This Week’s Discoveries | 13 March 2018
- 13 March 2018
- This Week's Discoveries
Niels Bohrweg 2
2333 CA Leiden
Harnessing plant-Streptomyces interactions for the discovery of new antimicrobials.
Anne van der Meij (IBL) Anne is a PhD student is the group of Gilles van Wezel. She is a microbiologist with a love for filamentous bacteria called streptomycetes. These bacteria are well-known antibiotic producers and are therefore important for human health. Interestingly, a lot of streptomycetes form close associations with among others animals, insects and plants. Her PhD project is focused on plant-Streptomyces interactions since they have likely played a key role in the evolution of the chemical diversity of Streptomyces derived antibiotics. Our aim is to understand these interactions and use them as elicitors for the production of novel antibiotics.
How social sciences improve drug dosing
Elke Krekels (LACDR) Elke Krekels is currently employed as an assistant professor at the division of Systems Biomedicine and Pharmacology of the LACDR. Using quantitative pharmacological approaches her research focusses on developing evidence-based drug dosing guidelines for special patient populations and on developing zebrafish models for pre-clinical research with inproved inter-species scaling potential.
In drug development, accurate quantification of changes in disease severity is essential to establish drug effects needed for the assessment of efficacy and the development of optimal drug dosing guidelines. There are however diseases for which no measures are available to directly and objectively quantify disease severity. In these cases, disease severity may be quantified using clinical scores in which multiple items related to the disease are rated based on observations of a patient. Commonly, the analysis of multi-item tests is based on total scores, but since these do not stay true to the underlying numerical nature of these data, the statistical power of the analysis is reduced and as a result clinical trials may fail to proof statistically significant drug effects, preventing potential effective medicine from reaching the market.
Item response theory (IRT) was developed 70 years ago in social sciences, but its ideal applicability for the analysis of multi-item clinical scores was only recognized a few years ago. IRT uses all item-level data to derive an unobserved latent variable, that in this case reflects disease severity. The concept of IRT will be explained and illustrated with results obtained in the analysis of pain measurements in preverbal children.