 As we were ready to start, or before we start, I wanted to remind the chat members to please use your microphones and make sure you're close enough. Some of the people on the webcast are having trouble hearing. And I should turn this off, but for the people in the audience, please help yourselves to some Halloween treats. Sorry about the people on watching on the web. Well, our lab keeps promising they're going to get down the beaming things from placed building to building, things in people. I haven't heard any progress reports lately. Should I begin? Please proceed. All right. Thank you. Hi, everybody. My name is Suraj Dasgupta. I'm from Bursar. And today I will be talking about Bursar's efforts in determining scenario-based aggregate phthalate exposures to humans. I'm going to briefly talk about the contents of my presentation. I'm going to provide really quick, you know, the objective and background of this particular project. Then I'm going to do a quick recap of where we were in the previous CHAP meeting and, you know, the work that we've done so far. Next I will be showing some of the Excel workbooks that we have created for pregnant women and women of childbearing age. And these spreadsheets of workbooks, there are three of them. And they include the concentrations, the exposure factors, and then the final exposure calculations. Next, I'll be briefly talking about the results, and then have a short discussion about our ongoing efforts with infants and children, and finally, you know, the things that yet to be accomplished. One of the things that we're most concerned about are the results that you have for the women of childbearing age, and I think we spend more time on that and less time on other things. We can always go back and fill in some of the gaps. I don't want to spend time on the details of all the calculations, I'd rather spend time on the results because we had a bit of a conversation at lunch indicating that's where we need to start focusing our attention. Is that correct, Chris? Just to put it into a frame of reference of the results or where we want your attention being paid. Because we're going to start looking at it in terms of some of the things that have been done in the bio-monitoring data to compare the milligrams per kilogram per day for both means in the high end. So in that way, you know where our focus is, okay? All right, thanks, Paul. Yeah, I think what I'm going to do then is I can talk about the few slides and then I'll skip showing the Excel spreadsheets because I kind of did that before. I can directly move on to the results and I'm sure that, you know, when questions come up, we can revert back to the spreadsheets. That's perfect. I mean, in that way, then you've taken time using it effectively and people need to have follow-up and back-fill of information. You're always free to go backwards. Okay. Thank you. All right. So the overall objective of this project is to estimate, you know, aggregate human exposures. We were given a list of eight phthalates and the way to do that for this particular project was to analyze individual human exposures to the variety, to the various median sources and to come up with an aggregate estimate of exposures to phthalates across several subpopulations. So this graphic over here kind of, I think, provides a good overall, you know, pictorial representation of the, what I'd like to call like the three-dimensional problem that we have over here. So we have phthalates interacting with a variety of sources slash media. And then you have all the different exposure routes. And again, on top are the different subpopulations and how each of these parameters, if you might call it, you know, interact with each other. And that's the real significant challenge of this particular project. A quick recap, Versaar started working on this project with CPSC back in June of 2011. We presented some really preliminary results in the first CHAP meeting that we had, which was in July. And at that time, CHAP, you know, members were mainly interested in looking at some of the uncertainty, variability, and sensitivity of the data that we talked about, mainly as it related to the exposure factors. So far, we have completed all the analysis for the first subpopulation that we started with, which is pregnant women and women of childbearing age. And we have roughly, we've completed roughly about 50% for infants, toddlers and children. And they were two separate categories, but we've combined them together and we are proceeding with them as if they're like, you know, one. So that's where we stand so far. The Excel workbooks, the way they're organized are the concentrations. The concentrations workbook has all the concentration for all the thalates that we determined from our literature research. And it includes seven exposure outs. And it's not like we have concentrations for all thalates. It is whatever we found was, you know, pertinent or relevant to that particular media or out. And that kind of carries through to the exposure factors. The exposure factors were primarily compiled from the 2009 exposure factors handbook, which, you know, was published by EPA. But besides that, we've also done a lot of, you know, research and looked at various other sources to come up with these