 Hello. Recording is on. All right. Thank you for joining us. We are going to continue our introduction. All of the materials to date have been things that have been published and have been turned into the nf.mocomakers.com web app. Everything from this point forward is either exactly where we are now or totally net new. Almost all of this we're going to need to codify into parts of a web application and generalize what we're seeing here to other types of data, other types of data, specifically other genes, other cancer types, and just anything we can do to generalize our approach. So what we have, where we are right now is going into the development of the S-prime algorithm. So I will, Paul, I think most of these slides are on your side, so I'll just let you kind of talk through most of them. Okay. So, on this slide, it illustrates a little bit of why we wanted to make a change from S to a different indices that was more inclusive. The S indices is very good at handling situations where there is a classic inhibition. And by that, we mean, if you look at the curve where you have a sigmoidal curve that goes in terms of its response from downward. So we're looking at here, the sigmoid curve on the bottom right is going from 100% down to zero. That's a classic inhibition curve. But even in the original data, we had things that were not behaving well and we were having difficulty with. The one with the open circles is one that we had a problem with because it was very close to zero. And anything that is close to zero in terms of providing a flat curve was very difficult to deal with. Even more difficult to deal with was curves that we needed to use the S algorithm. And you can't take the log of a negative number. So we were stuck. And what we left on the table then was a lot of data that was either close to zero or had developed a negative number and we couldn't assay. This is not an unusual thing. It happens in many databases, but it does lead to an incomplete assessment of the overall impact of the compound. We're only looking at those compounds, in other words, that give us a classic inhibition curve, and that's not necessarily reflective of what is going on in all the cell lines. And this could, in turn, reflect from a therapeutic point of view, what would be happening in a population of patients. Some of them would respond. Some of them would have no change in the overall effect. And some of them, the tumors would continue to grow, which would be illustrated by the upper curve. And in preclinical studies, one of the things you want to be able to do is capture all the variations to get a good idea or at least a better idea of what's happening with the various drugs that you're interested in. And just focusing on those cell lines responses that give you classic inhibition again leaves things on the table and you have an incomplete assessment on the next slide thank you um i want to go back just briefly to put some numbers behind that so uh we had the original screen 1,912 drugs only 600 had parameters we could use the rest over a thousand were unqualified right and and so it's it's a large data impact that that we're calling out here okay so because of this large gap in cell lines that we just couldn't assay, we wanted to go to something different where we could generate, still generate a single value using the same input parameters that we did with S, which are based on the four parameter logistic function and also include negative and zero type values what we came up with is this a sine H transformation a sine H is a transformation that involves a log a natural log function so the ln the beginning of the open parentheses is to indicate that it's a natural log rather than a log function and this particular function is used widely in economics where there is is used widely in economics, where there is negative values, zero values, and also positive values, and has been used not only by us here, but by an increasing number of scientists in the chemistry and biology realms, where there is recognized to be a need for looking at, again, positive values, negative values, and zero. And fortunately for us, this function has been codified into Excel, Python, and R. So it's easy to use directly without having to develop it de novo. So on the next slide. Before we did move on to actually using it, we wanted to make sure that the S value and the S prime values were giving us numbers that would be essentially identical. And to do that, we did a comparison where we first plotted them, the two values, the s prime value and the s values against each other and what you can see from looking at the graph when the and the blue dots is that there is essentially a something that looks like a 45 degree angle where all the dots, all the data falls on a straight line. This graphically illustrates that there is a direct correlation between one and the other. We then also did a Pearson correlation coefficient analysis and that's reflected in the graph where it says R, R is equal to 0.999. And what that indicates, again, is that there is a direct proportional relationship between s and s prime, where one, an r equal to one would be a perfect fit. So we're very close to that. And we did this for a number of different cell lines and data outputs just to make sure that we weren't missing anything. So again, what this establishes is that we have, what we're looking at when we describe S and s prime is the similar endpoint. It's not the same number because one is in log space while the other one is in the natural log format. But nonetheless, there is a one-to-one correlation in terms of the overall data display. On the next slide, this is sort of a flow sheet of how I've been handling the data and we started to move this into some of the Python-like activities uh we initially start with a parameter input whatever data source that we're using of of