ATLA 45.2, May 2017

//ATLA 45.2, May 2017

CAAT News and Views

CAAT staff

Big Data, Big Deadlines Spur a Change in Toxicity Testing

CAAT Co-hosts Symposium on Social Housing of Laboratory Animals

Thomas Hartung Interviewed in Rigor Mortis: How Sloppy Science Creates Worthless Cures, Crushes Hope, and Wastes Billions

CAAT-Europe’s Costanza Rovida Cited in ECHA Newsletter

ALTEX Journal Celebrates 10 Years of Food for Thought

Recent Publications

You need to register (for free) to download this article. Please log in/register here.

2017-06-09T14:28:27+00:00 Tags: , |

Evaluation of a Human Neural Stem Cell Culture Method for Prediction of the Neurotoxicity of Anti-epileptics

Abdal-jabbar Al-Rubai, Peter Wigmore and Margaret K. Pratten

Human neural stem cells have been proposed as an in vitro model to predict neurotoxicity. In this study, the potential of in vitro cultures of human-derived neurospheres to predict the effects of various anti-epileptic drugs (sodium valproate, phenytoin, carbamazepine and phenobarbitone) was evaluated. In general, these drugs had no significant effects on cell viability, total cellular protein, and  neuronal process length at low doses, but at high doses these parameters were reduced significantly. Therapeutic doses of sodium valproate and phenytoin had a clear effect on neurosphere size and cell migration, with a significant reduction in both parameters when compared with the control group. The other drugs (carbamazepine and phenobarbitone) reduced neurosphere size and cell migration only at higher doses. The expression levels of glial fibrillary protein and tubulin III, which were used to identify astrocytes and neuronal cells, respectively, were reduced in a dose-dependent manner that became significant at high doses. The levels of glial fibrillary protein did not indicate any occurrence of reactive astrocytosis.

This article is currently only available in full to paid subscribers. Click here to subscribe, or you will need to log in/register to buy and download this article

Sample Size Estimation for Pilot Animal Experiments by Using a Markov Chain Monte Carlo Approach

Andreas Allgoewer and Benjamin Mayer

The statistical determination of sample size is mandatory when planning animal experiments, but it is usually difficult to implement appropriately. The main reason is that prior information is hardly ever available, so the assumptions made cannot be verified reliably. This is especially true for pilot experiments. Statistical simulation might help in these situations. We used a Markov Chain Monte Carlo (MCMC) approach to verify the pragmatic assumptions made on different distribution parameters used for power and sample size calculations in animal experiments. Binomial and normal distributions, which are the most frequent distributions in practice, were simulated for categorical and continuous endpoints, respectively. The simulations showed that the common practice of using five or six animals per group for continuous endpoints is reasonable. Even in the case of small effect sizes, the statistical power would be sufficiently
large (≥ 80%). For categorical outcomes, group sizes should never be under eight animals, otherwise a sufficient statistical power cannot be guaranteed. This applies even in the case of large effects. The MCMC approach demonstrated to be a useful method for calculating sample size in animal studies that lack prior data. Of course, the simulation results particularly depend on the assumptions made with regard to the distributional properties and effects to be detected, but the same also holds in situations where prior data are available. MCMC is therefore a promising approach toward the more informed planning of pilot research experiments involving the use of animals.

This article is currently only available in full to paid subscribers. Click here to subscribe, or you will need to log in/register to buy and download this article

The Use of Neurocomputational Models as Alternatives to Animal Models in the Development of Electrical Brain Stimulation Treatments

Anne Beuter

Recent publications call for more animal models to be used and more experiments to be performed, in order to better understand the mechanisms of neurodegenerative disorders, to improve human health, and to develop new brain stimulation treatments. In response to these calls, some limitations of the current animal models are examined by using Deep Brain Stimulation (DBS) in Parkinson’s disease as an illustrative example. Without focusing on the arguments for or against animal experimentation, or on the history of DBS, the present paper argues that given recent technological and theoretical advances, the time has come to consider bioinspired computational modelling as a valid alternative to animal models, in order to design the next generation of human brain stimulation treatments. However, before computational neuroscience is fully integrated in the translational process and used as a substitute for animal models, several obstacles need to be overcome. These obstacles are examined in the context of institutional, financial, technological and behavioural lock-in. Recommendations include encouraging agreement to change long-term habitual practices, explaining what alternative models can achieve, considering economic stakes, simplifying administrative and regulatory constraints, and carefully examining possible conflicts of interest.

This article is currently only available in full to paid subscribers. Click here to subscribe, or you will need to log in/register to buy and download this article