In the world of chemical or human health risk analysis there seem to be several clouds forming over the horizon: mixture-based toxicology and interpretation, data-poor extrapolation to human exposure, and high-dose chronic to sub-chronic low-dose dose-response extrapolation. These opportunities force us to approach risk analysis as an art, and necessitates the inclusion of decision analysis into chemical screening procedures.
One problem whose urgency is increasing is data-poor extrapolation from animal to human dose-response relationships. Not only are there tens of thousands of compounds that are not regulated and have no publicly available data, but there are also entirely new types of chemicals produced by technological innovation for which existing toxicological approaches may not be appropriate.
Traditionally, risk scientists make this approximation (and similar others) by proposing a reference dose. The reference dose (RfD) is an unenforceable standard postulating a daily oral human exposure for which no appreciable risk of adverse effects attributable to exposure to the given compound likely exist. The reference dose is obtained from a point of departure for which either the lowest dose producing effects, or the highest dose for which no effects have been observed (i.e., LOAEL or NOAEL) that has been divided by uncertainty factors reflecting the uncertainties introduced by extrapolation between species and data quality contexts. Roger Cooke (and several commentators) discuss the RfD, concluding that the approach needs to be updated to incorporate probabilistic interpretation of these uncertainties, but there seems to be disagreement on how to update the RfD. In his Risk Analysis article, “Conundrums with Uncertainty Facors,” Cooke argues that this approach not only relies on inappropriate statistical independence assumptions, but that this is analogous to the engineering design application of safety factors. By not employing a probabilistic approach, we promulgate uneconomic guidelines at best, while at worst we are overconfident in the in our risk mitigation.
Cooke’s paper illustrates a probabilistic approach to obtaining estimates of dose-response relations from combined animal, human, data-poor, and data-rich results in a chemical toxicity knowledge base founded on Bayesian Belief Networks (in his example, non-parametric, continuous BBNs). He demonstrates the possibility of employing nonparametric or generalizable statistical methods to obtain a probabilistic understanding of the response of interest in the context of the chemical’s toxicological knowledge base. This in in contrast to the uncertainty factor approach which presupposes there is only limited understanding of the dose-response relationship at relevant human exposures which we might hope to obtain. While we are a ways away from abandoning the RfD approach, Cooke acknowledges that it may be difficult to rely only on dose-response modeling. His approach initializes on current practice, while promising a rapid and simple inference mechanism capable of deriving indicators in toxicological indicators and amenable to inclusion in broader decision-making models.