 Good morning everyone and this is the seventh meeting of the CHAP on fall 8th and hopefully it's lucky seven and just a reminder that today's meeting is open to the public. It's being webcast and recorded and I would just like to remind all the speakers to use your microphone so that the people in the room and the people listening in can hear. In about an hour, Lisa will be down for the lunch orders. If I broke up the actual body of the report we'd have put into fit, so yeah, that's fine. There is a contents just inside that first page. Are you ready? Russ? Chris? Are you ready? Yes. Andreas? Holder. Okay. All right. Welcome all the CHAP members again. Thank you all for making the trip. Some of you from long distances. What I'd like to do today is begin to finalize our document. I think there are some sections hopefully that we can do that. What I'd like to do with Chris is start with your section and Mike, if we can bring that section up on the screen, so that'd be section 2F has an index approach. And the strategy for today is to go through section 2F and 2E, the human biomonitoring, 2G, the scenario-based aggregate phthalate exposure assessment, which should lead us into then section 5, the criteria and section 5B, the recommendations. Okay. So the idea is to see if we can get through the recommendations and finalize those. And then tomorrow we will go back and look at other sections of the short report. So, Chris, you want to lead the discussion on your section. And now is the time to bring up any additions, corrections, ideas at the end of this discussion. We will have this section ready to go to the reviewers. Chris? Oh, that is on. Okay. I haven't edited this since our phone call weeks back. But largely you'll see the section may be a little bit redundant in the sense of trying to start with the choice of the approach for quantitative risk assessment. I think that's largely going to be dealt with earlier. But I think just to set it, I thought just a couple paragraphs would be fine there on page 37 as way of an introduction to the hazard index. At the bottom of page 37, the definition of hazard quotient, which is then used in the hazard index, because the figures now include hazard quotients, which will be, that just helps that definition. All of the exposure part has been moved to the Holger section. So this is largely just sort of setting up. The reason why we're using the hazard index, here's the hazard index, and then what data we're using it on. So on page 38, the NHANES and the Study for Future Families. And then the next section, summary description of methods used is just what chemicals are in the analysis. And then the three choices, the reference doses, description that moves on to page 39 that the three cases. And then the method section ends with the definition on page 39 of the margin of exposure. And the rest of the section is largely just trying to summarize what was done. A lot of the figures and things. Oh, I guess one thing to point out is we are using now the sampling weights in the NHANES data. So the inference then can be based on, you know, the U.S. non-institutionalized, well, population, but we're focusing on the age range for reproducible, you know, for reproductive age. That's for the NHANES. I'm not sure. What else? I notice under case one, you have highlighted other reasons. Do we want to either supply other reasons or delete that? That was actually addressing, there was a question, I think some of the, somebody internally reviewed it, I thought, and asked the question of why did you use Cortencamp and Faust instead of other papers? I don't really know why we use Cortencamp and Faust, except, I mean, honestly, Andreas presented that to us at one of our earlier meetings, and it included so much of what we were interested in, you know, why not? It really wasn't a comparison of their evaluation versus somebody else's. Is that an important question for us to sort of nail down? Are there other possibilities that we could have used? I don't think there are, are there? Are there other data sets? But then you're biased. Well, it was certainly a recent paper. There is a Benson paper. I haven't looked at the comparison of the two. That's a good point. Maybe Benson should be mentioned here, but there's no contradiction because both Benson and Cortencamp and Faust used the cumulative risk assessment method based on the assumption of those additions. But for the sake of completeness, I think we should mention Benson, Benson, the single-awesome paper. But I don't think we can, you know, we can't compare all possible papers. No. Maybe Benson, maybe I'll look back at Benson and see how it compares to Cortencamp and Faust in terms of... There are differences in the outcomes, but there's unanimity in terms of what approach to use. I think the latter is the more important thing here. Andreas, you mentioned that in your section. Did you not, their paper, the Benson approach or the Benson paper? No, I have to check again. My section was mainly concerned with going through the experimental evidence, not... May I make a suggestion here? Sort of for... It's a little thing, but just to make this clearer, right at the beginning of page 37, that we distinguish very clearly between those methods, concepts, dose addition, and independent action as concepts for the evaluation of experimental data as opposed to their use in cumulative risk assessment. So this distinction is made between the assessment on these concepts in assessing experimental data and their use as methods for cumulative risk assessment. For example, hazard index approach is one of these cumulative risk assessment approaches based on dose addition. There are... This distinction is important because the cumulative risk assessment approaches make some simplifying assumptions in comparison to when these concepts are used in the evaluation of experimental data. Maybe that could... I mean, it needs a sentence or two to distinguish that and make that clear. Are you talking about for the second paragraph? Yeah, yeah. Let's put some words to that right now. Yeah, give me five minutes and you carry on, give me five minutes and then we'll get