factors. And these are basically the behavioral or the human activities that need to be studied along with the concentrations to determine these overall thalate exposures. Finally, the workbook that has the calculations, it has calculations, exposure estimates for individual thalates. And it also has aggregate numbers. And I can show you that a little later. All right, so I'm not going to get into the spreadsheets. I'm directly going to move on to the results and discussions. One thing that I'd like to just point out before I start is that I know that one of the concerns was, you know, about the variability and the sensitivity of the data. And we have tried to address that a little bit. And you will see that in the subsequent graphs and charts. This table shows the aggregate exposures to the eight thalates that we have. I think this is not bright enough or something. I can actually open up the spreadsheet that has that. So what I have here are the total daily exposures to pregnant women from all the sources and all the exposure routes for eight thalates. And the distinction between the two, one is without concentrations and the other one is with concentrations. Now, when we were doing this exercise, we went back and forth several times with Paul and Mike. And at this point, I think I'd like to thank them for their continuous efforts in helping us out. But we found out that for some of the routes, for example, dermal handling, and this has to do with how some of the environmental products or sources or media result in thalate exposures. The equations that we had to calculate exposure estimates did not include concentrations. And what we determined were some of the numbers were really, really high. So what we decided to do was try to look at it from a slightly different perspective. So we decided to incorporate concentrations. But the concentrations that we included were a fractional amount. So if you can think of it like a reduction factor. So concentrations were included in the equations. And the differences that you see between the two columns are a result of, if you include the concentrations, which is nothing but it reduces the numbers. So that's where we stand. Sorry, can you elaborate that a little to an ignorant person like myself? This is not immediately intuitive and obvious. Concentrations of what? Concentrations of thalates in a particular product. For example, if I'm trying to assess the exposure that I will have if I hold this plastic pen, I can look at the behavioral patterns like the amount of time and a date that I'm going to hold this and exchange the amount that will get transferred to my skin. That's one way to look at it. The other way to look at it is look at those and then multiply that times the actual amount of thalate that might be present in this product. That's the fractional concentration that I'm talking about. So what you see in the column on the right-hand side excludes that amount. And the one on the left includes that. And the important thing over here is to see the big difference. And we've kind of gone back and forth about which is the best way. Our understanding so far is that certain cases, the ones with concentrations, are better. And in some other cases, it might be better to use it without concentration. So I just decided to present everything. Going back to this one, yes, yes, these are the average. I'm sorry. So basically, this is the average from all different roots of exposure. Correct. So if we look at it on page 7, our draft here. My apologies. If we go back, we have table 2 on page 7 in chapter 10. So anything we'd be looking at for comparison would be related to a mean or an average, OK? Not for the 90th percentile, trying to give us some foundation from which to operate. Bidness aggregate, it's not for individual sources at this point. This is not disengaged to dermal versus ingestion. The fact that those two columns are so different, are you saying that it's going to be a case by case basis for us to decide which one of the columns to look at? Or is there one that's, I mean, I don't know what. I can show you some of the other results that I have. And maybe at the end of the presentation, it might be clear what to do. Because I think that in certain cases, for the most part, the ones with concentrations make sense. The ones in which you may be in a couple of circumstances, you may want to go with something else. But for the most part, you use the concentrations. Yeah, and it makes things a lot more palatable in terms of reality, OK? So focus on the left, for the most part, and not on the right. Mike, is that what you and I agreed on? Absolutely, yes. We agreed upon that. In the final report, we're probably just going to focus on concentrations unless otherwise deciding that it would be worthwhile to include something else. The next table that I have over here compares our results with the warmoth paper. So as Paul pointed out, and I should have mentioned that before, our results are the mean aggregate values. And the two columns on the right are the 50th and the 95th percentile values that warmoth estimated. And again, these are comparable because these are total aggregate, whatever, exposures per kilogram body weight for women. And I have a graphic which kind of gives a better