Extrapolating In Vitro Results to Predict Human Toxicity

The neurodegenerative hypothesis of depression and the influence of antidepressant medications

Depression is a complex neurological disease that holds many theories on its aetiology and pathophysiology. The monoamine strategy of treating depression with medications to increase levels of monoamines in the (extra)synapse, primarily through the inhibition of monoamine transporters, does not always work, as seen in patients that lack a response to multiple anti-depressant exposures, as well as a lack of depressive symptoms in healthy volunteers exposed to monoamine reduction. Depression is increasingly being understood not as a single condition, but as a complex interplay of adaptations in various systems, including inflammatory responses and neurotransmission pathways in the brain. This understanding has led to the development of the neurodegenerative hypothesis of depression. This hypothesis, which is gaining widespread acceptance posits that both oxidative stress and inflammation play significant roles in the pathophysiology of depression. This article is a review of the literature focused on neuroinflammation in depression, as well as summarised studies of anti-inflammatory and antioxidant effects of antidepressants.

Multistate models of developmental toxicity: Application to valproic acid-induced malformations in the zebrafish embryo

For the determination of acute toxicity of chemicals in zebrafish (Danio rerio) embryos, the OECD test guideline 236, relative to the Fish Embryo Toxicity Test (FET), stipulates a dose-response analysis of four lethal core endpoints and a quantitative characterization of abnormalities including their time-dependency. Routinely, the data are analyzed at the different observation times separately. However, observations at a given time strongly depend on the previous effects and should be analyzed jointly with them. To solve this problem, we developed multistate models for occurrence of developmental malformations and live events in zebrafish embryos exposed to eight concentrations of valproic acid (VPA) the first five days of life. Observations were recorded daily per embryo. We statistically infer on model structure and parameters using a numerical Bayesian framework. Hatching probability rate changed with time and we compared five forms of its time-dependence; a constant rate, a piecewise constant rate with a fixed hatching time at 48 h post fertilization, a piecewise constant rate with a variable hatching time, as well as a Hill and Gaussian form. A piecewise constant function of time adequately described the hatching data. The other transition rates were conditioned on the embryo body concentration of VPA, obtained using a physiologically-based pharmacokinetic model. VPA impacted mostly the malformation probability rate in hatched and non-hatched embryos. Malformation reversion probability rates were lowered by VPA. Direct mortality was low at the concentrations tested, but increased linearly with internal concentration. The model makes full use of data and gives a finer grain analysis of the teratogenic effects of VPA in zebrafish than the OECD-prescribed approach. We discuss the use of the model for obtaining toxicological reference values suitable for inter-species extrapolation. A general result is that complex multistate models can be efficiently evaluated numerically.

An In Vitro Model of Diabetic Retinal Vascular Endothelial Dysfunction and Neuroretinal Degeneration

BACKGROUND

Diabetic retinopathy (DR) is a leading cause of blindness in working-age populations. Proper in vitro DR models are crucial for exploring pathophysiology and identifying novel therapeutic targets. This study establishes a rational in vitro diabetic retinal neuronal-endothelial dysfunction model and a comprehensive downstream validation system.

METHODS

Human retinal vascular endothelial cells (HRMECs) and retinal ganglion cells (RGCs) were treated with different glucose concentrations with mannitol as matched osmotic controls. Cell proliferation and viability were evaluated by the Cell Counting Kit-8. Cell migration was measured using a transwell migration assay. Cell sprouting was assessed by a tube formation assay. The VEGF expression was assessed by ELISA. RGCs were labeled by neurons and RGC markers TUJ1 and BRN3A for quantitative and morphological analysis. Apoptosis was detected using PI/Hoechst staining and TUNEL assay and quantified by ImageJ.

RESULTS

Cell proliferation and migration in HRMECs were significantly higher in the 25 mM glucose-treated group (p < 0.001) but lower in the 50 mM and 100 mM groups (p < 0.001). The permeability and the apoptotic index in HRMECs were statistically higher in the 25 mM, 50 mM, and 100 mM groups (p < 0.05). The tube formation assay found that all the parameters were significantly higher in the 25 mM and 50 mM groups (p < 0.001) concomitant with the elevated VEGFA expression in HRMECs (p = 0.016). Cell viability was significantly lower in the 50 mM, 100 mM, and 150 mM groups in RGCs (p _50mM = 0.013, p _100mM = 0.019, and p _150mM = 0.002). Apoptosis was significantly elevated, but the proportion of RGCs with neurite extension was significantly lower in the 50 mM, 100 mM, and 150 mM groups (p _50mM < 0.001, p _100mM < 0.001, and p _150mM < 0.001).

CONCLUSIONS

We have optimized glucose concentrations to model diabetic retinal endothelial (25-50 mM) or neuronal (50-100 mM) dysfunction in vitro, which have a wide range of downstream applications.

Quantitative in vitro to in vivo extrapolation of tissues toxicity

Predicting repeated-dosing in vivo drug toxicity from in vitro testing and omics data gathering requires significant support in bioinformatics, mathematical modeling and statistics. We present here the major aspects of the work devoted within the framework of the European integrated Predict-IV to pharmacokinetic modeling of in vitro experiments, physiologically based pharmacokinetic (PBPK) modeling, mechanistic models of toxicity for the kidney and brain, large scale dose-response analyses methods and biomarker discovery tools. All of those methods have been applied to various extent to the drug datasets developed by the project's partners. Our approach is rather generic and could be adapted to other drugs or drug candidates. It marks a successful integration of the work of the different teams toward a common goal of predictive quantitative in vitro to in vivo extrapolation.