back. Okay, just more of a clarification that there's the three cases, Chris. In reading through it, I couldn't really distill down what were the main differences between the case. I mean, is there a way to bullet or describe it better or maybe a paragraph saying how they differ in terms of, I guess, assumptions and how that would lead to maybe differences in calculations? Yeah, it's a good point. It's sort of a summary of the motivation of the three. And it's hard to distinguish the differences between them based on what's written so far, at least for me. The differences in the motivation or the differences in the values? The motivation, yeah. I think maybe Holger could input there as well, but my thinking on that is the case one is published, reference dose is published approach. It's just sort of based on what's in the literature with a little bit of variation because we added DINP and DIDP. I think DIDP to what you did. Case two, and I think the distinction is case one largely is looking at selecting a reference dose per chemical, not sort of relative to each other. But I think the neat thing about case two, it was actually Holger's idea to actually think about these as equipotent chemicals, which were comments that Earl Gray has made. And then with those assumptions, that really is sort of a comparison across chemicals, one at a time. So it's more of a set, an evaluation. So I think case two sort of has that flavor. And then case three is largely just representing all the work that the committee's done in terms of selecting reference doses. So I agree with you. I can try to write something that says we chose three cases largely because we wanted to see the impact of different values. On the analysis. And then maybe motivate what they are and then say them. Is that what you're thinking? Yes. And especially case three, there's not a lot there. But after you motivate them and describe them, I mean very briefly I would say would it, I mean then you're going to get results for the different cases. Do you then conclude that one is more appropriate? No, I think largely they are sort of in agreement. Qualitatively what the results are. Which I think is an important point to say it doesn't matter too much which of the cases you choose. I said it says in all cases studying, the hazard index value is dominated by DEHP. No, not three different methods. The cases are just different reference doses. Well I think because there was a rationale behind choosing these three different cases there's no problem in giving the arguments to it. And as Chris pointed out, we were motivated by several reasons to do this three case approach. One of them also was Thunberg's presentation when he said we have to give an idea of the uncertainty of the data. And we all know that if, for example, we look at the reference dose of DHP which is based on a study from 1954 that we have to have a look at more recent data and this is kind of reflected in this approach here of the three different cases. Yeah, we have to put it in perfect right. That was lost in the... Okay, but we need to capture this right now because if we go away from the table we're going to forget this. So let's start doing that. We've got the time. So what's the process? Who's going to do the edits and... Mike is going to do the edits on what's up on the screen. Okay. May I... That's a good start. May I make a suggestion? Should we collect the points for this section and then have a bit of a break, give drafting assignments to members of the panel and then reconvene in half an hour after the break? How's that suggestion? I mean I've got notes for three areas that want to be revised. Rather than go into detail and define the worth smithing detail here and now, why don't we collect the issue, go for a break, implement it, reconvene and then look at the product. I agree because I think that will save a lot of time. Yeah. Do I make edits and then email him to Mike to put back together? Mike's the most... Whatever's easiest. Okay, I think that's fine instead of... It's otherwise it's going to be hard. Maybe if you... Holger, your last comment was important too. Also that... Yeah, how... Did you capture that Holger's point, Chris? Why don't we go offline and let them work? We will do this together. Okay, and I wanted to make a point about case three. I think we ought to have included in the rationale there that we did that in response to the charge that we do a de novo analysis. Case three into a very different light. Are we interested in actually doing a comparison of the references across the three? Do we need to discuss that or is it just the matter of showing it? Again, it's a space issue. My thinking was we were going to make this a short section but if we want to go on and on, we could actually say, well, the EHP was chosen by them. I think it's in the... Is it all in the appendix? The table... Isn't the table here? No, I guess it's in the appendix. But my point was do we need to actually discuss... Let me back away. What I was trying... I think the value of the three cases is we don't have to sweat which of the values... Which of the reference doses for each chemical is the best. We're trying to see in the variability of the reference doses, do we have a significant change in the outcome? And we don't. So I don't think we need to say too much about a head-to-head comparison of why one reference dose may be better than the other, right? Not the comparison but the rationale for choosing the three cases. We're talking about a paragraph. We can do this in three sentences. We're talking only about a paragraph to tie things together in the beginning. Two hundred words. And then the conclusions... Twelve words. That's what we need. Okay, Paul, what are you saying now? You're saying we need to add something to the conclusion as written? A small statement about the conclusion in respect to cases. Why? Yes, adding something almost like what Chris just said. Basically that although the case is differed, the reference doses, et cetera, the bottom line result was similar. Chris said it much better but something like that just so it's clear. I just had a question about the process. So if we're