representation. So this is where we stand so far. This is like the overall thing. The three main ones that stand up are about the same that warmoth also found. The point is that this is for our mean, 95th percentile. So when you're looking at women of childbearing age, we've come with concentration data along, which is where I came down on being the most logical thing to do. Warmoth is not representative. Where does that data come from? Warmoth? Or me? Where does the warmoth data come from? It's German paper. What population? What's the population? Europe, I think. European data. It has actually various sub-populations. It includes children, infants, adults, women as well. So we've got a, this is a little bit different than ours. This is about the best comparison we have. Because again, his is modeling also. But his model estimates are much, much lower. The mean that what we have here, that's the point, is that he's missing something. But we can tell from our analysis. Do you know what's driving that major difference? We'll show you in a few minutes. We're trying to, what I'm trying to do is we're trying to keep us focused on the aggregate first because Chris was very concerned at lunch that we would be off in many, many different directions. What we're doing now is once we get this down some degree of coherence, we can go and look and see, well, where is this coming from? Why we're seeing the differences? Chris, is that okay with you? I'm serious because your points were well taken at lunchtime. Okay, this kind of shows the differences of our overall aggregate results if we include concentrations and if we do not. So clearly, as I've mentioned before, the numbers without concentrations are significantly higher. Now let's look at what really is driving these things. If you look at the pie chart on the left, that has the results with concentrations. And again, it's just kind of... Let's talk about that for a minute. Okay, all right. So roughly about 50% is DEP. About a little over 25% is DHP and then about 15 to 20% is DBP and the others are kind of fractional amounts. If we consider the three main, the three thalates that have the largest exposures, we then break it up according to what contributes and you'll see over here, for example, for DEP, it's completely ingestion, ingestion direct, which is nothing but food and beverages. I'd like to point out here that we have the maximum amount of data for food and beverages that we've collected. Well documented, good data, and it includes things right from drinking water to beer and wine, alcohol, and all sorts of food categories. For DBP, it's dermal handling. Dermal handling, some of the things that contribute to dermal handling include the human interactions with things like plastics, furniture, arts and crafts, items for infants and children, things like that. And again, DEHP, it's mainly food and beverages that kind of drive the numbers. This comes, DEHP, I'm sorry, DHP. I'm gonna skip this because this is the result when we do not use concentrations, but over here, you can just quickly say that it's mainly dermal handling that kind of drives and then ingestion indirect. It includes only one cosmetic product, which is lipstick, and it includes soil and dust. And of course, dermal handling, I mentioned some time back. Next, I'd like to, I'm sorry. Paul, did you have a question? Yeah, I think, which were the roots that we were most insignificant? Cosmetics, well, no. Paints. Paints. Paints. For each one of them, which was the one that were? Now, one thing. Well, there was one or two roots that are in trivial for all of them. I think it was paints, right? Paints, yes. But again, the thing that I'd like to point out about paints is we had very limited concentration data for paints, so that's, statistically, it's definitely not that significant. And in each case, inhalation can be quite, very variable. In some cases, it's high, in some cases, it's non-existent. To be honest with you, I was a little surprised. I thought inhalation would be higher. So did I, but we were called off guard, which indicates that all these phthalates have different roots of entry based upon the way in which they evolve from the products and are used by people. We decided to create some box plots. These show the variability of, this basically has all the exposures for the eight individual phthalates. It kind of gives you an idea of how variable the data is, outliers, whatever. And I've also put the number of records that we have. So you can see in some cases, the data is limited. For example, DIDP, I just have five exposure numbers. But that's the best that we could come up so far. The y-axis actually is total exposure and all the units are micrograms per kilograms on a daily basis. But I think what we're talking here is one to three orders of magnitude difference. Correct, yeah, that's it. I think that's the most important thing to note there that if you look at this, it's one times 10 to the zero down to about minus seven times 10 to the minus two in terms of median for, have a wide range of variability in terms of the exposure based upon the chemical alone. It's an awful large differential. That's a log scale by the way. The variability we see here is literally the same we see in the bio monitoring studies. I