Nest visitors of Vespula wasps and their potential use for biological control in an invaded range

The common and the German wasp, Vespula vulgaris and V. germanica, have proved to be prolific invasive species capable of degrading local ecosystems and costing invaded countries millions of dollars annually. Despite clear incentive, control strategies are yet to have any significant deleterious impact on invasive populations. Several species of arthropods are known to inhabit Vespula nests and feed upon developing larvae as either parasitoids or predators. Recent control strategies propose the use of such parasitoids as agents of biocontrol against invasive wasps (Volucella inanis in particular). Despite a general understanding of parasitoid ecology, some aspects such as prevalence, distribution, and behaviour remain limited. Here, we surveyed natural enemy prevalence in wasp nests over the period of three years and we tested larvae prey preference of two Volucella species, V. inanis and V. zonaria towards Vespula wasps. We find V. inanis to be the most prevalent of four prominent candidates for Vespid biocontrol—V. inanis, V. zonaria, Sphecophaga vesparum, and Metoecus paradoxus. Using two-choice assays, we find larvae of V. inanis to have slight yet significant prey preference for V. vulgaris larvae over V. germanica larvae, whilst V. zonaria display no preference. Furthermore, V. inanis were not averse to still predating upon V. germanica, doing so in 41% of trials. Prior exposure has no effect on the prey-preference. Our work provides experimental evidence that V. inanis is a promising candidate for biocontrol of invasive Vespula wasps, as the larvae predate on both target species of Vespula and display no exclusive preference among them

Comparison of In Vitro Stereoselective Metabolism of Bupropion in Human, Monkey, Rat, and Mouse Liver Microsomes

Background and Objectives Bupropion is an atypical antidepressant and smoking cessation aid associated with wide intersubject variability. This study compared the formation kinetics of three phase I metabolites (hydroxybupropion, threohydrobupropion, and erythrohydrobupropion) in human, marmoset, rat, and mouse liver microsomes. The objective was to establish suitability and limitations for subsequent use of nonclinical species to model bupropion central nervous system (CNS) disposition in humans. Methods Hepatic microsomal incubations were conducted separately for the R- and S-bupropion enantiomers, and the formation of enantiomer-specific metabolites was determined using LC-MS/MS. Intrinsic formation clearance (CLint) of metabolites across the four species was determined from the formation rate versus substrate concentration relationship. Results The total clearance of S-bupropion was higher than that of R-bupropion in monkey and human liver microsomes. The contribution of hydroxybupropion to the total racemic bupropion clearance was 38%, 62%, 17%, and 96% in human, monkey, rat, and mouse, respectively. In the same species order, threohydrobupropion contributed 53%, 23%, 17%, and 3%, and erythrohydrobupropion contributed 9%, 14%, 66%, and 1.3%, respectively, to racemic bupropion clearance. Conclusion The results demonstrate that phase I metabolism in monkeys best approximates that observed in humans, and support the preferred use of this species to investigate possible pharmacokinetic factors that influence the CNS disposition of bupropion and contribute to its high intersubject variability

Prediction of brain clozapine and norclozapine concentrations in humans from a scaled pharmacokinetic model for rat brain and plasma pharmacokinetics

BACKGROUND: Clozapine is highly effective in treatment-resistant schizophrenia, although, there remains significant variability in the response to this drug. To better understand this variability, the objective of this study was to predict brain extracellular fluid (ECF) concentrations and receptor occupancy of clozapine and norclozapine in human central nervous system by translating plasma and brain ECF pharmacokinetic (PK) relationships in the rat and coupling these with known human disposition of clozapine in the plasma. METHODS: Unbound concentrations of clozapine and norclozapine were measured in rat brain ECF using quantitative microdialysis after subcutaneous administration of a 10 mg/kg single dose of clozapine or norclozapine. These data were linked with plasma concentrations obtained in the same rats to develop a plasma-brain ECF compartmental model. Parameters describing brain ECF disposition were then allometrically scaled and linked with published human plasma PK to predict human ECF concentrations. Subsequently, prediction of human receptor occupancy at several CNS receptors was based on an effect model that related the predicted ECF concentrations to published concentration-driven receptor occupancy parameters. RESULTS: A one compartment model with first order absorption and elimination best described clozapine and norclozapine plasma concentrations in rats. A delay in the transfer of clozapine and norclozapine from plasma to the brain ECF compartment was captured using a transit compartment model approach. Human clozapine and norclozapine concentrations in brain ECF were simulated, and from these the median percentage of receptor occupancy of dopamine-2, serotonin-2A, muscarinic-1, alpha-1 adrenergic, alpha-2 adrenergic and histamine-1 for clozapine, and dopamine-2 for norclozapine were consistent with values reported in the literature. CONCLUSIONS: A PK model that relates clozapine and norclozapine disposition in rat plasma and brain, including blood-brain barrier transport, was developed. Using allometry and published human plasma PK, the model was successfully translated to predict clozapine and norclozapine concentrations and accordant receptor occupancy of both agents in human brain. These predicted exposure and occupancy measures at several receptors that bind clozapine may be employed to extend our understanding of clozapine's complex behavioral effects in humans

