Priorities for mixtures health effects research.

Abstract In order to better inform scientific decision making in the occupational environment, we need a better understanding of the toxicology of mixed exposures. In particular, we need an understanding of the dose–response relationship from the level of individual or population exposure down to the molecular level (and then back up again from the molecular level to the specific health-related response of the organism as a whole). Mixtures toxicology is proving to be different from single-chemical toxicology in several fundamental but barely recognized ways: • complex chemical mixtures may consist of thousands of (often unidentified) components, each often at very low doses, but together constituting significant exposure levels; • exposure is nearly always via multiple routes, pathways; • other stressors such as noise, heat, infection, etc., may play a significant role in the overall environmental health response; • interactions are potentially many and varied: pharmacokinetic and pharmacodynamic interactions may occur at the same site, or at different sites via complex physiological processes (including defense mechanisms); • cumulative effects of different exposures/stresses over time need to be considered (altering the “baseline” susceptibility of the individual). Knowledge gained in mixtures research should be able to improve current risk assessment and mitigation or intervention methods. In NIOSH's National Occupational Research Agenda (NORA) three priority areas have been identified: • Epidemiology: Improvement of statistical tools to identify mixed effects from available epidemiological data (“confounding factors are mixtures effects”). Awareness of disease states with significant environmental components (Gulf War Syndrome (GWS), Chronic Fatigue Syndrome (CFS), Multiple Chemical Sensitivity (MCS), etc.). Recognition and investigation of the complexity of disease related responses to multiple (simultaneous and serial) stressors (immune system, endocrine system, nervous system, etc.). Wider emphasis on relatively new concepts such as susceptibility, which links the genetic and environmental components of a disease. Disease itself is of course also a stressor. • Laboratory approaches: Methods need to be developed to understand and integrate experimental data from the molecular to the whole organism level for understanding multiple data (proteomics, genomics, and metabolomics/metabonomics) from mixed exposures. Understanding and prediction of precursors to adverse health effects will inevitably lead to identification of useful biomarkers of effect, and to earlier and more effective intervention strategies. We also need to improve our ability to forecast interaction effects from mixed exposures using less costly cellular-based screening tools and computer modeling (e.g. QSAR), and develop improved models for large-scale studies of the nature of chemical interactions that lump responses by chemical classes. • Modeling as integrator of data: Development and validation of mechanism-based models and predictive tools are essential for improving current risk assessment processes for mixtures. For example, current linked physiologically-based pharmacokinetic/pharmacodynamic (PBPK/PD) models for multiple simultaneous exposures to chemicals (such as BTEX) need to be extended to more complex mixtures, and new statistical methods of dealing with possibly thousands of components need to be developed. Ultimately, such models may become an integral part of a model of the “virtual human” via computer simulation.