 Recording is on. Hopefully this will be very useful for the whole group. We're looking to sharing this video and these slides out. And this will be a good foundation for everybody to kind of get caught up on the biology and also some of the direction that we have for how the algorithm is evolving. So historically, again, we've looked at high throughput screening data. This is the literal data from our original data set. So this is the MIP 3.0 data set that we currently have on our web application. You'll see that it goes from 0 to 10 data points, and those are activity levels. And that for each of those activity levels, we also have a concentration. That's the C values here. And there's one drug per entry, and a drug can occur multiple times based on if it's a different cell line that's being tested. So, again, we looked at multiple cell lines. Mostly, most of them were the test lines, which were the benign tumors. And those all looked like this, 5.5, 6.2, 9.5.11b, 9.5.6. We had one that was non-tumor and also from an NF1 patient, which was this plus minus, and it was this IPN NF9511C. And so hopefully with more exposure and familiarity, you'll start to be able to eyeball and identify this one as the reference. So generally, there's one reference and four test lines in what we ended up publishing with. There was more in the raw data. We just didn't use a lot of the raw data, either because they were largely because the reference lines weren't as didn't have as much information in them when if we were to use them as the reference or for the test lines there's actually one that had like a duplicate that just wasn't very helpful for us so we just discarded it. So we talked about this being a dose-response curve, and then the technical term here being four-parameter regression model. And the parameters of the regression model are, of course, A, D, C. And then technically there's a B in there for actually creating the sigmoidal shape. And then technically there's a B in there for actually creating the sigmoidal shape. And we're using, we're actually, we didn't actually end up using this literal formula directly. Behind the scenes, we had a program that Paul has that was doing this for us when we were creating these charts. creating these these charts um but we we our our algorithm of delta s or delta s prime really is only using a c and d um all right so we talked about the there are different naming conventions ec50 ac50 effective concentration active concentration. IC is really only relevant for inhibitory things, so inhibitory concentration. So only when the curve goes down would we use that. Largely, these are interchangeable for our purposes. So if you see EC50, AC50, for us, largely it's interchangeable. All right, so we now start to define something new. This is based on our algorithm and our approach, which is the term effectiveness. We are defining effectiveness as the height difference between these two. So the A is the asymptote extrapolated to zero drug concentration, D is the asymptote at the infinite drug concentration, and we are finding the size of this and calling that effectiveness. You'll note that this will generate a negative number if the curve goes up. Now it really does depend on the response that you're getting. So remember that in the high throughput screen we're giving a chemotherapy, what we expect to have happen is cell death that's that's that would be less luminance less activity you know you you give more drug more of it dies that would be what you would think um in some situations for some of the compounds the there was actually cell proliferation right and so in the curve actually went up we didn't we didn't use those drugs in our original study because they didn't they didn't work for treatment and you know we we kind of filtered them out but it was it was it was a it was very interesting and notable that there are things that that don't behave as you would expect. Now that's still interesting science, right? Like you want to know why is it proliferating? What's going on here? But from a treatment perspective, it wasn't relevant for treatments exactly, not directly at least. And well, I guess what we're calling out here is that there's another name for this effectiveness you'll sometimes see, which is amax is that right paul yes okay so you'll see that in some literature some papers i'm not super relevant some of our some of our data uh uh files will have that as well as we move forward okay that's good yeah one of the things that we're trying to do is generalize to other data sets and adapting to their terminology is is actively part of the challenge especially for us coders who have to deal with the raw data all right so relative activity um historically has been based on the difference between ac15 area under the curve. Actually, Paul, I'm going to let you take this slide if you could, please. Sure. Drug screens have been used for a fairly long time going back to about the 50s. And the mainstay in terms of trying to compare one drug activity against another has been either a comparison of the AC 50 which reflects potency or the area under the curve the area under the curve is a more total effect of the drug. And it is indirectly a measure of effectiveness. But both of these methods have been known for many years to have issues in terms of their predictability. They are incomplete in terms of their predictability. They are incomplete in terms of their ability to predict what's going to happen in a, especially as