I got to give you quicker view about the difference between different TA's, different therapeutic areas. In some therapeutic areas you use surrogate end, points earlier end points if you can do that instead of waiting for the actual clinical effect. Yeah, you take diabetes, in diabetes for instance you look at fasting blood glucose as a surrogate marker of, of looking at the effect eh, effect of your product on, on, on, on a. Here in treatment of diabetes instead of going to looking at HIV. So we will take longer time. So here and for glucose you just wait two weeks or three weeks instead of waiting yeah a number of weeks up to two months. Yeah, viral load for instance is a good biomarker for HIV. Products which have an effect on HIV, it's effects can be very very effectively be followed by viral load. So, instead of designing the study to look at the mortality of HIV, you actually look at viral load and design your phase two study based on that. Yeah, in some other therapeutic areas. You don't have reliable biomarkers and maybe oncology could be a good example of that one for majority of tumor types. The standard role and the standard for effect would be overall survival which, which, which is on the effect end point which happens depending on the tumor type. Forward in time you may, you may need to go yeah, in the, in the case of breast cancer years or for some more aggressive cancers earlier. But, but the yeah, that's, that's what the yeah, what the regulatory agencies want to see would be the actual clinical, clinical end point. Yeah, and then in some cases if you're lucky enough you have a surrogate marker, is marker is a biomarker, which has been in the virtue. By virtue of large, large studies it has been shown and proven that it could be replacing the actual all clinical end point and then blood pressure could be good example of that. Yeah, just two quick charts of showing you the differences in outcomes in phase two crossed the therapeutic areas. Far to left you see eh, column saying for all, all phase two studies the success rate is about 30%. Eh, ema, eh, this chart is being updated on an annual basis because that, that success rate is changing quite rapidly. Yeah, and then you have a difference of course depending on different therapeutic areas. Some therapeutic areas are having less success survival rate in, in, in phase two setting. Meaning that nine out of ten drugs, for instance if you look at the GI it has pretty low success rate. Majority of the drugs tested there in phase two die for instance. let me then go and show you phase three. The outcome of that one for all phase three studies is you have about 75% success rate. And again in variabilioty across different therapeutic areas. Sometimes you can try to figure out a correlation between your success rate between phase two and phase three. And it has to do again with the, with the discussion we kept earlier on, that if you keep the hurdle too high phase two, the few products which go to phase three actually has a lot lot higher potential for success. And then you may be able to see some of that in here, I would look at it for respiratory instance. You see, here in phase three, the respiratory rate is 95% in outcome success in phase three. And if you go to phase two, you see the respiratory success is about 15%. Yeah, and that gives me an idea that, that they had the pretty tough criteria in phase two for respiratory drugs for them to pass the hurdle of phase two. But the products which passed the hurdle of phase two and then to phase three were [INAUDIBLE] were 95% successful. So again, this is a balance between phase three and phase two, how you play it out. With the risk you take, you can balance it out where you want to take it. I'm going to give you about a couple of examples and finish up my presentation. First example is status and the effect of two different products. One of them is a gemcabene up and the second one is ezetimibe on and the affect of them on coronary artery disease, when you combine them with different statins. Yeah, so we did actually apply the modelling simulation which I told you earlier is one of the tools that we have in trying to find out. You figure out the potential outcome in phase three without having the data in phase two. And that's the beauty of it that if you have the other type of models you actually don't need to do the phase three study you can actually predict the outcome of phase three studies. So here I've I'm showing five different curves, and these are simple. Correlation between dose and the LDL yeah, change from from the baseline for different products, for five different status. A well known fact that these are all well published, you know it. If we can take this data point and we can model for them and we can predict yeah, yeah, or, or estimate the parameters of this, this, this this, this relationship, mathematically. and then, you look at the two other products, the new products that you want to test it. You have ezetimibe on top, and you have the the new product on bottom. And you do the same thing. You do again, you develop a exposure response for each of them. Yeah, and, and then on the right hand side you see I've two curves. In one of them I've taken statin and I've added ezetimible to it far right hand side. And other box here I've taken same product statin and added in, in gemcabene on it. And I look at the additive function, if the top curve would be, after the status was given stain and the bottom curve would be that is given in combination. So if you look at the for right hand side, you can see that when the product is ezetimibe combination, is it a? You have a steady effect and additive effect of that additional combination therapy which, which is sustained across different doses and it goes from zero to 80. You see the difference between two kilos of sustain meaning if you add up a new product in top of the previous one, you have a benefit, added benefit. If then you look at the the new test product, you can see that the effect is not really there, I mean there is in the beginning. But when you increase the dose of statin itself, the added bebefit you got from the combination therapy is becoming less and less. So when your, are you in the range of 60 milligram or 80 milligram. The additional effect added benefit of, of, of of the product is fairly small. Just based on this information the the product was discontinued or the, or the concept was discontinued. So it was not real big need to go to phase three setting, and run hundreds of patients to find this out. The second example here would be just the example of a single arm study. In phase two study and with drug X, a potent inhibitor of VEGFR's in thyroid cancer, we have primary objective would be You look at the activity of the drug in advanced thyroid measured by, by response rates overall. And then there's a number of secondary objectives listed we look at the few clinical objectives, we look at population PK analysis and so forth. This, this it's like if you view about the statistical expectations, where you get to know all hypothesis and alternative hypothesis, that was acceptable or not. Yeah, and the number of patients they recruited and then in this slide, I'm giving you a view about what the actual results end up to be. Yeah, it's a [UNKNOWN] charts. Yeah, everything going downwards, meaning a decrease in the tumor size. And then, this, and the information from this study could be interpreted as, Kaplan-Meier charts. this chart shows you, you have the survival, PFS progression free survival of 18.6 months. Okay, what does it really say? Its a single arm study and that's where the issue comes as we were talking before. In single arm study, we don't have any reference points, so in order for me to interpret this result, if this drug is better or not, I need to go again and start searching literature. For other products which are being used for Thyroid cancer. How many months, in fact, they have. Are they less or more than 18.6 months? Yeah, but by the virtue of that form I'm bringing a bias, because I don't know what patient population they use, what type of equipment they use in order to look at the the size of tumor and so forth. And as you know, medical technology is increasing, too. Going to become better and better. So if the study that I'm looking as reference was done ten years ago, the CT Scanner ten years ago were not as good as CT Scanners today. So you look at the size of tumor a lot lot better today than you were doing it ten 15 years ago. So again, it's difficult for me to find a good reference point when you have single arm studies and that was what I wanted to depict with this example. You have just one arm, you don't really know the reference point.