uh particular importance for us is to be able to make sure that we're capturing the asymptote at the zero concentration the abs and the abs and the asymptote at the infinite concentration plus the AC50. Those three values for us are the absolutely critical values to allow us to assess the value S or S prime. We then separate the cell lines by sets. And by that, I mean either reference, we designate reference cell lines or test cell lines by sets. And by that I mean either reference, we designate reference cell lines or test cell lines. And these have the same or similar definitions as we were, as Matthew had described previously. We then determine what we're calling EFF or effectiveness. determine what we're calling EFF or effectiveness, and that is the difference between the two asymptotes, the zero minus the infinity, the infinite drug dose, leaving temporarily the AC50 alone. The AC50, however, is then incorporated such that we incorporate both effectiveness and potency into one value. That's in the upper right-hand corner. That value is then transformed then transformed using the A sine H transformation. And then we just repeat everything for all the individual compound cell line combinations. Then the mean S prime is derived for each compound and cell line. And based on that, those means, we calculate the delta S prime, which is the difference between the S prime from the reference cell lines and the S prime from whatever we're designating as the test cell lines. we're designating as the test cell lines. This fundamentally is the core of what it is that we're doing, and everything builds out of this. On the next slide, this is a curve of an actual data set that we had. I just wanted to show you what the overall curve looks like. You can see that it has basically two components or two major components, one of which is curvilinear, which is seen on the upper portion going from about 15 down to about 5, and on the other side going from about negative 15 to about negative five as well in the middle there's a linear component and this is this is totally typical of a sine h transformations there's a linear portion and curvilinear portions to the overall curve i want to uh yeah i'll just leave it at that for the time being on the next matthew uh this was intended more for the biologists but um talk about it anyway uh but talk about it anyway. I wanted to provide for people an overview of what known chemotherapeutic agents may look like in this one cell line, just by way of reference. So the red dots that are superimposed on the blue line, blue curvilinear line, represent various drugs and their average S prime value. And you can see that there is a, for all these known chemotherapeutic drugs that are actually used in patients, there is a variation that goes down. a preferential ranking that you can come up with based on where they are on the curve and what number they actually are generating. Again, all of these drugs represent known chemotherapeutic agents, and they go from the topmost one, near 15, which are very nonspecific and will kill just about any cell, to ones that are more specific in terms of their biological function as they go down. And that specificity is in part reflected in what you see in the variance. Okay, on the next slide. Before continuing, is the source for this chart, is this the MIP 3.0 data set or is this the depth map? No, this is the depth map data. Okay. All right. Thanks. So everything that we'll be talking about from here on out is done with the depth map data. And to make a connection for you, Hassan, the depth map data is a net new data set. So we don't have anything published on it. We are just getting to it right now. It's what we'll be starting to work with more and more. I misspoke. So there will be a couple of slides before we make that transition. This one was actually made, the one with the blue line here was made with depth map data. The next slide is actually a regression back to the plexiform neurofibroma line. But it was made using the S prime transformation. And one of the reasons that I put this in here was to show that if you look at the MEK inhibitors, which are known to be effective in treating plexiform neurofibroma, you see that the cell lines that we were looking at were more sensitive as indicated by the yellow than essentially any other inhibitor that we looked at. Here I put in BRAF inhibitors because they're in the same pathway but it even so the MEK inhibitors by just gross ranking were much superior in terms of their overall ability to to inhibit cell growth on the the next, Matthew. One of the things that you can do with these is also to pool the various, maybe before I do this, go back to the previous slide. Thank you. Thank you. And while you can look individually at each one of these drugs in the MEK inhibitor column, for example, another approach is just to pool all the numbers under that. So if you use a pooled mean of the delta S from all the MEK inhibitors, a pooled mean of the delta S from all the MEK inhibitors, you will still get a negative value that is indicative of the entire class of compounds being more sensitive in terms of the plexiform neurofibromas. And you don't see see that for example in in the BRAF inhibitors or to a much lower extent this provides a very facile and quick way of being able to take a quick overview of what's going on without having to resort to looking at individual inhibitors you can look at the entire class, which was done on the next slide, Matthew. Thanks. Before we continue, Hassan, I want to take a pause and just make sure that you're tracking, because this is an important part. Is it clear how we can look at a category of drug type like MEK inhibitor and like make a judgment on it that it is or is not if you know effective at treating something based on looking at its pooled delta S prime value is that do you