Data Sciences Facility Core [DSFC]

PBPK models are powerful tools in environmental health, and can integrate various types and sizes of PK data sets into a common analysis framework. This approach can be enhanced significantly by incorporating the anatomy and physiology of the underlying biological system into the framework through the use of PBPK models, which have a much wider range of applicability than traditional empirical compartmental PK models. This greater applicability domain is particularly important when trying to understand population PK, since it is generally infeasible to obtain empirical data over the full range of variability in environmentally exposed populations and communities.

Unlike traditional compartmental models, PBPK model parameters are not uniquely identifiable based on experimental PK data alone, as it is infeasible for human populations to obtain all the information (tissue volumes and flows, tissue-specific metabolism and transport, and tissue-specific compound concentrations) needed to identify all their parameter values on the basis of maximum likelihood alone. It has therefore been proposed that population PBPK models, like other mechanistic models, are best parameterized in a Bayesian framework, which seamlessly integrates the prior information available from the literature on PBPK parameter values and correlations. A population model further helps Bayesian integration of prior information, since priors rest on population parameters (e.g., average blood volume) rather than on individual values.

The PBPK model–population model–Bayesian framework results in a clear, elegant, versatile, flexible approach that does not depend on asymptotic or Gaussian approximations. This approach has been demonstrated in multiple case studies of environmental or occupational toxicants, including several by Dr. Chiu. These include use of population PBPK models for bi-directional translation of mouse population-based models to human health and risk assessment.

While in vitro data offer a great potential to fill knowledge gaps related to toxicity information, several factors that can significantly influence in vivo toxicity such as bioavailability, metabolic and renal clearance, and protein binding, are not accounted for in in vitro test data. Therefore, the incorporation of toxicokinetic dosimetry modeling is a necessary element of the vision to move toxicity testing away from in vivo rodent studies to human-based in vitro assays and enable direct comparisons with human exposures for evaluating risk.

IVIVE forms a critical link from Stressors to Responses, one of the Center’s themes. IVIVE models also account for population variability in toxicokinetics, and therefore also touch on the theme of Individuals to Populations. Moreover, IVIVE approaches have been demonstrated to have a substantial impact on hazard prioritization and ranking, thereby providing critical information to inform the theme of Community, Regulation, and Policy. Because of the large number of chemicals the Center will investigate, both individually and in combination, an essential element of the DSFC is to maintain, and augment as necessary, a repository of models and their parameters in a readily computable form. Pearce et al. recently developed an open-source library “httk” (high-throughput toxicokinetics) in the “R” statistical software program that includes toxicokinetic models and pre-calculated RTK factors, as well as computer code for dynamic simulation and Monte Carlo sampling using several different toxicokinetic model formulations. Center investigators have also extended these approaches to mixtures.

A number of commercial and freely available software packages can conduct dose-response modeling, but their use in evaluating toxicological datasets is still quite limited compared to the traditional application of pair-wise statistical tests to determine “no observed adverse effect levels” (NOAELs).

The limitations of the NOAEL as a starting point for toxicological risk assessment have been recognized for decades, and it is generally accepted that the BMD, introduced by Crump, is more scientifically appropriate. The BMD is the dose associated with a specific size of effect, the benchmark response (BMR; we use M, for magnitude of effect, to denote this value). The BMD_M is estimated, with associated confidence interval/statistical distribution, by statistical model fitting to dose-response data. In order to facilitate use of toxicological data generated by Center investigators in regulatory decision-making, BMD modeling services will be provided.

We previously developed a standardized workflow for BMD modeling that was applied to hundreds of datasets, and which can be adapted for supported Center investigator needs. Additionally, the recent advent of high-throughput and population-based in vitro and in vivo models presents a new challenge. We have recently demonstrated development of a hierarchical Bayesian workflow for dose-response modeling of population data, which has a particular application to population-based in vitro data. This approach enabled a statistically rigorous analysis of population variability toxic responses, providing key information on toxicodynamic susceptibility.

