A Multiscale Approach for Assessing the Interactions of Environmental and Biological Systems in a Holistic Health Risk Assessment Framework
Advances in computing processing power and in availability of environmental and biological data have allowed the development and application of comprehensive modeling systems that utilize a holistic, integrated, approach for assessing the interactions of environmental and biological systems across multiple scales of spatiotemporal extent and biological organization. This approach allows mechanism-based environmental health risk assessments in a person-oriented framework, which accounts for simultaneous exposures to contaminants from multiple media, routes, and pathways. The conceptual basis and example applications of the Modeling ENvironment for TOtal Risk (MENTOR), and the DOse–Response Information ANalysis system (DORIAN) are presented.
KeywordsComprehensive modeling systemsDORIANMENTOREnvironmental healthRisk assessmentsExposures to mixtures
1 Introduction: The Evolution of Environmental Risk Assessment
The evolving focus of environmental human and ecological health risk analysis
Past: single pathway analysis of risk
Present: multiple pathway analysis of risk
Future: integrated “person-oriented” systems analysis of risk
Mixtures of contaminants with environmental and biological interactions
Multiple contaminant sources
Multiple contaminant sources
Multiple co-occurring chemical and nonchemical stressors affecting an individual
Single medium environmental fate and transport
Linked fate and transport in different environmental media
Dynamically integrated multimedia fate and transport in the environmental and biological systems
Single exposure route
Multiple exposure routes
Aggregate/cumulative exposure and dose analysis
Phenotype-based toxicity with susceptibility considerations
Mechanistic linkage of phenotype with genotype
Primary human health criteria for individual contaminants
Chemical and exposure-route specific risk for “standard individuals”
Aggregated risk for diverse human populations (with susceptible subpopulations)
Quantitative uncertainty and variability resolved for specific environmental and biological processes
The following sections present a summary overview of a continuing research effort, funded primarily by the US Environmental Protection Agency (USEPA), to develop, evaluate, and apply mechanistic and diagnostic computational modeling tools intended to support comprehensive analyses of all steps in the “environmental health sequence” of Fig. 1. This effort has resulted in the evolution of two “libraries of software components,” involving both modeling and data modules, developed in C++, Java, and Matlab, and operating, in conjunction with database engines, on customized Linux clusters. These libraries constitute the Modeling ENvironment for TOtal Risk studies (MENTOR) that addresses the “source-to-dose” steps, and the DOse–Response Information ANalysis system (DORIAN) for the biological “dose-to-effect” steps. It should be noted that the analyses supported by MENTOR and DORIAN are in principle “bi-directional,” i.e. allowing not only calculations of dose and biological effect from environmental and exposure information, but also, under certain conditions, the reconstruction of environmental exposure patterns from appropriate biomarker data.
2 The Modeling Environment for Total Risk Studies (MENTOR)
▪ MENTOR-1A (Georgopoulos et al. 2005a) employs a “one atmosphere” approach to characterize simultaneous exposures of individuals or populations to multiple, co-occurring atmospheric contaminants (e.g. ozone, fine particles, air toxics, etc.). The “one atmosphere” approach places emphasis on accounting for the dynamic physical and chemical interactions of the contaminants, in both ambient air and in specific residential, occupational, and vehicular microenvironments.
▪ MENTOR-4M (Georgopoulos et al. 2005b, 2006) provides a unified multiroute/multipathway modeling framework for aggregate and cumulative exposure assessments. The focus is on contaminants that are present in multiple environmental media and on mixtures of such contaminants.
▪ MENTOR-3P is a modular, “generalized” physiologically-based pharmacokinetic modeling framework for human populations that allows simultaneous consideration of multiple, co-occurring chemicals in a consistent manner, while incorporating information for attributes of intra- and inter-individual variation and variability, either physiological or biochemical, from various recent databases (CDC 2005; The Lifeline Group 2006).
▪ MENTOR-2E provides computational tools appropriate for characterizing exposures specifically for emergency events, as a means for improving planning for emergency response. Accordingly, emphasis is given to the critical attributes of physicochemical processes across the various temporal and spatial scales that are relevant to the evolution of the emergency event (Georgopoulos et al. 2004; Stenchikov et al. 2006).
▪ MENTOR-DOT provides a wide range of state-of-the-art diagnostic tools, such as the Stochastic Response Surface Method (SRSM) (e.g. Balakrishnan et al. 2003, 2005), the High Dimensional Model Representation (HDMR) (e.g. Wang et al. 2005a), and Bayesian Markov Chain Monte Carlo (MCMC) optimization techniques (e.g. Balakrishnan et al. 2003), which allow comprehensive sensitivity/uncertainty analysis of complex system models, systematic complex model reduction to obtain Fast Equivalent Operational Models (FEOMs), and efficient model/data fusion.