it applies to high throughput screening. So there is known to be a need for something that improves the predictability especially when you think of you're talking about thousands of drugs and if you have large errors in your predictability the utility of those screens goes way down so we're hoping to address that in with some of the work that we're doing. On the next slide. Okay. All of what we're doing is based on dose response curves. But if you just take a look at the various dose response curves, you can see that just trying to get an understanding of what's going on by the various curves is really difficult trying to assess where the the ec50s are even just visually looking at them it's it's hard to appreciate exactly what's going on part of the problem is that the curves are not always classically sigmoidal they have kind of funny shapes uh the effectiveness that is the height of the the difference between the the upper asymptote and the lower asymptote is frequently not full scale that becomes a difficulty in terms of interpretation, especially as it applies to the EC50 values. What happens there is you get a skewing upwards of the EC50 values, and that introduces a lot of analytical ambiguity, because instead of looking at changes in one dimension, you're looking at changes in two dimensions. one dimension you're looking at changes in two dimensions. So just based on this I hope you can appreciate that you know there really is a lot of funkiness that's going on and there is a need for wanting to change to something that's a little bit more effective on the next slide yeah right before we move on I just wanted because this is a really important for the rationale for why Delta s prime in particular but Delta s as well you see how the the curve with the white dots is not a full sigmoid is is that clear to both hassan uh is that clear to you yeah it's like you can't describe it with a polynomial is that what you're saying well it it's it's just not the full model right our model is for the full sigmoid and this is like less than the full sigmoid it's maybe like half yeah it's truncated on both ends yeah like the wired dots the white dots one yeah and and so what what that means is that we don't really know where the ac50 is right and and that you know if we we can make a guess the algorithm can do do an interpretation but it's it's challenging right like we know that it's probably off and it would be better in the ideal world, they would have had more concentration samples and you would have just seen more of it. But that's just not always the case with practical data. Now, the other one that's a weird shape is this all black dots one where it just, it's hard to call that a sigmoid. I mean, it just slams down. And so it just, it has a different type of response than what you would typically expect a very sensitive and narrow response. And, you know, there just wasn't good sampling, right when the key activity change was happening. So that's also a morphing. That's a challenge, right? It means that it's not following the sigmoid model that we're hoping and there's a deviation so it just means in both both of those cases that's going to be harder to interpret that data so that's really important because what we what we're proposing coming up is that we have our approach is a little bit better at working with those styles of data than the area under the curve and the traditional AC50 style. So this is basically the main point of the research. It's one of the main reasons why we think we're better. Yeah, just to put it like bluntly, like it's one of the reasons because we can deal with more practical, realistic data. It's more than that, though. We can also do cross-cell line analysis. But just at the most simplest level, yes, ours is better than what they're doing in industry right now. Because in industry right now, they're not compensating for the fact that sometimes the data is not complete or it's just not showing a good dose response curve they're kind of assuming an ideal the the biggest problem right now is not taking into account that the the drugs are that the drugs are not having a full scale effect. So instead of going from 100 down to zero, many drugs will have something much less than that. So they plateau at the lower end at 50, or in this case, and the one Matthew was showing that has a very sharp drop off it's 75 percent more or less so you're only getting a a change a net change of 25 percent in terms of the kill rate and if you don't take that into consideration uh all of your estimates about what's good or effective are can become less than accurate. That's very critical. All right. Thanks, Paul. Did you want to keep going or did you want me to continue? I think normally you take over here so you can just call me if you need something. Okay. All right. So now we're going to start going down the train of thought on what was our algorithm? What did we do different in our original paper, the delta S? First of all, we are introducing a combined value, which is a single number. For me, S stands for score because I was always thinking of turning this into a ranking of which compounds might be more relevant. But it's a single value, so however you want to think about it. And it combines the effectiveness, which we defined before, which is the height of the asymptote, and what we're calling potency, which is that AC50 value. And it's combining it here as a fraction and creating a single value S. This approach comes from receptor ligand