Document Mm2MdaRZGm1jjRyg5y7LzLLy

BIO-MEDICAL RESEARCH DOCUMENT DESCRIPTION FORM 63 68 69 76 Duplicate in all cards:L-" /? "7-- year as-1961- File number [Right justify [Numeric only] Author(s), as Last Name FS (No Punctuation) and coden for journal as JAMA preceeded by one blank space 1 S&hn&./r/. 20 21 MA 40 41 77 78 Sub-Index Code 60 61 62 11 12 13 Title of Report; end with space-hyphen-hyphen-space. Follow with Index Terms, separated from each other with comma--space. Avoid other punctuation; do not abbreviate. 1 _2 7~a (3 n --------------------- --------------------------------- - pc $-QT'9:/'/J>A-D--0<ULu i~o 61 62 21 i /toss* 22 23 24 30 CO o o roo Sourrce (Journal, Vol., Number, Pages, Date) Brief Summary 12 10 SUMMARY: 61 62 61 62 63 64 0 TESTIMONY ON: PROPOSED STANDARD FOR OCCUPATIONAL EXPOSURE TO VINYL CHLORIDE udCd2?5 R&S 110003 BY: Marvin A. Schneiderman, Ph.D. Associate Director for Field Studies and .Statistics, DCCP National Cancer InstituteBethesda, Maryland 20014 r> - 1 'J T.COC A LtSSi - ,*CA $ cOj BASED ON: 2- 0 3 & Remarks made at the New York Academy of 2_o i Sciences Working Group on Toxicity of Vinyl . Chloride, Poly-vinyl Chloride, New York, New York^/'j . Ho 3,0 May 10-11, 1974 and to be published in The Annals of the New York Academy of Sciences jHe? yz> bo 7 ^ & 07 / ' ***- June 25, 1974 There are difficult problems in extrapolating the results of animal experiments to possible results in man. Small animal experiments done at moderate to high doses must be used to tell us about what is likley to happen in a very much larger population of humans exposed at (hopefully) substantially lower doses. The usual way to extrapolate to responses at low doses is to assert the existence of some mathematical model of dose-response and then compute the response at the desired dose or compute the dose for the desired response. Presently two classes of mathematical models are advocated for in-mouse extrapolation. There are the models that deal with yes-no data, the dichotomous data models. These are concerned with whether or not an effect occurred. . The data presented by Professor Mai torn' in his experiment BT-1 after 52' weeks of exposure and 130 weeks of observation are of the yes-no type: Dose (ppm) 0 50 250 500 2500 6000 10,000 ANGIOSARCOMAS Positive Total Animals Animals 0/68 0/64* 4/67 7/67 13/74 14/72 7/69 R&S 110004 * Later data reported one positive at this dose \ 2 The most common models used to fit such data are: 1. The Probit Model - which assumes a normal (Gaussian) distribution of sensitivities in the exposed population to the material tested, usually against the logarithm of the dose. 2. The Logit Model - which assumes a response mechanism similar to first order chemical kinetics with a damping effect at upper response levels as "receptor sites," (places on, or in, the cell where the reaction takes place), etc., are used up or become occupied. 3. The "Witness" Model - including the one-hit and multi-hit models assume that an effect occurs when a vulnerable portion of the cell receives n hits, n being some integer, from 1. Some multi-hit models have been used to describe the rapid increase with age of cancer incidence in man [1], The assumption is made that n assaults on the cell are necessary to begin the irreversible changes that later show themselves as clinical cancer. The one-hit model is often used in describing the dose-response for radiation effects. 4. The Extreme-value Model - attempts to describe rare events such as the number of floods that will exceed a given height during some long time interval. In a limiting form, the extreme-value model becomes the onehit model and is, in fact, a generalized "hitness" model without the constraint that the n be an integer. The major difference among these models lies in the estimated effects at the very low doses in which we are interested. The doses necessary to achieve Q low response.levels (i.e. 10 ) are substantially different depending on the model used. The different extrapolations that would arise from the flaltoni data (including data reported after the New York Academy of Sciences meeting, R&S 110005 May 10-11, 1974) assuming several of these models are shown in Table 1. CO 110006 For the logit model a slope of 2.303 (in logarithms to the base 10) was chosen because this corresponds to the one-hit model. A slope 50% larger (3.454) was also used to show the large effect of change in slope. Figure 1 shows these comparisons. Table 2 shows estimated response rates at doses lower than those used in the Maltoni experiment. (INSERT TABLES 1 & 2 HERE) The results shown in Tables 1 and 2 are of considerable consequence in establishing "safe" levels or in estimating responses at any suggested levels of exposure.