The 1970s were an exciting time for the development and attention given to estimating
human health risk for environmental and occupational exposures. In the 1960s much
attention and concern was given to the epidemiological studies by Richard Doll on
cigarette smoking and lung cancer, Irving Selikoff’s studies of asbestos workers, Rachel
Carson’s book Silent Spring, and the adverse health effects of ionizing
radiation observed by the Atomic Bomb Causality Commission in Hiroshima. This all helped
lead to the creation of the Environmental Protection Agency (EPA) as well as the
National Institute of Environmental Health Sciences (NIEHS) in 1966 and its National
Toxicology Program (NTP) in 1976. Also at the international level, the International
Agency for Research on Cancer (IARC) of the World Health Organization began their
monograph series of the evaluation of cancer risks to humans in 1972. There have been
over 100 of these important monographs that include for each topic evaluations of
sources of the material under study along with evaluations of the toxicology,
epidemiology, and mechanistic evaluations. The monographs, however, do not attempt to
quantify the cancer risk. The EPA’s Integrated Risk Information System (IRIS) reports do
provide quantitative estimates of risk. There are now over 900 substances listed in the
IRIS directory of reports. There are now about 600 NTP reports from NIEHS. These reports
are the result of rodent tests of various chemicals. The typical study consists of 300
animals (150 mice, 150 rats) exposed to a maximum tolerated dose (MTD), 1/2 the MTD, and
a control group exposed for 2 years with thus 50 animals per dose group. What was of
particular importance is how one analyzes the rodent data and how to translate any
positive rodent effects to human risk particularly to low doses for setting
environmental and occupational exposure limits. The major risk assessment challenges in
the 1970s were high- to low-dose extrapolation and “mouse to man” extrapolation. The
primary focus was on cancer effects since they were of greatest concern from a public
health viewpoint.Back in the 1950s the idea of a safety factor approach to set acceptable limits on
exposures was developed. This was especially of importance for food additive policies.
The use of a factor of 100 applied to the highest “no effect level” in a study was used.
A factor of 500 was applied if instead one used the “lowest positive effect level.” The
use of 100 was based on the idea that a factor of 10 when extrapolating from animals to
humans and incorporated another factor of 10 to account for differential sensitivities
within the human population. Weil[1] proposed using a factor of 5000 from the lowest positive effect dose because of
uncertainties in animal to man extrapolation. Instead of a simple safety factor
approach, the log-probit method of Mantel and Bryan[2] in 1961 attempted to fit a function (the probit) to the observed dose–response
data and then to estimate effects at given low doses. This seemed appropriate for cancer
since it was not accepted that thresholds exist for carcinogen exposures. This helped to
set off a lot of varied activity in selecting appropriate dose–response functions for
the purpose of estimating low-dose cancer effects for regulatory exposure limits. The
logit function has provided similar fits; however, the choice of dose–response function
can make a considerable difference at very low doses.For extrapolation purposes, carcinogenesis data consisted of 2 types. The usual form
gives lifetime incidences at various doses for estimation of a dose incidence curve and
its associated error probabilities (eg, probit, logit, one-hit). The second type of
dose–response modeling is time-to-occurrence as a function of exposed dose. The
time-to-occurrence would typically be time of death due to the cancer of interest or
time of first appearance of the particular tumor of interest. This brought forward
competing distributions. Albert and Altshuler[3] considered the lognormal distribution. The other popular choice was the Weibull
distribution (Pike[4] and Peto et al[5]).What received the most attention has been the Armitage and Doll’s[6] multistage model of cancer. Mathematical algorithms were developed at NIEHS by
Guess and Crump.[7] In 1976, Crump et al[8] published a basic paper on the fundamental carcinogenic process and its effect on
low-dose risk. Kenny Crump continued to develop programs for multistage analyses after
returning from his year’s visit to NIEHS. These analysis have and continue to be applied
in various EPA projects. A review and analysis of the mathematical models of risk was
published in 1978 by Whittemore and Keller.[9] It should also be mentioned that during these years there was excitement and hope
that the salmonella microsome mutagenicity test could actually replace the animal
experiments (see McCann and Ames[10]). Since there is a reasonable correlation between animal cancer potency and
degree of mutation in the Ames test, the test could help to be used for priority setting
in animal bioassays. Also, an Ames positive assay adds to the strength of the animal
bioassay results.The question of extrapolating animal cancer results to man has been of considerable
concern. For a given compound, there are likely to be species differences in absorption,
metabolism, and excretion. Extrapolation of dose exposure from laboratory animal studies
to man seems to be best when based upon dose per surface area or equivalently 2/3 power
of body weight.For an extensive and detailed review of risk assessment as has been briefly discussed
here, one is referred to the study by Hoel et al.[11] The article presents the position of the Public Health Service on risk assessment
as of 1975.
Personal Reflections
The years of the 1970s were very interesting and exciting for those in the
statistical/mathematical areas. When I first came to NIEHS shortly after its
establishment, it was especially invigorating due to its connections with National
Institutes of Health (NIH) in Bethesda, NCI, and DCRT in particular, as well as
interactions with faculty and students at University of North Carolina (UNC). By our
close proximity to UNC, there was the opportunity to interact with their faculty and
to supervise some of their PhD student’s dissertation work. For example, Chris
Portier was a student of mine, and for his dissertation, we worked on the best
bioassay design for low-dose extrapolation. Chris stayed on at NIEHS after his
degree and much later became director of the National Center for Health Statistics.
National Institute of Environmental Health Sciences had funds for visiting scientist
who allowed me to invite Kenny Crump to visit for a year and introduce him to the
multistage model of cancer for which he successfully developed very important
statistical algorithms used by government agencies for cancer risk estimation. The
1970s were especially fulfilling by the number of committees working on cancer
reports. This especially included the NIH, IARC, and EPA. Working on these
committees provided the opportunity to make new friends and colleagues. This was
personally very satisfying for me. Finally, the 1970s were a time of challenging
research problems with rapid and interesting results in the area of quantitative
human risk assessment.