pharmacology which just means the part of pharmacology that relates to how things are binding and it can be used to rank the inhibitory effect of compounds I know Paul and I go back and forth on the role of S. I'm much more interested in delta S, but S by itself does allow you to look at a compound and say, for this compound, what is its overall single value effectiveness and potency? If you just want to look at it really quickly and judge it, it's very effective. So this is that approach of a single value S ranking. And what we have here is all of every single compound from our data set was plotted with its S value. And so this is just all 1,900 compounds just plotted. And, you know, by coincidence, it happens to have a decent curve. Paul, I'm going to let you kind of go into the explanation a little bit. Okay. So when we do this, part of the reason that we're looking at transforming everything into a log is because the numbers actually cover a wide range of values, many magnitudes of difference. is due to the the change in the AC 50 which can vary quite quite a bit yeah one thing to make clear actually going back to concentrations when when when scientists use concentrations they tend to use uh exponentially more concentration right so uh this this is actually a log concentration down here um so you know micromolar millimolar molar you know like they're dramatically changing the amount of concentration so So they never really use a linear scale. And we can actually see that if we go back for a second to our concentration data, we're going from this 0, 0, 0, 7, 8, 9 to 0.2 here. And then we're all the way up to like 1, 5, and 15, and 46, right? So the concentrations are, you know know logarithmic mean more and and that's because for you to see a larger impact a lot of times you need exponentially more compound to see that effect so that's why when we say that the AC 50 varies dramatically, what we're saying is the concentration range varies dramatically. And that's why looking at it in log space, which will kind of flatten it a little bit, is going to help us. And it makes everything a little bit, certainly much easier to graph. And from just a mathematical perspective, it makes it a little bit easier to understand because you're talking about values that are relatively restricted in numerical space. So again, the S value we're defining as being the log of the effectiveness over the AC50. And this by itself monitors the inhibitory effective activity of a given compound in a single cell line. And nearly all of these values are going to be positive. And what that indicates is that the compounds are having an inhibitory effect. I want to point out here that all of our numbers are based on log and log values have certain limitations. And one of the key limitations is that you can't take the log of a negative number. And that will become more important as we start to make a transition over to S prime. But from a practical point of view, it means that if the curve is going up instead of down in terms of its concentration and response and then we can't analyze it using just s alone it using just S alone. But you can use it for all compounds that are inhibitory, and you can use it to rank cells and compound responses. So from just selecting something that is going to be beneficial in terms of at least one cell line, you can make a nice ranking and apply statistics to that to make sure that the, for example, the 95% confidence intervals are separated from each other. And that gives confidence in terms of practicality of separation. On the next, Matthew. OK. So that leads us to now looking at the differences between cell lines. As I mentioned before, S is good at looking at relative compound efficiencies in a single cell line, but very frequently what is even more important than that is looking at the differences between a non-tumor situation and a tumor line and that is particularly important when we're talking about the nf1 syndrome and the cell lines that we get from them the nf1 syndrome is non-tumor The NF1 syndrome is non-tumor. Any patient that comes in with the NF1 syndrome, most of their body is non-tumor. And you want to treat them, and you're targeting just the tumor cell lines, the tumors that are in that patient. So you want to be able to get a differential effect where you're treating the tumor cell lines and killing those off selectively and sparing the normal tissue. The particular caveat here is that this treatment is not short term. These patients have a genetic disease, so they're going to have this situation where you have tumor versus non-tumor for the balance of their life. So the treatments have to be designed to maximize the tumor to non-tumor ratio because they're on lifetime therapies. That's very different from, for example, treating a malignant cancer, where the treatment is brutal, but it's relatively short-term. And it's designed to kill the tumors and not kill too many of the normal cells. So the patient survives, even though maybe his hair falls out, he has stomach issues, but there is survival. This is a little bit different. What here we're monitoring is the change, which we call delta, we're monitoring is for is the change which we call delta between a reference set of lines whatever we define as a reference and whatever we define as our test lines in this case particular case it's tumor lines so delta s then is the change in S going from one set of conditions to another, tumor versus non-tumor. This allows us to do