- Thus, for the Sprague-Dawley rats, exposed for 1 year, 5 times a week, 7 hours a day (Maltoni's experiment BT-1) at 50 ppm, we would expect as an upper limit, any where from QG5 positives out of 100 animals exposed to 2.3 positives out of 100 animals exposed, depending on the dose- . response model chosen. At 1 ppm this range (per 100 animals) is .0024 to .047 again depending on the modfel chosen. These estimates, of course, are for Sprague-Dawley rats. What this implies for men, exposed different ways, for different time periods, probably exposed to other co-carcinogens, etc. is not obvious. A conservative view is that genetically heterogenous man is it at least as sensitive as the S-D rat, and these estimates should be looked upon as minimum risks to man. The other class of models is concerned with time to response. Using them to estimate safe doses is based on the obvious fact that everyone must die of something at some time and a new cause of death is important only if it will occur early enough in the lifespan to shorten life. A letter in SCIENCE- [2] summed up the arguments this way, "...the only relevant parameter in such discussions is how the average lifespan of a person within a given population may be affected by...exposure. How can...'extra deaths' per year... R&S 110007 4 be translated into reduced lifespan?" In the data reported so far by Mai torn" and others, information about ^ to appearance of the tumors and time to death of the animals has not been reported, so these "latent period" or "time-to-response" models are not applicable here. When Maltoni reports tumor deaths observed up to 130 or 135 weeks this does not mean that all tumors appeared only at the very end of the experiment. There are difficulties with both classes of models for low dose effects. In the mid-range of doses where the experiments are usually done, the data are usually adequately fit by any one of the yes-no models. An example constructed by Cornfield [3] shows how similar the three major models are over a 256-fold range of doses. If given perfect data, we can't tell these models apart in the 2 to 98% range of response, this says that the data you can get often tells you too little about the data you can't get, i.c. you still won't know with certainty what model to use. 'MEGA-MOUSE MANIPULATIONS Under these circumstances it would seem reasonable to attempt to conduct experiments in the dose-range in which we are interested and where . the usual models give widely different answers. This has led to suggestions for the mega-mouse experiment. The experiment will have to be mega-mouse to be capable of detecting low levels of damage (say of the order of 1 in 10^) and/or small reduction in survival, etc. To demonstrate, at a single dose level, that the response rate is less than 1 in a million, with say 95% confidence and with no spontaneous tumors in untreated animals, would require three million animals each in both a treatment and control group. For a carcinogenesis experiment these animals would have to be maintained on the appropriate regimens for a life-time (about 18 months to 2 years for mice). Obviously millions of animals cannot be started on experiments all at once,^^ t i J 5 experiments would have to be conducted sequentially with great care that animals are appropriately randomized and identified at each stage of the separate subexperiments and that appropriate controls are set up at each subexperiment, etc. To make certain that one had sufficiently sensitive animals it probably would be necessary to set up (relatively small) parallel positive controls -- animals treated with known carcinogens at suitable doses, .to show that the experiment was biologically capable of detecting materials at least as carcinogenic as known carcinogens. The numbers added v/ould not be great, but added precautions have to-be taken when knov/n carcinogens are t being handled. If we take into account that the untreated animals may develop spontaneous tumors, then to detect an increase of 1 in 10^ over the spontaneous incidence could require a study several thousand times larger than one in which there were no spontaneous tumors. . ..