Arsenic in Rice: Just One Victory

Breaking the Mold

When arsenic in apple juice became a public issue in 2011, the FDA needed a risk assessment.  It was generally presumed by agency management that the toxicologists who worked for the Center for Food Safety and Applied Nutrition should produce a proper risk assessment; meaning an assessment that conforms to standardized EPA guidelines.  But, there was a problem: The most recent EPA (2005) cancer risk assessment guidelines don’t even begin to work for a naturally occurring toxicant like arsenic.  If they did, the EPA wouldn't still have a cancer slope factor that hasn’t been updated since 1988.  If they did, the EPA wouldn't have needed to contract out the cost-benefit analysis for the 2001 Arsenic Drinking Water Rule.  If they did, the FDA wouldn’t be in the position they were in.

So, for arsenic in apple juice we did something else instead (Carrington et al, 2013).  A similar analysis was released last week on the topic of arsenic in rice (FDA, 2016).  While the dose response analysis in those assessments doesn’t conform to the 2005 EPA guidelines, it is consistent with the general notion of separate risk assessment and risk management processes (i.e. NRC, 1983), and it is also consistent with the less prescriptive 1986 EPA cancer risk assessment guidelines.  The most significant departure of the FDA analyses from those guidelines is that they use a probability tree (another old idea) to characterize the uncertainty with the extrapolation from high to low doses.  The most obvious result of using this technique is that it does a more comprehensive job of characterizing the uncertainty associated with generating low-dose estimates from high-dose observations.  But, the more important advantage is that it changes the choice of what dose-response model “should” be used for making a regulatory decision into a different issue from its historical counterparts:

• NRC 1983 and 1986 EPA guidelines.  A default model was justified by policy unless a scientific argument could be presented for deviating from it.  The problem with this was that toxicologists could never make a scientific argument that was certain enough for the policy default to be overturned
• 2005 EPA Guidelines. The policy default became mandatory.  Scientific arguments were prohibited by the Point of Departure.
• Arsenic in Rice Assessment.   While the use of a probability tree means absolute certainty is not required, scientific arguments are mandatory.  Using a probability tree doesn’t eliminate the possibility of political bias, at least it doesn;t require it.  Furthermore, probability trees at least make it possible to frown upon self-interested biases as they occur.

The subjective weights and probabilities in the rice risk assessment are my own.  While no one frowned upon my potential political biases, no one who reviewed the risk assessment expressed an opinion about how the alternative models were weighted either, nor did they suggest alternative models that might be used instead.  That’s a shame that I attribute to the fact that the 2005 guidelines essentially shut down the market for theoretical reasoning in toxicology.  Instead, the current fashion seems to be that a statistical analysis will resolve all the uncertainties with a purely empirical approach – which means Toxicology has largely been supplanted by Epidemiology.  I beg to differ.

Another, more novel, technique introduced in the apple juice and rice assessments is that both analyses use a parametric bootstrapping technique (akin to a Monte-Carlo simulation) to represent the uncertainties in the dose estimates used to characterize the dose-response relationship.  As with the probability tree, there are two advantages.  First, there is better characterization of the uncertainty associated with the dose-response relationship.  Second, (and again, more importantly) it is no longer necessary to decide when the dose estimates for human epidemiology studies are “good enough” to proceed with a characterization of a dose-response relationship. 

The Devils in the Detail

Using a probability tree to raise the lid on Toxicology reveals a wide array of wriggling quantitative issues.  One of the reasons the EPA guidelines have proscribed relatively simple dose response models is because choosing a model for political reasons (e.g. to be precautionary) is only possible when there is a clear relationship between a scientific assumption made and its regulatory implications.   With a simple model that choice can often be made irrespective of any other scientific issues.  With more complicated models that may not be true.  For example, whether or not a nonlinear dose-response model is “more conservative” (i.e. yields a higher risk estimate) may depend on what the dose is.

The impetus for precautionary assumptions has led to the notion that in toxicology science and policy are inextricably linked.  For example, the FDA (2014) fish risk-benefit analysis contains several tables of ‘assumptions vs implications’ that were introduced because the Office of Management and Budget thought they were necessary to explicate potential biases.  But, sometimes the assumptions were essentially indisputable or had no clear political implications associated with them – so we tried to make some implications up.  In fact, the linkage between science and policy isn’t real at all – it’s done by design.  The biology underlying cancer and other diseases is very complicated, and the dose-response models typically used to generate estimates are approximations at best.  Trying to manipulate them to achieve a predetermined result tends to be obvious to anyone who isn’t doing the same.

But, make no mistake; the biology is complicated.  While the dose-response function used to characterize the risk from arsenic in rice serves as a nice exemplar of what cancer guidelines could be, the assessment itself is far from perfect.  There are many scientific issues yet to be addressed, some which are unique to arsenic.  For example:

• The models used in the rice assessment are standard models that are in EPA Benchmark Dose modeling software.  Those are perfectly adequate for characterizing the range of what the risks associated with exposure to arsenic might plausibly be, but I would hesitate to say that they are up to the task of characterizing what the risk is most likely to be – no matter how the alternative models are weighted.  That problem might be addressed by adding one or more complex biological (i.e. toxicokinetic/toxicodynamic) models to the probability tree, and perhaps eliminating the models there now.  
• The dose-response models in the apple and rice risk assessments are based on the analysis of the single cohort.  While the single studies chosen were the best available, a more concerted effort should develop a dose-response model that is reasonably consistent with all reported observations.
• There is evidence that exposures earlier in life are more important.  The rice dose-response model dealt with that issue by just focusing on exposures under the age of 50, but a model that parameterizes the temporal component of the cause-effect relationship would be far superior (i.e. a time-to-tumor analysisof some sort).   At least theoretically, it should be possible to produce a model that is consistent with both the data from Taiwan (that is better for characterizing the influence of dose), and the results from Chile (that are more useful for characterizing the influence of age at time of exposure).  However, Individual subject data would be necessary to do that.

There are a host of other nagging scientific details that could be dealt with; but they only become important when the goal is to produce estimates that scientifically defensible, as opposed to simply conforming to a default procedure justified solely by agency policy.  The EPA has far more resources to devote to dose-response analyses than the FDA did.  If they can awaken from the dogmatic slumber induced by their own guidelines, perhaps they can do much better.

References

Carrington CD, Murray C, and Tao, S. (2013). A Quantitative Assessment of Inorganic Arsenic in Apple Juice.

U.S. Environmental Protection Agency (1986).  Guidelines for Carcinogen Risk Assessment.  EPA/630/R-00/004

U.S. Environmental Protection Agency (2005).  Guidelines for Carcinogen Risk Assessment.  EPA/630/P-03/001F.

U.S. Food and Drug Administration (2014).  Quantitative Assessment of the Net Effects on Fetal Neurodevelopment from Eating Commercial Fish (As Measured by IQ and also by Early Age Verbal Development in Children). 

U.S. Food and Drug Administration (2016).  Arsenic in Rice and Rice Products: Risk Assessment Report

Official Post Soundtrack

Rundgren, Todd (1973).  Just One Victory.  In: A Wizard, A True Star, Track 19.

Post Notes

Thesis Post #60.  It also the first of a two part commentary on the Arsenic in Rice and Rice Products: Risk Assessment Report released by the FDA last week.  In addition, it is an executive summary of many other blog topics, which is why there are many links to other essays.