comparisons across cell lines and different compounds relative to some whatever we end up calling the reference lines. So we can use this type of Delta S anytime we want to look at a difference between cells that are of connective tissue origin, for example, and those that are of epithelial origin. that have one set of gene features nf1 positive for example versus ones that are nf1 negative again as an example and in the first set of papers that we did this is the approach that we use that we use s uh and s prime as our two indices to look at the effectiveness of drugs across uh both non-tumor and tumor lines in our first paper and that's delta s yeah. Thank you. So just wanted to go back for a second for a side discussion, which is also on the impact of units. It is it's been a criticism internally, that, you know, we want to be very cautious with these indices. One thing to keep in mind is if you look at the units of EFF over AC50, AC50 is a concentration. EFF is a percentage, generally speaking, so it's kind of unitless. But we do have at least the AC50 units. What that means is that because this is a log scale, units have an outsized of impact right so one thing to be very aware of is when you're doing this kind of ranking it only really works when the units are consistent across all the samples you can't mix micro molar and molar concentrations you have to have consistent units because the they will skew the log value very dramatically. Small changes can have really big impacts in log space. So that's one thing to keep in mind. One of the reasons I'm also particularly a fan of the delta S is because it helps us deal with a lot of the units issues. You still have to be consistent, but at least theoretically, you can see how because of the way log math works, this subtraction of the reference minus test log spaces is equivalent to this kind of ratio. So when you start looking at the code, we use the term ratios a lot. And the reason we use the term ratios is because ratios also represent this basic delta S subtraction. So it's important to know that A, there is mathematical equivalency between this kind of subtraction and the ratios. But also, if you're worried about the units like I am, it becomes less mathematically relevant once you have this kind of ratios here, where obviously... Right, because the ratio, because the units cancel out. Right. So that's one of the reasons delta S is also, at least mathematically, very reassuring, at least by the time you get to that point. Just to make one thing clear, in case it wasn't, the reference line is the non-tumor tissue, and the test line generally is the tumor tissue. Before we proceed, are there any questions, Hassan, so far? No, I'm good. You can go on. Okay. All right. Now, when we did our delta S, obviously, you need two cell lines. You need a reference and a test line. And if you recall, our original data set had one reference and four test lines. One of the things that we were able to do to improve the predictability of our algorithm is we created a delta S for every single test line. So we kept the reference line constant. We use the same one over and over again, but we changed the test lines to other tumors, right? Other plexiform neurofibroma tumors. And then we took a mean of that. So we ended up actually moving from just a single delta S, which is one reference, one test, to a mean delta S, which was a single reference in four test lines. And that's what we ended up introducing here is this delta S log mean, which covers at least four of the entries, assuming that they all had valid data. If one of the sigmoid curves was really broken like it was not sigmoidal we would generally toss out that cell line and we had a criteria in our paper that we needed at least three out of four to be in good shape for us to to analyze it so we could still create a mean with three out of four or four 4 out of 4, ideally. If it was less than that, it wasn't something we could analyze, and we would just discard that compound. So what this shows, again, is a list of all of our compounds, all 1,900 compounds. And now we're looking at the the Delta s log mean and what you see is that there is there's some things that are above zero some things that are below zero and there's a bunch of years at zero so if you think about this base of subtraction if you have a zero response, it means that there was no difference between the tumor and non-tumor tissue. Whatever they did, they did them exactly the same together. There was no relative difference. And what I was particularly more interested in was the drug sensitivity stuff, because what that is saying is that there was more of a difference in the tumor line than in the non-tumor line, the test line versus the reference. And in those cases, you're getting a negative delta S or negative delta S mean. So looking at this curve in particular, we were able to kind of make a claim or make a justification that at 0.5 and up, we're calling all of those compounds drug resistant. the effect in the test line in the tumor lines was less than the reference was. And then at this negative 0.5, we're saying it's drug sensitive, like we're categorizing these as drug-sensitive compounds, meaning that the test line had more of an effect than the tumor line. We're seeing a difference. And then for the zeros, I don't think we gave it a label, but it's basically undifferentiated, I guess is what we're calling it. There was no big difference. Is there anything else, Paul, you wanted