` .Even with unlimited funds to conduct these experiments, the probability that they could be conducted with no mistakes in handling, feeding, loss of animals or any other of the myriad of common laboratory errors that ruin the best planned experiments of mice by men, is very low. Purely logistical problems might guarantee failure. Such laboratory errors, considered as noise, could tend to overwhelm any signal present in the data,making it still more difficult to differentiate between treated and control animals. The statistical remedy is to enlarge the experiment by yet .another large factor increasing the risk of further blunders that could wipe put the gains of the increased experiment size. In this kind of experiment if one wanted to show "safety" of a material, quite possibly unconscious pressures would exist to do a poor job. The more errors made, the less likely it is to show a difference R&S 110008 6 between treated and controls, between signal and noise. Finally, at low dose levels results may be ambiguous, with confusion possible between spontaneous and induced tumors. Thus, we think the mega-mouse experiment is liable to end in a ditch. It does not have as much to commend it on second thought as it looked to have at first. IF NOT MEGA-MOUSE, THEN WHAT? In view of these problems, what are the prospects that the low dose proposed experiments/by the Manufacturing Chemists Association at the New York Academy meeting will provide useful information? We have computed, for Professor Mai ton BT-1 data, that a dose as low as 1 part per million is almost certain to have a risk of less than 1-2 in 10,000. What could an experiment at a dose of 1 PPM using several hundred animals show? Hardly anything? We would urge the MCA people to review their design plans. If they cannot design a potentially more productive-practical experiment perhaps they should abandon the whole thing Difficulties arise in> applying any mathematical model at very low dcse^^ Does the slope of the response curve in the mid-dose range hold for very low doses? Is the slope likely to become steeper or shallower at very low doses? If steeper, then any dose suggested as "safe" will be even safer than we think. On the other hand if the slope is shallower, then any "safe" dose will be less safe than we think. Armitage [4] showed that at high doses the curves flattened out -- that there seemed to be highly resistant animals. Is there symmetry in the dose-response relationship? If there is then we should look for flattening at the low end of the response curve, too. Mantel, Heston and Gurian [5] seem to have showed this. B&S 110009 MANTEL-BRYAN EXTRAPOLATION Nathan Mantel has suggested that in view of these many problems in extrapolation it might be useful, in attempting to compute a safe dose, to fix upon one of the models for which there seems to be adequate experience and a good biological basis, and to fix upon some suitably shallow slope, and with appropriate 95% or 99% upper limit of response calculations, extrapolate to a level which can be agreed upon as a "virtually safe" level. This approach is embodied in the Mantel-Bryan procedure [G] and in recent modifications [7]. Mantel and Bryan suggest using the log probit model, with a slope of R&S 110010 1 probit per 10-fold dilution. Both have been criticized. With respect to the model Richard Peto [8], among others, has suggested that it has little biological basis and that in the field in which most work has been done at low dose levels -- radiation carcinogenesis -- a one-hit model is appropriately conservative [9], Some of Peto's own work recently'would imply that a 2-hit the model would fit the data better. Such a model is less conservative than/probTt with slope = 1. The criticism of the M-B choice of slope runs in both directions.' There are workers who think it is too shallow. A recent review of about 180 papers in the carcinogenesis dose-response literature by the Franklin Institute DO] has turned up many examples of shallower slopes implying .that a slope of .1 is too steep. LATENT PERIOD MODELS - The models involving time to response, need more development. Most