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Safety & Health

Presentation: A Risk-Ranking Model - Application to Chemical Contaminants

by Dr. Barry Hooberman

(Slide)

DR. HOOBERMAN: Okay. Okay, once again I will preface this by saying this is a risk-ranking model. It has not been implemented into the work that Phares has done. This is a spreadsheet model, and I will take you through it and we will see if we can come up with a ranking that makes some sense.

(Slide)

Remember, we are talking about relative risk. You have heard that a lot of times. You have also heard that we have limited data, so we had to make many assumptions. We will document those and hopefully address those and refine those at a future time. And, remember, this is also a work in progress.

(Slide)

You haven’t heard this enough, but health risk is related to some function of health consequence score and an exposure score. And I want to caution you against a common perception. Everybody needs to remember that the health consequences is not the same as risk. You have to factor in the exposure.

(Slide)

Okay, I am going to go through our exposure. This was the subject of our last Public Meeting, so I am not going to spend a lot of time on it, but I am just going to summarize it up real quick.

We have our contaminants in the ingredients, so we get an initial exposure estimate of what a contaminant is in each ingredient. We follow it through manufacturing and we modify that level of exposure according to manufacturing processes that are used. Is there heat? Is there solvent? Things like that.

Then we come up with an estimate of the contaminant in ingredient in each of the ingredients in the finished feed. They all get mixed together. We total all the contaminants from each of the ingredients to get a single estimate of what the contaminants are in the finished feed.

(Slide)

Then we are going to, through some magic from Albert there on the bottom, and then we are going to -- I mean, go back and say that -- so then we start looking at the diets for swine, which was the subject of our last meeting. We had three diets, starter, grower and finisher, and we looked at -- they have slightly different ingredients, so we calculated the potential exposures in each of those diets.

Then we make certain assumptions for animals about exposures. Are they getting excreted from the animal? What’s the flow through the animal? Are they bio-accumulating in the animal? Things like that, in order to estimate some sort of animal effects.

I will tell you now that we have not finished addressing acute versus chronic effects in the animal or if a young pig is going to have a different toxicity susceptibility than an older pig. We are going to do that, but we haven’t done that yet, and so you are not going to see that in these -- in what I am going to present to you today.

Then you look through it. Okay, so the animal has had some exposure over its lifetime and then it is going to go off -- if it is a food-producing animal, it is going to go off for slaughter, and we need to make some estimates that about -- again, about the pharmacokinetics of the contaminant in the animal, how much is going to stay in there, where it is going to distribute.

When we generally look at residues, we look at muscle, kidney, liver, fat. Those are the main edible portions that we look at, for swine, anyway. For other animals, you might look at milk or eggs.

So we make certain assumptions of where the contaminant is going to distribute in order to try to get some sort of estimate of a human exposure is going to be. And, again, we are probably looking at long-term human exposures. We are not estimating short-term. And probably you don’t have too many chronic human exposures -- I’m sorry, acute human toxicity issues, from the consumption of meat, generally. Those are exceptional cases, I would think.

(Slide)

Okay, so here is something we presented in the last meeting, just to give you a quick run-through. On the left column, you see some of the ingredients that go into a starter swine feed, and then we can come up with some sort of estimate of the contamination level.

For Dioxins in corn, you could see 50 percent of our samples came up positive and the mean level is .0091 parts per trillion.

Now, for the stuff you are going to see today, we have made another assumption, and we are taking the worst case assumption. We are going to assume that 100 percent is contaminated, and not 50 percent. These are all things that we need to modify and refine the model, do once we get it implemented in Phares’s nice little model system.

(Slide)

Okay, this is just a quick summary. This is the manufacturing factors I am talking about, the modifying factors, you know. So we look at how corn, a process example in this case, corn is ground, so we are going to look at any way that grinding or chopping would affect the contaminant level.

There is a solvent extracted soybean meal as another contaminant. It gets ground, but it is also a solvent extraction that may affect the concentration of certain organic materials.

(Slide)

Then here is another example from our last meeting. You can see we started off with initial -- from the food contaminant program -- the feed contaminant program, I’m sorry, the FCP column. Those are levels that we found in data. And then the second column shows whether there is any processing effect scores, if it is getting modified at all. In this case, Dioxins don’t change too much. And then adjusting a final concentration based on their processing score.

(Slide)

Okay, just to focus where we are going a little bit, so we are going to come up with these estimates for each of the ingredients, total up the -- for example, the Dioxins in each of the ingredients that go into the swine -- say, a starter swine diet. And so to get an estimate of the total Dioxins in the diet for a starter pig, a pig on a starter diet -- I’m sorry -- and that is the finished feed.

Now, we also have, after that, some consideration about further processing. In transport/storage, generally for the most of the chemicals, that is not changing too much. For a microbial pathogen, that might change, depending on storage conditions. You may have growth conditions or you may have recontamination by rats, birds, things like that. So we have kind of left that out as a place holder: Should we have data that we can fill in and say “yes?” But these things may happen at a certain frequency and we can adjust our exposure estimates by that before it actually gets consumed by the animals.

(Slide)

Okay, and then we are going to move on, and as I said before, we are going to estimate animal exposures, looking at the diet, how many weeks they are on each of the diets, and the consumption of the amount of feed that the pigs intake and to get some sort of estimate of what the animals are exposed to. And then we are going to go on further and get some estimate what humans may be exposed to from consumption of those animals.

