A Multi-Factorial Approach to Understanding and Predicting Brain Exposure to Pharmacologic Agents

Development of effective neurotherapeutics is limited by an inability to deliver sufficient drug mass to sites of action within the brain, due to specialized structural and functional characteristics of the interface between the CNS and the systemic circulation (the blood-brain barrier; BBB). A potential solution to this critical challenge involves chemical inhibition of P-glycoprotein (P-gp), an important barrier transporter expressed at the blood-brain interface that attenuates brain uptake of numerous CNS-targeted agents. To determine how modulation of brain exposure to a model P-gp substrate could impact pharmacologic response, the relationship between response of a human brain tumor cell line and exposure to the antineoplastic paclitaxel was explored in vitro. Mathematical modeling of these data demonstrated that substantial inhibition of BBB P-gp could significantly reduce the paclitaxel dose required to elicit a meaningful antitumor effect. As P-gp is expressed in many tissues, selective inhibition of BBB P-gp is desirable for increasing brain partitioning of P-gp substrates. A brain-targeted approach for P-gp inhibitors via nasal delivery therefore was explored. The nasal route conferred compound-specific brain exposure advantages among three model inhibitors. However, the ability of this route to deliver a mass of drug required to confer the desired pharmacologic effect was insufficient; the profound inhibition of BBB P-gp needed for meaningful increases in brain exposure after systemic administration of paclitaxel could not be achieved. A fundamental limitation of the model system was slow paclitaxel equilibration between blood and brain. Mathematical simulations explored relationships between accuracy of assessment of brain partitioning, including the influence of P-gp on partitioning, and sampling time. These simulations identified a previously undescribed effect of peripheral distribution on the kinetics of brain partitioning. Using data-mining and targeted prospective experimentation, distribution into a peripheral pharmacokinetic compartment was identified as a novel mechanistic explanation for previously unexplained time-dependent decreases, rather than increases, in brain partitioning of the antiepileptic, valproate. Taken together, these studies identify an important cause of erroneous assessment of P-gp impact on brain exposure, elucidate fundamental kinetic processes underlying the distribution of drugs into brain, and establish a framework for metrics that can provide appropriate descriptions of CNS exposure

Influence of Enterohepatic Recycling on the Time Course of Brain-to-Blood Partitioning of Valproic Acid in Rats

A widely used metric of substrate exposure in brain is the brain-to-serum partition coefficient (Kp,brain; Cbrain/Cserum), most appropriately determined at distribution equilibrium between brain tissue and serum. In some cases, Cbrain/Cserum can peak and then decrease, as opposed to monotonically increasing to a plateau, precluding accurate estimation of partitioning. This “overshoot” has been observed with compounds that undergo enterohepatic recycling (ER), such as valproic acid (VPA). Previous simulation experiments identified a relationship between overshoot in the Cbrain/Cserum versus time profile and distribution into a peripheral “compartment” (e.g., the ER loop). This study was conducted to evaluate model predictions of that relationship. Initial experiments tested the ability of activated charcoal, antibiotics, or Mrp2 deficiency to impair VPA ER in rats, thereby limiting the apparent volume of distribution associated with ER. Mrp2 deficiency (significantly) and antibiotics (moderately) interrupted VPA ER. Subsequently, brain partitioning was evaluated in the presence versus absence of ER modulation. Although overshoot was not eliminated completely, deconvolution revealed that overshoot was reduced in Mrp2-deficient and antibiotic-treated rats. Consistent with model predictions, overshoot was higher after antibiotic treatment (moderate ER interruption) than in Mrp2 deficiency (substantial ER interruption). Steady-state Kp,brain was unaffected by experimental manipulation, also consistent with model predictions. These data support the hypothesis that Cbrain/Cserum may overshoot Kp,brain based on the extent of peripheral sequestration. Consideration of this information, particularly for compounds that undergo significant extravascular distribution, may be necessary to avoid erroneous estimation of Kp,brain

Glutathione as a Biomarker in Parkinson’s Disease: Associations with Aging and Disease Severity

Objectives. Oxidative stress contributes to Parkinson’s disease (PD) pathophysiology and progression. The objective was to describe central and peripheral metabolites of redox metabolism and to describe correlations between glutathione (Glu) status, age, and disease severity. Methods. 58 otherwise healthy individuals with PD were examined during a single study visit. Descriptive statistics and scatterplots were used to evaluate normality and distribution of this cross-sectional sample. Blood tests and magnetic resonance spectroscopy (MRS) were used to collect biologic data. Spearman’s rank-order correlation coefficients were used to evaluate the strength and direction of the association. The Unified PD Rating Scale (UPDRS) and the Patient-Reported Outcomes in PD (PRO-PD) were used to rate disease severity using regression analysis. Results. Blood measures of Glu decreased with age, although there was no age-related decline in MRS Glu. The lower the blood Glu concentration, the more severe the UPDRS (P=0.02, 95% CI: −13.96, −1.14) and the PRO-PD (P=0.01, 95% CI: −0.83, −0.11) scores. Discussion. These data suggest whole blood Glu may have utility as a biomarker in PD. Future studies should evaluate whether it is a modifiable risk factor for PD progression and whether Glu fortification improves PD outcomes