of them are concerned with mean or median times to appearance of cancers while clearly what we need is the time for 1% or 0.1% (or some smaller number perhaps) to develop cancer. If this event didn't occur until a late age then the risk, o however looked at,wou1d obviously be small. Gail [n] has pointed out that with one particular model of the distribution of time to appearance, a cancer with a median age of appearance (if there were no other causes of death) of 300 years (e.g. lung cancer) would nonetheless reduce life span for those developing it by about 8 years. There is also a psychological problem in working with these models, that perhaps someday will be overcome. It's the problem of dealing with ah unfamiliar currency. Is something which costs 72 rials expensive or cheap? If you are an Iranian you will have little trouble .working this out, but if you're an American tourist you will have to do some translating. How important is a cause of death which shortens average-lifespan at birth by 20 minutes-, or 2 months, or 2 years? Kow do you translate that into your own experience? As an aid., or as the first line in the tourist's-phrase book, please notice that all cancer, the second leading cause of death in the United States,reduces lifespan at birth, by less than 2 years. The time-to-appearance models are not free of model assumptions. The A1bert-Altschuler model assumes a log-normal distribution of occurrence times. Gehanj2 ] has shown-that this model implies unusual biological R&S 110011 behavior with a non-monotone hazard rate. The model that Peto and Pike and their colleagues prefer is the so-called Weibul model which is much better behaved, and does conform better to what is currently known about the biology. ' In addition the models developed so far are what the statistician calls "fitting" or "graduation" models. They fit or graduate the observed data, but it is not clear whether they can be extrapolated safely far outside 9 the observable range; they do ncit incorporate a "guarantee" that the response will be below a certain level. The Mantel-Bryan procedure does attempt to produce such a "guarantee." The latent period models have not only'inherent experimental (observational) problems but also some unsolved mathematical problems. It's not hard to observe superficial tumors in experimental animals, but what is the time-to-appearance of an internal tumor that kills the animal? That doesn't kill the animal? Do we have to develop experiments with serial sacrifice, designed into them? Again Richard Peto [13] has some suggestions on how to handle some of these data. Gail points out one further difficulty that the time-to-appearance models have brought more vividly to our attention. These are the "competing risk" problems. Suppose a material can lead to death from any one of a group of causes -- not just cancer. If this material shortens life, so that all the exposed individuals (animals or humans) die early before cancer is likely to occur it may even look like a cancer preventive. This is in the class of the cartoonist, Webster's, advice on how to keep from growing old -- walk in front of a bus. .More statistical sophistication is needed to handle the problem of competing risks. With yes-no models survival data as well as the simple "yes-no" response must be considered. Did the animals live long enough to develop cancer? Or, was the material so effective in eliminating all other causes of death that all animals died of cancer at B&S 110012 a.very late age? When first looked at /it appears that there are large differences in concept between yes-no models and the time-to-appearance models. However, Hoel and Chand [14] have shown how they are related. The Albert-Altschuler models, using a log- '. ' . 