Estimating the number needed to treat from continuous outcomes in randomised controlled trials: methodological challenges and worked example using data from the UK Back Pain Exercise and Manipulation (BEAM) trial

Background Reporting numbers needed to treat (NNT) improves interpretability of trial results. It is unusual that continuous outcomes are converted to numbers of individual responders to treatment (i.e., those who reach a particular threshold of change); and deteriorations prevented are only rarely considered. We consider how numbers needed to treat can be derived from continuous outcomes; illustrated with a worked example showing the methods and challenges. Methods We used data from the UK BEAM trial (n = 1, 334) of physical treatments for back pain; originally reported as showing, at best, small to moderate benefits. Participants were randomised to receive 'best care' in general practice, the comparator treatment, or one of three manual and/or exercise treatments: 'best care' plus manipulation, exercise, or manipulation followed by exercise. We used established consensus thresholds for improvement in Roland-Morris disability questionnaire scores at three and twelve months to derive NNTs for improvements and for benefits (improvements gained+deteriorations prevented). Results At three months, NNT estimates ranged from 5.1 (95% CI 3.4 to 10.7) to 9.0 (5.0 to 45.5) for exercise, 5.0 (3.4 to 9.8) to 5.4 (3.8 to 9.9) for manipulation, and 3.3 (2.5 to 4.9) to 4.8 (3.5 to 7.8) for manipulation followed by exercise. Corresponding between-group mean differences in the Roland-Morris disability questionnaire were 1.6 (0.8 to 2.3), 1.4 (0.6 to 2.1), and 1.9 (1.2 to 2.6) points. Conclusion In contrast to small mean differences originally reported, NNTs were small and could be attractive to clinicians, patients, and purchasers. NNTs can aid the interpretation of results of trials using continuous outcomes. Where possible, these should be reported alongside mean differences. Challenges remain in calculating NNTs for some continuous outcomes

Simultaneous Pharmacokinetic Analysis of Nitrate and its Reduced Metabolite, Nitrite, Following Ingestion of Inorganic Nitrate in a Mixed Patient Population

Purpose: The pharmacokinetic properties of plasma NO3- and its reduced metabolite, NO2-, have been separately described, but there has been no reported attempt to simultaneously model their pharmacokinetics following NO3- ingestion. This report describes development of such a model from retrospective analyses of concentrations largely obtained from primary endpoint efficacy trials. Methods: Linear and non-linear mixed effects analyses were used to statistically define concentration dependency on time, dose, as well as patient and study variables, and to integrate NO3- and NO2- concentrations from studies conducted at different times, locations, patient groups, and several studies in which sample range was limited to a few hours. Published pharmacokinetic studies for both substances were used to supplement model development. Results: A population pharmacokinetic model relating NO3- and NO2- concentrations was developed. The model incorporated endogenous levels of the two entities, and determined these were not influenced by exogenous NO3- delivery. Covariate analysis revealed intersubject variability in NO3- exposure was partially described by body weight differences influencing volume of distribution. The model was applied to visualize exposure versus response (muscle contraction performance) in individual patients. Conclusions: Extension of the present first-generation model, to ultimately optimize NO3- dose versus pharmacological effects, is warranted

Application of machine learning to predict reduction in total PANSS score and enrich enrollment in schizophrenia clinical trials