reviewing Chris and Holger's section in the short report, should we at the same time look through what's in the appendix or leave that separate and not discuss it? Yeah, I mean maybe not now, but we need to make sure that there are no inconsistencies. And I think that we can probably do between the time we leave here and the time the report is submitted and the person who has written the appendix can do that and make sure those two go together. What you're asking to do is sort of exactly what we did with the exposure chapter, the whole appendix we reviewed about three and a half weeks ago. And that's why we came up with some changes in numbers because we realized some things that were in there were very, very old and we wanted to replace them if things are new and then try to come up with a level of consistency. So what you're talking about I think is perfectly feasible but not for now. Okay, any other comments for Chris on section 2F? So during the break they will compose corrections that we will then look at after the break. Then we will move on to Holger's section. We probably could have done this one first, but I don't know why I didn't second. Yeah, it's the alphabet. I don't know the alphabet. Is that it? Ladies first, okay. Now I just have to find the beginning of Holger's section. There it is. Down on page 26 on the bottom. So Chris and me drafted this section mainly for four reasons. First it was the novel approach to analyze the exposure in five different populations. That's the two enhanced populations, women and pregnant women. And three population subsets from the SFF study, study for future families. That's the pre and postnatal women exposure. And there we have a data set also on the exposure of infants from month 0 to 37. So with this data investigation we could compare the data with each other. We could compare this data set with previous published data sets from the US but also from other studies worldwide. We could have a look at the correlations between the metabolites within each population. And that's a very important step for the end. We could compare this data with the aggregate exposure data from Paul's and the Weiser approach, which of course is not yet finished because we just got the last data in a couple of days ago. And with this approach then we could pave the way for the hazard quotient approach, the individual risk assessment approach and the hazard index approach, the cumulative risk assessment approach which has already been presented by Chris. Correlations are relatively low. It just has to be acknowledged in the conclusion. There are correlations but none of them I don't think is any. They're moderate to low. It says that the sources of this stuff are all over the place. I think we might... Do we do it together? Do we draft some comments on these correlations together? Chris and me and you? Absolutely. Because I think this is also of maybe some implications for the external aggregate exposure approach. Yeah, which may in fact have more uncertainty because there's no coherency among the studies. One study that's been done to say, here are all these sources, here's the impact on the time scale and all that stuff. That's why the correlations are best. But I think some input from your side would be fruitful to help us. So we could draft something later on. I'll do it right now while you guys are doing the other thing. Yeah, that's one of the things... I'm just going to add to the conclusion. As someone who doesn't do this sort of thing when I read this, that's the one thing that I sort of needed to really understand your section, Hoger, was what do those correlations mean? It means there's a lot of uncertainty in terms of sources. And that would be great to say, I think. Does say that we have levels, a unified approach to addressing where the levels came from. Yeah, I think it would be... We'll add that to the correlation discussion right now. But I think it's also some strength that you're seeing in two different data sets somewhat similar correlation pattern. You know, I don't think we should... It's correlations are less than 0.5. I'm not sure if it's a pattern. Or if a lack of a pattern. The only thing you can say is that the low weight and the high weight and the molecular weight compound seem to have some relationship, but it's not very strong. Remember, it's less than 0.5. That means that basically it's explaining less than 20% of the variance of the entire data set. A lot more things out there. And I think saying that sort of thing is important. Well, I'll say that right now. And somewhere we've got to talk about the relationship between the biomonitoring data and the year data, the Versar data, and how they relate. I think we did that. No, I'm... Not correlations, but in terms of the ranking of the levels. In fact, that was the last... I think that was the last table that you got two days ago or yesterday. Where we basically compared the biomonitoring results to the Versar to the CPSC results. And you can say they're within an organ or magnitude, which is probably better than I ever expected in most cases. And is there a discussion about that table? That has to be added. That's the only thing that has to be added. Because we've got that. Are you there? Okay. This gets back to something that I think Russ published. Not exactly that. So in table five on page 36, I looked at it too quickly a minute ago. These are actually correlations of prenatal versus postnatal. And I think that's important in just the fact that this is spot urine. And on the same women, but from different points in time, down the diagonal they are correlations of the chemical with itself, but at two different points in time. And I think Russ has published analyses of multiple spot urines over time. And you end up being able to say, even though I think your conclusion on some of your papers was that spot urines are useful in epidemiology studies to track exposures. Classifying, yeah. So we're seeing that again. So maybe that would be an important point just to bolster the idea that there are limitations to the spot urine, but there is some validation as the word or something because of what we're seeing here. And