to call out here? No, other than to say that these ultimately were changed into what you see on some of the yeah so we then now having calculated delta s uh we then looked at this from a kind of a pathways perspective or maybe a mechanism perspective and what we had done in our original paper is paul was able to manually curate the targets for those compounds into categories, like which pathway does this affect. So this is, again, the PIK3 mTOR pathway, which is one of the ones that we called out before. There are certain ones involved in the cell membrane, certain ones in the DNA cell cycle, and there were some wild cards down here we were very interested in as well. But we were able to bucket these and kind of look at them as an aggregate and look and say, what does this class of compounds do? So for everything affecting that PI3K mTOR pathway, what does it do and also within a pathway which ones were the strongest signal so the size of the bars are showing how much of a signal how much of a Delta s there was and the Reds are all the drug sensitive compounds and you can see those values here in the delta S log mean column. So we're ranking them by this delta S log mean and how many of the cell lines had good values. Many had four out of four, like we see with this chlamypramine up here. And then you see that there are cell lines that are more responsive and less responsive. And this is going to be true with real world patients as well. So this cell line here was very responsive to treatment. This one, 6-2, was a little bit of a problem child. It responded often, but not always. But then this 11B and 95.6, it was decently responsive. So this is some of the advantage that we had, especially in the structure of the original high-throughput drug screen, where there was a plurality of cell lines tested. And that was really advantageous to us because we could make a claim that, look, this is what you would see across a set of people right you have different mutations different profiles for the genetics but on average we're still seeing something nice and ideally you know from a from a research perspective the more cell lines that can be screened, the better, right? Like we wish our log mean was, you know, had hundreds or thousands of cell lines. That would be great. It's very expensive to run high throughput screening, but there's this idea that as time goes on, you can add more and more of these cell lines and then the log mean value becomes more and more accurate. And we're very excited about that. We hope to have more certainty the more data sets we encounter and the more studies are run. But there's this idea that we are creating these pools or functional domains and we're able to kind of assess things as a pool. Now, this is drug sensitive. It is fair to say that drug resistant is interesting scientifically. It's not what you'd necessarily prescribe directly because obviously it's not preferentially killing the tumor. But if you want to understand the pathway involved, if you're trying to discover the pathway and what are all the implications, you know, there is useful information here. Like, why? Why are these ones that affect the tub and the TOP2A? Why are they the way they are? You know, it's very useful. And then Paul had mentioned that there is a nuanced use case where sometimes you're giving somebody uh an agonist which you know is promoting something like maybe cell death but you're also going to give them the opposite inhibitor in a small concentration to kind of dial in an effect paul how would you describe that well no you got it right on point. Okay. So, yeah, you would, you, even though this is not the primary thing you want for killing your tumors, it still might be part of a combination therapy, or at least you'd want to study it scientifically to know why these proteins and genes are involved at all. It was also very helpful in terms of us being able to validate that the drugs were doing what we expected them to do. Validation is a very important component of making an algorithm like this. I mean, algorithms are very cheap, but getting ones that are predictive and having numbers that make sense according to established biology, that's a little bit harder and so uh part of this exercise was to make sure that we were making uh we're having outcomes that were entirely or at least largely consistent with known biology both from a drug sensitive perspective as well as a drug-resistant perspective. And then that in turn speaks to the power of the algorithm. Matthew? Matthew Cooke Thank you. All right, let's continue. So this side is focusing and narrowing in. For each of those categories, what were the top two most drug sensitive compounds? And so if you want to think of this like a doctor, right, like I need to prescribe something, you probably want to target some pathway, right? Like I want to, I, you know, I've already tried an mTOR inhibitor and it didn't do anything. I need an alternative, right? Well, this set of buckets are providing independent alternatives. They're doing different things. They're different parts of the cell cycle. So this is a way of saying these are alternatives, and this hopefully would be very useful for at least the researchers, if not the prescribers at some point, on saying, well, let me see what the top candidates are in these different parts of the pathway. pathway. Again, because of the way genetics works, you know, there's a lot of variability in it. You know, people have mutations in different