Virtual models (vMs) are computer representations of biological processes (at various levels of organization) used to resolve complex system behaviors and emergent properties that cannot be understood solely from studying the individual parts. Computational power is now available to develop vMs capable of supporting predictive toxicology, and of reducing the dependence on in vivo animal studies for basic research and risk assessment purposes. The ultimate goal is to simulate in vivo adaptive responses to environmental change, and to predict the affects of various perturbations on system behaviors. Examples of vMs are presented from research in the fields of physiology, pharmacology, toxicology, and risk assessment.

Estimates of internal dosimetry for acrylamide (AA, 2-propenamide; CASRN: 79-06-1) and its active metabolite glycidamide (GA) were compared using either biomarkers of internal exposure (hemoglobin adduct levels in rats and humans) or a PBTK model (Sweeney et al., 2010). The resulting impact on the human equivalent dose (HED, oral exposures), the human equivalent concentration (HEC, inhalation), and final reference values was also evaluated. Both approaches yielded similar AA HEDs and HECs for the most sensitive noncancer effect of neurotoxicity, identical oral reference doses (RfD) of 2 × 10−3 mg AA/kg bw/d, and nearly identical inhalation reference concentrations (RfC = 0.006 mg/m3 and 0.007 mg/m3, biomarker and PBTK results, respectively). HED and HEC values for carcinogenic potential were very similar, resulting in identical inhalation unit risks of 0.1/(mg AA/m3), and nearly identical oral cancer slope factors (0.4 and 0.5/mg AA/kg bw/d), biomarker and PBTK results, respectively. The concordance in estimated HEDs, HECs, and reference values from these two diverse methods increases confidence in those values. Advantages and specific application of each approach are discussed. (Note: Reference values derived with the PBPK model were part of this research, and do not replace values currently posted on IRIS: http://www.epa.gov/iris/toxreviews/0286tr.pdf.)

Biologically based dose-response (BBDR) models predict health outcomes (response) resulting from the presence of a toxicant at a biological target (dose). The benefits of BBDR models are many, and research programs are increasingly focusing on mechanistic research to support model development; however, progress has been slow. Impediments to progress include the complexity of dose response modeling, the need for a multidisciplinary team and consistent funding support, and difficulty in identifying and extracting the needed data. Of immediate concern is the lack of transparency of published models to the supporting data and literature, difficulty in accessing model code and simulation conditions sufficient to allow independent replication of results, and absence of well-defined quality criteria. Suggestions are presented to improve the development and use of BBDR models in risk assessment and to address the above limitations. Examples from BBDR models for methylmercury neurotoxicity and 5-fluorouracil embryotoxicity are presented to illustrate the suggestions including what kinds of databases are needed to support model development and transparency, quality assurance for modeling, and how the internet can advance database development and collaboration within the biological modeling community.