Assessing the toxicity of a sample for its risk to human health is key throughout the life sciences. This assessment is perhaps most visible in terms of the pharmaceutical industry, e.g., drug failures due to liver- or cardio-toxicity. There is also considerable toxicity testing that occurs for environmental toxicants as well as consumer product safety.
In addition, there are various regulatory initiatives (for example, REACH in Europe) to reduce the use of animals for toxicity testing, initially for nonpharmaceutical products. Couple this overwhelming rise in the volume of toxicity testing with the fact that many traditional methods of in vitro toxicity testing offer low predictivity, or use subjective, manual methods and there is now a perfect storm that is driving innovation and the desire for both better tools and more relevant, predictive models.
High-content methods based on automated imaging are able to generate multiple endpoints of size, shape, texture, and intensity in individual cells in each well, thereby reducing losses in data integrity due to population averaging. Because each cell is measured independently, subtle changes in physiology can be detected and quantified robustly. Additionally, high content allows previously nonaddressable targets to be evaluated, particularly those where a morphological change occurs in cells.