Absorption efficiency of RESV through mouse skin using 3 bases in different tissues.

<p>One mg of RESV dissolved in ethanol was applied directly (EtOH) or mixed with hydrophilic ointment (HO), macrogol (Ma) or CMC gel (CMC), and swabbed on mouse dorsal skin. After 4 h mice were sacrificed, metabolites were extracted from tissues and analyzed by LC-MS. Peak areas of RESV-SULF (<b>A</b>), trans-RESV-3-O-GLUC (<b>B</b>), cis-RESV-3-O-GLUC (<b>C</b>), DH-RESV-SULF (<b>D</b>), DH-RESV-GLUC (<b>E</b>) and tryptophan (<b>F</b>) were normalized using peak areas of spiked internal standards (HEPES and PIPES). Peak areas for each compound are presented as means ± SD of 3 mice. Statistical significance was assessed with Tukey’s test: *P<0.05.</p

Metabolism of Skin-Absorbed Resveratrol into Its Glucuronized Form in Mouse Skin

<div><p>Resveratrol (RESV) is a plant polyphenol, which is thought to have beneficial metabolic effects in laboratory animals as well as in humans. Following oral administration, RESV is immediately catabolized, resulting in low bioavailability. This study compared RESV metabolites and their tissue distribution after oral uptake and skin absorption. Metabolomic analysis of various mouse tissues revealed that RESV can be absorbed and metabolized through skin. We detected sulfated and glucuronidated RESV metabolites, as well as dihydroresveratrol. These metabolites are thought to have lower pharmacological activity than RESV. Similar quantities of most RESV metabolites were observed 4 h after oral or skin administration, except that glucuronidated RESV metabolites were more abundant in skin after topical RESV application than after oral administration. This result is consistent with our finding of glucuronidated RESV metabolites in cultured skin cells. RESV applied to mouse ears significantly suppressed inflammation in the TPA inflammation model. The skin absorption route could be a complementary, potent way to achieve therapeutic effects with RESV.</p></div

RESV metabolism in HepG2 (human hepatocytes), HaCaT (human keratinocytes), and C2C12 (mouse myoblasts).

<p>Cells were treated with 20 or 200 µM RESV for 4 h. After washing with PBS, cells were lysed, and metabolites were extracted and analyzed by LC-MS. Peak areas of RESV–SULF (<b>A</b>), trans-RESV-3-O-GLUC (<b>B</b>) and cis-RESV-3-O-GLUC (<b>C</b>) were normalized by peak areas of spiked internal standards (HEPES and PIPES). Peak areas for each compound are presented as means ± SD of 3 samples (except for HaCaT 200 µM RESV, 2 samples). Statistical significance was assessed using Dunnett’s test: *P<0.05.</p

Detected RESV and DH-RESV metabolites were identified using standard compounds (STD), MS/MS spectrum analysis (MS/MS), or <i>m/z</i> value (MS).

<p>Detected RESV and DH-RESV metabolites were identified using standard compounds (STD), MS/MS spectrum analysis (MS/MS), or <i>m/z</i> value (MS).</p

An Integrated Computational/Experimental Model of Lymphoma Growth

<div><p>Non-Hodgkin's lymphoma is a disseminated, highly malignant cancer, with resistance to drug treatment based on molecular- and tissue-scale characteristics that are intricately linked. A critical element of molecular resistance has been traced to the loss of functionality in proteins such as the tumor suppressor <i>p53</i>. We investigate the tissue-scale physiologic effects of this loss by integrating <i>in vivo</i> and immunohistological data with computational modeling to study the spatiotemporal physical dynamics of lymphoma growth. We compare between drug-sensitive <i>Eμ-myc Arf-/-</i> and drug-resistant <i>Eμ-myc p53-/-</i> lymphoma cell tumors grown in live mice. Initial values for the model parameters are obtained in part by extracting values from the cellular-scale from whole-tumor histological staining of the tumor-infiltrated inguinal lymph node <i>in vivo</i>. We compare model-predicted tumor growth with that observed from intravital microscopy and macroscopic imaging <i>in vivo</i>, finding that the model is able to accurately predict lymphoma growth. A critical physical mechanism underlying drug-resistant phenotypes may be that the <i>Eμ-myc p53-/-</i> cells seem to pack more closely within the tumor than the <i>Eμ-myc Arf-/-</i> cells, thus possibly exacerbating diffusion gradients of oxygen, leading to cell quiescence and hence resistance to cell-cycle specific drugs. Tighter cell packing could also maintain steeper gradients of drug and lead to insufficient toxicity. The transport phenomena within the lymphoma may thus contribute in nontrivial, complex ways to the difference in drug sensitivity between <i>Eμ-myc Arf-/-</i> and <i>Eμ-myc p53-/-</i> tumors, beyond what might be solely expected from loss of functionality at the molecular scale. We conclude that computational modeling tightly integrated with experimental data gives insight into the dynamics of Non-Hodgkin's lymphoma and provides a platform to generate confirmable predictions of tumor growth.</p> </div

Algorithm flowchart.