have any questions on that? Is that clear? I see you as unmuted, but I can't hear you. I see that apparently there's a cutoff for what's more sensitive and more resistant. So like if it's negative, like beyond negative one, then it's sensitive. Yeah, that's right. Yeah. And so that's for a single compound, right? So selumetinib is the one that is approved for the treatment of NF, and surprisingly, it's not that good. It's kind of in this, well, it's sort of, we're predicting it's sort of effective, but we're really saying it's unequivocal. It's not really a big difference between what it's doing to tumor versus non-tumor tissue. But some of these other ones look like really promising candidates, like all these ones up here. Now, that's great when we're talking on a drug-by-drug basis, but now we're going to kind of blur our eyes and say, well, we're just going to look at it from a pathways perspective. So we know that the MEK pathway is affected in NF1 because the RAS is not able to turn off and RAS activates MEK and then MEK does things downstream that are cell growth and cell proliferation. So drugs that are MEK inhibitors we would predict to be helpful because we're turning off something that is there's too much signaling for. So we would predict that mek inhibitors uh would be good at helping with this type of um signaling disorder and in fact that is in fact what we're finding um and so now we're kind of instead of talking about it from a compound by compound basis now we're kind of looking at it from a pathways perspective where we're looking at these categories of compounds and then there's this single value here which again yeah if it's negative it's it's more sensitive um that we're using for that so is is that clear do you have any questions on that yeah so what's the purpose of the pooled mean delta S prime well let's let's suppose that let's take a hypothetical you're an investigator that is working in this general area and you have a compound that is brand new. And you know that it affects MEK, but you don't have a lot of data on it. So if you have an MEK inhibitor and you look at this data and you look at the pooled mean, you can project that your compound, if it is an MEK inhibitor, is likely to have an effect on this pathway and be potentially effective in the treatment of plexiform neurofibroma. That's just one example that we can come up with very quickly. So what we're going to do is generalize this over different types of compounds? We'll generalize it from one class of compounds to another class of compounds so that we can look collectively at BRAF inhibitors or in this case MEK inhibitors. or in this case, MEK inhibitors. But we can do that if you go on to the next slide, Matthew, for a range of other classes of inhibitors. So those specifically targeting AKT1 or BRAF or epithelial growth factor receptor or anywhere down that list. So these are now pooled means. And you can look down the list and you can say, well, the one that is targeting the gene that is responsible for MEK, and that's MAP2K1. That is the gene that makes MEK. None of the rest have as promising a value. And all of these targets would be expected to be responsive to some extent in the overall pathway. So why is the value of negative 2.6 more promising than the others? Is it like, is there like a sweet spot? Because it's like negative one too big and 3.7. Like, how do you like, I don't understand the color scale. The color scale is just, is based on the previous slide. So again, we're looking at values that are range in activity for the MEK inhibitors from one extreme of minus 7.8 down to 0.4. Yeah, let me take a chance here on this one. So one of the intuitions to remember about the delta S indices, whether it's delta S or delta S prime, is that you're doing reference minus test. So you have non-tumor tissue signal minus tumor signal. And what you want is a drug that is preferential for the tumors and leaving the non-tumor tissue alone, essentially. So you would want things that are more negative. You would want a drug that's targeting the tumors but leaving the non-tumor tissue alone and if you can have a bigger negative then you're saying that there's more of an impact on the tumor than on the normal normal cell that's a good thing like you want to really preferentially target tumors so um when we're saying that we're we're color coding the negatives, it's because we're saying the magnitude matters and the direction matters. Go ahead, Hassan. So SREF stands for the non-tumor tissues, the ones we want to protect. Oh, so now the more negative, the better, basically? Yeah. The more negative, the better, basically? Yeah. The more negative, the better. Okay. And I'm just starting to connect the dots. And like the log EFE over AC50, what is that again? This is just a mathematical transformation of this minus. These minuses are exactly the same meaning as this. So you can actually just at this point ignore the bottom one. It's not super important, but it means mathematically the exact same thing as log ref minus log test. It's just some log math tricks. And remember that the EFF and the AC50 are terms that we derive from the dose response curve oh yeah yeah so eff is then equal to the difference between the the two asymptotes the high one and the low one so a minus d and c in the middle here is the ac 50. Okay. All right. Thank you. And then going back to this color coding now, so go to make that connection. So we do want the highest magnitude negative, right? There is meaning there. There is also meaning in the highest magnitude positive, but it's, it's like the opposite of what we want. There's something opposite going on. So scientifically, it's intriguing. Why is there more happening in the normal tissue than in the tumors? So it's good for investigation, but it's not necessarily going to be the compound you want to prescribe. It's