No abstract

Germinal matrix hemorrhage (GMH), affecting about 1 in 300 births, is a major perinatal disease with lifelong neurological consequences. Yet despite advances in neonatal medicine, there is no effective intervention. GMH is characterized by localized bleeding in the germinal matrix (GM), due to inherent vessel fragility unique to this developing brain region. Studies have shown that reduced TGFβ signaling contributes to this vascular immaturity. We have previously shown that a region-specific G-protein-coupled receptor pathway in GM neural progenitor cells regulates integrin β8, a limiting activator of pro-TGFβ. In this study, we use mice to test whether this regional pathway can be harnessed for GMH intervention. We first examined the endogenous dynamics of this pathway and found that it displays specific patterns of activation. We then investigated the functional effects of altering these dynamics by chemogenetics and found that there is a narrow developmental window during which this pathway is amenable to manipulation. Although high-level activity in this time window interferes with vessel growth, moderate enhancement promotes vessel maturation without compromising growth. Furthermore, we found that enhancing the activity of this pathway in a mouse model rescues all GMH phenotypes. Altogether, these results demonstrate that enhancing neurovascular signaling through pharmacological targeting of this pathway may be a viable approach for tissue-specific GMH intervention. They also demonstrate that timing and level are likely two major factors crucial for success. These findings thus provide critical new insights into both brain neurovascular biology and the intervention of GMH.

Pharmacokinetics (PK) is the impact of the body on the drug and pharmacodynamics (PD) is the impact of the drug on the body. The effect of the drug on the target population of patients is predicted using models during population PK/PD analysis. This type of analysis is becoming more and more common in clinical trials, but creating the dataset structure required by the widely used non-linear mixed effects modelling software called NONMEM H is often the nightmare part of the process. It usually takes months to prepare the NONMEM dataset before the pharmacokineticist feels that it is ready for them to use. It is also produced after unblinding, adding to the delay in finalising the dataset before analysis can be performed with it. This delay can lead to holding back decisions about future trials, or those decisions are then made without taking into account the population PK analysis report. This paper will look at the issues which cause problems when creating a NONMEM dataset, and what steps we can take to avoid these problems and minimize the time taken to create the final dataset.