places. They also have mutations in other parts of their genome that interact with parts of these pathways. So you never know exactly how it's going to play out with a single patient, but you can make some predictions if you have their genetic sequencing, for example, or you just know what you've tried with them before, where you've given them prescribed drugs and saw that there was little to no effect, and then you have to move on to something else. But this is a good way, again, this is a good way of... This can also be... Yeah, go ahead. This can also be very helpful from a drug development perspective because instead of starting now with 1,912 drugs, you can narrow it down to just a handful, maybe 10, that have slightly different mechanisms of action and are candidates for advanced preclinical studies, which are very, very expensive. So it's a way to weed out relatively quickly and with hopefully a degree of accuracy drugs or compounds that can be moved on into advanced preclinical studies. That's really important especially for rare diseases that don't have a lot of attention paid to them. Matthew? Matthew Kuehnert Now, it's fair to say that the science that we're providing should add value to the community. If you look at some of these, some of these are approved drugs for other things, like this one is a statin right here and vertiporphine is known as a photosensitized photosensitizer it these are some of these have been studied so again our original data set was a mix of approved drugs and experimental drugs some of these are approved drugs and they're not ones you would expect like that. We wouldn't really have predicted a statin would have an effect here, but that is something that we found. So not only are we ranking, but we're also bringing to light compounds that researchers just may not be aware of or they wouldn't have thought to look at. And we're giving justification for why they should probably slow down and look at this a little bit closer and so that's part of the value that we're actually providing the research process here is not only we're giving you a ranking but we're finding things that are unexpected and interesting and we can we can ask at least in follow-up studies why or what can we do to validate this oddball effect and then go deeper on it all right so let's summarize a few things this is this is a conclusion of the work we've done on the Delta S uh so the the in the final version of the paper we we actually required four out of four cell lines have good data we decided that that was the most conservative thing we could do because if we had three out of four we would have to introduce some weird statistics uh it just wasn't worth it for us we wanted to be very conservative with any of the claims we were making so we we did end up publishing with just four out of four being required to justify a strong log mean delta S. But internal discussions, we were comfortable with three out of four of the tumor lines having a positive delta S sign. S sign. And one of the details here that we skipped from the statistics, but we're going to bring this up right now, is that when we do the delta S, we are given a score for those dose response concentration data points of how much like a sigmoid is it? How much like our model is it? And this is the R-squared value. It goes from 0 to 1. 1 is a perfect fitting data points that perfectly fit a model. 0 is completely unrelated to a model, completely random scattering. And we set a threshold for our tumor and non-tumor lines of 0.8 or better for how sigmoidal the data is. This is intentional. So one of the things that we factored into making a threshold like this, when you have a drug that you're trying to prescribe, if it has one of these really sharp angles, it's very hard to dose that, right? You can't really prescribe somebody a dosage and expect it to be like 20% down or 30% down. It's just going to make a sudden uncontrollable jump. 20% down or 30% down, it's just going to make a sudden uncontrollable jump. So generally, that's not going to be a good candidate for treatment, because you can't give somebody a pill and know exactly what effect they're going to have. It's just not dosable. And other things like this other one with the white dots where it has you know it's incomplete well that's also going to have a bad r squared value and depending on how bad we may not be able to use it maybe just you know our day the the points that were observed just there was not enough there to extrapolate what the ac50 was or, you know, that we were comfortable calling that a sigmoidal curve. So one of the good things with the sigmoidal curve is that there's this region that is dosable, that has this kind of, you know, gradiation as far as what the effect is for a concentration. Yeah, not all biology does that. some are this snapped shut thing very dramatic change but nonetheless we were preferring ones that were more traditionally pharmacologically relevant which would have that more of a sigmoid curve than not so using the r squared to filter out data did a lot of things for us. Maybe there was sampling error in the original data. Maybe that's why the R squared was bad. Maybe there was incomplete data, poor data coverage, or maybe the shape was just not clinically relevant, right? Just didn't have good dosability. So there are a number of reasons that kind of justify wanting and preferring a better r squared and and 0.8 was kind of our our threshold for that now what that meant um for our analysis