would agree that the variability is pretty consistent. Absolute magnitude, I'm not sure are similar. Some of the numbers are surprising to me. Partially higher or low? Higher. Again, putting this into the context of what we're looking at, some of these chemicals, much greater exposure. And it all depends upon how it's distributed among these women in terms of the frequency and the distribution function of the actual final averages. This is very similar to the previous one. Only over here, the data has been categorized based on the different exposure outs. And again, I have the number of records that we have. So for example, pains is just four. On the other hand, ingestion and direct, which is food, which I was mentioning sometime back, 48, so that's better. Again, you see the same kind of variability. But the mean between these are not that different. If you take a look at the mean values, they're all, in comparison, they're all within one order of magnitude. Variability can be quite large because you have ingestion and direct, which goes from almost nothing to above one. But the mean is within about one, one and a half orders of magnitude. So the contributions from the different routes are equivalent, given the uncertainties we're dealing with. Just a question. Are you talking about the results? We're talking. I'm trying to interpret what he's saying because he did not have the ability to hear what we're pulgar and where Chris was coming from at lunchtime, and I'm trying to fill in some gaps. I'm sorry. Thanks, Paul. And then I thought we could see the different exposure routes and see how the phthalates interact, or what sort of numbers we get. So if you look at cosmetics, it's just two of them out of the eight. It's mainly DEP and DBP. The others are, I mean, not even there. Again, paints, it's just two of them. So this kind of tells, gives us a little bit of, idea of how sensitive some are to the routes in comparison to the others. Again, over here, we can mainly focus on the two graphs on the left-hand side, because those are the ones with concentrations. The one on top is dermal handling, and the one below that is dermal internal, and the dermal internal is mainly, it includes adult toys, and it includes medical devices. So again, just a few of them stand out in comparison to the others. Injection indirect and injection direct on the left-hand side, and that's what we've done so far. I mean, we can clearly do a lot of analysis with the data. I mean, it's really good, but I just wanted to give y'all an idea of where we stand so far, just a quick snapshot of the results. If I'd just like to, for a minute, and thank you, because I know that you have been working extremely hard on this, and it's still a work in progress, and if you can imagine, I mean, I knew this was a big project, but it's always bigger than I think, and if you can imagine all the many steps in this search in the literature to get the input values, and then to get the exposure factors out, and we're going through this sort of layer by layer, and I think we found the errors in the equations like leaving out the concentration term or where the units didn't work outright, and right now, we're looking at things, for example, in the concentration data, if there's one study that's driving it up or down, and also right now, for some of the exposure scenarios, I know we have the right equations and the right exposure factors, but I'm not sure all the, they're all applying, that the exposure factors are being applied to the right scenario or vice versa, so I think we're close, but these are definitely not final numbers. But that's why I wanted to go back to that one slide where we talked across the different routes. Could you go back to that one? Even if we aren't at the final version, I'm not gonna see very much differential in terms of the median where each one of these routes reside unless there's some powerful study or powerful set of data that we've ignored, because right now, it basically says that we have somewhat consistency across the different routes with a few being a little bit more important than the other, but it's gonna be a matter of what the sources are. That would be most important. That's where the variability, I think, is going to drive the analysis, is the different types of sources that are contributing. Moving on to our ongoing efforts with infants, toddlers, and children, we kind of classified according to the age group, so we have infants up to one year, toddlers between one and three, and children from three to 12. Just a quick update of where we stand so far. We have compiled most of the concentrations specifically for infants and children, and this is in addition to all the concentrations that we have for adults or women so far. We've also combined a lot of the exposure factors for children. We have not yet done the calculations for the exposures, but we're very close, and we have some really, really interesting questions that we've kind of come across, and we would like to have the opportunity to discuss them with Mike and Paul, and I don't know if this is the right time, but I can maybe showcase one example. I'm actually gonna open up a Word document which has a list of questions, but we can talk about one of them. This relates to