designed to identify risk (e.g., not sufficiently powered, incompletely reported, does not include positive controls, or otherwise has inadequate mechanisms for detecting adverse events), it is not scientifically valid to use such information to mitigate suggested risk from other sources. Only negative data originating from well-designed studies or other credible sources may mitigate, if not fully eliminate, concerns raised by other sources of information and even well-designed, credible data are often not appropriate to use this way as discussed below. The basic principle that “absence of evidence of risk does not indicate there is no risk” leads to the question of how to weigh seemingly inconsistent data where some information suggests a risk and other information does not. How to compare this type of information is discussed here, with particular emphasis on inconsistencies between animal and human data.

Because of the relevance of human data, serious adverse events arising from randomized clinical trials, spontaneous reports with strong attribution, or case series are generally more compelling than other categories of data when they raise the level of concern. In general, if there is scientifically based evidence from human studies indicating that a concern for safety exists, then the lack of adverse events in animal studies, in vitro studies, or even other human studies cannot be used to overrule or disregard the evidence of harm. The absence of adverse findings in animal studies, no matter how well designed, does not prove that pathological effects will not occur in humans; thus, the absence of an effect or observation in animals cannot mitigate concern raised by human data.

Whereas animal studies cannot be used to mitigate findings of toxicity in humans, animal testing can and should be used to further investigate adverse events that have been reported in humans but for which sufficient attribution cannot be reached. For example, animal data may be used to identify problems specific to particular formulations and sources or products (e.g., in their content, contamination, bioavailability) by comparing groups of animals given the different formulations. This approach might be used, for example, to identify the presence of a contaminant due to a novel processing technology by comparing the effects of feeding the two formulations to the appropriate animal species. Appropriate positive controls would, of course, be necessary to conclude that one formulation has a different effect than another formulation. Also, animal models of human conditions and physiological states can be used to uncover particular vulnerabilities in humans in order to determine specific circumstances under which the dietary supplement ingredient may cause safety concerns in humans by modeling particular conditions (e.g., an animal model of diabetes).

Similarly, it is rarely appropriate to discard observations of adverse effects in animals simply because similar effects were not observed in humans. Evidence of risk from well-designed animal studies using appropriate