one observation is when you look across the diagonal on table five, apart from the dibutal, all the other correlations are very similar, which, you know, you would think if there are different prenatal, postnatal sources, you know, that some may be closer to zero, some may be closer to 0.5, 0.3, but all of them are around 0.3 or so, which is, I think interesting, you know, basically telling us that irregardless of whether you're talking about DEP or DEHP, there's similar correlations and strength. Low correlations. They're low, but more similar to each other. Right. Given that there's such different sources. You know, for DEP, the sources are so different than for DEHP, but the correlations are nearly identical, really. But I think they're moderate to weak. One thing that we're missing, when the exposure occurs and the time when it shows up in the urine or in, you know, anything. We don't know did exposure occur on day X or day Y or day Z for all these different compounds and the fact that you're seeing here may be the residual correlation because there's a low level of constant input into the bodily fluids from phthalates and this is the baseline correlation which is about 9% of the variance. But if you wanted to go any farther, you have to link back these levels that are in the urine to the point in time when it occurred and then figure out how to establish a time course between exposure and dose. That is what is missing here is the time because biomarkers only give you, as you say, an instant time. If it's a 24-hour sample, it's only a 24-hour sample for that period of time backwards from when the exposure occurred and what remains. So it's not a simple analysis. There are a lot of issues with respect to time that one has to take into consideration but this may be the baseline. Over time, this is what you see is variance that's due to what stays in the body over a regular, over during a person's month in the life, year in the life. I agree completely. What I was just struck me as being odd was DEP, which is coming more likely from personal care products, has a similar correlation between the mother and the infant as does DHP, which is primarily coming from diet. I think a mother, you eat every day and the mother uses their personal products every day. And so therefore there's a constant baseline there and it's the fact that there's no peaks which indicate that there's really a lot of issues on elimination and transformation that are not understood. Metabolism is not understood. Think of it over time. People have a very regular pattern of use. Those patterns of use go all over the place. Can I just point out that there's something funny in the heading for this table? Shouldn't it read Pearson Collation Estimates for Estimated Daily Intake Values in mothers or of mothers? I don't know, there's something funny in that heading. And I stand corrected. It's not prenatal and postnatal, it's postnatal and baby. We had other tables that were preimposed. It is postnatal and with the infant, right. There were different categories. I didn't know which one was, I read it too fast. Yes, so you're right. We could say for estimated daily intake values in mothers, for postnatal values, does postnatal imply mothers? Like mothers? Eternal values? Postnatal values. Okay, good. I'd like to make a point to what Paul was just saying, because you used the word missing. Do we want to capture what you just said in terms of the section we have yet to write completely, the missing data part? Do we want to talk about that? I'm writing that right now, okay. Thank you. And then you'll have certainty later on. Paul, in your opinion, what do we make out of the correlations set of the high molecular weight satellites and low molecular weight satellites? In a way, it means that those who are exposed to one high molecular weight satellite are, with certain variability, also exposed to the other high molecular weight satellites. Yeah, I think that's about all you've been saying. We have to assume with some certainty that those who are exposed to highly to one high molecular weight satellite are also higher exposed to other high molecular weight satellites. It's probably very common, because these things are in everything. Not dealing with a unique source, which would, it was a unique source, we'd see a spike somewhere. Some compound would be way out of whack with everything else. Not, that's why you're seeing this low level. I think this might also be of importance for both the cumulative hazard index approach and also for the, let's say, worst case approach from your side, that we add up the 95th percentiles from the aggregate exposures. So, with some rationale behind it, because we can say the ones that are high exposed to one high molecularity today are also high exposed to the other one. That's fair. Any other comments for Hoger? Mike, is there an appendix for this section 2, Hoger, or no? No, this is it. It's in here. Well, it's, I think the, it's in the hazard index section, is they were combined. And if you chose to leave the tables in the main part, but we can maybe one table take to the appendix. But I've heard of a lot of people here that told me that they like the numbers. Looking at the numbers. 2B, are these, that take those tables? 