Clinical trial efficiency, defined as facilitating patient enrollment, and reducing the time to reach safety and efficacy decision points, is a critical driving factor for making improvements in therapeutic development. The present work evaluated a machine learning (ML) approach to improve phase II or proof-of-concept trials designed to address unmet medical needs in treating schizophrenia. Diagnostic data from the Clinical Antipsychotic Trials of Intervention Effectiveness (CATIE) trial were used to develop a binary classification ML model predicting individual patient response as either "improvement," defined as greater than 20% reduction in total Positive and Negative Syndrome Scale (PANSS) score, or "no improvement," defined as an inadequate treatment response (<20% reduction in total PANSS). A random forest algorithm performed best relative to other tree-based approaches in model ability to classify patients after 6 months of treatment. Although model ability to identify true positives, a measure of model sensitivity, was poor (<0.2), its specificity, true negative rate, was high (0.948). A second model, adapted from the first, was subsequently applied as a proof-of-concept for the ML approach to supplement trial enrollment by identifying patients not expected to improve based on their baseline diagnostic scores. In three virtual trials applying this screening approach, the percentage of patients predicted to improve ranged from 46% to 48%, consistently approximately double the CATIE response rate of 22%. These results show the promising application of ML to improve clinical trial efficiency and, as such, ML models merit further consideration and development

The PTTG1-binding factor (PBF/PTTG1IP) regulates p53 activity in thyroid cells

The PTTG1-Binding Factor (PBF/PTTG1IP) has an emerging repertoire of roles, especially in thyroid biology, and functions as a proto-oncogene. High PBF expression is independently associated with poor prognosis and lower disease-specific survival in human thyroid cancer. However, the precise role of PBF in thyroid tumorigenesis is unclear. Here, we present extensive evidence demonstrating that PBF is a novel regulator of p53, a tumor suppressor protein with a key role in maintaining genetic stability, which is infrequently mutated in differentiated thyroid cancer. By coimmunoprecipitation and proximity ligation assays, we show that PBF binds specifically to p53 in thyroid cells, and significantly represses transactivation of responsive promoters. Further, we identify that PBF decreases p53 stability by enhancing ubiquitination, which appears dependent on the E3 ligase activity of Mdm2. Impaired p53 function was evident in a transgenic mouse model with thyroid-specific PBF over-expression (PBF-Tg), which had significantly increased genetic instability as indicated by FISSR-PCR analysis. Consistent with this, ~40% of all DNA repair genes examined were repressed in PBF-Tg primary cultures, including genes with critical roles in maintaining genomic integrity such as Mgmt, Rad51 and Xrcc3. Our data also revealed that PBF induction resulted in upregulation of the E2 enzyme Rad6 in murine thyrocytes, and was associated with Rad6 expression in human thyroid tumors. Overall, this work provides novel insights into the role of the proto-oncogene PBF as a negative regulator of p53 function in thyroid tumorigenesis, where PBF is generally over-expressed and p53 mutations are rare compared to other tumor types

Aptamer-based multiplexed proteomic technology for biomarker discovery

Interrogation of the human proteome in a highly multiplexed and efficient manner remains a coveted and challenging goal in biology. We present a new aptamer-based proteomic technology for biomarker discovery capable of simultaneously measuring thousands of proteins from small sample volumes (15 [mu]L of serum or plasma). Our current assay allows us to measure ~800 proteins with very low limits of detection (1 pM average), 7 logs of overall dynamic range, and 5% average coefficient of variation. This technology is enabled by a new generation of aptamers that contain chemically modified nucleotides, which greatly expand the physicochemical diversity of the large randomized nucleic acid libraries from which the aptamers are selected. Proteins in complex matrices such as plasma are measured with a process that transforms a signature of protein concentrations into a corresponding DNA aptamer concentration signature, which is then quantified with a DNA microarray. In essence, our assay takes advantage of the dual nature of aptamers as both folded binding entities with defined shapes and unique sequences recognizable by specific hybridization probes. To demonstrate the utility of our proteomics biomarker discovery technology, we applied it to a clinical study of chronic kidney disease (CKD). We identified two well known CKD biomarkers as well as an additional 58 potential CKD biomarkers. These results demonstrate the potential utility of our technology to discover unique protein signatures characteristic of various disease states. More generally, we describe a versatile and powerful tool that allows large-scale comparison of proteome profiles among discrete populations. This unbiased and highly multiplexed search engine will enable the discovery of novel biomarkers in a manner that is unencumbered by our incomplete knowledge of biology, thereby helping to advance the next generation of evidence-based medicine