The extended statistical analysis of toxicity tests using standardised effect sizes (SESs): a comparison of nine published papers

Specific reprogramming of alpha cells to insulin-producing cells by short glucagon promoter-driven Pdx1 and MafA

Endogenous reprogramming of pancreas-derived non-beta cells into insulin-producing cells is a promising approach to treat type 1 diabetes (T1D). One strategy that has yet to be explored is the specific delivery of insulin-producing essential genes, Pdx1 and MafA, to pancreatic alpha cells to reprogram the cells into insulin-producing cells in an adult pancreas. In this study, we used an alpha cell-specific glucagon (GCG) promoter to drive Pdx1 and MafA transcription factors to reprogram alpha cells to insulin-producing cells in chemically induced and autoimmune diabetic mice. Our results showed that a combination of a short glucagon-specific promoter with AAV serotype 8 (AAV8) can be used to successfully deliver Pdx1 and MafA to pancreatic alpha cells in the mouse pancreas. Pdx1 and MafA expression specifically in alpha cells were also able to correct hyperglycemia in both induced and autoimmune diabetic mice. With this technology, targeted gene specificity and reprogramming were accomplished with an alpha-specific promotor combined with an AAV-specific serotype and provide an initial basis to develop a novel therapy for the treatment of T1D.

Comparative Study of Multitask Toxicity Modeling on a Broad Chemical Space

Acute toxicity is one of the most challenging properties to predict purely with computational methods due to its direct relationship to biological interactions. Moreover, toxicity can be represented by different end points: it can be measured for different species using different types of administration, etc., and it is questionable if the knowledge transfer between end points is possible. We performed a comparative study of prediction multitask toxicity for a broad chemical space using different descriptors and modeling algorithms and applied multitask learning for a large toxicity data set extracted from the Registry of Toxic Effects of Chemical Substances (RTECS). We demonstrated that multitask modeling provides significant improvement over single-output models and other machine learning methods. Our research reveals that multitask learning can be very useful to improve the quality of acute toxicity modeling and raises a discussion about the usage of multitask approaches for regulation purposes. Our MultiTox models are freely available in OCHEM platform ( ochem.eu/multitox ) under CC-BY-NC license.

Genetically Defined Strains in Drug Development and Toxicity Testing

There is growing concern about the poor quality and lack of repeatability of many pre-clinical experiments involving laboratory animals. According to one estimate as much as $28 billion is wasted annually in the USA alone in such studies. A decade ago the FDA's "Critical path" white paper noted that "The traditional tools used to assess product safety-animal toxicology and outcomes from human studies-have changed little over many decades and have largely not benefited from recent gains in scientific knowledge. The inability to better assess and predict product safety leads to failures during clinical development and, occasionally, after marketing." Repeat-dose 28-days and 90-days toxicity tests in rodents have been widely used as part of a strategy to assess the safety of drugs and chemicals but their repeatability and power to detect adverse effects have not been formally evaluated.The guidelines (OECD TG 407 and 408) for these tests specify the dose levels and number of animals per dose but do not specify the strain of animals which should be used. In practice, almost all the tests are done using genetically undefined "albino" rats or mice in which the genetic variation, a major cause of inter-individual and strain variability, is unknown and uncontrolled. This chapter suggests that a better strategy would be to use small numbers of animals of several genetically defined strains of mice or rats instead of the undefined animals used at present. Inbred strains are more stable providing more repeatable data than outbred stocks. Importantly their greater phenotypic uniformity should lead to more powerful and repeatable tests. Any observed strain differences would indicate genetic variation in response to the test substance, providing key data. We suggest that the FDA and other regulators and funding organizations should support research to evaluate this alternative.