Comparison of current (A) and proposed (B) toxicity testing paradigms.

<p>The current approach (Panel A) to setting regulatory standards involves interpretation of the most sensitive end point observed in animal studies. Low-dose extrapolation requires obtaining a point of departure from the results of the animal studies and the use of either linear or threshold extrapolation plus application of uncertainty factors to the point of departure. Other extrapolations between species or across exposure routes are sometime conducted with pharmacokinetic modeling of the tissue doses that are associated with adverse effects. A similar sequence of steps can be envisioned for setting standards based on pathway assays (Panel B). Likely hazards are determined by the sensitivity of the various toxicity pathway assays; extrapolations require assessing adverse consequences of <i>in vitro</i> exposures and use of computational systems biology pathway (CSBP) and pharmacokinetic modeling to set a standard for human exposure related to mg/kg/day ingested or ppm (parts per million) in the inhaled air.</p

A schematic figure illustrating progressive activation of a prototype toxicity pathway, with attendant discrete phenotypic transitions.

<p>Text on right of figure shows proposed approaches for characterizing discrete transitions through multiple cellular phenotypes, i.e., (1) basal function, (2) minimally perturbed cellular states, (3) upregulation of adaptive, homeostatic gene batteries, and finally (4) overtly adverse states with excessive pathway perturbations. The structure of the circuitry with various embedded, nonlinear feedback loops is schematized in the middle panels. In the context of <i>in vitro</i> assay design, varying free concentrations of a test compound in the test media lead to increasing activation of pathway component (signaling protein) A, thereby driving pathway perturbations of signaling components B and C, and further signaling events downstream of C. Each of these steps is expected to be associated with specific alterations in gene expression, phenotypic read-out and pathway activation, identified by CSBP modeling. As the concentration increases, various portions of the network would be sequentially activated until full activation was achieved (indicated by progressively darker shading of pathway components). Full pathway activation triggers a robust response throughout the pathway circuitry, leading to adverse outcomes measurable in the cellular assay.</p

Using prototype pathways and compounds: the fast track for implementing the 2007 NRC TT21C vision.

<p>The next step in moving forward will be to see the entire 2007 TT21C vision placed in practice with a group of pathways and specific prototype compounds preferentially affecting the target pathways. The use of compounds with robust animal toxicity profiles will allow a mapping of <i>in vivo</i> responses associated with apical end points and intermediate end points against new <i>in vitro</i> toxicity pathway assay results. The comparison will permit a better sense of the relationship between <i>in vitro</i> and <i>in vivo</i> responses in key pathways and the relevant estimates of risk. Prototype compounds for the DNA-damage pathway could include DNA-damaging compounds – polyaromatic hydrocarbons, formaldehyde, and alkylating agents, etc. – and compounds causing DNA damage indirectly through oxidative stress pathways, such as flavonoids. The output of the two approaches in relation to risk estimates and estimates of safe region of exposure could also be compared to assess correspondence or lack of correspondence in the approaches.</p

Stress response pathways and network motifs.

<p>(<b>A</b>) <b>Typical structure of a stress response pathway</b> (adapted from Simmons et al. <a href="http://www.plosone.org/article/info:doi/10.1371/journal.pone.0020887#pone.0020887-Simmons1" target="_blank">[26]</a>). The so-called eight canonical stress response pathways, conserved broadly across eukaryotes, have a common structure (common motifs) for sensing damage and mounting a transcriptional response to counteract the stress. (<b>B</b>) <b>Common network motifs in intracellular response pathways.</b> Three elements, (genes/proteins) X, Y, and Z, in a pathway can regulate each other to form: (<b>i</b>) a negative feedback loop; (<b>ii</b>) a positive feedback loop; (<b>iii</b>) a coherent feed-forward loop, where X activates Y, and both X and Y activate Z; and (<b>iv</b>) an incoherent feed-forward loop, where X activates both Y and Z, but Y suppresses Z. Two transcription factors X and Y can regulate each other through, for instance: (<b>v</b>) a double-negative feedback loop; or (<b>vi</b>) a double-negative feedback loop with positive autoregulation. Sharp arrows denote activation; flat arrows denote suppression.</p

p53-mediated perturbation of DNA damage response pathways that affect mutagenesis.