doing the opposite of what you want. But you can study it to find out more information. But as far as like drug ability, as far as like what might actually solve the problem, this bottom one. Okay. Any other questions, Hasan? No, I'm good. Okay. Paul? Okay okay on the next slide okay so you can take this same approach uh and sort of expand it and that's what was done here to look at a wider range of genes and uh you know i don't want to really spend a lot of time here other than to say that you can expand it. Instead of looking at just a handful of genes, you can look at a much larger number of genes. And the value that what I'd like to bring your attention to is the one value in green. That's the same one that we were looking at before. And you can see just by scanning the overall grid that there was really only two values that were higher than that, or maybe three. two values that were higher than that, or maybe three. But for the most part, the overall sensitivity of this target is fairly distinct. But this gives a way of us to be able in just a you know in a if you're interested in a very quick overview of what's going on by individual gene you can you can do that and still retain the underlying drug specificity which you can go into if you have an interest. This is just another layer that we can put on for the convenience of the individual investigator that may want to be looking at the data or inputting data for analysis. On the next slide, Matthew. So we have been thinking that we want to be able to take the S indices and be able to make them generally applicable not only to just the plexiform neurofibroma lines but as many other cases that might be of interest. But we have a foundation a foundation in looking at NF1 genes and the difference between reference lines that have no mutation in the NF1 genes and ones that do, which is the basic difference between the last set of studies with neurofibroma lines and the test lines. the the last set of studies with neurofibroma lines and the test lines we hypothesized that other types of tumor cells that contained nf1 mutated genes might be we might be able to detect changes in drug sensitivity in a similar way but we would be using a slightly different way of but we would be using a slightly different way of analyzing the data. Because we have no normal tissue to look at, we would have to look at something slightly different. So the data that we're going to be discussing from here on is based on drug response data that we found in DepMap, which is a data portal for cancer. And in particular, we're looking at one data source, and that's indicated in the CSV file underneath that. And Matthew is just typing in a source that you can look at specifically to find that CVS file. So if we just continue with that, I'd like to just show you a little bit of that file. And this is the, this actually comes from that depth map, a CVS file, CSV file. And of interest to us was the fact that there is an upper limit, a lower limit in yellow, and in green, an EC50. These are the key terms that we use in determining S values. Now what they have done with what these investigators have done is normalize the data so that the upper limit always is normalized to one. So what they're making it, the range here is the upper limit or the upper asymptote is always fixed to one. The lower asymptote can then descend under ideal conditions it would be to zero but it can go actually below that the ac50 then has the same equivalency as as what we've been calling ac50 the uh there's some interesting points here and I'd like to just show you that if you look at the upper and lower limit and sort of the slightly orange lines here, you can see that if you start at one, the numbers actually go up for the first two values. They're going from one to 2.7, 2.5. What's happening here is that the cell number is increasing, those curves are going down. In light blue, you see starting values or starting asymptotes of one and ending asymptotes essentially at one as well. So those are just flat lines there was essentially no effect so then if we we look at the total number of positive and negative values in the in this entire uh data set uh the number of positive values that I'm calling greater than zero or less than zero were quite heavily skewed so that there's almost something that approaches a 50-50 split between the curves that are going up and the curves that are going down. between the curves that are going up and the curves that are going down. And classically, one would not be able to analyze these data unless they have true inhibitory curves. And you can see, if you go to the IC50 column, you just see lots of numbers not available. IC50 column, you just see lots of numbers not available. And there's only two from this group that actually have numbers that follow a classic inhibition curve, and you can figure out what's going on. We're leaving, obviously, a tremendous number of samples on the table that can't be analyzed because they're either going up the wrong way for an inhibitory curve or they're flat. So not only we were having trouble, but lots of other investigators are having trouble with the same problem. Some curves go up and they can't be handled effectively in terms of current data structures. Next, Matthew. Okay. This is probably overkill, so I'm not going to to a variety of different cancer cell lines with MEK inhibitors and rank them. So we looked at pancreatic cancers, lung cancers, cancers of the central nervous system, breast cancers, and colorectal cancers. And you can then break them down by their gene product, whether they are NF1 competent or NF1 compromised. That's the difference between the top and bottom. So that's an easy way for us to be able to look at it. And again, I don't want to spend a lot of time on this because there's just a lot of information here. On the next