Abstract
Mechanistic information is used in the field of risk assessment in order to clarify two controversial methodological issues, the selection of inference guides and the definition of standards of evidence. In this paper we present an analysis of the concept of mechanistic information in risk assessment by recurring to previous philosophical analyses of mechanistic explanation. Our conclusion is that the conceptual analysis of mechanistic explanation facilitates a better characterization of the concept of mechanistic information. However, it also shows that the use of this kind of information in risk assessment is heavily influenced by pragmatic factors, which have not been sufficiently taken into account in philosophical analysis. Mechanistic models are like hypothesis that have to be validated empirically. Due to their dependence on the standards of evidence, they are subject to the same pragmatic factors. Therefore, recurring to mechanistic information does not lead to closure of the methodological controversies in risk assessment.
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Notes
It could be argued that Cummins’ (1975) analysis of functions is mostly a mechanistic one. On his view, to attribute a function to something is to explain the functioning of the system of which this something forms part.
An important consequence of mechanistic explanation-based analysis has been the strengthening of the link between the analysis of scientific explanation and the function of models in scientific practice (Bokulich 2011).
‘Mechanistic information’ in risk assessment, as we are using it in this paper, has to be distinguished from other, different understandings of this concept, particularly in the analysis of causality in biological systems (Bogen and Machamer 2011).
Mechanistic information is used in risk assessment as evidence in order to evaluate hypotheses like extrapolation models or inference guides. It can also be applied to issues like, for instance, the additivity of various health and environmental effects of chemical substances in mixtures. In this paper we will therefore use the expressions “mechanistic information” and “mechanistic evidence” indistinctively.
Modes of action are usually highly specific to the context in which they have been identified and described (a specific illness, a particular organ, etc.). It cannot be presumed that the same mode of action can be applied to a different context. Equally, a particular outcome (like a disease) can be produced through different modes of action. And the same toxic substance may produce different diseases in different organs via differing modes of action. So, establishing a mode of action encompasses various necessary steps: (1) identification of the key events that lead to the illness, (2) analysis of the experimental evidence that supports the hypothesized mode of action, and 3) comparative analysis of various possible modes of action (Schoeny et al. 2006).
For a mechanism of action to be clearly defined and characterized, we would need detailed data on the metabolism of the substance in question, molecular target(s) and the organs in which effects can occur, modification of biochemical pathways and consequences in the organism of this modification, physiological responses, overall effect on the organisms but also on the population or ecosystem, causal and temporal interrelations, as well as dose response parameters for each mechanistic step (Borgert et al. 2004).
As Glennan (2005) points out, mechanisms are usually generated and refined in a gradual manner, so there really does not exist any clear-cut frontier between sketches and schemata.
In explanation the use of variables is more common than the use of strict parameters (Woodward 2003, 234).
On this view, a mechanistic model can be understood in analogy to a mathematical formula that provides variables for a type of phenomenon. The concrete values for the variables are determined by the specific phenomenon to which the model is applied. Given that the model is a representation of the mechanism that causes a given phenomenon, it can be interpreted as a causal explanation, even if a very general and abstract one.
This, however, is not considered indicative of the possibility of atrazine causing cancer in humans, at least in the U.S. regulatory regime, because of differences between rats and humans.
In many cases substances that at high doses produce harm in living organisms will cease to do so if the dose of exposure falls below a certain threshold level. In other cases, exposure will produce harm even at very low doses, i.e., there is no threshold. The scientific and regulatory information available in most cases refers exclusively to high-dose exposures because it is difficult or impossible to gather information on real-life (low dose) exposures. It is for this reason that scientists and regulators have to decide which kind of extrapolation (from known high-dose exposure to low dose exposure) to hypothesize, one in which a threshold is presumed (first case) or, alternatively, a linear model (second case).
Those who argue for hormesis theory (which states that with decreasing dose of exposure the direction of a biological response may change) consider that low exposures may produce beneficial effects (see Elliott 2011).
The case of atrazine we have presented in the previous section shows that mechanistic information can be used to question the applicability of animal data in humans, in this particular case due to doubts about if the (well established) mode of action of atrazine in rats could be reproduced in the human body.
Cranor’s proposal is aimed at the reduction of false negatives, for considering them in the regulatory context generally more costly for society than false positives. However, Leuridan and Weber (2011), in line with their above mentioned view on standards of evidence, propose to use mechanistic information in order to minimize false positives.
Borgert et al. (2004) consider that a large amount of professional judgment is needed to decide if several substances share a mode of action. They argue that “while an empirical demonstration of dose addition has classically been used to infer common modes of action, the reciprocal statement cannot be made” (Borgert et al. 2004, 94). In other words, without experimental data that prove additivity of the effects of two substances, additivity cannot be presumed on the sole basis of sharing the same mode of action.
See note 14.
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Acknowledgments
This work has received support from the Spanish Ministry for the Economy and Competitiveness and European Commission FEDER funds (research projects La explicación basada en mecanismos en la evaluación de riesgos [FFI2010-20227/FISO] and La evaluación de beneficios como ciencia reguladora: las declaraciones de salud de los alimentos funcionales [FFI2013-42154-P]).
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Luján, J.L., Todt, O. & Bengoetxea, J.B. Mechanistic Information as Evidence in Decision-Oriented Science. J Gen Philos Sci 47, 293–306 (2016). https://doi.org/10.1007/s10838-015-9306-8
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DOI: https://doi.org/10.1007/s10838-015-9306-8