the inhalation exposure of infants and children. The thing that we are currently having a difficulty with is we know exactly the human activity patterns of the behavior of women, but how does that translate to the exposure that a child is gonna have or infant when the women holds the baby or nurses and stuff like that? So we know that it has to be a reduction factor, but how do we determine that? It's not something that's easily available in literature. We have to make some judgment calls. We've done a lot of literature search. We've consulted with people, but have not been able to come up with a good estimate. So those are some things that we need to address for this particular category. Just as an example for fragrances, we have a number, the number of times that somebody uses it, but then how does that translate to, how do we use that and say, okay, this is the amount that the infant would be exposed to as far as fragrances? Sort of like secondhand tobacco smoke. But third, well, a child who's breathing, breathing, well that would be tertiary, but secondhand would be the inhalation factor where you have material that's being evaporated off the woman's body. So you have inhalation secondary and you're right, tertiary could be the amount that's from contact. Blowing, salting, behavior. Yeah, so that's a question, how we handle that. Is a real situation. Right, that's mostly what I have for today. The things that need to be done, we need to review the data for what we've been doing so far with infants and children. And as Mike pointed out, we kind of need to provide the finishing touches for pregnant women. The last category that we have to address are adults, which would include women and men. And then we need to also address the data quality issues. This is being a challenge to say the least. We've tried to do a little bit with the results, but we kind of need to also do it with respect to the actual data, like the concentrations and even the exposure factors. So those are some of the outstanding things that need to be accomplished and we're hoping that we can work very closely with Paul and Mike and address those things and ultimately finish this project. One of the things, questions I have for the chap is we started, we said that the women of reproductive age would be the first priority. Infants would be the second. And then farther down, you know, maybe other children or in the general population. And I'm wondering, do we need all of those? I mean, general population was our lowest priority. I'm not sure we even need it. I mean, I wrote it in to cover all our bases. I would think to speed the process of getting these things finalized, we drop the adults and only worry about women of child bearing age and infants, young children. Is that, Phillip and Brent, do you feel comfortable with that? You want to leave the adults in? I think the pregnant women and children really parallel what we're doing in other parts of this discussion. So I think that makes sense. Got parallelism throughout the report. Good. Yeah, I mean, I think, of course, Serge is doing the work. I think once you do the other scenarios, doing general population won't be that hard. But if push comes to shove, we don't really need it. Yeah, and we agree with that. The one thing that I'd like to point out, and I think we might have missed this, have we thought about teenagers? Although women of child bearing age and pregnant women, they kind of, a lot of the studies, they start really early, but teenage boys, we've not really considered that. So I'm thinking, should we also exclude them? Well, I think when I wrote up the task, the thinking was that it's really prenatal exposures were the number one concern, and then neonatal after that. And then probably the others, I mean, at least the thinking was that they are less important, and that's how we did it the way we did. Of course, teenagers are, in fact, still developing. So I guess I would just leave, I mean, that's why we wrote the task the way we did. But that's really a question for the chap. You wanna weigh in on this? I can tell. You probably also have to weigh the time aspect of prioritizing and not being able to get to months, or? I was thinking in terms of what would, if we had the data, say on teenagers, what would that add to our deliberations? It adds another whole large group of women who use cosmetics. Pardon me? Maybe even more cosmetics. So what you're talking about in terms of exposure, you're talking about a very large user base, and within that, there's also, logically, some of them will be pregnant. So that part of it I might consider, but adults in general, I'd just ignore it. In, as far as Tina, I don't know what the chap would do on the hazard side. I mean, would you do that any differently? After having seen the interpretation of the first set, data set on pregnant women, I would say, we should first think about, think, talk about the data again, and put it into perspective. And I think after that, we might see that we don't really need to go further on that road. And in terms of the bottom line as to whether to ban, or deals with toys and child care articles, and the way they're defined in the statute, toys means for kids up to 12, or I guess it's up to an including in child care articles