Adaptive measurements of urban runoff quality

An approach to adaptively measure runoff water quality dynamics is introduced, focusing specifically on characterizing the timing and magnitude of urban pollutographs. Rather than relying on a static schedule or flow‐weighted sampling, which can miss important water quality dynamics if parameterized inadequately, novel Internet‐enabled sensor nodes are used to autonomously adapt their measurement frequency to real‐time weather forecasts and hydrologic conditions. This dynamic approach has the potential to significantly improve the use of constrained experimental resources, such as automated grab samplers, which continue to provide a strong alternative to sampling water quality dynamics when in situ sensors are not available. Compared to conventional flow‐weighted or time‐weighted sampling schemes, which rely on preset thresholds, a major benefit of the approach is the ability to dynamically adapt to features of an underlying hydrologic signal. A 28 km2 urban watershed was studied to characterize concentrations of total suspended solids (TSS) and total phosphorus. Water quality samples were autonomously triggered in response to features in the underlying hydrograph and real‐time weather forecasts. The study watershed did not exhibit a strong first flush and intraevent concentration variability was driven by flow acceleration, wherein the largest loadings of TSS and total phosphorus corresponded with the steepest rising limbs of the storm hydrograph. The scalability of the proposed method is discussed in the context of larger sensor network deployments, as well the potential to improving control of urban water quality.Key PointsAn Internet‐enabled sensor node autonomously adapts to weather forecasts and hydrograph features to collect water quality samplesFirst flush was not observed and peak loadings were primarily driven by erosion and flashinessCompared to present methods, our framework significantly reduces manpower and resource requirements in the study of water quality dynamicsPeer Reviewedhttp://deepblue.lib.umich.edu/bitstream/2027.42/135503/1/wrcr22370.pdfhttp://deepblue.lib.umich.edu/bitstream/2027.42/135503/2/wrcr22370_am.pd

Human impact parameterizations in global hydrological models improves estimates of monthly discharges and hydrological extremes: a multi-model validation study

Human activities have a profound influence on river discharge, hydrological extremes, and water-related hazards. In this study, we compare the results of five state-of-the-art global hydrological models (GHMs) with observations to examine the role of human impact parameterizations (HIP) in the simulation of the mean, high, and low flows. The analysis is performed for 471 gauging stations across the globe and for the period 1971-2010. We find that the inclusion of HIP improves the performance of GHMs, both in managed and near-natural catchments. For near-natural catchments, the improvement in performance results from improvements in incoming discharges from upstream managed catchments. This finding is robust across GHMs, although the level of improvement and reasons for improvement vary greatly by GHM. The inclusion of HIP leads to a significant decrease in the bias of long-term mean monthly discharge in 36-73% of the studied catchments, and an improvement in modelled hydrological variability in 31-74% of the studied catchments. Including HIP in the GHMs also leads to an improvement in the simulation of hydrological extremes, compared to when HIP is excluded. Whilst the inclusion of HIP leads to decreases in simulated high-flows, it can lead to either increases or decreases in low-flows. This is due to the relative importance of the timing of return flows and reservoir operations and their associated uncertainties. Even with the inclusion of HIP, we find that model performance still not optimal. This highlights the need for further research linking the human management and hydrological domains, especially in those areas with a dominant human impact. The large variation in performance between GHMs, regions, and performance indicators, calls for a careful selection of GHMs, model components, and evaluation metrics in future model applications

Global analysis of urban surface water supply vulnerability

This study presents a global analysis of urban water supply vulnerability in 71 surface-water supplied cities, with populations exceeding 750 000 and lacking source water diversity. Vulnerability represents the failure of an urban supply-basin to simultaneously meet demands from human, environmental and agricultural users. We assess a baseline (2010) condition and a future scenario (2040) that considers increased demand from urban population growth and projected agricultural demand. We do not account for climate change, which can potentially exacerbate or reduce urban supply vulnerability. In 2010, 35% of large cities are vulnerable as they compete with agricultural users. By 2040, without additional measures 45% of cities are vulnerable due to increased agricultural and urban demands. Of the vulnerable cities in 2040, the majority are river-supplied with mean flows so low (1200 liters per person per day, l/p/d) that the cities experience 'chronic water scarcity' (1370 l/p/d). Reservoirs supply the majority of cities facing individual future threats, revealing that constructed storage potentially provides tenuous water security. In 2040, of the 32 vulnerable cities, 14 would reduce their vulnerability via reallocating water by reducing environmental flows, and 16 would similarly benefit by transferring water from irrigated agriculture. Approximately half remain vulnerable under either potential remedy

Corrigendum: Global analysis of urban surface water supply vulnerability (2014 Environ. Res. Lett. 9 104004)

Salem, Oregon was inadvertently included in the analysis, but it does not meet the study threshold population of 750 000 people. This reduces the number of cities in this study from 71 to 70. Changes to figure 1 , and tables 1 , 2 and 3 as well as the text are shown below. The changes do not affect the overall results or conclusions