depended on which line had it if if if you had a bad r squared on your reference line we just couldn't we couldn't assess anything about that that uh that that compound um because the reference line we only had one of them so if it was bad there's nothing we could do but if it was good and one of the tests was bad that's not a problem right so if three out of the four tests had good r squared values we could still analyze it um we would just yeah we would discard the third the fourth one but we had three out of four that was good enough for us so um that is the r squared value it is very important for the uh for the delta s approach we we will call out and give you a preview that it plays much less of an impact in our S prime algorithm coming up, but it plays a strong impact in our Delta S study. It was a really critical quality control for a whole study. And so when things didn't work, when we did have misses, what were the common reasons? One was the low R squared. As I captured before, that's a lot of reasons, sampling error, or just not the dose range wasn't right. Delta S, however, could, or the S value could be determined to have a negative EFF. And again, there's no way to take a log of a negative EFF of a negative value. So those values are probably discarded as well. And again, this would be the cells grew. So we didn't really care in our original study that we were discarding those because we were just trying to say, these are the things that are more, that are prescribable to kill the tumors. We didn't care about what was causing tumors to grow, but scientifically we would care. We would want to know the mechanisms and pathways involved either way. So that was a limitation of the Delta S that we're going to get rid of going forward. And then of course the AC50 was not always able to be determined maybe the concentration response curve was flat so you imagine that delta S was really really squished and it just almost looks like a flat line there's almost no way to find halfway through a relevant inflection point so we did see a lot of flat responses. And that's a problem. We didn't have a good way to work with data that looked really flat. So again, not super relevant for the drug-sensitive category, but it is relevant for the science and the understanding. So we'll address that going forward as well. understanding. So we'll address that going forward as well. So to qualify what, you know, how all of our filters and how it all kind of played out, out of the 1,912 compounds, 600 passed the drug screens. So obviously a narrow subset, but the upside was that we thought that the data that we were getting was very reliable because we had these filters for quality. And because especially we were doing these averages and such, like the data that when we made a claim saying like these things are more drug sensitive than in tumors than non-tumors, we felt very comfortable saying that. But there's, I think what's been a thorn in our side from a research perspective is, well, that's still a lot of compounds that we're just not, we're screening out early on. We're not doing anything with that data. So that's kind of what's priming us for the future work going into the Delta S prime. So just to give you a preview, I think what we'll do, Paul, if you think we could probably wrap up at this point and then continue, I guess tomorrow, does that work for folks? It's fine with me Hassan sorry would you be comfortable continuing the rest of this tomorrow yeah and also when will we go through the technical part after yeah probably, I mean, we're at 30 out of 50. We should be able to finish up almost all of it. Maybe we can make a push next time to finish all of it. I think probably we can finish in one more skip a lot of the data slides and just primarily focus on the concepts. And that will cut down the total amount of time very substantially. Most of these are data containing slides and they may be of more interest to the biologists. Yeah. Yeah, I think we can hit the last of this in one more meeting. So before we do that, let me just give you a sneak preview of where we're going with Delta S prime. So Delta S prime has a format similar to S. So this is the S prime. And the S prime is also a log, but it's a natural log. And it uses the same input variables, A, D, and C, but it does it in a slightly different way. Now, to give you a sneak preview, this is going to allow us to look at curves both where the A and D goes down but also where A and D goes up. It's also going to allow us to do when A and D are relatively flat and it's hard to calculate C. So it's going to give us a lot more ability to calculate all of those values we were just discarding earlier on, now we're going to have actual values for it. So we'll go into it a little bit different, a little bit more coming up, but a lot of this is very one-to-one with our approach for delta S. We have an S and then a delta S, we have an S prime and then a delta S prime. So we'll get into that more coming up. But this that we're starting with the delta S prime is all of the new work. This is where you will be coming on to the project. Everything else up to this point is background. But it's background that very directly affects our approach for the S prime. So yeah, why don't we hop on a call tomorrow at 1130 and we'll keep going with it. All right, sounds great. Thank you for your time. All right, thank you. Thanks Paul. All right, bye bye. Bye.
Historical Work on delta S - Computational Biology - Part 2