is up to three, so. That's actually my last slide, so I'm done. But if he has any questions, if you wanna look at the data, I have the spreadsheets. I'm more than happy to show anything. So one question I have is based on the discussion at lunch. Could you at least give us a top level view of how you went from all the different exposure routes to calculating a total? Absolutely. I'm sure there were assumptions you made there, but I missed it, because I'm... Now, I did not address those details, but I can show you, close this. All right, so we begin by collecting the concentrations. So we have all the concentrations in the form of a database, if you might call it, where we have the thalate exposure route, where it comes from, the media, the population that it addresses, and all the information that tells us where the data came from, how it was converted, and what the final units, what the final numbers are, and what the final units are. So this is the beginning. The next thing would be to look at the exposure factors, that is the human behavioral patterns. So if you see over here, all the data is categorized based on the different exposure routes. So if you look at inhalation, give you an example here. What we have are numbers for things like, what is the inhalation rate, and we have detailed comments about how these numbers were obtained. Most of them were obtained from the exposure factors, handbook, and the handbook kind of gives a lot of details about the study that was done, and ranges, variabilities, and things like that. So we have certain important factors, or parameters that govern the activities, the human activities. So we have them compiled over here along with our explanations. And then this data gets combined with the concentrations that we collected for that particular exposure route, and that particular phthalate or media in an equation. And the equations were something that we worked very closely with Mike, Paul, for example, this is the equation that we use for inhalation, and it tells you how we come up with daily exposures for given body weight. But how do you get behavior in there? So not everybody has the same, how do you... Well, the behaviors and the factors handbook are categorized according to subpopulations. So the numbers that they have are for women of a particular age group, and they've done various studies of the different activities on a daily basis and come up with some characteristic numbers or the distribution. Yes, and that's published by the EPA. And so these get combined in another spreadsheet. So for example, we have the concentrations over here or all the different phthalates that we have, this combined, and we've calculated the mean in the 95th percentile, we have all the exposure factors that we obtained, and these all get combined in that equation to give us daily inhalation exposures in micrograms per kilograms per day. And we have two estimates here, one which uses the average concentration and the other which uses the 95th percentile value for the concentration. Now we have done this for all the seven exposure routes, for all phthalates. What we do then is we kind of aggregate for each exposure route, we aggregate the numbers. So what this tells you is you don't have anything for DNOP or DINP, but these are some of the total exposure numbers for inhalation, for all the phthalates that we collected. So we do this and then the final step is to combine those aggregates across the different exposure routes and come up with this total number for each phthalate. So it's a step. So that's the table that we saw it again with? Exactly, yes, yes. So what if you were gonna take that to a higher intake? Evan, combine the 95th percentiles because as I said that have to be really, main thing I think is to leave the 95th percentiles disaggregated because otherwise you're gonna come, not everybody's gonna have the 95th percentile of all the different routes of exposure. It just doesn't add up, you may have one or two but the main thing is to show the distribution of the 95th percentiles across the different chemicals so that we know which ones can be most significant based upon the numbers that you get in each category. So based on your judgment, would you be able to come up with a ballpark figure for a high intake? For us the entire? Looked at different routes of exposure and said, you know it's largely food here so let's take the food and maybe, I don't. You can do anything you want. I mean the thing is that you can take the 95th percentile for food and that will be a reasonable estimate. But do you add that to the 95th percentile for cosmetic ingestion? No, I don't think that would be reasonable. But didn't your tables, didn't you have a table that said for particular chemicals? It's largely, this one's 75% food and 25% for the medicine. That was for the means, all right? All those, the results are from the mean value. So we took the mean values and then calculated the statistics out of that. The mean values, you have plenty of wiggle room in the means. It's when you get to the extreme. Did you use the same proportions for the extremes? Extreme values with the proportions of the means? It would not make any sense. Because not everybody, risk assessment, the way it used to be done years ago, is