Investigation of ifosfamide and chloroacetaldehyde renal toxicity through integration of in vitro liver-kidney microfluidic data and pharmacokinetic-system biology models.

We have integrated in vitro and in silico data to describe the toxicity of chloroacetaldehyde (CAA) on renal cells via its production from the metabolism of ifosfamide (IFO) by hepatic cells. A pharmacokinetic (PK) model described the production of CAA by the hepatocytes and its transport to the renal cells. A system biology model was coupled to the PK model to describe the production of reactive oxygen species (ROS) induced by CAA in the renal cells. In response to the ROS production, the metabolism of glutathione (GSH) and its depletion were modeled by the action of an NFE2L2 gene-dependent pathway. The model parameters were estimated in a Bayesian context via Markov Chain Monte Carlo (MCMC) simulations based on microfluidic experiments and literature in vitro data. Hepatic IFO and CAA in vitro intrinsic clearances were estimated to be 1.85 x 10(-9) µL s(-1) cell(-1) and 0.185 x 10(-9) µL s(-1) cell(-1) ,respectively (corresponding to an in vivo intrinsic IFO clearance estimate of 1.23 l h(-1) , to be compared to IFO published values ranging from 3 to 10 l h(-1) ). After model calibration, simulations made at therapeutic doses of IFO showed CAA renal intracellular concentrations ranging from 11 to 131 µM. Intracellular CAA concentrations above 70 µM induced intense ROS production and GSH depletion. Those responses were time and dose dependent, showing transient and non-linear kinetics. Those results are in agreement with literature data reporting that intracellular CAA toxic concentrations range from 35 to 320 µM, after therapeutic ifosfamide dosing. The results were also consistent with in vitro CAA renal cytotoxicity data.

First pass intestinal and liver metabolism of paracetamol in a microfluidic platform coupled with a mathematical modeling as a means of evaluating ADME processes in humans.

We developed a microfluidic platform to investigate paracetamol intestinal and liver first pass metabolism. This approach was coupled with a mathematical model to estimate intrinsic in vitro parameters and to predict in vivo processes. The kinetic modeling estimated the paracetamol and paracetamol sulfate permeabilities, the sulfate and glucuronide effluxes in the intestine compartment. Based on a gut model, we estimated intrinsic intestinal clearance of between 26 and 77 L/h for paracetamol in humans, a permeability of 10 L/h, and a gut availability between 0.17 and 0.53 (compared to 0.95-1 in vivo). The role played by the liver in paracetamol metabolism was estimated via in vitro intrinsic clearances of 7.6, 13.6, and 11.5 µL/min/10(6) cells for HepG2/C3a, rat primary hepatocytes, and human primary hepatocytes, respectively. Based on a parallel tube model to describe the liver, the paracetamol hepatic clearance, and the paracetamol hepatic availability in humans were estimated at 6.5 mL/min/kg of bodyweight (BDW) and 0.7, respectively (when compared to 5 mL/min/kg of BDW and 0.77 to 0.88 for in vivo values, respectively). The drug availability was predicted ranging between 0.24 and 0.41 (0.88 in vivo). The overall approach provided a first step in an integrated strategy combining in silico/in vitro methods based on microfluidic for evaluating drug absorption, distribution and metabolism processes.

Occupational exposure to cobalt: a population toxicokinetic modeling approach validated by field results challenges the biological exposure index for urinary cobalt.

This study modeled the urinary toxicokinetics of cobalt exposure based on 507 urine samples from 16 workers, followed up for 1 week, and 108 related atmospheric cobalt measurements to determine an optimal urinary cobalt sampling strategy at work and a corresponding urinary exposure threshold (UET). These data have been used to calibrate a population toxicokinetic model, taking into account both the measurement uncertainty and intra- and interindividual variability. Using the calibrated model, urinary sampling sensitivity and specificity performance in detecting exposure above the 20 microg/m(3) threshold limit value - time-weighted average (TLV-TWA) has been applied to identify an optimal urine sampling time. The UET value is obtained by minimizing misclassification rates in workplace exposures below or above the TLV. Total atmospheric cobalt concentrations are in the 5-144 microg/m(3) range, and total urinary cobalt concentrations are 0.5-88 microg/g creatinine. A two-compartment toxicokinetic model best described urinary elimination. Terminal elimination half-time from the central compartment is 10.0 hr (95% confidence interval [8.3-12.3]). The optimal urinary sampling time has been identified as 3 hr before the end of shift at the end of workweek. If we assume that misclassification errors are of equal cost, the UET associated with the TLV of 20 microg/m(3) is 5 microg/L, which is lower than the ACGIH-recommended biological exposure index of 15 microg/L.

Stochasticity in physiologically based kinetics models: implications for cancer risk assessment.

In case of low-dose exposure to a substance, its concentration in cells is likely to be stochastic. Assessing the consequences of this stochasticity in toxicological risk assessment requires the coupling of macroscopic dynamics models describing whole-body kinetics with microscopic tools designed to simulate stochasticity. In this article, we propose an approach to approximate stochastic cell concentration of butadiene in the cells of diverse organs. We adapted the dynamics equations of a physiologically based pharmacokinetic (PBPK) model and used a stochastic simulator for the system of equations that we derived. We then coupled kinetics simulations with a deterministic hockey stick model of carcinogenicity. Stochasticity induced substantial modifications relative to dose-response curve, compared with the deterministic situation. In particular, there was nonlinearity in the response and the stochastic apparent threshold was lower than the deterministic one. The approach that we developed could easily be extended to other biological studies to assess the influence of stochasticity at macroscopic scale for compound dynamics at the cell level.

Analysis of Time-Series Gene Expression Data to Explore Mechanisms of Chemical-Induced Hepatic Steatosis Toxicity.

Non-alcoholic fatty liver disease (NAFLD) represents a wide spectrum of disease, ranging from simple fatty liver through steatosis with inflammation and necrosis to cirrhosis. One of the most challenging problems in biomedical research and within the chemical industry is to understand the underlying mechanisms of complex disease, and complex adverse outcome pathways (AOPs). Based on a set of 28 steatotic chemicals with gene expression data measured on primary hepatocytes at three times (2, 8, and 24 h) and three doses (low, medium, and high), we identified genes and pathways, defined as molecular initiating events (MIEs) and key events (KEs) of steatosis using a combination of a time series and pathway analyses. Among the genes deregulated by these compounds, the study highlighted OSBPL9, ALDH7A1, MYADM, SLC51B, PRDX6, GPAT3, TMEM135, DLGDA5, BCO2, APO10LA, TSPAN6, NEURL1B, and DUSP1. Furthermore, pathway analysis indicated deregulation of pathways related to lipid accumulation, such as fat digestion and absorption, linoleic and linolenic acid metabolism, calcium signaling pathway, fatty acid metabolism, peroxisome, retinol metabolism, and steroid metabolic pathways in a time dependent manner. Such transcription profile analysis can help in the understanding of the steatosis evolution over time generated by chemical exposure.