Phospholipidosis in rats treated with amiodarone: serum biochemistry and whole genome micro-array analysis supporting the lipid traffic jam hypothesis and the subsequent rise of the biomarker BMP.

To provide mechanistic insight in the induction of phospholipidosis and the appearance of the proposed biomarker di-docosahexaenoyl (C22:6)-bis(monoacylglycerol) phosphate (BMP), rats were treated with 150 mg/kg amiodarone for 12 consecutive days and analyzed at three different time points (day 4, 9, and 12). Biochemical analysis of the serum revealed a significant increase in cholesterol and phospholipids at the three time points. Bio-analysis on the serum and urine detected a time-dependent increase in BMP, as high as 10-fold compared to vehicle-treated animals on day 12. Paralleling these increases, micro-array analysis on the liver of treated rats identified cholesterol biosynthesis and glycerophospholipid metabolism as highly modulated pathways. This modulation indicates that during phospholipidosis-induction interactions take place between the cationic amphiphilic drug and phospholipids at the level of BMP-rich internal membranes of endosomes, impeding cholesterol sorting and leading to an accumulation of internal membranes, converting into multilamellar bodies. This process shows analogy to Niemann-Pick disease type C (NPC). Whereas the NPC-induced lipid traffic jam is situated at the cholesterol sorting proteins NPC1 and NPC2, the amiodarone-induced traffic jam is thought to be located at the BMP level, demonstrating its role in the mechanism of phospholipidosis-induction and its significance for use as a biomarker.

Modeling anti-KLH ELISA data using two-stage and mixed effects models in support of immunotoxicological studies.

During preclinical drug development, the immune system is specifically evaluated after prolonged treatment with drug candidates, because the immune system may be an important target system. The response of antibodies against a T-cell-dependent antigen is recommenced by the FDA and EMEA for the evaluation of immunosuppression/enhancement. For that reason, we developed a semiquantitative enzyme-linked immunosorbent assay to measure antibodies against keyhole limpet hemocyanin. To our knowledge, the analysis of this kind of data is at this moment not yet fully explored. In this article, we describe two approaches for modeling immunotoxic data using nonlinear models. The first is a two-stage model in which we fit an individual nonlinear model for each animal in the first stage, and the second stage consists of testing possible treatment effects using the individual maximum likelihood estimates obtained in the first stage. In the second approach, the inference about treatment effects is based on a nonlinear mixed model, which accounts for heterogeneity between animals. In both approaches, we use a three-parameter logistic model for the mean structure.

MODELING ANTI-KLH ELISA DATA USING TWO-STAGE AND MIXED EFFECTS MODELS IN SUPPORT OF IMMUNOTOXICOLOGICAL STUDIES.

During preclinical drug development, the immune system is specifically evaluated after prolonged treatment with drug candidates, because the immune system may be an important target system. The response of antibodies against a T-cell-dependent antigen is recommenced by the FDA and EMEA for the evaluation of immunosuppression/enhancement. For that reason, we developed a semiquantitative enzyme-linked immunosorbent assay to measure antibodies against keyhole limpet hemocyanin. To our knowledge, the analysis of this kind of data is at this moment not yet fully explored. In this article, we describe two approaches for modeling immunotoxic data using nonlinear models. The first is a two-stage model in which we fit an individual nonlinear model for each animal in the first stage, and the second stage consists of testing possible treatment effects using the individual maximum likelihood estimates obtained in the first stage. In the second approach, the inference about treatment effects is based on a nonlinear mixed model, which accounts for heterogeneity between animals. In both approaches, we use a three-parameter logistic model for the mean structure.