they'd say well who is the most exposed individual? Well they put some lunatic on top of a hill. They'd eat nothing but hazardous waste. They'd drink nothing but hazardous waste. They would breed nothing but hazardous waste and their offspring would have three heads. It made no sense because there was no logic behind that risk estimate. What you have to do is base it upon the most probable route of exposure where a person may in fact be in the 95th percentile. For example, with cosmetics. It would be a woman who would use cosmetics three, four, five, six times a day and do that every day or most days. And it'd be a small percentage of the women who would do that. That would be your most exposed individual. But can I translate that and say that that woman would have the same high exposures for food? No, they may be a vegetarian and they may only eat organic foods. So they wouldn't get the high exposures. That's where the issue becomes one of coming up with numbers that are reasonable for one route. And if you want to translate to another, you have to come up with a case study that allows me to translate that to another venue. In the meantime, you could always say the woman who had the very high exposures cosmetics would probably be around the mean with everything else unless you have something else to say that will make it higher or lower. That's where judgment comes in. But could it actually be, I hear what you're saying that you're gonna have green babies if you're not careful by having all the... But if the most outrageous extreme case puts us in a good position relative to a reference dose then we don't need to do further, we'll look, right? If we look at a very unreasonable, worse case and if it's close and if we're trying to create this margin of exposure table and if it's still orders of magnitude below a reference dose, then we're okay. That's a different question. If you wanna use it for a calculation to see in fact where your margin of safety is with respect to the extremes, you can always define that calculation according to the extreme numbers and just note that this is not what we're assuming is an exposure to an individual but this is something to see whether or not the reality of the most highly exposed individual could in fact exceed a reference dose. Yeah, you can do it that way. That's more than reasonable thing to do. But again, that has to be couched that you don't take that number and run it off with it in another direction. You're using it for a design value, which is fine. So it's a top tier number that if it's... If you really wanted to look further into it, you could make further assumptions and say, well, this is assuming you're high in one and not in the other thing that comes down to here. But I think just to get this into a... For your purposes, that's fine. What you want to do, it's fine. I can say that they could provide you with those numbers. Do you want to use a 95th percent top? I think we're going to do a 95th percent top. So that would work. I think we already have those numbers for women of childbearing age, right? Can you show them? I know we have those numbers by root, by root. I can do a quick plot if that would illustrate it better. This is for dermal cosmetics. Red is, of course, 95th. The horizontal axis is phthalate? Yeah, yeah, the individual phthalates. And that actually comes from here. Well, this is actually all DEP. Those are different products. Yeah, the products are different, actually. So this is by one phthalate for all different products? Yes, yes. Let's get our heads together. Is DEP... DEP only? By product. Yes. DEP and DBP, actually. There are two of them. You wanted the numbers? So if I was going to create a high, outrageously high value for high intake estimates, what would I put in? Okay, let's see. And that would be for DBP, DBP found in deodorants, or rather, exposure to pregnant women from deodorants. And it would be 1.03 to the power minus one, micrograms per kilogram, body weight on a daily basis. Sorry, say it again, 1.0. One, e to the power, so 0.1. 0.1 micrograms. Sorry, but you're saying that's just deodorant? Yes. You can sum them up for... That's what I'm asking. Can you do the sum for me? What you do is take the 95th... Yeah, I can do this. Take that plot. Get the plot back up. Can you just sum that column? Just sum the column, yeah. Yeah, I can do that. That's what I would do. Most outrageous for... Well, not across chemicals. Could you give it to... This is per chemical. See, this is the total for cosmetics for DBP. High number, 95th percent high, and this is for DEP. This is DEP, this is DBP. These are the numbers. So it's about what was it about? 0.8, both are 0.8. 