'll get into but to give you a preview uh it has a an upper asymptote uh and then the the the slope of that s the middle of the s there's a slope there and right at the middle point there's also a concentration called ac50 or where 50 of the activity is happening and then there's a few other parameters the r squared is how good of a fit to the model it is. So if you have points that don't follow an S shape, the R squared will be low. But if they perfectly follow an S shape, they'll be one. So generally it goes between zero and one for how close it fits that. And then AUC stands for area under the curve. It's another way of doing analysis where you just take the area under the sigmoid curve. Sometimes people calculate that and use that. We didn't end up using that currently, but that's actually future work that we have coming up. So when we look at which the cell lines are that we actually end up using, they have these labels. So these are the labels for four different cell lines are that we actually end up using they have these labels so these are the labels for four different cell lines they all start with this ipn that's a convention that we inherited from the original uh the original uh uh authors that did the study and you'll become familiar with them. You know, we kind of focus on the last parts of it. So 0.55, 6, 2, a, uh, nine, five, one, one B nine, five point six. Those are all the tumor cell lines are, we only used one non-tumor cell line as our reference. And that's this nine, five, one, one C. So you'll become more and more familiar with that as you work with the data. But we do know which ones are which. And again, our the one thing to know about the non-tumor, it is non-tumor, but it still comes from an NF1 patient. And that's actually ideal for the kind of analysis that we want to do. Who would be receiving this treatment is NF1 patients. And what we essentially want to do is we want to find compounds that are more able to kill tumors than non-tumor tissue. So we want to leave things like their brain and their heart alone. We just want to kill the tumor. So we want to find compounds that have more activity in these cell lines than in this one. This one we want to see very little activity because that means it's killing tumor tissue. And you can get this online. This is a public synapse. This is publicly published data. You can request access to it, and they just give it to you right get this online. This is a public synapse. This is publicly published data. You can request access to it, and they just give it to you right away these days. All right, so this is more of a deep dive into what a dose-response curve would look like. Again, you're creating this model, which is the sigmoid curve. And essentially, when you have very low concentration, you know, you'd have very little effect. And then at some point, you've added enough concentration to start having an effect. And then at some other point, you've given so much that you've saturated the effect. There's no more effect you can get by giving more of the compound. And so that's why you have this sigmoids and they they have these asymptotes here uh the upper and the lower and so what we're calling a uh is this this upper asymptote and d is this lower asymptote so we're going to follow that notation in some of our future slides, but when you see A and D, we're talking about the activity levels at those asymptotes. Now, there's a special point right here in the middle, which is the AC50, and that is the concentration where there's half of the maximal activity. You'll see other similar terminology, EC50, which is 50% of the effective concentration. And then sometimes you'll see IC50, which just has to do with inhibitory concentration. They all kind of mean the same thing. There is a different emphasis, like inhibitory is a certain type of reaction. But the AC50 and the EC50s50s for our purposes are interchangeable and you you will see both in literature this formula by the way is the formula for creating the sigmoid curve so if you're if you're creating your model this is how you would create the model for it but it's it's enough for us to look just actually at A, D, and C. That's the heart of our algorithms, just A, D, and C for the most part. So A, again, is extrapolated as the asymptote at the zero drug concentration all the way up here. And D is extrapolated to an infinite drug concentration over here um when you you know you obviously we're not actually going to these those extremes but we are able to extrapolate using the model um all right and i think we'll probably stop just about here. Basically, just to give you a preview of what we have coming up, what we actually care about are the relative differences between different types of responses. So you see that there are some responses that follow a good sigmoid curve and then you're able to look at the AC50 for one of them and the AC50 for another and make a judgment call on which one is maybe more or less effective. So we'll get into some of that at our next meeting but it's going to eventually become, how can we create a single value that kind of adds both the information from the height difference between the asymptotes and that C value into a single value? And then how can we create a comparison of that value in the tumor line, which is SREF, and then the non-tumor line, which is S-Test. So we'll get into that more in our next session. But basically, understanding at this point that sigmoid curves are at the heart of analyses for the high throughput screening data, that we're looking at concentration and activity response, and that we're able to generalize our data, looking just at these points, A, D and C, which we're gonna use to power our algorithm or our formula coming up. So at this point, are there any questions? Nope, it's pretty clear. Okay, great. I'm gonna turn off recording. Give me a second.
NF1 Biology and Foundations - Computational Biology - Part 1