<p>Refer to <a href="http://www.ploscompbiol.org/article/info:doi/10.1371/journal.pcbi.1003008#s2" target="_blank"><b>Materials and Methods</b></a> and <b><a href="http://www.ploscompbiol.org/article/info:doi/10.1371/journal.pcbi.1003008#pcbi.1003008.s004" target="_blank">Text S1</a></b> for equations. Using the cellular-scale data, we measured values for proliferation and apoptosis for both drug-sensitive and drug-resistant tumors and calculated corresponding values for the model mitosis and apoptosis parameters <i>λ</i>_M and <i>λ</i>_A. We solved <a href="http://www.ploscompbiol.org/article/info:doi/10.1371/journal.pcbi.1003008#pcbi.1003008.e002" target="_blank">Eq. (2)</a> for the local levels of cell substrates <i>n</i> at each time step of simulation of tumor growth. The parameters were input into <a href="http://www.ploscompbiol.org/article/info:doi/10.1371/journal.pcbi.1003008#pcbi.1003008.e010" target="_blank">Eq. (3)</a> to numerically calculate the source mass terms <i>S_i</i>, which were then used in <a href="http://www.ploscompbiol.org/article/info:doi/10.1371/journal.pcbi.1003008#pcbi.1003008.e001" target="_blank">Eq. (1)</a> to compute the volume fractions of viable <i>ρ</i>_V and <i>ρ</i>_D dead tissue. These fractions were used in <a href="http://www.ploscompbiol.org/article/info:doi/10.1371/journal.pcbi.1003008#pcbi.1003008.e011" target="_blank">Eq. (4)</a> to obtain the tumor tissue growth velocity.</p

Model of RESV metabolism after oral and transdermal administration.

<p>Arrows indicate metabolism of RESV and dashed arrows indicate predicted metabolism of DH-RESV. Numbers beside the arrows are explained in the text.</p

Schematic showing integrated computational/experimental modeling strategy involving both cell- and tumor-scale measurements.

<p>(<b>A</b>) Functional relationships involving cell-scale parameters such as proliferation (Ki-67), apoptosis (Caspase-3), and hypoxia (HIF-1α) are defined based on experimental observations, e.g., from immunohistochemistry the density of viable tissue as a function of vascularization is shown in the third panel (red: highest density; yellow: lowest; blue: vessels). These functional relationships as well as parameter values measured experimentally are then used as input to the model to create simulations of lymphoma growth. A sample simulated tumor cross-section showing vascularized viable tissue (highest density in red, lowest in yellow, with vessel cross-sections as small blue dots) is shown at the far right. (<b>B</b>) Lymphoma observations regarding size, morphology, and vasculature from macroscopic imaging of an inguinal lymph node in live mice provide part of the tumor-scale information to validate the model simulations. Note the pre-existing vasculature in the lymph node (in the center of each frame) from which oxygen and nutrients are supplied to the tissue. For comparison, a control group of lymph nodes in animals without tumors is also shown.</p

Scheme to obtain the cellular-scale experimental data.

<p>Lymphomas (shown as large orange sphere) were grown <i>in vivo</i> by tail vein injection of either drug-sensitive <i>Eμ-myc/Arf-/-</i> or drug-resistant <i>Eμ-myc/p53-/-</i> lymphoma cells. The inguinal lymph node tumor was excised, fixed, and sliced for histology sections (5 µm apart) every 100 µm along the tumor. A total of five sets (S1 through S5) of histology sections were obtained (for simplicity, the figure only shows three sets). The sections in each set were stained for cell viability (H&E), hypoxia (HIF-1α), proliferation (Ki-67), apoptosis (Caspase-3), and vascularization (CD-31).</p

Representation of the lymph node by the computational model.

<p>(A) Diagram highlighting a typical lymph node structure. (B) Simulation output from the model showing an incipient tumor (dark red) forming in the center of the node. Afferent lymphatic vessels are collectively represented as one incoming tube on the top, and the efferent vessel is at the bottom. (C) The simulated distribution of oxygen (brown color) released by the blood vasculature within the node remains uniform at this initial stage.</p