0.8 micrograms per kilogram per day. Okay, but that's just for cosmetics. Yes. Now can you give me that with food and all the other things? Yeah, I would have to... And then I could sum all those for the crazy case. That's your worst possible, possible, possible, want it to be done, we could do that. You guys do that for me? No, we will do that for you. Yes, yes, absolutely. That's what you want for your extreme value estimate. For a margin of exposure table? We could do that. That now? Yeah, we could probably, right? Can we do that now? Yeah. Give me about six or seven minutes to do that, but I can do it. You take a break and let him do it and come back. It's a good time for a break. Okay, we'll do it. Very good job, by the way. Thank you. Thanks, Serge. This is 95th and this is 50th. And this is the total, these are the final numbers across all media, all exposure routes. And this is in terms of micrograms per kilograms per day. And just to kind of put it in perspective, if we take these numbers, if we multiply it by 70 kilograms, which is what we assume the average body weight would be, this would be the total exposure to phthalates in milligrams per day. Worst case scenario. And for TINP, it's not possible to calculate that? The reason I only did it for this is because these numbers, you see, they are the same. The reason being, we added these concentrations later, so we did not calculate the 95th. So we only have the means for that. So that's why I've just highlighted these. That's why you see the differences. There are no differences over here. Although I made changes to the calculations, that's because we don't have those, the 95th percentile concentrations for those. But again, I mean, we can definitely make, go ahead and change that. It would not be a significant difference to the overall because you see those three other ones that really drive the numbers. Okay, what we're gonna try to do now is get back to our assessment of phthalates and our recommendations and basically chemical by chemical and we'll start with analysis that Chris has done with a subset of the phthalates for which we have the most data. And I'll let Chris lead the discussion and her analysis that's been done on the fly. She's gonna present that on this. So this actually comes from, I'm sorry about the table getting all mixed up, but this comes from Andreas's ideas this morning about trying to, I think, think more qualitatively and not so quantitatively, maybe as a first look at things. So the idea was to have some kind of a margin of exposure table based on a ratio of the reference doses to modeling, this is now for the high intake and then there's another table for the median intake. Two different versions, one from the modeling data and the other from the biomonitoring data that Holger and I have been working on which is the 99th percentile from the NHANES data. So the next two columns there that are actually squared off is just those corresponding ratios. And then we were trying to make it more simple. I think I would propose to get rid of those next two columns but just so you could see the calculation, the last two columns that are all looking funky are trying to get to an idea of how many orders of magnitude above between the high intake estimate and the reference dose. So if you agreed the way we calculated that and I guess we could do that different ways but so for example, DEP had a, suppose we use a reference dose of 7,500, the modeling exposure had 582. So the ratio of 7,500 to 582 is actually 13 and we would say that's the rounded margin of exposure of one order of magnitude or 10 or should we, I'm not sure what that column should be. So do we call that a 10 or a? Just a little thing. You've based this on reference doses. It should be points of departure. Margin of exposures are calculated in relation to points of departure. Well, so in all cases it would be a factor of 100 off because we've used the uncertainty factors of 100 all the way down. Do you have to say that we made up the RFD of DEP? Right, so DEP may not be the good place to start. Those are ordered based on molecular weight so that was why it's at the top of the table. But would you like for me to recalculate them or can you look at it this way or? But Andres, my question for you is based on your discussion this morning, is that, ignore that the last two, the margin of exposure columns, just look at the rounded margin of exposure columns, if those were corrected based on actually a point of departure compared to the high intake values, is that the kind of feel that you were after that there are some chemicals that are much closer and others that are further apart and that might give us some guidance about where to go, which isn't, so there's a lot of variation, a lot of unknowns, a lot of all of that, but at least it's ballparks, which may be a good place to start. There would be cases when we could maybe have some information about hazard indices, but otherwise we, this is, it seems to me, I mean, I guess we have to figure out what kind of margin of exposure we're interested in if you multiply those by 100 each, but there are some pretty. So how do you want to use this, margin of exposure will work? So Andres could probably address that better than I, but the idea was that, I think if we focus in too narrowly on just looking at a hazard index or a hazard quotient, how close is it to one, there are so many things that can go up and down based on different behaviors, different, you know, different substitutions and things that we don't have access to. So the idea was to look at things, at least maybe beginning, more qualitatively and not so quantitatively. So although we're looking at numbers, we're trying to be ballpark numbers here. There's something else wrong there. Can I ask for five minutes interruption to? To fix it? Yeah, shall we do that? Yes. Okay.