slide. Before we continue, I just wanted to go back. One of the things that we're starting to do, and this is part of that generalization that we're talking about things that we're starting to do, and this is part of that generalization that we're talking about, is we're starting to look across different cancer types. You see here, pancreas, lung, central nervous system, breast, and colorectal. These are ones we suspect may be related to NF1 in some way, but it doesn't matter. The point is that we're generalizing to other cancer types. And we actually have that in the depth map data. It's right here. If we see this CLE underscore name, the CLE is the first part before the underscore, and that's the cell line. But what comes after it is the tissue where it comes from. This actually, it's abbreviated in the image, but it says prostate. So this would be the prostate cancers that these data samples are for and we have that for all kinds of tissues lung and all these other ones so we have the data there to be able to say this is lung cancer this is colon cancer this is prostate cancer um and and we also know um because of the analysis that that that we are able to do on the target data, which is over here after mechanism of action, it's not shown, but we know the target for each of these compounds. So we're able to say, okay, well, drug target like MEK and cancer type. So we were creating a matrix of data and there's that is a huge matrix that we could potentially generalize in DetMap. A matrix of all possible genes and all possible cancer types and then within those you know doing our our delta s algorithm so that's we you don't have to understand that very deeply right now but there's just this idea that you know we can take the work we've done and and broaden it by looking at cancer types and additional types of compound classifications and. And gene classifications. Yeah, gene classifications. Let's skip this one, Matthew. Okay. So one of the things that's very important for us to be able to establish is that, and one of the questions that will come up is, is your evaluation any better or worse than what's currently available? So there are two systems that are widely used, one of which is looking at the AC50, and the other is the area under the curve. And specifically what you're looking at when you're talking about relative differences is the change between each. So it would be a change in the AC50 from one condition to another condition or the change in area under the curve from one condition to another. addition to another. So this was put in to show that as something similar to what we provided in the first study, that what the delta S or in this particular case, the delta S prime is looking at is different statistically from the difference that you see in EC50 values, which is located in the second over from the right-hand side, or the delta AUC, which is right next to it. And this was established using a Pearson correlation coefficient, where you're looking at a correlation fit of going from 1 to 0. A correlation of 1 would be a perfect fit. A correlation of 0 is be a perfect fit. A correlation of zero is a totally random fit. And the point of this is that the Delta S is looking at a distinct, a distinctly different endpoint than either the EC50 or the area under the curve. And it is more closely aligned with the EFF, which is the difference between the two asymptotes. That is to say, its effectiveness. And this is important and an area that we will be building out because what this will provide is something that addresses the known deficiencies in AC50 and area under the curve evaluations. They're not very predictive right now. We believe that using the Delta S values, you can get a much higher and much better predictive outcome. And we will be moving to address this because it will provide not only us, but other investigators a bit more confidence in what these values are actually doing and what the high throughput screening is actually meaning in terms of outcome. Okay, next. Okay. We spent a lot of time looking at MEK inhibitors. This is just to show that you can take the same approach and not only look at MEK inhibitors, but you can expand it to a variety of others depending on on individual cancer type so the cancer types that we were looking at were pretty much the same or are the same but what we've done here is just change the gene target so we're looking at not only at MEK inhibitors, but PIK3, which is another gene, AKT, yet another gene, and P38. All of these represent different pathway alternatives. And there is stratification here. So this builds into an overall view that we can go back then to relook at and stratify what's affecting in the various pathways, which is going to be a future focus of some of this work. Thank you. One thing to make a connection here, Hasan, because A, this is a net new data set, and B, we're trying to generalize across it. It's a massive data set. I mean, we're just looking at four things here, MEK, PIK3, AKT, P38. data set i mean we're just looking at four things here mek pik3 aktp uh 38 we want our web application to be able to have those um these uh genes as drop downs in a filter right so like instead of paul having to go into excel and manually craft these complicated analyses that's what we want the web application to do. We want the web application to be able to have a set of cancers. Maybe we could use these ones or we could have a bigger one, but be able to select which of these genes a researcher wants to focus on. a researcher wants to focus on. And so this, what Paul is proposing, becomes the foundation for an interactive web tool meant for investigators. And so that's a lot of the work that we're gonna wanna work on. That's part of the generalizing it is. We'll go back over what Paul had proposed slower and work with you on the code part of it. But the goal