Metabolites of PPI-2458, a selective, irreversible inhibitor of methionine aminopeptidase-2: structure determination and In vivo activity

The natural product fumagillin exhibits potent antiproliferative and antiangiogenic properties. The semisynthetic analog PPI-2458, (3R,4S,5S,6R)-5-methoxy-4-(2R,3R)-2-methyl-3-(3-methylbut-2-enyl) oxiran-2-yl]-1-oxaspiro2.5]octan-6-yl] N-(2R)-1-amino-3-methyl-1-oxobutan-2-yl]carbamate, demonstrates rapid inactivation of its molecular target, methionine aminopeptidase-2 (MetAP2), and good efficacy in several rodent models of cancer and inflammation with oral dosing despite low apparent oral bioavailability. To probe the basis of its in vivo efficacy, the metabolism of PPI-2458 was studied in detail. Reaction phenotyping identified CYP3A4/5 as the major source of metabolism in humans. Six metabolites were isolated from liver microsomes and characterized by mass spectrometry and nuclear resonance spectroscopy, and their structures were confirmed by chemical synthesis. The synthetic metabolites showed correlated inhibition of MetAP2 enzymatic activity and vascular endothelial cell growth. In an ex vivo experiment, MetAP2 inhibition in white blood cells, thymus, and lymph nodes in rats after single dosing with PPI-2458 and the isolated metabolites was found to correlate with the in vitro activity of the individual species. In a phase 1 clinical study, PPI-2458 was administered to patients with non-Hodgkin lymphoma. At 15 mg administered orally every other day, MetAP2 in whole blood was 80% inactivated for up to 48 hours, although the exposure of the parent compound was only similar to 10% that of the summed cytochrome P450 metabolites. Taken together, the data confirm the participation of active metabolites in the in vivo efficacy of PPI-2458. The structures define a metabolic pathway for PPI-2458 that is distinct from that of TNP-470 ((3R, 4S, 5S, 6R)-5-methoxy-4-(2R, 3R)-2-methyl-3-(3-methylbut-2-enyl)oxiran-2-yl]-1-oxaspiro2.5]octan-6 -yl] N-(2-chloroacetyl)carbamate). The high level of MetAP2 inhibition achieved in vivo supports the value of fumagillin-derived therapeutics for angiogenic diseases.

Molecular dynamics simulations of PPI dendrimer-drug complexes

Dendrimeric nanoparticles are potential drug delivery devices which can enhance the solubility of hydrophobic drugs, thus increasing their bioavailability and sustained release action. A quantitative understanding of the dendrimer-drug interactions can give valuable insight into the solubility and release profile of hydrophobic drug molecules in various solvent conditions. Fully atomistic molecular dynamics (MD) simulations have been performed to study the interactions of G5 PPIEDA (G5 ethylenediamine cored poly(propylene imine)) dendrimer and two well known drugs (Famotidine and Indomethacin) at different pH conditions. The study suggested that at low pH the dendrimer-drug complexes are thermodynamically unstable as compared to neutral and high pH conditions. Calculated Potential of Mean Force (PMF) by umbrella sampling showed that the release of drugs from the dendrimer at low pH is spontaneous, median release at neutral pH and slow release at high pH. In addition, Molecular Mechanics Poisson-Boltzmann Surface Area (MM-PBSA) binding free energy calculations were also performed at each umbrella sampling window to identify the various energy contributions. To understand the effect of dendrimer chemistry and topology on the solubility and release profile of drugs, this study is extended to explore the solubility and release profile of phenylbutazone drug complexed with G3 poly(amidoamine) and G4 diaminobutane cored PPI dendrimers. The results indicate that the pH-induced conformational changes in dendrimer, ionization states, dendrimer type and pK(a) of the guest molecules influence the free energy barrier and stability of complexation, and thus regulate drug loading, solubility and release.

Personal and Public Involvement and its impact - Monitoring, measuring and evaluating the impact of Personal and Public Involvement (PPI) in Health and Social Care in Northern Ireland.

Introduction
This report details the findings from research conducted across Northern Ireland’s Health and Social Care Trusts during 2015 which examines the current state of Personal and Public Involvement (PPI). This is about how service users, carers and patients engage with staff, management and directors of statutory health and social care organisations. Most statutory health and social care organisations must, under legislation, meet the requirements of PPI. PPI has been part of health and social care policy in Northern Ireland since 2007 and became law two years later with the introduction of the Health and Social Care Reform Act (2009). It is, therefore, timely that PPI is now assessed in this systematic way in order to both examine the aspects which are working well and to highlight those areas where improvements need to be made. As far as possible, this Summary Report is written in an accessible way, avoiding jargon and explaining key research terms, so as to ensure it is widely understood. This is in keeping with established good practice in service user involvement research. This summary, therefore, gives a picture of PPI in Northern Ireland currently. There is also a fuller report which gives a lot more details about the research and findings. Information on this is available from the Public Health Agency and/or the Patient and Client Council.

Forecasting industry-level CPI and PPI inflation : does exchange rate pass-through matter?