Typically, MENTOR simulations consist of: (1) Estimation of (multimedia) background levels of environmental pollutants (in air, water, and soil) through either (a) multivariate spatiotemporal analysis of monitor data or (b) regional-scale environmental model predictions; (2) Estimation of local multimedia pollutant levels in an appropriate administrative unit (such as a census tract) or a conveniently defined grid through either (a) field study measurements, (b) subgrid “corrections” of regional model estimates, or (c) application of a local scale environmental model; (3) Characterization of essential demographic attributes of populations (geographic density, age, gender, race, occupation, income, etc.); (4) Development of activity event sequences for each member of a sample population representing the population of concern for the exposure period through either (a) existing databases from composites of past studies (for baseline assessment) or (b) study-specific information (special registries); (5) Estimation of multimedia levels and temporal profiles of pollutants in various microenvironments (streets, residences, offices, restaurants, vehicles, etc.) through either (a) field study measurements or (b) microenvironmental mass-balance modeling (air), drinking water distribution modeling (water), and dietary concentration modeling (food); (6) Calculation of appropriate inhalation rates, as well as drinking water and food consumption rates for the members of the sample population, through a combination of the physiological attributes of the study subjects and the activities pursued during the individual exposure events; and (7) Biologically-based target tissue dose (toxicokinetic) modeling. This last step provides a significant advantage over past practices in exposure assessment, as it allows model evaluation against field measurements of biomarkers, when such information is available.
3 The Dose–Response Information Analysis System (DORIAN)
A specific objective of the DORIAN effort is to produce generalized, integrated, physiologically based models for the “coupled” toxicokinetics and toxicodynamics of contaminants of concern and their mixtures (Abdel-Rahman and Kauffman 2004; Danhof et al. 2007). These models are designed so as to describe quantitatively both the processes affecting the contaminant in the organism (absorption, distribution, metabolism, elimination) and the processes resulting to toxicity due to the contaminant. So, the biologically-based toxicodynamic models link target tissue molecular and cellular events (e.g. receptor activation, changes in ion channel functions, etc.) to consequent biochemical and physiological changes.
The DORIAN development effort benefits directly from a wide range of ongoing activities in computational cell biology (e.g. Slepchenko et al. 2002; Takahashi et al. 2003), computational physiology (e.g. Crampin et al. 2004; Strategy for the EuroPhysiome (STEP) Consortium 2006), toxicogenomics (e.g. NRC 2005; UK Environment Agency 2003; USEPA 2004), systems toxicology (e.g. USEPA 2003b; BMBF 2002; Waters et al. 2003) and other related fields, taking place in North America, Europe, Japan, New Zealand, and elsewhere. Outcomes from these research efforts are regularly incorporated in the DORIAN system to ensure its relevance and usefulness.
4 Examples: Selected Case Studies
The various applications of MENTOR to a wide range of environmental problems, and the recent applications of combined MENTOR and DORIAN modules that were reported here, demonstrate the feasibility and the potential of holistic, “person-oriented” approaches in studying environmental health issues. The selected references listed in this work demonstrate that there is extensive activity in North America, Europe, Japan, New Zealand, and elsewhere, towards the development and deployment of methods that rely on a systematic integration of exposure and biological processes for understanding the “coupled dynamics” of environmental and human health states (see also Schwartz and Collins 2007). As such methods evolve toward maturity and widespread acceptance, they can be reasonably expected to provide various opportunities for rethinking and reevaluating environmental and public health policy practices, by taking into account the variability among individuals that simultaneously experience multiple, possibly interacting, environmental stressors.
Support for this work has been provided primarily by the USEPA-funded Environmental Bioinformatics and Computational Toxicology Center (ebCTC) under STAR Grant No. GAD R 832721-010, and the USEPA funded Center for Exposure and Risk Modeling (CERM) under Cooperative Agreement no. CR-83162501. This work has not been reviewed by and does not represent the opinions of the funding agency. Appreciation is extended to the research team of CCL, with special thanks to Profs S. Isukapalli and S. W. Wang, as well as to A. Sasso, Y. C. Yang, and P. Shade. Thanks are also due to Prof P.J. Lioy (CERM), Prof W. Welsh (ebCTC), Dr W. Tong (USFDA-NCTR Center for Toxicoinformatics), and to the numerous USEPA and EOHSI collaborators who have contributed to this research.