2A, I guess, or the really fine font? Maybe we could put table one into the appendix if we need to the conversion factors. Mike, there isn't any additional biomonitoring data in tab seven, is there? Well, I think tab seven was sections FN, ENF, biomonitoring, and hazard index were originally one. That's why there's still one appendix. They were originally one document. Then we split them into two parts for the short report, the main body. So a lot of the, there is an equivalent. Yeah, well, there is a table one in tab seven, the appendix. It's a little more detailed even. Well, we need to sort of edit down this appendix, though, basically, because so much of it is in both places. I'm not sure what happened. I think the, I guess the appendix is going to be somewhat of a standalone thing, just a longer version. So it's, I mean, there's going to be some redundancy. In the appendix, you describe case one, two, and three with a bit more detail. I mean, I would almost bring some of that into the short part of the report, and then you may not need to repeat it. I mean, that's one way of, on page 14 and 15. Savings, 100 percent. After what I'm writing this, one of the things that I can say is that maybe the biomonitoring data is most useful in human or risk assessment, but it can tell us nothing about the sources that can contribute to the high risk, or identify the populations at high risk, in which. I think I disagree with that in the sense of, I think that the biomonitoring data allows representation of real exposure per person, and not, you know, hypothetical, everybody has a meeting, everybody has high exposures. So, and every, every exposure, so each woman in this case, you know, has her own mixture. And so you're seeing the distribution of, of how those different mixtures look across a population. So I don't think we could say we don't know anything about high exposures. Didn't say we didn't know anything. Represent, I'm going to read many cases that when you deal with them, when you're dealing with urine values, it's only going to give you a spot piece of data. Data could be at the tail end of one's exposure curve, it could be at the beginning. I have no idea what the range was of where the timing of the exposure was. I think that's true on a per, on a per person basis, but if you look at across a population, I think you are seeing, I mean, I think it'd be reasonable to assume that there would be some women who may have just had the exposure, and other women who are hours away from the exposure, but because you're looking at that distribution, you're getting that representation. But isn't that the mean? And if the representation is your averaging? You couldn't go back and say that based upon the mean value, the values we're using for our intake or for, are going to be representative of the person who's either got very low or high exposure. Say that for the fact? I interject, that's a confusion here between statements for particular individuals and statements for a population. But the point to take out of this, a home from this in my opinion, is that you're making intake estimates for entire populations. Right, I agree. So as Chris said, and I'm sure Holger will agree, that is possible if you measure spot urine concentrations for large groups of people. Otherwise, your reservations are, of course, totally valid. It's how we phrase it. And your other point is also well-taken and well-recognized, and that is that on the basis of those biomonitoring data, you cannot make inferences as to the sources. That's why source-root fate modeling is important. I just want to figure out exactly what this intake value represents. An average over a population. That's what I figure, it's an average. Could that be emphasized? I think it needs to be. I think that's limiting to say it's an average over a population. The middle of that distribution is the average across the population. But there are some in that upper tail, there are people who have women who have high exposures. Now, we don't know where it came from. We know it's there. But the neat thing about the biomonitoring data is that we can see how these chemicals are together in the same person. We don't have to make assumptions across chemicals. That's something that we have a problem with otherwise. I'm still seeing how what you're saying is different than what I'm saying. When you say, maybe I'm just reacting to when you say on the average, it makes me think that all we can interpret, maybe that's not what you're saying, but I'm thinking all we can interpret is the middle of this distribution. And I would say no, we can look at the tail of the upper tail and the lower tail. But the numbers that we're using to find the hazard index are based upon what? A summation of the hazard index for each individual. Each person is their own hazard index. Right, and then if that's the case, and what you're coming up with is a hazard index for that chemical? For the set of chemicals. So therefore, what do you get? And a hazard index distribution for the population. Right, but that's the key point. That's the key point. It's a hazard index for the population, but what is the value that you're representing? When you sum this across, when we sum across doing a, when we're doing a cumulative risk assessment, are you summing the 90th percentile, the 95th percentile, the 50th percentile? No, see, okay, no, see, that's, okay, here's the important point. What's in the numerator here is actually the daily intake estimates for each woman. So each woman has their own hazard index. It's not based on the population value of exposure. It is based on the concentrations on urine from that woman. And that woman had multiple concentrations from the different chemicals. And so then those are then weighted with the appropriate reference doses, and then summed up. So each woman has her own hazard index. So that's, I think, the really cool thing about this, which is different than what's the typical hazard index approach. You're basically using all of the values, rather than a median or a 95th percentile, or you're using each value for each woman. So if that hasn't come out, that's the big point, right? If that hasn't come out, then we need to really- That hasn't come out. To like, demonstrate the calculation or do something very basic to get that really clear. They have the benefit of just explaining it. Can we go back to the 2F with a second? On page 28, you do say that. I took a while to get to it. It's there. Not on page 28. Under dose extrapolation. Yeah, this paragraph could probably be raised better. But it is there. It is there, yeah. But it took a while for me to- Should it be in that section or back in the- 2F. Yeah. It is something I drafted, but it should rather be in pre-section. 