10 normal time to appearance of tumors correspond to the Mantel-Bryan choice of the probit model where observations would be made at one instant in time for experimental animals exposed to different doses. Similarly the Weibul- based models, favored by Peto and Pike correspond to the "hitness" models, R&S 110013 when animals are all sacrificed at one time, and observed for presence or absence of tumors. MORE TROUBLES No matter the model used, there still remain a substantial group of unsolved problems. One is the problem of interactions of materials. Beginning with the early initiator-promoter studies of Berenblum [15], research workers have been aware that materials affect each others effects and often do not operate in an "independence vacuum." .Richard Doll [16] has shown that in two important environmental exposures, asbestos and radon (in the exposed uranium miners), the interaction with cigarette smoking was multiplicative rather than additive. This is a frightening * prospect which implies that the problems'that follow the addition of new materials to man's environment may be much more serious than just adding another drop to the pool. Some work has been done on allowing for the "natural" incidence of tumors in control groups but there is not agreement on the appropriate procedure to use -- simple subtraction, the Abbott's correction, or a slightly more complicated "functional" method proposed by Albert and Altschuler [17J. From the experimentalist's point of view the most unfortunate effect of natural tumor incidence is that it substantially increases the experiment sizes needed to show differences between the treated and control groups. n Then there are questions of dose. Should an animal experiment be a lifetime feeding experiment? A series of pulsed-doses, with zero exposure in between? An exposure for a short time, followed by no further exposure? Exposure in utero? Exposure only relatively late in life? Each of these is meaningful and could have a counterpart in human exposure. How one extrapolates from one to the other is now known. ESTABLISHING ACCEPTASLE RISK LEVELS Applying the extrapolations from almost any model will entail problems and sometimes hardship. When people talk about levels they "can live with" they must be reminded that these could be levels that other people might die from. We therefore recommended that the appropriate "safe" level (i.e. a risk of 1 in a billion, or 1 in a million, etc.) be chosen after considering the following: , (a) Is the material already in the environment? If so, how extensively is it used? What are its economic uses? Its R&S 110014 health uses? (i.e. is it a medicine for use in a very serious condition? a mild condition? a self-limiting condition?). (b) Is it a new material? What will it be used for? What are its -I m^ * economic uses? Its health uses? For example, with materials now in the environment and of substantial economic importance, limiting risk of 1x10"^, reducing in subsequent 7 ft . years to IxlCf and then to 1x10 may be appropriate. For new materials not yet in the environment, the limiting risk could be set at 1x10 -8 , unless there are overwhelming health or economic reasons to indicate higher risk levels. The setting of a limiting risk level should be an "open market" operation. 12 In the early application of the arithmetic of the models, consideration can be given to how many persons are or will be, exposed to the material. If there are limited numbers of persons exposed to the material, as an initial "safe" level, possibly a risk level as high as 1x10" 5 may be permitted. This level will then have to be progressively reduced to a country-wide or world-wide, universal lower risk. This means a first risk of 1x10-5 for those "exposed," and perhaps 1x10-10 for those apparently "unexposed," (but who may even be exposed to a "rub-off" level much as the non-smoking person maybe exposed to the smoke from someone else's cigarette) This proposal involves a double standard and raises serious ethical issues as to whether seme portions of the population should be assigned higher risk suffer the losses -- so that other lower risk portions of the population might gather the gains -- another "open-market" question. Finally, ways are needed to include non-experiniental evidence in setting up safe doses. For Example, if man has been exposed to a material for a .long time (over 30 years is a long time) and no untoward irreversible effects seen, a v'ay is needed to incorporate these data into the arithmetic. No objective procedures now exist for doing this. Perhaps, as an interim measure until objective procedures are developed, experts could be called upon to modify the "safe" dose. This might be done by a change in the allowable risk level, i.e. a change from 10 "8 to 10""6 . Perhaps the experts should be restricted in making changes in the risk level to no more than 10.0-fold, i.e. from 10" to 10". OUT-MOUSE