3 Minute 3Rs December 2018

It's the December episode of 3-Minute 3Rs, brought to you by , the NC3Rs (www.nc3rs.org.uk), the North American 3Rs Collaborative (www.na3rsc.org) and Lab Animal (www.nature.com/laban)The papers behind the pod:1. Optimizing the design of population-based patient-derived tumor xenograft studies to better predict clinical response https://bit.ly/2GwGJ7N2. An In Vitro Model of Hematotoxicity https://bit.ly/2Ezgaw33. Found In Translation https://go.nature.com/2UVJ0g6[NC3Rs] Every cancerous tumor is different and treating two tumors with the same drug may result in two very different outcomes, which contributes to the high failure rate during cancer drug discovery. One experiment type gaining interest to model this tumor heterogeneity is screening a population of patient-derived tumor xenograft models, or PDX models. However, there is little guidance available for these experiments. This risks poor experimental design that could result in a study being under or overpowered. Both these scenarios waste animals. A new freely available interactive tool from a team at AstraZeneca, published in Disease Models & Mechanisms, enables scientists to design robust and reproducible population PDX studies. The tool developed by Floc’h et al explores the design of population PDX studies and how this can impact the risk of both false-negative and false-positive results. Each experiment for a specific drug or cancer type is unique so the tool cannot provide exact recommendations, but what it can do is support you in selecting the optimal design for your experiment whilst ensuring the 3Rs are also considered. Details for how to access the tool can be found via the link in the description.[NA3RsC] Mature blood cells and bone marrow are common targets of drug toxicity. In addition to leading to significant complications for patients receiving antineoplastic agents for cancer treatment, these hematotoxic effects can be limiting, preventing administration of the doses needed for therapeutic efficacy. Screening for potential toxicity to the hematopoietic system in animals and in human cell-based in vitro approaches is critical in early drug development. The CFU assay has been validated as the traditional screen for hematotoxicity, but has limitations. A publication in Current Protocols in Toxicology by Mahalingaiah and colleagues describes the next-generation, high-throughput assay as a refined approach. A liquid culture, lineage-specific, in vitro cell differentiation model offers multiple advantages over the traditional assay, making it a versatile and useful tool to screen compounds. In addition to decreasing the number of animals required for hematoxicity determination, this human, cell-based in vitro assay is a useful alternative to in vivo studies for investigative work in understanding mechanisms of lineage-specific hematotoxicity at different stages of differentiation.[LA] So you’ve made a mouse model of disease, applied a treatment, and measured gene expression. Now what? You might think the gene with the greatest changes in expression is going to be the most important for the disease. But there’s a pretty big evolutionary gap between mouse and man. From just your experiment, do you really know how relevant that gene will be? To better bridge the species gap, researchers from Technion have developed a new statistical model that takes decades of previously recorded gene expression data in mouse and human into account. They call it Found in Translation, or FIT for short. For new mouse data, FIT calculates a per-gene effect size and predicts which gene in your mouse might be most relevant to humans. It can potentially rescue genes that might not otherwise make the cut for further studies and de-prioritize those important to a mouse but perhaps less so to a person. No new mice experiments required. You can find all the details about FIT in Nature Methods, and you can try it out yourself with your own gene expression data at mouse2man.org. Hosted on Acast. See acast.com/privacy for more information.