(Slide)

Okay, so here is a run-through -- very draft -- just to show you an example. The yellow stuff is a lower stuff, red stuff is the higher stuff. This is just the exposure rankings from what we ran through.

So you can see DON and PCBs are kind of low-end on exposures for both swine and humans. Down at the bottom, Fumonisin is a higher exposure. Remember, this is all just a relative ranking. We are not ranking this -- these are not straight numbers or anything like that.

We have categorized it. In other words, we have come up with some sort of numerical estimate. And then we group them into Categories 1 through 7 just to see how the data fall out. And there is the categorization that you see.

In this case, there is not a lot of difference between swine and humans in exposure. There are a few instances -- Dioxin is a little bit different, Dioxin is a little bit higher in swine than in humans. I am not sure what the other differences are. There are not too many other differences, actually.

(Slide)

Okay, so that is the exposure side of the equation. Now we will look at the health consequences side. Remember, health consequence scores are determined by a severity score and a potency score. You have heard that already numerous times. And then we are going to combine them in some sort of ranking system, and this, say, you know, high in severity and high in potency is going to give you a higher health consequence score.

(Slide)

Okay, so here is the ranking scheme that we have used up to now. Again, this may be changed -- we have to look and see as we go ahead and validate the model and see if it is making sense, passes the laugh test, so to speak.

So, severity scores are ranked from 1 to 10 for these chemical contaminants, 1 being you don’t see much, 10 being death. I don’t think I scored anything as a 10 at this point in time. We did have some cancer-causing agents, potential cancer-causing agents. Remember, swine probably are not going to get cancer because they will be sent off for slaughter before the end of their lifetime, far before the end of their normal lifetime, so it is unlikely that you are going to see long-term exposures leading to cancerous effects in swine. But you -- that is not to say you won’t see that in humans, thinking that humans are not normally sent off for slaughter most of the time. So there is the scheme that we used.

(Slide)

Now, for potency, what we did is we set up -- we tried to develop what we called an “AEL” -- this goes back 2 meetings ago -- which is some sort of Acceptable Exposure Limit. We tried to pick an acronym that was not used by anybody else because we don’t want to imply that it has a regulatory impact.

So we calculated what would be its equivalent, essentially, to an ADI in concept -- what is an Acceptable Daily Intake? ADIs, for those who want to know, are set by looking from animal studies generally, setting a no observable effect level and dividing by a safety factor.

So, we have our -- we tried to come up with some sort of ADI-like number, an AEL for each of the hazards. And then we just took the reciprocal because a high AEL is actually a low toxicity number. So -- but we wanted higher numbers for higher risks -- for higher health consequences. So we just did a reciprocal and sorted it out that way and found the ranking, and then grouped them into categories and gave us assignments of 1, 3 or 10.

(Slide)

Okay, so here is our health consequences ranking, swine on the right side this time, humans on the left, low health consequences on the top, high health consequences on the bottom.

There are a few differences here. Lead was a little bit -- swine seemed to be a little bit more susceptible to lead than humans are, so they -- I’m sorry -- less susceptible, so lead got a lower ranking for health consequences in swine than humans.

Aflatoxin is actually -- if you could follow the color scheme -- is actually a little bit lower ranking for swine, again because of the cancer issue, whereas Aflatoxin causes cancer in humans, so it got a little bit higher health consequence score.

I think we need to work on this a little bit better to get some differentiation between humans and swine.

Remember, we started with the assumption that humans and swine are going to behave similarly in response to these chemicals unless we had some data to show that -- something to substantiate that there is a difference. By no means have we exhausted our resources on that.

We need to go talk to a lot of veterinarians and animal scientists and see if those were correct assumptions and see where the problems may be. But this is where we are now.

(Slide)

So we have got a ranking in exposure and health consequences, and so far we -- so we can get an overall risk ranking for our chemical contaminants.

You can see what is happening here. Fumonisin is the highest relative risk that we found in this ranking. Aflatoxin came out in the second grouping as far as high, despite the fact that we are not really concerned with cancer in swine, but the exposures were sufficiently high to keep it -- to push it into a high overall risk number.

You see Dioxin in swine was a little bit higher -- quite a bit higher -- than Dioxin in humans. We will have to go back and look at that. I think that was the basis of exposure.

Now, remember, there are a lot of assumptions that go into estimating what humans are going to be exposed by. A lot of it is based on pharmacokinetics. And we certainly need to refine those estimates.

There are some -- there are a couple other small differences. You could see the lead is changed a little bit from humans, to a little bit higher risk in humans than swine, because, remember, when we went back to the health consequences, lead was found to be -- swine a little bit less susceptible to lead toxicity than humans.

(Slide)

So this is where we have gotten to so far. Here is a ranking of risk of chemical contaminants. I will have more to say on interpretation in the next talk, but this is where we are at.

I think that is my last slide -- yes.

Any questions?

(No response)

I know that was kind of a fast run-through, but remember this is our 3rd meeting on the model itself, and so we have covered health consequences and the hazards in the 1st meeting and the 2nd meeting we have covered exposure. So now we are trying to put it all together.

No questions?

(No response)

Okay. We may get you to 3:30 after all. And so there is one more talk.