EXTRAPOLATION After the in-mouse extrapolation problem comes extrapolation from mouse to man - the out-mouse.problem. David Rail [18], in a recent symposium r &S 110015 13 on Statistics and the Environment, at the National Academy of Sciences, dealt with some of the issues very well. We will not attempt here to do more than summarize parts of his discussion, in place using Rail's own language. He points out the difficulties in extrapolating "from an inbred mouse strain to a genetically heterogeneous population, such as man." It appears reasonable (to us) that an inbred strain is likely to have a steeper dose-response curve than would a more heterogeneous population. Rail asks, "If a heterogeneous population is a collection of inbred strains, each with its own dose-response curve, what then would the dose-response curve be for this heterogeneous population?" . Rail also, speaks of problems of relative size of the experimental animals and man which lead him to recommend that doses are more likely transferable on the basis of relative surface area (or `2/3 power of weight) than on a mg/kg basis. He notes the differences in metabolic rates, ar.d in the ratio of blood volume to circulation time which would lead to materials being retained by man relatively longer than they are retained by the smaller experimental animals. If this is true, then equivalent doses of carcinogens could be more carcinogenic for man than for the mouse. Rail also raised questions of plasma protein and tissue binding with the remark that small mammals tend to bind compounds less extensively than man. Because hepatic metabolism is often of major consequence Rail notes that "in general, small mammals metabolize compounds more rapidly than large ones; in general herbivores metabolize compounds more rapidly than carnivores ...The entire area of metabolic disposition of foreign compounds is of major importance." 30 </> O oa o> R&S 110017 i `t The generation time of cells, their rates of repair processes, etc., obviously will affect malignant or mutagenic change. "In the mouse...the generation time of the rapidly proliferating cell population is about half of that in man, so that a cancer in a mouse is likely to appear earlier in a mouse's life span than in a man's." This observation has considerable importance with respect to the time-to-appearance models. We must recognize that experimental animals usually live in a controlled environment, with relatively uniform diets, temperature, humidity, exposure to other agents, etc. Man, as a free living animal,has a much more diversified experience -- some of which will make some of him more sensitive, more susceptible and more likely to succumb to environmental assaults. Some experimenters have argued that the appropriate experimental animal would be a non-inbred animal. Using non-inbred animals reduces the reproducibility of experiments, requiring larger experiments to give answers with assurance. Another suggestion is to conduct experiments in several species using inbred animals of each species and using results in the most sensitive.of the speci to extrapolate to man. In spite of all these difficulties, it still seems possible to extrapolate from mouse to man -- at least from the median mouse to some hypothetical median man. To allow for responses of the non-median man, for the more sensitive individuals of the species, will require introducing additional factors. It is less than reasonable to take a no-effect level in a relatively small animal experiment, (for the kinds of effects we are considering here, * 100 animals at a dose is a small experiment) and translate it to a TLV or some similar "safe" standard. 15 COST-BENEFIT, OR WHOSE OX IS GORED? The remaining issues considered here are ones in which the statisticians have no special competence. On the other hand there's nothing in their training to make them more than normally incompetent, so we think we can talk about these issues, as well (or as badly) as other scientists. These issues are the ones usually lumped together under the heading of "cost-benefit," or, in less elegant language "whose ox is gored?" There v/ere some direct references to this in the N.Y. Academy mtg. when Mr. Mazzocchi asserted that issues of safety v/ould have to be resolved by adversary proceedings. This disturbed some people who then proposed the