Methods for population-based risk prediction for the purposes of informing environmental health decisions continue to evolve, with increasing emphasis on use of in vitro data, dose-response modeling, and probabilistic analyses of uncertainty and variability. Recent National Academy of Sciences reports have recommended development of predictive risk estimates that provide stakeholders and decision-makers with a fuller characterization of the potential population human health effects associated with different levels of exposure.

Recently, the World Health Organization (WHO) developed a harmonized probabilistic approach based on the concept of the “target human dose” (denoted HD_M^I) that responds directly to these National Academy of Sciences recommendations. While similar to the traditional “reference dose” (RfD) in its derivation, it differs in several important ways. First, being based on dose-response modeling, it is always tied to a specific health effect, unlike a NOAEL or RfD. Second, it is a harmonized approach applicable to both cancer and non-cancer effects. Furthermore, it extends existing decision benchmarks by quantitatively defining the protection goals in terms of population incidence (I) and magnitude (M) of the critical effect in the human population. This allows for incorporation into regulatory impact analyses that require economic benefit-cost estimates. Finally, it uses uncertainty distributions derived from historical and/or chemical-specific data and analyses in order to derive confidence intervals that quantify the level of uncertainty in the overall result.

This core provides tailored tools to support four categories of center activities: data collection, integration/curation, data analytics, and visualization. For data collection, remote sensing and LiDAR data can be employed to develop spatial sampling schemes. When combined with demographic and socioeconomic data, the center can develop cluster or stratified sampling strategies for cross-sectional or longitudinal survey methods. Regarding data integration, geospatial and temporal features can be the primary data linkage, while other formats of data can be converted into geospatial database by geocoding/georeferencing/georectification. As the projects use sensor networks (e.g., biosensing, environmental monitoring, crowd-sensing), contextual data that capture the dynamics of the environment and human exposure can be obtained and scaled for analysis. Next, geoprocessing, spatial interpolation, spatial autocorrelation, spatial machine learning methods can be applied to model global and local geospatial processes or address spatial dependence and heterogeneity. Lastly, static maps (e.g., choropleth maps, heatmaps) and interactive webmaps and dashboards will be developed to visualize dataset and project results. In addition, DSFC personnel have experience leveraging virtual and augmented reality technologies for realistic representations of the environment. Three-dimensional digital models will be developed based on LiDAR point clouds in ESRI CityEngine and game engines (e.g., Unreal, Unity) to simulate various environmental hazards (e.g., flooding, oil spill).

Texas EJ communities face compounded hazards that manifest as “acute-on-chronic” stressors. With the increasing recognition that health disparities are geospatial – closely tied to the spatial-temporal heterogeneity of environmental and social stressor distribution. For example, populations in closer proximity to Toxic Release Inventory sites, industrial land uses, or transportation corridors run a higher risk of being affected by toxics through various exposure pathways; these areas are often occupied by communities of color. By integrating domains of multisource and multi-type data (e.g., climate extremes, environment, infrastructure, socioeconomic, and health), this core can identify vulnerable populations with specific exposure levels, pathways, and scales relevant to their unique needs, further increasing the validity and accuracy of methodologies and the robustness of their findings.

Geospatial technology can not only reveal distributive injustice in existing environmental stressors and health disparities, but also promote procedural justice through enabling information access and meaningful participation of community members, thus facilitating equitable allocation of resources and decision-making. DSFC will utilize a GeoDesign framework with six key modeling approaches, representation models, process models, evaluation models, change models, impact models, and decision models, to support community-based participatory research. The representation and process models aim to enable community-driven problem identification through efficient spatial data sharing and modeling the individual, household, and community level health risk factors. The evaluation and change models utilize scenario-based approaches to evaluate health outcomes based on community co-design solutions. The impact and decision models assess the primary health outcomes and auxiliary benefits of the solutions, as well as any unintended social consequences.