is to have something interactive that investigators can kind of, they want to look at what they want to look at, but having it in a snapshot, a visual chart, even with the color coding that can let them look at any combination of cancer type and compound category that they want to look at. So that'll be some of the superpowers that we're giving the researchers coming up. All right, Paul. One thing I'd like to bring your attention to, Hassan, is the color code down at the bottom, which is yellow, gray, and sort of an off red. code. So you can call sensitive things one, things that are equivocal zero, and things that are resistant as as minus one, for example, let me turn that around the sensitive as as minus one, the equivocal is zero, and the resistant as one. So we so we keep the same numerical structure. So the negative numbers are are something we want and beneficial. So on the next slide, you can take and use that same numerical system and using the same data, just transformed into a slightly different way, you can now graph the data looking at the same different way. You can now graph the data looking at the same inhibitors or inhibitor classes that we were looking at before and align them by cancer type. So that the dark blue here, all those bars reflect pancreatic cancers. All the red bars indicate breast cancers, for example. But this is just, there are many ways to different, to capture the data and present it. the data and present it. And this is one of the dilemmas that we're having right now is how best to present the data when you're talking about extremely large sets of data and how to try to capsulize it for the viewer to make sense of it. And that will be a continuing focus as well as we move forward. Okay. And I think we're second to the end here. So if we go on to the next slide, Matthew. Next slide, Matthew. Just before we proceed, in case it wasn't clear, the magnitude of these bars is the count of the occurrence of something being in one of these categories of sensitive, equivocal or resistant. Is that right, Paul? Yes. Okay. So that's how we're kind of, if we ignore the actual number values and just kind of categorize them, that's how we're making kind of broad categorical assessments saying that, well, in breast cancer, MEKs have this strong negativity, but not pancreatic cancer. So we can make a differential determination on what treatment should be based on whether it's pancreas or breast and say, well, don't use MEK inhibitors in pancreatic cancer. They're not going to do what you think they should, but do use them in the MEK. So that is, you know, it goes back to what the doctors are able to prescribe a patient because most times when you have cancer, these days, they're actually going to sample the cancer and do a genetic profile so they'll know which mutations you have and this this is all just for the nf1 mutation scenario so if you have the nf1 mutation you're going to get something with breast cancer that you're not going to want to have if you have the nf1 mutation and pancreas pancreatic cancer so that's some of the you know as far as like the impact we're making for people this is this and pancreas, pancreatic cancer. So that's some of the, you know, as far as like the impact we're making for people, this is an important slide because it really puts together what a doctor might be interested in. If they know what a patient's genetic profile is for their tumors, it will affect their treatment options that they should be prescribing. Do you have any questions at this point, Hassan? No, it's pretty clear. OK, so just based on what Matthew was saying, you can then bring this back to just looking at the mean delta S values, knowing that based on the previous slides, there's some cancers that are preferentially sensitive to MEK inhibitors as a class. So returning to the S prime inhibitors, you look at their overall inhibition rate, and you can select a single chemical or a single drug that would be the most efficient in terms of overall kill rate. overall kill rate. So that provides, again, additional information from a therapeutic perspective, as well as it provides information from a drug development point of view, because you're balancing then projected outcome for an individual drug, as well as in its target class. And that's very useful information for selecting drugs that you want to move forward toward clinical trials. And I think there's just one more slide, Matthew. And I think there's just one more slide, Matthew. We do have some long range goals that we're not working on robustly at the moment. But those involve trying to do combinations of drug evaluations. In most current, in many cases where people actually have cancers now, they frequently are getting not one drug, but two or in sometimes cases, more drugs at a given time or sequence. And this is to help provide, to address different pathways and kill mechanisms. So with the long range goal of trying to totally eliminate the cancers themselves. But this combination drug evaluation is a daunting problem and has been for many years because there's many interactive pathways. And one of the reasons that we've been spending a lot of time on talking about pathways is because when we get to combination drug evaluation, those are going to play a dominant role. And ultimately, we think that with a lot of work, where we want to be able to get is to evaluations via neural network modeling that can save us all a lot of time in terms of trying to make projections based on just the preclinical data that goes into the dose response curves. Matthew? Matthew Kuehnert Thank you. So at this time, I'm going to turn off recording. Thank you so much for helping us get this together, Hasan. It's very helpful. So let me go ahead and get that off. Hasan Qureshi Thank you, Paul and Matthew.
delta S' and Future Work - Computational Biology - Part 3