2F, where is that? We're here. That's afterwards. I'm telling you, this is- But that has to be set up later. The whole section is the exposure section. But I think that maybe, I think what Andreas and I are probably saying is that it needs to be stated here, and then it needs to be stated again. Because if it stayed here a little bit more clearly, then when you go later on, you feel more comfortable. Because this is where it's calculated, right? Where the calculation is done. This is when the daily intakes are estimated, but it doesn't- It's not summed or anything in this section. No, no, but this is what you- But the whole point of what you just laid to me was that the point about the fact that this is where the daily intake is calculated in the district. So maybe just a little bit clearer. Again, two sentences saying at the beginning that, you know, this is how we did it. Which is exactly as you described it to me. That makes things clearer. But that indeed is the difference between Chris approach and mine in the way we present the data. If you look into my tables, the 95th percentile for one of the late is not the same as the 95th percentile for the other in terms of it's another woman. It could be- it is not the same person who has the 95th percentile there and there while in Chris approach the 95th percentile is the cumulative exposure of all of the late exposures for the individual woman. So it could be a maximum exposure for one of the late and the 50th percentile for another of the late and the 25th percentile for the others. So that- that's- Now you're just confusing me. Sorry. Well, because Holger's looking at individual phthalates in this section and Chris is doing the cumulative part. Right. So I think what Holger's point is the 90th- there is a woman, there is a woman who has the- at the 95th level, 95th percentile level. And in terms of hazard index, right, by taking a multiple, you know, a multivariate distribution, putting it down to one index, you can see, you know, it's only one dimensional then, right? So that's different than on Holger's table, where you look at 95th percentile of one chemical and 95th percentile of the second chemical, that's not the same person. No, it shouldn't be. That's what the correlation matrix is saying, too. It ain't because otherwise it would be much higher correlation. If the- if the 95th percentile and the 75th and the 50th are all the same, for each- for every woman at the same one, well then the correlations would be much higher. Scatter indicates that the 95th for A, B, or C is distributed among other women. That's what makes the correlations low because you don't know what the source is or you don't know the time course of the source is to when you see the expression of the compound or metabolite in the urine. It makes sense while we're seeing 0.3, 0.4, and we'll see the scatter plot. Holger and Chris, do you think you can come up with text that will clarify the two sections? Yes. I was kind of expecting you to say no. We're up for the task. Something that Paul and I can understand. Yes, our limited ability to bars to the trees. No, actually what I'm trying to do is I'm trying to get it to the public because we're going to have to explain this to a series of people who are going to have to make recommendations that have to understand this beyond what we can as scientists. Out those kinds of pieces of art for people to wade through this kind of detailed analysis, especially when the data sources are so, so different and sometimes disparate. Yeah, let's do that. Holger, here's some comments. This is probably a good time to take a break and do some writing and we'll come back in at 11 o'clock. That's for this. Okay. Well, if you want to do your whole section, that's fine. Either edit your section would be, well, I guess it depends on how much stuff there is. If it's, if you're scattered around, if you could send me your section otherwise, send me a paragraph just so I know where to insert it. That perfectly clear. Mike, while you're doing that, I think maybe we'll go on and talk about Paul's section, which we can do from our hard copy. Okay, so if the chap would turn to their page 43. Yes. Were you making some changes with Mike? Yes. Yeah, you want to talk about those? Well, they're mostly additions because we have to put some framing around the non-valley substitutes. Okay, so that's primarily what we've done is added some text to the introduction to the results and whatever to enter the conclusion to dealing with that particular issue, those that particular issue. We also, I'm also adding text, also add text for the table that was given us yesterday on the comparison between the intakes that we calculated by estimating exposure versus what has been seen in the actual studies. And the primary result there is that the results are within the factor of one order of magnitude, which considering the estimations and the way in which biomonitoring results are obtained spot samples, which is not too bad considering a first shot. If you want to improve that, we got to get more data. And I think that's the summary of what we're saying there in terms of the value and the use of the data can be for a risk assessment. But with the qualifiers that there are uncertainties and uncertainties in terms of unknowns because no one takes the data, no one gets the work done. You know, we hear a lot of complaints about the lack of real exposure data. Well, that's not the fault of the chap, it's the fault of the community not doing the work. And I think the more we remain uncertain, the harder it is for us to reduce those uncertainties in a timely way. So that's one of the points I've made in terms of the chapter. I think we've done pretty well considering the limitations that are associated with the data sets. And it does frost me that we don't have enough exposure data. Beautiful voice there, nice and clearly to understand. Do you think I'm okay now? My Mick Jagger approach is going to work. Very well, very nice. It suits you so well. Anyway, back to seriousness. I think that's where we are right now and I think that's the best we can do. And I think what Mike is doing, he's going to be making changes. And that additional table I think is valuable for the cumulative risk assessment that's going to be done later on so it can be referred back to in the report. In addition, just to let you know as I did write an uncertainty section. Yep. And I think it lays out some of the critical issues that need