Fabricating Microfluidic Valve Master Molds in SU‐8 Photoresist

Multilayer soft lithography has become a powerful tool in analytical chemistry, biochemistry, material and life sciences, and medical research. Complex fluidic micro-circuits require reliable components that integrate easily into microchips. We introduce two novel approaches to master mold fabrication for constructing in-line micro-valves using SU-8. Our fabrication techniques enable robust and versatile integration of many lab-on-a-chip functions including filters, mixers, pumps, stream focusing and cell-culture chambers, with in-line valves. SU-8 created more robust valve master molds than the conventional positive photoresists used in multilayer soft lithography, but maintained the advantages of biocompatibility and rapid prototyping. As an example, we used valve master molds made of SU-8 to fabricate PDMS chips capable of precisely controlling beads or cells in solution

A Liver-Centric Multiscale Modeling Framework for Xenobiotics.

We describe a multi-scale, liver-centric in silico modeling framework for acetaminophen pharmacology and metabolism. We focus on a computational model to characterize whole body uptake and clearance, liver transport and phase I and phase II metabolism. We do this by incorporating sub-models that span three scales; Physiologically Based Pharmacokinetic (PBPK) modeling of acetaminophen uptake and distribution at the whole body level, cell and blood flow modeling at the tissue/organ level and metabolism at the sub-cellular level. We have used standard modeling modalities at each of the three scales. In particular, we have used the Systems Biology Markup Language (SBML) to create both the whole-body and sub-cellular scales. Our modeling approach allows us to run the individual sub-models separately and allows us to easily exchange models at a particular scale without the need to extensively rework the sub-models at other scales. In addition, the use of SBML greatly facilitates the inclusion of biological annotations directly in the model code. The model was calibrated using human in vivo data for acetaminophen and its sulfate and glucuronate metabolites. We then carried out extensive parameter sensitivity studies including the pairwise interaction of parameters. We also simulated population variation of exposure and sensitivity to acetaminophen. Our modeling framework can be extended to the prediction of liver toxicity following acetaminophen overdose, or used as a general purpose pharmacokinetic model for xenobiotics

Standalone simulation of sub-cellular model.

<p>Results of the standalone run of the sub-cellular model using parameter set <b>REFSIM</b> and an initial concentration of APAP of 0.1mM (15<i>μ</i>g/ml).</p

Time course of a standalone simulation of the sinusoid model in CC3D using the parameters set REFSIM.

<p>A simulated 3 second square pulse of APAP was pushed into the left end of the vessel lumen for three seconds starting one second into the simulation. The concentration of APAP in the blood and hepatocytes is given by the heat map scale at left and time progresses from top to bottom. Blood components are created at the periportal (left) end and a constant force is exerted on the blood components to induce blood flow through the simulated sinusoid. The temporal scales was adjusted so that the blood speed in the simulation was equivalent to 200 <i>μ</i>m/s, giving a transit time of a blood component through the sinusoid of one second.</p

CC3D Parameters.

<p>CC3D Parameters.</p

Plasma concentrations versus time for APAP and metabolites for HMPCsim6.

<p>Plasma concentrations versus time for APAP and metabolites simulated with the complete multiscale model using the best fit parameter set <b>HMPCsim6</b>. Symbols are as described in <a href="http://www.plosone.org/article/info:doi/10.1371/journal.pone.0162428#pone.0162428.g007" target="_blank">Fig 7</a>.</p

Fixed point sensitivity of model outputs to single parameter variations.

<p>The horizontal axis lists the model’s parameters grouped by the sub-model. Parameters for the PBPK model start with “pbpk_”, the sinusoid models with “cc3d_” and the subcellular model with “sc_”. The vertical axis lists the model’s outputs duplicated for each of the input parameter sets. The “Average” row is the average of all the sensitivities in the column for the particular parameter set. Each square in the heat map is the relative change of a model output divided by the relative change of the model parameter in single-parameter-variation simulation. Dark space indicates little influence of a parameter on the model outputs, bright regions reflect strong influence of a parameter and white regions represent sensitivities greater than one.</p

Sensitivity comparisons for formation of NAPQI-GSH.

<p>Comparison of the sensitivities for the formation of NAPQI-GSH (NAPQIGSH_Sum) versus the average parameter sensitivities about the fixed point <b>REFSIM</b>. Axis are as described in <a href="http://www.plosone.org/article/info:doi/10.1371/journal.pone.0162428#pone.0162428.g013" target="_blank">Fig 13</a>.</p

Plasma concentrations calculated using parameter set LNsim8.

<p>This parameter set represents a hypothetical chemical species with ADME behavior significantly different than APAP. Symbols are as described in <a href="http://www.plosone.org/article/info:doi/10.1371/journal.pone.0162428#pone.0162428.g007" target="_blank">Fig 7</a> and the APAP <i>in</i> <i>vivo</i> data is included for comparison.</p