alternative to adversary activity, cooperation between science, industry and the labor-unions. It seems to us that v/e really have no alternative to the adversary-type activities when different people have different interests. The scientist who believes that he can resolve all through the use of his science alone impresses us as being out of touch. Does this mean that no cooperation is in. order? Of course not. As statisticians, we could not work without the data on who was exposed, how much he was exposed to, and for how long he was exposed. These data must come from industry. To follow-up exposed people we need data from industry and union and government sources. Evaluating animal results, requires cooperation with our laboratory science colleagues. But cooperation does not mean suspension of critical faculties, or suspension of scientific disbelief. If our laboratory colleague has designed a non-productive experiment, we must tell him and try to help him design one that will yield something. If v/e have misinterpreted or misused data, somebody must tell us. (Usually somebody does.) And the telling R&S 110018 16 need not be too.gentlemanly, either.. We would urge saying a lot of these things out in the open where other people can see and hear, too, because these "other people" have a stake in all this - their lives, possibly. We don't find it too dreadful that two letters discussing an industrial R&S 110019 exposure to a carcinogen in a recent MEW ENGLAND JOURNAL OF MEDICINE [19] read like exchanges of "you're an expletive deleted." Some good might come of the exchange. As for "whose ox is gored", we have already referred to the ethical issue of choosing some people to be at risk so that some others (or "Society") might benefit. We believe a good shibboleth in cost-benefit discussions might consist of the Cornfield questions: "Cost to whom? Benefit fo whom?" The decisions on the balancing must be made with affected parties included no matter how hard it is to do. It's enough to sit in on a hearing on the possible banning of a pesticide and to see the Department of Agriculture on one side, and the Department of Health, Education and Welfare on the other to know that "The Government" is not a monolith and cannot make the decision, either. SUMMARY . . Where does this all leave us? It leaves us able to develop rather good animal data at dose levels that don't really interest us. That's a first-class highway that takes us where we don't want to go. It leaves us unlikely to be able to develop good data at "realistic" doses. To extrapolate animal results to man exposed at these "realistic" doses today requires assuming a mathematical model of dose-response in the animal, and conservative use of this model. Then v:e have to jump from one species to another in ignorance of the terrain of the landing site, i.e. the many species differences. However, wc will have REFERENCES 1. Armitage, P. & Doll, R.: The Age Distribution of Cancer and a Multi-Stage Theory of Carcinogenesis. Brit. J. of Cancer 8, #1, 1-12, 1954. 2. Trefall, H.: Extra Deaths. Science 182, #4114, 1973. 3. FDA Advisory Cmte. on Protocols for Safety Evaluation: Panel on Carcinogenesis Report on Cancer Testing in the Safety Evaluation of Food Additives and Pesticides. Toxicology & Applied Pharmacology 20: 419, 1971. 4. Armitage, P.: An Examination of Some Experimental Cancer Data in the Light of the One-Hit Theory of Infectivity Titrations. J. Natl. Ca. Inst. 23: 1313-1330, 1959. 5. Mantel, N., Heston, W.E., and Gurian, J.M.: Thresholds in Linear DoseResponse Models for Carcinogenesis. J. Natl. Ca. Inst. 27, #1, 203-215, 196 6. Mantel, N. and Bryan, W.R.: "Safety" Testing of Carcinogenic Agents. J. Natl. Ca. Inst. 27: 455-470, 1961. 7. Mantel, N., Bohidar, N.R., Brown, C.C., Ciminera, J.L., & Tukey, J.W.: An Improved "Mantel-Bryan" Procedure for "Safety" Testing of Carcinogens Covering the Case of Heterogeneous Data. Submitted for Publication to JNCI, 1974. 8. Peto, R., Lee, P.M. and Paige, W.S.: Statistical Analysis of the Bioassay of Continuous Carcinogens. Brit. J. of Cancer 26, 258-261, 1972. 9. Personal Correspondence 9 10.. Craig, P.: Carcinogen Dose-Response Relationship. Progress Report to the National Cancer Institute by the Franklin Institute Research Laboratories, Philadelphia, May 7, 1974, Contract No. 43268. Unpublished. 