to be dealt with. And from the standpoint of the non-thalli substitutes, again, the cry is for data. Because what we're doing right now is we're just working on one route of entry. That's non-dietary ingestion from, you know, teeters and soft toys that people can suck on. And that's insufficient to make any conclusions about exposure. And especially from cumulative exposure, we can't make, we haven't got a prayer of making any comparisons because we don't know how the non-thalli substitutes would compare in terms of just which sources they would be in, how they bind to those sources, and things of that sort. And the nature of the frequency of the eye contact of kids with them, okay? We are progressing toward an end, and we should have by the end of today or tomorrow text to match the comments that I just gave you. Any comments from CHAP members? Are we set on this order being biomonitoring data, the hazard index, and then this section? Yeah, I think so because that's real data. At least you have something to hang your hat on. I mean, we're making estimates. They're good estimates as best as you can make with the data we have available. But the biomonitoring, I think, is a tone that it's real data. And it's a lot easier to start interpreting from real data in comparison to the quote quote, the hypothetical that's based upon a lot of indirect information. Should that sort of be a preamble to this section describing, I mean, I just, I'm looking at the page prior to this as margin of exposure estimates, and then we go straight into air products and toys. I mean, maybe there needs to be a transition. How's the reader supposed to read this? Is it relative to what they've just read in terms of biomonitoring? Is it, what's the objective? So what do you say, Chris, do you want to have the hazard index approach after this? No, I don't know, any order is, I think this order is fine. I just think there needs to be more of a transition between where we are with biomonitoring data and in the beginning of this. I mean, do we just need to have a little, I don't know if it's. I would like to support that idea. I think the scenario-based exposures in G should either be before or after E, the biomonitoring, and then the way is paved for dealing with F hazard index. Yeah, I think before might be a reasonable way to go. Yeah, that's what I mean. I think before the hazard index might be fine. That's right. But right after biomonitoring would probably be wise because then you don't have this disconnect. Yeah, I agree, totally agree with that. I thought that's what we agreed to the last time. Maybe I was out of the room. I think, well, I was out of the room. I thought we had it that way and I suggest changing it. You were to blame. Now wait, but what was just suggested and what you just said, I think we're two different things. Why? Because I think we just suggested that your section go before biomonitoring. Before hazard, but after biomonitoring, because biomonitoring is the real data. But I think biomonitoring needs to go right before hazard index, doesn't it? Because those are really tied together. The alternative would be to put pole stuff before biomonitoring, then biomonitoring, then hazard index. That's what I thought that could be done too. That's what I thought, I think that's the most reasonable way. Okay, but the only difference will be that in our section now we include this new table that actually compares the biomonitoring data to the exposure calculated data. If that's okay with you, we're fine. No problem, it can be taken out and then put at the end of biomonitoring in a special section. Okay, that actually would be good. Take the text that I just wrote up, which is fine. And take the table and then put that at the end of the biomonitoring and that solves both of your problems and say how to link it together. The text I just gave you, instead of being at the end of the results for this section, take it at the end of the results for the biomonitoring section for comparative analysis with the estimate exposure. I'm okay with that. Yeah, that would then pave the way for the analysis with hazard index, etc., and plus. I think there's a real novelty and the striking thing is, and I've never seen that in that clarity elsewhere, that both broadly speaking, exposure assessment methods yield comparable results. Okay, no, that's perfectly reasonable. That makes a nice, consistent flow by taking that one section out and moving it. Okay, I'm easy. It's already been written. So, we want to have biomonitoring, then exposure first. No, exposure then biomonitoring and then exposure, biomonitoring, hazard index. And then the one table, which we were going to add to exposure, put the end of biomonitoring with the text that I just wrote for that table transferred also, okay? Okay, got it. And there's some grammatical problems I had with some of the text that I'll point out to you that I couldn't make sense of a couple of sentences, so I'll point this out to you. That's fine, I probably wrote it in my sleep. Yeah, so I used to do my mathematics problems many years ago. Well, it's like they were written by a left-handed person. You got it, all right, but seriously, I think this sounds a lot better for how we want to establish the basis for which to, I think, do the risk assessment in the end if we go each one of those. And actually, what it does also, and thinking about it, it does lay the foundation, which we explain in the text, that the phthalates and toys are a small part of the overall exposure. So saying that first, then going to biomonitoring and then going to hazard index at least lays a foundation as to what we're dealing with in terms of the percent contribution of the phthalates and toys to the overall process, which is explained in that section. So, yeah, I think that makes a lot of sense. Mike, you will shift those around for us. Yeah. Now, anyway, I'm not sure exactly where the changes are, but this is... You want to point those out, Chris? Well, before we do that, though, I'm just thinking about what we talked about with the biomonitoring data. We had a discussion, I think, added at the end about correlations. And now we're