<p>A negative feedback motif composed of p53, Mdm2, and others (not shown) can produce undamped oscillations in response to radiation-induced double strand breaks. A partial AND (pAND) gate is used to indicate that to produce inheritable mutations, both DNA damage and cell proliferation (DNA replication) are required, and that the two processes likely contribute to mutagenesis in a multiplicative manner. Activation of the apoptosis pathway works to mitigate mutagenesis by killing cells with severe or unrepairable DNA damage. Pointed arrows indicate activation and blunted arrows indicate inhibition.</p

Schematic of the physiologically based toxicokinetic (PBTK) models for the study of naphthalene toxicokinetics

<p><b>Copyright information:</b></p><p>Taken from "PBTK Modeling Demonstrates Contribution of Dermal and Inhalation Exposure Components to End-Exhaled Breath Concentrations of Naphthalene"</p><p></p><p>Environmental Health Perspectives 2007;115(6):894-901.</p><p>Published online 14 Feb 2007</p><p>PMCID:PMC1892111.</p><p>This is an Open Access article: verbatim copying and redistribution of this article are permitted in all media for any purpose, provided this notice is preserved along with the article's original DOI</p> Pulmonary uptake of naphthalene in the personal breathing-zone and pulmonary clearance from the blood compartment are added to a previously published dermatotoxicokinetic model (). Abbreviations in the PBTK model: , input rate constant for dermal exposure; , permeability coefficient for the viable epidermis; , exposed surface area; , stratum corneum:viable epidermis partition coefficient; , blood flow rate to skin; , viable epidermis:blood partition coefficient; , pulmonary ventilation rate; , blood:air partition coefficient; , blood flow rate to fat; , fat:blood partition coefficient; , blood flow rate to other tissue; , other tissue:blood partition coefficient; , extraction ratio

Plots comparing the PBTK model simulations to experimentally measured naphthalene concentrations in blood from 10 study volunteers () who were dermally exposed to JP-8 on the volar forearm

<p><b>Copyright information:</b></p><p>Taken from "PBTK Modeling Demonstrates Contribution of Dermal and Inhalation Exposure Components to End-Exhaled Breath Concentrations of Naphthalene"</p><p></p><p>Environmental Health Perspectives 2007;115(6):894-901.</p><p>Published online 14 Feb 2007</p><p>PMCID:PMC1892111.</p><p>This is an Open Access article: verbatim copying and redistribution of this article are permitted in all media for any purpose, provided this notice is preserved along with the article's original DOI</p

Normalized sensitivity coefficients for the end-exhaled breath concentrations

<p><b>Copyright information:</b></p><p>Taken from "PBTK Modeling Demonstrates Contribution of Dermal and Inhalation Exposure Components to End-Exhaled Breath Concentrations of Naphthalene"</p><p></p><p>Environmental Health Perspectives 2007;115(6):894-901.</p><p>Published online 14 Feb 2007</p><p>PMCID:PMC1892111.</p><p>This is an Open Access article: verbatim copying and redistribution of this article are permitted in all media for any purpose, provided this notice is preserved along with the article's original DOI</p> Parameters were adjusted at the 1% level

Dose response from high throughput gene expression studies and the influence of time and cell line on inferred mode of action by ontologic enrichment

<table><tbody><tr> <td>Presented at the Annual Society of Toxicology meeting</td></tr></tbody></table

Pollution Trees: Identifying Similarities among Complex Pollutant Mixtures in Water and Correlating Them to Mutagenicity

There are relatively few tools available for computing and visualizing similarities among complex mixtures and in correlating the chemical composition clusters with toxicological clusters of mixtures. Using the “intersection and union ratio (IUR)” and other traditional distance matrices on contaminant profiles of 33 specific water samples, we used “pollution trees” to compare these mixtures. The “pollution trees” constructed by neighbor-joining (NJ), maximum parsimony (MP), and maximum likelihood (ML) methods allowed comparison of similarities among these samples. The mutagenicity of each sample was then mapped to the “pollution tree”. The IUR-distance-based measure proved effective in comparing chemical composition and compound level differences between mixtures. We found a robust “pollution tree” containing seven major lineages with certain broad characteristics: treated municipal water samples were different from raw water samples and untreated rural drinking water samples were similar with local water sources. The IUR-distance-based tree was more highly correlated to mutagenicity than were other distance matrices, i.e., MP/ML methods, sampling group, region, or water type. IUR-distance-based “pollution trees” may become important tools for identifying similarities among real mixtures and examining chemical composition clusters in a toxicological context