Screening out irrelevant cell-based models of disease

The common and persistent failures to translate promising preclinical drug candidates into clinical success highlight the limited effectiveness of disease models currently used in drug discovery. An apparent reluctance to explore and adopt alternative cell-and tissue-based model systems, coupled with a detachment from clinical practice during assay validation, contributes to ineffective translational research. To help address these issues and stimulate debate, here we propose a set of principles to facilitate the definition and development of disease-relevant assays, and we discuss new opportunities for exploiting the latest advances in cell-based assay technologies in drug discovery, including induced pluripotent stem cells, three-dimensional (3D) co-culture and organ-on-a-chip systems, complemented by advances in single-cell imaging and gene editing technologies. Funding to support precompetitive, multidisciplinary collaborations to develop novel preclinical models and cell-based screening technologies could have a key role in improving their clinical relevance, and ultimately increase clinical success rates

Non-standard errors

In statistics, samples are drawn from a population in a data-generating process (DGP). Standard errors measure the uncertainty in estimates of population parameters. In science, evidence is generated to test hypotheses in an evidence generating process (EGP). We claim that EGP variation across researchers adds uncertainty: Non-standard errors (NSEs). We study NSEs by letting 164 teams test the same hypotheses on the same data. NSEs turn out to be sizable, but smaller for better reproducible or higher rated research. Adding peer-review stages reduces NSEs. We further find that this type of uncertainty is underestimated by participants

Rosiglitazone Improves Survival and Hastens Recovery from Pancreatic Inflammation in Obese Mice

Obesity increases severity of acute pancreatitis (AP) by unclear mechanisms. We investigated the effect of the PPAR-gamma agonist rosiglitazone (RGZ, 0.01% in the diet) on severity of AP induced by administration of IL-12+ IL-18 in male C57BL6 mice fed a low fat (LFD) or high fat diet (HFD), under the hypothesis that RGZ would reduce disease severity in HFD-fed obese animals. In both LFD and HFD mice without AP, RGZ significantly increased body weight and % fat mass, with significant upregulation of adiponectin and suppression of erythropoiesis. In HFD mice with AP, RGZ significantly increased survival and hastened recovery from pancreatic inflammation, as evaluated by significantly improved pancreatic histology, reduced saponification of visceral adipose tissue and less severe suppression of erythropoiesis at Day 7 post-AP. This was associated with significantly lower circulating and pancreas-associated levels of IL-6, Galectin-3, osteopontin and TIMP-1 in HFD + RGZ mice, particularly at Day 7 post-AP. In LFD mice with AP, RGZ significantly worsened the degree of intrapancreatic acinar and fat necrosis as well as visceral fat saponification, without affecting other parameters of disease severity or inflammation. Induction of AP lead to major suppression of adiponectin levels at Day 7 in both HFD and HFD + RGZ mice. In conclusion, RGZ prevents development of severe AP in obese mice even though it significantly increases adiposity, indicating that obesity can be dissociated from AP severity by improving the metabolic and inflammatory milieu. However, RGZ worsens selective parameters of AP severity in LFD mice

Mechanotransduction and Vascular Resistance

International audienceMechanotransduction is the process by which any cell transduces (converts) a mechanical signal into chemical cues. The vessel wall is permanently sheared by the moving blood particles as well as stretched and compressed by the pressure applied by the blood. Multiple types of mechanical stress fields are associated with flow patterns and unsteadiness.Mechanosensing occurs locally at the plasma membrane. It relies on detection of local changes in protein conformation that lead to ion channel opening, protein unfolding, modified enzyme kinetics, and variations in molecular interactions following exposure of buried binding site or, conversely, hiding them.Mechanotransduction initiates several signaling pathways. Multiple mediators include: At the cell surface, G-protein-coupled and protein tyrosine kinase receptors, ion channels, enzymes, adhesion molecules, and specialized plasmalemmal nanodomains At the cell cortex, the cortical actin network that regulates the cell-surface mechanics and signaling adaptors and effectors (e.g., small monomeric guanosine triphosphatases and heterotrimeric guanine nucleotide-binding proteins, kinases, phosphatases, and ubiquitins, among others) In the cytosol, enzymes, scaffolds, carriers such as endosomes, calcium concentration, and transcription factors In the nucleus, nuclear pore carriers, enzymes, and the transcriptional and translational machineryMechanotransduction by vascular cells regulate the contraction–relaxation state of vascular smooth myocytes, thereby regulating locally and quickly the size of the vascular lumen, that is, the local vascular resistance to blood flow. Once experiencing an unusual mechanical stress, vascular smooth myocytes react by contracting or relaxing according to the magnitude of the mechanical stress, the value of which rises above or falls below the range in which it fluctuates in normal conditions. Moreover, they receive chemical and electrochemical signals from endotheliocytes, themselves sensing the wall shear stress at their wetted (luminal) surface.Mechanotransduction thus regulates locally blood flow more rapidly than the endocrine regulation by remote tissues and even than that of the nervous system, which transmits signals very rapidly via afferent nerves and, after processing in the centers of the spinal cord and brain, efferent nerves

Nonstandard Errors

In statistics, samples are drawn from a population in a data-generating process (DGP). Standard errors measure the uncertainty in estimates of population parameters. In science, evidence is generated to test hypotheses in an evidence-generating process (EGP). We claim that EGP variation across researchers adds uncertainty—nonstandard errors (NSEs). We study NSEs by letting 164 teams test the same hypotheses on the same data. NSEs turn out to be sizable, but smaller for more reproducible or higher rated research. Adding peer-review stages reduces NSEs. We further find that this type of uncertainty is underestimated by participants