saying at the end of that, we're going to put a comparison of a table that has a comparison of means or whatever, point estimates across the biomonitoring data and the exposure modeling, right? That's reasonable. Do we need to say something about... There's going to be similar point estimates within borders of magnitude, whatever. Do we need to say something about correlations or is that going too far? Didn't we say we couldn't really go that far with the biomonitoring? We can't talk about the sources. That's the key addition to that correlation is we can't talk about the sources because of the issues of space, time, and not everybody who's high is high with the same chemicals. So therefore, I think it works. And I think... Is that in what you wrote, Holger? Yeah, yeah, I put that down there. There was an addition that Holger and I worked on. OK, so it is there. And then we can go on to that saying, actually you can add, even with these maybe connecting set, even with these issues, all right, for the results we have, this is what we found in table X, that there is comparability, or at least some of the work. Mainly it's the average for this comparability because not everybody did the 95th percentile. We... Pardon me? That's it. Yeah, that's the addition. Is there any value in calculating hazard indices for the scenario exposures? I mean, it can be done. What do you mean? Well, just as you calculated a hazard index for the biomonering data, you can do it for the exposure assessment data, too. You want to add that? Can you do that? Chris? Yeah, I think you could, because you have those data in the summary table. I mean, the question is where to put it? In the same section, in the biomon... Any hazard index section. OK. If you can... Actually, that was... I'm not sure it's necessary. OK, here's why I would say that it's not necessary because it seems to me the logic is the evaluation of the exposure modeling seems to be very comparable with however we define comparable to the biomonering data. The advantage of the biomonering data for the hazard index is that you're actually representing, you know, the true mixtures per person. And so with that, then we go in and do the hazard index. So then to go back and say, well, we could then go back and do it the whole standard way. I don't know, it doesn't seem to fit the buildup of the decisions and the advantages that we've argued up to that point. I mean, I guess I'm just thinking if you wanted not so much the total, but the... If you want to do a source by source. Yeah. But you've already got, I mean, you've already got exposures from different sources. As a reviewer, I would think I would be left short by not having that done. And why didn't you do that? For my reviewer, I would say, I don't know why you did this. I mean, I would say, what advantage did that give you? I don't know. It's closure, completion. And if we did it by source, that would make it even more interesting. In fact, one of the viewers might come back and say, why didn't you do the hazard index by source since one of your charges is to look at how much hazard is being provided by children's toys compared to everything else. I see both points, but I would also be inclined to say that using population-based monitoring data as a richer data source to construct the hazard index stuff. Yes, I agree. So we should stick to that. But wouldn't it be a mid... On the other hand, the points you made, Michael and Paul, they emphasize the one advantage that stems from exposure-med modeling and that is you can be more specific about the sources. But to calculate hazard indices for specific sources is a little, shall we say, artificial. They see, because we haven't got a way to validate it. So here's my proposal for a middle way. We could have a paragraph, it could be quite short, which outlines and emphasizes the complementarities in these two approaches. So the complementarities, sorry, the advantages and how they complement each other. So it could say that the biomonitoring is more holistic and has the advantage of analyzing individual data and then population-based, but it does not get at specific sources, which is precisely what exposure modeling can do. And then with that in mind, we did an estimate on the hazard index for the sources. I would probably leave that out, but then the one key bridging assumption or the key point to point out then is that both exposure modeling and biomonitoring yield exposure estimates of comparable magnitude. We just don't know which sources are important, although the exposure estimation suggests clearly that it's diet. That wraps the whole thing around into a knot because the exposure modeling, why'd you do it? Well, it tells you that the biggest source will be diet and that you can't get at all from the biomonitoring data. So in that hazard index paragraph, once we wrap it all up, yeah, that's worth it. That's better than doing a, I think that's better than doing a hazard assessment or hazard index for the model data. But we do bring it and wrap it around by saying in the end that these hazard indices are being dominated by diet. Okay, that's fine. Recommending when we have that table that compares the two approaches to have a paragraph that kind of wraps it up. Yep, and I think that worked out really well. And who's going to write that? One, I can write it once we get it, once we get all the knots tied on it. I just have to see what it looks like when we change, we move that over to that section so that I don't say something that's silly when it's already in the section. It'll be right after this, but we have to wait until my friend here puts it in and puts the table in and then I can write that wrap around paragraph. It's not even a paragraph, it's a few sentences. But that's fine, I'll do that. I'll revisit that this afternoon, hopefully. Yep. Am I clear about that? I will do that. Still clear. Thank you. Are you happy with that, Chris? Yes, yes, that sounds good. Okay. Mike, should we break for lunch now and then come back and tackle Chris's revisions? Sure, I think it's the perfect time to break. Wouldn't you want to come back an hour? Yep, an hour. Okay. 130. Back in an hour.