We show that incorporating the effects of exchange rate pass-through into a model can help in obtaining superior forecasts of domestic, industry-level inflation. Our analysis is based on a multivariate system of domestic inflation, import prices and exchange rates that incorporates restrictions from economic theory. These are restrictions on the transmission channels of the exchange rate pass-through to domestic prices, and are presented as testable hypotheses that lead to model reduction. We provide the results of various tests, including causality and prior restrictions, which support the underlying economic arguments and the model we use. The forecasting results for our model suggest that it has a superior performance overall, jointly producing more accurate forecasts of domestic inflation.

Semantic and layered protein function prediction from PPI networks

Background The past few years have seen a rapid development in novel high-throughput technologies that have created large-scale data on protein-protein interactions (PPI) across human and most model species. This data is commonly represented as networks, with nodes representing proteins and edges representing the PPIs. A fundamental challenge to bioinformatics is how to interpret this wealth of data to elucidate the interaction of patterns and the biological characteristics of the proteins. One significant purpose of this interpretation is to predict unknown protein functions. Although many approaches have been proposed in recent years, the challenge still remains how to reasonably and precisely measure the functional similarities between proteins to improve the prediction effectiveness.

Results We used a Semantic and Layered Protein Function Prediction (SLPFP) framework to more effectively predict unknown protein functions at different functional levels. The framework relies on a new protein similarity measurement and a clustering-based protein function prediction algorithm. The new protein similarity measurement incorporates the topological structure of the PPI network, as well as the protein's semantic information in terms of known protein functions at different functional layers. Experiments on real PPI datasets were conducted to evaluate the effectiveness of the proposed framework in predicting unknown protein functions.

Conclusion The proposed framework has a higher prediction accuracy compared with other similar approaches. The prediction results are stable even for a large number of proteins. Furthermore, the framework is able to predict unknown functions at different functional layers within the Munich Information Center for Protein Sequence (MIPS) hierarchical functional scheme. The experimental results demonstrated that the new protein similarity measurement reflects more reasonably and precisely relationships between proteins.

Role of glutathione in schizophrenia? The effect of brain glutathione depletion on prepulse inhibition (PPI) in rats and mice

Reductions in brain glutathione (GSH) levels have been reported in schizophrenia. We investigated the effects of brain GSH depletion on prepulse inhibition (PPI), a model of sensorimotor gating which is disrupted in individuals with schizophrenia. It was hypothesized that GSH depletion would lead to disruption of PPI similar to that seen in schizophrenia and enhance the effect of increased dopamine release by amphetamine. Sprague-Dawley rats and C57Bl/6 mice were treated with saline or 2-cyclohexene-1-one (CHX, 75 mg/kg and 120 mg/kg respectively) to deplete brain GSH. 225 minutes later the animals were injected with amphetamine (2.5 mg/kg in rats and 25 mg/kg in mice). Total brain GSH levels were measured using an enzymatic recycling assay. Surprisingly, in rats CHX treatment prevented the disruption of PPI by amphetamine. Thus, while there was the expected disruption of PPI caused by amphetamine on its own (average %PPI reduced from 58 ± 5 to 44 ± 4), in combination with CHX, amphetamine had no significant effect (67 ± 4 vs. 63 ± 3, respectively). In contrast to rats, in mice CHX had no effect on PPI. Thus, amphetamine similarly disrupted PPI after saline (41 ± 5 vs. 28 ± 5) and CHX pretreatment (45 ± 6 vs. 26 ± 5). There were significant 40-63% depletions of GSH in frontal cortex and striatum of CHX-treated rats and mice. These data show that GSH depletion in the brain by CHX treatment did not induce the expected decrease in PPI. Because the levels of GSH depletion in this study were similar to those found in schizophrenia, these results cast doubt on a direct interaction between brain GSH levels and PPI disruption in this illness. In rats, CHX treatment prevented the disruption of PPI caused by amphetamine. We have observed that resting levels of GSH are lower in rats than in mice. It is plausible that some oxidative damage may occur after amphetamine treatment alone, which induces marked release of the electroactive species, dopamine. In mice with their higher levels of GSH (either with or without CHX treatment) and in control rats, this does not cause functional effects. However, in CHX-treated rats GSH levels are reduced to a point where amphetamine-induced dopamine release may cause increased metabolism and lipid peroxidation inducing a decrease in postsynaptic dopamine receptor function and consequently leading to an apparent inhibition of the disruption of PPI. In conclusion, while individuals with schizophrenia show disruption of PPI and reduced brain GSH levels, in rats and mice brain GSH depletion alone does not impact on PPI. In combination with a hyperdopaminergic state, functional effects on PPI regulation were found. These effects warrant further investigation.

Predicting protein functions from PPI networks using functional aggregation

Predicting protein functions computationally from massive protein–protein interaction (PPI) data generated by high-throughput technology is one of the challenges and fundamental problems in the post-genomic era. Although there have been many approaches developed for computationally predicting protein functions, the mutual correlations among proteins in terms of protein functions have not been thoroughly investigated and incorporated into existing prediction methods, especially in voting based prediction methods. In this paper, we propose an innovative method to predict protein functions from PPI data by aggregating the functional correlations among relevant proteins using the Choquet-Integral in fuzzy theory. This functional aggregation measures the real impact of each relevant protein function on the final prediction results, and reduces the impact of repeated functional information on the prediction. Accordingly, a new protein similarity and a new iterative prediction algorithm are proposed in this paper. The experimental evaluations on real PPI datasets demonstrate the effectiveness of our method.