11. Gail, M.: Measuring the Benefit of Reduced Exposure to Environmental Carcinogens. Submited to J. of Chron. Pis., 1974. 12. * Gehan, E.A.: Estimating Survival Function from the Life Table. J. Chron. Pis. 21: 629-644, 1969. 13. Peto, R.: Guidelines on the Analysis of Tumour Rates and Death Rates in Experimental Animals. Brit. J. of Cancer 29, 101, 1974. 14. Hoel, D. and Chand, N.: A Comparison of Models for Determining Safe Levels of Environmental Agents. Submitted for Publication, 1974. 15. Berenblum, I. and Shubik, P.: A New, Quantitative, Approach to the Study of the Stages of Chemical Carcinogenesis in the Mouse's Skin. Brit. J. of Ca. 1, #4, 383-391, 1947. 0300 H S18U I * . 17. knowledge of some important species similarities and that makes the jump i a lot less dangerous. With respect to costs and benefits we are just * beginning to understand some of the implications of the arithmetic. We have begun to see that there are few, if any, good ways of totalling the costs or computing the benefits [20]. Cost-benefit may turn out to be another blind alley. Tomorrow and the next day we must do the appropriate research on species differences in metabolism, in the mathematics of the modelling and extrapolation -- as a minimum. The socially-related issues of what is an acceptable risk; what are the costs; what are the benefits must be discussed in the open, freely. This implies recognizing that someone's costs may be someone else's benefits. (My medical costs are my physician's source of living.) The inputs to the cost-benefit algebra are not well worked out. Our ways of working must include the adversary approach as well as the pleasanter way of cooperation. And today, we must get to precautionary decisions for man's safety and health based on the road maps from animal data -- inadequate as they are. R&S 110021 ********** 16. Doll, R.: The Age Distribution of Cancer: Implications for Models of Carcinogenesis. J. of the Royal Stat. Soc. A, 134: 133-166, 1971. 17. Albert, R.E. and Altshuler, B.: Considerations Relating to the Formulation of Limits for Unavoidable Population Exposures to Environmental Carcinogens. In Radionuclide Carcinogenesis by Ballou, J.E. et al (eds.j, AEC Symposium Series, C0NF-72050, NTIS, Springfield, Va. 233-253, 1973. 18. Rail, D.P.: Problems of Low-Doses of Carcinogens. Presented at the NAS Symposium on Statistics and the Environment. Publication in Proceedings of the Symposium. (In Press) 19. Beavers, E.M.: Lung Cancer in Chloromethyl Methyl Ether Workers. (Letter) Figueroa, W.G. and Weiss, W.: Reply to above letter. Mew Ena. J. Med. 290, #17, 971-972, 1974. ---------- --------------- 20. Chemicals and Health. Science and Technology Policy Office, National Science Foundation, 1973. U.S. Government Printing Office, Washington, D.C. #3800-00159. R&S 110022 TABLE 1 <% EXTRAPOLATED 'SAFE' DOSE LEVEL (PPB) USING MALTONI'S LIVER ANGIOSARCOMA DATA* (99% ASSURANCE LEVEL) Extrapolation Model Probit (Slope = 1, Mantel-Bryan) Logit (Slope = 3.454) Logit (Slope = 2.303) and "One-Hit" "Classical" Weil 10"5 RISK LEVEL 10-6 Uf7 10-8 None Specified 25] (225) 574 (553) 23 (21.4) 31.6 29.2 11.3 (73.0) (26.1) (10.1) 124 26.7 5.74 (119) (25.7) (5.53) 2.'3 0.23 0.023 (2.14) (0.214)(0.0214) - .-- z 500 -- (not possible __ --_ 125-50 (25-10) * The data as of 130. weeks of observation up through dose level 500 ppm, but not higher, were used for extrapolations since the response curve flattened out at higher levels. The numbers in parenthesis show "safe" levels computed from data developed after 135 'weeks of observation, which included 1 liver angiosarcoma at a dose of 50 ppm. R&S 110023 / / TABLE 2 ESTIMATED RESPONSE RATES: LIVER ANGIOSARCOMA (99% UPPER CONFIDENCE LIMIT TN PARENTHESIS) . MODEL50 Probit (Slope = 1) Logit (Slope = 3.454) Logit (Slope = 2.303) and "One-Hit" .012 (.028) .0041 (.0085) .012 (.023) DOSE (PPM) 25 .0055 (.013) .0015 (.0030) .0061 (.012) 5 .00059 (.0018) .00013 (.00027) .0012 (.0023) T .000041 (.00015) .000012 (.000024) .00025X-000470 yj Based on data from Maltoni, experiment BT-Is 52 weeks of exposure and 135 weeks of observation ay C0 K O O ii I 1 . ' FRACTION OF. EXPERIMENTAL DOSE GIVING RESPONSE RATE 10"2 ' - USING SEVERAL EXTRAPOLATIONS Response Rate JO"'3 1 a `~c " '* / 10"6 10 "5 10"4 < --:----- 10"3 10"2 10"1 _0 Fraction of Dose Giving Response Rate 10 u i . R&S 110025